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2280 papers:

POPLPOPL-2020-Pavlogiannis #effectiveness #performance
Fast, sound, and effectively complete dynamic race prediction (AP), p. 29.
ASPLOSASPLOS-2020-ZhangKE #branch #execution
Exploring Branch Predictors for Constructing Transient Execution Trojans (TZ, KK, DE), pp. 667–682.
CGOCGO-2020-ParkLZM #fault #low cost
Low-cost prediction-based fault protection strategy (SP, SL, ZZ, SAM), pp. 30–42.
CSLCSL-2020-AngluinAF #ambiguity #automaton #query
Strongly Unambiguous Büchi Automata Are Polynomially Predictable With Membership Queries (DA, TA, DF), p. 17.
EDMEDM-2019-AndersonBB
Assessing the Fairness of Graduation Predictions (HA, AB, RSB).
EDMEDM-2019-AskinadzeC #education #student
Predicting Student Dropout in Higher Education Based on Previous Exam Results (AA, SC0).
EDMEDM-2019-AulckNVBW #mining
Mining University Registrar Records to Predict First-Year Undergraduate Attrition (LSA, DN, NV, JB, JW).
EDMEDM-2019-ChenLFG #online #scalability #student #tutorial
Predictors of Student Satisfaction: A Large-scale Study of Human-Human Online Tutorial Dialogues (GC, DL, RF, DG).
EDMEDM-2019-ColemanBS #strict #student
A Better Cold-Start for Early Prediction of Student At-Risk Status in New School Districts (CC, RSB, SS).
EDMEDM-2019-DoanS #performance #student
Rank-Based Tensor Factorization for Student Performance Prediction (TND, SS).
EDMEDM-2019-EmersonSSRMWMBL #modelling #programming #student
Predicting Early and Often: Predictive Student Modeling for Block-Based Programming Environments (AE, AS, CS, FJR, WM, ENW, BWM, KEB, JCL).
EDMEDM-2019-EmondV #3d #learning #performance #visualisation
Visualizing Learning Performance Data and Model Predictions as Objects in a 3D Space (BE, JJV).
EDMEDM-2019-GardnerYBB #design #modelling #perspective #replication
Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective (JG, YY, RSB, CB).
EDMEDM-2019-HuttGDD #modelling
Evaluating Fairness and Generalizability in Models Predicting On-Time Graduation from College Applications (SH, MG, ALD, SKD).
EDMEDM-2019-MandalapuG #student
Studying Factors Influencing the Prediction of Student STEM and Non-STEM Career Choice (VM, JG).
EDMEDM-2019-MaoZKPBC #programming #student
One minute is enough: Early Prediction of Student Success and Event-level Difficulty during Novice Programming Tasks (YM, RZ, FK, TWP, TB, MC).
EDMEDM-2019-MorsyK #knowledge-based
Neural Attentive Knowledge-based Model for Grade Prediction (SM, GK).
EDMEDM-2019-PigeauAP #case study
Success prediction in MOOCs: A case study (AP, OA, YP).
EDMEDM-2019-ReillyS #analysis #collaboration #problem #quality
Predicting the Quality of Collaborative Problem Solving Through Linguistic Analysis of Discourse (JMR, BS).
EDMEDM-2019-RenNLR #cumulative
Grade Prediction Based on Cumulative Knowledge and Co-taken Courses (ZR, XN, ASL, HR).
EDMEDM-2019-SherHG #learning #mobile #power of #student
Investigating effects of considering mobile and desktop learning data on predictive power of learning management system (LMS) features on student success (VS, MH, DG).
EDMEDM-2019-TatoND #hybrid #network #reasoning
Hybrid Deep Neural Networks to Predict Socio-Moral Reasoning Skills (AANT, RN, AD).
EDMEDM-2019-Venantd #complexity #concept #graph #semantics #towards
Towards the Prediction of Semantic Complexity Based on Concept Graphs (RV, Md).
EDMEDM-2019-WampflerKSSG #mobile #smarttech #using
Affective State Prediction in a Mobile Setting using Wearable Biometric Sensors and Stylus (RW, SK, BS, VRS, MG0).
EDMEDM-2019-WeitekampHMRK #learning #student #towards #using
Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning (DWI, EH, CJM, NR, KRK).
EDMEDM-2019-WhitehillAH #crowdsourcing #learning #what
Do Learners Know What's Good for Them? Crowdsourcing Subjective Ratings of OERs to Predict Learning Gains (JW, CA, BH).
EDMEDM-2019-Woodruff #architecture #education #interactive #machine learning #student
Predicting student academic outcomes in UK secondary phase education: an architecture for machine learning and user interaction (MW).
ICPCICPC-2019-SchnappingerOPF #classification #learning #maintenance #static analysis #tool support
Learning a classifier for prediction of maintainability based on static analysis tools (MS, MHO, AP, AF), pp. 243–248.
ICSMEICSME-2019-Sae-LimHS #automation #impact analysis #question
Can Automated Impact Analysis Techniques Help Predict Decaying Modules? (NSL, SH, MS), pp. 541–545.
ICSMEICSME-2019-SarkarRB #debugging
Improving Bug Triaging with High Confidence Predictions at Ericsson (AS, PCR, BB), pp. 81–91.
MSRMSR-2019-AhluwaliaFP #dataset #fault #named
Snoring: a noise in defect prediction datasets (AA, DF, MDP), pp. 63–67.
MSRMSR-2019-DamPN0GGKK #fault #lessons learnt #using
Lessons learned from using a deep tree-based model for software defect prediction in practice (HKD, TP, SWN, TT0, JCG, AG, TK, CJK), pp. 46–57.
MSRMSR-2019-HoangDK0U #fault #framework #learning #named
DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction (TH, HKD, YK, DL0, NU), pp. 34–45.
MSRMSR-2019-Treude0 #git #modelling #stack overflow #topic
Predicting good configurations for GitHub and stack overflow topic models (CT, MW0), pp. 84–95.
MSRMSR-2019-YangC0 #behaviour #development #source code #specification
Predicting co-changes between functionality specifications and source code in behavior driven development (AZHY, DAdC, YZ0), pp. 534–544.
SANERSANER-2019-YuBLKYX #empirical #fault #learning #rank
An Empirical Study of Learning to Rank Techniques for Effort-Aware Defect Prediction (XY, KEB, JL0, JWK, XY, ZX), pp. 298–309.
AIIDEAIIDE-2019-KartalHT #learning
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning (BK, PHL, MET), pp. 38–44.
CoGCoG-2019-KatonaSHDBDW #learning #using
Time to Die: Death Prediction in Dota 2 using Deep Learning (AK, RJS, VJH, SD, FB, AD, JAW), pp. 1–8.
CoGCoG-2019-KristensenB #game studies
Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games (JTK, PB), pp. 1–8.
CoGCoG-2019-NumminenVP #analysis #game studies
Predicting the monetization percentage with survival analysis in free-to-play games (RN, MV, TP), pp. 1–8.
CoGCoG-2019-RioCP #profiling
Profiling Players with Engagement Predictions (AFdR, PPC, ÁP), pp. 1–4.
CoGCoG-2019-YangHZYC0ML #game studies #mining #online
Mining Player In-game Time Spending Regularity for Churn Prediction in Free Online Games (WY, TH, JZ, GY, JC, LC0, SM, YEL), pp. 1–8.
FDGFDG-2019-GuitartTRCP #game studies #learning #video
From non-paying to premium: predicting user conversion in video games with ensemble learning (AG, SHT, AFdR, PPC, ÁP), p. 9.
CoGVS-Games-2019-Jercic #game studies #metric #performance #set #what
What could the baseline measurements predict about decision-making performance in serious games set in the financial context (PJ), pp. 1–4.
CIKMCIKM-2019-AharonKLSBESSZ
Soft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini Native (MA, YK, RL, OS, AB, NE, AS, AS, AZ), pp. 2793–2801.
CIKMCIKM-2019-ArianAAKSS #feature model #network
Feature Enhancement via User Similarities Networks for Improved Click Prediction in Yahoo Gemini Native (MA, EA, MA, YK, OS, RS), pp. 2557–2565.
CIKMCIKM-2019-BaiYK0LY #graph #network
Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction (LB, LY, SSK, XW0, WL0, ZY), pp. 2293–2296.
CIKMCIKM-2019-ChengZYTN0 #framework
A Dynamic Default Prediction Framework for Networked-guarantee Loans (DC, YZ, FY, YT, ZN, LZ0), pp. 2547–2555.
CIKMCIKM-2019-ChoiAA #online
Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems (JIC, AA, EA), pp. 1281–1290.
CIKMCIKM-2019-FangSCG #fine-grained
Fine-Grained Fuel Consumption Prediction (CF, SS, ZC, AG), pp. 2783–2791.
CIKMCIKM-2019-IslamMR #graph #named #network #social #using
NActSeer: Predicting User Actions in Social Network using Graph Augmented Neural Network (MRI, SM, NR), pp. 1793–1802.
CIKMCIKM-2019-JiangCBWYN #learning #smarttech
Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices (JYJ, ZC, ALB, WW0, SDY, DN), pp. 2773–2781.
CIKMCIKM-2019-JiaoXZZ #graph #network
Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention (YJ, YX, JZ, YZ), pp. 419–428.
CIKMCIKM-2019-KimSRLW #learning
Deep Learning for Blast Furnaces: Skip-Dense Layers Deep Learning Model to Predict the Remaining Time to Close Tap-holes for Blast Furnaces (KK, BS, SHR, SL, SSW), pp. 2733–2741.
CIKMCIKM-2019-LiCWZW #feature model #graph #interactive #modelling #named #network
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction (ZL, ZC, SW, XZ, LW0), pp. 539–548.
CIKMCIKM-2019-PanWWYZZ #matrix #network
Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction (ZP, ZW, WW, YY, JZ, YZ), pp. 2683–2691.
CIKMCIKM-2019-QiuW0 #multi #problem
Question Difficulty Prediction for Multiple Choice Problems in Medical Exams (ZQ, XW, WF0), pp. 139–148.
CIKMCIKM-2019-SalhaLHTV #graph
Gravity-Inspired Graph Autoencoders for Directed Link Prediction (GS, SL, RH, VAT, MV), pp. 589–598.
CIKMCIKM-2019-TanMYYDWTYWCCY #named #network #risk management
UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction (QT, AJM, MY, BY, HD, VWSW, YKT, TCFY, GLHW, JYLC, FKLC, PCY), pp. 109–118.
CIKMCIKM-2019-TaoGFCYZ #game studies #learning #multi #named #online
GMTL: A GART Based Multi-task Learning Model for Multi-Social-Temporal Prediction in Online Games (JT, LG, CF, LC, DY, SZ), pp. 2841–2849.
CIKMCIKM-2019-TianLWT #re-engineering
Time Series Prediction with Interpretable Data Reconstruction (QT, JL, DW, AT), pp. 2133–2136.
CIKMCIKM-2019-WangLL #interactive #network
Neighborhood Interaction Attention Network for Link Prediction (ZW, YL, WL), pp. 2153–2156.
CIKMCIKM-2019-WangLLW #towards
Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor (JW, QL0, ZL, SW), pp. 1703–1712.
CIKMCIKM-2019-WangRCR0R #graph #learning
Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning (SW, PR, ZC, ZR, JM0, MdR), pp. 1623–1632.
CIKMCIKM-2019-WangZDSZHYB
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction (YW, LZ, QD, FS, BZ, YH, WY, YB), pp. 349–358.
CIKMCIKM-2019-WuWLH019a #named #overview #rating
ARP: Aspect-aware Neural Review Rating Prediction (CW, FW, JL, YH, XX0), pp. 2169–2172.
CIKMCIKM-2019-WuWQLH0 #gender #microblog #representation
Neural Gender Prediction in Microblogging with Emotion-aware User Representation (CW, FW, TQ, JL, YH, XX0), pp. 2401–2404.
CIKMCIKM-2019-XinEBYLZY0 #multi #online
Multi-task based Sales Predictions for Online Promotions (SX, ME, JB, CY, ZL, XZ, YY, CW0), pp. 2823–2831.
CIKMCIKM-2019-XuZL #incremental #kernel #online
New Online Kernel Ridge Regression via Incremental Predictive Sampling (SX, XZ, SL), pp. 791–800.
CIKMCIKM-2019-YangDTTZQD #composition #learning #relational #visual notation
Learning Compositional, Visual and Relational Representations for CTR Prediction in Sponsored Search (XY, TD, WT, XT, JZ, SQ, ZD), pp. 2851–2859.
CIKMCIKM-2019-YangLSB #behaviour #interactive
Exploring The Interaction Effects for Temporal Spatial Behavior Prediction (HY, TL, YS, EB), pp. 2013–2022.
CIKMCIKM-2019-YangWCW #graph #network #using
Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks (YY, ZW, QC, LW), pp. 2161–2164.
CIKMCIKM-2019-YuanHYZCDL
Improving Ad Click Prediction by Considering Non-displayed Events (BWY, JYH, MYY, HZ, CYC, ZD, CJL), pp. 329–338.
CIKMCIKM-2019-YuanWLWHX #memory management #overview #rating
Neural Review Rating Prediction with User and Product Memory (ZY, FW, JL, CW, YH, XX0), pp. 2341–2344.
CIKMCIKM-2019-ZhaoZXQJ0 #named
AIBox: CTR Prediction Model Training on a Single Node (WZ, JZ, DX, YQ, RJ, PL0), pp. 319–328.
ECIRECIR-p1-2019-AlmquistJ #towards
Towards Content Expiry Date Determination: Predicting Validity Periods of Sentences (AA, AJ), pp. 86–101.
ECIRECIR-p1-2019-BahrainianZMC #information retrieval #query #topic
Predicting the Topic of Your Next Query for Just-In-Time IR (SAB, FZ, IM, FC), pp. 261–275.
ECIRECIR-p1-2019-FardBW #network
Relationship Prediction in Dynamic Heterogeneous Information Networks (AMF, EB, KW0), pp. 19–34.
ECIRECIR-p1-2019-GuptaKKL #correlation #evaluation #information retrieval #metric #ranking
Correlation, Prediction and Ranking of Evaluation Metrics in Information Retrieval (SG, MK, VK, ML), pp. 636–651.
ECIRECIR-p1-2019-OttoHE #image
“Is This an Example Image?” - Predicting the Relative Abstractness Level of Image and Text (CO, SH, RE), pp. 711–725.
ECIRECIR-p1-2019-ZhangJ #image #network #twitter
Image Tweet Popularity Prediction with Convolutional Neural Network (YZ0, AJ), pp. 803–809.
ECIRECIR-p2-2019-JolyGBKPSGBVPSS #challenge #identification
LifeCLEF 2019: Biodiversity Identification and Prediction Challenges (AJ, HG, CB, SK, MP, MS, HG, PB, WPV, RP, JS, FRS, HM), pp. 275–282.
ECIRECIR-p2-2019-Masood #adaptation #internet #modelling #risk management
Adapting Models for the Case of Early Risk Prediction on the Internet (RM), pp. 353–358.
ECIRECIR-p2-2019-NikolenkoTMSA #named #rating
AspeRa: Aspect-Based Rating Prediction Model (SIN, ET, VM, IS, AA), pp. 163–171.
ECIRECIR-p2-2019-PenhaCCGS #automation #classification #documentation #performance
Document Performance Prediction for Automatic Text Classification (GP, RRC, SDC, MAG, RLTS), pp. 132–139.
ICMLICML-2019-BehpourLZ #learning #probability
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings (SB, AL, BDZ), pp. 563–572.
ICMLICML-2019-ChengYRB #optimisation #policy
Predictor-Corrector Policy Optimization (CAC, XY, NDR, BB), pp. 1151–1161.
ICMLICML-2019-FarinaKBS
Stable-Predictive Optimistic Counterfactual Regret Minimization (GF, CK, NB, TS), pp. 1853–1862.
ICMLICML-2019-FrancP #learning #nondeterminism #on the
On discriminative learning of prediction uncertainty (VF, DP), pp. 1963–1971.
ICMLICML-2019-LuiseSPC #rank
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction (GL, DS, MP, CC), pp. 4193–4202.
ICMLICML-2019-NguyenLKB #detection #multi
Anomaly Detection With Multiple-Hypotheses Predictions (DTN, ZL, MK, TB), pp. 4800–4809.
ICMLICML-2019-PetersonB0GR
Cognitive model priors for predicting human decisions (JCP, DB, DR0, TLG, SJR), pp. 5133–5141.
ICMLICML-2019-RaghuBSOKMK #nondeterminism
Direct Uncertainty Prediction for Medical Second Opinions (MR, KB, RS, ZO, RDK, SM, JMK), pp. 5281–5290.
ICMLICML-2019-TangR
The Variational Predictive Natural Gradient (DT, RR), pp. 6145–6154.
ICMLICML-2019-X #bound #named #policy #using
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction, pp. 3692–3702.
KDDKDD-2019-ChenLPS #twitter #using
Using Twitter to Predict When Vulnerabilities will be Exploited (HC0, RL, NP, VSS), pp. 3143–3152.
KDDKDD-2019-ChenZBXL #behaviour #what
Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction (CC, LZ, JB0, CX, TYL), pp. 2376–2384.
KDDKDD-2019-DengRN #graph #learning #social
Learning Dynamic Context Graphs for Predicting Social Events (SD, HR, YN), pp. 1007–1016.
KDDKDD-2019-DeyZSN #effectiveness #named #personalisation
PerDREP: Personalized Drug Effectiveness Prediction from Longitudinal Observational Data (SD, PZ, DS, KN), pp. 1258–1268.
KDDKDD-2019-DingZP0H #modelling
Modeling Extreme Events in Time Series Prediction (DD, MZ, XP, MY0, XH0), pp. 1114–1122.
KDDKDD-2019-Do0V #graph transformation #network #policy
Graph Transformation Policy Network for Chemical Reaction Prediction (KD, TT0, SV), pp. 750–760.
KDDKDD-2019-FeiTL #learning #multi #word
Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction (HF, ST, PL0), pp. 834–842.
KDDKDD-2019-GengLLJXZYLZ #named #network
LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction (YaG, QL, TL, LJ, LX, DZ, WY, WL, YZ), pp. 2439–2447.
KDDKDD-2019-GuoHJZW0 #behaviour #multi #network #realtime #using
Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior (LG, LH, RJ, BZ, XW, BC0), pp. 1984–1992.
KDDKDD-2019-HuangWZZZYC #behaviour #modelling #multi #online
Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics (CH0, XW, XZ, CZ, JZ, DY, NVC), pp. 2613–2622.
KDDKDD-2019-JiangSHSXCWKS #named
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events (RJ, XS, DH, XS, TX, ZC, ZW, KSK, RS), pp. 2114–2122.
KDDKDD-2019-KalraWB0
Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising (AK, CW, CB, YC0), pp. 2819–2827.
KDDKDD-2019-KeXZBL #framework #learning #named #online
DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks (GK, ZX, JZ, JB0, TYL), pp. 384–394.
KDDKDD-2019-KitadaIS #effectiveness #multi #network #using
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives (SK, HI, YS), pp. 2069–2077.
KDDKDD-2019-KrausF #personalisation #sequence
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching (MK, SF), pp. 2643–2652.
KDDKDD-2019-KumarZL #interactive #network
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (SK, XZ, JL), pp. 1269–1278.
KDDKDD-2019-Li0WGYK #adaptation #kernel #learning #multi
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points (ZL, JZ0, QW0, YG, JY, CK), pp. 2848–2856.
KDDKDD-2019-LiHCSWZP #graph
Predicting Path Failure In Time-Evolving Graphs (JL, ZH, HC, JS, PW, JZ, LP), pp. 1279–1289.
KDDKDD-2019-LiST #classification #higher-order #markov #multi #network #random
Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification (CL, DS, DT), pp. 1141–1151.
KDDKDD-2019-LiuCGD #parametricity
Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data - An Application to Climate Prediction (YL, JC, ARG, JGD), pp. 2556–2564.
KDDKDD-2019-LuoHHLZ #named #quality
AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018 (ZL, JH, KH, XL, PZ), pp. 1842–1850.
KDDKDD-2019-MengZXZX #network
A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction (QM, HZ, KX, LZ, HX), pp. 14–24.
KDDKDD-2019-OkawaIK0TU #information management #process
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information (MO, TI, TK, YT0, HT, NU), pp. 373–383.
KDDKDD-2019-OuyangZLZXLD #network
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction (WO, XZ, LL, HZ, XX, ZL, YD), pp. 2078–2086.
KDDKDD-2019-PanLW00Z #learning #using
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (ZP, YL, WW, YY0, YZ0, JZ), pp. 1720–1730.
KDDKDD-2019-PanMRSF #learning #multi #online
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising (JP, YM, ALR, YS, AF), pp. 2689–2697.
KDDKDD-2019-PiBZZG #behaviour #modelling
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (QP, WB, GZ, XZ, KG), pp. 2671–2679.
KDDKDD-2019-SchonD0 #fault #how #using
The Error is the Feature: How to Forecast Lightning using a Model Prediction Error (CS, JD, RM0), pp. 2979–2988.
KDDKDD-2019-SheehanMTUJBLE #development #using #wiki
Predicting Economic Development using Geolocated Wikipedia Articles (ES, CM, MT, BU, NJ, MB, DBL, SE), pp. 2698–2706.
KDDKDD-2019-SheetritNKS #probability
Temporal Probabilistic Profiles for Sepsis Prediction in the ICU (ES, NN, DK, YS), pp. 2961–2969.
KDDKDD-2019-SinghWWWKMDLK #migration #social #social media
Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration (LS, LW, YW, YW, CK, SM, KD, YL, KK), pp. 1975–1983.
KDDKDD-2019-TranS #feature model #scalability
Seasonal-adjustment Based Feature Selection Method for Predicting Epidemic with Large-scale Search Engine Logs (TQT, JS), pp. 2857–2866.
KDDKDD-2019-WangJCJ #behaviour #named
TUBE: Embedding Behavior Outcomes for Predicting Success (DW, TJ, NVC, MJ0), pp. 1682–1690.
KDDKDD-2019-WangYCW00 #graph #matrix #modelling
Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling (YW, HY, HC, TW, JX0, KZ0), pp. 1227–1235.
KDDKDD-2019-WuGGWC #modelling #recommendation
Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination (QW, YG, XG, PW, GC), pp. 447–457.
KDDKDD-2019-XuH0D #network #social
Link Prediction with Signed Latent Factors in Signed Social Networks (PX, WH, JW0, BD), pp. 1046–1054.
KDDKDD-2019-YabeTSSU #behaviour #using #web
Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior (TY, KT, TS, YS, SVU), pp. 2707–2717.
KDDKDD-2019-YeSDFTX #multi #network
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network (JY, LS, BD, YF, XT, HX), pp. 305–313.
KDDKDD-2019-ZhangTDZW #health #named #risk management
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records (XSZ, FT, HHD, JZ, FW), pp. 2487–2495.
KDDKDD-2019-ZhengXKWMAY #linear #using
Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression (YZ, LX, TK, JW0, HM, RA, KY), pp. 2278–2286.
OOPSLAOOPSLA-2019-GencRXB #bound #concurrent #detection
Dependence-aware, unbounded sound predictive race detection (KG, JR, YX, MDB), p. 30.
ASEASE-2019-GongJWJ #empirical #evaluation #fault
Empirical Evaluation of the Impact of Class Overlap on Software Defect Prediction (LG, SJ, RW, LJ), pp. 698–709.
ASEASE-2019-LiuHGN #source code
Predicting Licenses for Changed Source Code (XL, LH, JG, VN), pp. 686–697.
ESEC-FSEESEC-FSE-2019-Caulo #fault #metric #taxonomy
A taxonomy of metrics for software fault prediction (MC), pp. 1144–1147.
ESEC-FSEESEC-FSE-2019-ChenCCHCM
Predicting breakdowns in cloud services (with SPIKE) (JC, JC, PC, KH, SC, TM), pp. 916–924.
ESEC-FSEESEC-FSE-2019-JimenezRPSTH
The importance of accounting for real-world labelling when predicting software vulnerabilities (MJ, RR, MP, FS, YLT, MH), pp. 695–705.
ESEC-FSEESEC-FSE-2019-MaddilaBN #case study #scalability
Predicting pull request completion time: a case study on large scale cloud services (CSM, CB, NN), pp. 874–882.
ESEC-FSEESEC-FSE-2019-Zhou0X0JLXH #fault #learning #locality
Latent error prediction and fault localization for microservice applications by learning from system trace logs (XZ, XP0, TX, JS0, CJ, DL, QX, CH), pp. 683–694.
ICSE-2019-AmarR #debugging #fault #locality #mining
Mining historical test logs to predict bugs and localize faults in the test logs (AA, PCR), pp. 140–151.
ICSE-2019-CabralMSM #evolution #fault #latency #verification
Class imbalance evolution and verification latency in just-in-time software defect prediction (GGC, LLM, ES, SM), pp. 666–676.
ICSE-2019-HaZ #configuration management #named #network #performance
DeepPerf: performance prediction for configurable software with deep sparse neural network (HH, HZ0), pp. 1095–1106.
ICSE-2019-PanCP0L #modelling #verification
Easy modelling and verification of unpredictable and preemptive interrupt-driven systems (MP, SC, YP0, TZ0, XL), pp. 212–222.
ICSE-2019-RutledgePKOPZ #execution #symbolic computation
Zero-overhead path prediction with progressive symbolic execution (RR, SP, HAK, AO, MP, AGZ), pp. 234–245.
ASPLOSASPLOS-2019-SivathanuCSZ #learning #named
Astra: Exploiting Predictability to Optimize Deep Learning (MS, TC, SSS, LZ), pp. 909–923.
ASPLOSASPLOS-2019-XuV0 #graph #named
PnP: Pruning and Prediction for Point-To-Point Iterative Graph Analytics (CX, KV, RG0), pp. 587–600.
CASECASE-2019-AshouriNSG #using
Day-ahead Prediction of Building Occupancy using WiFi Signals (AA, GRN, ZS, HBG), pp. 1237–1242.
CASECASE-2019-ChengaT
Neural-Network-Based Heart Motion Prediction for Ultrasound-Guided Beating-Heart Surgery (LC, MT), pp. 437–442.
CASECASE-2019-ColomboGARTBG #approach #constraints #mobile
Parameterized Model Predictive Control of a Nonholonomic Mobile Manipulator: A Terminal Constraint-Free Approach (RC, FG, VA, PR, ST, LB, SKG), pp. 1437–1442.
CASECASE-2019-CrowleyZAS #detection
Set-Based Predictive Control for Collision Detection and Evasion (JC, YZ, BA, RGS), pp. 541–546.
CASECASE-2019-GunaySY0D
An inquiry into the predictability of failure events in chillers and boilers (BG, ZS, CY, WS0, DD), pp. 222–227.
CASECASE-2019-LauerL #machine learning
Plan instability prediction by machine learning in master production planning (TL, SL), pp. 703–708.
CASECASE-2019-MaggipintoSZM #case study #fault #multi #process #what
What are the Most Informative Data for Virtual Metrology? A use case on Multi-Stage Processes Fault Prediction (MM, GAS, FZ, SFM), pp. 1796–1801.
CASECASE-2019-PippiaLCSS #approach
Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building (TP, JL, RDC, JS, BDS), pp. 1243–1248.
CASECASE-2019-ShenSLBZDWXW
PredNet and CompNet: Prediction and High-Precision Compensation of In-Plane Shape Deformation for Additive Manufacturing (ZS, XS, YL, YB, XZ, XD, LW, GX, FYW0), pp. 462–467.
CASECASE-2019-TanNAGL #case study #data-driven #scheduling
Data-Driven Surgical Duration Prediction Model for Surgery Scheduling: A Case-Study for a Practice-Feasible Model in a Public Hospital (KWT, FNHLN, BYA, JG, SSWL), pp. 275–280.
CASECASE-2019-TanNNL #data-driven #process
Data-Driven Decision-Support for Process Improvement through Predictions of Bed Occupancy Rates (KWT, QYN, FNHLN, SSWL), pp. 133–139.
CASECASE-2019-XuLWMWLS #data-driven #fault #maintenance
Data-Driven Fault Diagnostics and Prognostics for Predictive Maintenance: A Brief Overview* (GX, ML0, JW, YM, JW, FL, WS0), pp. 103–108.
CASECASE-2019-ZhaoXSLSW #3d #network
Nonlinear Deformation Prediction and Compensation for 3D Printing Based on CAE Neural Networks (MZ, GX, XS, CL, ZS, HW), pp. 667–672.
CASECASE-2019-ZhouWXS00G #approach #learning #modelling #personalisation
A Model-Driven Learning Approach for Predicting the Personalized Dynamic Thermal Comfort in Ordinary Office Environment (YZ, XW, ZX, YS, TL0, CS0, XG), pp. 739–744.
ICSTICST-2019-MaoCZ #mutation testing #testing
An Extensive Study on Cross-Project Predictive Mutation Testing (DM, LC, LZ), pp. 160–171.
ICSTICST-2019-PatersonCAKFM #empirical #fault #testing #using
An Empirical Study on the Use of Defect Prediction for Test Case Prioritization (DP, JC, RA, GMK, GF, PM), pp. 346–357.
TAPTAP-2019-AichernigPSW #case study #learning #testing
Predicting and Testing Latencies with Deep Learning: An IoT Case Study (BKA, FP, RS, AW), pp. 93–111.
ICSAICSA-2018-EismannWKK #component #dependence #modelling #parametricity #performance #runtime
Modeling of Parametric Dependencies for Performance Prediction of Component-Based Software Systems at Run-Time (SE, JW, JvK, SK), pp. 135–144.
ICSAICSA-2018-Yasaweerasinghelage #architecture #data analysis #modelling #performance #privacy #simulation #using
Predicting the Performance of Privacy-Preserving Data Analytics Using Architecture Modelling and Simulation (RY, MS, IW, HYP), pp. 166–175.
EDMEDM-2018-EagleCSBSL #modelling #online #student
Predictive Student Modeling for Interventions in Online Classes (ME, TC, JS, MJB, JCS, JL).
EDMEDM-2018-EagleCSM #modelling
Predicting Individualized Learner Models Across Tutor Lessons (ME, ATC, JCS, BMM).
EDMEDM-2018-GitinabardKLW #behaviour #certification #social
Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features (NG, FK, CL, EYW).
EDMEDM-2018-KarumbaiahBS #game studies #learning #student
Predicting Quitting in Students Playing a Learning Game (SK, RSB, VJS).
EDMEDM-2018-KimVG #learning #named #performance #student
GritNet: Student Performance Prediction with Deep Learning (BHK, EV, VG).
EDMEDM-2018-PytlarzPPP #network #student #transaction #what
What can we learn from college students' network transactions? Constructing useful features for student success prediction (IP, SP, MP, RP).
EDMEDM-2018-RajendranKCLB #behaviour #learning
Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior (RR, AK, KEC, DTL, GB).
EDMEDM-2018-SahebiB #performance #student
Student Performance Prediction by Discovering Inter-Activity Relations (SS, PB).
EDMEDM-2018-SheshadriGLBH #case study #online #performance #student
Predicting Student Performance Based on Online Study Habits: A Study of Blended Courses (AS, NG, CL, TB, SH).
EDMEDM-2018-SlimHOB #student
Predicting Student Enrollment based on Student and College Characteristics (AS, DH, TO, TB).
EDMEDM-2018-WangZHLJ
Prediction of Academic Achievement Based on Digital Campus (ZW, XZ, JH, XL, YJ).
EDMEDM-2018-WinchellMLGP #learning #student
Textbook annotations as an early predictor of student learning (AW, MM, ASL, PG, HP).
ICPCICPC-2018-XuLTLZLX #fault #set
Cross version defect prediction with representative data via sparse subset selection (ZX, SL, YT, XL, TZ0, JL0, JX0), pp. 132–143.
ICSMEICSME-2018-0006LEL #feature model #higher-order #interactive
Predicting Higher Order Structural Feature Interactions in Variable Systems (SF0, LL, AE, RELH), pp. 252–263.
ICSMEICSME-2018-Alsolai #maintenance #object-oriented #using
Predicting Software Maintainability in Object-Oriented Systems Using Ensemble Techniques (HA), pp. 716–721.
ICSMEICSME-2018-JansenHT #case study #detection #energy #evolution #network #spreadsheet
Detecting and Predicting Evolution in Spreadsheets - A Case Study in an Energy Network Company (BJ, FH, ET), pp. 645–654.
MSRMSR-2018-AcciolyBSC08 #java #open source
Analyzing conflict predictors in open-source Java projects (PRGA, PB, LS, GC), pp. 576–586.
MSRMSR-2018-BulmerMD #developer #ide #machine learning
Predicting developers' IDE commands with machine learning (TB, LM, DED), pp. 82–85.
MSRMSR-2018-MiddletonMGMMWM08 #developer #git
Which contributions predict whether developers are accepted into github teams (JM, ERMH, DG, AWM, RM, DW, SM), pp. 403–413.
MSRMSR-2018-ShahbazianNM #architecture #implementation #towards
Toward predicting architectural significance of implementation issues (AS, DN, NM), pp. 215–219.
SANERSANER-2018-LiuLGZX #fault #metric
Connecting software metrics across versions to predict defects (YL, YL, JG, YZ, BX), pp. 232–243.
SANERSANER-2018-PascarellaPB #debugging
Re-evaluating method-level bug prediction (LP, FP, AB), pp. 592–601.
SANERSANER-2018-SotoG #debugging #probability #using
Using a probabilistic model to predict bug fixes (MS, CLG), pp. 221–231.
SANERSANER-2018-XuLLZ #analysis #component #fault #hybrid #kernel #learning
Cross-version defect prediction via hybrid active learning with kernel principal component analysis (ZX, JL0, XL, TZ0), pp. 209–220.
SCAMSCAM-2018-KiniT #developer #fault #metric #research
[Research Paper] Periodic Developer Metrics in Software Defect Prediction (SOK, AT), pp. 72–81.
SCAMSCAM-2018-PaceTG #architecture #research #smell #towards #using
[Research Paper] Towards Anticipation of Architectural Smells Using Link Prediction Techniques (JADP, AT, DG), pp. 62–71.
SEFMSEFM-2018-BabaeeGF #framework #learning #runtime #statistics #using #verification
Prevent : A Predictive Run-Time Verification Framework Using Statistical Learning (RB, AG, SF), pp. 205–220.
AIIDEAIIDE-2018-PurdyWHR #metric #quality
Predicting Generated Story Quality with Quantitative Measures (CP, XW, LH, MR), pp. 95–101.
AIIDEAIIDE-2018-WigginsKMMBWL #education #game studies
Affect-Based Early Prediction of Player Mental Demand and Engagement for Educational Games (JBW, MK, WM, BWM, KEB, ENW, JCL), pp. 243–249.
CoGCIG-2018-AungBDCKYW #dataset #learning #scalability
Predicting Skill Learning in a Large, Longitudinal MOBA Dataset (MA, VB, AD, PIC, AVK, CY, ARW), pp. 1–7.
CoGCIG-2018-GainaLP #experience
General Win Prediction from Agent Experience (RDG, SML, DPL), pp. 1–8.
CoGCIG-2018-KummerNP
Applying Commitment to Churn and Remaining Players Lifetime Prediction (LBMK, JCN, ECP), pp. 1–8.
CIKMCIKM-2018-0002WYTZZ #named #semistructured data #social
vec2Link: Unifying Heterogeneous Data for Social Link Prediction (FZ0, BW, YY, GT, KZ, TZ), pp. 1843–1846.
CIKMCIKM-2018-ArabzadehFZNB #dependence #twitter
Causal Dependencies for Future Interest Prediction on Twitter (NA, HF, FZ, AN, EB), pp. 1511–1514.
CIKMCIKM-2018-ChenKBRKKB #analysis #behaviour
Predictive Analysis by Leveraging Temporal User Behavior and User Embeddings (CC, SK, HB, RAR, EK, BK, RCB), pp. 2175–2182.
CIKMCIKM-2018-ChenL #evolution
Exploiting Structural and Temporal Evolution in Dynamic Link Prediction (HC, JL0), pp. 427–436.
CIKMCIKM-2018-ChenLZY #rating
Heterogeneous Neural Attentive Factorization Machine for Rating Prediction (LC0, YL, ZZ, PSY), pp. 833–842.
CIKMCIKM-2018-ChenWH #graph #network
Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction (YC, ZW, XH), pp. 1655–1658.
CIKMCIKM-2018-DedieuMZV #modelling #streaming
Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming (AD, RM, ZZ, HV), pp. 607–616.
CIKMCIKM-2018-GongLZLZK #matrix #online
Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization (YG, ZL, JZ0, WL0, YZ, CK), pp. 1243–1252.
CIKMCIKM-2018-HuangZZC #named #network
DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction (CH0, JZ, YZ, NVC), pp. 1423–1432.
CIKMCIKM-2018-HuNY #framework #linear #performance
A Fast Linear Computational Framework for User Action Prediction in Tencent MyApp (YH, DN, JY), pp. 2047–2055.
CIKMCIKM-2018-KhodabakhshFZB #social #streaming
Predicting Personal Life Events from Streaming Social Content (MK, HF, FZ, EB), pp. 1751–1754.
CIKMCIKM-2018-LiangLWC #maintenance
Long-Term RNN: Predicting Hazard Function for Proactive Maintenance of Water Mains (BL, ZL, YW0, FC0), pp. 1687–1690.
CIKMCIKM-2018-LiTL #towards
Towards Explainable Networked Prediction (LL, HT, HL), pp. 1819–1822.
CIKMCIKM-2018-Liu0SZ #network
Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News (QL, XC0, SS, SZ), pp. 1603–1606.
CIKMCIKM-2018-LvZCXYL #network
Homepage Augmentation by Predicting Links in Heterogenous Networks (JL, JZ, WC, QX, ZY, QL0), pp. 1611–1614.
CIKMCIKM-2018-MaYXCZG #knowledge-based #named
KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare (FM, QY, HX, RC, JZ, JG0), pp. 743–752.
CIKMCIKM-2018-NechaevCG #linked data #open data #social #social media
Type Prediction Combining Linked Open Data and Social Media (YN, FC, CG), pp. 1033–1042.
CIKMCIKM-2018-SharchilevRROSR
Web-based Startup Success Prediction (BS, MR, AYR, DO, PS, MdR), pp. 2283–2291.
CIKMCIKM-2018-WuYZGWC #process
Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction (QW, CY, HZ, XG, PW, GC), pp. 517–526.
CIKMCIKM-2018-WuZSW #hybrid #modelling #social
Hybrid Deep Sequential Modeling for Social Text-Driven Stock Prediction (HW, WZ, WS, JW), pp. 1627–1630.
CIKMCIKM-2018-YangCR #process
Recurrent Spatio-Temporal Point Process for Check-in Time Prediction (GY, YC, CKR), pp. 2203–2211.
CIKMCIKM-2018-ZhaoX0ZLZ #approach #comprehension #learning #on the
On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning based Approach (JZ, JX, RZ0, PZ, CL, FZ), pp. 1413–1422.
CIKMCIKM-2018-ZhuLYZ0W #framework #learning
A Supervised Learning Framework for Prediction of Incompatible Herb Pair in Traditional Chinese Medicine (JZ, YL, SY, SZ, ZY0, CW), pp. 1799–1802.
ECIRECIR-2018-BahrainianMC #topic
Predicting Topics in Scholarly Papers (SAB, IM, FC), pp. 16–28.
ECIRECIR-2018-McDonaldMO18a #overview #towards #using
Towards Maximising Openness in Digital Sensitivity Review Using Reviewing Time Predictions (GM, CM, IO), pp. 699–706.
ECIRECIR-2018-OzdikisRN #statistics #twitter
Spatial Statistics of Term Co-occurrences for Location Prediction of Tweets (OO, HR, KN), pp. 494–506.
ECIRECIR-2018-ZhangWXLS #graph #precise
Discriminative Path-Based Knowledge Graph Embedding for Precise Link Prediction (MZ, QW, WX, WL, SS), pp. 276–288.
ICMLICML-2018-BonakdarpourCBL
Prediction Rule Reshaping (MB, SC, RFB, JL), pp. 629–637.
ICMLICML-2018-BrukhimG #modelling
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction (NB, AG), pp. 658–666.
ICMLICML-2018-GarciaCEd #learning
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction (AG0, CC, SE, FdB), pp. 1681–1689.
ICMLICML-2018-GhoshalH #learning #modelling #polynomial
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time (AG, JH), pp. 1749–1757.
ICMLICML-2018-HefnyM0SG #network #policy
Recurrent Predictive State Policy Networks (AH, ZM, WS0, SSS, GJG), pp. 1954–1963.
ICMLICML-2018-ItoYF #estimation #optimisation
Unbiased Objective Estimation in Predictive Optimization (SI, AY, RF), pp. 2181–2190.
ICMLICML-2018-JangKS #video
Video Prediction with Appearance and Motion Conditions (YJ, GK, YS), pp. 2230–2239.
ICMLICML-2018-KimW #approximate #bound #self #string
Self-Bounded Prediction Suffix Tree via Approximate String Matching (DK0, CJW), pp. 2664–2672.
ICMLICML-2018-Koriche #combinator #compilation #game studies
Compiling Combinatorial Prediction Games (FK), pp. 2761–2770.
ICMLICML-2018-LiptonWS #black box #detection
Detecting and Correcting for Label Shift with Black Box Predictors (ZCL, YXW, AJS), pp. 3128–3136.
ICMLICML-2018-MenschB #programming
Differentiable Dynamic Programming for Structured Prediction and Attention (AM, MB), pp. 3459–3468.
ICMLICML-2018-PanS #learning
Learning to Speed Up Structured Output Prediction (XP, VS), pp. 3993–4002.
ICMLICML-2018-PearceBZN #approach #learning
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach (TP, AB, MZ, AN), pp. 4072–4081.
ICMLICML-2018-PleissGWW #process
Constant-Time Predictive Distributions for Gaussian Processes (GP, JRG, KQW, AGW), pp. 4111–4120.
ICMLICML-2018-QiJZ #earley #parsing #sequence
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction (SQ, BJ, SCZ), pp. 4168–4176.
ICMLICML-2018-SanyalKGK #as a service #named
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service (AS, MJK, AG, VK), pp. 4497–4506.
ICMLICML-2018-Streeter #algorithm #approximate #modelling
Approximation Algorithms for Cascading Prediction Models (MS), pp. 4759–4767.
ICMLICML-2018-WangGLWY #learning #towards
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning (YW, ZG, ML, JW0, PSY), pp. 5110–5119.
ICMLICML-2018-WenHSZCL #network #recognition
Deep Predictive Coding Network for Object Recognition (HW, KH, JS, YZ, EC, ZL), pp. 5263–5272.
ICMLICML-2018-WichersVEL #video
Hierarchical Long-term Video Prediction without Supervision (NW, RV, DE, HL), pp. 6033–6041.
ICMLICML-2018-YoonJS18a #dataset #generative #modelling #multi #named #network #using
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks (JY, JJ, MvdS), pp. 5685–5693.
ICPRICPR-2018-BartoliLBB
Context-Aware Trajectory Prediction (FB, GL, LB, ADB), pp. 1941–1946.
ICPRICPR-2018-BhaskaruniML #framework #testing
Estimating Prediction Qualities without Ground Truth: A Revisit of the Reverse Testing Framework (DB, FPM, CL), pp. 49–54.
ICPRICPR-2018-ChengNTZ
Prediction Defaults for Networked-guarantee Loans (DC, ZN, YT, LZ0), pp. 361–366.
ICPRICPR-2018-DehzangiM #behaviour #using
Unobtrusive Driver Drowsiness Prediction Using Driving Behavior from Vehicular Sensors (OD, SM), pp. 3598–3603.
ICPRICPR-2018-Dou0 #2d #3d #network
2D and 3D Convolutional Neural Network Fusion for Predicting the Histological Grade of Hepatocellular Carcinoma (TD, WZ0), pp. 3832–3837.
ICPRICPR-2018-GaolLH0W #automation #learning #multi
Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning (LG, WL, ZH, DH0, YW), pp. 3592–3597.
ICPRICPR-2018-HeKMP
Aggregated Sparse Attention for Steering Angle Prediction (SH, DK, YM, NP), pp. 2398–2403.
ICPRICPR-2018-HouCLWX #network
Predicting Traffic Flow via Ensemble Deep Convolutional Neural Networks with Spatio-temporal Joint Relations (JH, JC, SL, JW, QX), pp. 1487–1492.
ICPRICPR-2018-LiangLJXL #benchmark #dataset #metric #multi #named
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction (LL, LL, LJ, DX, ML), pp. 1598–1603.
ICPRICPR-2018-LinLJ #named #ranking
R2-ResNeXt: A ResNeXt-Based Regression Model with Relative Ranking for Facial Beauty Prediction (LL, LL, LJ), pp. 85–90.
ICPRICPR-2018-LiYL #clustering #detection #documentation #image
Page Object Detection from PDF Document Images by Deep Structured Prediction and Supervised Clustering (XL, FY, CLL), pp. 3627–3632.
ICPRICPR-2018-Pham0V #graph #memory management #network #process
Graph Memory Networks for Molecular Activity Prediction (TP, TT0, SV), pp. 639–644.
ICPRICPR-2018-TayanovKS #classification #learning #using
Prediction-based classification using learning on Riemannian manifolds (VT, AK, CYS), pp. 591–596.
ICPRICPR-2018-WuLXFPL #network
Context-Aware Attention LSTM Network for Flood Prediction (YW, ZL, WX, JF, SP, TL), pp. 1301–1306.
ICPRICPR-2018-WuXFPL #network
Local and Global Bayesian Network based Model for Flood Prediction (YW, WX, JF, SP, TL), pp. 225–230.
ICPRICPR-2018-ZhaoYT #network #order #using
Pen Tip Motion Prediction for Handwriting Drawing Order Recovery using Deep Neural Network (BZ, MY, JT), pp. 704–709.
KDDKDD-2018-0001YSLQT
A Treatment Engine by Predicting Next-Period Prescriptions (BJ0, HY, LS, CL, YQ, JT), pp. 1608–1616.
KDDKDD-2018-AyhanCS
Predicting Estimated Time of Arrival for Commercial Flights (SA, PC, HS), pp. 33–42.
KDDKDD-2018-BeeckMSVD #machine learning
Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion (TODB, WM, KS0, BV, JD), pp. 606–615.
KDDKDD-2018-ChenYWWNL #metric #named #network
PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction (HC, HY, WW0, HW0, QVHN, XL), pp. 1177–1186.
KDDKDD-2018-DuLSH #towards
Towards Explanation of DNN-based Prediction with Guided Feature Inversion (MD, NL, QS, XH), pp. 1358–1367.
KDDKDD-2018-FoxAJPW #learning #multi
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories (IF, LA, MJ, RPB, JW), pp. 1387–1395.
KDDKDD-2018-GohSVH #learning #rule-based #using
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (GBG, CS, AV, NOH), pp. 302–310.
KDDKDD-2018-HangPN #behaviour #student
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction (MH, IP, JN), pp. 321–330.
KDDKDD-2018-HulotAJ #effectiveness #towards
Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems (PH, DA, SDJ), pp. 378–386.
KDDKDD-2018-KuangCAXL
Stable Prediction across Unknown Environments (KK, PC0, SA, RX, BL0), pp. 1617–1626.
KDDKDD-2018-LiaoZWMCYGW #learning #sequence
Deep Sequence Learning with Auxiliary Information for Traffic Prediction (BL, JZ, CW0, DM, TC, SY, YG, FW), pp. 537–546.
KDDKDD-2018-LiHZ #graph
E-tail Product Return Prediction via Hypergraph-based Local Graph Cut (JL, JH, YZ), pp. 519–527.
KDDKDD-2018-LiRMLKC #named
TATC: Predicting Alzheimer's Disease with Actigraphy Data (JL, YR, HM, ZL0, TK, HC), pp. 509–518.
KDDKDD-2018-LiuTZ #modelling
Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation (BL, PNT, JZ), pp. 1784–1793.
KDDKDD-2018-MaGSYZZ #health #risk management
Risk Prediction on Electronic Health Records with Prior Medical Knowledge (FM, JG0, QS, QY, JZ, AZ), pp. 1910–1919.
KDDKDD-2018-QiuTMDW0 #learning #named #social
DeepInf: Social Influence Prediction with Deep Learning (JQ, JT, HM, YD, KW, JT0), pp. 2110–2119.
KDDKDD-2018-Ruhrlander0U #modelling #using
Improving Box Office Result Predictions for Movies Using Consumer-Centric Models (RPR, MB0, MU), pp. 655–664.
KDDKDD-2018-ShenLOLZC #framework #named #network #novel
StepDeep: A Novel Spatial-temporal Mobility Event Prediction Framework based on Deep Neural Network (BS, XL, YO, ML, WZ, KMC), pp. 724–733.
KDDKDD-2018-SunTYWZ #identification #modelling
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models (MS, FT, JY, FW, JZ), pp. 793–801.
