Stem signific$ (all stems)

85 papers:

CHI-2015-VatavuW #analysis #elicitation #formal method #metric #tool support

Formalizing Agreement Analysis for Elicitation Studies: New Measures, Significance Test, and Toolkit (RDV, JOW), pp. 1325–1334.

DUXU-UI-2015-SahitoCHHS #tablet

Significance of Line Length for Tablet PC Users (WAS, HIC, ZH, SRH, FS), pp. 587–596.

ECIR-2015-Dutta #approximate #mining #named #statistics #string #using

MIST: Top-k Approximate Sub-string Mining Using Triplet Statistical Significance (SD), pp. 284–290.

KDD-2015-Llinares-LopezS #mining #mutation testing #performance #permutation #testing

Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing (FLL, MS, LP, KMB), pp. 725–734.

ICSE-v1-2015-ZouWXZSM #algorithm #detection #float #search-based

A Genetic Algorithm for Detecting Significant Floating-Point Inaccuracies (DZ, RW, YX, LZ, ZS, HM), pp. 529–539.

PPoPP-2015-VassiliadisPCALBVN #energy #programming #runtime

A programming model and runtime system for significance-aware energy-efficient computing (VV, KP, CC, CDA, SL, NB, HV, DSN), pp. 275–276.

SIGMOD-2014-AroraSB #graph #mining #statistics

Mining statistically significant connected subgraphs in vertex labeled graphs (AA, MS, AB), pp. 1003–1014.

SCAM-2014-Abi-AntounCVG #abstract interpretation #graph #question #using

Are Object Graphs Extracted Using Abstract Interpretation Significantly Different from the Code? (MAA, SC, RV, AG), pp. 245–254.

SCSM-2014-LeonardoFGPSSUWC #identification #social #social media #trust

Identifying Locations of Social Significance: Aggregating Social Media Content to Create a New Trust Model for Exploring Crowd Sourced Data and Information (ADL, SF, AG, FP, WS, TS, AU, DW, JBC), pp. 170–177.

CIKM-2014-LiuLK #classification #information management #performance #using

Using Local Information to Significantly Improve Classification Performance (WL, DL, RK), pp. 1947–1950.

KDD-2014-SchubertWK #detection #named #scalability #topic

SigniTrend: scalable detection of emerging topics in textual streams by hashed significance thresholds (ES, MW, HPK), pp. 871–880.

SIGIR-2014-Carterette #information retrieval #statistics #testing #theory and practice

Statistical significance testing in information retrieval: theory and practice (BC), p. 1286.

SAC-2014-AlharbiZ #predict #social

Exploring the significance of human mobility patterns in social link prediction (BA, XZ), pp. 604–609.

FSE-2014-MirakhorliFGWC #architecture #detection #monitoring #named

Archie: a tool for detecting, monitoring, and preserving architecturally significant code (MM, AF, AG, MW, JCH), pp. 739–742.

ICDAR-2013-ClausnerPA #documentation #evaluation #order #recognition

The Significance of Reading Order in Document Recognition and Its Evaluation (CC, SP, AA), pp. 688–692.

CSEET-2013-DebGG #case study #experience #re-engineering #social

Software engineering projects with social significance: An experience report at a minority university (DD, LG, MG), pp. 314–318.

HIMI-HSM-2013-Wesugi #approach #design #experience #novel #simulation

Design Approach of Simulation Exercise with Use of Device and Its Significance — Design of Novel Device for Realistic Experience of Being a Hemiplegia Patient (SW), pp. 315–324.

SIGIR-2013-UrbanoMM13a #comparison #evaluation #information retrieval #statistics #testing

A comparison of the optimality of statistical significance tests for information retrieval evaluation (JU, MM, DM), pp. 925–928.

REFSQ-2013-Cleland-HuangCK #agile #approach #architecture #requirements

A Persona-Based Approach for Exploring Architecturally Significant Requirements in Agile Projects (JCH, AC, EK), pp. 18–33.

ICLP-J-2013-MazuranSZ #datalog #declarative #horn clause

A declarative extension of horn clauses, and its significance for datalog and its applications (MM, ES, CZ), pp. 609–623.

SIGMOD-2012-MongioviBRSPF #mining #named #network

SigSpot: mining significant anomalous regions from time-evolving networks (abstract only) (MM, PB, RR, AKS, EEP, CF), p. 865.

VLDB-2012-SachanB #mining #statistics #string #using

Mining Statistically Significant Substrings using the Chi-Square Statistic (MS, AB), pp. 1052–1063.

ICPR-2012-Berrar #classification #comparison #null #testing #visual notation

Null QQ plots: A simple graphical alternative to significance testing for the comparison of classifiers (DPB), pp. 1852–1855.

KDD-2012-KawaleCOSLK #testing

Testing the significance of spatio-temporal teleconnection patterns (JK, SC, DO, KS, SL, VK), pp. 642–650.

KDIR-2012-Martiny #security

Unsupervised Discovery of Significant Candlestick Patterns for Forecasting Security Price Movements (KM), pp. 145–150.

