BibSLEIGH — predict tag

predict

predict

Tag #predict

2280 papers:

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