KDDKDD-2018-WaliaHCCLKNBAM #pipes and filters #risk management
A Dynamic Pipeline for Spatio-Temporal Fire Risk Prediction (BSW, QH, JC, FC, JL, NK, PN, JB, GA, MM), pp. 764–773.
KDDKDD-2018-WangJ
Smoothed Dilated Convolutions for Improved Dense Prediction (ZW, SJ), pp. 2486–2495.
KDDKDD-2018-YangSJ0 #clustering #ll #mobile #social
I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application (CY, XS, LJ, JH0), pp. 914–922.
KDDKDD-2018-YardimKMG #question
Can Who-Edits-What Predict Edit Survival? (ABY, VK, LM, MG), pp. 2604–2613.
KDDKDD-2018-YiZWLZ #distributed #network #quality
Deep Distributed Fusion Network for Air Quality Prediction (XY, JZ, ZW, TL, YZ0), pp. 965–973.
KDDKDD-2018-YuanZY #approach #learning #named
Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data (ZY, XZ, TY), pp. 984–992.
KDDKDD-2018-ZhaoAS0 #classification #dependence #performance #using
Prediction-time Efficient Classification Using Feature Computational Dependencies (LZ0, AAF, MS, KZ0), pp. 2787–2796.
KDDKDD-2018-ZhouZSFZMYJLG #network
Deep Interest Network for Click-Through Rate Prediction (GZ, XZ, CS, YF, HZ, XM, YY, JJ, HL, KG), pp. 1059–1068.
OOPSLAOOPSLA-2018-KalhaugeP #concurrent
Sound deadlock prediction (CGK, JP), p. 29.
OOPSLAOOPSLA-2018-MathurK0 #concurrent #detection #power of #what
What happens-after the first race? enhancing the predictive power of happens-before based dynamic race detection (UM, DK, MV0), p. 29.
PLDIPLDI-2018-0002ZLY #representation
A general path-based representation for predicting program properties (UA0, MZ, OL, EY), pp. 404–419.
PLDIPLDI-2018-RoemerGB #bound #concurrent #detection
High-coverage, unbounded sound predictive race detection (JR, KG, MDB), pp. 374–389.
ASEASE-2018-QuLCJCHZ #2d #fault #named #network #using
node2defect: using network embedding to improve software defect prediction (YQ, TL0, JC, YJ, DC, AH, QZ), pp. 844–849.
ESEC-FSEESEC-FSE-2018-ImtiazB #data-driven #requirements #towards
Towards data-driven vulnerability prediction for requirements (SMI, TB), pp. 744–748.
ESEC-FSEESEC-FSE-2018-LinHDZSXLLWYCZ
Predicting Node failure in cloud service systems (QL, KH, YD, HZ0, KS, YX, JGL, CL, YW, RY, MC, DZ), pp. 480–490.
ICSE-2018-BenninKPMM #approach #fault #named
MAHAKIL: diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction (KEB, JK, PP, AM, SM), p. 699.
ICSE-2018-DamevskiCSKP #behaviour #developer #ide #modelling #topic #using
Predicting future developer behavior in the IDE using topic models (KD, HC, DCS, NAK, LLP), p. 932.
ICSE-2018-HabayebMMB #debugging #markov #on the #using
On the use of hidden Markov model to predict the time to fix bugs (MH, SSM, AVM, ABB), p. 700.
ICSE-2018-HerboldTG #benchmark #case study #comparative #fault #metric
A comparative study to benchmark cross-project defect prediction approaches (SH, AT, JG), p. 1063.
ICSE-2018-McIntoshK #case study #fault
Are fix-inducing changes a moving target?: a longitudinal case study of just-in-time defect prediction (SM, YK), p. 560.
ASPLOSASPLOS-2018-EvtyushkinRAP #branch #named
BranchScope: A New Side-Channel Attack on Directional Branch Predictor (DE, RR, NBAG, DP), pp. 693–707.
ASPLOSASPLOS-2018-MishraILH #energy #latency #learning #named
CALOREE: Learning Control for Predictable Latency and Low Energy (NM, CI, JDL, HH), pp. 184–198.
CASECASE-2018-LowO #hybrid #implementation #realtime #using
Real-time Implementation of Nonlinear Model Predictive Control for Mechatronic Systems Using a Hybrid Model (SL, DO), pp. 164–167.
CASECASE-2018-NaugB #data-driven #energy
A Data Driven Method for Prediction of Energy Demand in Commercial Buildings (AN, GB), pp. 335–340.
CASECASE-2018-PedrielliJ
Simulation-Predictive Control for Manufacturing Systems (GP, FJ), pp. 1310–1315.
ICSTICST-2018-HemmatiS #testing
Investigating NLP-Based Approaches for Predicting Manual Test Case Failure (HH, FS), pp. 309–319.
ICSAICSA-2017-NassarS #fault #metric #question #traceability
Traceability Metrics as Early Predictors of Software Defects? (BN, RS), pp. 235–238.
ICSAICSA-2017-Yasaweerasinghelage #architecture #latency #modelling #simulation #using
Predicting Latency of Blockchain-Based Systems Using Architectural Modelling and Simulation (RY, MS, IW), pp. 253–256.
JCDLJCDL-2017-WeihsE #learning #metric
Learning to Predict Citation-Based Impact Measures (LW, OE), pp. 49–58.
EDMEDM-2017-Askinadze0 #distance #student
Application of the Dynamic Time Warping Distance for the Student Drop-out Prediction on Time Series Data (AA, SC0).
EDMEDM-2017-GardnerB #evaluation #towards
Toward Replicable Predictive Model Evaluation in MOOCs (JG, CB).
EDMEDM-2017-HongB #learning #using
A Prediction and Early Alert Model Using Learning Management System Data and Grounded in Learning Science Theory (WJH, MLB).
EDMEDM-2017-IsholaM
Predicting Prospective Peer Helpers to Provide Just-In-Time Help to Users in Question and Answer Forums (OMI, GM).
EDMEDM-2017-KaiAPBMWM #behaviour #online #student
Predicting Student Retention from Behavior in an Online Orientation Course (SK, JMLA, LP, RSB, KM, HW, MM).
EDMEDM-2017-MadaioLCO #mining #using
Using Temporal Association Rule Mining to Predict Dyadic Rapport in Peer Tutoring (MAM, RL, JC, AO).
EDMEDM-2017-NamFC #learning #semantics #word
Predicting Short- and Long-Term Vocabulary Learning via Semantic Features of Partial Word Knowledge (SN, GAF, KCT).
EDMEDM-2017-RenNR
Grade Prediction with Temporal Course-wise Influence (ZR, XN, HR).
EDMEDM-2017-WanDGP #using
Dropout Prediction in MOOCs using Learners' Study Habits Features (HW, JD, XG, DP).
EDMEDM-2017-WanDGYL #online #performance
Predicting Performance in a Small Private Online Course (HW, JD, XG, QY, KL).
EDMEDM-2017-XieMSEBH #adaptation #learning #online #student
Student Learning Strategies to Predict Success in an Online Adaptive Mathematics Tutoring System (JX, SM, KTS, AE, RSB, XH).
EDMEDM-2017-ZengCZKC
Dropout Prediction in Home Care Training (WZ, SCC, BZ, RK, CLC).
ICPCICPC-2017-CatolinoPLFZ #assessment #developer #empirical
Developer-related factors in change prediction: an empirical assessment (GC, FP, ADL, FF, AZ), pp. 186–195.
ICSMEICSME-2017-FanLZZZ #algorithm #challenge #empirical #fault #privacy
The Utility Challenge of Privacy-Preserving Data-Sharing in Cross-Company Defect Prediction: An Empirical Study of the CLIFF&MORPH Algorithm (YF, CL, XZ, GZ, YZ), pp. 80–90.
ICSMEICSME-2017-Gupta #maintenance #mining #process #using
Improving Software Maintenance Using Process Mining and Predictive Analytics (MG0), pp. 681–686.
ICSMEICSME-2017-HanLXLF #learning #using
Learning to Predict Severity of Software Vulnerability Using Only Vulnerability Description (ZH, XL0, ZX, HL, ZF0), pp. 125–136.
ICSMEICSME-2017-HuangXL #fault #modelling
Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction (QH, XX0, DL0), pp. 159–170.
ICSMEICSME-2017-LiJZZ #fault #kernel #learning #multi
Heterogeneous Defect Prediction Through Multiple Kernel Learning and Ensemble Learning (ZL0, XYJ, XZ, HZ0), pp. 91–102.
ICSMEICSME-2017-Schroeder0KPASH #industrial
Predicting and Evaluating Software Model Growth in the Automotive Industry (JS, CB0, AK, HP, MA0, MS, TH), pp. 584–593.
MSRMSR-2017-BisongTB #modelling #performance
Built to last or built too fast?: evaluating prediction models for build times (EB, ET, OB), pp. 487–490.
MSRMSR-2017-DehghanNBLD #empirical #implementation
Predicting likelihood of requirement implementation within the planned iteration: an empirical study at IBM (AD0, AN, KB, JL, DED), pp. 124–134.
MSRMSR-2017-MadeyskiK #dataset #fault #idea
Continuous defect prediction: the idea and a related dataset (LM, MK), pp. 515–518.
MSRMSR-2017-NiL #classification #effectiveness #using
Cost-effective build outcome prediction using cascaded classifiers (AN, ML0), pp. 455–458.
MSRMSR-2017-RahmanRK #code review #developer #experience #overview #using
Predicting usefulness of code review comments using textual features and developer experience (MMR0, CKR, RGK), pp. 215–226.
SANERSANER-2017-AltingerHSGW #fault #performance
Performance tuning for automotive Software Fault Prediction (HA, SH, FS, JG, FW), pp. 526–530.
SANERSANER-2017-LiuLNB #fault #locality #modelling #search-based #testing #using
Improving fault localization for Simulink models using search-based testing and prediction models (BL, L, SN, LCB), pp. 359–370.
AIIDEAIIDE-2017-RavariSSD #game studies #hybrid #online
Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny (YNR, PS, RS, AD), pp. 207–213.
AIIDEAIIDE-2017-SarkarC #game studies
Level Difficulty and Player Skill Prediction in Human Computation Games (AS, SC), pp. 228–233.
CHI-PLAYCHI-PLAY-2017-GuckelsbergerSG #case study #experience
Predicting Player Experience without the Player.: An Exploratory Study (CG, CS, JG, PAC), pp. 305–315.
CHI-PLAYCHI-PLAY-2017-HamalainenMTT #game studies #physics #simulation
Predictive Physics Simulation in Game Mechanics (PH, XM, JT, JT), pp. 497–505.
CoGCIG-2017-BertensGP #big data #game studies #multi #scalability
Games and big data: A scalable multi-dimensional churn prediction model (PB, AG, AP), pp. 33–36.
CoGCIG-2017-JeonYYK #game studies #mobile #performance
Extracting gamers' cognitive psychological features and improving performance of churn prediction from mobile games (JJ, DY, SIY, KJK), pp. 150–153.
DiGRADiGRA-2017-Donaldson
I Predict a Riot: Making and Breaking Rules and Norms in League of Legends (SD).
FDGFDG-2017-CleghernLOR #modelling #using
Predicting future states in DotA 2 using value-split models of time series attribute data (ZC, SL, OYÖ, DLR), p. 10.
CIKMCIKM-2017-BhamidipatiKM #scalability
A Large Scale Prediction Engine for App Install Clicks and Conversions (NB, RK, SM), pp. 167–175.
CIKMCIKM-2017-CaoSCOC #comprehension #named
DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades (QC, HS, KC, WO, XC), pp. 1149–1158.
CIKMCIKM-2017-DongSWGZ #interactive #modelling #social
Weakly-Guided User Stance Prediction via Joint Modeling of Content and Social Interaction (RD, YS, LW0, YG, YZ), pp. 1249–1258.
CIKMCIKM-2017-HansenHAL
Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care (CH, CH0, SA, CL), pp. 2095–2098.
CIKMCIKM-2017-HuangPLLMC #learning
An Ad CTR Prediction Method Based on Feature Learning of Deep and Shallow Layers (ZH, ZP, QL0, BL, HM, EC), pp. 2119–2122.
CIKMCIKM-2017-LiuH #adaptation #framework #multi #personalisation
A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection (ZL, MH), pp. 1169–1177.
CIKMCIKM-2017-MaYC #named #video
LARM: A Lifetime Aware Regression Model for Predicting YouTube Video Popularity (CM, ZY, CWC), pp. 467–476.
CIKMCIKM-2017-MehrotraASYZKK #modelling
Deep Sequential Models for Task Satisfaction Prediction (RM, AHA, MS, EY, IZ, AEK, MK), pp. 737–746.
CIKMCIKM-2017-MenonL #process
Predicting Short-Term Public Transport Demand via Inhomogeneous Poisson Processes (AKM, YL), pp. 2207–2210.
CIKMCIKM-2017-NiuZ #collaboration #recommendation #sequence
Collaborative Sequence Prediction for Sequential Recommender (SN, RZ), pp. 2239–2242.
CIKMCIKM-2017-SanjoK #semantics
Recipe Popularity Prediction with Deep Visual-Semantic Fusion (SS, MK), pp. 2279–2282.
CIKMCIKM-2017-TayTPH #graph #multi #network
Multi-Task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs (YT, LAT, MCP, SCH), pp. 1029–1038.
CIKMCIKM-2017-WangCHLSY #matrix
Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services (YW, JC, LH0, WL, LS, PSY), pp. 939–947.
CIKMCIKM-2017-WangDYT #comprehension #mobile #network #social
Understanding and Predicting Weight Loss with Mobile Social Networking Data (ZW, TD, DY, JT), pp. 1269–1278.
CIKMCIKM-2017-Yang17a #matrix
Bayesian Heteroscedastic Matrix Factorization for Conversion Rate Prediction (HY), pp. 2407–2410.
CIKMCIKM-2017-YaoZHB #named #semantics
SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories (DY, CZ, JHH, JB), pp. 2411–2414.
CIKMCIKM-2017-ZhangYEALL #analysis #social
Predicting Startup Crowdfunding Success through Longitudinal Social Engagement Analysis (QZ, TY, ME, SA0, VL, BTL), pp. 1937–1946.
CIKMCIKM-2017-ZhaoT #correlation #modelling
Modeling Temporal-Spatial Correlations for Crime Prediction (XZ, JT), pp. 497–506.
ECIRECIR-2017-ArguelloA0 #performance #query #using
Using Query Performance Predictors to Reduce Spoken Queries (JA, SA, FD0), pp. 27–39.
ECIRECIR-2017-ChifuDMM #query
Human-Based Query Difficulty Prediction (AGC, SD, SM, JM), pp. 343–356.
ECIRECIR-2017-ClosBWC #network #social
Predicting Emotional Reaction in Social Networks (JC, AB, NW, GC), pp. 527–533.
ECIRECIR-2017-GiachanouGMC #sentiment
Sentiment Propagation for Predicting Reputation Polarity (AG, JG, IM, FC), pp. 226–238.
ECIRECIR-2017-ManotumruksaMO #matrix #network #rating #social #word
Matrix Factorisation with Word Embeddings for Rating Prediction on Location-Based Social Networks (JM, CM, IO), pp. 647–654.
ECIRECIR-2017-RoiteroMM #effectiveness #question #topic
Do Easy Topics Predict Effectiveness Better Than Difficult Topics? (KR, EM, SM), pp. 605–611.
ECIRECIR-2017-SkowronLFS #music #retrieval
Predicting Genre Preferences from Cultural and Socio-Economic Factors for Music Retrieval (MS, FL, BF, MS), pp. 561–567.
ECIRECIR-2017-ZarrinkalamFBK #twitter
Predicting Users' Future Interests on Twitter (FZ, HF, EB, MK), pp. 464–476.
ICMLICML-2017-BelangerYM #energy #learning #network
End-to-End Learning for Structured Prediction Energy Networks (DB, BY, AM), pp. 429–439.
ICMLICML-2017-BojanowskiJ #learning
Unsupervised Learning by Predicting Noise (PB, AJ), pp. 517–526.
ICMLICML-2017-DempseyMSDGMR #named
iSurvive: An Interpretable, Event-time Prediction Model for mHealth (WHD, AM, CKS, MLD, DHG, SAM, JMR), pp. 970–979.
ICMLICML-2017-HartfordLLT #approach #flexibility
Deep IV: A Flexible Approach for Counterfactual Prediction (JSH, GL, KLB, MT), pp. 1414–1423.
ICMLICML-2017-KohL #black box #comprehension
Understanding Black-box Predictions via Influence Functions (PWK, PL), pp. 1885–1894.
ICMLICML-2017-MishraAM #modelling
Prediction and Control with Temporal Segment Models (NM, PA, IM), pp. 2459–2468.
ICMLICML-2017-PanYTB #nondeterminism #process
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control (YP, XY, EAT, BB), pp. 2760–2768.
ICMLICML-2017-PathakAED #self
Curiosity-driven Exploration by Self-supervised Prediction (DP, PA, AAE, TD), pp. 2778–2787.
ICMLICML-2017-SilverHHSGHDRRB #learning
The Predictron: End-To-End Learning and Planning (DS, HvH, MH, TS, AG, TH, GDA, DPR, NCR, AB, TD), pp. 3191–3199.
ICMLICML-2017-SunVGBB #learning
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction (WS0, AV, GJG, BB, JAB), pp. 3309–3318.
ICMLICML-2017-VillegasYZSLL #learning
Learning to Generate Long-term Future via Hierarchical Prediction (RV, JY, YZ, SS, XL, HL), pp. 3560–3569.
KDDKDD-2017-0013H #learning #paradigm
Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics (XL0, JH), pp. 285–294.
KDDKDD-2017-AokiAM
Luck is Hard to Beat: The Difficulty of Sports Prediction (RYSA, RMA, POSVdM), pp. 1367–1376.
KDDKDD-2017-ChamberlainCLPD #using
Customer Lifetime Value Prediction Using Embeddings (BPC, ÂC, CHBL, RP, MPD), pp. 1753–1762.
KDDKDD-2017-DebGIPVYY #automation #learning #named #network #policy
AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments (SD, ZG, SI, SCP, SV, HY, JY), pp. 1783–1792.
KDDKDD-2017-GongNSG #health
Predicting Clinical Outcomes Across Changing Electronic Health Record Systems (JJG, TN, PS, JVG), pp. 1497–1505.
KDDKDD-2017-ItoF #optimisation
Optimization Beyond Prediction: Prescriptive Price Optimization (SI, RF), pp. 1833–1841.
KDDKDD-2017-JiaKNGCWK #incremental
Incremental Dual-memory LSTM in Land Cover Prediction (XJ, AK, GN, JG, KC, PCW, VK), pp. 867–876.
KDDKDD-2017-LakkarajuKLLM #algorithm #problem
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (HL, JMK, JL, JL, SM), pp. 275–284.
KDDKDD-2017-LiuSLMLX #functional
Functional Zone Based Hierarchical Demand Prediction For Bike System Expansion (JL, LS, QL, JM, YL, HX), pp. 957–966.
KDDKDD-2017-MaCZYSG #bidirectional #named #network
Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks (FM, RC, JZ, QY, TS, JG0), pp. 1903–1911.
KDDKDD-2017-MottiniA #network #pointer #using
Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction (AM, RAA), pp. 1575–1583.
KDDKDD-2017-TolomeiSHL
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (GT, FS, AH, ML), pp. 465–474.
KDDKDD-2017-TongCZCWYYL #approach #online #platform #scalability
The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms (YT, YC, ZZ, LC0, JW0, QY0, JY, WL), pp. 1653–1662.
KDDKDD-2017-WoodsAMM #feedback #modelling #using
Formative Essay Feedback Using Predictive Scoring Models (BW, DA, SM, EM), pp. 2071–2080.
KDDKDD-2017-YamadaLGCWKKMC
Convex Factorization Machine for Toxicogenomics Prediction (MY, WL, AG, JC, KW, SAK, SK, HM, YC), pp. 1215–1224.
KDDKDD-2017-YangZH #algorithm #towards
Local Algorithm for User Action Prediction Towards Display Ads (HY, YZ, JH), pp. 2091–2099.
KDDKDD-2017-YilmazEF
Predicting Optimal Facility Location without Customer Locations (EY, SE, HF), pp. 2121–2130.
KDDKDD-2017-Yu
Three Principles of Data Science: Predictability, Stability and Computability (BY), p. 5.
KDDKDD-2017-ZhangAQ #multi
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (LZ, CCA, GJQ), pp. 2141–2149.
KDDKDD-2017-ZhangC
Weisfeiler-Lehman Neural Machine for Link Prediction (MZ, YC), pp. 575–583.
AdaEuropeAdaEurope-2017-DaiB #execution #realtime #roadmap #worst-case
Predicting Worst-Case Execution Time Trends in Long-Lived Real-Time Systems (XD, AB), pp. 87–101.
PEPMPEPM-2017-KlinikHJP #higher-order #workflow
Predicting resource consumption of higher-order workflows (MK, JH, JMJ, RP), pp. 99–110.
PLDIPLDI-2017-KiniM0 #linear
Dynamic race prediction in linear time (DK, UM, MV0), pp. 157–170.
ASEASE-2017-IncertoTT #adaptation #performance #self
Software performance self-adaptation through efficient model predictive control (EI, MT, CT), pp. 485–496.
ASEASE-2017-RolfsnesMB #recommendation
Predicting relevance of change recommendations (TR, LM, DWB), pp. 694–705.
ASEASE-2017-Sultana #metric #towards #using
Towards a software vulnerability prediction model using traceable code patterns and software metrics (KZS), pp. 1022–1025.
ESEC-FSEESEC-FSE-2017-FuM17a #fault #learning
Revisiting unsupervised learning for defect prediction (WF0, TM), pp. 72–83.
ESEC-FSEESEC-FSE-2017-HuijgensLSRGR #agile #delivery #metric #mining #power of
Strong agile metrics: mining log data to determine predictive power of software metrics for continuous delivery teams (HH, RL, DS, HR, GG, DR), pp. 866–871.
ICSE-2017-GopalakrishnanS #architecture #question #source code #topic
Can latent topics in source code predict missing architectural tactics? (RG, PS, MM, MG), pp. 15–26.
ASPLOSASPLOS-2017-ChenYGKMT #named #precise
Prophet: Precise QoS Prediction on Non-Preemptive Accelerators to Improve Utilization in Warehouse-Scale Computers (QC0, HY, MG, RSK, JM, LT), pp. 17–32.
CASECASE-2017-0004SWPL #data-driven #industrial #optimisation
Data-based predictive optimization for by product gas system in steel industry (JZ0, CS, WW0, WP, QL), p. 87.
CASECASE-2017-ArboGG #industrial #on the
On model predictive path following and trajectory tracking for industrial robots (MHA, EIG, JTG), pp. 100–105.
CASECASE-2017-GohSS #learning #modelling
A model-based learning controller with predictor augmentation for non-stationary conditions and time delay in water shooting (CFG, GLGS, KS), pp. 1110–1117.
CASECASE-2017-JiangJG #optimisation
Optimization of sensor location for improving wind power prediction accuracy (ZJ, QSJ, XG), pp. 1220–1225.
CASECASE-2017-KeL #mobile #robust #using #visual notation
Visual servoing of constrained differential-drive mobile robots using robust tube-based predictive control (FK, ZL0), pp. 1073–1078.
CASECASE-2017-KinghorstGLCYFZ #algorithm #approach #maintenance #markov #modelling #search-based
Hidden Markov model-based predictive maintenance in semiconductor manufacturing: A genetic algorithm approach (JK, OG, ML, HLC, KY, JF, MZ, BVH), pp. 1260–1267.
CASECASE-2017-KongN #approach #fault #using
A practical yield prediction approach using inline defect metrology data for system-on-chip integrated circuits (YK, DN), pp. 744–749.
CASECASE-2017-LaiJG #energy #learning #parametricity
An integrated physical-based and parameter learning method for ship energy prediction under varying operating conditions (XL, XJ, XG), pp. 1180–1185.
CASECASE-2017-LiuZ
A rolling ARMA method for ultra short term wind power prediction (YL, YZ), pp. 1232–1236.
CASECASE-2017-Pu0L #distributed
Model predictive control for distributed microgrid system with unbalanced loads (YP, JW0, SL), pp. 1622–1627.
CGOCGO-2017-CumminsP0L #benchmark #metric #modelling
Synthesizing benchmarks for predictive modeling (CC, PP, ZW0, HL), pp. 86–99.
FASEFASE-2017-AraujoNN #debugging #effectiveness #on the
On the Effectiveness of Bug Predictors with Procedural Systems: A Quantitative Study (CWA, IN, DJN), pp. 78–95.
QoSAQoSA-2016-PitakratOHG #approach #architecture #online
An Architecture-Aware Approach to Hierarchical Online Failure Prediction (TP, DO, AvH, LG), pp. 60–69.
JCDLJCDL-2016-KehoeT #similarity
Predicting Medical Subject Headings Based on Abstract Similarity and Citations to MEDLINE Records (AKK, VIT), pp. 167–170.
JCDLJCDL-2016-NezhadbiglariGA
Early Prediction of Scholar Popularity (MN, MAG, JMA), pp. 181–190.
EDMEDM-2016-al-RifaieYd #performance
Investigating Swarm Intelligence for Performance Prediction (MMaR, MYK, Md), pp. 264–269.
EDMEDM-2016-AshenafiRR #student
Predicting Student Progress from Peer-Assessment Data (MMA, MR, GR), pp. 270–275.
EDMEDM-2016-BotelhoAH #interactive #modelling
Modeling Interactions Across Skills: A Method to Construct and Compare Models Predicting the Existence of Skill Relationships (AFB, SA, NTH), pp. 292–297.
EDMEDM-2016-BoyerV #modelling #robust
Robust Predictive Models on MOOCs : Transferring Knowledge across Courses (SB, KV), pp. 298–305.
EDMEDM-2016-Bydzovska #analysis #comparative #performance #student
A Comparative Analysis of Techniques for Predicting Student Performance (HB), pp. 306–311.
EDMEDM-2016-CraigHXFH #behaviour #identification #learning #persistent
Identifying relevant user behavior and predicting learning and persistence in an ITS-based afterschool program (SDC, XH, JX, YF, XH), pp. 581–582.
EDMEDM-2016-DominguezBU #learning #modelling
Predicting STEM Achievement with Learning Management System Data: Prediction Modeling and a Test of an Early Warning System (MD, MLB, PMU), pp. 589–590.
EDMEDM-2016-GonzalezMRM #case study #collaboration
Meta-learning for predicting the best vote aggregation method: Case study in collaborative searching of LOs (AZG, VHM, CR, MEPM), pp. 656–657.
EDMEDM-2016-MinWPVBMFWL #interactive #multimodal #student
Predicting Dialogue Acts for Intelligent Virtual Agents with Multimodal Student Interaction Data (WM, JBW, LP, AKV, KEB, BWM, MF, ENW, JCL), pp. 454–459.
EDMEDM-2016-Nam #adaptation #behaviour #learning
Predicting Off-task Behaviors for Adaptive Vocabulary Learning System (SN), pp. 672–674.
EDMEDM-2016-RauP #modelling #representation
Adding eye-tracking AOI data to models of representation skills does not improve prediction accuracy (MAR, ZAP), pp. 622–623.
EDMEDM-2016-RenRJ #modelling #multi #performance #using
Predicting Performance on MOOC Assessments using Multi-Regression Models (ZR, HR, AJ), pp. 484–489.
EDMEDM-2016-SahebiLB #modelling #performance #student
Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain (SS, YRL, PB), pp. 502–506.
EDMEDM-2016-SalesWP #algebra #student
Student Usage Predicts Treatment Effect Heterogeneity in the Cognitive Tutor Algebra I Program (AS, AW, JP), pp. 207–214.
EDMEDM-2016-SharmaBGPD16a #education #multimodal #named #network
LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos (AS, AB, AG, SP, OD), pp. 215–222.
EDMEDM-2016-SharmaBGPD16a_ #education #multimodal #named #network
LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos (AS, AB, AG, SP, OD), pp. 215–222.
EDMEDM-2016-StapelZP #learning #online #performance #student
An Ensemble Method to Predict Student Performance in an Online Math Learning Environment (MS, ZZ, NP), pp. 231–238.
EDMEDM-2016-SweeneyLRJ #approach #performance #recommendation #student
Next-Term Student Performance Prediction: A Recommender Systems Approach (MS, JL, HR, AJ), p. 7.
EDMEDM-2016-TomkinsRG #behaviour #case study #performance #student
Predicting Post-Test Performance from Student Behavior: A High School MOOC Case Study (ST, AR, LG), pp. 239–246.
EDMEDM-2016-VailWGBWL
The Affective Impact of Tutor Questions: Predicting Frustration and Engagement (AKV, JBW, JFG, KEB, ENW, JCL), pp. 247–254.
EDMEDM-2016-WenMWDHR #collaboration #integration #learning #online
Transactivity as a Predictor of Future Collaborative Knowledge Integration in Team-Based Learning in Online Courses (MW, KM, XW0, SD, JDH, CPR), pp. 533–538.
EDMEDM-2016-Yee-KingGd #collaboration #learning #metric #online #social #student #using
Predicting student grades from online, collaborative social learning metrics using K-NN (MYK, AGR, Md), pp. 654–655.
ICSMEICSME-2016-LevinY #developer #maintenance #process #semantics #using
Using Temporal and Semantic Developer-Level Information to Predict Maintenance Activity Profiles (SL, AY), pp. 463–467.
ICSMEICSME-2016-PalombaZFLO #debugging #performance #smell #using
Smells Like Teen Spirit: Improving Bug Prediction Performance Using the Intensity of Code Smells (FP, MZ, FAF, ADL, RO), pp. 244–255.
MSRMSR-2016-KikasDP #git #using
Using dynamic and contextual features to predict issue lifetime in GitHub projects (RK, MD, DP), pp. 291–302.
SANERSANER-2016-MachoMP #category theory #commit #source code
Predicting Build Co-changes with Source Code Change and Commit Categories (CM, SM, MP0), pp. 541–551.
SANERSANER-2016-XuXLC #clustering #fault #feature model #information management #named
MICHAC: Defect Prediction via Feature Selection Based on Maximal Information Coefficient with Hierarchical Agglomerative Clustering (ZX, JX, JL0, XC), pp. 370–381.
SCAMSCAM-2016-JimenezPT #case study #kernel #linux #modelling
Vulnerability Prediction Models: A Case Study on the Linux Kernel (MJ, MP, YLT), pp. 1–10.
AIIDEAIIDE-2016-DrachenLKRSRK #agile #game studies #mobile
Rapid Prediction of Player Retention in Free-to-Play Mobile Games (AD, ETL, YK, PSR, RS, JR, DK), pp. 23–29.
AIIDEAIIDE-2016-Valls-VargasZO #natural language
Predicting Proppian Narrative Functions from Stories in Natural Language (JVV, JZ, SO), pp. 107–113.
CoGCIG-2016-ClericoCPMTFGJ #classification #game studies #video
Biometrics and classifier fusion to predict the fun-factor in video gaming (AC, CC, MP, PEM, ST, THF, JCG, PLJ), pp. 1–8.
CoGCIG-2016-SephtonCDHS #android #mining #using
Using association rule mining to predict opponent deck content in android: Netrunner (NS, PIC, SD, VJH, NHS), pp. 1–8.
CoGCIG-2016-ShakerA #experience #learning
Transfer learning for cross-game prediction of player experience (NS, MAZ), pp. 1–8.
CoGCIG-2016-SifaSDOB #game studies #learning #representation
Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning (RS, SS, AD, CO, CB), pp. 1–8.
CoGCIG-2016-TamassiaRSDZH #approach #game studies #markov #modelling #online
Predicting player churn in destiny: A Hidden Markov models approach to predicting player departure in a major online game (MT, WLR, RS, AD, FZ, MH), pp. 1–8.
CIKMCIKM-2016-AgrawalC #data mining #mining #using
A Fatigue Strength Predictor for Steels Using Ensemble Data Mining: Steel Fatigue Strength Predictor (AA, ANC), pp. 2497–2500.
CIKMCIKM-2016-AmeriFCR #analysis #framework #student
Survival Analysis based Framework for Early Prediction of Student Dropouts (SA, MJF, RBC, CKR), pp. 903–912.
CIKMCIKM-2016-Bao #modelling #process #self
Modeling and Predicting Popularity Dynamics via an Influence-based Self-Excited Hawkes Process (PB), pp. 1897–1900.
CIKMCIKM-2016-Duong-TrungSS #matrix #realtime #twitter
Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression (NDT, NS, LST), pp. 1973–1976.
CIKMCIKM-2016-HansenLM #query #using #web
Ensemble Learned Vaccination Uptake Prediction using Web Search Queries (NDH, CL, KM), pp. 1953–1956.
CIKMCIKM-2016-HuangWW
Crowdsourcing-based Urban Anomaly Prediction System for Smart Cities (CH0, XW, DW0), pp. 1969–1972.
CIKMCIKM-2016-ImamoriT #twitter
Predicting Popularity of Twitter Accounts through the Discovery of Link-Propagating Early Adopters (DI, KT), pp. 639–648.
CIKMCIKM-2016-JatowtKT #using #wiki
Predicting Importance of Historical Persons using Wikipedia (AJ, DK, KT), pp. 1909–1912.
CIKMCIKM-2016-LinCWC #optimisation #realtime
Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget (CCL, KTC, WCHW, MSC), pp. 2143–2148.
CIKMCIKM-2016-LiuKL #social #social media #using
Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media (XL, XK, YL), pp. 2179–2184.
CIKMCIKM-2016-MishraRX #process
Feature Driven and Point Process Approaches for Popularity Prediction (SM, MAR, LX), pp. 1069–1078.
CIKMCIKM-2016-NegiC #network #social
Link Prediction in Heterogeneous Social Networks (SN, SC), pp. 609–617.
CIKMCIKM-2016-QiuSCCK #behaviour #crowdsourcing #named
CrowdSelect: Increasing Accuracy of Crowdsourcing Tasks through Behavior Prediction and User Selection (CQ, ACS, BC, JC, DRK), pp. 539–548.
CIKMCIKM-2016-WangKBC
Webpage Depth-level Dwell Time Prediction (CW, AK, CB, YC0), pp. 1937–1940.
CIKMCIKM-2016-WangPVR #named
CRISP: Consensus Regularized Selection based Prediction (PW, KKP, BV, CKR), pp. 1019–1028.
CIKMCIKM-2016-WangSYLFXB16a #multi
Empowering Truth Discovery with Multi-Truth Prediction (XW0, QZS, LY, XL0, XSF, XX, BB), pp. 881–890.
CIKMCIKM-2016-WeiWWLX #hybrid #named
ZEST: A Hybrid Model on Predicting Passenger Demand for Chauffeured Car Service (HW, YW, TW, YL, JX0), pp. 2203–2208.
CIKMCIKM-2016-ZengZMZW #clustering #network
Exploiting Cluster-based Meta Paths for Link Prediction in Signed Networks (JZ, KZ0, XM, FZ, HW), pp. 1905–1908.
CIKMCIKM-2016-ZhangGWHH #network #twitter
Retweet Prediction with Attention-based Deep Neural Network (QZ0, YG, JW, HH, XH), pp. 75–84.
CIKMCIKM-2016-ZhangXLGFY #multi
Multi-source Hierarchical Prediction Consolidation (CZ, SX, YL, JG0, WF0, PSY), pp. 2251–2256.
CIKMCIKM-2016-ZhengWPYFX #big data #using
Urban Traffic Prediction through the Second Use of Inexpensive Big Data from Buildings (ZZ, DW0, JP, YY, CF, LFX), pp. 1363–1372.
ECIRECIR-2016-ArguelloAD #performance #query #using
Using Query Performance Predictors to Improve Spoken Queries (JA, SA, FD0), pp. 309–321.
ECIRECIR-2016-MiottoLD #health #learning
Deep Learning to Predict Patient Future Diseases from the Electronic Health Records (RM, LL0, JTD), pp. 768–774.
ECIRECIR-2016-SchedlZ #locality #music #retrieval #web
Fusing Web and Audio Predictors to Localize the Origin of Music Pieces for Geospatial Retrieval (MS, FZ), pp. 322–334.
ECIRECIR-2016-WangGLXC #learning #multi #representation
Multi-task Representation Learning for Demographic Prediction (PW, JG, YL, JX0, XC), pp. 88–99.
ECIRECIR-2016-ZhangDW #case study #category theory #learning #multi
Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction (WZ0, TD, JW0), pp. 45–57.
ICMLICML-2016-AJFMS #cumulative #learning
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control (PLA, CJ, MCF0, SIM, CS), pp. 1406–1415.
ICMLICML-2016-BelangerM #energy #network
Structured Prediction Energy Networks (DB, AM), pp. 983–992.
ICMLICML-2016-Hernandez-Lobato #multi #optimisation
Predictive Entropy Search for Multi-objective Bayesian Optimization (DHL, JMHL, AS, RPA), pp. 1492–1501.
ICMLICML-2016-LeKYC #learning #online #sequence
Smooth Imitation Learning for Online Sequence Prediction (HML0, AK, YY, PC0), pp. 680–688.
ICMLICML-2016-MeshiMWS
Train and Test Tightness of LP Relaxations in Structured Prediction (OM, MM, AW, DAS), pp. 1776–1785.
ICMLICML-2016-SunVBB #learning
Learning to Filter with Predictive State Inference Machines (WS0, AV, BB, JAB), pp. 1197–1205.
ICMLICML-2016-TrouillonWRGB
Complex Embeddings for Simple Link Prediction (TT, JW, SR0, ÉG, GB), pp. 2071–2080.
ICMLICML-2016-YoonAHS #named
ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission (JY, AMA, SH, MvdS), pp. 1680–1689.
ICPRICPR-2016-AydinKAA #automation #random #using
Automatic personality prediction from audiovisual data using random forest regression (BA, AAK, OA, LA), pp. 37–42.
ICPRICPR-2016-CarvajalWSL #automation #contest #towards
Towards Miss Universe automatic prediction: The evening gown competition (JC, AW, CS, BCL), pp. 1089–1094.
ICPRICPR-2016-CorniaBSC #multi #network
A deep multi-level network for saliency prediction (MC, LB, GS0, RC), pp. 3488–3493.
ICPRICPR-2016-GrantSZG #network #visualisation
Predicting and visualizing psychological attributions with a deep neural network (EG, SS, MZ, MvG), pp. 1–6.
ICPRICPR-2016-GrenierBV
Taking into account stereoisomerism in the prediction of molecular properties (PAG, LB, DV), pp. 1542–1547.
ICPRICPR-2016-Kamkar0LPV #graph #using
Stable clinical prediction using graph support vector machines (IK, SG0, CL0, DQP, SV), pp. 3332–3337.
ICPRICPR-2016-KaurKS #video
Prediction based seam carving for video retargeting (HK, SK, DS), pp. 877–882.
ICPRICPR-2016-LeeL #process
Human activity prediction based on Sub-volume Relationship Descriptor (DGL, SWL), pp. 2060–2065.
ICPRICPR-2016-Li0RNVAL #multi
Multiple adverse effects prediction in longitudinal cancer treatment (CL0, SG0, SR, VN0, SV, DA, TL), pp. 3156–3161.
ICPRICPR-2016-NorooziMNEA #classification #recognition
Fusion of classifier predictions for audio-visual emotion recognition (FN, MM, AN, SE, GA), pp. 61–66.
ICPRICPR-2016-OgawaMDCFH #adaptation #multi #performance
A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation (TO, SHRM, ED, JC, NHF, HH), pp. 2222–2227.
ICPRICPR-2016-RoyCSC #classification
Meta-regression based pool size prediction scheme for dynamic selection of classifiers (AR, RMOC, RS, GDCC), pp. 216–221.
ICPRICPR-2016-SarafianosNK #estimation
Predicting privileged information for height estimation (NS, CN, IAK), pp. 3115–3120.
ICPRICPR-2016-TamboB #behaviour
Temporal dynamics of tip fluorescence predict cell growth behavior in pollen tubes (ALT, BB), pp. 1171–1176.
ICPRICPR-2016-Valverde-Rebaza #network #social
Exploiting social and mobility patterns for friendship prediction in location-based social networks (JCVR, MR, PP, AdAL), pp. 2526–2531.
ICPRICPR-2016-WangB #network
Link prediction via Supervised Dynamic Network Formation (YW0, LB0), pp. 4160–4165.
ICPRICPR-2016-ZhaoIBJ #fault #using
Wind turbine fault prediction using soft label SVM (RZ, MRAI, KPB, QJ), pp. 3192–3197.
ICPRICPR-2016-ZhaoMFQH #performance #using
Fast motion deblurring using gyroscopes and strong edge prediction (JZ, JM, BF, SQ, FH), pp. 739–744.
ICPRICPR-2016-ZhongSL #adaptation #off the shelf #recognition
Transferring from face recognition to face attribute prediction through adaptive selection of off-the-shelf CNN representations (YZ, JS, HL0), pp. 2264–2269.
KDDKDD-2016-0002GOL #modelling #scalability
Business Applications of Predictive Modeling at Scale (QZ0, SG, PO, YL), pp. 2139–2140.
KDDKDD-2016-AyhanS
Aircraft Trajectory Prediction Made Easy with Predictive Analytics (SA, HS), pp. 21–30.
KDDKDD-2016-BaoWL #approach #data-driven #network #resource management
From Prediction to Action: A Closed-Loop Approach for Data-Guided Network Resource Allocation (YB, HW, XL0), pp. 1425–1434.
KDDKDD-2016-BotezatuGBW #reliability #towards
Predicting Disk Replacement towards Reliable Data Centers (MMB, IG, JB, DW), pp. 39–48.
KDDKDD-2016-BrooksKG #data-driven #ranking #using
Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights (JB, MK, JVG), pp. 49–55.
KDDKDD-2016-ChakrabortyVJS #using
Predicting Socio-Economic Indicators using News Events (SC, AV, SJ, LS), pp. 1455–1464.
KDDKDD-2016-ChenJ
Predicting Matchups and Preferences in Context (SC0, TJ), pp. 775–784.
KDDKDD-2016-DengSDZYL #network
Latent Space Model for Road Networks to Predict Time-Varying Traffic (DD, CS, UD, LZ, RY, YL0), pp. 1525–1534.
KDDKDD-2016-HuLL #profiling #social
Collective Sensemaking via Social Sensors: Extracting, Profiling, Analyzing, and Predicting Real-world Events (YH, YRL, JL), pp. 2127–2128.
KDDKDD-2016-KhanB #behaviour #modelling
Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries (MRK, JEB), pp. 145–154.
KDDKDD-2016-LakkarajuBL #framework #set
Interpretable Decision Sets: A Joint Framework for Description and Prediction (HL, SHB, JL), pp. 1675–1684.
KDDKDD-2016-LiuNZZYCWZC #e-commerce
Repeat Buyer Prediction for E-Commerce (GL, TTN, GZ, WZ, JY, JC, MW0, PZ, WC), pp. 155–164.
KDDKDD-2016-MadaioCHZCHCD #named
Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta (MAM, STC, OLH, WZ, XC0, MHA, DHC, BD), pp. 185–194.
KDDKDD-2016-NakagawaSKTT #approach #mining #performance
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining (KN, SS, MK, KT, IT), pp. 1785–1794.
KDDKDD-2016-NieGY #multi
Predict Risk of Relapse for Patients with Multiple Stages of Treatment of Depression (ZN, PG, JY), pp. 1795–1804.
KDDKDD-2016-Ribeiro0G #classification #quote #trust #why
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier (MTR, SS0, CG), pp. 1135–1144.
KDDKDD-2016-SalehiRLP #approach #risk management #robust
Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach (MS, LIR, TML, AP), pp. 245–254.
KDDKDD-2016-TanFLWLLPXH #adaptation #algorithm #scalability
Scalable Time-Decaying Adaptive Prediction Algorithm (YT, ZF, GL, FW, ZL, SL, QP, EPX, QH), pp. 617–626.
KDDKDD-2016-ZhangZMCZA #linear #modelling #named #scalability
GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction (XZ, YZ, YM, BCC, LZ, DA), pp. 363–372.
MoDELSMoDELS-2016-FalknerSC #modelling #performance
Model-driven performance prediction of systems of systems (KEF, CS, VC), p. 44.
POPLPOPL-2016-KatzEY #modelling #using
Estimating types in binaries using predictive modeling (OK, REY, EY), pp. 313–326.
ASEASE-2016-XuYXXCL #developer #network #online #semantics
Predicting semantically linkable knowledge in developer online forums via convolutional neural network (BX, DY, ZX, XX, GC, SL), pp. 51–62.
ASEASE-2016-YangHKIBZX #clustering #dependence #empirical
An empirical study on dependence clusters for effort-aware fault-proneness prediction (YY, MH, JK, SSI, DB, YZ, BX), pp. 296–307.
FSEFSE-2016-PiorkowskiHNFSB #developer
Foraging and navigations, fundamentally: developers' predictions of value and cost (DP, AZH, TN, SDF, CS, MMB), pp. 97–108.
FSEFSE-2016-Xu0ZX #analysis #debugging #detection #python
Python predictive analysis for bug detection (ZX, PL0, XZ0, BX), pp. 121–132.
FSEFSE-2016-YangZLZL0XL #fault #modelling
Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models (YY, YZ, JL, YZ, HL, LX0, BX, HL), pp. 157–168.
ICSE-2016-MullerF #metric #online #quality #using
Using (bio)metrics to predict code quality online (SCM, TF0), pp. 452–463.
ICSE-2016-Tantithamthavorn #automation #classification #fault #modelling #optimisation #parametricity
Automated parameter optimization of classification techniques for defect prediction models (CT, SM, AEH, KM), pp. 321–332.
ICSE-2016-WangLT #automation #fault #learning #semantics
Automatically learning semantic features for defect prediction (SW0, TL, LT0), pp. 297–308.
ICSE-2016-ZhangZZH #classification #fault #using
Cross-project defect prediction using a connectivity-based unsupervised classifier (FZ0, QZ, YZ0, AEH), pp. 309–320.
ASPLOSASPLOS-2016-JeonHKERC #interactive #latency #named #parallel
TPC: Target-Driven Parallelism Combining Prediction and Correction to Reduce Tail Latency in Interactive Services (MJ, YH, HK, SE, SR, ALC), pp. 129–141.
ASPLOSASPLOS-2016-YoonSCC #data flow #named
PIFT: Predictive Information-Flow Tracking (MKY, NS, YC, MC), pp. 713–725.
CASECASE-2016-AmannAS #online #realtime
Online real-time scheduled model predictive feedforward control for impounded river reaches applied to the Moselle river (KUA, EA, OS), pp. 1276–1281.
CASECASE-2016-ChuSS #modelling #probability #realtime
Stochastic Lagrangian Traffic flow modeling and real-time traffic prediction (KCC, RS, KS), pp. 213–218.
CASECASE-2016-MengZZT #power management #process
A new model for predicting power consumption of machining processes: A turning case (LM, MZ, CZ, GT), pp. 1289–1294.
CASECASE-2016-SabbaghiH #3d #process
Predictive model building across different process conditions and shapes in 3D printing (AS, QH), pp. 774–779.
CASECASE-2016-ShigematsuTS
Tip-over prevention control of a teleoperated excavator based on ZMP prediction (KS, TT, SS), pp. 1380–1386.
CASECASE-2016-TohidiH #adaptation #multi #process #self
Self-Tuning Adaptive Multiple Model Predictive Control with application to pH Control process (AT, HH), pp. 1219–1224.
CASECASE-2016-TsaiWPXLFZ #behaviour #process #smarttech #using
Context-aware activity prediction using human behavior pattern in real smart home environments (MJT, CLW, SKP, YX, TYL, LCF, YCZ), pp. 168–173.
CASECASE-2016-YangLWC #architecture #hybrid
A hybrid tool life prediction scheme in cloud architecture (HCY, YYL, MNW, FTC), pp. 1160–1165.
ICSTICST-2016-YuWHH #concurrent #source code #testing
Predicting Testability of Concurrent Programs (TY, WW, XH, JHH), pp. 168–179.
HTHT-2015-BurelMHA #behaviour #community #online
Predicting Answering Behaviour in Online Question Answering Communities (GB, PM, YH, HA), pp. 201–210.
HTHT-2015-ChungL #framework #platform
A Long-Term Study of a Crowdfunding Platform: Predicting Project Success and Fundraising Amount (JC, KL), pp. 211–220.
HTHT-2015-PrasetyoH
Twitter-based Election Prediction in the Developing World (NDP, CH), pp. 149–158.
JCDLJCDL-2015-SalahEldeenN #resource management
Predicting Temporal Intention in Resource Sharing (HMS, MLN), pp. 205–214.
JCDLJCDL-2015-ZhouGKT
No More 404s: Predicting Referenced Link Rot in Scholarly Articles for Pro-Active Archiving (KZ, CG, MK0, RT), pp. 233–236.
SIGMODSIGMOD-2015-HuangZYDLND0Z #big data
Telco Churn Prediction with Big Data (YH, FZ, MY, KD, YL, BN, WD, QY, JZ), pp. 607–618.