KDIR-2012-QuirogaMH #sequence

Frequent and Significant Episodes in Sequences of Events — Computation of a New Frequency Measure based on Individual Occurrences of the Events (OQ, JM, SH), pp. 324–328.

MoDELS-2012-PfeifferW #development

Cross-Language Support Mechanisms Significantly Aid Software Development (RHP, AW), pp. 168–184.

MoDELS-2012-PfeifferW #development

Cross-Language Support Mechanisms Significantly Aid Software Development (RHP, AW), pp. 168–184.

LICS-2012-Parys #on the

On the Significance of the Collapse Operation (PP), pp. 521–530.

WICSA-2011-MiksovicZ #architecture #information management #metamodelling #requirements

Architecturally Significant Requirements, Reference Architecture, and Metamodel for Knowledge Management in Information Technology Services (CM, OZ), pp. 270–279.

DAC-2011-KarakonstantisBATGR

Significance driven computation on next-generation unreliable platforms (GK, NB, CDA, GT, VG, KR), pp. 290–291.

HT-2011-TakahashiOYIOT #analysis #using #wiki

Evaluating significance of historical entities based on tempo-spatial impacts analysis using Wikipedia link structure (YT, HO, MY, HI, SO, KT), pp. 83–92.

ICALP-v1-2011-FortnowS #robust #simulation

Robust Simulations and Significant Separations (LF, RS), pp. 569–580.

ICSE-2011-Mirakhorli #approach #architecture #requirements

Tracing architecturally significant requirements: a decision-centric approach (MM), pp. 1126–1127.

VLDB-2010-CaoCJ10a #mining #semantics

Mining Significant Semantic Locations From GPS Data (XC, GC, CSJ), pp. 1009–1020.

CSMR-2010-OzkayaPGC #architecture #evolution #requirements #using

Using Architecturally Significant Requirements for Guiding System Evolution (IO, JADP, AG, SC), pp. 127–136.

ECIR-2010-RedpathGMC #collaboration #recommendation

Collaborative Filtering: The Aim of Recommender Systems and the Significance of User Ratings (JR, DHG, SIM, LC), pp. 394–406.

ICPR-2010-BharathM #empirical #on the #online #recognition #word

On the Significance of Stroke Size and Position for Online Handwritten Devanagari Word Recognition: An Empirical Study (AB, SM), pp. 2033–2036.

KDD-2010-FangNF

Discovering significant relaxed order-preserving submatrices (QF, WN, JF), pp. 433–442.

KDD-2010-GoorhaU #roadmap

Discovery of significant emerging trends (SG, LHU), pp. 57–64.

KDIR-2010-KleizaKT #approach #documentation #identification #query #semantics #similarity #visualisation #word

Semantic Identification and Visualization of Significant Words within Documents — Approach to Visualize Relevant Words within Documents to a Search Query by Word Similarity Computation (KK, PK, KDT), pp. 481–486.

HPDC-2010-AgrawalMHC #estimation #named #parallel #sequence #statistics

MPIPairwiseStatSig: parallel pairwise statistical significance estimation of local sequence alignment (AA, SM, DH, ANC), pp. 470–476.

IJCAR-2010-AbourbihBBM #automation #calculus

A Single-Significant-Digit Calculus for Semi-Automated Guesstimation (JAA, LB, AB, FM), pp. 354–368.

PODS-2009-KirschMPPUV #approach #identification #performance #statistics

An efficient rigorous approach for identifying statistically significant frequent itemsets (AK, MM, AP, GP, EU, FV), pp. 117–126.

CIKM-2009-AnWZH #database #scalability

Diverging patterns: discovering significant frequency change dissimilarities in large databases (AA, QW, JZ, XH), pp. 1473–1476.

SIGIR-2009-SmuckerAC #evaluation #information retrieval #statistics #testing

Agreement among statistical significance tests for information retrieval evaluation at varying sample sizes (MDS, JA, BC), pp. 630–631.

SIGMOD-2008-YanCHY #graph #mining

Mining significant graph patterns by leap search (XY, HC, JH, PSY), pp. 433–444.

CIKM-2008-AlqadahB08a #clustering #detection #set

Detecting significant distinguishing sets among bi-clusters (FA, RB), pp. 1455–1456.

KDD-2008-MoiseS #approach #clustering #novel #statistics

Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering (GM, JS), pp. 533–541.

SAC-2008-YuB #detection #effectiveness #multi #performance

A fast and effective method to detect multiple least significant bits steganography (XY, NB), pp. 1443–1447.

ITiCSE-2007-RavehHY #student

Transforming a high school student project in computer science into a significant scientific achievement (BR, BH, CY), p. 331.

CIAA-2007-Supol

Significant Subpatterns Matching (JS), pp. 317–319.

ICEIS-AIDSS-2007-Calvo-FloresNGF07a #empirical

An Empirical Study of Significant Variables for Trading Strategies (MDCF, JFNN, EGG, CMF), pp. 330–335.