SIGMODSIGMOD-2015-PrasadFGMLXHR #data transfer #distributed #performance #scalability
Large-scale Predictive Analytics in Vertica: Fast Data Transfer, Distributed Model Creation, and In-database Prediction (SP, AF, VG, JM, JL, VX, MH, IR), pp. 1657–1668.
SIGMODSIGMOD-2015-ZhouT #named
SMiLer: A Semi-Lazy Time Series Prediction System for Sensors (JZ, AKHT), pp. 1871–1886.
EDMEDM-2015-AltrabshehCF #feedback #student
Predicting Learning-Related Emotions from Students' Textual Classroom Feedback (NA, MC, SF), pp. 436–439.
EDMEDM-2015-BotelhoAWH #performance #student #using
Predicting Student Aptitude Using Performance History (AFB, SAA, HW, NTH), pp. 622–623.
EDMEDM-2015-Bydzovska #performance #towards
Towards Freshmen Performance Prediction (HB), pp. 602–603.
EDMEDM-2015-CastroACH #modelling #student
Building Models to Predict Hint-or-Attempt Actions of Students (FEVC, SA, TC, NTH), pp. 476–479.
EDMEDM-2015-ChaplotRK #named #student
SAP: Student Attrition Predictor (DSC, ER, JK), pp. 635–636.
EDMEDM-2015-EagleHB #estimation #interactive #network #problem
Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments (ME, AH, TB), pp. 342–349.
EDMEDM-2015-Gonzalez-Brenes #adaptation #empirical #evaluation #paradigm
Your Model Is Predictive - but Is It Useful? Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation (JPGB, YH0), pp. 187–194.
EDMEDM-2015-InwegenWAH #modelling
The Effect of the Distribution of Predictions of User Models (EVI, YW0, SA, NTH), pp. 620–621.
EDMEDM-2015-LuoK0 #classification #performance #student
Discrimination-Aware Classifiers for Student Performance Prediction (LL, IK, WL0), pp. 384–387.
EDMEDM-2015-LuoSMG #student #using #word
Predicting Student Grade based on Free-style Comments using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons (JL, SES, TM, KG), pp. 396–399.
EDMEDM-2015-OlsenAR #collaboration #learning #performance #student
Predicting Student Performance In a Collaborative Learning Environment (JKO, VA, NR), pp. 211–217.
EDMEDM-2015-OstrowDH #algorithm #optimisation #performance #student
Optimizing Partial Credit Algorithms to Predict Student Performance (KO, CD, NTH), pp. 404–407.
EDMEDM-2015-Riofrio-Luzcando #3d #student
A Model for Student Action Prediction in 3D Virtual Environments for Procedural Training (DRL, JR), pp. 614–615.
EDMEDM-2015-RollinsonB #modelling #policy
From Predictive Models to Instructional Policies (JR, EB), pp. 179–186.
EDMEDM-2015-RuizUF #interactive #monitoring #student
Predicting Students' Outcome by Interaction Monitoring (SR, MU, IFC), pp. 598–599.
EDMEDM-2015-SameiOKNDBG #modelling #question
Modeling Classroom Discourse: Do Models of Predicting Dialogic Instruction Properties Generalize across Populations? (BS, AO, SK, MN, SKD, NB, ACG), pp. 444–447.
EDMEDM-2015-SnowPJMB #experience #student
Achievement versus Experience: Predicting Students' Choices during Gameplay (ELS, MOSP, MEJ, DSM, RSB), pp. 564–565.
EDMEDM-2015-TangGMP #effectiveness #order
Desirable Difficulty and Other Predictors of Effective Item Orderings (ST, HG, EM, ZAP), pp. 416–419.
EDMEDM-2015-ViePGBB #adaptation #modelling #performance #scalability #testing
Predicting Performance on Dichotomous Questions: Comparing Models for Large-Scale Adaptive Testing (JJV, FP, JBG, EB, YB), pp. 618–619.
EDMEDM-2015-WhitehillWLCR #automation #student #towards
Beyond Prediction: Towards Automatic Intervention in MOOC Student Stop-out (JW, JJW, GL, CAC, JR), pp. 171–178.
ITiCSEITiCSE-2015-Cukierman #learning #process #student
Predicting Success in University First Year Computing Science Courses: The Role of Student Participation in Reflective Learning Activities and in I-clicker Activities (DC), pp. 248–253.
ICSMEICSME-2015-NucciPSBOL #debugging #developer #on the
On the role of developer’s scattered changes in bug prediction (DDN, FP, SS, GB, RO, ADL), pp. 241–250.
MSRMSR-2015-AltingerSDW #dataset #embedded #fault #industrial #modelling #novel
A Novel Industry Grade Dataset for Fault Prediction Based on Model-Driven Developed Automotive Embedded Software (HA, SS, YD, FW), pp. 494–497.
MSRMSR-2015-ChoetkiertikulD #risk management
Characterization and Prediction of Issue-Related Risks in Software Projects (MC, HKD, TT, AG), pp. 280–291.
MSRMSR-2015-ErcanSB #automation #stack overflow
Automatic Assessments of Code Explanations: Predicting Answering Times on Stack Overflow (SE, QS, AB), pp. 442–445.
MSRMSR-2015-GoderieGGB #named #stack overflow
ETA: Estimated Time of Answer Predicting Response Time in Stack Overflow (JG, BMG, BvG, AB), pp. 414–417.
MSRMSR-2015-HashimotoTMM #effectiveness #fact extraction #optimisation #performance
Extracting Facts from Performance Tuning History of Scientific Applications for Predicting Effective Optimization Patterns (MH, MT, TM, KM), pp. 13–23.
SANERSANER-2015-XiaLMSH #co-evolution
Cross-project build co-change prediction (XX, DL, SM, ES, AEH), pp. 311–320.
ICALPICALP-v1-2015-SkorskiGP
Condensed Unpredictability (MS, AG, KP), pp. 1046–1057.
AIIDEAIIDE-2015-MullerFKKMSSG #collaboration #named
HeapCraft: Quantifying and Predicting Collaboration in Minecraft (SM0, SF, MK, SK, RPM, BS, RWS, MHG), pp. 156–162.
AIIDEAIIDE-2015-SifaHRDKB #game studies #mobile
Predicting Purchase Decisions in Mobile Free-to-Play Games (RS, FH, JR, AD, KK, CB), pp. 79–85.
AIIDEAIIDE-2015-StanescuBB #using
Using Lanchester Attrition Laws for Combat Prediction in StarCraft (MAS, NAB, MB), pp. 86–92.
AIIDEAIIDE-2015-Valls-VargasOZ #game studies #segmentation
Exploring Player Trace Segmentation for Dynamic Play Style Prediction (JVV, SO, JZ), pp. 93–99.
CoGCIG-2015-AsayamaMFN #game studies #performance #realtime #video
Prediction as faster perception in a real-time fighting video game (KA, KM, KiF, MN), pp. 517–522.
CoGCIG-2015-MoudrikBN #game studies
Evaluating Go game records for prediction of player attributes (JM, PB, RN), pp. 162–168.
CoGCIG-2015-XieDKC #data transformation #representation
Predicting player disengagement and first purchase with event-frequency based data representation (HX, SD, DK, PIC), pp. 230–237.
ICGTICGT-2015-DrewesHM #parsing #top-down
Predictive Top-Down Parsing for Hyperedge Replacement Grammars (FD, BH, MM), pp. 19–34.
CHICHI-2015-OttleyYC #how #visualisation
Personality as a Predictor of User Strategy: How Locus of Control Affects Search Strategies on Tree Visualizations (AO, HY, RC), pp. 3251–3254.
CHICHI-2015-SutherlandHY #automation
The Role of Environmental Predictability and Costs in Relying on Automation (SCS, CH, MEY), pp. 2535–2544.
CHICHI-2015-ZhangC #modelling #policy #social #social media
Modeling Ideology and Predicting Policy Change with Social Media: Case of Same-Sex Marriage (AXZ, SC), pp. 2603–2612.
CHICHI-2015-ZugerF #developer #using
Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors (MZ, TF), pp. 2981–2990.
CSCWCSCW-2015-FarrahiEC #community #mobile
Predicting a Community’s Flu Dynamics with Mobile Phone Data (KF, RE, MC), pp. 1214–1221.
HCIHCI-DE-2015-IgaTAF
Study of Uninterruptible Duration Prediction Based on PC Operation (HI, TT, KA, KF), pp. 350–359.
HCIHCI-IT-2015-MeleMR #communication #type system #user interface
Beyond Direct Gaze Typing: A Predictive Graphic User Interface for Writing and Communicating by Gaze (MLM, DM, CER), pp. 66–77.
HCIHCI-IT-2015-MijovicMMMKG #fault #human-computer #towards
Towards Creation of Implicit HCI Model for Prediction and Prevention of Operators’ Error (PM, MM, MM, IM, VK, IG), pp. 341–352.
HCIHCI-IT-2015-MullerLBSKSW #data-driven #network #overview #using
Using Neural Networks for Data-Driven Backchannel Prediction: A Survey on Input Features and Training Techniques (MM, DL, LB, MS, KK, SS, AW), pp. 329–340.
HCIHCI-UC-2015-FrauCT #mobile #probability #prototype #visualisation
Graphic Visualization of Probabilistic Traffic/Trajectory Predictions in Mobile Applications. A First Prototype and Evaluations for General Aviation Purposes (GF, FDC, DT), pp. 154–164.
HCIHIMI-IKD-2015-ArgyleLG #evaluation
Evaluation of Data Display Methods in a Flash Flood Prediction Tool (EMA, CL, JJG), pp. 15–22.
HCILCT-2015-FlanaganYSH #fault
Prediction of Learner Native Language by Writing Error Pattern (BF, CY, TS, SH), pp. 87–96.
HCISCSM-2015-GerritsenB #analysis
Simulation-Based Prediction and Analysis of Collective Emotional States (CG, WRJvB), pp. 118–126.
ICEISICEIS-v1-2015-PecliGPMFTTDFCG #learning #problem #reduction
Dimensionality Reduction for Supervised Learning in Link Prediction Problems (AP, BG, CCP, CM, FF, FT, JT, MVD, SF, MCC, RRG), pp. 295–302.
ICEISICEIS-v1-2015-VermaHBPD #enterprise #information management #repository
Access Prediction for Knowledge Workers in Enterprise Data Repositories (CKV, MH, SB, APW, SD), pp. 150–161.
ICEISICEIS-v2-2015-SariK #analysis #debugging #monitoring #using
Bug Prediction for an ATM Monitoring Software — Use of Logistic Regression Analysis for Bug Prediction (ÖS, OK), pp. 382–387.
CIKMCIKM-2015-AwadallahKOJ #query
Characterizing and Predicting Voice Query Reformulation (AHA, RGK, UO, RJ), pp. 543–552.
CIKMCIKM-2015-BagdouriO15a #microblog #on the
On Predicting Deletions of Microblog Posts (MB, DWO), pp. 1707–1710.
CIKMCIKM-2015-ChenLZWZM
Does Vertical Bring more Satisfaction?: Predicting Search Satisfaction in a Heterogeneous Environment (YC, YL, KZ0, MW, MZ0, SM), pp. 1581–1590.
CIKMCIKM-2015-DingSGHYH #network #sentiment #video
Video Popularity Prediction by Sentiment Propagation via Implicit Network (WD, YS, LG, XH, RY, TH), pp. 1621–1630.
CIKMCIKM-2015-JiangLSW #behaviour #clustering #matrix #twitter
Message Clustering based Matrix Factorization Model for Retweeting Behavior Prediction (BJ, JL, YS, LW), pp. 1843–1846.
CIKMCIKM-2015-JoLR #analysis #topic
Time Series Analysis of Nursing Notes for Mortality Prediction via a State Transition Topic Model (YJ, NL, CPR), pp. 1171–1180.
CIKMCIKM-2015-LinZWLL0C #approach #data-driven #parametricity #pipes and filters
Data Driven Water Pipe Failure Prediction: A Bayesian Nonparametric Approach (PL, BZ, YW0, ZL, BL0, YW0, FC0), pp. 193–202.
CIKMCIKM-2015-LiuWW #collaboration #interactive #multi #representation
Collaborative Prediction for Multi-entity Interaction With Hierarchical Representation (QL0, SW, LW0), pp. 613–622.
CIKMCIKM-2015-LiuYWW
A Convolutional Click Prediction Model (QL0, FY, SW, LW0), pp. 1743–1746.
CIKMCIKM-2015-PreumSQ #adaptation #behaviour #multi #named #personalisation
MAPer: A Multi-scale Adaptive Personalized Model for Temporal Human Behavior Prediction (SMP, JAS, YQ), pp. 433–442.
CIKMCIKM-2015-SinghPK0MG #case study #dataset
The Role Of Citation Context In Predicting Long-Term Citation Profiles: An Experimental Study Based On A Massive Bibliographic Text Dataset (MS0, VP, SK, TC0, AM0, PG), pp. 1271–1280.
CIKMCIKM-2015-ValletBAMK #video
Characterizing and Predicting Viral-and-Popular Video Content (DV, SB, SA, AM, MAK), pp. 1591–1600.
CIKMCIKM-2015-WangKBC #online
Viewability Prediction for Online Display Ads (CW, AK, CB, YC0), pp. 413–422.
ECIRECIR-2015-ChongDL #modelling #topic #using
Prediction of Venues in Foursquare Using Flipped Topic Models (WHC, BTD, EPL), pp. 623–634.
ECIRECIR-2015-JungL #approach
A Discriminative Approach to Predicting Assessor Accuracy (HJJ, ML), pp. 159–171.
ECIRECIR-2015-SadeghiBMSSV #process
Predicting Re-finding Activity and Difficulty (SS, RB, PM, MS, FS, DV), pp. 715–727.
ICMLICML-2015-AnavaHZ #online
Online Time Series Prediction with Missing Data (OA, EH, AZ), pp. 2191–2199.
ICMLICML-2015-GiguereRLM #algorithm #kernel #problem #string
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction (SG, AR, FL, MM), pp. 2021–2029.
ICMLICML-2015-GlobersonRSY #how #question
How Hard is Inference for Structured Prediction? (AG, TR, DS, CY), pp. 2181–2190.
ICMLICML-2015-Hernandez-Lobato15a #constraints #optimisation
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (JMHL, MAG, MWH, RPA, ZG), pp. 1699–1707.
ICMLICML-2015-LeeY #category theory #strict
Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (TL, SY), pp. 2483–2492.
ICMLICML-2015-LianHRLC #multi #process
A Multitask Point Process Predictive Model (WL, RH, VR, JEL, LC), pp. 2030–2038.
ICMLICML-2015-LiuY #graph #learning
Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.
ICMLICML-2015-SteinhardtL15a #learning #modelling
Learning Fast-Mixing Models for Structured Prediction (JS, PL), pp. 1063–1072.
KDDKDD-2015-BabaKNKGIAKIHKS #low cost
Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries (YB, HK, YN, EK, PPG, RIM, AA, MK, SI, TH, MK, SS, KK, KT, MS, MB, NU, MK, NN), pp. 1681–1690.
KDDKDD-2015-BeutelAF #behaviour #detection #graph #modelling
Graph-Based User Behavior Modeling: From Prediction to Fraud Detection (AB, LA, CF), pp. 2309–2310.
KDDKDD-2015-CaruanaLGKSE #modelling
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission (RC, YL, JG, PK, MS, NE), pp. 1721–1730.
KDDKDD-2015-DentonWPBF #hashtag #image
User Conditional Hashtag Prediction for Images (ED, JW, MP, LDB, RF), pp. 1731–1740.
KDDKDD-2015-DongZTCW #named #network
CoupledLP: Link Prediction in Coupled Networks (YD, JZ, JT, NVC, BW), pp. 199–208.
KDDKDD-2015-FeldmanNPR #mining #online
Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions (RF, ON, AP, BR), pp. 1779–1788.
KDDKDD-2015-IkonomovskaJD #realtime #using
Real-Time Bid Prediction using Thompson Sampling-Based Expert Selection (EI, SJ, AD), pp. 1869–1878.
KDDKDD-2015-JiangZZY #e-commerce #recommendation
Life-stage Prediction for Product Recommendation in E-commerce (PJ, YZ, YZ, QY), pp. 1879–1888.
KDDKDD-2015-KimYTM #framework #sequence
A Decision Tree Framework for Spatiotemporal Sequence Prediction (TK, YY, SLT, IM), pp. 577–586.
KDDKDD-2015-LiCS #elicitation
Predicting Voice Elicited Emotions (YL, JDC, LJS), pp. 1969–1978.
KDDKDD-2015-LiLMWP #timeline #twitter
Click-through Prediction for Advertising in Twitter Timeline (CL, YL, QM, DW, SP), pp. 1959–1968.
KDDKDD-2015-MinorDC #algorithm #data-driven #evaluation #process
Data-Driven Activity Prediction: Algorithms, Evaluation Methodology, and Applications (BM, JRD, DJC), pp. 805–814.
KDDKDD-2015-NagarajanWBNBHT #analysis
Predicting Future Scientific Discoveries Based on a Networked Analysis of the Past Literature (MN, ADW, BJB, IBN, SB, PJH, METD, SB, AKA, JJL, SR, CMB, CRP, LK, AML, AL, HZ, SB, GW, YC, LAD, WSS, OL), pp. 2019–2028.
KDDKDD-2015-NoriKYII #modelling #multi
Simultaneous Modeling of Multiple Diseases for Mortality Prediction in Acute Hospital Care (NN, HK, KY, HI, YI), pp. 855–864.
KDDKDD-2015-PotashBLMRWRJMG #health #modelling
Predictive Modeling for Public Health: Preventing Childhood Lead Poisoning (EP, JB, AL, SM, AR, JW, ER, EJ, RM, RG), pp. 2039–2047.
KDDKDD-2015-Pratt #machine learning #protocol #proving
Proof Protocol for a Machine Learning Technique Making Longitudinal Predictions in Dynamic Contexts (KBP), pp. 2049–2058.
KDDKDD-2015-QiATSA
State-Driven Dynamic Sensor Selection and Prediction with State-Stacked Sparseness (GJQ, CA, DST, DMS, PA), pp. 945–954.
KDDKDD-2015-SomanchiALEG #using
Early Prediction of Cardiac Arrest (Code Blue) using Electronic Medical Records (SS, SA, AL, EE, RG), pp. 2119–2126.
KDDKDD-2015-SunWH #approach #named #risk management
LINKAGE: An Approach for Comprehensive Risk Prediction for Care Management (ZS, FW, JH), pp. 1145–1154.
KDDKDD-2015-TangQM #named #network #scalability
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks (JT, MQ, QM), pp. 1165–1174.
KDDKDD-2015-VeeriahDQ #architecture #learning
Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction (VV, RD, GJQ), pp. 1205–1214.
KDDKDD-2015-WangCMBYR
Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction (ZW, PC, SRM, JSB, JY, NR), pp. 1285–1294.
KDDKDD-2015-WangYLXXCR #using
Regularity and Conformity: Location Prediction Using Heterogeneous Mobility Data (YW, NJY, DL, LX, XX, EC, YR), pp. 1275–1284.
KDDKDD-2015-WeiLMCRS #using
Predicting Serves in Tennis using Style Priors (XW, PL, SM, PC, MR, SS), pp. 2207–2215.
KDDKDD-2015-WuYC #realtime
Predicting Winning Price in Real Time Bidding with Censored Data (WCHW, MYY, MSC), pp. 1305–1314.
KDDKDD-2015-XuSB #learning
Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction (TX, JS, JB), pp. 1345–1354.
KDDKDD-2015-ZhaoEHRL #named #process #self #twitter
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (QZ, MAE, HYH, AR, JL), pp. 1513–1522.
KDDKDD-2015-ZhouM #approach #kernel
Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach (ZZ, DSM), pp. 2297–2303.
KDDKDD-2015-ZhuYH #clustering #optimisation
Co-Clustering based Dual Prediction for Cargo Pricing Optimization (YZ, HY, JH), pp. 1583–1592.
MLDMMLDM-2015-Al-SaleemAAB #education #mining #performance #student
Mining Educational Data to Predict Students’ Academic Performance (MAS, NAK, SAO, GB), pp. 403–414.
MLDMMLDM-2015-DhulekarNOY #graph #learning #mining
Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning (ND, SN, BO, BY), pp. 32–52.
RecSysRecSys-2015-LiWTM #community #rating #recommendation #social
Overlapping Community Regularization for Rating Prediction in Social Recommender Systems (HL, DW, WT, NM), pp. 27–34.
RecSysRecSys-2015-MaksaiGF #evaluation #metric #online #performance #recommendation
Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics (AM, FG, BF), pp. 179–186.
SEKESEKE-2015-AssuncaoFLSV #automaton #generative #markov #modelling #named #network #probability
SANGE — Stochastic Automata Networks Generator. A tool to efficiently predict events through structured Markovian models (JA, PF, LL, AS, JMV), pp. 581–584.
SEKESEKE-2015-ChenM #empirical #fault
An empirical study on predicting defect numbers (MC, YM), pp. 397–402.
SEKESEKE-2015-FiondellaGL #automation
A Smartphone-based System for Automated Congestion Prediction (LF, SSG, NL), pp. 195–200.
SEKESEKE-2015-Murillo-MoreraJ #algorithm #approach #framework #learning #search-based #using
A Software Defect-Proneness Prediction Framework: A new approach using genetic algorithms to generate learning schemes (JMM, MJ), pp. 445–450.
SEKESEKE-2015-TunnellA #fault #modelling #release planning #using
Using Time Series Models for Defect Prediction in Software Release Planning (JT, JA), pp. 451–454.
SEKESEKE-2015-WangG #approach #hybrid #novel
A Novel Hybrid Approach for Diarrhea Prediction (YW, JG), pp. 168–173.
SIGIRSIGIR-2015-ArkhipovaGKS #evaluation
Search Engine Evaluation based on Search Engine Switching Prediction (OA, LG, IK, PS), pp. 723–726.
SIGIRSIGIR-2015-HeBVAR #evaluation #framework #quality #ranking #refinement
Untangling Result List Refinement and Ranking Quality: a Framework for Evaluation and Prediction (JH, MB, APdV, LA, MdR), pp. 293–302.
SIGIRSIGIR-2015-KongLLZCA
Predicting Search Intent Based on Pre-Search Context (WK, RL, JL, AZ, YC, JA), pp. 503–512.
SIGIRSIGIR-2015-LiKF #behaviour #matrix
Predicting User Behavior in Display Advertising via Dynamic Collective Matrix Factorization (SL, JK, YF), pp. 875–878.
SIGIRSIGIR-2015-LiuCTS0MZ
Different Users, Different Opinions: Predicting Search Satisfaction with Mouse Movement Information (YL, YC, JT, JS, MZ, SM, XZ), pp. 493–502.
SIGIRSIGIR-2015-SchuthHR #metric
Predicting Search Satisfaction Metrics with Interleaved Comparisons (AS, KH, FR), pp. 463–472.
SIGIRSIGIR-2015-SongNZAC #learning #multi #network #social #volunteer
Multiple Social Network Learning and Its Application in Volunteerism Tendency Prediction (XS, LN, LZ, MA, TSC), pp. 213–222.
MoDELSMoDELS-2015-KetataMFLC #migration #modelling #performance
Performance prediction upon toolchain migration in model-based software (AK, CM, SF, JHL, KC), pp. 302–311.
SPLCSPLC-2015-ValovGC #comparison #empirical #performance #variability
Empirical comparison of regression methods for variability-aware performance prediction (PV, JG, KC), pp. 186–190.
POPLPOPL-2015-RaychevVK
Predicting Program Properties from “Big Code” (VR, MTV, AK), pp. 111–124.
ASEASE-2015-ChoetkiertikulD #classification #using
Predicting Delays in Software Projects Using Networked Classification (T) (MC, HKD, TT, AG), pp. 353–364.
ASEASE-2015-NamK #dataset #fault #named
CLAMI: Defect Prediction on Unlabeled Datasets (T) (JN, SK), pp. 452–463.
ASEASE-2015-SarkarGSAC #configuration management #low cost #performance
Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T) (AS, JG, NS, SA, KC), pp. 342–352.
ASEASE-2015-ZhangGBC #configuration management #fourier #learning #performance
Performance Prediction of Configurable Software Systems by Fourier Learning (T) (YZ, JG, EB, KC), pp. 365–373.
ESEC-FSEESEC-FSE-2015-JingWDQX #fault #learning #metric #representation
Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning (XYJ, FW, XD, FQ, BX), pp. 496–507.
ESEC-FSEESEC-FSE-2015-KimNYCK #api #fault #named #performance #testing
REMI: defect prediction for efficient API testing (MK, JN, JY, SC, SK), pp. 990–993.
ESEC-FSEESEC-FSE-2015-NamK #fault
Heterogeneous defect prediction (JN, SK), pp. 508–519.
ESEC-FSEESEC-FSE-2015-RosenGS #commit #risk management
Commit guru: analytics and risk prediction of software commits (CR, BG, ES), pp. 966–969.
ESEC-FSEESEC-FSE-2015-RotellaCG #reliability
Predicting field reliability (PR, SC, DG), pp. 986–989.
ICSEICSE-v1-2015-GhotraMH #classification #fault #modelling #performance
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models (BG, SM, AEH), pp. 789–800.
ICSEICSE-v1-2015-HuangLR #analysis #concurrent #named
GPredict: Generic Predictive Concurrency Analysis (JH, QL, GR), pp. 847–857.
ICSEICSE-v1-2015-PetersML #fault #named #privacy
LACE2: Better Privacy-Preserving Data Sharing for Cross Project Defect Prediction (FP, TM, LL), pp. 801–811.
ICSEICSE-v1-2015-Tantithamthavorn #fault #modelling #performance
The Impact of Mislabelling on the Performance and Interpretation of Defect Prediction Models (CT, SM, AEH, AI, KiM), pp. 812–823.
ICSEICSE-v2-2015-AndersonSD #case study #industrial
Striving for Failure: An Industrial Case Study about Test Failure Prediction (JA, SS, HD), pp. 49–58.
ICSEICSE-v2-2015-CaglayanTBHMC #fault #industrial #metric #replication
Merits of Organizational Metrics in Defect Prediction: An Industrial Replication (BC, BT, ABB, MH, AM, EC), pp. 89–98.
ICSEICSE-v2-2015-EtienneMAD #modelling #performance #process #proving #trust
Improving Predictability, Efficiency and Trust of Model-Based Proof Activity (JFÉ, MM, FA, VD), pp. 139–148.
ICSEICSE-v2-2015-TanTDM #fault #online
Online Defect Prediction for Imbalanced Data (MT, LT, SD, CM), pp. 99–108.
SACSAC-2015-AhrndtBFA #adaptation
Predictability in human-agent cooperation: adapting to humans’ personalities (SA, BB, JF, SA), pp. 474–479.
SACSAC-2015-AlemerienM #metric #named #usability #user interface #visual notation
SLC: a visual cohesion metric to predict the usability of graphical user interfaces (KA, KM), pp. 1526–1533.
SACSAC-2015-ChaudhuriMG #network #using
QoS prediction for network data traffic using hierarchical modified regularized least squares rough support vector regression (AC, SM, SKG), pp. 659–661.
SACSAC-2015-LoffRM #platform #social #social media
Predicting well-being with geo-referenced data collected from social media platforms (JL, MR, BM), pp. 1167–1173.
SACSAC-2015-MakrisVV #classification
Classification model for predicting cost slippage in governmental ICT projects (CM, PV, JV), pp. 1238–1241.
SACSAC-2015-ManhaesCZ #automation #performance #source code #student #towards
Towards automatic prediction of student performance in STEM undergraduate degree programs (LMBM, SMSdC, GZ), pp. 247–253.
SACSAC-2015-Puffitsch #analysis #bound #branch
Persistence-based branch misprediction bounds for WCET analysis (WP), pp. 1898–1905.
SACSAC-2015-SidneyMRH #performance #set #similarity
Performance prediction for set similarity joins (CFS, DSM, LAR, TH), pp. 967–972.
SACSAC-2015-Valverde-Rebaza #modelling #naive bayes #network #online #social
A naïve Bayes model based on ovelapping groups for link prediction in online social networks (JCVR, AV, LB, TdPF, AdAL), pp. 1136–1141.
SACSAC-2015-XuanLXT #empirical #fault #metric #set #using
Evaluating defect prediction approaches using a massive set of metrics: an empirical study (XX, DL, XX, YT), pp. 1644–1647.
SACSAC-2015-XuYYHHK #multi #using
Solar irradiance forecasting using multi-layer cloud tracking and numerical weather prediction (JX, SY, DY, DH, JH, PK), pp. 2225–2230.
SACSAC-2015-ZhangYLC #concept #debugging #mining #repository
Predicting severity of bug report by mining bug repository with concept profile (TZ, GY, BL, ATSC), pp. 1553–1558.
CASECASE-2015-ArdakaniORJ #generative #realtime #using
Real-time trajectory generation using model predictive control (MMGA, BO, AR, RJ), pp. 942–948.
CASECASE-2015-FarhanPWL #algorithm #machine learning #using
Predicting individual thermal comfort using machine learning algorithms (AAF, KRP, BW, PBL), pp. 708–713.
CASECASE-2015-Jin0S #energy #pattern matching #pattern recognition #recognition
Power prediction through energy consumption pattern recognition for smart buildings (MJ, LZ, CJS), pp. 419–424.
CASECASE-2015-JinQH #fault #geometry
Out-of-plane geometric error prediction for additive manufacturing (YJ, SJQ, QH), pp. 918–923.
CASECASE-2015-KruseW
Application of the Smith-Åström Predictor to robot force control (DK, JTW), pp. 383–388.
CASECASE-2015-LinZW #realtime #using
Using real-time sensing data for predicting future state of building fires (CCL, GZ, LLW), pp. 1313–1318.
CASECASE-2015-LiuY #modelling #self
A self-organizing method for predictive modeling with highly-redundant variables (GL, HY), pp. 1084–1089.
CASECASE-2015-LiuZ #adaptation #human-computer #modelling #process
Adaptive predictive ANFIS based human arm movement modeling and control in machine-human cooperative GTAW process (YL, YZ), pp. 1465–1470.
CASECASE-2015-LuanH #3d #geometry #modelling
Predictive modeling of in-plane geometric deviation for 3D printed freeform products (HL, QH), pp. 912–917.
CASECASE-2015-MeddouriDF #analysis #generative #induction #performance #using
Performance analysis of an autonomous induction generator under different operating conditions using predictive control (SM, LAD, LF), pp. 1118–1124.
CASECASE-2015-NguyenWKLH #geometry #modelling
Predictive models for the geometrical characteristics of channels milled by abrasive waterjet (TDN, JW, NMK, HL, QPH), pp. 1459–1464.
CASECASE-2015-NouaouriSA #data mining #mining #problem
Evidential data mining for length of stay (LOS) prediction problem (IN, AS, HA), pp. 1415–1420.
CASECASE-2015-SustoM #approach #machine learning #multi
Slow release drug dissolution profile prediction in pharmaceutical manufacturing: A multivariate and machine learning approach (GAS, SFM), pp. 1218–1223.
CASECASE-2015-WangZ #modelling #simulation
A prediction method for interior temperature of grain storage via dynamics models: A simulation study (DW, XZ), pp. 1477–1483.
CASECASE-2015-ZakharovZYJ #algorithm #configuration management #distributed #fault tolerance #optimisation #performance
A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control (AZ, EZ, MY, SLJJ), pp. 886–891.
CGOCGO-2015-RohouSS #branch #performance #trust
Branch prediction and the performance of interpreters: don’t trust folklore (ER, BNS, AS), pp. 103–114.
DATEDATE-2015-BaldwinBRPB #analysis #array #using
Gait analysis for fall prediction using hierarchical textile-based capacitive sensor arrays (RB, SB, RR, CP, NB), pp. 1293–1298.
DATEDATE-2015-BaranowskiFKLTW #online
On-line prediction of NBTI-induced aging rates (RB, FF, SK, CL, MBT, HJW), pp. 589–592.
DATEDATE-2015-FaravelonFP #branch #performance #simulation
Fast and accurate branch predictor simulation (AF, NF, FP), pp. 317–320.
DATEDATE-2015-LaerEMWJ #multi
Coherence based message prediction for optically interconnected chip multiprocessors (AVL, CE, MRM, PMW, TMJ), pp. 613–616.
DATEDATE-2015-SinglaKUO #mobile #platform #power management
Predictive dynamic thermal and power management for heterogeneous mobile platforms (GS, GK, AKU, ÜYO), pp. 960–965.
DATEDATE-2015-VargasQM #question
OpenMP and timing predictability: a possible union? (RV, EQ, AM), pp. 617–620.
HPCAHPCA-2015-DuweJ0 #fault #latency
Correction prediction: Reducing error correction latency for on-chip memories (HD, XJ, RK), pp. 463–475.
HPCAHPCA-2015-PapadopoulouTSM #design
Prediction-based superpage-friendly TLB designs (MMP, XT, AS, AM), pp. 210–222.
HPCAHPCA-2015-PeraisS #effectiveness #framework #named
BeBoP: A cost effective predictor infrastructure for superscalar value prediction (AP, AS), pp. 13–25.
PDPPDP-2015-AltomareCT #data mining #energy #migration #mining #modelling #virtual machine
Energy-Aware Migration of Virtual Machines Driven by Predictive Data Mining Models (AA, EC, DT), pp. 549–553.
PDPPDP-2015-ConinckKVSBMF #algebra #matrix #parallel #scalability #towards
Towards Parallel Large-Scale Genomic Prediction by Coupling Sparse and Dense Matrix Algebra (ADC, DK, FV, OS, BDB, SM, JF), pp. 747–750.
TACASTACAS-2015-ZengSLH #precise
A Method for Improving the Precision and Coverage of Atomicity Violation Predictions (RZ, ZS, SL, XH), pp. 116–130.
CAVCAV-2015-SinghG #programming
Predicting a Correct Program in Programming by Example (RS, SG), pp. 398–414.
ICLPICLP-J-2015-BlackmoreRE #approach #compilation #effectiveness #embedded #logic programming
A logic programming approach to predict effective compiler settings for embedded software (CB, OR, KE), pp. 481–494.
ICTSSICTSS-2015-AltingerHGW #fault #novel
Novel Insights on Cross Project Fault Prediction Applied to Automotive Software (HA, SH, JG, FW), pp. 141–157.
CBSECBSE-2014-NoorshamsRRKR #architecture #modelling #performance #statistics
Enriching software architecture models with statistical models for performance prediction in modern storage environments (QN, RR, AR, SK, RHR), pp. 45–54.
ECSAECSA-2014-ChiprianovFSP #architecture #distributed #embedded #modelling #performance #realtime
Architectural Support for Model-Driven Performance Prediction of Distributed Real-Time Embedded Systems of Systems (VC, KEF, CS, GP), pp. 357–364.
QoSAQoSA-2014-BuhnovaCF #modelling #overview #reliability
Failure data collection for reliability prediction models: a survey (BB, SC, LF), pp. 83–92.
HTHT-2014-0001IAS #on the
On the predictability of talk attendance at academic conferences (CS, JI, MA, GS), pp. 279–284.
JCDLJCDL-2014-ChakrabortyKGGM #approach #learning #towards
Towards a stratified learning approach to predict future citation counts (TC, SK, PG, NG, AM), pp. 351–360.
JCDLJCDL-2014-DibieMS #approach #behaviour #comprehension #education #online #using
A computational approach to understanding and predicting the behavior of educators using an online curriculum planning tool (OD, KEM, TS), pp. 487–488.
SIGMODSIGMOD-2014-PangKFF #commit #named
PLANET: making progress with commit processing in unpredictable environments (GP, TK, MJF, AF), pp. 3–14.
VLDBVLDB-2014-WuWHN #execution #nondeterminism #query
Uncertainty Aware Query Execution Time Prediction (WW, XW, HH, JFN), pp. 1857–1868.
VLDBVLDB-2015-InoueOT14 #branch #performance #set
Faster Set Intersection with SIMD instructions by Reducing Branch Mispredictions (HI, MO, KT), pp. 293–304.
VLDBVLDB-2015-ZhangWWY14 #behaviour #social
Inferring Continuous Dynamic Social Influence and Personal Preference for Temporal Behavior Prediction (JZ, CW, JW, JXY), pp. 269–280.
EDMEDM-2014-BeheshtiD #assessment #performance #set
Predictive performance of prevailing approaches to skills assessment techniques: Insights from real vs. synthetic data sets (BB, MCD), pp. 409–410.
EDMEDM-2014-ChiSBC #assessment #game studies #question #student
Choice-based Assessment: Can Choices Made in Digital Games Predict 6th-Grade Students' Math Test Scores? (MC, DLS, KPB, DBC), pp. 36–43.
EDMEDM-2014-Cordova-Sanchez #education
Relevancy prediction of micro-blog questions in an educational setting (MCS, PR, LS, JF), pp. 415–416.
EDMEDM-2014-ForsythGPMS #learning
Discovering Theoretically Grounded Predictors of Shallow vs. Deep- level Learning (CF, ACG, PIPJ, KKM, BS), pp. 229–232.
EDMEDM-2014-Garcia-SaizPZ #education #metric #power of
The predictive power of the SNA metrics for education (DGS, CP, MEZ), pp. 419–420.
EDMEDM-2014-GrafsgaardWBWL #data type #learning #multimodal #tutorial
Predicting Learning and Affect from Multimodal Data Streams in Task-Oriented Tutorial Dialogue (JFG, JBW, KEB, ENW, JCL), pp. 122–129.
EDMEDM-2014-JiangWSWO #behaviour #performance
Predicting MOOC performance with Week 1 Behavior (SJ, AEW, KS, MW, DKO), pp. 273–275.
EDMEDM-2014-JoshiFRNB #interactive #standard
Generalizing and Extending a Predictive Model for Standardized Test Scores Based On Cognitive Tutor Interactions (AJ, SF, SR, TN, SRB), pp. 369–370.
EDMEDM-2014-KaserKG #analysis #learning #parametricity
Different parameters - same prediction: An analysis of learning curves (TK, KRK, MHG), pp. 52–59.
EDMEDM-2014-KhajahWLM #difference #learning #modelling
Integrating latent-factor and knowledge-tracing models to predict individual differences in learning (MK, RW, RVL, MM), pp. 99–106.
EDMEDM-2014-KimPSJ #comparison #learning #linear #online #student #using
Predicting students' learning achievement by using online learning patterns in blended learning environments: Comparison of two cases on linear and non-linear model (JK, YP, JS, IHJ), pp. 407–408.
EDMEDM-2014-Lang #student #using
The Use of Student Confidence for Prediction & Resolving Individual Student Knowledge Structure (CL), pp. 438–440.
EDMEDM-2014-PedroOBH #education #interactive
Predicting STEM and Non-STEM College Major Enrollment from Middle School Interaction with Mathematics Educational Software (MOSP, JO, RSB, NTH), pp. 276–279.
EDMEDM-2014-Salmeron-Majadas #interactive
Exploring indicators from keyboard and mouse interactions to predict the user affective state (SSM, OCS, JB), pp. 365–366.
EDMEDM-2014-SilvaPC #classification #video
A Predictive Model for Video Lectures Classification (PS, RP, EC), pp. 325–326.
EDMEDM-2014-XiongAH #performance
Improving Retention Performance Prediction with Prerequisite Skill Features (XX, SA, NTH), pp. 375–376.
SANERCSMR-WCRE-2014-PanichellaOL #fault #modelling
Cross-project defect prediction models: L’Union fait la force (AP, RO, ADL), pp. 164–173.
ICSMEICSME-2014-ParizyTK #design #fault
Software Defect Prediction for LSI Designs (MP, KT, YK), pp. 565–568.
ICSMEICSME-2014-Shihab #quality
Practical Software Quality Prediction (ES), pp. 639–644.
MSRMSR-2014-0001MKZ #fault #towards
Towards building a universal defect prediction model (FZ, AM, IK, YZ), pp. 182–191.
MSRMSR-2014-FukushimaKMYU #empirical #fault #modelling #using
An empirical study of just-in-time defect prediction using cross-project models (TF, YK, SM, KY, NU), pp. 172–181.
MSRMSR-2014-GarciaS #debugging #open source
Characterizing and predicting blocking bugs in open source projects (HVG, ES), pp. 72–81.
MSRMSR-2014-MondalRS #co-evolution #ranking
Prediction and ranking of co-change candidates for clones (MM, CKR, KAS), pp. 32–41.
SCAMSCAM-2014-CaiJSZZ #analysis #named
SENSA: Sensitivity Analysis for Quantitative Change-Impact Prediction (HC, SJ, RAS, YJZ, YZ), pp. 165–174.
FMFM-2014-ForejtKNS #analysis #communication #precise #source code
Precise Predictive Analysis for Discovering Communication Deadlocks in MPI Programs (VF, DK, GN, SS), pp. 263–278.
CHI-PLAYCHI-PLAY-2014-TakataloH #experience #user interface
Predicting the metascore with a subjective user experience data (JMET, JH), pp. 441–442.
CoGCIG-2014-GarnettGEGO #online
Predicting unexpected influxes of players in EVE online (RG, TG0, TE, EG, PO), pp. 1–8.
CoGCIG-2014-HadijiSDTKB
Predicting player churn in the wild (FH, RS, AD, CT, KK, CB), pp. 1–8.
CoGCIG-2014-RungeGGF #game studies #social
Churn prediction for high-value players in casual social games (JR, PG, FG, BF), pp. 1–8.
FDGFDG-2014-BechtB #game studies #named #video
meIRL-BC: Predicting player positions in video games (IB, SB).
FDGFDG-2014-YangHR #game studies #identification
Identifying patterns in combat that are predictive of success in MOBA games (PY, BEH, DLR).
CHICHI-2014-ForlinesMGB #crowdsourcing #network #social
Crowdsourcing the future: predictions made with a social network (CF, SM, LG, RB), pp. 3655–3664.
CHICHI-2014-HongA #modelling #performance #recommendation #user interface #using
Novice use of a predictive human performance modeling tool to produce UI recommendations (KWH, RSA), pp. 2251–2254.
CHICHI-2014-KierasH #modelling #towards #visual notation
Towards accurate and practical predictive models of active-vision-based visual search (DEK, AJH), pp. 3875–3884.
CHICHI-2014-NoorRHRWM
28 frames later: predicting screen touches from back-of-device grip changes (MFMN, AR, SH, SR, JW, RMS), pp. 2005–2008.
CHICHI-2014-PasqualW #using
Mouse pointing endpoint prediction using kinematic template matching (PTP, JOW), pp. 743–752.
CHICHI-2014-PielotOKO #mobile
Didn’t you see my message?: predicting attentiveness to mobile instant messages (MP, RdO, HK, NO), pp. 3319–3328.
CHICHI-2014-YurutenZP #mobile #process
Predictors of life satisfaction based on daily activities from mobile sensor data (OY, JZ, PHZP), pp. 497–500.
CSCWCSCW-2014-ChoudhuryCHH #facebook
Characterizing and predicting postpartum depression from shared facebook data (MDC, SC, EH, AH), pp. 626–638.
CSCWCSCW-2014-MitraG #people
The language that gets people to give: phrases that predict success on kickstarter (TM, EG), pp. 49–61.
HCIHCI-AIMT-2014-MohammedSS
Gaze Location Prediction with Depth Features as Auxiliary Information (RAAM, LS, OGS), pp. 281–292.
HCIHCI-AIMT-2014-SchmidtW #gesture #multi
Prediction of Multi-touch Gestures during Input (MS, GW), pp. 158–169.
HCIHCI-AS-2014-LandyLNPLM #analysis #comparative #development
Finding Directions to a Good GPS System — A Comparative Analysis and Development of a Predictive Model (JL, TL, NN, PP, EL, PM), pp. 454–465.
HCIHCI-AS-2014-LeeYJ #using
Data Preloading Technique using Intention Prediction (SL, JY, DYJ), pp. 32–41.
HCIHIMI-AS-2014-ZhangXCZL #fault #probability
Predictive Probability Model of Pilot Error Based on CREAM (XZ, HX, YC, LZ, GL), pp. 296–304.
HCIHIMI-DE-2014-Rodriguez #algorithm #image #matlab
Prediction or Guess? Decide by Looking at Two Images Generated by a “MATLAB MySQL” Algorithm (CR0), pp. 87–97.
HCILCT-TRE-2014-GoodeC #collaboration #online #performance
Online Collaboration: Individual Involvement Used to Predict Team Performance (AWG, GC), pp. 408–416.
HCISCSM-2014-Hu #health #social #social media
Health Slacktivism on Social Media: Predictors and Effects (CWH), pp. 354–364.
HCISCSM-2014-SolingerHHFL #approach #facebook #multi #social #social media
Beyond Facebook Personality Prediction: — A Multidisciplinary Approach to Predicting Social Media Users’ Personality (CS, LMH, SHH, RF, CL), pp. 486–493.
CAiSECAiSE-2014-FolinoGP #low level #mining #modelling #multi #process
Mining Predictive Process Models out of Low-level Multidimensional Logs (FF, MG, LP), pp. 533–547.
CAiSECAiSE-2014-MaggiFDG #monitoring #process
Predictive Monitoring of Business Processes (FMM, CDF, MD, CG), pp. 457–472.
CAiSECAiSE-2014-SenderovichWGM #mining #process #queue
Queue Mining — Predicting Delays in Service Processes (AS, MW, AG, AM), pp. 42–57.
EDOCEDOC-2014-LassenMV #twitter
Predicting iPhone Sales from iPhone Tweets (NBL, RM, RV), pp. 81–90.
ICEISICEIS-v1-2014-CaetanoLC #approach #case study #data-driven
A Data-driven Approach to Predict Hospital Length of Stay — A Portuguese Case Study (NC, RMSL, PC), pp. 407–414.
ICEISICEIS-v1-2014-FolinoGP #framework #modelling
A Framework for the Discovery of Predictive Fix-time Models (FF, MG, LP), pp. 99–108.
ICEISICEIS-v1-2014-MamcenkoG #mobile #using
Customer Churn Prediction in Mobile Operator Using Combined Model (JM, JG), pp. 233–240.
CIKMCIKM-2014-ChangZGCXT #modelling #online
Predicting the Popularity of Online Serials with Autoregressive Models (BC, HZ, YG, EC, HX, CT), pp. 1339–1348.
CIKMCIKM-2014-ChenHH #named #video
Clairvoyant: An Early Prediction System For Video Hits (HC, QH, LH), pp. 2054–2056.
CIKMCIKM-2014-DavletovAC #using
High Impact Academic Paper Prediction Using Temporal and Topological Features (FD, ASA, AC), pp. 491–498.
CIKMCIKM-2014-LiuLB
Predicting Search Task Difficulty at Different Search Stages (CL, JL, NJB), pp. 569–578.
CIKMCIKM-2014-LiuXD #mining #network
Relationship Emergence Prediction in Heterogeneous Networks through Dynamic Frequent Subgraph Mining (YL, SX, LD), pp. 1649–1658.
CIKMCIKM-2014-OzdemirayA #performance #query
Query Performance Prediction for Aspect Weighting in Search Result Diversification (AMO, ISA), pp. 1871–1874.
CIKMCIKM-2014-TangHCL #interactive
Predictability of Distrust with Interaction Data (JT, XH, YC, HL), pp. 181–190.
CIKMCIKM-2014-TaoW #performance #query
Query Performance Prediction By Considering Score Magnitude and Variance Together (YT, SW), pp. 1891–1894.
CIKMCIKM-2014-YuX #interactive #learning #network #scalability #social
Learning Interactions for Social Prediction in Large-scale Networks (XY, JX), pp. 161–170.
ECIRECIR-2014-Arguello
Predicting Search Task Difficulty (JA), pp. 88–99.
ECIRECIR-2014-GrausTBR #concept #generative #pseudo #social
Generating Pseudo-ground Truth for Predicting New Concepts in Social Streams (DG, MT, LB, MdR), pp. 286–298.
ECIRECIR-2014-OstroumovaBCTG #crawling #policy #web
Crawling Policies Based on Web Page Popularity Prediction (LO, IB, AC, AT, GG), pp. 100–111.
ICMLICML-c1-2014-SeldinBCA #multi
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations (YS, PLB, KC, YAY), pp. 280–287.
ICMLICML-c1-2014-ZhouT #generative #network #probability
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (JZ, OGT), pp. 745–753.
ICMLICML-c2-2014-AgarwalKKSV #multi #scalability
Least Squares Revisited: Scalable Approaches for Multi-class Prediction (AA, SMK, NK, LS, GV), pp. 541–549.
ICMLICML-c2-2014-BratieresQNG #graph #grid #process #scalability
Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications (SB, NQ, SN, ZG), pp. 334–342.
ICMLICML-c2-2014-CortesKM
Ensemble Methods for Structured Prediction (CC, VK, MM), pp. 1134–1142.
ICMLICML-c2-2014-HuS #machine learning #multi
Multi-period Trading Prediction Markets with Connections to Machine Learning (JH, AJS), pp. 1773–1781.
ICMLICML-c2-2014-SuGR #network
Structured Prediction of Network Response (HS, AG, JR), pp. 442–450.
ICMLICML-c2-2014-WuCLY #behaviour #consistency #learning #network #social
Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks (SHW, HHC, KHL, PSY), pp. 298–306.
ICMLICML-c2-2014-YanLXH
Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising (LY, WJL, GRX, DH), pp. 802–810.
ICPRICPR-2014-GienTCL #fuzzy #learning #multi
Dual Fuzzy Hypergraph Regularized Multi-label Learning for Protein Subcellular Location Prediction (JG, YYT, CLPC, YL), pp. 512–516.
ICPRICPR-2014-JoshiGG #automation #using #visual notation
Automatic Prediction of Perceived Traits Using Visual Cues under Varied Situational Context (JJ, HG, RG), pp. 2855–2860.
ICPRICPR-2014-LiYLYWH #classification #multi
Multi-view Based AdaBoost Classifier Ensemble for Class Prediction from Gene Expression Profiles (LL, ZY, JL, JY, HSW, GH), pp. 178–183.
ICPRICPR-2014-SuDAGSLLPLLT #automation
Automated Prediction of Glasgow Outcome Scale for Traumatic Brain Injury (BS, TAD, AKA, TG, TS, SL, CCTL, BCP, CKL, TYL, CLT), pp. 3245–3250.
ICPRICPR-2014-SuiTX
An Unsupervised Band Selection Method Based on Overall Accuracy Prediction (CS, YT, YX), pp. 3756–3761.
ICPRICPR-2014-WangWH #framework #learning #multi #risk management
A Multi-task Learning Framework for Joint Disease Risk Prediction and Comorbidity Discovery (XW, FW, JH), pp. 220–225.
ICPRICPR-2014-ZhouHG
Exploring Brain Tumor Heterogeneity for Survival Time Prediction (MZ, LOH, DBG), pp. 580–585.
KDDKDD-2014-BarbieriBM #why
Who to follow and why: link prediction with explanations (NB, FB, GM), pp. 1266–1275.
KDDKDD-2014-ChiaS #mining #scalability
Scalable noise mining in long-term electrocardiographic time-series to predict death following heart attacks (CCC, ZS), pp. 125–134.
KDDKDD-2014-Horvitz #people
Data, predictions, and decisions in support of people and society (EH), p. 2.
KDDKDD-2014-KapoorSSY #approach
A hazard based approach to user return time prediction (KK, MS, JS, TY), pp. 1719–1728.
KDDKDD-2014-PoaloH #case study #modelling
Predictive modeling in practice: a case study from sprint (TDP, JH), p. 1517.
KDDKDD-2014-RadosavljevikP #interface #modelling #scalability
Large scale predictive modeling for micro-simulation of 3G air interface load (DR, PvdP), pp. 1620–1629.
KDDKDD-2014-SiposFMW #maintenance
Log-based predictive maintenance (RS, DF, FM, ZW), pp. 1867–1876.
KDDKDD-2014-SongZSS #behaviour #scalability
Prediction of human emergency behavior and their mobility following large-scale disaster (XS, QZ, YS, RS), pp. 5–14.
KDDKDD-2014-TamhaneISDA #analysis #risk management #student
Predicting student risks through longitudinal analysis (AT, SI, BS, MD, JA), pp. 1544–1552.
KDDKDD-2014-TayebiEGB #embedded #learning #using
Spatially embedded co-offence prediction using supervised learning (MAT, ME, UG, PLB), pp. 1789–1798.
KDDKDD-2014-VarshneyCF0FM #enterprise
Predicting employee expertise for talent management in the enterprise (KRV, VC, SWF, JW, DF, AM), pp. 1729–1738.
KDDKDD-2014-WangZQWD #multi #risk management
Clinical risk prediction with multilinear sparse logistic regression (FW, PZ, BQ, XW, ID), pp. 145–154.
KDDKDD-2014-YaoTXL
Predicting long-term impact of CQA posts: a comprehensive viewpoint (YY, HT, FX, JL), pp. 1496–1505.
KDDKDD-2014-ZhangYZ #multi
Meta-path based multi-network collective link prediction (JZ, PSY, ZHZ), pp. 1286–1295.
KDIRKDIR-2014-BydzovskaB #student #towards
Towards Student Success Prediction (HB, MB), pp. 162–169.
KDIRKDIR-2014-FahedBB #algorithm #mining
Episode Rules Mining Algorithm for Distant Event Prediction (LF, AB, AB), pp. 5–13.
KMISKMIS-2014-SelmiHA #feedback #mining
Opinion Mining for Predicting Peer Affective Feedback Helpfulness (MS, HH, EA), pp. 419–425.
KMISKMIS-2014-VelosoPSSRA0 #data mining #mining #modelling #realtime
Real-Time Data Mining Models for Predicting Length of Stay in Intensive Care Units (RV, FP, MFS, ÁMS, FR, AA, JM), pp. 245–254.
KRKR-2014-Michael #learning
Simultaneous Learning and Prediction (LM).
KRKR-2014-SazonauSB #owl #performance #question
Predicting Performance of OWL Reasoners: Locally or Globally? (VS, US, GB).
RecSysRecSys-2014-KimC #collaboration
Bayesian binomial mixture model for collaborative prediction with non-random missing data (YDK, SC), pp. 201–208.
RecSysRecSys-2014-XuPA #ranking #recommendation
Controlled experimentation in recommendations, ranking & response prediction (YX, RP, JA), p. 389.
RecSysRecSys-2014-YuanMZS #sentiment
Exploiting sentiment homophily for link prediction (GY, PKM, ZZ, MPS), pp. 17–24.
SEKESEKE-2014-FinlayPC
Synthetic Minority Over-sampling TEchnique (SMOTE) for Predicting Software Build Outcomes (JF, RP, AMC), pp. 546–551.
SEKESEKE-2014-SouzaRS #empirical #using
A Proposal for the Improvement of Project’s Cost Predictability using Earned Value Management and Historical Data of Cost — An Empirical Study (ADdS, ARR, DCSdS), pp. 729–734.
SEKESEKE-2014-WangGZ #network #using
Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (YW, JG, ZZ), pp. 501–506.
SEKESEKE-2014-WangKN #classification #fault #metric #performance
Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction (HW, TMK, AN), pp. 540–545.
SIGIRSIGIR-2014-BianYC #microblog #network
Predicting trending messages and diffusion participants in microblogging network (JB, YY, TSC), pp. 537–546.
SIGIRSIGIR-2014-BingGLNW #classification #segmentation #web
Web page segmentation with structured prediction and its application in web page classification (LB, RG, WL, ZYN, HW), pp. 767–776.
SIGIRSIGIR-2014-EugsterRSKBRJK
Predicting term-relevance from brain signals (MJAE, TR, MMAS, IK, OB, NR, GJ, SK), pp. 425–434.
SIGIRSIGIR-2014-HeGKLS #web
Predicting the popularity of web 2.0 items based on user comments (XH, MG, MYK, YL, KS), pp. 233–242.
SIGIRSIGIR-2014-HuSL #rating
Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction (LH, AS, YL), pp. 345–354.
SIGIRSIGIR-2014-JeonKHHECR #parallel #web
Predictive parallelization: taming tail latencies in web search (MJ, SK, SwH, YH, SE, ALC, SR), pp. 253–262.
SIGIRSIGIR-2014-KatzSKSR #performance #query
Wikipedia-based query performance prediction (GK, AS, OK, BS, LR), pp. 1235–1238.
SIGIRSIGIR-2014-KimHWZ
Comparing client and server dwell time estimates for click-level satisfaction prediction (YK, AHA, RWW, IZ), pp. 895–898.
SIGIRSIGIR-2014-KongMFYZ #realtime
Predicting bursts and popularity of hashtags in real-time (SK, QM, LF, FY, ZZ), pp. 927–930.
SIGIRSIGIR-2014-NguyenL #microblog #network #on the
On predicting religion labels in microblogging networks (MTN, EPL), pp. 1211–1214.
SIGIRSIGIR-2014-PerezJ #microblog #performance #query #retrieval
Predicting query performance in microblog retrieval (JARP, JMJ), pp. 1183–1186.
SIGIRSIGIR-2014-RaiberK
Query-performance prediction: setting the expectations straight (FR, OK), pp. 13–22.
SIGIRSIGIR-2014-RavivKC #performance #query #retrieval
Query performance prediction for entity retrieval (HR, OK, DC), pp. 1099–1102.
SIGIRSIGIR-2014-Sebastian #clustering #semantics #using
Cluster links prediction for literature based discovery using latent structure and semantic features (YS), p. 1275.
SIGIRSIGIR-2014-SongSWA #web
Context-aware web search abandonment prediction (YS, XS, RW, AHA), pp. 93–102.
SIGIRSIGIR-2014-WangSCHHW #modelling
Modeling action-level satisfaction for search task satisfaction prediction (HW, YS, MWC, XH, AHA, RWW), pp. 123–132.
SKYSKY-2014-Fernandez-Utrilla #behaviour #web
An Unified Behaviour Model to Predict Web 2.0 Adoption as a Tool for Software-Knowledge Sharing (MFU, PFU, GM), pp. 3–18.
OOPSLAOOPSLA-2014-ZhaoWZDSSW #automaton #probability #sequence
Call sequence prediction through probabilistic calling automata (ZZ, BW, MZ, YD, JS, XS, YW), pp. 745–762.
AdaEuropeAdaEurope-2014-SaezC #kernel #realtime
Integrated Schedulers for a Predictable Interrupt Management on Real-Time Kernels (SS, AC), pp. 134–148.
PLDIPLDI-2014-HuangMR #abstraction #concurrent #control flow #detection
Maximal sound predictive race detection with control flow abstraction (JH, POM, GR), p. 36.
ICSEICSE-2014-JingYZWL #fault #learning #taxonomy
Dictionary learning based software defect prediction (XYJ, SY, ZWZ, SSW, JL), pp. 414–423.
ICSEICSE-2014-RahmanKBD #debugging #statistics
Comparing static bug finders and statistical prediction (FR, SK, ETB, PTD), pp. 424–434.
SACSAC-2014-AlharbiZ #social
Exploring the significance of human mobility patterns in social link prediction (BA, XZ), pp. 604–609.
SACSAC-2014-FdhilaR #co-evolution #collaboration #process
Predicting change propagation impacts in collaborative business processes (WF, SRM), pp. 1378–1385.
SACSAC-2014-HusemannR #multi #scalability #video
Introduction of a multi-layer predictive search strategy for scalable video coding (RH, VR), pp. 985–986.
SACSAC-2014-ManhaesCZ #architecture #named #using
WAVE: an architecture for predicting dropout in undergraduate courses using EDM (LMBM, SMSdC, GZ), pp. 243–247.
SACSAC-2014-VasconcelosAG #code review #what
What makes your opinion popular?: predicting the popularity of micro-reviews in foursquare (MAV, JMA, MAG), pp. 598–603.
SACSAC-2014-XiaLWZ #analysis
Build system analysis with link prediction (XX, DL, XW, BZ), pp. 1184–1186.
CASECASE-2014-ChenLL #adaptation #design #using
Design of lane keeping system using adaptive model predictive control (BCC, BCL, KL), pp. 922–926.
CASECASE-2014-HsiehHP #generative #using
Improving the stability and fuel economy for Belt-Starter Generator Mild HEV at idle speed using model predict control (FCH, YDH, YWP), pp. 916–921.
CASECASE-2014-HuangNXCSD #3d #approach #geometry #modelling
Predictive modeling of geometric deviations of 3D printed products — A unified modeling approach for cylindrical and polygon shapes (QH, HN, KX, YC, SS, TD), pp. 25–30.
CASECASE-2014-KhodabakhshianFW #performance
Predictive control of the engine cooling system for fuel efficiency improvement (MK, LF, JW), pp. 61–66.
CASECASE-2014-LuoHA #online
Online trajectory tracking based on model predictive control for Service Robot (RCL, KCH, RA), pp. 1238–1243.
CASECASE-2014-NodaMNKOI #behaviour #maintenance #online
Online maintaining behavior of high-load and unstable postures based on whole-body load balancing strategy with thermal prediction (SN, MM, SN, YK, KO, MI), pp. 1166–1171.
CASECASE-2014-ShamsAY #modelling
Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs (IS, SA, KY), pp. 576–581.
CASECASE-2014-SustoWPZJOM #adaptation #flexibility #machine learning #maintenance
An adaptive machine learning decision system for flexible predictive maintenance (GAS, JW, SP, MZ, ABJ, PGO, SFM), pp. 806–811.
DACDAC-2014-Bhushan #injection #visual notation
A Rigorous Graphical Technique for Predicting Sub-harmonic Injection Locking in LC Oscillators (PB), p. 8.
DATEDATE-2014-AhmadC #performance #simulation
Fast STA prediction-based gate-level timing simulation (TBA, MJC), pp. 1–6.
DATEDATE-2014-AlhammadP #execution #manycore #parallel #thread
Time-predictable execution of multithreaded applications on multicore systems (AA, RP), pp. 1–6.
DATEDATE-2014-AyariABCKR
New implementions of predictive alternate analog/RF test with augmented model redundancy (HA, FA, SB, MC, VK, MR), pp. 1–4.
DATEDATE-2014-Huang14a #manycore #network #performance
Leveraging on-chip networks for efficient prediction on multicore coherence (LH), pp. 1–4.
DATEDATE-2014-KeramidasMKN
Spatial pattern prediction based management of faulty data caches (GK, MM, AK, DN), pp. 1–6.
DATEDATE-2014-MarianiPZS #design #named #scheduling #simulation #using
DeSpErate: Speeding-up design space exploration by using predictive simulation scheduling (GM, GP, VZ, CS), pp. 1–4.
DATEDATE-2014-NelsonNMKG #composition #kernel #named #realtime
CoMik: A predictable and cycle-accurately composable real-time microkernel (AN, ABN, AMM, MK, KG), pp. 1–4.
DATEDATE-2014-ReinekeW #performance #resource management
Impact of resource sharing on performance and performance prediction (JR, RW), pp. 1–2.
DATEDATE-2014-YangHKKCPK #parallel #simulation
Predictive parallel event-driven HDL simulation with a new powerful prediction strategy (SY, JH, DK, NK, DC, JP, JK), pp. 1–3.
DATEDATE-2014-ZhangLHCW #multi #performance
Joint Virtual Probe: Joint exploration of multiple test items’ spatial patterns for efficient silicon characterization and test prediction (SZ, FL, CKH, KTC, HW), pp. 1–6.
HPCAHPCA-2014-AhnYC #architecture #named
DASCA: Dead Write Prediction Assisted STT-RAM Cache Architecture (JA, SY, KC), pp. 25–36.
HPCAHPCA-2014-WangDDS #concurrent #memory management #multi #named #source code #thread
DraMon: Predicting memory bandwidth usage of multi-threaded programs with high accuracy and low overhead (WW, TD, JWD, MLS), pp. 380–391.
PDPPDP-2014-GhaneAS #multi
An Opto-electrical NoC with Traffic Flow Prediction in Chip Multiprocessors (MG, MA, HSA), pp. 440–443.
PPoPPPPoPP-2014-LiuTHB #detection #named
PREDATOR: predictive false sharing detection (TL, CT, ZH, EDB), pp. 3–14.
ISSTAISSTA-2014-NistorR #developer #named #performance #problem #smarttech
SunCat: helping developers understand and predict performance problems in smartphone applications (AN, LR), pp. 282–292.
QoSAQoSA-2013-FeugasMD #evolution #process #quality
A causal model to predict the effect of business process evolution on quality of service (AF, SM, LD), pp. 143–152.
ICDARICDAR-2013-HassaineAAJ #contest #gender
ICDAR 2013 Competition on Gender Prediction from Handwriting (AH, SAM, JMA, AJ), pp. 1417–1421.
ICDARICDAR-2013-RabeuxJVD #documentation #evaluation #quality
Quality Evaluation of Ancient Digitized Documents for Binarization Prediction (VR, NJ, AV, JPD), pp. 113–117.
TPDLTPDL-2013-AlhooriF #question #ranking #social
Can Social Reference Management Systems Predict a Ranking of Scholarly Venues? (HA, RF), pp. 138–143.
VLDBVLDB-2013-HendawiBM #framework #named #network #query #scalability
iRoad: A Framework For Scalable Predictive Query Processing On Road Networks (AMH, JB, MFM), pp. 1262–1265.
VLDBVLDB-2013-PopescuBEA #named #runtime #scalability #towards
PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics (ADP, AB, VE, AA), pp. 1678–1689.
VLDBVLDB-2013-WuCHN #concurrent #database #execution #query #towards
Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads (WW, YC, HH, JFN), pp. 925–936.
VLDBVLDB-2013-XueZZXYTJZ #named #privacy
DesTeller: A System for Destination Prediction Based on Trajectories with Privacy Protection (AYX, RZ, YZ, XX, JY, YT), pp. 1198–1201.
VLDBVLDB-2013-ZhouTWN #2d #learning #named #probability
R2-D2: a System to Support Probabilistic Path Prediction in Dynamic Environments via “Semi-Lazy” Learning (JZ, AKHT, WW, WSN), pp. 1366–1369.
ITiCSEITiCSE-2013-McDonald #mobile
A location prediction project on mobile devices (CM), p. 320.
SIGITESIGITE-2013-GodaHM #correlation #performance #self
Correlation of grade prediction performance and validity of self-evaluation comments (KG, SH, TM), pp. 35–42.
CSMRCSMR-2013-LamkanfiD #debugging
Predicting Reassignments of Bug Reports — An Exploratory Investigation (AL, SD), pp. 327–330.
CSMRCSMR-2013-SurianTLCL #network
Predicting Project Outcome Leveraging Socio-Technical Network Patterns (DS, YT, DL, HC, EPL), pp. 47–56.
CSMRCSMR-2013-XiaLWYLS #algorithm #case study #comparative #debugging #learning
A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction (XX, DL, XW, XY, SL, JS), pp. 331–334.
ICSMEICSM-2013-GharehyaziePF #developer #process #social
Social Activities Rival Patch Submission for Prediction of Developer Initiation in OSS Projects (MG, DP, VF), pp. 340–349.
ICSMEICSM-2013-LeL #approach #automation #effectiveness #fault #locality #tool support
Will Fault Localization Work for These Failures? An Automated Approach to Predict Effectiveness of Fault Localization Tools (TDBL, DL), pp. 310–319.
ICSMEICSM-2013-TabaKZHN #debugging #using
Predicting Bugs Using Antipatterns (SEST, FK, YZ, AEH, MN), pp. 270–279.
ICSMEICSM-2013-TianLS #analysis #debugging #multi #named
DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis (YT, DL, CS), pp. 200–209.
MSRMSR-2013-HuW #fault #using
Using citation influence to predict software defects (WH, KW), pp. 419–428.
MSRMSR-2013-PetersMM #fault
Better cross company defect prediction (FP, TM, AM), pp. 409–418.
IFMIFM-2013-RuksenasCH #behaviour #evaluation #interactive
Integrating Formal Predictions of Interactive System Behaviour with User Evaluation (RR, PC, MDH), pp. 238–252.
AIIDEAIIDE-2013-StanescuHEGB
Predicting Army Combat Outcomes in StarCraft (MS, SPH, GE, RG, MB).
CoGCIG-2013-BuckleyCK
Predicting skill from gameplay input to a first-person shooter (DB, KC0, JDK), pp. 1–8.
CoGCIG-2013-ChoKC #adaptation #order
Replay-based strategy prediction and build order adaptation for StarCraft AI bots (HCC, KJK, SBC), pp. 1–7.
CHICHI-2013-ChoudhuryCH #behaviour #social #social media
Predicting postpartum changes in emotion and behavior via social media (MDC, SC, EH), pp. 3267–3276.
CHICHI-2013-HuttoYG #twitter
A longitudinal study of follow predictors on twitter (CJH, SY, EG), pp. 821–830.
CHICHI-2013-ReineckeYMMZLG #complexity #quantifier #visual notation
Predicting users’ first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness (KR, TY, LM, RM, YZ, JL, KZG), pp. 2049–2058.
CHICHI-2013-Salvucci #interactive
Distraction beyond the driver: predicting the effects of in-vehicle interaction on surrounding traffic (DDS), pp. 3131–3134.
CSCWCSCW-2013-HsiehHCT #community #exclamation #online #quote #social #volunteer
“Welcome!”: social and psychological predictors of volunteer socializers in online communities (GH, YH, IC, KNT), pp. 827–838.
HCIDHM-HB-2013-BatainehMA
Artificial Neural Network-Based Prediction of Human Posture (MB, TM, KAM), pp. 305–313.
HCIDHM-SET-2013-KongZC #behaviour #risk management
Personality and Attitudes as Predictors of Risky Driving Behavior: Evidence from Beijing Drivers (JK, KZ, XC), pp. 38–44.
HCIDHM-SET-2013-MurataKEH #behaviour #metric #using
Prediction of Drowsy Driving Using Behavioral Measures of Drivers — Change of Neck Bending Angle and Sitting Pressure Distribution (AM, TK, TE, TH), pp. 78–87.
HCIDUXU-CXC-2013-WangTKS #evaluation #eye tracking #performance
Banner Evaluation Predicted by Eye Tracking Performance and the Median Thinking Style (MYW, DLT, CTK, VCS), pp. 129–138.
HCIDUXU-WM-2013-JimenezM #design #evaluation #smarttech
Design and Evaluation of a Predictive Model for Smartphone Selection (YJ, PM), pp. 376–384.
HCIHCI-AMTE-2013-McDougallR
Ease of Icon Processing Can Predict Icon Appeal (SM, IR), pp. 575–584.
HCIHCI-AS-2013-HuaG #comprehension #difference #evaluation #protocol #safety #usability
Usability Evaluation of a Voluntary Patient Safety Reporting System: Understanding the Difference between Predicted and Observed Time Values by Retrospective Think-Aloud Protocols (LH, YG), pp. 94–100.
HCIHCI-IMT-2013-TruongNTD #collaboration #word
Collaborative Smart Virtual Keyboard with Word Predicting Function (CTT, DHNH, MTT, ADD), pp. 513–522.
HCIHCI-UC-2013-KawabeIN
A Refuge Location Prediction System for When a Tsunami Has Occurred (AK, TI, YN), pp. 295–300.
HCIHIMI-D-2013-UedaA #people #using
Prediction of the Concern of People Using CGM (YU, YA), pp. 284–292.
HCIOCSC-2013-HallCW #approach #community
Well-Being’s Predictive Value — A Gamified Approach to Managing Smart Communities (MH, SC, CW), pp. 13–22.
EDOCEDOC-2013-ValjaOISJ #modelling
Modeling and Prediction of Monetary and Non-monetary Business Values (MV, , MEI, MvS, PJ), pp. 153–158.
ICEISICEIS-J-2013-BevacquaCFGP13a #data-driven #framework #monitoring #process
A Data-Driven Prediction Framework for Analyzing and Monitoring Business Process Performances (AB, MC, FF, MG, LP), pp. 100–117.
ICEISICEIS-J-2013-LiL13a #object-oriented
Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System (LL, HL), pp. 209–225.
ICEISICEIS-v1-2013-BevacquaCFGP #abstraction #adaptation #approach #process
A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances (AB, MC, FF, MG, LP), pp. 56–65.
ICEISICEIS-v2-2013-LiL #agile #network #object-oriented #process #using
Predicting Fault-proneness of Object-Oriented System Developed with Agile Process using Learned Bayesian Network (LL, HL), pp. 5–16.
CIKMCIKM-2013-BaragliaMNS #learning #named
LearNext: learning to predict tourists movements (RB, CIM, FMN, FS), pp. 751–756.
CIKMCIKM-2013-GhoreishiS #query
Predicting event-relatedness of popular queries (SNG, AS), pp. 1193–1196.
CIKMCIKM-2013-KamathC #learning #what
Spatio-temporal meme prediction: learning what hashtags will be popular where (KYK, JC), pp. 1341–1350.
CIKMCIKM-2013-LiaoPPL #behaviour #mining #mobile #on the #smarttech
On mining mobile apps usage behavior for predicting apps usage in smartphones (ZXL, YCP, WCP, PRL), pp. 609–618.
CIKMCIKM-2013-LiMWLX #network #on the #online #social
On popularity prediction of videos shared in online social networks (HL, XM, FW, JL, KX), pp. 169–178.
CIKMCIKM-2013-McGeeCC #social #social media
Location prediction in social media based on tie strength (JM, JC, ZC), pp. 459–468.
CIKMCIKM-2013-MishraRT #network #process
Estimating the relative utility of networks for predicting user activities (NM, DMR, PT), pp. 1047–1056.
CIKMCIKM-2013-SpeicherBG #exclamation #interactive #web
TellMyRelevance!: predicting the relevance of web search results from cursor interactions (MS, AB, MG), pp. 1281–1290.
CIKMCIKM-2013-TanCHKS
Instant foodie: predicting expert ratings from grassroots (CT, EHC, DAH, GK, AJS), pp. 1127–1136.
CIKMCIKM-2013-ZhuZPWZY #network #process #social
Predicting user activity level in social networks (YZ, EZ, SJP, XW, MZ, QY), pp. 159–168.
ECIRECIR-2013-Cleger-TamayoFHT #quality #recommendation
Being Confident about the Quality of the Predictions in Recommender Systems (SCT, JMFL, JFH, NT), pp. 411–422.
ECIRECIR-2013-DemeesterNTDH #web
Snippet-Based Relevance Predictions for Federated Web Search (TD, DN, DT, CD, DH), pp. 697–700.
ECIRECIR-2013-LagnierDGG #information management #network #social #using
Predicting Information Diffusion in Social Networks Using Content and User’s Profiles (CL, LD, ÉG, PG), pp. 74–85.
ECIRECIR-2013-OlteanuPLA #web
Web Credibility: Features Exploration and Credibility Prediction (AO, SP, XL, KA), pp. 557–568.
ECIRECIR-2013-RaiberK #effectiveness #metric #using
Using Document-Quality Measures to Predict Web-Search Effectiveness (FR, OK), pp. 134–145.
ICMLICML-c1-2013-GiguereLMS #algorithm #approach #bound #learning
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction (SG, FL, MM, KS), pp. 107–114.
ICMLICML-c1-2013-HamiltonFP #modelling
Modelling Sparse Dynamical Systems with Compressed Predictive State Representations (WLH, MMF, JP), pp. 178–186.
ICMLICML-c1-2013-MehtaG #bound
Sparsity-Based Generalization Bounds for Predictive Sparse Coding (NAM, AGG), pp. 36–44.
ICMLICML-c3-2013-Agarwal #algorithm #multi
Selective sampling algorithms for cost-sensitive multiclass prediction (AA), pp. 1220–1228.
ICMLICML-c3-2013-JancsaryNR #learning
Learning Convex QP Relaxations for Structured Prediction (JJ, SN, CR), pp. 915–923.
ICMLICML-c3-2013-JoseGAV #kernel #learning #performance
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction (CJ, PG, PA, MV), pp. 486–494.
ICMLICML-c3-2013-LondonHTG
Collective Stability in Structured Prediction: Generalization from One Example (BL, BH, BT, LG), pp. 828–836.
ICMLICML-c3-2013-RastegariCFHD
Predictable Dual-View Hashing (MR, JC, SF, HDI, LSD), pp. 1328–1336.
ICMLICML-c3-2013-RossZYDB #learning #policy
Learning Policies for Contextual Submodular Prediction (SR, JZ, YY, DD, DB), pp. 1364–1372.
KDDKDD-2013-CuiJYWZY #approach #data-driven #network
Cascading outbreak prediction in networks: a data-driven approach (PC, SJ, LY, FW, WZ, SY), pp. 901–909.
KDDKDD-2013-KuoYHKL #network #social #statistics #using
Unsupervised link prediction using aggregative statistics on heterogeneous social networks (TTK, RY, YYH, PHK, SDL), pp. 775–783.
KDDKDD-2013-LeeNBC #social
Link prediction with social vector clocks (CL, BN, UB, PC), pp. 784–792.
KDDKDD-2013-McMahanHSYEGNPDGCLWHBK
Ad click prediction: a view from the trenches (HBM, GH, DS, MY, DE, JG, LN, TP, ED, DG, SC, DL, MW, AMH, TB, JK), pp. 1222–1230.
KDDKDD-2013-MontgomerySCM #experience
Experience from hosting a corporate prediction market: benefits beyond the forecasts (TAM, PMS, MJC, PEM), pp. 1384–1392.
KDDKDD-2013-TranPLHBV #framework #risk management
An integrated framework for suicide risk prediction (TT, DQP, WL, RH, MB, SV), pp. 1410–1418.
KDDKDD-2013-Varian
Predicting the present with search engine data (HV), p. 4.
KDDKDD-2013-WangBLZL
Psychological advertising: exploring user psychology for click prediction in sponsored search (TW, JB, SL, YZ, TYL), pp. 563–571.
KDDKDD-2013-WeissDB #quality
Improving quality control by early prediction of manufacturing outcomes (SMW, AD, RJB), pp. 1258–1266.
KDDKDD-2013-XiangYFWTY #learning #multi
Multi-source learning with block-wise missing data for Alzheimer’s disease prediction (SX, LY, WF, YW, PMT, JY), pp. 185–193.
KDDKDD-2013-YiCLSY #online #performance
Predictive model performance: offline and online evaluations (JY, YC, JL, SS, TWY), pp. 1294–1302.
KDDKDD-2013-ZhangWNLWY #how #named #network #social #visualisation
LAFT-Explorer: inferring, visualizing and predicting how your social network expands (JZ, CW, YN, YL, JW, PSY), pp. 1510–1513.
KDDKDD-2013-ZhengDMZ #collaboration #interactive #matrix #multi
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (XZ, HD, HM, SZ), pp. 1025–1033.
KDDKDD-2013-ZhouTWN #approach #probability
A “semi-lazy” approach to probabilistic path prediction (JZ, AKHT, WW, WSN), pp. 748–756.
KDIRKDIR-KMIS-2013-ChenYTH #behaviour
The Disulfide Connectivity Prediction with Support Vector Machine and Behavior Knowledge Space (HYC, CBY, KTT, CYH), pp. 112–118.
KDIRKDIR-KMIS-2013-FerchichiBF #adaptation #approach #image
An Approach based on Adaptive Decision Tree for Land Cover Change Prediction in Satellite Images (AF, WB, IRF), pp. 82–90.
MLDMMLDM-2013-MaziluCGRHT #detection #learning
Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson’s Disease (SM, AC, EG, DR, JMH, GT), pp. 144–158.
MLDMMLDM-2013-ParimiC
Pre-release Box-Office Success Prediction for Motion Pictures (RP, DC), pp. 571–585.
MLDMMLDM-2013-PoziMD #estimation
Density Ratio Estimation in Support Vector Machine for Better Generalization: Study on Direct Marketing Prediction (MSMP, AM, AD), pp. 275–280.
MLDMMLDM-2013-SappP #classification #clustering
Accuracy-Based Classification EM: Combining Clustering with Prediction (SS, AP), pp. 458–465.
RecSysRecSys-2013-Adamopoulos #rating #recommendation
Beyond rating prediction accuracy: on new perspectives in recommender systems (PA), pp. 459–462.
RecSysRecSys-2013-AdamopoulosT #collaboration #recommendation #using
Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems (PA, AT), pp. 351–354.
RecSysRecSys-2013-GaoHBLLZ #social #social media
Improving user profile with personality traits predicted from social media content (RG, BH, SB, LL, AL, TZ), pp. 355–358.
RecSysRecSys-2013-Steck #evaluation #ranking #recommendation
Evaluation of recommendations: rating-prediction and ranking (HS), pp. 213–220.
RecSysRecSys-2013-TangGHL #overview #rating
Context-aware review helpfulness rating prediction (JT, HG, XH, HL), pp. 1–8.
RecSysRecSys-2013-TiroshiBKCK #network #social
Cross social networks interests predictions based ongraph features (AT, SB, MAK, TC, TK), pp. 319–322.
SEKESEKE-2013-CalikliB #bias #developer #fault
The Impact of Confirmation Bias on the Release-based Defect Prediction of Developer Groups (, AB), pp. 461–466.
SEKESEKE-2013-YuanYL #debugging #fine-grained #source code
Bug Prediction for Fine-Grained Source Code Changes (ZY, LY, CL), pp. 381–387.
SIGIRSIGIR-2013-ChandarWC #documentation
Document features predicting assessor disagreement (PC, WW, BC), pp. 745–748.
SIGIRSIGIR-2013-GuoJLYA #interactive #mining #mobile #web
Mining touch interaction data on mobile devices to predict web search result relevance (QG, HJ, DL, SY, EA), pp. 153–162.
SIGIRSIGIR-2013-SondakSK #query
Estimating query representativeness for query-performance prediction (MS, AS, OK), pp. 853–856.
SIGIRSIGIR-2013-WestWH #process #query
Here and there: goals, activities, and predictions about location from geotagged queries (RW, RWW, EH), pp. 817–820.
SIGIRSIGIR-2013-Wu #behaviour #how #online #using
How far will you go?: characterizing and predicting online search stopping behavior using information scent and need for cognition (WCW), p. 1149.
SIGIRSIGIR-2013-ZhangWYW #learning #network
Learning latent friendship propagation networks with interest awareness for link prediction (JZ, CW, PSY, JW), pp. 63–72.
RERE-2013-ShiWL #evolution #learning
Learning from evolution history to predict future requirement changes (LS, QW, ML), pp. 135–144.
REFSQREFSQ-2013-ProynovaP #feedback
Factors Influencing User Feedback on Predicted Satisfaction with Software Systems (RP, BP), pp. 96–111.
ASEASE-2013-Ganai #incremental #performance #reasoning
Efficient data race prediction with incremental reasoning on time-stamped lock history (MKG), pp. 37–47.
ASEASE-2013-GuoCASW #approach #learning #performance #statistics #variability
Variability-aware performance prediction: A statistical learning approach (JG, KC, SA, NS, AW), pp. 301–311.
ASEASE-2013-IzsoSBHR #graph #metric #performance #precise #query #towards
Towards precise metrics for predicting graph query performance (BI, ZS, GB, ÁH, IR), pp. 421–431.
ASEASE-2013-JiangTK #fault #personalisation
Personalized defect prediction (TJ, LT, SK), pp. 279–289.
ASEASE-2013-ScannielloGMM #clustering #fault #using
Class level fault prediction using software clustering (GS, CG, AM, TM), pp. 640–645.
ESEC-FSEESEC-FSE-2013-RahmanPHD #bias #fault
Sample size vs. bias in defect prediction (FR, DP, IH, PTD), pp. 147–157.
ESEC-FSEESEC-FSE-2013-SilicDS #clustering #reliability #web #web service
Prediction of atomic web services reliability based on k-means clustering (MS, GD, SS), pp. 70–80.
ESEC-FSEESEC-FSE-2013-ZhangC #effectiveness #fault #modelling
A cost-effectiveness criterion for applying software defect prediction models (HZ, SCC), pp. 643–646.
ICSEICSE-2013-HaiducRBOLM #code search #quality #query #source code
Query quality prediction and reformulation for source code search: the refoqus tool (SH, GDR, GB, RO, ADL, AM), pp. 1307–1310.
ICSEICSE-2013-HerzigJZ #classification #debugging #how
It’s not a bug, it’s a feature: how misclassification impacts bug prediction (KH, SJ, AZ), pp. 392–401.
ICSEICSE-2013-LewisLSZOW #case study #debugging #developer
Does bug prediction support human developers? findings from a google case study (CL, ZL, CS, XZ, RO, EJWJ), pp. 372–381.
ICSEICSE-2013-Souza #using
A proposal for the improvement of project’s cost predictability using EVM and historical data of cost (ADdS), pp. 1447–1449.
ICSEICSE-2013-ZhangGV #empirical
Predicting bug-fixing time: an empirical study of commercial software projects (HZ, LG, SV), pp. 1042–1051.
SACSAC-2013-BasgaluppBSC #approach
Software effort prediction: a hyper-heuristic decision-tree based approach (MPB, RCB, TSdS, ACPLFC), pp. 1109–1116.
SACSAC-2013-KannanMDS #navigation #using
Predictive indoor navigation using commercial smart-phones (BK, FM, MBD, KPS), pp. 519–525.
SACSAC-2013-LinCLG #approach #data-driven #distributed #learning
Distributed dynamic data driven prediction based on reinforcement learning approach (SYL, KMC, CCL, NG), pp. 779–784.
SACSAC-2013-MaiaB #modelling #network #performance #query
Sensor-field modeling based on in-network data prediction: an efficient strategy for answering complex queries in wireless sensor networks (JEBM, AB), pp. 554–559.
SACSAC-2013-MinerviniFdE #rank #semantics
Rank prediction for semantically annotated resources (PM, NF, Cd, FE), pp. 333–338.
SACSAC-2013-Perez-PalacinCM #named #trade-off
log2cloud: log-based prediction of cost-performance trade-offs for cloud deployments (DPP, RC, JM), pp. 397–404.
SACSAC-2013-SinghR #algorithm #architecture #optimisation
Meta-learning based architectural and algorithmic optimization for achieving green-ness in predictive workload analytics (NS, SR), pp. 1169–1176.
SACSAC-2013-UmemotoYNT #behaviour #query
Predicting query reformulation type from user behavior (KU, TY, SN, KT), pp. 894–901.
CASECASE-2013-ChenHCHW #automation #maintenance
Automatic baseline-sample-selection scheme for baseline predictive maintenance (CFC, YSH, FTC, HCH, SCW), pp. 183–188.
CASECASE-2013-KwadzogahZL #bibliography #perspective
Model predictive control for HVAC systems — A review (RK, MZ, SL), pp. 442–447.
CASECASE-2013-ParisioMVJ #approach
A scenario-based predictive control approach to building HVAC management systems (AP, MM, DV, KHJ), pp. 428–435.
CASECASE-2013-SharabianiDBCND #machine learning
Machine learning based prediction of warfarin optimal dosing for African American patients (AS, HD, AB, LC, EN, KD), pp. 623–628.
CASECASE-2013-SustoJOM #multi #process
Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes (GAS, ABJ, PGO, SFM), pp. 201–206.
CASECASE-2013-SustoSPPMB #fault #maintenance
A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation (GAS, AS, SP, DP, SFM, AB), pp. 195–200.
CASECASE-2013-TranH13a #composition
Plug-and-play predictive control of modular nonlinear systems with coupling delays (TT, QPH), pp. 699–704.
CASECASE-2013-YeWZML #approach #network #optimisation #scalability
A signal split optimization approach based on model predictive control for large-scale urban traffic networks (BLY, WW, XZ, WJM, JL), pp. 904–909.
DACDAC-2013-AsenovART #performance
Predicting future technology performance (AA, CA, CR, ET), p. 6.
DACDAC-2013-KleebergerGS #evaluation #modelling #performance #standard
Predicting future product performance: modeling and evaluation of standard cells in FinFET technologies (VK, HEG, US), p. 6.
DACDAC-2013-MaricAV #adaptation #energy #hybrid #named #reliability
APPLE: adaptive performance-predictable low-energy caches for reliable hybrid voltage operation (BM, JA, MV), p. 8.
DATEDATE-2013-ChenD #parallel #simulation #using
Optimized out-of-order parallel discrete event simulation using predictions (WC, RD), pp. 3–8.
DATEDATE-2013-KodakaTSYKTXSUTMM #manycore #power management
A near-future prediction method for low power consumption on a many-core processor (TK, AT, SS, AY, TK, TT, HX, TS, HU, JT, TM, NM), pp. 1058–1059.
DATEDATE-2013-LifaEP #linear
Dynamic configuration prefetching based on piecewise linear prediction (AAL, PE, ZP), pp. 815–820.
DATEDATE-2013-MuradoreQF #network
Model predictive control over delay-based differentiated services control networks (RM, DQ, PF), pp. 1117–1122.
HPCAHPCA-2013-BonannoCLMPS #branch
Two level bulk preload branch prediction (JB, AC, DL, UM, BP, AS), pp. 71–82.
HPCAHPCA-2013-FarooqKJ #branch #compilation
Store-Load-Branch (SLB) predictor: A compiler assisted branch prediction for data dependent branches (MUF, K, LKJ), pp. 59–70.
HPCAHPCA-2013-RobatmiliLEGSPBK #architecture #effectiveness #how #manycore
How to implement effective prediction and forwarding for fusable dynamic multicore architectures (BR, DL, HE, MSSG, AS, AP, DB, SWK), pp. 460–471.
HPCAHPCA-2013-SubramanianSKJM #in memory #memory management #named #performance
MISE: Providing performance predictability and improving fairness in shared main memory systems (LS, VS, YK, BJ, OM), pp. 639–650.
PDPPDP-2013-Atoofian #consistency
Consistency Check through O-GEHL Predictors (EA), pp. 218–224.
PDPPDP-2013-JokhioALPL #in the cloud #resource management #video
Prediction-Based Dynamic Resource Allocation for Video Transcoding in Cloud Computing (FJ, AA, SL, IP, JL), pp. 254–261.
ICLPICLP-J-2013-ArbelaezTC #parallel #runtime #satisfiability #using
Using sequential runtime distributions for the parallel speedup prediction of SAT local search (AA, CT, PC), pp. 625–639.
ICSTICST-2013-CanforaLPOPP #fault #multi
Multi-objective Cross-Project Defect Prediction (GC, ADL, MDP, RO, AP, SP), pp. 252–261.
ICSTICST-2013-CarrozzaCNPR #analysis #industrial
Analysis and Prediction of Mandelbugs in an Industrial Software System (GC, DC, RN, RP, SR), pp. 262–271.
ICTSSICTSS-2013-BadriBF #case study #empirical #testing
Predicting the Size of Test Suites from Use Cases: An Empirical Exploration (MB, LB, WF), pp. 114–132.
ISSTAISSTA-2013-GuiSLSDW #model checking #reliability #testing
Combining model checking and testing with an application to reliability prediction and distribution (LG, JS, YL, YJS, JSD, XW), pp. 101–111.
VMCAIVMCAI-2013-JohnNN #network
Knockout Prediction for Reaction Networks with Partial Kinetic Information (MJ, MN, JN), pp. 355–374.
QoSAQoSA-2012-Groenda #modelling #performance
Improving performance predictions by accounting for the accuracy of composed performance models (HG), pp. 111–116.
WICSA-ECSAWICSA-ECSA-2012-FrancoBR #architecture #automation #reliability
Automated Reliability Prediction from Formal Architectural Descriptions (JMF, RB, MZR), pp. 302–309.
WICSA-ECSAWICSA-ECSA-2012-RathfelderBKR #email #monitoring #performance #scalability #using
Workload-aware System Monitoring Using Performance Predictions Applied to a Large-scale E-Mail System (CR, SB, KK, RHR), pp. 31–40.
HTHT-2012-HartigH #linked data #modelling #open data #query
Query prediction with context models for populating personal linked data caches (OH, TH), pp. 325–326.
HTHT-2012-KhabiriCK #realtime #semantics #web
Predicting semantic annotations on the real-time web (EK, JC, KYK), pp. 219–228.
SIGMODSIGMOD-2012-GiatrakosDGSS #data type #distributed #geometry #monitoring
Prediction-based geometric monitoring over distributed data streams (NG, AD, MNG, IS, AS), pp. 265–276.
VLDBVLDB-2012-SwitakowskiBZ
From Cooperative Scans to Predictive Buffer Management (MS, PAB, MZ), pp. 1759–1770.
CSMRCSMR-2012-GoulaoFWA #analysis #case study #comparative #evolution #using
Software Evolution Prediction Using Seasonal Time Analysis: A Comparative Study (MG, NF, MW, FBeA), pp. 213–222.
CSMRCSMR-2012-HosseiniNG #debugging #using
A Market-Based Bug Allocation Mechanism Using Predictive Bug Lifetimes (HH, RN, MWG), pp. 149–158.
CSMRCSMR-2012-KarusD #xml
Predicting Coding Effort in Projects Containing XML (SK, MD), pp. 203–212.
ICSMEICSM-2012-HmoodEKR #open source
Applying technical stock market indicators to analyze and predict the evolvability of open source projects (AH, ME, IK, JR), pp. 613–616.
MSRMSR-2012-BettenburgNH #fault #modelling
Think locally, act globally: Improving defect and effort prediction models (NB, MN, AEH), pp. 60–69.
MSRMSR-2012-GigerPG #analysis #empirical
Can we predict types of code changes? An empirical analysis (EG, MP, HCG), pp. 217–226.
SCAMSCAM-2012-BusingeSB #eclipse #plugin
Compatibility Prediction of Eclipse Third-Party Plug-ins in New Eclipse Releases (JB, AS, MvdB), pp. 164–173.
WCREWCRE-2012-AbebeATAG #fault #question #smell
Can Lexicon Bad Smells Improve Fault Prediction? (SLA, VA, PT, GA, YGG), pp. 235–244.
WCREWCRE-2012-TianLS #classification #debugging #fine-grained #information retrieval #nearest neighbour
Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction (YT, DL, CS), pp. 215–224.
CoGCIG-2012-WistubaSP #comparison
Comparison of Bayesian move prediction systems for Computer Go (MW, LS, MP), pp. 91–99.
FDGFDG-2012-Stein
Sports newsgames: prediction, speculation, and accuracy (AS), pp. 5–10.
CHICHI-2012-FitchettC #named #what
AccessRank: predicting what users will do next (SF, AC), pp. 2239–2242.
CHICHI-2012-NavalpakkamC #experience
Mouse tracking: measuring and predicting users’ experience of web-based content (VN, EFC), pp. 2963–2972.
CHICHI-2012-SwearnginCJB #generative #legacy #modelling #performance
Easing the generation of predictive human performance models from legacy systems (AS, MBC, BEJ, RKEB), pp. 2489–2498.
CSCWCSCW-2012-FugelstadDMKMTS #community #online #what
What makes users rate (share, tag, edit...)?: predicting patterns of participation in online communities (PF, PD, JFM, JK, CAM, LGT, MS), pp. 969–978.
CSCWCSCW-2012-Gilbert12a
Predicting tie strength in a new medium (EG), pp. 1047–1056.
CSCWCSCW-2012-RzeszotarskiK #learning #wiki #word
Learning from history: predicting reverted work at the word level in wikipedia (JMR, AK), pp. 437–440.
CSCWCSCW-2012-TripathiB #experience #modelling
Predicting creativity in the wild: experience sample and sociometric modeling of teams (PT, WB), pp. 1203–1212.
CAiSECAiSE-2012-KhazankinSD #crowdsourcing
Predicting QoS in Scheduled Crowdsourcing (RK, DS, SD), pp. 460–472.
ICEISICEIS-v1-2012-AstiazaraB #energy
Application of an Artificial Immune System to Predict Electrical Energy Fraud and Theft (MVA, DACB), pp. 265–271.
CIKMCIKM-2012-ChengTH #database #effectiveness #keyword #query
Predicting the effectiveness of keyword queries on databases (SC, AT, VH), pp. 1213–1222.
CIKMCIKM-2012-DiriyeWBD #comprehension #web
Leaving so soon?: understanding and predicting web search abandonment rationales (AD, RW, GB, STD), pp. 1025–1034.
CIKMCIKM-2012-EldardiryN #analysis #classification #graph #how
An analysis of how ensembles of collective classifiers improve predictions in graphs (HE, JN), pp. 225–234.
CIKMCIKM-2012-GuoLA #fine-grained #interactive #web
Predicting web search success with fine-grained interaction data (QG, DL, EA), pp. 2050–2054.
CIKMCIKM-2012-HuangCLL #probability #process #social #using
Predicting aggregate social activities using continuous-time stochastic process (SH, MC, BL, DL), pp. 982–991.
CIKMCIKM-2012-HuangNHT #network #social #trust
Trust prediction via aggregating heterogeneous social networks (JH, FN, HH, YCT), pp. 1774–1778.
CIKMCIKM-2012-KangLC #category theory
Predicting primary categories of business listings for local search (CK, JL, YC), pp. 2591–2594.
CIKMCIKM-2012-KanhabuaN12a #query #retrieval
Estimating query difficulty for news prediction retrieval (NK, KN), pp. 2623–2626.
CIKMCIKM-2012-KolesnikovLT
Predicting CTR of new ads via click prediction (AK, YL, VT), pp. 2547–2550.
CIKMCIKM-2012-KootiMGC #network #online #social
Predicting emerging social conventions in online social networks (FK, WAM, PKG, MC), pp. 445–454.
CIKMCIKM-2012-KrikonCK #performance #retrieval
Predicting the performance of passage retrieval for question answering (EK, DC, OK), pp. 2451–2454.
CIKMCIKM-2012-KupavskiiOUUSGK #twitter
Prediction of retweet cascade size over time (AK, LO, AU, SU, PS, GG, AK), pp. 2335–2338.
CIKMCIKM-2012-KurlandRS #clustering #ranking
Query-performance prediction and cluster ranking: two sides of the same coin (OK, FR, AS), pp. 2459–2462.