CIKM-2007-SmuckerAC #comparison #evaluation #information retrieval #statistics #testing

A comparison of statistical significance tests for information retrieval evaluation (MDS, JA, BC), pp. 623–632.

ICSM-2006-RompaeyBD #smell

Characterizing the Relative Significance of a Test Smell (BVR, BDB, SD), pp. 391–400.

EDOC-2006-HosslerBS #modelling

Significant Productivity Enhancement through Model Driven Techniques: A Success Story (JH, MB, SS), pp. 367–373.

ICPR-v2-2006-ClimentS #segmentation #visual notation

Visually Significant Dynamics for Watershed Segmentation (JC, AS), pp. 341–344.

KDD-2006-GaoGEJ #clustering

Discovering significant OPSM subspace clusters in massive gene expression data (BJG, OLG, ME, SJMJ), pp. 922–928.

KDD-2006-Webb

Discovering significant rules (GIW), pp. 434–443.

ISSTA-2006-KiviluomaKM #architecture #aspect-oriented #behaviour #monitoring #runtime #using

Run-time monitoring of architecturally significant behaviors using behavioral profiles and aspects (KK, JK, TM), pp. 181–190.

SAC-2005-Morimoto #mining #transitive

Optimized transitive association rule: mining significant stopover between events (YM), pp. 543–544.

DAC-2004-GoldmanKBBBCSV #question #statistics

Is statistical timing statistically significant? (RG, KK, CB, AB, SYB, EC, LS, CV), p. 498.

ITiCSE-2004-ChesnevarGM #automaton #formal method #learning

Didactic strategies for promoting significant learning in formal languages and automata theory (CIC, MPG, AGM), pp. 7–11.

ICML-2004-JakulinB #interactive #testing

Testing the significance of attribute interactions (AJ, IB).

ICPR-v4-2004-TothSWA #clustering #detection #using

Detection of Moving Shadows using Mean Shift Clustering and a Significance Test (DT, IS, AW, TA), pp. 260–263.

KDD-2004-NeillM #agile #clustering #detection

Rapid detection of significant spatial clusters (DBN, AWM), pp. 256–265.

KDD-2004-ZhangPT #on the #statistics

On the discovery of significant statistical quantitative rules (HZ, BP, AT), pp. 374–383.

ICML-2003-KotnikK #learning #self

The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy (CK, JKK), pp. 369–375.

KDD-2003-TaoMF #framework #mining #using

Weighted Association Rule Mining using weighted support and significance framework (FT, FM, MMF), pp. 661–666.

ICSE-2003-InoueYFYMK #component #rank

Component Rank: Relative Significance Rank for Software Component Search (KI, RY, HF, TY, MM, SK), pp. 14–24.

ICPR-v2-2002-Maloof #analysis #machine learning #on the #statistics #testing

On Machine Learning, ROC Analysis, and Statistical Tests of Significance (MAM), pp. 204–207.

ICML-2001-LatinneSD #classification #multi #problem

Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensing (PL, MS, CD), pp. 298–305.

SAC-2001-ZhouCH #correlation #identification #optimisation #problem #set #using

Identifying the most significant pairwise correlations of residues in different positions of helices: the subset selection problem using least squares optimization (XZ, GC, MTH), pp. 51–55.

ICPR-v3-2000-VassZ #analysis #component #fault #image #performance

Enhanced Significance-Linked Connected Component Analysis for High Performance Error Resilient Wavelet Image Coding (JV, XZ), pp. 3075–3078.

KR-2000-BesnardS

Significant Inferences : Preliminary Report (PB, TS), pp. 401–410.

ITiCSE-1999-Szejko #quality

An exercise in evaluating significance of software quality criteria (SS), p. 199.

CIKM-1999-SwanA

Extracting Significant Time Varying Features from Text (RCS, JA), pp. 38–45.

ICPR-1996-Garcia-SilventeFG #approach #image #multi

A multi-channel-based approach for extracting significant scales on gray-level images (MGS, JFV, JAG), pp. 231–235.

ICPR-1996-Kupeev #algorithm #detection #on the

On significant maxima detection: a fine-to-coarse algorithm (KYK), pp. 270–274.

ICPR-1996-ModayurS #3d #statistics #using

3D matching using statistically significant groupings (BRM, LGS), pp. 238–242.

KDD-1994-BhandariB #data analysis #on the #statistics

On the Role of Statistical Significance in Exploratory Data Analysis (ISB, SB), pp. 61–72.

ML-1992-MuggletonSB

Compression, Significance, and Accuracy (SM, AS, MB), pp. 338–347.

SIGIR-1991-Cleverdon #testing

The Significance of the Cranfield Tests on Index Languages (CWC), pp. 3–12.

ML-1989-GamsK #empirical #learning

New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains (MG, AK), pp. 99–103.

HCI-SES-1987-Watanabe #architecture #automation #interface

Human-Interface Architecture: Its Significance for Office Automation System (HW), pp. 279–294.