CIKMCIKM-2012-KurlandSHRCR #framework #probability
Back to the roots: a probabilistic framework for query-performance prediction (OK, AS, SH, FR, DC, OR), pp. 823–832.
CIKMCIKM-2012-KustarevUMS #performance #query
Session-based query performance prediction (AK, YU, AM, PS), pp. 2563–2566.
CIKMCIKM-2012-LiuLCBZ
Exploring and predicting search task difficulty (JL, CL, MJC, NJB, XZ), pp. 1313–1322.
CIKMCIKM-2012-LongBDC #e-commerce
Enhancing product search by best-selling prediction in e-commerce (BL, JB, AD, YC), pp. 2479–2482.
CIKMCIKM-2012-MarkovitsSKC #performance #query #retrieval
Predicting query performance for fusion-based retrieval (GM, AS, OK, DC), pp. 813–822.
CIKMCIKM-2012-ShinSD #multi
Multi-scale link prediction (DS, SS, ISD), pp. 215–224.
CIKMCIKM-2012-SouihliS #probability #query #xml
Demonstrating ProApproX 2.0: a predictive query engine for probabilistic XML (AS, PS), pp. 2734–2736.
CIKMCIKM-2012-WangC #learning #word
Learning to predict the cost-per-click for your ad words (CJW, HHC), pp. 2291–2294.
CIKMCIKM-2012-YangZW #collaboration #incremental #scalability #using
Scalable collaborative filtering using incremental update and local link prediction (XY, ZZ, KW), pp. 2371–2374.
ECIRECIR-2012-AlhadiGKN #microblog #monitoring #named
LiveTweet: Monitoring and Predicting Interesting Microblog Posts (ACA, TG, JK, NN), pp. 569–570.
ECIRECIR-2012-BoudinND #query #using
Using a Medical Thesaurus to Predict Query Difficulty (FB, JYN, MD), pp. 480–484.
ECIRECIR-2012-GaugazSDIGH #impact analysis
Predicting the Future Impact of News Events (JG, PS, GD, TI, MG, NH), pp. 50–62.
ECIRECIR-2012-OghinaBTR #social #social media #using
Predicting IMDB Movie Ratings Using Social Media (AO, MB, MT, MdR), pp. 503–507.
ECIRECIR-2012-SondhiVZ #reliability
Reliability Prediction of Webpages in the Medical Domain (PS, VGVV, CZ), pp. 219–231.
ECIRECIR-2012-ZhouCHLJ #query #web
Assessing and Predicting Vertical Intent for Web Queries (KZ, RC, MH, ML, JMJ), pp. 499–502.
ICMLICML-2012-BalasubramanianL #multi
The Landmark Selection Method for Multiple Output Prediction (KB, GL), p. 41.
ICMLICML-2012-DavisCBPPC #clustering #relational
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events (JD, VSC, EB, DP, PLP, MC), p. 172.
ICMLICML-2012-DoppaFT
Output Space Search for Structured Prediction (JRD, AF, PT), p. 107.
ICMLICML-2012-EbanBSG #learning #online #sequence
Learning the Experts for Online Sequence Prediction (EE, AB, SSS, AG), p. 38.
ICMLICML-2012-HartikainenSS #modelling
State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction (JH, MS, SS), p. 96.
ICMLICML-2012-KoepkeB #performance
Fast Prediction of New Feature Utility (HAK, MB), p. 130.
ICMLICML-2012-MenonJVEO #ranking
Predicting accurate probabilities with a ranking loss (AKM, XJ, SV, CE, LOM), p. 88.
ICMLICML-2012-SamdaniR #learning #performance
Efficient Decomposed Learning for Structured Prediction (RS, DR), p. 200.
ICMLICML-2012-SarkarCJ #network #parametricity
Nonparametric Link Prediction in Dynamic Networks (PS, DC, MIJ), p. 246.
ICMLICML-2012-SchwingHPU #modelling #performance #visual notation
Efficient Structured Prediction with Latent Variables for General Graphical Models (AGS, TH, MP, RU), p. 216.
ICMLICML-2012-ShanKRBSR #matrix #probability #using
Gap Filling in the Plant Kingdom — Trait Prediction Using Hierarchical Probabilistic Matrix Factorization (HS, JK, PBR, AB, FS, MR), p. 47.
ICMLICML-2012-SheffetMI #behaviour
Predicting Consumer Behavior in Commerce Search (OS, NM, SI), p. 233.
ICMLICML-2012-ShivaswamyJ #learning #online
Online Structured Prediction via Coactive Learning (PS, TJ), p. 12.
ICMLICML-2012-Zhu #feature model #modelling #parametricity
Max-Margin Nonparametric Latent Feature Models for Link Prediction (JZ), p. 154.
ICPRICPR-2012-FilipHS
Predicting environment illumination effects on material appearance (JF, MH, JS), pp. 2075–2078.
ICPRICPR-2012-HidoM #feature model
Temporal feature selection for time-series prediction (SH, TM), pp. 3557–3560.
ICPRICPR-2012-HiradeY #learning
Ensemble learning for change-point prediction (RH, TY), pp. 1860–1863.
ICPRICPR-2012-KumarRS #learning
Learning to predict super resolution wavelet coefficients (NK, NKR, AS), pp. 3468–3471.
ICPRICPR-2012-MatsuoK #behaviour #monitoring
Prediction of drowsy driving by monitoring driver’s behavior (HM, AK), pp. 3390–3393.
ICPRICPR-2012-MiaoLZ #fault #feature model
Cost-sensitive feature selection with application in software defect prediction (LM, ML, DZ), pp. 967–970.
ICPRICPR-2012-SemenovichSG #modelling
Predicting onsets of genocide with sparse additive models (DS, AS, BEG), pp. 3549–3552.
ICPRICPR-2012-TakahashiI
Predicting battery life from usage trajectory patterns (TT, TI), pp. 2946–2949.
ICPRICPR-2012-YeD #learning
Learning features for predicting OCR accuracy (PY, DSD), pp. 3204–3207.
ICPRICPR-2012-ZhaoSS #learning
Importance-weighted label prediction for active learning with noisy annotations (LZ, GS, RS), pp. 3476–3479.
KDDKDD-2012-ChengZAMZZN #multi
Multimedia features for click prediction of new ads in display advertising (HC, RvZ, JA, EM, RZ, YZ, VN), pp. 777–785.
KDDKDD-2012-ChenMTJ #metric
Playlist prediction via metric embedding (SC, JLM, DT, TJ), pp. 714–722.
KDDKDD-2012-ChenY
Position-normalized click prediction in search advertising (YC, TWY), pp. 795–803.
KDDKDD-2012-Douglas #modelling #multi
Leveraging predictive modeling to reduce signal theft in a multi-service organization environment (SD), p. 1004.
KDDKDD-2012-KimLX #image #multi #process #using #web
Web image prediction using multivariate point processes (GK, FFL, EPX), pp. 1068–1076.
KDDKDD-2012-OlteanuS #clustering #correlation #energy #named #network #nondeterminism
DAGger: clustering correlated uncertain data (to predict asset failure in energy networks) (DO, SJvS), pp. 1504–1507.
KDDKDD-2012-RaederSDPP #design #robust
Design principles of massive, robust prediction systems (TR, OS, BD, CP, FJP), pp. 1357–1365.
KDDKDD-2012-YuDRZY #classification #multi
Transductive multi-label ensemble classification for protein function prediction (GXY, CD, HR, GZ, ZY), pp. 1077–1085.
KDIRKDIR-2012-GarciaGG #information management #performance #validation
Predicting the Efficiency with Knowledge Discovery of a Budgeted Company: A Cuban University — Validation through Three Semesters (LIG, IG, RG), pp. 315–318.
KDIRKDIR-2012-IkebeKT #learning #smarttech #using
Friendship Prediction using Semi-supervised Learning of Latent Features in Smartphone Usage Data (YI, MK, HT), pp. 199–205.
KMISKMIS-2012-PortelaPS #data mining #mining #modelling #pervasive
Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine (FP, FP, MFS), pp. 81–88.
KRKR-2012-PradeR #logic #similarity
Homogeneous Logical Proportions: Their Uniqueness and Their Role in Similarity-Based Prediction (HP, GR).
MLDMMLDM-2012-DiezC #approach #classification #multi
A Multiclassifier Approach for Drill Wear Prediction (AD, AC), pp. 617–630.
MLDMMLDM-2012-Herrera-YagueZ #network
Prediction of Telephone User Attributes Based on Network Neighborhood Information (CHY, PJZ), pp. 645–659.
MLDMMLDM-2012-KalpakisYHMSSS #analysis #permutation #using
Outcome Prediction for Patients with Severe Traumatic Brain Injury Using Permutation Entropy Analysis of Electronic Vital Signs Data (KK, SY, PFMH, CFM, LGS, DMS, TMS), pp. 415–426.
RecSysRecSys-2012-EkstrandR #algorithm #recommendation
When recommenders fail: predicting recommender failure for algorithm selection and combination (MDE, JR), pp. 233–236.
RecSysRecSys-2012-SheehanP #named #personalisation #student
pGPA: a personalized grade prediction tool to aid student success (MS, YP), pp. 309–310.
SEKESEKE-2012-JangidPE #analysis #mobile #sentiment #using
A Mobile Application for Stock Market Prediction Using Sentiment Analysis (KJ, PP, ME), pp. 13–18.
SEKESEKE-2012-WangKWN #empirical #fault #metric
An Empirical Study of Software Metric Selection Techniques for Defect Prediction (HW, TMK, RW, AN), pp. 94–99.
SIGIRSIGIR-2012-AgichteinWDB #comprehension #continuation
Search, interrupted: understanding and predicting search task continuation (EA, RWW, STD, PNB), pp. 315–324.
SIGIRSIGIR-2012-AnderkaSL #quality #wiki
Predicting quality flaws in user-generated content: the case of wikipedia (MA, BS, NL), pp. 981–990.
SIGIRSIGIR-2012-CarmelK #information retrieval #performance #query
Query performance prediction for IR (DC, OK), pp. 1196–1197.
SIGIRSIGIR-2012-Cummins #modelling #monte carlo #performance #simulation #using
Investigating performance predictors using monte carlo simulation and score distribution models (RC), pp. 1097–1098.
SIGIRSIGIR-2012-KongFSL #microblog #twitter
Predicting lifespans of popular tweets in microblog (SK, LF, GS, KL), pp. 1129–1130.
SIGIRSIGIR-2012-LiEV
Want a coffee?: predicting users’ trails (WL, CE, APdV), pp. 1171–1172.
SIGIRSIGIR-2012-MacdonaldTO #learning #online #query #scheduling
Learning to predict response times for online query scheduling (CM, NT, IO), pp. 621–630.
SIGIRSIGIR-2012-MahdabiAKC #automation #concept #query #refinement #using
Automatic refinement of patent queries using concept importance predictors (PM, LA, MK, FC), pp. 505–514.
SIGIRSIGIR-2012-PantelGAH #modelling #social
Social annotations: utility and prediction modeling (PP, MG, OA, KH), pp. 285–294.
SIGIRSIGIR-2012-QumsiyehN #multi #personalisation #recommendation
Predicting the ratings of multimedia items for making personalized recommendations (RQ, YKN), pp. 475–484.
SIGIRSIGIR-2012-YangSLZC #network #social
Friend or frenemy?: predicting signed ties in social networks (SHY, AJS, BL, HZ, YC), pp. 555–564.
OOPSLAOOPSLA-2012-WuZSJGS #behaviour #correlation
Exploiting inter-sequence correlations for program behavior prediction (BW, ZZ, XS, YJ, YG, RS), pp. 851–866.
POPLPOPL-2012-SmaragdakisESYF #concurrent #detection #polynomial
Sound predictive race detection in polynomial time (YS, JE, CS, JY, CF), pp. 387–400.
RERE-2012-MaxwellAS #evolution #requirements
Managing changing compliance requirements by predicting regulatory evolution (JCM, AIA, PPS), pp. 101–110.
ASEASE-2012-LuCC #fault #learning #reduction #using
Software defect prediction using semi-supervised learning with dimension reduction (HL, BC, MC), pp. 314–317.
ASEASE-2012-Nogueira #complexity #testing
Predicting software complexity by means of evolutionary testing (AFN), pp. 402–405.
ASEASE-2012-SeoK
Predicting recurring crash stacks (HS, SK), pp. 180–189.
ASEASE-2012-SharT #validation #web
Predicting common web application vulnerabilities from input validation and sanitization code patterns (LKS, HBKT), pp. 310–313.
ASEASE-2012-WestermannHKF #automation #performance
Automated inference of goal-oriented performance prediction functions (DW, JH, RK, RF), pp. 190–199.
FSEFSE-2012-CaglayanMCBAT #fault #metric #named
Dione: an integrated measurement and defect prediction solution (BC, ATM, , AB, TA, BT), p. 20.
FSEFSE-2012-FarzanMRS #concurrent #source code
Predicting null-pointer dereferences in concurrent programs (AF, PM, NR, FS), p. 47.
FSEFSE-2012-RahmanPD #fault
Recalling the “imprecision” of cross-project defect prediction (FR, DP, PTD), p. 61.
ICSEICSE-2012-BhattacharyaINF #analysis #evolution #graph
Graph-based analysis and prediction for software evolution (PB, MI, IN, MF), pp. 419–429.
ICSEICSE-2012-HataMK #debugging #fine-grained
Bug prediction based on fine-grained module histories (HH, OM, TK), pp. 200–210.
ICSEICSE-2012-PetersM #fault #privacy
Privacy and utility for defect prediction: Experiments with MORPH (FP, TM), pp. 189–199.
ICSEICSE-2012-SharT #injection #mining #sql
Mining input sanitization patterns for predicting SQL injection and cross site scripting vulnerabilities (LKS, HBKT), pp. 1293–1296.
ICSEICSE-2012-SiegmundKKABRS #automation #detection #performance
Predicting performance via automated feature-interaction detection (NS, SSK, CK, SA, DSB, MR, GS), pp. 167–177.
ICSEICSE-2012-ZimmermannNGM #debugging
Characterizing and predicting which bugs get reopened (TZ, NN, PJG, BM), pp. 1074–1083.
SACSAC-2012-BanthiaG #fault #modelling #quality
Investigating fault prediction capabilities of five prediction models for software quality (DB, AG), pp. 1259–1261.
SACSAC-2012-BasgaluppBR #maintenance
Predicting software maintenance effort through evolutionary-based decision trees (MPB, RCB, DDR), pp. 1209–1214.
SACSAC-2012-BoulilaEFS #adaptation #approach #database #image
High level adaptive fusion approach: application to land cover change prediction in satellite image databases (WB, KSE, IRF, BS), pp. 21–22.
SACSAC-2012-DrumondRS #information management #knowledge base #rdf
Predicting RDF triples in incomplete knowledge bases with tensor factorization (LD, SR, LST), pp. 326–331.
SACSAC-2012-GriffithOS #collaboration #rating
Investigations into user rating information and predictive accuracy in a collaborative filtering domain (JG, CO, HS), pp. 937–942.
SACSAC-2012-MenorPB #kernel #probability #using
Probabilistic prediction of protein phosphorylation sites using kernel machines (MM, GP, KB), pp. 1393–1398.
SACSAC-2012-SarroMFG #algorithm #analysis #fault #search-based #using
A further analysis on the use of Genetic Algorithm to configure Support Vector Machines for inter-release fault prediction (FS, SDM, FF, CG), pp. 1215–1220.
SACSAC-2012-SchluterC #correlation #detection #markov #modelling #using
Hidden markov model-based time series prediction using motifs for detecting inter-time-serial correlations (TS, SC), pp. 158–164.
CCCC-2012-FarooqCJ #branch #compilation
Compiler Support for Value-Based Indirect Branch Prediction (MUF, LC, LKJ), pp. 185–199.
CGOCGO-2012-ParkCA #graph #modelling #using
Using graph-based program characterization for predictive modeling (EP, JC, MAA), pp. 196–206.
DACDAC-2012-DonkohLS #adaptation #design #hybrid #using
A hybrid and adaptive model for predicting register file and SRAM power using a reference design (ED, AL, ES), pp. 62–67.
DACDAC-2012-KumarBKV #analysis #source code #using
Early prediction of NBTI effects using RTL source code analysis (JAK, KMB, HK, SV), pp. 808–813.
DACDAC-2012-LiANSVZ #design #physics
Guiding a physical design closure system to produce easier-to-route designs with more predictable timing (ZL, CJA, GJN, CCNS, NV, NYZ), pp. 465–470.
DACDAC-2012-RoyC #analysis
Predicting timing violations through instruction-level path sensitization analysis (SR, KC), pp. 1074–1081.
DACDAC-2012-SinhaYCCC #design #modelling
Exploring sub-20nm FinFET design with predictive technology models (SS, GY, VC, BC, YC), pp. 283–288.
DACDAC-2012-VelamalaSSC #matter #physics #statistics
Physics matters: statistical aging prediction under trapping/detrapping (JBV, KS, TS, YC), pp. 139–144.
DATEDATE-2012-MuradoreQF #network
Predictive control of networked control systems over differentiated services lossy networks (RM, DQ, PF), pp. 1245–1250.
DATEDATE-2012-Sadooghi-AlvandiAM #branch #towards
Toward virtualizing branch direction prediction (MSA, KA, AM), pp. 455–460.
DATEDATE-2012-SharifiAR #named
TempoMP: Integrated prediction and management of temperature in heterogeneous MPSoCs (SS, RZA, TSR), pp. 593–598.
DATEDATE-2012-TanLXTC #branch #energy #stack
Energy-efficient branch prediction with Compiler-guided History Stack (MT, XL, ZX, DT, XC), pp. 449–454.
DATEDATE-2012-ZuluagaBT #case study #design #trade-off
Predicting best design trade-offs: A case study in processor customization (MZ, EVB, NPT), pp. 1030–1035.
LCTESLCTES-2012-FangLZLCZ #analysis #multi
Improving dynamic prediction accuracy through multi-level phase analysis (ZF, JL, WZ, YL, HC, BZ), pp. 89–98.
LCTESLCTES-2012-ZuluagaKMP #design
“Smart” design space sampling to predict Pareto-optimal solutions (MZ, AK, PAM, MP), pp. 119–128.
PDPPDP-2012-AchourN #approach #clustering #multi #performance
A Performance Prediction Approach for MPI Routines on Multi-clusters (SA, WN), pp. 125–129.
PDPPDP-2012-DaniASB #concurrent #named #parallel #source code #thread
TCP: Thread Contention Predictor for Parallel Programs (AMD, BA, YNS, CB), pp. 19–26.
PDPPDP-2012-WangYLC #migration
Packet Triggered Prediction Based Task Migration for Network-on-Chip (CW, LY, LL, TC), pp. 491–498.
PPoPPPPoPP-2012-NugterenC #adaptation #parallel #performance
The boat hull model: adapting the roofline model to enable performance prediction for parallel computing (CN, HC), pp. 291–292.
ISSTAISSTA-2012-LiRCS #debugging #detection #precise
Residual investigation: predictive and precise bug detection (KL, CR, CC, YS), pp. 298–308.
CBSECBSE-2011-OtteGS #component #deployment #distributed #embedded #enterprise #realtime
Predictable deployment in component-based enterprise distributed real-time and embedded systems (WO, ASG, DCS), pp. 21–30.
QoSAQoSA-ISARCS-2011-BroschBKR #architecture #fault tolerance #reliability
Reliability prediction for fault-tolerant software architectures (FB, BB, HK, RHR), pp. 75–84.
QoSAQoSA-ISARCS-2011-KlattRK #communication #framework #integration #quality
Integration of event-based communication in the palladio software quality prediction framework (BK, CR, SK), pp. 43–52.
WICSAWICSA-2011-FaniyiBEK #architecture #security
Evaluating Security Properties of Architectures in Unpredictable Environments: A Case for Cloud (FF, RB, AE, RK), pp. 127–136.
WICSAWICSA-2011-RathfelderK #architecture #component
Palladio Workbench: A Quality-Prediction Tool for Component-Based Architectures (CR, BK), pp. 347–350.
DRRDRR-2011-RabeuxJD #documentation #evaluation #fault
Ancient documents bleed-through evaluation and its application for predicting OCR error rates (VR, NJ, JPD), pp. 1–10.
HTHT-2011-SeroussiBZ #modelling #personalisation #rating #using
Personalised rating prediction for new users using latent factor models (YS, FB, IZ), pp. 47–56.
HTHT-2011-SquicciariniSLW #adaptation #image #named #policy #resource management
A3P: adaptive policy prediction for shared images over popular content sharing sites (ACS, SS, DL, JW), pp. 261–270.
SIGMODSIGMOD-2011-DugganCPU #concurrent #database #performance
Performance prediction for concurrent database workloads (JD, , OP, EU), pp. 337–348.
SIGMODSIGMOD-2011-KantereDGA #query
Predicting cost amortization for query services (VK, DD, GG, AA), pp. 325–336.
VLDBVLDB-2012-PavloJZ11 #execution #modelling #on the #optimisation #parallel #transaction
On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems (AP, EPCJ, SBZ), pp. 85–96.
CSEETCSEET-2011-HaleJG #performance #student
Predicting individual performance in student project teams (MLH, NJ, RFG), pp. 11–20.
ITiCSEITiCSE-2011-GiannakosV #education #identification
Identifying the predictors of educational webcasts’ adoption (MNG, PV), p. 376.
CSMRCSMR-2011-KraftKNCH #embedded #maintenance #research
Software Maintenance Research in the PROGRESS Project for Predictable Embedded Software Systems (JK, HMK, TN, IC, HH), pp. 335–338.
CSMRCSMR-2011-LamkanfiDSV #algorithm #debugging #mining
Comparing Mining Algorithms for Predicting the Severity of a Reported Bug (AL, SD, QDS, TV), pp. 249–258.
CSMRCSMR-2011-MendeKP #case study #fault #integration #on the #testing
On the Utility of a Defect Prediction Model during HW/SW Integration Testing: A Retrospective Case Study (TM, RK, JP), pp. 259–268.
ICPCICPC-2011-TothVBG #complexity #metric #process
Adding Process Metrics to Enhance Modification Complexity Prediction (GT, AZV, ÁB, TG), pp. 201–204.
ICSMEICSM-2011-KhomhCZSD #fault #testing #using
Predicting post-release defects using pre-release field testing results (FK, BC, YZ, AS, DD), pp. 253–262.
ICSMEICSM-2011-KobayashiMIHKY #fault #impact analysis #named #scalability
ImpactScale: Quantifying change impact to predict faults in large software systems (KK, AM, KI, YH, MK, TY), pp. 43–52.
ICSMEICSM-2011-NishizonoMVM #comprehension #empirical #evolution #industrial #maintenance #metric #source code
Source code comprehension strategies and metrics to predict comprehension effort in software maintenance and evolution tasks — an empirical study with industry practitioners (KN, SM, RV, KiM), pp. 473–481.
ICSMEICSM-2011-RomanoP #interface #java #metric #source code #using
Using source code metrics to predict change-prone Java interfaces (DR, MP), pp. 303–312.
MSRMSR-2011-BhattacharyaN #debugging #modelling #question
Bug-fix time prediction models: can we do better? (PB, IN), pp. 207–210.
MSRMSR-2011-GigerPG #debugging #fine-grained #source code
Comparing fine-grained source code changes and code churn for bug prediction (EG, MP, HCG), pp. 83–92.
WCREWCRE-2011-AryaniPLMN #dependence #question #using
Can We Predict Dependencies Using Domain information? (AA, FP, ML, ANM, ON), pp. 55–64.
WCREWCRE-2011-TanPPZ #clustering #fault #quality
Assessing Software Quality by Program Clustering and Defect Prediction (XT, XP, SP, WZ), pp. 244–248.
CoGCIG-2011-SynnaeveB11a #game studies
A Bayesian model for opening prediction in RTS games with application to StarCraft (GS, PB), pp. 281–288.
FDGFDG-2011-HarrisonR #behaviour #using
Using sequential observations to model and predict player behavior (BEH, DLR), pp. 91–98.
CHICHI-2011-John #design #modelling #performance #recommendation #user interface #using
Using predictive human performance models to inspire and support UI design recommendations (BEJ), pp. 983–986.
CHICHI-2011-SporkaFKPHM #named #using
CHANTI: predictive text entry using non-verbal vocal input (AJS, TF, SHK, OP, PH, ISM), pp. 2463–2472.
CHICHI-2011-TausczikP #online #quality
Predicting the perceived quality of online mathematics contributions from users’ reputations (YRT, JWP), pp. 1885–1888.
CHICHI-2011-WobbrockJS #2d #fault #modelling
Modeling and predicting pointing errors in two dimensions (JOW, AJ, KS), pp. 1653–1656.
CSCWCSCW-2011-ParkKKLS #sentiment
The politics of comments: predicting political orientation of news stories with commenters’ sentiment patterns (SP, MK, JK, YL, JS), pp. 113–122.
HCIDHM-2011-ChiouCC #3d #reliability
The Effects of Landmarks and Training on 3D Surface Anthropometric Reliability and Hip Joint Center Prediction (WKC, BHC, WYC), pp. 3–11.
HCIDHM-2011-HowardY #case study
Predicting Support Reaction Forces for Standing and Seated Tasks with Given Postures-A Preliminary Study (BH, J(Y), pp. 89–98.
HCIDHM-2011-MarlerKJ #analysis
Optimization-Based Posture Prediction for Analysis of Box Lifting Tasks (TM, LK, RJ), pp. 151–160.
HCIDHM-2011-MobusEG #composition
Predicting the Focus of Attention and Deficits in Situation Awareness with a Modular Hierarchical Bayesian Driver Model (CM, ME, HG), pp. 483–492.
HCIDHM-2011-ZouZYBGC
An Alternative Formulation for Determining Weights of Joint Displacement Objective Function in Seated Posture Prediction (QZ, QZ, J(Y, RB, JG, AC), pp. 231–242.
HCIHCI-MIIE-2011-LinH #adaptation #performance
Predicting the Effects of Time-Gaps for Adaptive Cruise Control (ACC) on Bus Driver Performance (BTWL, SLH), pp. 435–443.
HCIHIMI-v1-2011-YanoAFJ #modelling #performance #process
Feasibility Study of Predictive Human Performance Modeling Technique in Field Activities (NY, TA, SF, BEJ), pp. 180–189.
HCIOCSC-2011-PujariK #approach #machine learning #recommendation
A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.
ICEISICEIS-v1-2011-LiZS #network
A New Network Traffic Prediction Model in Cognitive Networks (DL, RZ, XS), pp. 427–435.
ICEISICEIS-v1-2011-NiknafsSRR #analysis #comparative
Comparative Analysis of Three Techniques for Predictions in Time Series Having Repetitive Patterns (AN, BS, MMR, GR), pp. 177–182.
ICEISICEIS-v2-2011-FronzaSSV #approach #towards
Toward a Non Invasive Control of Applications — A Biomedical Approach to Failure Prediction (IF, AS, GS, JV), pp. 83–91.
ICEISICEIS-v2-2011-LongLG #algorithm #performance #search-based
High-speed Railway based on Genetic Algorithm for Prediction of Travel Choice (CL, JL, YG), pp. 26–31.
CIKMCIKM-2011-AmodeoBB #hybrid #modelling
Hybrid models for future event prediction (GA, RB, UB), pp. 1981–1984.
CIKMCIKM-2011-ChiangNTD #network
Exploiting longer cycles for link prediction in signed networks (KYC, NN, AT, ISD), pp. 1157–1162.
CIKMCIKM-2011-DengLPCX #ad hoc #database #graph #query #reachability
Predicting the optimal ad-hoc index for reachability queries on graph databases (JD, FL, YP, BC, JX), pp. 2357–2360.
CIKMCIKM-2011-DhillonSS #information management #learning #modelling #multi #web
Semi-supervised multi-task learning of structured prediction models for web information extraction (PSD, SS, SKS), pp. 957–966.
CIKMCIKM-2011-FeiJYLH #approach #behaviour #learning #multi #social
Content based social behavior prediction: a multi-task learning approach (HF, RJ, YY, BL, JH), pp. 995–1000.
CIKMCIKM-2011-GaoDG
Temporal link prediction by integrating content and structure information (SG, LD, PG), pp. 1169–1174.
CIKMCIKM-2011-HarveyCRC #collaboration #modelling #rating
Bayesian latent variable models for collaborative item rating prediction (MH, MJC, IR, FC), pp. 699–708.
CIKMCIKM-2011-HopcroftLT
Who will follow you back?: reciprocal relationship prediction (JEH, TL, JT), pp. 1137–1146.
CIKMCIKM-2011-KeikhaSCC #documentation #effectiveness #feedback #pseudo
Predicting document effectiveness in pseudo relevance feedback (MK, JS, WBC, FC), pp. 2061–2064.
CIKMCIKM-2011-LakkarajuA #social #social media
Attention prediction on social media brand pages (HL, JA), pp. 2157–2160.
CIKMCIKM-2011-LiYL #power of #random
Link prediction: the power of maximal entropy random walk (RHL, JXY, JL), pp. 1147–1156.
CIKMCIKM-2011-LymberopoulosZKBL #mobile
Location-aware click prediction in mobile local search (DL, PZ, ACK, KB, JL), pp. 413–422.
CIKMCIKM-2011-MoghaddamJE #online #overview #personalisation #quality #recommendation
Review recommendation: personalized prediction of the quality of online reviews (SM, MJ, ME), pp. 2249–2252.
CIKMCIKM-2011-MohtaramiALT #nondeterminism #sentiment
Predicting the uncertainty of sentiment adjectives in indirect answers (MM, HA, ML, CLT), pp. 2485–2488.
CIKMCIKM-2011-ShiLY #graph
Collective prediction with latent graphs (XS, YL, PSY), pp. 1127–1136.
CIKMCIKM-2011-YanTLSL #learning
Citation count prediction: learning to estimate future citations for literature (RY, JT, XL, DS, XL), pp. 1247–1252.
CIKMCIKM-2011-YinHD #analysis #microblog
Structural link analysis and prediction in microblogs (DY, LH, BDD), pp. 1163–1168.
ECIRECIR-2011-BuccioMS #framework #towards #using
Towards Predicting Relevance Using a Quantum-Like Framework (EDB, MM, DS), pp. 755–758.
ECIRECIR-2011-ChilukaAP #approach #recommendation #scalability
A Link Prediction Approach to Recommendations in Large-Scale User-Generated Content Systems (NC, NA, JAP), pp. 189–200.
ECIRECIR-2011-TackstromM #fine-grained #modelling #sentiment
Discovering Fine-Grained Sentiment with Latent Variable Structured Prediction Models (OT, RTM), pp. 368–374.
ICMLICML-2011-BrouarddS #kernel
Semi-supervised Penalized Output Kernel Regression for Link Prediction (CB, FdB, MS), pp. 593–600.
ICMLICML-2011-CossalterYZ #adaptation #approximate #kernel #scalability
Adaptive Kernel Approximation for Large-Scale Non-Linear SVM Prediction (MC, RY, LZ), pp. 409–416.
ICMLICML-2011-DekelGSX #distributed #online
Optimal Distributed Online Prediction (OD, RGB, OS, LX), pp. 713–720.
ICMLICML-2011-GerrishB
Predicting Legislative Roll Calls from Text (SG, DMB), pp. 489–496.
ICMLICML-2011-Reyzin
Boosting on a Budget: Sampling for Feature-Efficient Prediction (LR), pp. 529–536.
ICMLICML-2011-UrnerSB
Access to Unlabeled Data can Speed up Prediction Time (RU, SSS, SBD), pp. 641–648.
KDDKDD-2011-BilenkoR #personalisation
Predictive client-side profiles for personalized advertising (MB, MR), pp. 413–421.
KDDKDD-2011-BrucknerS #game studies #problem
Stackelberg games for adversarial prediction problems (MB, TS), pp. 547–555.
KDDKDD-2011-Dhurandhar #using
Improving predictions using aggregate information (AD), pp. 1118–1126.
KDDKDD-2011-IfrimW #biology #bound #classification #coordination #sequence
Bounded coordinate-descent for biological sequence classification in high dimensional predictor space (GI, CW), pp. 708–716.
KDDKDD-2011-LiuHLMPV #modelling #quality #visual notation
Latent graphical models for quantifying and predicting patent quality (YL, PyH, RL, SM, CP, AV), pp. 1145–1153.
KDDKDD-2011-MenonCGAK #collaboration #using
Response prediction using collaborative filtering with hierarchies and side-information (AKM, KPC, SG, DA, NK), pp. 141–149.
KDDKDD-2011-ScellatoNM #network #social
Exploiting place features in link prediction on location-based social networks (SS, AN, CM), pp. 1046–1054.
KDDKDD-2011-SteinbergM #modelling #optimisation
Broad scale predictive modeling and marketing optimization in retail sales (DS, FFM), p. 780.
KDDKDD-2011-TorgoO #2d
2D-interval predictions for time series (LT, OO), pp. 787–794.
KDDKDD-2011-ValizadeganJW #learning #multi
Learning to trade off between exploration and exploitation in multiclass bandit prediction (HV, RJ, SW), pp. 204–212.
KDDKDD-2011-WangPSGB #social
Human mobility, social ties, and link prediction (DW, DP, CS, FG, ALB), pp. 1100–1108.
KDDKDD-2011-ZhangCWY #behaviour #comprehension #modelling
User-click modeling for understanding and predicting search-behavior (YZ, WC, DW, QY), pp. 1388–1396.
KDDKDD-2011-ZhangLWGZG #data type #modelling #performance
Enabling fast prediction for ensemble models on data streams (PZ, JL, PW, BJG, XZ, LG), pp. 177–185.
KDDKDD-2011-ZhouYLY #learning #multi
A multi-task learning formulation for predicting disease progression (JZ, LY, JL, JY), pp. 814–822.
KDIRKDIR-2011-BonninBB #mining #web
Handling Tabbing and Backward References for Predictive Web Usage Mining (GB, AB, AB), pp. 503–509.
KDIRKDIR-2011-HagenauLN #feature model
Impact of Feature Selection and Feature Types on Financial Stock Price Prediction (MH, ML, DN), pp. 303–308.
KDIRKDIR-2011-RenC #markov #modelling #transaction
Users Interest Prediction Model — Based on 2nd Markov Model and Inter-transaction Association Rules (YR, ALC), pp. 244–249.
RecSysRecSys-2011-Bellogin #performance #recommendation
Predicting performance in recommender systems (AB), pp. 371–374.
RecSysRecSys-2011-CamposDS #evaluation #matrix #recommendation #testing #towards
Towards a more realistic evaluation: testing the ability to predict future tastes of matrix factorization-based recommenders (PGC, FD, MASM), pp. 309–312.
RecSysRecSys-2011-IsaacmanICM #distributed #rating
Distributed rating prediction in user generated content streams (SI, SI, AC, MM), pp. 69–76.
RecSysRecSys-2011-KorenS #named #personalisation #rating
OrdRec: an ordinal model for predicting personalized item rating distributions (YK, JS), pp. 117–124.
RecSysRecSys-2011-SymeonidisTM #multi #network #rating #recommendation #social
Product recommendation and rating prediction based on multi-modal social networks (PS, ET, YM), pp. 61–68.
SEKESEKE-2011-FronzaSSV #using
Failure Prediction based on Log Files Using the Cox Proportional Hazard Model (IF, AS, GS, JV), pp. 456–461.
SEKESEKE-2011-GaoK #fault
Software Defect Prediction for High-Dimensional and Class-Imbalanced Data (KG, TMK), pp. 89–94.
SEKESEKE-2011-KhoshgoftaarGN #case study #comparative #quality
A Comparative Study of Different Strategies for Predicting Software Quality (TMK, KG, AN), pp. 65–70.
SEKESEKE-2011-RiazMT #maintenance #overview #relational
Maintainability Predictors for Relational Database-Driven Software Applications: Results from a Survey (MR, EM, EDT), pp. 420–425.
SIGIRSIGIR-2011-CuiWLOYS #ranking #social #what
Who should share what?: item-level social influence prediction for users and posts ranking (PC, FW, SL, MO, SY, LS), pp. 185–194.
SIGIRSIGIR-2011-CumminsJO #performance #query #standard #using
Improved query performance prediction using standard deviation (RC, JMJ, CO), pp. 1089–1090.
SIGIRSIGIR-2011-GuoWZAD #comprehension #why
Why searchers switch: understanding and predicting engine switching rationales (QG, RWW, YZ, BA, STD), pp. 335–344.
SIGIRSIGIR-2011-KanhabuaBM #ranking
Ranking related news predictions (NK, RB, MM), pp. 755–764.
SIGIRSIGIR-2011-KanhabuaN #performance #query
Time-based query performance predictors (NK, KN), pp. 1181–1182.
SIGIRSIGIR-2011-LiuADGMPS #web
Predicting web searcher satisfaction with existing community-based answers (QL, EA, GD, EG, YM, DP, IS), pp. 415–424.
SIGIRSIGIR-2011-OchiOO #rating #using #word
Rating prediction using feature words extracted from customer reviews (MO, MO, RO), pp. 1205–1206.
SIGIRSIGIR-2011-YangYD #analysis #network
Award prediction with temporal citation network analysis (ZY, DY, BDD), pp. 1203–1204.
SIGIRSIGIR-2011-YuanCM
Predicting eBay listing conversion (TTY, ZC, MM), pp. 1335–1336.
SIGIRSIGIR-2011-ZhangCB #behaviour
Predicting users’ domain knowledge from search behaviors (XZ, MJC, NJB), pp. 1225–1226.
SPLCSPLC-2011-SiegmundRKGAK #non-functional #product line #scalability
Scalable Prediction of Non-functional Properties in Software Product Lines (NS, MR, CK, PGG, SA, SSK), pp. 160–169.
RERE-2011-FitzgeraldLF #feature model
Early failure prediction in feature request management systems (CF, EL, AF), pp. 229–238.
ASEASE-2011-GanaiAWGB #concurrent #multi #named #testing #thread
BEST: A symbolic testing tool for predicting multi-threaded program failures (MKG, NA, CW, AG, GB), pp. 596–599.
ASEASE-2011-MenziesBMZC #estimation #fault #modelling
Local vs. global models for effort estimation and defect prediction (TM, AB, AM, TZ, DRC), pp. 343–351.
ASEASE-2011-RathfelderKE #automation #capacity #performance #using
Capacity planning for event-based systems using automated performance predictions (CR, SK, DE), pp. 352–361.
ASEASE-2011-ZhangMPL #monitoring #runtime
Run-time systems failure prediction via proactive monitoring (PZ, HM, AP, XL), pp. 484–487.
ESEC-FSEESEC-FSE-2011-LeeNHKI #fault #interactive #metric
Micro interaction metrics for defect prediction (TL, JN, DH, SK, HPI), pp. 311–321.
ICSEICSE-2011-KimZWG #fault
Dealing with noise in defect prediction (SK, HZ, RW, LG), pp. 481–490.
ICSEICSE-2011-KoziolekSBWBKTMK #case study #evolution #industrial #quality
An industrial case study on quality impact prediction for evolving service-oriented software (HK, BS, CGB, RW, SB, KK, MT, RM, AK), pp. 776–785.
ICSEICSE-2011-NguyenNP #fault #topic
Topic-based defect prediction (TTN, TNN, TMP), pp. 932–935.
ICSEICSE-2011-Petricic #component #deployment #embedded
Predictable dynamic deployment of components in embedded systems (AP), pp. 1128–1129.
SACSAC-2011-BabichCPK #abstraction #case study #eclipse #fault #object-oriented #using
Using a class abstraction technique to predict faults in OO classes: a case study through six releases of the Eclipse JDT (DB, PJC, JFP, BMGK), pp. 1419–1424.
SACSAC-2011-ChamorroDA
Evolutionary computation for the prediction of secondary protein structures (AEMC, FD, JSAR), pp. 1082–1087.
SACSAC-2011-LiuZ #memory management #realtime
Exploiting time predictable two-level scratchpad memory for real-time systems (YL, WZ), pp. 395–396.
SACSAC-2011-ShinLSL #concurrent #monitoring #multi #scheduling #thread
Predictable multithread scheduling with cycle-accurate thread progress monitor (YS, SL, MS, SL), pp. 627–628.
SACSAC-2011-TranCS #permutation
Prediction of permuted super-secondary structures in β-barrel proteins (VDT, PC, JMS), pp. 110–111.
SACSAC-2011-ZhaoGFC #approach #smarttech
A system context-aware approach for battery lifetime prediction in smart phones (XZ, YG, QF, XC), pp. 641–646.
CASECASE-2011-PampuriSLN #maintenance
Proportional hazard model with ℓ1 Penalization applied to Predictive Maintenance in semiconductor manufacturing (SP, AS, CDL, GDN), pp. 250–255.
CASECASE-2011-PurwinsNBHKLPW
Regression methods for prediction of PECVD Silicon Nitride layer thickness (HP, AN, BB, UH, AK, BL, GP, KW), pp. 387–392.
CASECASE-2011-SustoBL #maintenance
A Predictive Maintenance System for Silicon Epitaxial Deposition (GAS, AB, CDL), pp. 262–267.
CGOCGO-2011-ParkPCCS #modelling #optimisation
Predictive modeling in a polyhedral optimization space (EP, LNP, JC, AC, PS), pp. 119–129.
DACDAC-2011-AadithyaVDR #impact analysis #named #probability #random
MUSTARD: a coupled, stochastic/deterministic, discrete/continuous technique for predicting the impact of random telegraph noise on SRAMs and DRAMs (KVA, SV, AD, JSR), pp. 292–297.
DACDAC-2011-DongL #performance
Efficient SRAM failure rate prediction via Gibbs sampling (CD, XL), pp. 200–205.
DATEDATE-2011-AkessonG #architecture #integration #memory management #modelling
Architectures and modeling of predictable memory controllers for improved system integration (BA, KG), pp. 851–856.
DATEDATE-2011-BartoliniCTB #distributed #energy #multi #self
A distributed and self-calibrating model-predictive controller for energy and thermal management of high-performance multicores (AB, MC, AT, LB), pp. 830–835.
DATEDATE-2011-DrmanacSWWA #multi #optimisation #parametricity #testing
Multidimensional parametric test set optimization of wafer probe data for predicting in field failures and setting tighter test limits (DGD, NS, LW, LCW, MSA), pp. 794–799.
DATEDATE-2011-GhermanMECB #concurrent #fault #self
Error prediction based on concurrent self-test and reduced slack time (VG, JM, SE, SC, YB), pp. 1626–1631.
DATEDATE-2011-GuoCSCWH #debugging #design #empirical #verification
Empirical design bugs prediction for verification (QG, TC, HS, YC, YW, WH), pp. 161–166.
DATEDATE-2011-KimCY #distributed #simulation
A new distributed event-driven gate-level HDL simulation by accurate prediction (DK, MJC, SY), pp. 547–550.
DATEDATE-2011-PenolazziSH #energy #multi #performance
Predicting bus contention effects on energy and performance in multi-processor SoCs (SP, IS, AH), pp. 1196–1199.
DATEDATE-2011-WolfG #integration
SoC infrastructures for predictable system integration (PvdW, JG), pp. 857–862.
HPCAHPCA-2011-BrownPT #concurrent #migration #performance #set #thread
Fast thread migration via cache working set prediction (JAB, LP, DMT), pp. 193–204.
HPCAHPCA-2011-Seznec #branch #estimation
Storage free confidence estimation for the TAGE branch predictor (AS), pp. 443–454.
LCTESLCTES-2011-SarkarMR #manycore #migration
Predictable task migration for locked caches in multi-core systems (AS, FM, HR), pp. 131–140.
PDPPDP-2011-AchourAKN #automation #clustering #named #performance #source code #towards
MPI-PERF-SIM: Towards an Automatic Performance Prediction Tool of MPI Programs on Hierarchical Clusters (SA, MA, BK, WN), pp. 207–211.
PDPPDP-2011-CorneaB #benchmark #distributed #metric #performance #using
Performance Prediction of Distributed Applications Using Block Benchmarking Methods (BFC, JB), pp. 183–190.
ICSTICST-2011-CzerwonkaDNTT #analysis #case study #experience #named
CRANE: Failure Prediction, Change Analysis and Test Prioritization in Practice — Experiences from Windows (JC, RD, NN, AT, AT), pp. 357–366.
ISSTAISSTA-2011-HuangZ #concurrent #persuasion
Persuasive prediction of concurrency access anomalies (JH, CZ), pp. 144–154.
VMCAIVMCAI-2011-LopesR #distributed #model checking
Distributed and Predictable Software Model Checking (NPL, AR), pp. 340–355.
QoSAQoSA-2010-BabkaTB #modelling #performance #random #validation
Validating Model-Driven Performance Predictions on Random Software Systems (VB, PT, LB), pp. 3–19.
QoSAQoSA-2010-BroschKBR #architecture #component #reliability
Parameterized Reliability Prediction for Component-Based Software Architectures (FB, HK, BB, RHR), pp. 36–51.
DRRDRR-2010-TerradesSGVJ #detection
Interactive-predictive detection of handwritten text blocks (ORT, NS, AG, EV, AJ), pp. 1–10.
HTHT-2010-CorletteS #network #online #scalability #social
Link prediction applied to an open large-scale online social network (DC, FMSI), pp. 135–140.
VLDBVLDB-2010-AkdereCU #query #streaming
Database-support for Continuous Prediction Queries over Streaming Data (MA, , EU), pp. 1291–1301.
EDMEDM-2010-Gonzalez-BrenesM
Predicting Task Completion from Rich but Scarce Data (JPGB, JM), pp. 291–292.
EDMEDM-2010-SoundranayagamY #learning #order #question
Can Order of Access to Learning Resources Predict Success? (HS, KY), pp. 323–324.
ITiCSEITiCSE-2010-DennyLHDP #performance #programming #self
Self-predicted and actual performance in an introductory programming course (PD, ALR, JH, DBD, HCP), pp. 118–122.
CSMRCSMR-2010-BeckerHTKK #component #modelling #quality #reverse engineering
Reverse Engineering Component Models for Quality Predictions (SB, MH, MT, KK, JK), pp. 194–197.
CSMRCSMR-2010-MendeK #fault #modelling
Effort-Aware Defect Prediction Models (TM, RK), pp. 107–116.
CSMRCSMR-2010-NadiHM #set #using
Does the Past Say It All? Using History to Predict Change Sets in a CMDB (SN, RCH, SM), pp. 97–106.
ICSMEICSM-2010-KameiMMMAH #debugging #modelling #using
Revisiting common bug prediction findings using effort-aware models (YK, SM, AM, KiM, BA, AEH), pp. 1–10.
MSRMSR-2010-DAmbrosLR #comparison #debugging
An extensive comparison of bug prediction approaches (MD, ML, RR), pp. 31–41.
MSRMSR-2010-LamkanfiDGG #debugging
Predicting the severity of a reported bug (AL, SD, EG, BG), pp. 1–10.
MSRMSR-2010-NugrohoCA #design #java #metric #uml
Assessing UML design metrics for predicting fault-prone classes in a Java system (AN, MRVC, EA), pp. 21–30.
MSRMSR-2010-RobbesPL #game studies #ide #interactive
Replaying IDE interactions to evaluate and improve change prediction approaches (RR, DP, ML), pp. 161–170.
WCREWCRE-2010-ShihabIKIOAHM #case study #debugging #eclipse
Predicting Re-opened Bugs: A Case Study on the Eclipse Project (ES, AI, YK, WMI, MO, BA, AEH, KiM), pp. 249–258.
CoGCIG-2010-ButlerD #game studies
Partial observability during predictions of the opponent's movements in an RTS game (SB0, YD), pp. 46–53.
CoGCIG-2010-GohN
Pleasure propagation to reward predictors (CKG, AN), pp. 62–68.
CoGCIG-2010-MahlmannDTCY #behaviour
Predicting player behavior in Tomb Raider: Underworld (TM, AD, JT, AC, GNY), pp. 178–185.
CHICHI-2010-ArifS #cost analysis #fault
Predicting the cost of error correction in character-based text entry technologies (ASA, WS), pp. 5–14.
CHICHI-2010-JeonKC #online #quality
Re-examining price as a predictor of answer quality in an online q&a site (GYJ, YMK, YC), pp. 325–328.
CHICHI-2010-LiuR #mobile
Predicting Chinese text entry speeds on mobile phones (YL, KJR), pp. 2183–2192.
CHICHI-2010-Sylvan #community #online
Predicting influence in an online community of creators (ES), pp. 1913–1916.
CHICHI-2010-WangZC #gesture #mobile #named #problem
SHRIMP: solving collision and out of vocabulary problems in mobile predictive input with motion gesture (JW, SZ, JFC), pp. 15–24.
ICEISICEIS-AIDSS-2010-CacoveanuBP #framework
Evaluating Prediction Strategies in an Enhanced Meta-learning Framework (SC, CVB, RP), pp. 148–156.
ICEISICEIS-AIDSS-2010-KrohaN #classification #roadmap
Classification of Market News and Prediction of Market Trends (PK, RN), pp. 187–192.
ICEISICEIS-AIDSS-2010-Vilas-BoasSPSR #data mining #mining
Hourly Prediction of Organ Failure and Outcome in Intensive Care based on Data Mining Techniques (MVB, MFS, FP, ÁMS, FR), pp. 270–277.
ICEISICEIS-J-2010-PotoleaCL #evaluation #framework
Meta-learning Framework for Prediction Strategy Evaluation (RP, SC, CL), pp. 280–295.
CIKMCIKM-2010-AzizR #data flow #multi #robust #semistructured data
Robust prediction from multiple heterogeneous data sources with partial information (MSA, CKR), pp. 1857–1860.
CIKMCIKM-2010-BatalH #classification #using
Constructing classification features using minimal predictive patterns (IB, MH), pp. 869–878.
CIKMCIKM-2010-BhattCP #network #scalability #social
Predicting product adoption in large-scale social networks (RB, VC, RP), pp. 1039–1048.
CIKMCIKM-2010-HauffKA #comparison #performance #query
A comparison of user and system query performance predictions (CH, DK, LA), pp. 979–988.
CIKMCIKM-2010-LiBS #online #overview #ranking
Affinity-driven prediction and ranking of products in online product review sites (HL, SSB, AS), pp. 1745–1748.
CIKMCIKM-2010-WhiteBD #using
Predicting short-term interests using activity-based search context (RWW, PNB, STD), pp. 1009–1018.
CIKMCIKM-2010-ZhaoC
Term necessity prediction (LZ, JC), pp. 259–268.
ECIRECIR-2010-BelloginC #approach #collaboration #performance
A Performance Prediction Approach to Enhance Collaborative Filtering Performance (AB, PC), pp. 382–393.
ECIRECIR-2010-Collins-ThompsonB #classification #performance #query
Predicting Query Performance via Classification (KCT, PNB), pp. 140–152.
ECIRECIR-2010-HauffAHJ #effectiveness #evaluation #performance #query
Query Performance Prediction: Evaluation Contrasted with Effectiveness (CH, LA, DH, FdJ), pp. 204–216.
ECIRECIR-2010-TsagkiasWR #modelling #online
News Comments: Exploring, Modeling, and Online Prediction (MT, WW, MdR), pp. 191–203.
ECIRECIR-2010-XingZH #image #query #retrieval
Query Difficulty Prediction for Contextual Image Retrieval (XX, YZ, MH), pp. 581–585.
ICMLICML-2010-CaoLY #learning #multi
Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains (BC, NNL, QY), pp. 159–166.
ICMLICML-2010-Cesa-BianchiGVZ #graph #random
Random Spanning Trees and the Prediction of Weighted Graphs (NCB, CG, FV, GZ), pp. 175–182.
ICMLICML-2010-DinculescuP #approximate
Approximate Predictive Representations of Partially Observable Systems (MD, DP), pp. 895–902.
ICMLICML-2010-GraepelCBH
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine (TG, JQC, TB, RH), pp. 13–20.
ICMLICML-2010-LayB #classification #using
Supervised Aggregation of Classifiers using Artificial Prediction Markets (NL, AB), pp. 591–598.
ICMLICML-2010-SakumaA #online #privacy
Online Prediction with Privacy (JS, HA), pp. 935–942.
ICPRICPR-2010-AmanoK #using
Appearance Control Using Projection with Model Predictive Control (TA, HK), pp. 2832–2835.
ICPRICPR-2010-KaferHWKR #recognition
Recognition and Prediction of Situations in Urban Traffic Scenarios (EK, CH, CW, FK, HJR), pp. 4234–4237.
ICPRICPR-2010-KryszczukHS #orthogonal #using
Direct Printability Prediction in VLSI Using Features from Orthogonal Transforms (KK, PH, RS), pp. 2764–2767.
ICPRICPR-2010-SakarKSG #clustering #feature model
Prediction of Protein Sub-nuclear Location by Clustering mRMR Ensemble Feature Selection (COS, OK, HS, FG), pp. 2572–2575.
ICPRICPR-2010-ScharfenbergerCF #artificial reality #using
Driver Body-Height Prediction for an Ergonomically Optimized Ingress Using a Single Omnidirectional Camera (CS, SC, GF), pp. 298–301.
ICPRICPR-2010-SuS #process
Latent Fingerprint Core Point Prediction Based on Gaussian Processes (CS, SNS), pp. 1634–1637.
ICPRICPR-2010-TsaiHTC #detection #pipes and filters #scalability #using
Learning-Based Vehicle Detection Using Up-Scaling Schemes and Predictive Frame Pipeline Structures (YMT, KYH, CCT, LGC), pp. 3101–3104.
ICPRICPR-2010-UyarBCB #development #network
Bayesian Networks for Predicting IVF Blastocyst Development (AU, ABB, HNC, MB), pp. 2772–2775.
ICPRICPR-2010-WuBT #image
The Good, the Bad, and the Ugly: Predicting Aesthetic Image Labels (YW, CB, CT), pp. 1586–1589.
KDDKDD-2010-BozorgiSSV #heuristic #learning
Beyond heuristics: learning to classify vulnerabilities and predict exploits (MB, LKS, SS, GMV), pp. 105–114.
KDDKDD-2010-JahrerTL #recommendation
Combining predictions for accurate recommender systems (MJ, AT, RAL), pp. 693–702.
KDDKDD-2010-KhoslaCLCHL #approach #machine learning
An integrated machine learning approach to stroke prediction (AK, YC, CCYL, HKC, JH, HL), pp. 183–192.
KDDKDD-2010-KumarGM #data mining #fault #health #mining
Data mining to predict and prevent errors in health insurance claims processing (MK, RG, ZSM), pp. 65–74.
KDDKDD-2010-LeroyCB
Cold start link prediction (VL, BBC, FB), pp. 393–402.
KDDKDD-2010-LichtenwalterLC
New perspectives and methods in link prediction (RL, JTL, NVC), pp. 243–252.
KDDKDD-2010-YangO #feature model #probability #using
Feature selection for support vector regression using probabilistic prediction (JBY, CJO), pp. 343–352.
KDDKDD-2010-YinXHD #personalisation #probability
A probabilistic model for personalized tag prediction (DY, ZX, LH, BDD), pp. 959–968.
KDIRKDIR-2010-BonninBB #detection #modelling #parallel #web
Detecting Parallel Browsing to Improve Web Predictive Modeling (GB, AB, AB), pp. 504–509.
KDIRKDIR-2010-Ghosh #locality #mining #modelling #multi #scalability #using
Actionable Mining of Large, Multi-relational Data using Localized Predictive Models (JG), pp. 9–10.
KDIRKDIR-2010-JunGRO #image #process
Predicting Ground-based Aerosol Optical Depth with Satellite Images Via Gaussian Processes (GJ, JG, VR, ZO), pp. 370–375.
KEODKEOD-2010-KambhampatiSP #metric
Dysphonia Measures in Parkinson’s Disease and Their use in Prediction of Its Progression (CK, MS, NP), pp. 104–109.
KRKR-2010-GebserGISSTV #biology #consistency #network #nondeterminism #programming #scalability #set
Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming (MG, CG, MI, TS, AS, ST, PV).
RecSysRecSys-2010-Baeza-Yates #query #recommendation
Query intent prediction and recommendation (RABY), pp. 5–6.
RecSysRecSys-2010-BenchettaraKR #approach #collaboration #machine learning #recommendation
A supervised machine learning link prediction approach for academic collaboration recommendation (NB, RK, CR), pp. 253–256.
RecSysRecSys-2010-DesarkarSM #collaboration #graph #rating
Aggregating preference graphs for collaborative rating prediction (MSD, SS, PM), pp. 21–28.
RecSysRecSys-2010-MayerMSJ #social
Common attributes in an unusual context: predicting the desirability of a social match (JMM, SM, RPS, QJ), pp. 337–340.
RecSysRecSys-2010-SymeonidisTM #network #similarity #social #transitive
Transitive node similarity for link prediction in social networks with positive and negative links (PS, ET, YM), pp. 183–190.
SEKESEKE-2010-PaikariRR #case study #comparative #fault #reasoning #using
A Comparative Study of Attribute Weighting Techniques for Software Defect Prediction Using Case-based Reasoning (EP, MMR, GR), pp. 380–386.
SEKESEKE-2010-Radlinski #development #quality #using
Software Development Effort and Quality Prediction Using Bayesian Nets and small Local Qualitative Data (LR), pp. 113–116.
SEKESEKE-2010-WilliamsGRW #health
Predicting Project Health Prior to Inception (RW, JG, KR, MW), pp. 640–644.
SIGIRSIGIR-2010-ArapakisAJ #comparison #modelling #personalisation #topic
A comparison of general vs personalised affective models for the prediction of topical relevance (IA, KA, JMJ), pp. 371–378.
SIGIRSIGIR-2010-BalasubramanianKC10a #performance #query #web
Predicting query performance on the web (NB, GK, VRC), pp. 785–786.
SIGIRSIGIR-2010-ChenYLZQ #classification #personalisation #query #question
Predicting query potential for personalization, classification or regression? (CC, MY, SL, TZ, HQ), pp. 725–726.
SIGIRSIGIR-2010-ClementsSVR #behaviour #using
Using flickr geotags to predict user travel behaviour (MC, PS, APdV, MJTR), pp. 851–852.
SIGIRSIGIR-2010-FeildAJ
Predicting searcher frustration (HAF, JA, RJ), pp. 34–41.
SIGIRSIGIR-2010-HauffJKA #quality #query
Query quality: user ratings and system predictions (CH, FdJ, DK, LA), pp. 743–744.
SIGIRSIGIR-2010-LiuYHA #adaptation #analysis #performance #sentiment
S-PLASA+: adaptive sentiment analysis with application to sales performance prediction (YL, XY, XH, AA), pp. 873–874.
SIGIRSIGIR-2010-ShahP #community #quality
Evaluating and predicting answer quality in community QA (CS, JP), pp. 411–418.
SIGIRSIGIR-2010-ShtokKC #modelling #statistics #using
Using statistical decision theory and relevance models for query-performance prediction (AS, OK, DC), pp. 259–266.
SIGIRSIGIR-2010-WhiteH10a #query #web
Predicting escalations of medical queries based on web page structure and content (RWW, EH), pp. 769–770.
ICSEICSE-2010-Cruz #case study #fault #metric #uml
Exploratory study of a UML metric for fault prediction (AECC), pp. 361–364.
ICSEICSE-2010-GuoZNM #debugging #empirical
Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows (PJG, TZ, NN, BM), pp. 495–504.
ICSEICSE-2010-KlasEMHG #case study #fault #industrial #metric
Transparent combination of expert and measurement data for defect prediction: an industrial case study (MK, FE, JM, KH, OvG), pp. 119–128.
ICSEICSE-2010-Schroter10a #developer #interactive
Predicting build outcome with developer interaction in Jazz (AS), pp. 511–512.
ICSEICSE-2010-ZhengL #collaboration #reliability
Collaborative reliability prediction of service-oriented systems (ZZ, MRL), pp. 35–44.
SACSAC-2010-FurukawaOMI #behaviour #social
Prediction of social bookmarking based on a behavior transition model (TF, SO, YM, MI), pp. 1741–1747.
SACSAC-2010-KimKMY #multi #network #probability #using
Probabilistic context prediction using time-inferred multiple pattern networks (YHK, WK, KM, YY), pp. 1015–1019.
SACSAC-2010-MatosBM #network #towards
Towards in-network data prediction in wireless sensor networks (TBM, AB, JEBM), pp. 592–596.
SACSAC-2010-NinagawaE #modelling #network #probability #using
Link prediction using probabilistic group models of network structure (AN, KE), pp. 1115–1116.
SACSAC-2010-OssaPSG #algorithm #graph #low cost #web
Referrer graph: a low-cost web prediction algorithm (BdlO, AP, JS, JAG), pp. 831–838.
CASECASE-2010-BraunS #approach #constraints #logic
A Mixed Logical Dynamic Model Predictive Control approach for handling industrially relevant transportation constraints (MWB, JS), pp. 966–971.
CASECASE-2010-HanSL #random
Modified generalized predictive power control for wireless networked systems with random delays (CH, DS, ZL), pp. 509–514.
CASECASE-2010-JiangKM #monitoring
Residual life prediction for systems subject to condition monitoring (RJ, MJK, VM), pp. 106–111.
CASECASE-2010-KumarKST #programming
A mathematical programming for predicting molecular formulas in accurate mass spectrometry (SK, MK, RS, KT), pp. 246–251.
CASECASE-2010-MacwanNB #approach #bound #multi #novel #online #probability
On-line target-motion prediction for autonomous multirobot search in realistic terrains with time-expanding boundaries: A novel probabilistic approach (AM, GN, BB), pp. 662–667.
CASECASE-2010-SchirruPN #maintenance #process #robust
Particle filtering of hidden Gamma processes for robust Predictive Maintenance in semiconductor manufacturing (AS, SP, GDN), pp. 51–56.
CASECASE-2010-SouzaPC #analysis #distributed #implementation #network
Distributed model predictive control applied to urban traffic networks: Implementation, experimentation, and analysis (FAdS, VBP, EC), pp. 399–405.
CASECASE-2010-ZengB #mobile
Collision avoidance for nonholonomic mobile robots among unpredictable dynamic obstacles including humans (LZ, GMB), pp. 940–947.
CGOCGO-2010-JiangZTMGSG #behaviour #correlation #statistics
Exploiting statistical correlations for proactive prediction of program behaviors (YJ, EZZ, KT, FM, MG, XS, YG), pp. 248–256.
DACDAC-2010-CochranR #consistency #detection #runtime
Consistent runtime thermal prediction and control through workload phase detection (RC, SR), pp. 62–67.
DACDAC-2010-DhimanMR #modelling #online #using
A system for online power prediction in virtualized environments using Gaussian mixture models (GD, KM, TR), pp. 807–812.
DACDAC-2010-HuangCKT #named #network
NTPT: on the end-to-end traffic prediction in the on-chip networks (YSCH, KCKC, CTK, SYT), pp. 449–452.
DACDAC-2010-XieD #variability
Representative path selection for post-silicon timing prediction under variability (LX, AD), pp. 386–391.
DATEDATE-2010-AliARA #algorithm #design #energy #evaluation
Evaluation and design exploration of solar harvested-energy prediction algorithm (MIA, BMAH, JR, DA), pp. 142–147.
DATEDATE-2010-AndalamRG #multi #thread #using
Deterministic, predictable and light-weight multithreading using PRET-C (SA, PSR, AG), pp. 1653–1656.
DATEDATE-2010-GellertPZFVS #architecture #design #energy #smt
Energy-performance design space exploration in SMT architectures exploiting selective load value predictions (AG, GP, VZ, AF, LNV, CS), pp. 271–274.
DATEDATE-2010-PenolazziSH #energy #operating system #performance #realtime
Predicting energy and performance overhead of Real-Time Operating Systems (SP, IS, AH), pp. 15–20.
DATEDATE-2010-ShafiqueBH #adaptation #energy #estimation #named #runtime #video
enBudget: A Run-Time Adaptive Predictive Energy-Budgeting scheme for energy-aware Motion Estimation in H.264/MPEG-4 AVC video encoder (MS, LB, JH), pp. 1725–1730.
HPCAHPCA-2010-FarooqCJ
Value Based BTB Indexing for indirect jump prediction (MUF, LC, LKJ), pp. 1–11.
HPDCHPDC-2010-YuanYWZ #named #parallel #scheduling #strict
PV-EASY: a strict fairness guaranteed and prediction enabled scheduler in parallel job scheduling (YY, GY, YW, WZ), pp. 240–251.
ISMMISMM-2010-TianFG #parallel #using
Speculative parallelization using state separation and multiple value prediction (CT, MF, RG), pp. 63–72.
PDPPDP-2010-MinhW #parallel #using
Using Historical Data to Predict Application Runtimes on Backfilling Parallel Systems (TNM, LW), pp. 246–252.
PPoPPPPoPP-2010-AleenSP #execution #streaming
Input-driven dynamic execution prediction of streaming applications (FA, MS, SP), pp. 315–324.
PPoPPPPoPP-2010-ZhaiCZ #named #parallel #performance #scalability #using
PHANTOM: predicting performance of parallel applications on large-scale parallel machines using a single node (JZ, WC, WZ), pp. 305–314.
ICSTICST-2010-ZimmermannNW #security
Searching for a Needle in a Haystack: Predicting Security Vulnerabilities for Windows Vista (TZ, NN, LAW), pp. 421–428.
ISSTAISSTA-2010-ArcuriIB #analysis #effectiveness #formal method #random testing #testing
Formal analysis of the effectiveness and predictability of random testing (AA, MZZI, LCB), pp. 219–230.
ISSTAISSTA-2010-OstrandW #fault
Software fault prediction tool (TJO, EJW), pp. 275–278.
CBSECBSE-2009-HauckKKR #component #execution #modelling #performance
Modelling Layered Component Execution Environments for Performance Prediction (MH, MK, KK, RHR), pp. 191–208.
QoSAQoSA-2009-ChanP #architecture #behaviour #composition #process
Compositional Prediction of Timed Behaviour for Process Control Architecture (KC, IP), pp. 86–100.
QoSAQoSA-2009-GhezziT #performance
Predicting Performance Properties for Open Systems with KAMI (CG, GT), pp. 70–85.
QoSAQoSA-2009-KrogmannSBKMM #architecture #feedback #performance #using #visualisation
Improved Feedback for Architectural Performance Prediction Using Software Cartography Visualizations (KK, CMS, SB, MK, AM, FM), pp. 52–69.
HTHT-2009-Choudhury #modelling #online #process #social #social media
Modeling and predicting group activity over time in online social media (MDC), pp. 349–350.
ICDARICDAR-2009-FairhurstA #profiling
An Investigation of Predictive Profiling from Handwritten Signature Data (MCF, MCDCA), pp. 1305–1309.
JCDLJCDL-2009-LiC #approach #graph #kernel #machine learning #recommendation
Recommendation as link prediction: a graph kernel-based machine learning approach (XL, HC), pp. 213–216.
VLDBVLDB-2009-CandeaPV #concurrent #scalability
A Scalable, Predictable Join Operator for Highly Concurrent Data Warehouses (GC, NP, RV), pp. 277–288.
VLDBVLDB-2009-UnterbrunnerGAFK #performance
Predictable Performance for Unpredictable Workloads (PU, GG, GA, DF, DK), pp. 706–717.
VLDBVLDB-2009-ZhangCJOZ #effectiveness #nondeterminism #query
Effectively Indexing Uncertain Moving Objects for Predictive Queries (MZ, SC, CSJ, BCO, ZZ), pp. 1198–1209.
EDMEDM-2009-CetintasSXH #correctness #low level #problem
Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems (SC, LS, YPX, CH), pp. 230–239.
EDMEDM-2009-DekkerPV #case study #student
Predicting Students Drop Out: A Case Study (GD, MP, JV), pp. 41–50.
EDMEDM-2009-ZafraV #learning #multi #programming #search-based #student
Predicting Student Grades in Learning Management Systems with Multiple Instance Learning Genetic Programming (AZ, SV), pp. 309–318.
ITiCSEITiCSE-2009-RodrigoBJADELPST #behaviour
Affective and behavioral predictors of novice programmer achievement (MMTR, RSB, MCJ, ACMA, TD, MBVEL, SALL, SAMSP, JOS, EST), pp. 156–160.
CSMRCSMR-2009-ElishE #case study #comparative #maintenance #object-oriented
Application of TreeNet in Predicting Object-Oriented Software Maintainability: A Comparative Study (MOE, KOE), pp. 69–78.
CSMRCSMR-2009-MendeKL #evolution #fault #modelling #scalability
Evaluating Defect Prediction Models for a Large Evolving Software System (TM, RK, ML), pp. 247–250.
ICSMEICSM-2009-AnbalaganV #debugging #on the #open source
On predicting the time taken to correct bug reports in open source projects (PA, MAV), pp. 523–526.
ICSMEICSM-2009-JiaSYL #data transformation #difference #question #set
Data transformation and attribute subset selection: Do they help make differences in software failure prediction? (HJ, FS, YY, QL), pp. 519–522.
ICSMEICSM-2009-WongC #impact analysis #logic #modelling
Predicting change impact from logical models (SW, YC), pp. 467–470.
ICSMEICSM-2009-Zimmermann #debugging #development #mining #process
Changes and bugs — Mining and predicting development activities (TZ), pp. 443–446.
MSRMSR-2009-EkanayakeTGB #concept #fault #quality #using
Tracking concept drift of software projects using defect prediction quality (JE, JT, HCG, AB), pp. 51–60.
MSRMSR-2009-ShinBOW #fault #question
Does calling structure information improve the accuracy of fault prediction? (YS, RMB, TJO, EJW), pp. 61–70.
LATALATA-2009-NakamuraHT
Prediction of Creole Emergence in Spatial Language Dynamics (MN, TH, ST), pp. 614–625.
FMFM-2009-WangKGG #analysis #concurrent #source code
Symbolic Predictive Analysis for Concurrent Programs (CW, SK, MKG, AG), pp. 256–272.
CoGCIG-2009-CowleyCBH #behaviour #modelling #using
Analyzing player behavior in Pacman using feature-driven decision theoretic predictive modeling (BC, DC, MMB, RJH), pp. 170–177.
CoGCIG-2009-WeberM #approach #data mining #mining
A data mining approach to strategy prediction (BGW, MM), pp. 140–147.
FDGFDG-2009-WetzelJG #information management #resource management
Predicting opponent resource allocations when qualitative and contextual information is not available (BW, SJ, MLG), pp. 333–334.
CHICHI-2009-BuscherCM #eye tracking #using #web #what
What do you see when you’re surfing?: using eye tracking to predict salient regions of web pages (GB, EC, MRM), pp. 21–30.
CHICHI-2009-DunlopT #feedback
Tactile feedback for predictive text entry (MDD, FT), pp. 2257–2260.
CHICHI-2009-GilbertK #social #social media
Predicting tie strength with social media (EG, KK), pp. 211–220.
CHICHI-2009-LoveJTH #assessment #learning
Learning to predict information needs: context-aware display as a cognitive aid and an assessment tool (BCL, MJ, MTT, MH), pp. 1351–1360.
HCIDHM-2009-AmantiniC #behaviour #fault #simulation
A Simple Simulation Predicting Driver Behavior, Attitudes and Errors (AA, PCC), pp. 345–354.
HCIDHM-2009-FuLYB
Simulation-Based Discomfort Prediction of the Lower Limb Handicapped with Prosthesis in the Climbing Tasks (YF, SL, MY, YB), pp. 512–520.
HCIHCD-2009-Stephane #analysis #behaviour #simulation
User Behavior Patterns: Gathering, Analysis, Simulation and Prediction (LS), pp. 322–331.
HCIHCI-NIMT-2009-SadP #modelling #word
Modeling Word Selection in Predictive Text Entry (HHS, FP), pp. 725–734.
HCIHCI-NT-2009-JohnS #modelling #towards #usability
Toward Cognitive Modeling for Predicting Usability (BEJ, SS), pp. 267–276.
HCIHIMI-II-2009-AyodeleZK #approach #email #machine learning
Email Reply Prediction: A Machine Learning Approach (TA, SZ, RK), pp. 114–123.
ICEISICEIS-J-2009-NachevHS #fuzzy #network #using
Insolvency Prediction of Irish Companies Using Backpropagation and Fuzzy ARTMAP Neural Networks (AN, SH, BS), pp. 287–298.
ICEISICEIS-J-2009-PapatheocharousA #classification #fuzzy
Classification and Prediction of Software Cost through Fuzzy Decision Trees (EP, ASA), pp. 234–247.
CIKMCIKM-2009-HungP #clustering #network
Clustering object moving patterns for prediction-based object tracking sensor networks (CCH, WCP), pp. 1633–1636.
CIKMCIKM-2009-LiBS #analysis
Blog cascade affinity: analysis and prediction (HL, SSB, AS), pp. 1117–1126.
CIKMCIKM-2009-MabroukehE #mining #ontology #semantics #using #web
Using domain ontology for semantic web usage mining and next page prediction (NRM, CIE), pp. 1677–1680.
CIKMCIKM-2009-MaHR #framework #incremental #mobile #named
iLoc: a framework for incremental location-state acquisition and prediction based on mobile sensors (YM, RH, DR), pp. 1367–1376.
CIKMCIKM-2009-SunLL #case study #category theory #classification #performance #what
What makes categories difficult to classify?: a study on predicting classification performance for categories (AS, EPL, YL), pp. 1891–1894.
CIKMCIKM-2009-TsagkiasWR #online
Predicting the volume of comments on online news stories (MT, WW, MdR), pp. 1765–1768.
CIKMCIKM-2009-WhiteD #behaviour
Characterizing and predicting search engine switching behavior (RWW, STD), pp. 87–96.
CIKMCIKM-2009-WuB #probability
Predicting the conversion probability for items on C2C ecommerce sites (XW, AB), pp. 1377–1386.
ECIRECIR-2009-HauffAH #evaluation #performance #query
The Combination and Evaluation of Query Performance Prediction Methods (CH, LA, DH), pp. 301–312.
ECIRECIR-2009-MoshfeghiAPJ #collaboration #rating #recommendation #semantics
Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering (YM, DA, BP, JMJ), pp. 54–65.
ECIRECIR-2009-TsagkiasLR
Exploiting Surface Features for the Prediction of Podcast Preference (MT, ML, MdR), pp. 473–484.
ICMLICML-2009-BoulariasC #policy
Predictive representations for policy gradient in POMDPs (AB, BCd), pp. 65–72.
ICMLICML-2009-Daume #search-based
Unsupervised search-based structured prediction (HDI), pp. 209–216.
ICMLICML-2009-GarnettOR
Sequential Bayesian prediction in the presence of changepoints (RG, MAO, SJR), pp. 345–352.
ICMLICML-2009-JetchevT #learning
Trajectory prediction: learning to map situations to robot trajectories (NJ, MT), pp. 449–456.
ICMLICML-2009-KarampatziakisK #learning
Learning prediction suffix trees with Winnow (NK, DK), pp. 489–496.
ICMLICML-2009-KunegisL #graph transformation #learning
Learning spectral graph transformations for link prediction (JK, AL), pp. 561–568.
ICMLICML-2009-Makino #network #representation
Proto-predictive representation of states with simple recurrent temporal-difference networks (TM), pp. 697–704.
ICMLICML-2009-Smith #natural language #summary #tutorial
Tutorial summary: Structured prediction for natural language processing (NAS), p. 20.
ICMLICML-2009-XuWS #learning
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning (LX, MW, DS), pp. 1137–1144.
ICMLICML-2009-YuLZG #collaboration #parametricity #random #scalability #using
Large-scale collaborative prediction using a nonparametric random effects model (KY, JDL, SZ, YG), pp. 1185–1192.
KDDKDD-2009-AttenbergPS #behaviour #modelling
Modeling and predicting user behavior in sponsored search (JA, SP, TS), pp. 1067–1076.
KDDKDD-2009-DeodharG #mining
Mining for the most certain predictions from dyadic data (MD, JG), pp. 249–258.
KDDKDD-2009-MonrealePTG #mining #named
WhereNext: a location predictor on trajectory pattern mining (AM, FP, RT, FG), pp. 637–646.
KDDKDD-2009-SculleyMBB
Predicting bounce rates in sponsored search advertisements (DS, RGM, SB, RJB), pp. 1325–1334.
MLDMMLDM-2009-DashevskiyL
Predictions with Confidence in Applications (MD, ZL), pp. 775–786.
MLDMMLDM-2009-NikovskiR #modelling
Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series (DN, GR), pp. 734–748.
RecSysRecSys-2009-CamposFHR #collaboration
Measuring predictive capability in collaborative filtering (LMdC, JMFL, JFH, MARM), pp. 313–316.
RecSysRecSys-2009-GivonL #recommendation
Predicting social-tags for cold start book recommendations (SG, VL), pp. 333–336.
RecSysRecSys-2009-MarlinZ #collaboration #ranking
Collaborative prediction and ranking with non-random missing data (BMM, RSZ), pp. 5–12.
SEKESEKE-2009-KocaguneliTBTC #analysis #fault #metric #named
Prest: An Intelligent Software Metrics Extraction, Analysis and Defect Prediction Tool (EK, AT, ABB, BT, BC), pp. 637–642.
SEKESEKE-2009-LounisAS #approach #impact analysis #maintenance
Predicting Maintainability expressed as Change Impact: A Machine-learning-based Approach (HL, MKA, HAS), pp. 122–128.
SEKESEKE-2009-XuZW #network
Long-term Prediction of Wireless Network Traffic (ZX, ZZ, WW), pp. 473–479.
SIGIRSIGIR-2009-ArapakisKJK #modelling #topic
Modeling facial expressions and peripheral physiological signals to predict topical relevance (IA, IK, JMJ, IK), pp. 728–729.
SIGIRSIGIR-2009-Collins-ThompsonB #performance #query #using
Estimating query performance using class predictions (KCT, PNB), pp. 672–673.
SIGIRSIGIR-2009-DiazA #adaptation #feedback
Adaptation of offline vertical selection predictions in the presence of user feedback (FD, JA), pp. 323–330.
SIGIRSIGIR-2009-GuoA #interactive #segmentation
Beyond session segmentation: predicting changes in search intent with client-side user interactions (QG, EA), pp. 636–637.
SIGIRSIGIR-2009-HauffA #effectiveness #performance #query #question
When is query performance prediction effective? (CH, LA), pp. 829–830.
SIGIRSIGIR-2009-KonigGW #query
Click-through prediction for news queries (ACK, MG, QW), pp. 347–354.
SIGIRSIGIR-2009-KumaranC #quality #query #using
Reducing long queries using query quality predictors (GK, VRC), pp. 564–571.
SIGIRSIGIR-2009-ScholerG #evaluation #query
A case for improved evaluation of query difficulty prediction (FS, SG), pp. 640–641.
SIGIRSIGIR-2009-TomsF #analysis #behaviour
Predicting stopping behaviour: a preliminary analysis (EGT, LF), pp. 750–751.
SIGIRSIGIR-2009-WangLF #automation #fuzzy #using
Automatic URL completion and prediction using fuzzy type-ahead search (JW, GL, JF), pp. 634–635.
SIGIRSIGIR-2009-WhiteBC #information management
Predicting user interests from contextual information (RWW, PB, LC), pp. 363–370.
TOOLSTOOLS-EUROPE-2009-ConejeroFGHJ #metric
Early Crosscutting Metrics as Predictors of Software Instability (JMC, EF, AG, JH, EJ), pp. 136–156.
AdaSIGAda-2009-Lathrop #ada #branch #dynamic analysis
Dynamic analysis of branch mispredictions in Ada (SML), pp. 79–84.
ASEASE-2009-MunkbyS #fault tolerance #type inference
Type Inference for Soft-Error Fault-Tolerance Prediction (GM, SS), pp. 65–75.
ASEASE-2009-ShivajiWAK #debugging
Reducing Features to Improve Bug Prediction (SS, EJWJ, RA, SK), pp. 600–604.
ESEC-FSEESEC-FSE-2009-ZimmermannNGGM #empirical #fault #process #scalability
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process (TZ, NN, HG, EG, BM), pp. 91–100.
ICSEICSE-2009-Hassan #complexity #fault #using
Predicting faults using the complexity of code changes (AEH), pp. 78–88.
ICSEICSE-2009-WolfSDN #analysis #communication #developer #network #social #using
Predicting build failures using social network analysis on developer communication (TW, AS, DD, THDN), pp. 1–11.
SACSAC-2009-ChunLSC #multi #using #video
An enhanced multi-view video compression using the constrained inter-view prediction (SC, SL, KS, KC), pp. 1811–1815.
SACSAC-2009-ConceicaoMC #mobile #modelling
A nonlinear mobile robot modeling applied to a model predictive controller (ASC, APM, JPC), pp. 1186–1187.
SACSAC-2009-KangGC #scalability
A new inter-layer prediction scheme for spatial scalability with different frame rates (JK, GG, KC), pp. 1779–1783.
SACSAC-2009-MathuriyaBHH #manycore #named #scalability
GTfold: a scalable multicore code for RNA secondary structure prediction (AM, DAB, CEH, SCH), pp. 981–988.
SACSAC-2009-RossTA #data type #online
Online annotation and prediction for regime switching data streams (GJR, DKT, NMA), pp. 1501–1505.
SACSAC-2009-TanakaKTN #navigation #using
A destination prediction method using driving contexts and trajectory for car navigation systems (KT, YK, TT, SN), pp. 190–195.
SACSAC-2009-Xie
Improved AdaBoost.M1 of decision trees with confidence-rated predictions (ZX), pp. 1462–1466.
SACSAC-2009-XuLQ #privacy
Privacy preserving churn prediction (SX, SL, MQ), pp. 1610–1614.
ASPLOSASPLOS-2009-DimitrovZ #approach #automation #debugging #validation
Anomaly-based bug prediction, isolation, and validation: an automated approach for software debugging (MD, HZ), pp. 61–72.
ASPLOSASPLOS-2009-WegielK
Dynamic prediction of collection yield for managed runtimes (MW, CK), pp. 289–300.
CASECASE-2009-ChengP
A fusion prognostics method for remaining useful life prediction of electronic products (SC, MGP), pp. 102–107.
CASECASE-2009-KumarWKSS #analysis #fuzzy #using #variability
A fuzzy filtering based system for maximal oxygen uptake prediction using heart rate variability analysis (MK, MW, SK, NS, RS), pp. 604–608.
CCCC-2009-GaoLXN #concurrent #recursion #source code #thread
Exploiting Speculative TLP in Recursive Programs by Dynamic Thread Prediction (LG, LL, JX, TFN), pp. 78–93.
CGOCGO-2009-MaoS #evolution #learning #virtual machine
Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines (FM, XS), pp. 92–101.
DACDAC-2009-DrmanacLW #process #variability
Predicting variability in nanoscale lithography processes (DGD, FL, LCW), pp. 545–550.
DATEDATE-2009-BellasiFS #analysis #modelling #multi #power management
Predictive models for multimedia applications power consumption based on use-case and OS level analysis (PB, WF, DS), pp. 1446–1451.
DATEDATE-2009-HanssonSG #composition #named #network
Aelite: A flit-synchronous Network on Chip with composable and predictable services (AH, MS, KG), pp. 250–255.
DATEDATE-2009-ShafiqueBH #approach #design #hardware #parallel #performance #video
A parallel approach for high performance hardware design of intra prediction in H.264/AVC Video Codec (MS, LB, JH), pp. 1434–1439.
HPCAHPCA-2009-DuanLP #architecture #estimation #metric #performance
Versatile prediction and fast estimation of Architectural Vulnerability Factor from processor performance metrics (LD, BL, LP), pp. 129–140.
HPCAHPCA-2009-MatsutaniKAY #architecture #latency
Prediction router: Yet another low latency on-chip router architecture (HM, MK, HA, TY), pp. 367–378.
HPCAHPCA-2009-ReddiGHWSB #using
Voltage emergency prediction: Using signatures to reduce operating margins (VJR, MSG, GHH, GYW, MDS, DMB), pp. 18–29.
HPDCHPDC-2009-GlasnerV #adaptation #runtime
Adaptive run-time prediction in heterogeneous environments (CG, JV), pp. 61–62.
HPDCHPDC-2009-LiSDZ #parallel #performance
Performance prediction based on hierarchy parallel features captured in multi-processing system (JL, FS, ND, QZ), pp. 63–64.
HPDCHPDC-2009-SonmezYIE #evaluation #queue #runtime
Trace-based evaluation of job runtime and queue wait time predictions in grids (OOS, NY, AI, DHJE), pp. 111–120.
PDPPDP-2009-TudelaL #optimisation #parallel
Parallel Protein Structure Prediction by Multiobjective Optimization (JCCT, JOL), pp. 268–275.
TACASTACAS-2009-FarzanM #complexity
The Complexity of Predicting Atomicity Violations (AF, PM), pp. 155–169.
CAVCAV-2009-Benini #manycore #performance #question
Predictability vs. Efficiency in the Multicore Era: Fight of Titans or Happy Ever after? (LB), p. 50.
CAVCAV-2009-CosteHLS #composition #design #industrial #modelling #performance #towards
Towards Performance Prediction of Compositional Models in Industrial GALS Designs (NC, HH, EL, WS), pp. 204–218.
ICSTICST-2009-GegickRW #component
Predicting Attack-prone Components (MG, PR, LAW), pp. 181–190.
ICSTICST-2009-WedyanAB #automation #detection #effectiveness #fault #refactoring #static analysis #tool support
The Effectiveness of Automated Static Analysis Tools for Fault Detection and Refactoring Prediction (FW, DA, JMB), pp. 141–150.
CBSECBSE-2008-KuperbergKR #behaviour #black box #component #modelling #parametricity #performance #using
Performance Prediction for Black-Box Components Using Reengineered Parametric Behaviour Models (MK, KK, RHR), pp. 48–63.
CBSECBSE-2008-MartensBKR #component #empirical #modelling #performance #reuse
An Empirical Investigation of the Effort of Creating Reusable, Component-Based Models for Performance Prediction (AM, SB, HK, RHR), pp. 16–31.
QoSAQoSA-2008-GallottiGMT #composition #model checking #probability #quality
Quality Prediction of Service Compositions through Probabilistic Model Checking (SG, CG, RM, GT), pp. 119–134.
HTHT-2008-ChoudhurySJS #communication #social #using
Dynamic prediction of communication flow using social context (MDC, HS, AJ, DDS), pp. 49–54.
EDMEDM-2008-FengBHK #question
Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standarized Test? (MF, JEB, NTH, KRK), pp. 107–116.
EDMEDM-2008-FengHBK #question #student
Can we predict which groups of questions students will learn from? (MF, NTH, JEB, KRK), pp. 218–225.
CSMRCSMR-2008-KenmeiAP #analysis #open source #scalability
Trend Analysis and Issue Prediction in Large-Scale Open Source Systems (BK, GA, MDP), pp. 73–82.
CSMRCSMR-2008-LagerstromJ #architecture #enterprise #maintenance #modelling #using
Using Architectural Models to Predict the Maintainability of Enterprise Systems (RL, PJ), pp. 248–252.
MSRMSR-2008-RatzingerSG #fault #on the #refactoring
On the relation of refactorings and software defect prediction (JR, TS, HCG), pp. 35–38.
WCREWCRE-2008-ZhouWGGL #approach #network
A Bayesian Network Based Approach for Change Coupling Prediction (YZ, MW, EG, HG, JL), pp. 27–36.
CoGCIG-2008-HladkyB #evaluation #game studies #modelling #video
An evaluation of models for predicting opponent positions in first-person shooter video games (SH, VB), pp. 39–46.
CHICHI-2008-GajosETCW #adaptation #user interface
Predictability and accuracy in adaptive user interfaces (KZG, KE, DST, MC, DSW), pp. 1271–1274.
CHICHI-2008-HarperRRK #online #quality
Predictors of answer quality in online Q&A sites (FMH, DRR, SR, JAK), pp. 865–874.
CHICHI-2008-KapoorH #case study #comparative #experience #modelling
Experience sampling for building predictive user models: a comparative study (AK, EH), pp. 657–666.
CHICHI-2008-OganAJ #learning #using
Pause, predict, and ponder: use of narrative videos to improve cultural discussion and learning (AO, VA, CJ), pp. 155–162.
CHICHI-2008-RatwaniMT #eye tracking #fault #using
Predicting postcompletion errors using eye movements (RMR, JMM, JGT), pp. 539–542.
ICEISICEIS-DISI-2008-PapatheocharousA #modelling
Size and Effort-Based Computational Models for Software Cost Prediction (EP, ASA), pp. 57–64.
ICEISICEIS-ISAS2-2008-CabreroGP
Combining Different Change Prediction Techniques (DCM, JG, MP), pp. 57–63.
ICEISICEIS-J-2008-PapatheocharousA08a #algorithm #approach #hybrid #modelling #network #search-based #using
Hybrid Computational Models for Software Cost Prediction: An Approach Using Artificial Neural Networks and Genetic Algorithms (EP, ASA), pp. 87–100.
CIKMCIKM-2008-DavisCBCB
Predicting individual disease risk based on medical history (DAD, NVC, NB, NAC, ALB), pp. 769–778.
CIKMCIKM-2008-HauffHJ #overview #performance #query
A survey of pre-retrieval query performance predictors (CH, DH, FdJ), pp. 1419–1420.
CIKMCIKM-2008-HauffMB #query #web
Improved query difficulty prediction for the web (CH, VM, RABY), pp. 439–448.
CIKMCIKM-2008-HsuC #social
A method to predict social annotations (MHH, HHC), pp. 1375–1376.
CIKMCIKM-2008-MahouiTSC #identification #modelling #using
Identification of gene function using prediction by partial matching (PPM) language models (MM, WJT, AKTS, SC), pp. 779–786.
CIKMCIKM-2008-WebbCP #web
Predicting web spam with HTTP session information (SW, JC, CP), pp. 339–348.
CIKMCIKM-2008-ZhaoBZY #community #network
Characterizing and predicting community members from evolutionary and heterogeneous networks (QZ, SSB, XZ, KY), pp. 309–318.
ECIRECIR-2008-HeLR #metric #query #using
Using Coherence-Based Measures to Predict Query Difficulty (JH, ML, MdR), pp. 689–694.
ECIRECIR-2008-ZhaoST #effectiveness #performance #query #similarity #using #variability
Effective Pre-retrieval Query Performance Prediction Using Similarity and Variability Evidence (YZ, FS, YT), pp. 52–64.
ICMLICML-2008-AllauzenMT #kernel #sequence
Sequence kernels for predicting protein essentiality (CA, MM, AT), pp. 9–16.
ICMLICML-2008-KakadeST #algorithm #multi #online #performance
Efficient bandit algorithms for online multiclass prediction (SMK, SSS, AT), pp. 440–447.
ICMLICML-2008-VovkZ #game studies
Prediction with expert advice for the Brier game (VV, FZ), pp. 1104–1111.
ICMLICML-2008-WingateS #exponential #learning #product line
Efficiently learning linear-linear exponential family predictive representations of state (DW, SPS), pp. 1176–1183.
ICMLICML-2008-YueJ #set #using
Predicting diverse subsets using structural SVMs (YY, TJ), pp. 1224–1231.
ICPRICPR-2008-LiCS #kernel #optimisation #video
An improved mean-shift tracker with kernel prediction and scale optimisation targeting for low-frame-rate video tracking (ZL, JC, NNS), pp. 1–4.
ICPRICPR-2008-ScrippsTCE #approach #matrix
A matrix alignment approach for link prediction (JS, PNT, FC, AHE), pp. 1–4.
ICPRICPR-2008-SuAL #realtime #robust
Robust real-time face alignment based on ASM with boosting regression for displacement prediction (YS, HA, SL), pp. 1–4.
KDDKDD-2008-LaxmanTW #generative #modelling #sequence #using
Stream prediction using a generative model based on frequent episodes in event sequences (SL, VT, RWW), pp. 453–461.
KDDKDD-2008-MeloAL #behaviour #metric #network #question
Can complex network metrics predict the behavior of NBA teams? (POSVdM, VAFA, AAFL), pp. 695–703.
KDDKDD-2008-ZengMLBM #analysis #using
Using predictive analysis to improve invoice-to-cash collection (SZ, PM, CAL, IMBM, CM), pp. 1043–1050.
RecSysRecSys-2008-DingZYZFB #collaboration #fault #statistics
Boosting collaborative filtering based on statistical prediction errors (SD, SZ, QY, XZ, RF, LDB), pp. 3–10.
RecSysRecSys-2008-KrishnanNNDK #online #recommendation
Who predicts better?: results from an online study comparing humans and an online recommender system (VK, PKN, MN, RTD, JAK), pp. 211–218.
RecSysRecSys-2008-Umyarov #performance #recommendation
Leveraging aggregate ratings for improving predictive performance of recommender systems (AU), pp. 327–330.
SEKESEKE-2008-BadriBS #approach #co-evolution #object-oriented
Predicting Change Propagation in Object-oriented Systems: a Control-call Path Based Approach and Associated Tool (LB, MB, DSY), pp. 103–110.
SEKESEKE-2008-Barros #information management #using
Predicting Software Project Size Using Project Generated Information (MdOB), pp. 149–154.
SEKESEKE-2008-DaiYZG #approach #composition #self #web #web service
Failure Prediction Based Self-healing Approach for Web Service Composition (YD, LY, BZ, KG), pp. 853–856.
SEKESEKE-2008-TurhanB #fault
Weighted Static Code Attributes for Software Defect Prediction (BT, ABB), pp. 143–148.
SEKESEKE-2008-XuSW #adaptation #fault #network
An Adaptive Neural Network with Dynamic Structure for Software Defect Prediction (ZX, NS, WW), pp. 79–84.
SEKESEKE-2008-YangDZ #performance #reliability
Reliability Oriented QoS Driven Composite Service Selection Based on Performance Prediction (LY, YD, BZ), pp. 215–218.
SIGIRSIGIR-2008-Al-MaskariSCA #effectiveness #question
The good and the bad system: does the test collection predict users’ effectiveness? (AAM, MS, PDC, EA), pp. 59–66.
SIGIRSIGIR-2008-DupretP
A user browsing model to predict search engine click data from past observations (GD, BP), pp. 331–338.
SIGIRSIGIR-2008-HeymannRG #social
Social tag prediction (PH, DR, HGM), pp. 531–538.
SIGIRSIGIR-2008-LiLRGA #community
Exploring question subjectivity prediction in community QA (BL, YL, AR, EVG, EA), pp. 735–736.
SIGIRSIGIR-2008-LiuBA #community
Predicting information seeker satisfaction in community question answering (YL, JB, EA), pp. 483–490.
SIGIRSIGIR-2008-RahurkarC
Predicting when browsing context is relevant to search (MR, SC), pp. 841–842.
RERE-2008-HerrmannD #requirements #research
Requirements Prioritization Based on Benefit and Cost Prediction: An Agenda for Future Research (AH, MD), pp. 125–134.
ASEASE-2008-DanielB #automation #effectiveness #testing #tool support
Predicting Effectiveness of Automatic Testing Tools (BD, MB), pp. 363–366.
ASEASE-2008-Jones #development #source code
Reflections on, and Predictions for, Support Systems for the Development of Programs (CBJ), pp. 7–8.
ASEASE-2008-JoshiS #java #parallel #source code #thread #type system
Predictive Typestate Checking of Multithreaded Java Programs (PJ, KS), pp. 288–296.
ASEASE-2008-SentillesPCH #component #development #embedded #ide #named
Save-IDE: An Integrated Development Environment for Building Predictable Component-Based Embedded Systems (SS, PP, IC, JH), pp. 493–494.
FSEFSE-2008-MeneelyWSO #analysis #developer #network #social
Predicting failures with developer networks and social network analysis (AM, LW, WS, JAO), pp. 13–23.
FSEFSE-2008-PinzgerNM #developer #network #question
Can developer-module networks predict failures? (MP, NN, BM), pp. 2–12.
ICSEICSE-2008-ChenSR #analysis #java #named #runtime
jPredictor: a predictive runtime analysis tool for java (FC, TFS, GR), pp. 221–230.
ICSEICSE-2008-CheungRMG #component #reliability
Early prediction of software component reliability (LC, RR, NM, LG), pp. 111–120.
ICSEICSE-2008-MoserPS #analysis #comparative #fault #metric #performance
A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction (RM, WP, GS), pp. 181–190.
ICSEICSE-2008-RuthruffPMER #approach #static analysis
Predicting accurate and actionable static analysis warnings: an experimental approach (JRR, JP, JDM, SGE, GR), pp. 341–350.
ICSEICSE-2008-ZimmermannN #analysis #dependence #fault #graph #network #using
Predicting defects using network analysis on dependency graphs (TZ, NN), pp. 531–540.
SACSAC-2008-BloomaCG #framework
A predictive framework for retrieving the best answer (MJB, AYKC, DHLG), pp. 1107–1111.
SACSAC-2008-DornS #3d #approximate #named
CReF: a central-residue-fragment-based method for predicting approximate 3-D polypeptides structures (MD, ONdS), pp. 1261–1267.
SACSAC-2008-SouzaOVO #approach #simulation #statistics
A statistical approach for prediction of projects based on simulation (MMdS, HCBdO, AMLdV, SRBO), pp. 23–27.
SACSAC-2008-WeiYLX #embedded #memory management #realtime
Flash memory management based on predicted data expiry-time in embedded real-time systems (PW, LY, ZL, XX), pp. 1477–1481.
ASPLOSASPLOS-2008-BurceaSMF
Predictor virtualization (IB, SS, AM, BF), pp. 157–167.
ASPLOSASPLOS-2008-ChoiPT #branch #thread
Accurate branch prediction for short threads (BC, LP, DMT), pp. 125–134.
CASECASE-2008-QianZ #pipes and filters #process
Optimal model predictive control of plasma pipe welding process (KQ, YZ), pp. 492–497.
CGOCGO-2008-KetterlinC #data access #recognition
Prediction and trace compression of data access addresses through nested loop recognition (AK, PC), pp. 94–103.
CGOCGO-2008-SalverdakZ #random
Accurate critical path prediction via random trace construction (PS, CT, CBZ), pp. 64–73.
DACDAC-2008-BastaniKWC #learning #set
Speedpath prediction based on learning from a small set of examples (PB, KK, LCW, EC), pp. 217–222.
DACDAC-2008-ChenLC
Predictive formulae for OPC with applications to lithography-friendly routing (TCC, GWL, YWC), pp. 510–515.
DACDAC-2008-ChoYBP #named #performance
ELIAD: efficient lithography aware detailed router with compact post-OPC printability prediction (MC, KY, YB, DZP), pp. 504–509.
DACDAC-2008-CookS #design #using
Predictive design space exploration using genetically programmed response surfaces (HC, KS), pp. 960–965.
DACDAC-2008-SenOA #multi #runtime #verification
Predictive runtime verification of multi-processor SoCs in SystemC (AS, VO, MSA), pp. 948–953.
DACDAC-2008-SuhendraM #clustering #multi
Exploring locking & partitioning for predictable shared caches on multi-cores (VS, TM), pp. 300–303.
DACDAC-2008-YeoLK #manycore
Predictive dynamic thermal management for multicore systems (IY, CCL, EJK), pp. 734–739.
DATEDATE-2008-CertnerLPTAD #approach #parallel #performance #source code
A Practical Approach for Reconciling High and Predictable Performance in Non-Regular Parallel Programs (OC, ZL, PP, OT, FA, ND), pp. 740–745.
DATEDATE-2008-ClothH #design #embedded #evaluation #mobile
Quantitative Evaluation in Embedded System Design: Predicting Battery Lifetime in Mobile Devices (LC, BRH), pp. 90–91.
HPCAHPCA-2008-DiaoS #cpu #process
Prediction of CPU idle-busy activity pattern (QD, JJS), pp. 27–36.
HPCAHPCA-2008-GaoMDZ #branch #correlation #locality #novel
Address-branch correlation: A novel locality for long-latency hard-to-predict branches (HG, YM, MD, HZ), pp. 74–85.
HPCAHPCA-2008-LeeKMP #using
Performance-aware speculation control using wrong path usefulness prediction (CJL, HK, OM, YNP), pp. 39–49.
HPCAHPCA-2008-MalikADF #named
PaCo: Probability-based path confidence prediction (KM, MA, VD, MIF), pp. 50–61.
HPCAHPCA-2008-MalikASWF #independence #parallel
Branch-mispredict level parallelism (BLP) for control independence (KM, MA, SSS, KMW, MIF), pp. 62–73.
HPCAHPCA-2008-RamosB #named
C-Oracle: Predictive thermal management for data centers (LER, RB), pp. 111–122.
HPCAHPCA-2008-SubramaniamPL #dependence #memory management #named #smt
PEEP: Exploiting predictability of memory dependences in SMT processors (SS, MP, GHL), pp. 137–148.
ISMMISMM-2008-BrabermanFGY #memory management #parametricity #requirements
Parametric prediction of heap memory requirements (VAB, FJF, DG, SY), pp. 141–150.
ICLPICLP-2008-Vidal #analysis #effectiveness #partial evaluation
Trace Analysis for Predicting the Effectiveness of Partial Evaluation (GV), pp. 790–794.
ICSTICST-2008-CiupaPLOM #object-oriented #on the #random testing #testing
On the Predictability of Random Tests for Object-Oriented Software (IC, AP, AL, MO, BM), pp. 72–81.
ICSTICST-2008-Vemuri #mobile #testing
Testing Predictive Software in Mobile Devices (VRV), pp. 440–447.
ICSTICST-2008-WareWS #metric #using
The Use of Intra-Release Product Measures in Predicting Release Readiness (MPW, FGW, MS), pp. 230–237.
TAPTAP-2008-WeyukerO #fault #question #what
What Can Fault Prediction Do for YOU? (EJW, TJO), pp. 18–29.
QoSAQoSA-2007-KoziolekBH #architecture #component #performance
Predicting the Performance of Component-Based Software Architectures with Different Usage Profiles (HK, SB, JH), pp. 145–163.
QoSAQoSA-2007-MarzollaM #performance #web #web service #workflow
Performance Prediction of Web Service Workflows (MM, RM), pp. 127–144.
QoSAQoSA-2007-RoshandelMG #architecture #reliability
A Bayesian Model for Predicting Reliability of Software Systems at the Architectural Level (RR, NM, LG), pp. 108–126.
HTHT-2007-MelguizoOJ #navigation #problem #web
Predicting and solving web navigation problems (MCPM, HvO, IJ), pp. 47–48.
ICDARICDAR-2007-BabaUS #online #recognition
Predictive DP Matching for On-Line Character Recognition (DB, SU, HS), pp. 674–678.
VLDBVLDB-2007-NandiJ #effectiveness
Effective Phrase Prediction (AN, HVJ), pp. 219–230.
ITiCSEITiCSE-2007-CaronnaSMIHT #using
Prediction of modulators of pyruvate kinase in smiles text using aprori methods (JSC, RS, JDM, VLI, KGH, JHT), p. 348.
CSMRCSMR-2007-SharafatT #approach #object-oriented #probability
A Probabilistic Approach to Predict Changes in Object-Oriented Software Systems (ARS, LT), pp. 27–38.
ICPCICPC-2007-MirarabHT #co-evolution #network #using
Using Bayesian Belief Networks to Predict Change Propagation in Software Systems (SM, AH, LT), pp. 177–188.
ICPCICPC-2007-Vivanco #algorithm #complexity #identification #metric #modelling #search-based #source code #using
Use of a Genetic Algorithm to Identify Source Code Metrics Which Improves Cognitive Complexity Predictive Models (RAV), pp. 297–300.
ICSMEICSM-2007-CapiluppiF #anti #open source
A model to predict anti-regressive effort in Open Source Software (AC, JFR), pp. 194–203.
ICSMEICSM-2007-HerraizGRG #evolution #on the
On the prediction of the evolution of libre software projects (IH, JMGB, GR, DMG), pp. 405–414.
ICSMEICSM-2007-Vivanco #approach #modelling #quality #using
Improving Predictive Models of Software Quality Using an Evolutionary Computational Approach (RV), pp. 503–504.
MSRMSR-2007-JoshiZRB #approach #debugging
Local and Global Recency Weighting Approach to Bug Prediction (HJ, CZ, SR, CB), p. 33.
MSRMSR-2007-KagdiM #dependence
Combining Single-Version and Evolutionary Dependencies for Software-Change Prediction (HHK, JIM), p. 17.
MSRMSR-2007-Panjer #debugging #eclipse
Predicting Eclipse Bug Lifetimes (LDP), p. 29.
MSRMSR-2007-Schroter #fault
Predicting Defects and Changes with Import Relations (AS), p. 31.
LATALATA-2007-AlhazovMR #network
Networks of Evolutionary Processors with Two Nodes Are Unpredictable (AA, CMV, YR), pp. 521–528.
CoGCIG-2007-ArakiYTT
Move Prediction in Go with the Maximum Entropy Method (NA, KY, YT, JT), pp. 189–195.
CHICHI-2007-CockburnGG #performance
A predictive model of menu performance (AC, CG, SG), pp. 627–636.
CHICHI-2007-HalversonH #human-computer #interactive #visual notation
A minimal model for predicting visual search in human-computer interaction (TH, AJH), pp. 431–434.
CHICHI-2007-LankCR #using
Endpoint prediction using motion kinematics (EL, YCNC, JR), pp. 637–646.
CHICHI-2007-PettittBS #information management #visual notation
An extended keystroke level model (KLM) for predicting the visual demand of in-vehicle information systems (MP, GEB, AS), pp. 1515–1524.
HCIDHM-2007-FritzscheB
Prediction of Discomfort During Arm Movements (FF, HB), pp. 66–73.
HCIDHM-2007-QiaoYY #equation
The Application of Kane Equation in the Impact Prediction of Human Motion (MQ, CY, XY), pp. 179–188.
HCIDHM-2007-XuWZZ #algorithm #complexity
An Epileptic Seizure Prediction Algorithm from Scalp EEG Based on Morphological Filter and Kolmogorov Complexity (GX, JW, QZ, JZ), pp. 736–746.
HCIDHM-2007-YangRMAH #validation
Validation of Predicted Posture for the Virtual Human SantosTM (JY, SR, TM, KAM, CH), pp. 500–510.
HCIHCI-AS-2007-BurgerB
Predicting the Outcome of a Computer Literacy Course Based on a Candidate’s Personal Characteristics (AJB, PJB), pp. 173–182.
HCIHCI-IDU-2007-StrybelVDKNCG #using
Predicting Perceived Situation Awareness of Low Altitude Aircraft in Terminal Airspace Using Probe Questions (TZS, KPLV, JPD, JK, TKN, VC, FPG), pp. 939–948.
HCIHCI-IDU-2007-VerpoortenLC #interactive
Task-Based Prediction of Interaction Patterns for Ambient Intelligence Environments (KV, KL, KC), pp. 1216–1225.
HCIHCI-MIE-2007-DerakhshiK #bound #fuzzy #network #sequence #using #word
Using Recurrent Fuzzy Neural Networks for Predicting Word Boundaries in a Phoneme Sequence in Persian Language (MRFD, MRK), pp. 50–59.
HCIHCI-MIE-2007-SerbanTM #behaviour #interface #learning
A Learning Interface Agent for User Behavior Prediction (GS, AT, GSM), pp. 508–517.
HCIHIMI-IIE-2007-Sanchez-PueblaAD #evaluation #interface #parametricity #validation
Validation of Critical Parameters for Predictive Evaluation of Notification System in Avionics Interfaces (MASP, IA, PD), pp. 1109–1118.
HCIHIMI-MTT-2007-KarshEAHSMPSAKSB #case study #quality
Do Beliefs About Hospital Technologies Predict Nurses’ Perceptions of Their Ability to Provide Quality Care? A Study in Two Pediatric Hospitals (BTK, KE, SA, RJH, MS, KM, NP, TS, JA, RK, KS, RLB), pp. 77–83.
ICEISICEIS-AIDSS-2007-BenschBRBSB #operating system #optimisation #self
Self-Learning Prediction System for Optimisation of Workload Management in a Mainframe Operating System (MB, DB, WR, MB, WGS, PB), pp. 212–218.
ICEISICEIS-AIDSS-2007-SturekRNS #reliability
A Decision Support System for Predicting the Reliability of a Robotic Dispensing System (JS, SR, PN, KS), pp. 289–296.
ICEISICEIS-AIDSS-2007-YingboJJ #learning #process #using #workflow
Using Decision Tree Learning to Predict Workflow Activity Time Consumption (YL, JW, JS), pp. 69–75.
CIKMCIKM-2007-LiMGDBM #process #using
Predicting individual priorities of shared activities using support vector machines (LL, MJM, WG, CD, BB, DRM), pp. 515–524.
CIKMCIKM-2007-PiwowarskiZ #modelling
Predictive user click models based on click-through history (BP, HZ), pp. 175–182.
ECIRECIR-2007-MasegosaJJ #independence
Evaluating Query-Independent Object Features for Relevancy Prediction (ARM, HJ, JMJ), pp. 283–294.
ICMLICML-2007-DavisCRP #approach #process
An integrated approach to feature invention and model construction for drug activity prediction (JD, VSC, SR, DP), pp. 217–224.
ICMLICML-2007-DietzBS
Unsupervised prediction of citation influences (LD, SB, TS), pp. 233–240.
ICMLICML-2007-GlobersonKCC #algorithm
Exponentiated gradient algorithms for log-linear structured prediction (AG, TK, XC, MC), pp. 305–312.
ICMLICML-2007-TitovH #incremental #network
Incremental Bayesian networks for structure prediction (IT, JH), pp. 887–894.
KDDKDD-2007-AgarwalM #modelling #scalability
Predictive discrete latent factor models for large scale dyadic data (DA, SM), pp. 26–35.
KDDKDD-2007-PandeySGGK #case study #interactive #network
Association analysis-based transformations for protein interaction networks: a function prediction case study (GP, MS, RG, TG, VK), pp. 540–549.
MLDMMLDM-2007-AburtoW #hybrid
A Sequential Hybrid Forecasting System for Demand Prediction (LA, RW), pp. 518–532.
MLDMMLDM-2007-Morzy #mining
Mining Frequent Trajectories of Moving Objects for Location Prediction (MM), pp. 667–680.
RecSysRecSys-2007-ZhangP #algorithm #collaboration #recommendation #recursion
A recursive prediction algorithm for collaborative filtering recommender systems (JZ, PP), pp. 57–64.
SEKESEKE-2007-AmouiST #network #using
Temporal Software Change Prediction Using Neural Networks (MA, MS, LT), pp. 380–385.
SEKESEKE-2007-HewettKM #order
Predicting Order of Likelihood of Defective Software Modules (RH, PK, AvdM), pp. 93–98.
SEKESEKE-2007-MoserPS #agile #development #incremental #modelling #using
Incremental Effort Prediction Models in Agile Development using Radial Basis Functions (RM, WP, GS), pp. 519–522.
SIGIRSIGIR-2007-Diaz #performance #using
Performance prediction using spatial autocorrelation (FD), pp. 583–590.
SIGIRSIGIR-2007-HuffmanH #how #question
How well does result relevance predict session satisfaction? (SBH, MH), pp. 567–574.
SIGIRSIGIR-2007-LiuHAY #named #performance #sentiment #using
ARSA: a sentiment-aware model for predicting sales performance using blogs (YL, XH, AA, XY), pp. 607–614.
SIGIRSIGIR-2007-MaKL #collaboration #effectiveness
Effective missing data prediction for collaborative filtering (HM, IK, MRL), pp. 39–46.
SIGIRSIGIR-2007-MasegosaJJ #documentation
Effects of highly agreed documents in relevancy prediction (ARM, HJ, JMJ), pp. 883–884.
SIGIRSIGIR-2007-ZhouC #performance #query #web
Query performance prediction in web search environments (YZ, WBC), pp. 543–550.
ASEASE-2007-JiangS #control flow #debugging #statistics
Context-aware statistical debugging: from bug predictors to faulty control flow paths (LJ, ZS), pp. 184–193.
ASEASE-2007-Kagdi #fine-grained #mining #source code
Improving change prediction with fine-grained source code mining (HHK), pp. 559–562.
ESEC-FSEESEC-FSE-2007-JoshiSS #effectiveness #testing
Predictive testing: amplifying the effectiveness of software testing (PJ, KS, MS), pp. 561–564.
ICSEICSE-2007-JinTHL #evaluation #information management #legacy #performance
Performance Evaluation and Prediction for Legacy Information Systems (YJ, AT, JH, YL), pp. 540–549.
ICSEICSE-2007-KimZWZ #fault
Predicting Faults from Cached History (SK, TZ, EJWJ, AZ), pp. 489–498.
DACDAC-2007-LiuS #process #scalability #statistics
Confidence Scalable Post-Silicon Statistical Delay Prediction under Process Variations (QL, SSS), pp. 497–502.
DATEDATE-2007-HamersE
Resource prediction for media stream decoding (JH, LE), pp. 594–599.
DATEDATE-2007-NarayanasamyCC #fault
Transient fault prediction based on anomalies in processor events (SN, AKC, BC), pp. 1140–1145.
DATEDATE-2007-SahinH #algorithm #architecture #hardware #interactive #performance
Interactive presentation: An efficient hardware architecture for H.264 intra prediction algorithm (ES, IH), pp. 183–188.
DATEDATE-2007-Srivastava #interactive #scalability
Interactive presentation: Radix 4 SRT division with quotient prediction and operand scaling (NRS), pp. 195–200.
HPCAHPCA-2007-QuinonesPG #branch #execution
Improving Branch Prediction and Predicated Execution in Out-of-Order Processors (EQ, JMP, AG), pp. 75–84.
FASEFASE-2007-RatzingerPG #evolution #fault #named
EQ-Mine: Predicting Short-Term Defects for Software Evolution (JR, MP, HCG), pp. 12–26.
TACASTACAS-2007-KuglerPSH #framework #logic #modelling
“Don’t Care” Modeling: A Logical Framework for Developing Predictive System Models (HK, AP, MJS, EJAH), pp. 343–357.
CADECADE-2007-KoprowskiM #dependence #satisfiability #using
Predictive Labeling with Dependency Pairs Using SAT (AK, AM), pp. 410–425.
CBSECBSE-2006-Hamlet #assembly
Defining “Predictable Assembly” (DH), pp. 320–327.
QoSAQoSA-2006-DuzbayevP #behaviour #runtime
Runtime Prediction of Queued Behaviour (ND, IP), pp. 78–94.
VLDBVLDB-2006-ChenRST #analysis
Bellwether Analysis: Predicting Global Aggregates from Local Regions (BCC, RR, JWS, PT), pp. 655–666.
ITiCSEITiCSE-2006-Chamillard #education #performance #student #using
Using student performance predictions in a computer science curriculum (ATC), pp. 260–264.
ICSMEICSM-2006-TomaszewskiGL #fault
A Method for an Accurate Early Prediction of Faults in Modified Classes (PT, HG, LL), pp. 487–496.
MSRMSR-2006-AskariH #evaluation #modelling #scalability
Information theoretic evaluation of change prediction models for large-scale software (MA, RCH), pp. 126–132.
MSRMSR-2006-KnabPB #fault #source code
Predicting defect densities in source code files with decision tree learners (PK, MP, AB), pp. 119–125.
RTARTA-2006-HirokawaM
Predictive Labeling (NH, AM), pp. 313–327.
AIIDEAIIDE-2006-McQuigganLL #approach #induction #interactive
Predicting User Physiological Response for Interactive Environments: An Inductive Approach (SWM, SL, JCL), pp. 60–65.
CHICHI-2006-AvrahamiH #communication #latency #modelling
Responsiveness in instant messaging: predictive models supporting inter-personal communication (DA, SEH), pp. 731–740.
CHICHI-2006-EngLTCHV #adaptation #automation #behaviour #generative #performance
Generating automated predictions of behavior strategically adapted to specific performance objectives (KE, RLL, IT, AC, AH, AHV), pp. 621–630.
CHICHI-2006-IqbalB #cost analysis
Leveraging characteristics of task structure to predict the cost of interruption (STI, BPB), pp. 741–750.
CHICHI-2006-KuriharaGOI #multimodal #recognition #speech
Speech pen: predictive handwriting based on ambient multimodal recognition (KK, MG, JO, TI), pp. 851–860.
CSCWCSCW-2006-AvrahamiH #communication
Communication characteristics of instant messaging: effects and predictions of interpersonal relationships (DA, SEH), pp. 505–514.
ICEISICEIS-AIDSS-2006-ThuD #risk management #using
Predicting Cardiovascular Risks — Using POSSUM, PPOSSUM and Neural Net Techniques (TNTT, DND), pp. 230–234.
ICEISICEIS-ISAS-2006-SubramaniamKG #behaviour #evolution #process
Business Processes: Behavior Prediction and Capturing Reasons for Evolution (SS, VK, DG), pp. 3–10.
CIKMCIKM-2006-ZhouC #framework #novel #performance #query #ranking #robust
Ranking robustness: a novel framework to predict query performance (YZ, WBC), pp. 567–574.
ICMLICML-2006-BonillaWACTO
Predictive search distributions (EVB, CKIW, FVA, JC, JT, MFPO), pp. 121–128.
ICMLICML-2006-BowlingMJNW #learning #policy #using
Learning predictive state representations using non-blind policies (MHB, PM, MJ, JN, DFW), pp. 129–136.
ICMLICML-2006-DeCoste #collaboration #matrix #using
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations (DD), pp. 249–256.
ICMLICML-2006-EngelhardtJB #visual notation
A graphical model for predicting protein molecular function (BEE, MIJ, SEB), pp. 297–304.
ICMLICML-2006-RudaryS #modelling #probability
Predictive linear-Gaussian models of controlled stochastic dynamical systems (MRR, SPS), pp. 777–784.
ICMLICML-2006-SternHG #game studies #ranking
Bayesian pattern ranking for move prediction in the game of Go (DHS, RH, TG), pp. 873–880.
ICMLICML-2006-WingateS #kernel #linear #modelling #probability
Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems (DW, SPS), pp. 1017–1024.
ICMLICML-2006-WolfeS
Predictive state representations with options (BW, SPS), pp. 1025–1032.
ICMLICML-2006-XuWSS #learning
Discriminative unsupervised learning of structured predictors (LX, DFW, FS, DS), pp. 1057–1064.
ICPRICPR-v1-2006-ZhuWCW #classification #human-computer #interface
GMM-Based Classification Method for Continuous Prediction in Brain-Computer Interface (XZ, JW, YC, YW), pp. 1171–1174.
ICPRICPR-v2-2006-Ekbal #classification #using
Improvement of Prediction Accuracy Using Discretization and Voting Classifier (AE), pp. 695–698.
ICPRICPR-v2-2006-Huang #kernel
A New Kernel Based on Weighted Cross-Correlation Coefficient for SVMs and Its Application on Prediction of T-cell Epitopes (JH), pp. 691–694.
ICPRICPR-v2-2006-LiA #estimation
Texture-Constrained Shape Prediction for Mouth Contour Extraction and its State Estimation (ZL, HA), pp. 88–91.
ICPRICPR-v3-2006-JingS
Scanner Artifact Removal in Simultaneous EEG-fMRI for Epileptic Seizure Prediction (MJ, SS), pp. 722–725.
ICPRICPR-v3-2006-KierA #classification #multi
Predicting the benefit of sample size extension in multiclass k-NN classification (CK, TA), pp. 332–335.
ICPRICPR-v3-2006-MoriUKTHS #gesture #recognition
Early Recognition and Prediction of Gestures (AM, SU, RK, RiT, TH, HS), pp. 560–563.
ICPRICPR-v3-2006-NguyenSQN #fuzzy #using
Traffic Prediction Using Ying-Yang Fuzzy Cerebellar Model Articulation Controller (MNN, DS, CQ, GSN), pp. 258–261.
ICPRICPR-v3-2006-WangB06b #multimodal #performance
Performance Prediction for Multimodal Biometrics (RW, BB), pp. 586–589.
ICPRICPR-v4-2006-LienY #performance #using
A Fast Mode Decision Method for H.264/AVC Using the Spatial-Temporal Prediction Scheme (CCL, CPY), pp. 334–337.
ICPRICPR-v4-2006-LiuH #automation #segmentation #speech
A Bayesian Predictive Method for Automatic Speech Segmentation (ML, TSH), pp. 290–293.
ICPRICPR-v4-2006-WongYL #identification
Identifying Weather Systems from Numerical Weather Prediction Data (KYW, CLY, PWL), pp. 841–844.
KDDKDD-2006-CaruanaEMRSFHK #mining
Mining citizen science data to predict orevalence of wild bird species (RC, MFE, AM, MR, DS, DF, WMH, SK), pp. 909–915.
KDDKDD-2006-ZhangPD #categorisation #graph #linear #modelling
Linear prediction models with graph regularization for web-page categorization (TZ, AP, BD), pp. 821–826.
SEKESEKE-2006-HewettKSA #fault #testing
Software Defect Data and Predictability for Testing Schedules (RH, AK, CS, AAA), pp. 499–504.
SIGIRSIGIR-2006-AgichteinBDR #interactive #learning #modelling #web
Learning user interaction models for predicting web search result preferences (EA, EB, STD, RR), pp. 3–10.
SIGIRSIGIR-2006-JeonCLP #framework #quality
A framework to predict the quality of answers with non-textual features (JJ, WBC, JHL, SP), pp. 228–235.
SIGIRSIGIR-2006-MurrayLC #behaviour #modelling #query
Action modeling: language models that predict query behavior (GCM, JJL, AC), pp. 681–682.
SPLCSPL-BOOK-2006-Immonen #architecture #reliability
A Method for Predicting Reliability and Availability at the Architecture Level (AI), pp. 373–422.
SPLCSPLC-2006-GanesanMY #product line
Predicting Return-on-Investment for Product Line Generations (DG, DM, KY), pp. 13–22.
QAPLQAPL-2006-SingerB
Return Value Prediction meets Information Theory (JS, GB), pp. 137–151.
ASEASE-2006-HassanZ #certification #using
Using Decision Trees to Predict the Certification Result of a Build (AEH, KZ), pp. 189–198.
ICSEICSE-2006-LiHSR #case study #experience #fault
Experiences and results from initiating field defect prediction and product test prioritization efforts at ABB Inc (PLL, JDH, MS, BR), pp. 413–422.
ICSEICSE-2006-NagappanBZ #component #metric #mining
Mining metrics to predict component failures (NN, TB, AZ), pp. 452–461.
ICSEICSE-2006-TwalaCS
Ensemble of missing data techniques to improve software prediction accuracy (BT, MC, MJS), pp. 909–912.
SACSAC-2006-DerisB #embedded
Branchless cycle prediction for embedded processors (KJD, AB), pp. 928–932.
SACSAC-2006-DorneichNPT #database #embedded #modelling #parallel #relational
Embedded predictive modeling in a parallel relational database (AD, RN, EPDP, FT), pp. 569–574.
SACSAC-2006-TsengCL #e-commerce #mining #navigation #personalisation
Mining and prediction of temporal navigation patterns for personalized services in e-commerce (VST, JCC, KWL), pp. 867–871.
ASPLOSASPLOS-2006-IpekMCSS #architecture #design #modelling
Efficiently exploring architectural design spaces via predictive modeling (EI, SAM, RC, BRdS, MS), pp. 195–206.
ASPLOSASPLOS-2006-LeeB #architecture #modelling #performance
Accurate and efficient regression modeling for microarchitectural performance and power prediction (BCL, DMB), pp. 185–194.
ASPLOSASPLOS-2006-ReddyRP #comprehension #fault tolerance #thread
Understanding prediction-based partial redundant threading for low-overhead, high- coverage fault tolerance (VKR, ER, SP), pp. 83–94.
CASECASE-2006-HuangLYZ #fuzzy
Fuzzy Model Predictive Control for a Comfort Air-Conditioning System (YH, NL, YY, JZ), pp. 530–533.
DACDAC-2006-OgrasM
Prediction-based flow control for network-on-chip traffic (ÜYO, RM), pp. 839–844.
DATEDATE-2006-Schoeberl #java
A time predictable Java processor (MS), pp. 800–805.
HPCAHPCA-2006-RileyZ #probability
Probabilistic counter updates for predictor hysteresis and stratification (NR, CBZ), pp. 110–120.
HPCAHPCA-2006-SubramaniamL #dependence #memory management #scalability #scheduling
Store vectors for scalable memory dependence prediction and scheduling (SS, GHL), pp. 65–76.
HPDCHPDC-2006-CaiKS #data type #named #network #performance
IQ-Paths: Predictably High Performance Data Streams across Dynamic Network Overlays (ZC, VK, KS), pp. 18–29.
HPDCHPDC-2006-DobberMK #effectiveness #grid #scalability
Effective Prediction of Job Processing Times in a Large-Scale Grid Environment (MD, RDvdM, GK), pp. 359–360.
HPDCHPDC-2006-DuanPF #data mining #detection #fault #grid
Data Mining-based Fault Prediction and Detection on the Grid (RD, RP, TF), pp. 305–308.
HPDCHPDC-2006-RenLEB #fine-grained
Resource Availability Prediction in Fine-Grained Cycle Sharing Systems (XR, SL, RE, SB), pp. 93–104.
HPDCHPDC-2006-SandholmLOO #grid #performance #resource management #using
Market-Based Resource Allocation using Price Prediction in a High Performance Computing Grid for Scientific Applications (TS, KL, JAO, JO), pp. 132–143.
PDPPDP-2006-RibasG #branch #using
Evaluating Branch Prediction Using Two-Level Perceptron Table (LVMR, RAdLG), pp. 145–148.
PDPPDP-2006-SilvaMG #branch
Extending the PPM Branch Predictor (ZCdS, JAM, RALG), pp. 259–262.
PPoPPPPoPP-2006-BrevikNW #bound #parallel
Predicting bounds on queuing delay for batch-scheduled parallel machines (JB, DN, RW), pp. 110–118.
FASEFASE-2006-RobbyDK #design #flexibility #metric #using
Using Design Metrics for Predicting System Flexibility (R, SAD, VAK), pp. 184–198.
CBSECBSE-2005-LiuG #performance #protocol #using
Performance Prediction of J2EE Applications Using Messaging Protocols (YL, IG), pp. 1–16.
WICSAWICSA-2005-BhattacharyaP #architecture #component #specification
Predicting Architectural Styles from Component Specifications (SB, DEP), pp. 231–232.
WICSAWICSA-2005-TangJHN #architecture #design #impact analysis #network
Predicting Change Impact in Architecture Design with Bayesian Belief Networks (AT, YJ, JH, AEN), pp. 67–76.
JCDLJCDL-2005-HuangLC #approach #collaboration
Link prediction approach to collaborative filtering (ZH, XL, HC), pp. 141–142.
VLDBVLDB-2005-ChenCLR
Prediction Cubes (BCC, LC, YL, RR), pp. 982–993.
CSMRCSMR-2005-HostJ #performance
Performance Prediction Based on Knowledge of Prior Product Versions (MH, EJ), pp. 12–20.
ICSMEICSM-2005-HassanH #fault
The Top Ten List: Dynamic Fault Prediction (AEH, RCH), pp. 263–272.
ICSMEICSM-2005-HayesZ #analysis #evolution #maintenance #metric
Maintainability Prediction: A Regression Analysis of Measures of Evolving Systems (JHH, LZ), pp. 601–604.
MSRMSR-2005-AntoniolRV #linear #mining #repository
Linear predictive coding and cepstrum coefficients for mining time variant information from software repositories (GA, VFR, GV), pp. 61–65.
SFMSFM-2005-AcquavivaABBBL #formal method #impact analysis #power management
A Methodology Based on Formal Methods for Predicting the Impact of Dynamic Power Management (AA, AA, MB, AB, EB, EL), pp. 155–189.
CHICHI-2005-BlackmonKP #effectiveness #navigation #problem
Tool for accurately predicting website navigation problems, non-problems, problem severity, and effectiveness of repairs (MHB, MK, PGP), pp. 31–40.
CHICHI-2005-DabbishKFK #comprehension #email
Understanding email use: predicting action on a message (LAD, REK, SRF, SBK), pp. 691–700.
CHICHI-2005-KaurH #behaviour #comparison
A comparison of LSA, wordNet and PMI-IR for predicting user click behavior (IK, AJH), pp. 51–60.
ICEISICEIS-v2-2005-CuellarDJ #network #problem #programming
An Application of Non-Linear Programming to Train Recurrent Neural Networks in Time Series Prediction Problems (MPC, MD, MdCPJ), pp. 35–42.
ICEISICEIS-v4-2005-RusselB #data transfer #grid #performance
Predicting the Performance of Data Transfer in a Grid Environment (ABMR, SB), pp. 176–181.
CIKMCIKM-2005-AgichteinC
Predicting accuracy of extracting information from unstructured text collections (EA, SC), pp. 413–420.
ICMLICML-2005-BachJ #composition #kernel #rank
Predictive low-rank decomposition for kernel methods (FRB, MIJ), pp. 33–40.
ICMLICML-2005-CarneyCDL #network #probability #using
Predicting probability distributions for surf height using an ensemble of mixture density networks (MC, PC, JD, CL), pp. 113–120.
ICMLICML-2005-DaumeM #approximate #learning #optimisation #scalability
Learning as search optimization: approximate large margin methods for structured prediction (HDI, DM), pp. 169–176.
ICMLICML-2005-LeiteB #classification #performance
Predicting relative performance of classifiers from samples (RL, PB), pp. 497–503.
ICMLICML-2005-LiuXC #graph #using
Predicting protein folds with structural repeats using a chain graph model (YL, EPX, JGC), pp. 513–520.
ICMLICML-2005-Niculescu-MizilC #learning
Predicting good probabilities with supervised learning (ANM, RC), pp. 625–632.
ICMLICML-2005-RennieS #collaboration #matrix #performance
Fast maximum margin matrix factorization for collaborative prediction (JDMR, NS), pp. 713–719.
ICMLICML-2005-SnelsonG #approximate
Compact approximations to Bayesian predictive distributions (ES, ZG), pp. 840–847.
ICMLICML-2005-TaskarCKG #approach #learning #modelling #scalability
Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.
ICMLICML-2005-Wiewiora #learning
Learning predictive representations from a history (EW), pp. 964–971.
ICMLICML-2005-WolfeJS #learning
Learning predictive state representations in dynamical systems without reset (BW, MRJ, SPS), pp. 980–987.
KDDKDD-2005-Ghani #online
Price prediction and insurance for online auctions (RG), pp. 411–418.
KDDKDD-2005-GruhlGKNT #online #power of
The predictive power of online chatter (DG, RVG, RK, JN, AT), pp. 78–87.
KDDKDD-2005-RaskuttiH
Predicting the product purchase patterns of corporate customers (BR, AH), pp. 469–478.
KDDKDD-2005-SongLTS #behaviour #modelling
Modeling and predicting personal information dissemination behavior (XS, CYL, BLT, MTS), pp. 479–488.
KDDKDD-2005-YangL #learning
Learning to predict train wheel failures (CY, SL), pp. 516–525.
KDDKDD-2005-YangWZ #data type
Combining proactive and reactive predictions for data streams (YY, XW, XZ), pp. 710–715.
MLDMMLDM-2005-BunkeDIK #analysis #graph #learning
Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning (HB, PJD, CI, MK), pp. 366–375.
MLDMMLDM-2005-HalveyKS #clustering #internet #navigation #using
Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files (MH, MTK, BS), pp. 174–183.
MLDMMLDM-2005-KurganH #approach #feature model #sequence
Prediction of Secondary Protein Structure Content from Primary Sequence Alone — A Feature Selection Based Approach (LAK, LH), pp. 334–345.
SEKESEKE-2005-LoYT #algorithm #mining
Weighted Binary Sequential Mining Algorithm with Application to the Next-Day Appearance Prediction (SL, JY, FCT), pp. 783–782.
SIGIRSIGIR-2005-JensenBGFC #learning #query #visual notation #web
Predicting query difficulty on the web by learning visual clues (ECJ, SMB, DAG, OF, AC), pp. 615–616.
MODELSMoDELS-2005-RodriguesRU #development #modelling #reliability
Reliability Prediction in Model-Driven Development (GNR, DSR, SU), pp. 339–354.
MODELSMoDELS-2005-RodriguesRU #development #modelling #reliability
Reliability Prediction in Model-Driven Development (GNR, DSR, SU), pp. 339–354.
PLDIPLDI-2005-Jimenez #branch
Code placement for improving dynamic branch prediction accuracy (DAJ), pp. 107–116.
POPLPOPL-2005-StoyleHBSN
Mutatis mutandis: safe and predictable dynamic software updating (GS, MWH, GMB, PS, IN), pp. 183–194.
PPDPPPDP-2005-PaluDP #heuristic #optimisation #parallel
Heuristics, optimizations, and parallelism for protein structure prediction in CLP(FD) (ADP, AD, EP), pp. 230–241.
ICSEICSE-2005-MockusZL #quality
Predictors of customer perceived software quality (AM, PZ, PLL), pp. 225–233.
ICSEICSE-2005-NagappanB #fault #metric #using
Use of relative code churn measures to predict system defect density (NN, TB), pp. 284–292.
SACSAC-2005-AngiulliBP #detection
Detection and prediction of distance-based outliers (FA, SB, CP), pp. 537–542.
SACSAC-2005-BalsaraR #model checking #search-based #using
Prediction of inherited and genetic mutations using the software model checker SPIN (ZB, SR), pp. 208–209.
SACSAC-2005-ChenA #analysis #approach #comparative #sequence #using
A new approach for gene prediction using comparative sequence analysis (RC, HHA), pp. 177–184.
SACSAC-2005-DavidssonHS #sequence
Comparing approaches to predict transmembrane domains in protein sequences (PD, JH, KS), pp. 185–189.
SACSAC-2005-HsiehCLT #detection #named #sequence
EXONSCAN: EXON prediction with Signal detection and Coding region AligNment in homologous sequences (SJH, YSC, CYL, CYT), pp. 202–203.
SACSAC-2005-HuangSMZGGP #approach #clustering
A clustering-based approach for prediction of cardiac resynchronization therapy (HH, LS, FM, SZ, MG, LG, JDP), pp. 260–266.
SACSAC-2005-LiY #classification #recursion #using
Using recursive classification to discover predictive features (FL, YY), pp. 1054–1058.
CASECASE-2005-DulluriSR
Predicting price-tag for customized goods (SD, PS, NRSR), pp. 136–141.
CGOCGO-2005-StephensonA #classification #using
Predicting Unroll Factors Using Supervised Classification (MS, SPA), pp. 123–134.
DATEDATE-2005-BurguiereR #branch #modelling
A Contribution to Branch Prediction Modeling in WCET Analysi (CB, CR), pp. 612–617.
DATEDATE-2005-LeeCALK #hardware #transaction
A Prediction Packetizing Scheme for Reducing Channel Traffic in Transaction-Level Hardware/Software Co-Emulation (JGL, MKC, KYA, SHL, CMK), pp. 384–389.
DATEDATE-2005-StuijkBMG #data type #multi #scalability
Predictable Embedding of Large Data Structures in Multiprocessor Networks-on-Chip (SS, TB, BM, MG), pp. 254–255.
DATEDATE-2005-WehmeyerM #embedded #memory management
nfluence of Memory Hierarchies on Predictability for Time Constrained Embedded Software (LW, PM), pp. 600–605.
HPCAHPCA-2005-ChandraGKS #architecture #multi #thread
Predicting Inter-Thread Cache Contention on a Chip Multi-Processor Architecture (DC, FG, SK, YS), pp. 340–351.
HPCAHPCA-2005-LauSC #classification
Transition Phase Classification and Prediction (JL, SS, BC), pp. 278–289.
HPCAHPCA-2005-TuckT #parallel #thread
Multithreaded Value Prediction (NT, DMT), pp. 5–15.
PDPPDP-2005-FritzscheRLFG #performance #re-engineering
A Performance Prediction for Iterative Reconstruction Techniques on Tomography (PCF, AR, EL, JJF, IG), pp. 92–99.
FASEFASE-2005-RodriguesRU #component #concurrent #reliability #using
Using Scenarios to Predict the Reliability of Concurrent Component-Based Software Systems (GNR, DSR, SU), pp. 111–126.
CBSECBSE-2004-EskenaziFH #component #composition #performance
Performance Prediction for Component Compositions (EME, AVF, DKH), pp. 280–293.
CBSECBSE-2004-MuskensC #component #multi #runtime
Prediction of Run-Time Resource Consumption in Multi-task Component-Based Software Systems (JM, MRVC), pp. 162–177.
DocEngDocEng-2004-Dymetman #authoring #documentation #editing
Chart-parsing techniques and the prediction of valid editing moves in structured document authoring (MD), pp. 229–238.
SIGMODSIGMOD-2004-PatelCC #named #performance
STRIPES: An Efficient Index for Predicted Trajectories (JMP, YC, VPC), pp. 637–646.
SIGMODSIGMOD-2004-TaoFPL
Prediction and Indexing of Moving Objects with Unknown Motion Patterns (YT, CF, DP, BL), pp. 611–622.
ITiCSEITiCSE-WGR-2004-RountreeRRH
Interacting factors that predict success and failure in a CS1 course (NR, JR, AVR, RH), pp. 101–104.
ICSMEICSM-2004-HassanH #co-evolution
Predicting Change Propagation in Software Systems (AEH, RCH), pp. 284–293.
SCAMSCAM-2004-BruntinkD #metric #object-oriented #testing #using
Predicting Class Testability using Object-Oriented Metrics (MB, AvD), pp. 136–145.
ICALPICALP-2004-Lyngso #complexity #modelling #pseudo
Complexity of Pseudoknot Prediction in Simple Models (RBL), pp. 919–931.
SEFMSEFM-2004-RouffVHTR #behaviour #formal method
Properties of a Formal Method for Prediction of Emergent Behaviors in Swarm-Based Systems (CR, AV, MGH, WT, JLR), pp. 24–33.
CHICHI-2004-HouriziJ #design
Designing to support awareness: a predictive, composite model (RH, PJ), pp. 159–166.
CHICHI-2004-JohnPSK #modelling #performance
Predictive human performance modeling made easy (BEJ, KCP, DDS, KRK), pp. 455–462.
CHICHI-2004-VeraHML #approach #constraints #interactive
A constraint satisfaction approach to predicting skilled interactive cognition (AHV, AH, MM, RLL), pp. 121–128.
CSCWCSCW-2004-NagelHA #communication
Predictors of availability in home life context-mediated communication (KSN, JMH, GDA), pp. 497–506.
ICEISICEIS-v1-2004-LuciaPS #empirical #maintenance #modelling
Assessing Effort Prediction Models for Corrective Software Maintenance — An Empirical Study (ADL, EP, SS), pp. 383–390.
ICEISICEIS-v2-2004-CuellarFJN #adaptation #network
An Adaptable Time-Delay Neural Network to Predict the Spanish Economic Indebtedness (MPC, WF, MdCPJ, RPP, MAN), pp. 457–460.
ICEISICEIS-v2-2004-CuellarNJP #algorithm #case study #comparative #network
A Comparative Study of Evolutionary Algorithms for Training Elman Recurrent Neural Networks to Predict Autonomous Indebtedness (MPC, AN, MdCPJ, RPP), pp. 461–464.
ICEISICEIS-v2-2004-WuW #probability #using #web
Predicting Web Requests Efficiently Using a Probability Model (SW, WW), pp. 48–53.
ICEISICEIS-v3-2004-PanedaMGGN #analysis #monitoring #video
Analysis and Configuration Methodology for Video on Demand Services Based on Monitoring Information and Prediction (XGP, DM, RG, VGG, ÁN), pp. 289–294.
ICEISICEIS-v5-2004-GattiM #communication #composition
CABA2L A Bliss Predictive Composition Assistant for AAC Communication Software (NG, MM), pp. 89–96.
ICEISICEIS-v5-2004-Pahnila #information management #personalisation
Predicting the User Acceptance of Personalized Information Systems: Case Medical Portal (SP), pp. 195–202.
ECIRECIR-2004-HungWS #bottom-up #clustering #top-down
Predictive Top-Down Knowledge Improves Neural Exploratory Bottom-Up Clustering (CH, SW, PS), pp. 154–166.
ICMLICML-2004-ChuGW #visual notation
A graphical model for protein secondary structure prediction (WC, ZG, DLW).
ICMLICML-2004-JamesS #learning
Learning and discovery of predictive state representations in dynamical systems with reset (MRJ, SPS).
ICMLICML-2004-QiMPG #automation
Predictive automatic relevance determination by expectation propagation (Y(Q, TPM, RWP, ZG).
ICMLICML-2004-RosencrantzGT #learning
Learning low dimensional predictive representations (MR, GJG, ST).
ICMLICML-2004-Zhang #algorithm #linear #probability #problem #scalability #using
Solving large scale linear prediction problems using stochastic gradient descent algorithms (TZ0).
ICPRICPR-v2-2004-ColleP #process
Relaxation Labeling Processes for Protein Secondary Structure Prediction (GC, MP), pp. 355–358.
ICPRICPR-v2-2004-MiyamotoUHIO #comparison
Comparison of Microarray-Based Predictive Systems for Early Recurrence of Cancer (TM, SU, YH, NI, MO), pp. 347–350.
ICPRICPR-v2-2004-XuanDKHCW #feature model #multi #profiling #robust
Robust Feature Selection by Weighted Fisher Criterion for Multiclass Prediction in Gene Expression Profiling (JX, YD, JIK, EPH, RC, YJW), pp. 291–294.
ICPRICPR-v4-2004-DharaC #approach #estimation #hybrid #using #video
Video Motion Estimation Using Prediction Based Hybrid Approach (BCD, BC), pp. 737–740.
ICPRICPR-v4-2004-JunY
Prediction of Fingerprint Orientation (JL, WYY), pp. 436–439.
ICPRICPR-v4-2004-LaiY #algorithm #fault #network
Successive-Least-Squares Error Algorithm on Minimum Description Length Neural Networks for Time Series Prediction (YNL, SYY), pp. 609–612.
ICPRICPR-v4-2004-ReddyS #estimation
A New Predictive Full-Search Block Motion Estimation (VSKR, SS), pp. 721–724.
KDDKDD-2004-CumbyFGK
Predicting customer shopping lists from point-of-sale purchase data (CMC, AEF, RG, MK), pp. 402–409.
KDDKDD-2004-HorvathGW #graph #kernel #mining
Cyclic pattern kernels for predictive graph mining (TH, TG, SW), pp. 158–167.
KDDKDD-2004-NakataT #mining
Mining traffic data from probe-car system for travel time prediction (TN, JiT), pp. 817–822.
KDDKDD-2004-YanVS
Predicting prostate cancer recurrence via maximizing the concordance index (LY, DV, OS), pp. 479–485.
SEKESEKE-2004-CostagliolaFGTV #development #using #web
Using COSMIC-FFP for Predicting Web Application Development Effort (GC, FF, CG, GT, GV), pp. 439–444.
SEKESEKE-2004-Cruz-LemusGORP #diagrams #fuzzy #statechart #uml #using
Predicting UML Statechart Diagrams Understandability Using Fuzzy Logic-Based Techniques (JACL, MG, JAO, FPR, MP), pp. 238–245.
SEKESEKE-2004-RyanO #development #information management #performance
Team Tacit Knowledge as a Predictor of Performance in Software Development Teams (SR, RO), pp. 312–317.
SIGIRSIGIR-2004-Buckley #comparative #ranking #retrieval #topic
Topic prediction based on comparative retrieval rankings (CB), pp. 506–507.
SIGIRSIGIR-2004-DiazJ #precise #query #using
Using temporal profiles of queries for precision prediction (FD, RJ), pp. 18–24.
SIGIRSIGIR-2004-LiuCKG
Context sensitive vocabulary and its application in protein secondary structure prediction (YL, JGC, JKS, VG), pp. 538–539.
SIGIRSIGIR-2004-WhiteJ04a
An implicit system for predicting interests (RWW, JMJ), p. 595.
TOOLSTOOLS-USA-2003-Bouktif04 #approach #quality #search-based #set
Improving Rule Set Based Software Quality Prediction: A Genetic Algorithm-based Approach (SB), pp. 227–241.
SACSAC-2004-Calderon-BenavidesGAGD #algorithm #collaboration #comparison #multi
A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings (MLCB, CNGC, JdJPA, JCGD, JD), pp. 1033–1039.
SACSAC-2004-LeccaPLC #probability #π-calculus
Predicting cell adhesion probability via the biochemical stochastic π-calculus (PL, CP, CL, GC), pp. 211–212.
ASPLOSASPLOS-2004-ShenZD #locality
Locality phase prediction (XS, YZ, CD), pp. 165–176.
CGOCGO-2004-WuBQEF
The Accuracy of Initial Prediction in Two-Phase Dynamic Binary Translators (YW, MBJ, JQ, OE, JF), pp. 227–238.
DATEDATE-v2-2004-WangMR #automation #megamodelling
Automated, Accurate Macromodelling of Digital Aggressors for Power/Ground/Substrate Noise Prediction (ZW, RM, JSR), pp. 824–829.
HPCAHPCA-2004-ChenYFM #effectiveness
Accurate and Complexity-Effective Spatial Pattern Prediction (CFC, SHY, BF, AM), pp. 276–287.
HPCAHPCA-2004-EhrhartP #scheduling #using
Reducing the Scheduling Critical Cycle Using Wakeup Prediction (TEE, SJP), pp. 222–231.
HPCAHPCA-2004-GandhiAS #branch
Reducing Branch Misprediction Penalty via Selective Branch Recovery (AG, HA, STS), pp. 254–264.
HPCAHPCA-2004-WenWPK
Exploiting Prediction to Reduce Power on Buses (VW, MW, YP, JK), pp. 2–13.
HPDCHPDC-2004-QiaoSD #empirical #multi #network
An Empirical Study of the Multiscale Predictability of Network Traffic (YQ, JAS, PAD), pp. 66–76.
PDPPDP-2004-GuiradoRRL #performance #using
Performance Prediction Using an Application-Oriented Mapping Tool (FG, AR, CR, EL), pp. 184–191.
FASEFASE-2004-ChatleyEKMU #plugin
Predictable Dynamic Plugin Systems (RC, SE, JK, JM, SU), pp. 129–143.
TACASTACAS-2004-SenRA #analysis #online #parallel #performance #safety #source code #thread
Online Efficient Predictive Safety Analysis of Multithreaded Programs (KS, GR, GA), pp. 123–138.
ICLPICLP-2004-DefourJP #component #modelling
Applying CLP to Predict Extra-Functional Properties of Component-Based Models (OD, JMJ, NP), pp. 454–455.
VLDBVLDB-2003-PapadiasTS #query
The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries (YT, DP, JS), pp. 790–801.
ICSMEICSM-2003-QuahT #metric #network #object-oriented #quality #using
Application of Neural Networks for Software Quality Prediction Using Object-Oriented Metrics (TSQ, MMTT), p. 116–?.
WCREWCRE-2003-DagpinarJ #comparison #empirical #maintenance #metric #object-oriented
Predicting Maintainability with Object-Oriented Metrics — An Empirical Comparison (MD, JHJ), pp. 155–164.
ICALPICALP-2003-Condon #design #problem
Problems on RNA Secondary Structure Prediction and Design (AC), pp. 22–32.
CHICHI-2003-HudsonFAAFKLY
Predicting human interruptibility with sensors: a Wizard of Oz feasibility study (SEH, JF, CGA, DA, JF, SBK, JCL, JY), pp. 257–264.
CAiSECAiSE-2003-ZhangJY #assessment #product line #quality
Quality Prediction and Assessment for Product Lines (HZ, SJ, BY), pp. 681–695.
ICEISICEIS-v2-2003-ShihFL #online
Customer Defection Prediction in Online Bookstores (YYS, KF, DRL), pp. 352–358.
ICEISICEIS-v2-2003-VogiatzisFP #information management
The Protein Structure Prediction Module of the Prot-Grid Information System (DV, DF, GAP), pp. 372–378.
CIKMCIKM-2003-Liben-NowellK #network #problem #social
The link prediction problem for social networks (DLN, JMK), pp. 556–559.
ICMLICML-2003-McGovernJ #identification #learning #multi #relational #using
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning (AM, DJ), pp. 528–535.
ICMLICML-2003-SinghLJPS #learning
Learning Predictive State Representations (SPS, MLL, NKJ, DP, PS), pp. 712–719.
KDDKDD-2003-GunduzO #behaviour #modelling #representation #web
A Web page prediction model based on click-stream tree representation of user behavior (SG, MTÖ), pp. 535–540.
KDDKDD-2003-LawrenceHC #modelling
Passenger-based predictive modeling of airline no-show rates (RDL, SJH, JC), pp. 397–406.
KDDKDD-2003-SahooORGMMVS #clustering #scalability
Critical event prediction for proactive management in large-scale computer clusters (RKS, AJO, IR, MG, JEM, SM, RV, AS), pp. 426–435.
KDDKDD-2003-SheCWEGB
Frequent-subsequence-based prediction of outer membrane proteins (RS, FC, KW, ME, JLG, FSLB), pp. 436–445.
SIGIRSIGIR-2003-JonesF #query #word
Query word deletion prediction (RJ, DCF), pp. 435–436.
PLDIPLDI-2003-DingZ #analysis #distance #locality #reuse
Predicting whole-program locality through reuse distance analysis (CD, YZ), pp. 245–257.
PLDIPLDI-2003-ErtlG #branch #optimisation #virtual machine
Optimizing indirect branch prediction accuracy in virtual machine interpreters (MAE, DG), pp. 278–288.
POPLPOPL-2003-HofmannJ #first-order #functional #source code
Static prediction of heap space usage for first-order functional programs (MH, SJ), pp. 185–197.
ASEASE-2003-GuoCS #fault #network
Predicting Fault Prone Modules by the Dempster-Shafer Belief Networks (LG, BC, HS), pp. 249–252.
ESEC-FSEESEC-FSE-2003-McCamantE #component #problem
Predicting problems caused by component upgrades (SM, MDE), pp. 287–296.
ICSEICSE-2003-MockusWZ #comprehension
Understanding and Predicting Effort in Software Projects (AM, DMW, PZ), pp. 274–284.
SACSAC-2003-MinOF #performance
Performance Prediction of Wormhole Switching in Hypercubes with Bursty Traffic Pattern (GM, MOK, JDF), pp. 985–989.
SACSAC-2003-VulloF #approach #recursion
A Recursive Connectionist Approach for Predicting Disulfide Connectivity in Proteins (AV, PF), pp. 67–71.
DACDAC-2003-HuM #clustering
Wire length prediction based clustering and its application in placement (BH, MMS), pp. 800–805.
DACDAC-2003-JessKNOV #parametricity #statistics
Statistical timing for parametric yield prediction of digital integrated circuits (JAGJ, KK, SRN, RHJMO, CV), pp. 932–937.
DATEDATE-2003-RongP #capacity
An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries (PR, MP), pp. 11148–11149.
HPCAHPCA-2003-ChenDA #branch #data flow #dependence
Dynamic Data Dependence Tracking and its Application to Branch Prediction (LC, SD, DHA), pp. 65–76.
HPCAHPCA-2003-Jimenez #branch
Reconsidering Complex Branch Predictors (DAJ), pp. 43–52.
HPCAHPCA-2003-SimonCF #branch
Incorporating Predicate Information into Branch Predictors (BS, BC, JF), pp. 53–64.
LCTESLCTES-2003-ZhaoCS #embedded #impact analysis #optimisation
Predicting the impact of optimizations for embedded systems (MZ, BRC, MLS), pp. 1–11.
PDPPDP-2003-AlmeidaGMRT #algorithm #clustering #on the
On the Prediction of Master-Slave algorithms over Heterogeneous Clusters (FA, DG, LMM, CR, JT), p. 433–?.
ICLPICLP-2003-BackofenW #approach #constraints #modelling
A Constraint-Based Approach to Structure Prediction for Simplified Protein Models That Outperforms Other Existing Methods (RB, SW), pp. 49–71.
ICTSSTestCom-2003-Weyuker
Prediction = Power (EJW), pp. 1–9.
CBSECBSE-2002-ChenGLL #component #enterprise #off the shelf #performance
Performance Prediction of COTS Component-based Enterprise Applications (SC, IG, AL, YL), p. 7.
CBSECBSE-2002-CrnkovicSSW #assembly #research
Anatomy of a Research Project in Predictable Assembly (IC, HS, JS, KW), p. 1.
CBSECBSE-2002-LarssonWNC #architecture #embedded #product line #using
Using Prediction Enabled Technologies for Embedded Product Line Architectures (ML, AW, CN, IC), p. 12.
CBSECBSE-2002-MorenoHW #component #empirical #modelling #standard #statistics #towards
Statistical Models for Empirical Component Properties and Assembly-Level Property Predictions: Toward Standard Labeling (GM, SH, KW), p. 10.
CBSECBSE-2002-StaffordM #component #reliability
Issues in Predicting the Reliability of Composed Components (JAS, JDM), p. 4.
CBSECBSE-2002-VecellioTS #behaviour #component
Containers for Predictable Behavior of Component-based Software (GJV, WMT, RMS), p. 2.
CBSECBSE-2003-JongeMC #component #runtime
Scenario-Based Prediction of Run-time Resource Consumption in Component-Based Software Systems (MdJ, JM, MRVC), p. 4.
CBSECBSE-2003-VecellioT #framework #policy
Infrastructure Support for Predictable Policy Enforcement (GV, WT), p. 13.
CBSECBSE-2003-WuMW #component #performance
Component Based Performance Prediction (XW, DM, MW), p. 3.
HTHT-2002-Davison #html #web
Predicting web actions from HTML content (BDD0), pp. 159–168.
HTHT-2002-ZhuHH #markov #modelling #using #web
Using Markov models for web site link prediction (JZ, JH, JGH), pp. 169–170.
CSMRCSMR-2002-YuSM #case study #industrial #metric #object-oriented #using
Predicting Fault-Proneness using OO Metrics: An Industrial Case Study (PY, TS, HAM), pp. 99–107.
ICSMEICSM-2002-BouktifSK #approach #modelling #quality
Combining Software Quality Predictive Models: An Evolutionary Approach (SB, HAS, BK), pp. 385–392.
CIAACIAA-2002-Bruggemann-KleinW #context-free grammar #on the #parsing
On Predictive Parsing and Extended Context-Free Grammars (ABK, DW), pp. 239–247.
ICALPICALP-2002-Marathe #complexity #towards
Towards a Predictive Computational Complexity Theory (MVM), pp. 22–31.
IFLIFL-2002-HammondM #behaviour
Predictable Space Behaviour in FSM-Hume (KH, GM), pp. 1–16.
ICEISICEIS-2002-KieltykaK #energy #network
The Application of Artificial Neural Networks for Heat Energy Use Prediction (LK, RK), pp. 526–529.
ICEISICEIS-2002-KrishnaswamyLZ #data mining #distributed #mining #optimisation #runtime
Supporting the Optimisation of Distributed Data Mining by Predicting Application Run Times (SK, SWL, ABZ), pp. 374–381.
CIKMCIKM-2002-LouL #performance #web
Efficient prediction of web accesses on a proxy server (WL, HL), pp. 169–176.
ICMLICML-2002-GoebelRB #composition #performance
A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance (MG, PJR, MB), pp. 211–218.
ICMLICML-2002-WangW #modelling #probability
Modeling for Optimal Probability Prediction (YW, IHW), pp. 650–657.
ICPRICPR-v1-2002-WechslerDLC #bound #using
Motion Prediction Using VC-Generalization Bounds (HW, ZD, FL, VC), pp. 151–154.
ICPRICPR-v2-2002-LateckiW #automation #recognition
Automatic Recognition of Unpredictable Events in Videos (LJL, DdW), pp. 889–892.
ICPRICPR-v3-2002-GarciaFRF #image #performance
Performance of the Kullback-Leibler Information Gain for Predicting Image Fidelity (JAG, JFV, RRS, XRFV), pp. 843–848.
ICPRICPR-v3-2002-PeiC #adaptation #fault #novel
Novel Error Concealment Method with Adaptive Prediction to the Abrupt and Gradual Scene Changes (SCP, YZC), pp. 827–830.
ICPRICPR-v3-2002-XueG #classification #performance #word
Performance Prediction for Handwritten Word Recognizers and Its Application to Classifier Combination (HX, VG), pp. 241–244.
ICPRICPR-v4-2002-BrittoSBS #recognition #string
A String Length Predictor to Control the Level Building of HMMs for Handwritten Numeral Recognition (AdSBJ, RS, FB, CYS), pp. 31–34.
KDDKDD-2002-JoshiAK #question
Predicting rare classes: can boosting make any weak learner strong? (MVJ, RCA, VK), pp. 297–306.
KRKR-2002-Saint-CyrL #how
Belief Extrapolation (or how to Reason About Observations and Unpredicted Change) (FDdSC, JL), pp. 497–508.
SEKESEKE-2002-JorgensenM #development #how #question #why
Combination of software development effort prediction intervals: why, when and how? (MJ, KM), pp. 425–428.
SIGIRSIGIR-2002-ChenLS
Predicting category accesses for a user in a structured information space (MC, ASL, JPS), pp. 65–72.
SIGIRSIGIR-2002-Cronen-TownsendZC #performance #query
Predicting query performance (SCT, YZ, WBC), pp. 299–306.
PLDIPLDI-2002-BurtscherDH #classification
Static Load Classification for Improving the Value Predictability of Data-Cache Misses (MB, AD, MH), pp. 222–233.
PLDIPLDI-2002-HenzingerK #embedded #realtime
The Embedded Machine: Predictable, Portable Real-Time Code (TAH, CMK), pp. 315–326.
ASEASE-2002-AzarPBKS #adaptation #algorithm #modelling #quality #search-based
Combining and Adapting Software Quality Predictive Models by Genetic Algorithms (DA, DP, SB, BK, HAS), pp. 285–288.
ASEASE-2002-GrosserSV #reasoning #using
Predicting Software Stability Using Case-Based Reasoning (DG, HAS, PV), p. 295–?.
SACSAC-2002-DinakarpandianK #proximity
BlOMIND-protein property prediction by property proximity profiles (DD, VK), pp. 168–172.
ASPLOSASPLOS-2002-LiJSVR #comprehension #control flow #operating system
Understanding and improving operating system effects in control flow prediction (TL, LKJ, AS, NV, JR), pp. 68–80.
DACDAC-2002-Sheehan
Osculating Thevenin model for predicting delay and slew of capacitively characterized cells (BNS), pp. 866–869.
DATEDATE-2002-BontempiK #data analysis #performance
A Data Analysis Method for Software Performance Prediction (GB, WK), pp. 971–976.
HPCAHPCA-2002-KampeSD #analysis #branch #fourier #using
The FAB Predictor: Using Fourier Analysis to Predict the Outcome of Conditional Branches (MK, PS, MD), pp. 223–232.
HPCAHPCA-2002-ParikhSZBS #branch
Power Issues Related to Branch Prediction (DP, KS, YZ, MB, MRS), pp. 233–244.
HPDCHPDC-2002-TaylorWGS #kernel #parallel #performance #using
Using Kernel Couplings to Predict Parallel Application Performance (VET, XW, JG, RLS), pp. 125–134.
HPDCHPDC-2002-VazhkudaiS #data transfer #grid
Predicting Sporadic Grid Data Transfers (SV, JMS), p. 188–?.
PDPPDP-2002-JuhaszC #execution #parallel
Execution Time Prediction for Parallel Data Processing Tasks (SJ, HC), pp. 31–38.
CBSECBSE-2001-Lau #certification #component
Component Certification and System Prediction: Is There a Role for Formality (KKL), p. 16.
CBSECBSE-2001-Schmidt #assembly #automation #component #towards
Trusted Components: Towards Automated Assembly with Predictable Properties (HWS), p. 14.
HTHT-2001-MendesCM #development #hypermedia #towards
Towards the prediction of development effort for hypermedia applications (EM, SC, NM), pp. 249–258.
ICDARICDAR-2001-DehkordiSA
Prediction of Handwriting Legibility (MED, NS, TA), pp. 997–1001.
VLDBVLDB-2001-CasatiDGS #comprehension #exception #process #quality
Improving Business Process Quality through Exception Understanding, Prediction, and Prevention (DG, FC, UD, MCS), pp. 159–168.
CSMRCSMR-2001-Evanco #fault #modelling
Prediction Models for Software Fault Correction Effort (WME), pp. 114–120.
ICSMEICSM-2001-MohapatraM #case study #fault
Defect Prevention through Defect Prediction: A Case Study at Infosys (SM, BM), pp. 260–272.
ICSMEICSM-2001-PoloPR #case study #legacy #maintenance #metric #source code #using
Using Code Metrics to Predict Maintenance of Legacy Programs: A Case Study (MP, MP, FR), pp. 202–208.
CHICHI-2001-JamesR #mobile #performance
Text input for mobile devices: comparing model prediction to actual performance (CLJ, KMR), pp. 365–371.
CHICHI-2001-Salvucci #architecture #interface #using
Predicting the effects of in-car interfaces on driver bahavior using a cognitive architecture (DDS), pp. 120–127.
CAiSECAiSE-2001-GeneroOPR #information management #maintenance #metric #object-oriented #using
Using Metrics to Predict OO Information Systems Maintainability (MG, JAO, MP, FPR), pp. 388–401.
ICEISICEIS-v1-2001-Edrees
Expert Systems for Disordered Prediction (SAE), pp. 475–480.
CIKMCIKM-2001-BlokHCJBA #database #design #information retrieval #optimisation #query #trade-off
Predicting the Cost-Quality Trade-Off for Information Retrieval Queries: Facilitating Database Design and Query Optimization (HEB, DH, SC, FdJ, HMB, PMGA), pp. 207–214.
ICMLICML-2001-HamerlyE
Bayesian approaches to failure prediction for disk drives (GH, CE), pp. 202–209.
ICMLICML-2001-Hutter #bound #sequence
General Loss Bounds for Universal Sequence Prediction (MH), pp. 210–217.
ICMLICML-2001-LangfordSM #bound #classification
An Improved Predictive Accuracy Bound for Averaging Classifiers (JL, MWS, NM), pp. 290–297.
KDDKDD-2001-Agrarwal #modelling
Applications of generalized support vector machines to predictive modeling (NA), p. 6.
KDDKDD-2001-CadezSM #modelling #probability #profiling #transaction #visualisation
Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction (IVC, PS, HM), pp. 37–46.
KDDKDD-2001-RossetNEVI #evaluation #modelling
Evaluation of prediction models for marketing campaigns (SR, EN, UE, NV, YI), pp. 456–461.
KDDKDD-2001-YangZL #mining #modelling #web
Mining web logs for prediction models in WWW caching and prefetching (QY, HHZ, ITYL), pp. 473–478.
SEKESEKE-2001-GeneroOPR #diagrams #information management #maintenance
Knowledge Discovery For Predicting Entity Relationship Diagram Maintainability (MG, JAO, MP, FPR), pp. 203–211.
SIGIRSIGIR-2001-HoashiMIH #algorithm #collaboration #query
Query Expansion Based on Predictive Algorithms for Collaborative Filtering (KH, KM, NI, KH), pp. 414–415.
SACSAC-2001-DeermanLP #algorithm #problem #search-based
Linkage-learning genetic algorithm application to the protein structure prediction problem (KRD, GBL, RP), pp. 333–339.
HPCAHPCA-2001-GoemanVB #difference #performance
Differential FCM: Increasing Value Prediction Accuracy by Improving Table Usage Efficiency (BG, HV, KDB), pp. 207–216.
HPCAHPCA-2001-JimenezL #branch
Dynamic Branch Prediction with Perceptrons (DAJ, CL), pp. 197–206.
HPCAHPCA-2001-TuneLTC
Dynamic Prediction of Critical Path Instructions (ET, DL, DMT, BC), pp. 185–195.
HPDCHPDC-2001-Dinda #online
Online Prediction of the Running Time of Tasks (PAD), pp. 383–382.
PDPPDP-2001-FolinoS #source code
Predictability of Cellular Programs Implemented with CAMELot (GF, GS), pp. 468–474.
PDPPDP-2001-GoncalvesPPSNS #architecture #branch #performance #smt
Evaluating the Effects of Branch Prediction Accuracy on the Performance of SMT Architectures (RG, MLP, GDP, TGSdS, POAN, RS), pp. 355–362.
PDPPDP-2001-GonzalezLRRSPP #source code
Predicting the Time of Oblivious Programs (JAG, CL, JLR, CR, FdS, FP, MP), pp. 363–368.
PDPPDP-2001-SchulzHT #communication #performance
Prediction of Communication Performance for Wide Area Computing Systems (JS, CH, DT), pp. 480–486.
ICSTSAT-2001-NuallainRB #behaviour #satisfiability
Ensemble-based prediction of SAT search behaviour (BÓN, MdR, JvB), pp. 278–289.
HTHT-2000-MendesH #development #towards #web
Towards the prediction of development effort for web applications (EM, WH), pp. 242–243.
CSMRCSMR-2000-JorgensenSK #maintenance
The Prediction Ability of Experienced Software Maintainers (MJ, DIKS, GK), pp. 93–100.
ICSMEICSM-2000-RamilL #case study #evolution #metric
Metrics of Software Evolution as Effort Predictors — A Case Study (JFR, MML), pp. 163–172.
CHICHI-2000-ChiPP #usability #web
The scent of a site: a system for analyzing and predicting information scent, usage, and usability of a Web site (EHhC, PP, JEP), pp. 161–168.
CHICHI-2000-SilfverbergMK #mobile
Predicting text entry speed on mobile phones (MS, ISM, PK), pp. 9–16.
CHICHI-2000-WatsonFM #quality #using
Using naming time to evaluate quality predictors for model simplification (BW, AF, AM), pp. 113–120.
ICMLICML-2000-Scheffer #performance
Predicting the Generalization Performance of Cross Validatory Model Selection Criteria (TS), pp. 831–838.
ICMLICML-2000-SingerV #learning #modelling #performance
Learning to Predict Performance from Formula Modeling and Training Data (BS, MMV), pp. 887–894.
ICPRICPR-v1-2000-Fernandez-VidalRMC #image #visual notation
Image Representational Model for Predicting Visual Distinctness of Objects (XRFV, RRS, JMB, JC), pp. 1689–1694.
ICPRICPR-v2-2000-Caelli #feature model #image #learning #modelling #performance
Learning Image Feature Extraction: Modeling, Tracking and Predicting Human Performance (TC), pp. 2215–2218.
ICPRICPR-v2-2000-PolickerG #algorithm #clustering #fuzzy
A New Algorithm for Time Series Prediction by Temporal Fuzzy Clustering (SP, ABG), pp. 2728–2731.
ICPRICPR-v4-2000-BarretoBA #visual notation
Model Predictive Control to Improve Visual Control of Motion: Applications in Active Tracking of Moving Targets (JPB, JB, HA), pp. 4732–4735.
KDDKDD-2000-GerstenWA #case study #experience #modelling #roadmap #tool support
Predictive modeling in automotive direct marketing: tools, experiences and open issues (WG, RW, DA), pp. 398–406.
KDDKDD-2000-KingKCD #data mining #functional #mining #sequence #using
Genome scale prediction of protein functional class from sequence using data mining (RDK, AK, AC, LD), pp. 384–389.
KDDKDD-2000-RaghavanBS #detection #process #using
Defection detection: using activity profiles to predict ISP customer vulnerability (NR, RMB, MS), pp. 506–515.
SIGIRSIGIR-2000-AmentoTH #documentation #quality #web
Does “authority” mean quality? predicting expert quality ratings of Web documents (BA, LGT, WCH), pp. 296–303.
SIGIRSIGIR-2000-PragerBCR
Question-answering by predictive annotation (JMP, EWB, AC, DRR), pp. 184–191.
AdaEuropeAdaEurope-2000-BarrazaPCC #development
An Application of the Chains-of-Rare-Events Model to Software Development Failure Prediction (NRB, JDP, BCF, FC), pp. 185–195.
ICSEICSE-2000-MiliCGZ00a #automation #reuse
Tracking, predicting and assessing software reuse costs: an automated tool (AM, SFC, RG, LZ), p. 785.
SACSAC-2000-GoodwinM #data mining #mining
Data Mining for Preterm Birth Prediction (LKG, SM), pp. 46–51.
SACSAC-2000-HaffnerREM #documentation #modelling
Modeling of Time and Document Aging for Request Prediction — One Step Further (EGH, UR, TE, CM), pp. 984–990.
ASPLOSASPLOS-2000-DuesterwaldB #less is more #profiling
Software Profiling for Hot Path Prediction: Less is More (ED, VB), pp. 202–211.
DACDAC-2000-GhazalNR #performance
Predicting performance potential of modern DSPs (NG, ARN, JMR), pp. 332–335.
DACDAC-2000-Sheehan
Predicting coupled noise in RC circuits by matching 1, 2, and 3 moments (BNS), pp. 532–535.
DACDAC-2000-VelevB #branch #exception #functional #multi #verification
Formal verification of superscale microprocessors with multicycle functional units, exception, and branch prediction (MNV, REB), pp. 112–117.
DATEDATE-2000-Sheehan
Predicting Coupled Noise in RC Circuits (BNS), pp. 517–521.
HPCAHPCA-2000-KaxirasY #communication #multi
Coherence Communication Prediction in Shared-Memory Multiprocessors (SK, CY), pp. 156–167.
HPCAHPCA-2000-LeeWY
Decoupled Value Prediction on Trace Processors (SJL, YW, PCY), pp. 231–240.
HPCAHPCA-2000-PatilE #alias #branch
Combining Static and Dynamic Branch Prediction to Reduce Destructive Aliasing (HP, JSE), pp. 251–262.
HPDCHPDC-2000-ShenC #architecture #distributed #multi #performance
A Distributed Multi-Storage Resource Architecture and I/O Performance Prediction for Scientific Computing (XS, ANC), pp. 21–30.
HPDCHPDC-2000-WangOCF #using
Using Idle Workstations to Implement Predictive Prefetching (JYQW, JSO, YC, MJF), pp. 87–94.
PDPPDP-2000-ZavanellaM #source code #using
Predictability of bulk synchronous programs using MPI (AZ, AM), pp. 118–123.
TPDLECDL-1999-DushayFL #distributed #library #performance
Predicting Indexer Performance in a Distributed Digital Library (ND, JCF, CL), pp. 142–166.
CSMRCSMR-1999-BengtssonB #architecture #maintenance
Architecture Level Prediction of Software Maintenance (PB, JB), pp. 139–147.
ICSMEICSM-1999-KhoshgoftaarAYJH #experience #fault #legacy #metric
Experience Paper: Preparing Measurements of Legacy Software for Predicting Operational Faults (TMK, EBA, XY, WDJ, JPH), p. 359–?.
CHICHI-1999-ToyodaS #editing #interface #multi #navigation #network #perspective
Hyper Mochi Sheet: A Predictive Focusing Interface for Navigating and Editing Nested Networks Through a Multi-Focus Distortion-Oriented View (MT, ES), pp. 504–511.
HCIHCI-CCAD-1999-Jacko #performance
The importance of clinical diagnoses in the prediction of performance on computer-based tasks for low vision users (JAJ), pp. 787–791.
HCIHCI-EI-1999-KolasinskiG #modelling
An Investigation into the Predictive Modeling of VE Sickness (EMK, RDG), pp. 147–151.
ICEISICEIS-1999-Habrant #database #learning #network #search-based
Structure Learning of Bayesian Networks from Databases by Genetic Algorithms-Application to Time Series Prediction in Finance (JH), pp. 225–231.
ICEISICEIS-1999-Lu #database #modelling #parallel #performance #process
Modelling Background Processes in Parallel Database Systems for Performance Prediction (KJL), pp. 101–108.
ICMLICML-1999-BontempiBB #learning
Local Learning for Iterated Time-Series Prediction (GB, MB, HB), pp. 32–38.
KDDKDD-1999-MannilaPS #using
Prediction with Local Patterns using Cross-Entropy (HM, DP, PS), pp. 357–361.
MLDMMLDM-1999-HongW #data mining #mining
Advanced in Predictive Data Mining Methods (SJH, SMW), pp. 13–20.
AdaSIGAda-1999-Boehm #future of #re-engineering
Predicting the future of computer systems and software engineering (BWB), p. 227.
ICSEICSE-1999-BenlarbiM #metric #morphism #polymorphism #risk management
Polymorphism Measures for Early Risk Prediction (SB, WLM), pp. 334–344.
ICSEICSE-1999-ConcepcionLS #concurrent #development #monitoring #multi #re-engineering #recursion #thread
The RMT (Recursive Multi-Threaded) Tool: A Computer Aided Software Engineering Tool for Monitoring and Predicting Software Development Progress (AIC, SL, SJS), pp. 660–663.
DACDAC-1999-PomerleauFB
Improved Selay Prediction for On-Chip Buses (RGP, PDF, GLB), pp. 497–501.
HPCAHPCA-1999-KaxirasG #performance #using
Improving CC-NUMA Performance Using Instruction-Based Prediction (SK, JRG), pp. 161–170.
HPCAHPCA-1999-NakraGS
Global Context-Based Value Prediction (TN, RG, MLS), pp. 4–12.
HPDCHPDC-1999-DindaO #evaluation #linear #modelling
An Evaluation of Linear Models for Host Load Prediction (PAD, DRO), pp. 87–96.
HPDCHPDC-1999-KapadiaFB #grid #modelling
Predictive Application-Performance Modeling in a Computational Grid Environment (NHK, JABF, CEB), pp. 47–54.
HPDCHPDC-1999-WolskiSH #cpu #grid
Predicting the CPU Availability of Time-shared Unix Systems on the Computational Grid (RW, NTS, JH), pp. 105–112.
LCTESLCTES-1999-SchneiderF #abstract interpretation #behaviour #pipes and filters
Pipeline Behavior Prediction for Superscalar Processors by Abstract Interpretation (JS, CF), pp. 35–44.
PPoPPPPoPP-1999-BagrodiaDDP #parallel #performance #scalability #simulation #using
Performance Prediction of Large Parallel Applications using Parallel Simulations (RB, ED, SD, TP), pp. 151–162.
PPoPPPPoPP-1999-Sundaram-StukelV #analysis #using
Predictive Analysis of a Wavefront Application using LogGP (DSS, MKV), pp. 141–150.
CIAAWIA-1998-MaurelPR #automaton
The Syntactic Prediction with Token Automata: Application to HandiAS System (DM, BLP, OR), pp. 100–109.
CSCWCSCW-1998-SarwarKBHMR #collaboration #quality #research #using
Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System (BMS, JAK, AB, JLH, BNM, JR), pp. 345–354.
ICMLICML-1998-NakamuraA #algorithm #collaboration #using
Collaborative Filtering Using Weighted Majority Prediction Algorithms (AN, NA), pp. 395–403.
ICMLICML-1998-Street #network
A Neural Network Model for Prognostic Prediction (WNS), pp. 540–546.
KDDKDD-1998-HandleyLR #learning
Learning to Predict the Duration of an Automobile Trip (SH, PL, FAR), pp. 219–223.
KDDKDD-1998-MegiddoS
Discovering Predictive Association Rules (NM, RS), pp. 274–278.
KDDKDD-1998-WeissH #learning #sequence
Learning to Predict Rare Events in Event Sequences (GMW, HH), pp. 359–363.
KDDKDD-1998-WuthrichPLCZL
Daily Prediction of Major Stock Indices from Textual WWW Data (BW, DP, SL, VC, JZ, WL), pp. 364–368.
KRKR-1998-WhiteBH #modelling
Building Models of Prediction Theories (GW, JB, WH), pp. 557–569.
SIGIRSIGIR-1998-McNabWWG #query
Predicting Query Times (RJM, YW, IHW, CG), pp. 355–356.
SIGIRSIGIR-1998-VogtC #information retrieval #performance
Predicting the Performance of Linearly Combined IR Systems (CCV, GWC), pp. 190–196.
ICSEICSE-1998-BinkleyS #dependence #maintenance #metric #runtime #validation
Validation of the Coupling Dependency Metric as a Predictor of Run-Time Failures and Maintenance Measures (ABB, SRS), pp. 452–455.
ASPLOSASPLOS-1998-StarkEP #branch
Variable Length Path Branch Prediction (JS, ME, YNP), pp. 170–179.
DATEDATE-1998-NicolaidisD #design #multi
Design of Fault-Secure Parity-Prediction Booth Multipliers (MN, RdOD), pp. 7–14.
DATEDATE-1998-PrietoRGPHR #approach #design #fault #layout #testing
An Approach to Realistic Fault Prediction and Layout Design for Testability in Analog Circuits (JAP, AR, IAG, EJP, JLH, AMDR), pp. 905–909.
HPCAHPCA-1998-VengroffG #branch #estimation #performance #re-engineering
Partial Sampling with Reverse State Reconstruction: A New Technique for Branch Predictor Performance Estimation (DEV, GRG), pp. 342–351.
HPDCHPDC-1998-HollingsworthK #adaptation
Prediction and Adaptation in Active Harmony (JKH, PJK), pp. 180–188.
LCTESLCTES-1998-FerdinandW #behaviour #on the #realtime
On Predicting Data Cache Behavior for Real-Time Systems (CF, RW), pp. 16–30.
PDPPDP-1998-RodriguezAM #algorithm #network #parallel #performance #using
Prediction of parallel algorithms performance on bus-based networks using PVM (RJR, CA, DM), pp. 57–63.
VLDBVLDB-1997-KraissW #documentation #migration #scalability
Vertical Data Migration in Large Near-Line Document Archives Based on Markov-Chain Predictions (AK, GW), pp. 246–255.
ICSMEICSM-1997-NiessinkV #maintenance
Predicting Maintenance Effort with Function Points (FN, HvV), pp. 32–39.
HCIHCI-CC-1997-MatiasS #modelling
Predictive Models of Carpal Tunnel Syndrome Among Office Personnel (ACM, GS), pp. 529–532.
HCIHCI-CC-1997-Venda #analysis #communication #performance
Ergodynamics in Analysis and Prediction of Communication Efficiency (VFV), pp. 833–836.
HCIHCI-SEC-1997-LaugheryP #case study #performance
Predicting Human Performance in Complex Systems-A Method and Case Study (KRLJ, BMP), pp. 75–78.
HCIHCI-SEC-1997-So #image #industrial #overview
Lag Compensation by Image Deflection and Prediction: A Review on the Potential Benefits to Virtual Training Applications for Manufacturing Industry (RHYS), pp. 997–1000.
ICMLICML-1997-CardieN #using
Improving Minority Class Prediction Using Case-Specific Feature Weights (CC, NN), pp. 57–65.
ICMLICML-1997-CohenD #case study #comparative #fault #induction #logic programming
A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction (WWC, PTD), pp. 66–74.
ICMLICML-1997-MooreSD #performance #polynomial
Efficient Locally Weighted Polynomial Regression Predictions (AWM, JGS, KD), pp. 236–244.
ICMLICML-1997-SakrLCHG #data access #learning #memory management #modelling #multi
Predicting Multiprocessor Memory Access Patterns with Learning Models (MFS, SPL, DMC, BGH, CLG), pp. 305–312.
ESECESEC-FSE-1997-Ebert #case study #development #experience
Experiences with Criticality Predictions in Software Development (CE), pp. 278–293.
ICSEICSE-1997-PighinZ #analysis #metric #statistics
A Predictive Metric Based on Discriminant Statistical Analysis (MP, RZ), pp. 262–270.
SACSAC-1997-KaiserLMGP #algorithm #hybrid #search-based
Polypeptide structure prediction: real-value versus binary hybrid genetic algorithms (CEK, GBL, LDM, GHGJ, RP), pp. 279–286.
SACSAC-1997-SongL #performance #scheduling
Performance prediction based loop scheduling for heterogeneous computing environment (YS, WML), pp. 413–421.
DACDAC-1997-SambandamH #architecture #behaviour #design #embedded #realtime
Predicting Timing Behavior in Architectural Design Exploration of Real-Time Embedded Systems (RSS, XH), pp. 157–160.
HPCAHPCA-1997-AugustCGH #architecture #branch
Architectural Support for Compiler-Synthesized Dynamic Branch Prediction Strategies: Rationale and Initial Results (DIA, DAC, JCG, WmWH), pp. 84–93.
HPCAHPCA-1997-WallaceB #branch #multi
Multiple Branch and Block Prediction (SW, NB), pp. 94–103.
HPDCHPDC-1997-FigueiraB
Predicting Slowdown for Networked Workstations (SMF, FB), pp. 92–101.
SOSPSOSP-1997-JonesRR #constraints #cpu #independence #performance #process #scheduling
CPU Reservations and Time Constraints: Efficient, Predictable Scheduling of Independent Activities (MBJ, DR, MCR), pp. 198–211.
STOCSTOC-1997-FreundSSW #using
Using and Combining Predictors That Specialize (YF, RES, YS, MKW), pp. 334–343.
PODSPODS-1996-TheodoridisS #performance
A Model for the Prediction of R-tree Performance (YT, TKS), pp. 161–171.
SIGMODSIGMOD-1996-LeflerSW #database #named #performance #simulation
DBSim: A Simulation Tool for Predicting Database Performance (ML, MS, CW), p. 548.
KDDAKDDM-1996-ApteH
Predicting Equity Returns from Securities Data (CA, SJH), pp. 541–560.
ICMLICML-1996-Ting
The Characterisation of Predictive Accuracy and Decision Combination (KMT), pp. 498–506.
ICPRICPR-1996-Garcia-SalicettiDGW #adaptation #online #recognition #word
Adaptive discrimination in an HMM-based neural predictive system for on-line word recognition (SGS, BD, PG, ZW), pp. 515–519.
ICPRICPR-1996-RebuffelS #estimation #framework
Estimation of depth-from-motion combining iterative prediction scheme and regularization framework (VR, JLS), pp. 466–470.
ICPRICPR-1996-StevensB #3d #multi #recognition
Interleaving 3D model feature prediction and matching to support multi-sensor object recognition (MRS, JRB), pp. 607–611.
KDDKDD-1996-KontkanenMT #data mining #finite #mining
Predictive Data Mining with Finite Mixtures (PK, PM, HT), pp. 176–182.
KDDKDD-1996-Lange #approach #empirical #recursion #using
An Empirical Test of the Weighted Effect Approach to Generalized Prediction Using Recursive Neural Nets (RL), pp. 183–188.
KDDKDD-1996-MasandP #comparison #modelling
A Comparison of Approaches for Maximizing Business Payoff of Prediction Models (BMM, GPS), pp. 195–201.
SASSAS-1996-AltFMW #abstract interpretation #behaviour
Cache Behavior Prediction by Abstract Interpretation (MA, CF, FM, RW), pp. 52–66.
FSEFSE-1996-RosenblumW #effectiveness #testing
Predicting the Cost-Effectiveness of Regression Testing Strategies (DSR, EJW), pp. 118–126.
ICSEICSE-1996-BasiliBCKMV #comprehension #maintenance #process
Understanding and Predicting the Process of Software Maintenance Release (VRB, LCB, SEC, YMK, WLM, JDV), pp. 464–474.
SACSAC-1996-BaishE #modelling #quality
Intelligent prediction techniques for software quality models (EB, CE), pp. 565–569.
SACSAC-1996-Cripps #performance #using
Using artificial neural nets to predict academic performance (AC), pp. 33–37.
ASPLOSASPLOS-1996-ChenCM #analysis #branch
Analysis of Branch Prediction Via Data Compression (ICKC, JTC, TNM), pp. 128–137.
ASPLOSASPLOS-1996-LipastiWS #locality
Value Locality and Load Value Prediction (MHL, CBW, JPS), pp. 138–147.
ASPLOSASPLOS-1996-SeznecJSM #branch #multi
Multiple-Block Ahead Branch Predictors (AS, SJ, PS, PM), pp. 116–127.
HPCAHPCA-1996-CalderGE
Predictive Sequential Associative Cache (BC, DG, JSE), pp. 244–253.
ISSTAISSTA-1996-Hamlet #dependence #testing
Predicting Dependability by Testing (RGH), pp. 84–91.
ICDARICDAR-v1-1995-BlandoKN #image #using
Prediction of OCR accuracy using simple image features (LRB, JK, TAN), pp. 319–322.
ICDARICDAR-v1-1995-Garcia-SalicettiDGMF #markov #online #recognition
A hidden Markov model extension of a neural predictive system for on-line character recognition (SGS, BD, PG, AM, DF), pp. 50–53.
CHICHI-1995-KierasWM #architecture #modelling #using
Predictive Engineering Models Using the EPIC Architecture for a High-Performance Task (DEK, SDW, DEM), pp. 11–18.
ICMLICML-1995-StreetMW #approach #induction #learning
An Inductive Learning Approach to Prognostic Prediction (WNS, OLM, WHW), pp. 522–530.
KDDKDD-1995-CortesDHV #capacity #complexity
Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates (CC, HD, DH, VV), pp. 51–56.
KDDKDD-1995-Glymour #modelling
Available Technology for Discovering Causal Models, Building Bayes Nets, and Selecting Predictors: The TETRAD II Program (CG), pp. 130–135.
OOPSLAOOPSLA-1995-GroveDGC
Profile-Guided Receiver Class Prediction (DG, JD, CG, CC), pp. 108–123.
PLDIPLDI-1995-CalderGLMMZ #branch
Corpus-Based Static Branch Prediction (BC, DG, DCL, JHM, MM, BGZ), pp. 79–92.
PLDIPLDI-1995-Patterson #branch
Accurate Static Branch Prediction by Value Range Propagation (JRCP), pp. 67–78.
ESECESEC-1995-Chamillard #analysis #case study #metric #performance #reachability
An Exploratory Study of Program Metrics as Predictors of Reachability Analysis Performance (ATC), pp. 343–361.
LCTESLCT-RTS-1995-HuangL #concurrent #execution #worst-case
Predicting the Worst-Case Execution Time of the Concurrent Execution of Instructions and Cycle-Stealing DMA I/O Operations (TYH, JWSL), pp. 1–6.
STOCSTOC-1995-KivinenW #linear
Additive versus exponentiated gradient updates for linear prediction (JK, MKW), pp. 209–218.
SIGMODSIGMOD-1994-DewanSHH #distributed #parallel #query
Predictive Dynamic Load Balancing of Parallel and Distributed Rule and Query Processing (HMD, SJS, MAH, JJH), pp. 277–288.
ICSMEICSM-1994-KhoshgoftaarS #maintenance
Improving Code Churn Predictions During the System Test and Maintenance Phases (TMK, RMS), pp. 58–67.
CHICHI-1994-MasuiN94b #editing #performance
Repeat and predict — two keys to efficient text editing (TM, KN), pp. 118–123.
ICMLICML-1994-AbeM #probability
A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars (NA, HM), pp. 3–11.
KDDKDD-1994-ApteH #generative
Predicting Equity Returns from Securities Data with Minimal Rule Generation (CA, SJH), pp. 407–418.
PLDIPLDI-1994-Krall #branch #replication
Improving Semi-static Branch Prediction by Code Replication (AK), pp. 97–106.
PLDIPLDI-1994-Wang #performance #precise
Precise Compile-Time Performance Prediction for Superscalar-Based Computers (KYW), pp. 73–84.
ASPLOSASPLOS-1994-YoungS #branch #correlation #using
Improving the Accuracy of Static Branch Prediction Using Branch Correlation (CY, MDS), pp. 232–241.
DATEEDAC-1994-BrashearMOPM #analysis #performance #statistics #using
Predicting Circuit Performance Using Circuit-level Statistical Timing Analysis (RBB, NM, CO, LTP, MRM), pp. 332–337.
HPDCHPDC-1994-SrbljicVB #consistency #distributed #memory management #performance
Performance Prediction for Different Consistency Schemes in Distributed Shared Memory Systems (SS, ZGV, LB), pp. 295–302.
VLDBVLDB-1993-Selinger #challenge #database
Predictions and Challenges for Database Systems in the Year 2000 (PGS), pp. 667–675.
ICSMECSM-1993-KhoshgoftaarML #case study #comparative #maintenance #modelling #testing
A Comparative Study of Predictive Models for Program Changes During System Testing and Maintenance (TMK, JCM, DLL), pp. 72–79.
HCIHCI-ACS-1993-YufikSV #evaluation #performance
Quantitative Evaluation and Performance Prediction (YMY, TBS, VFV), pp. 642–647.
CIKMCIKM-1993-LiZ #estimation
An Information Model for Use in Software Management Estimation and Prediction (NRL, MVZ), pp. 481–489.
ICMLICML-1993-Schwartz93a #scheduling
ATM SCheduling with Queuing Dely Predictions (DBS), pp. 306–313.
SEKESEKE-1993-Porter #classification #component
Developing and Analyzing Classification Rules for Predicting Faulty Software Components (AAP), pp. 453–461.
PLDIPLDI-1993-BallL #branch #for free
Branch Prediction For Free (TB, JRL), pp. 300–313.
PLDIPLDI-1993-BarrettZ #memory management #performance #using
Using Lifetime Predictors to Improve Memory Allocation Performance (DAB, BGZ), pp. 187–196.
SACSAC-1993-HashemiCST #monte carlo #network #paradigm
Prediction Capability of Neural Networks Trained by Monte-Carlo Paradigm (RRH, AHC, NLS, JRT), pp. 9–13.
SACSAC-1993-Jeffay #paradigm #performance #realtime
The Real-Time Producer/Consumer Paradigm: A Paradigm for the Construction of Efficient, Predictable Real-Time Systems (KJ), pp. 796–804.
SACSAC-1993-Jimenez-CedenoV #approach #communication #network #realtime
Centralized Packet Radio Network: A Communication Approach Suited for Data Collection in a Real-Time Flash Flood Prediction System (MJC, RVE), pp. 709–713.
SACSAC-1993-ShenDU #data flow
Packet Delay Prediction in Datagram Mesh Systems (ZS, PGD, LU), pp. 539–545.
DACDAC-1993-ChanSZ #array #on the #programmable
On Routability Prediction for Field-Programmable Gate Arrays (PKC, MDFS, JYZ), pp. 326–330.
CHICHI-1992-MarcusNRSV #user interface
Sci-fi at CHI: Cyberpunk novelists predict future user interfaces (AM, DAN, RR, BS, VV), pp. 435–437.
ICMLML-1992-Christiansen #learning #nondeterminism
Learning to Predict in Uncertain Continuous Tasks (ADC), pp. 72–81.
SEKESEKE-1992-WatsonBB #development #knowledge-based #maintenance
The Development of a Knowledge-Based System for Predicting Strategic Building Maintenance (IW, PB, AB), pp. 356–363.
AdaEuropeAdaEurope-1992-Lundberg #ada #parallel #source code
Predicting the Speedup of Parallel Ada Programs (LL), pp. 257–274.
PEPMPEPM-1992-Malmkjaer #source code
Predicting Properties of Residual Programs (KM), pp. 8–13.
ASPLOSASPLOS-1992-FisherF #branch
Predicting Conditional Branch Directions From Previous Runs of a Program (JAF, SMF), pp. 85–95.
ASPLOSASPLOS-1992-PanSR #branch #correlation #using
Improving the Accuracy of Dynamic Branch Prediction Using Branch Correlation (STP, KS, JTR), pp. 76–84.
VLDBVLDB-1991-FaloutsosNS #flexibility
Predictive Load Control for Flexible Buffer Allocation (CF, RTN, TKS), pp. 265–274.
CHICHI-1991-HowesY
Predicting the learnability of task-action mappings (AH, RMY), pp. 113–118.
ICMLML-1991-Walczak #induction #performance
Predicting Actions from Induction on Past Performance (SW), pp. 275–279.
PLDIPLDI-1991-Wall #behaviour #using
Predicting Program Behavior Using Real or Estimated Profiles (DWW), pp. 59–70.
PLDIBest-of-PLDI-1991-Wall91a #behaviour #using
Predicting program behavior using real or estimated profiles (with retrospective) (DWW), pp. 429–441.
ESECESEC-1991-Voas #impact analysis
A Dynamic Failure Model for Predicting the Impact that a Program Location has on the Program (JMV), pp. 308–331.
DACDAC-1991-SutanthavibulS
Dynamic Prediction of Critical Paths and Nets for Constructive Timing-Driven Placement (SS, ES), pp. 632–635.
CHICHI-1990-YoungW #analysis #concept #fault #using
Using a knowledge analysis to predict conceptual errors in text-editor usage (RMY, JW), pp. 91–98.
SEKESEKE-1990-Clark
A State Space Model for Currency Exchange Rate Prediction (JC), pp. 267–271.
PPoPPPPoPP-1990-ChangE #functional #implementation
An Implementation of a Barotropic Numerical Weather Prediction Model in the Functional Language SISAL (PSC, GKE), pp. 109–117.
CHICHI-1989-YoungGS #design #evaluation #interface #modelling #programmable
Programmable user models for predictive evaluation of interface designs (RMY, TRGG, TJS), pp. 15–19.
KRKR-1989-Rayner #problem #question
Did Newton Solve the “Extended Prediction Problem”? (MR), pp. 381–385.
ICMLML-1988-FisherS #concept
Concept Simplification and Prediction Accuracy (DHF, JCS), pp. 22–28.
HCIHCI-SES-1987-RuthG
Examining the Silent Majority: A Current Perspective on Predictive Variables Associated with Managerial and Clerical ADP Users (SRR, EPG), pp. 311–318.
SIGIRSIGIR-1987-RaitaT
Predictive Text Compression by Hashing (TR, JT), pp. 223–233.
DACDAC-1987-JainPP #design #pipes and filters #trade-off
Predicting Area-Time Tradeoffs for Pipelined Design (RJ, ACP, NP), pp. 35–41.
DACDAC-1987-MinaiWB #approach #evaluation #heuristic
A Discrete Heuristics Approach to Predictive Evaluation of Semi-Custom IC Layouts (AAM, RDW, FWB), pp. 770–776.
SIGIRSIGIR-1986-TeuholaR #using
Text Compression Using Prediction (JT, TR), pp. 97–102.
ICSEICSE-1985-TakahashiK #empirical #fault
An Empirical Study of a Model for Program Error Prediction (MT, YK), pp. 330–336.
VLDBVLDB-1981-Sevcik #database #performance #using
Data Base System Performance Prediction Using an Analytical Model (KCS), pp. 182–198.
PLDISCC-1979-Ball #optimisation
Predicting the effects of optimization on a procedure body (JEB), pp. 214–220.
ICSEICSE-1979-CurtisSM #complexity #metric #performance #replication
Third Time Charm: Stronger Replication of the Ability of Software Complexity Metrics to Predict Programmer Performance (BC, SBS, PM), pp. 356–360.
ICSEICSE-1979-RemusZ #quality
Prediction and Management of Program Quality (HR, SNZ), pp. 341–350.
DACDAC-1977-HellerMD #requirements
Prediction of wiring space requirements for LSI (WRH, WFM, WED), pp. 32–42.
ICSEICSE-1976-Shooman #modelling #reliability
Structural Models for Software Reliability Prediction (MLS), pp. 268–280.
DACDAC-1975-Fike #design #detection #fault #question
Predicting fault detectability in combinational circuits — a new design tool? (JLF), pp. 290–295.
STOCSTOC-1975-EhrenfeuchtR #formal method #on the
On (Un)predictability of Formal Languages (AE, GR), pp. 117–120.
DACDAC-1973-PillingS #logic
Computer-aided prediction of delays in LSI logic systems (DJP, HBS), pp. 182–186.
DACDAC-1969-Radke
A justification of, and an improvement on, a useful rule for predicting circuit-to-pin ratios (CER), pp. 257–267.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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