LEADER 00859cam0-22003011i-450- 001 990005979240403321 005 19980601 035 $a000597924 035 $aFED01000597924 035 $a(Aleph)000597924FED01 035 $a000597924 100 $a19980601d1958----km-y0itay50------ba 101 0 $aeng 102 $aGB 105 $ay-------001yy 200 1 $aMeetings$ethe Regulation of and Procedure at Meetings of Companies and Public Bodies and at Public Meetings$fF.D. HEAD 205 $a6. edition 210 $aLondon$cIsaac Pitman & Sons$d1958 215 $aXIII, 246 p.$d22 cm 676 $a346.06$v20$zita 700 1$aHead,$bFrederick Dewar$0404390 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005979240403321 952 $aVIII H 450$b64807$fFGBC 959 $aFGBC 996 $aMeetings$9583098 997 $aUNINA LEADER 13463nam 22008535 450 001 996466472203316 005 20200706121153.0 010 $a3-319-63558-1 024 7 $a10.1007/978-3-319-63558-3 035 $a(CKB)4340000000061646 035 $a(DE-He213)978-3-319-63558-3 035 $a(MiAaPQ)EBC6283218 035 $a(MiAaPQ)EBC5610443 035 $a(Au-PeEL)EBL5610443 035 $a(OCoLC)1001286196 035 $a(PPN)20384985X 035 $a(EXLCZ)994340000000061646 100 $a20170718d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKnowledge Science, Engineering and Management$b[electronic resource] $e10th International Conference, KSEM 2017, Melbourne, VIC, Australia, August 19-20, 2017, Proceedings /$fedited by Gang Li, Yong Ge, Zili Zhang, Zhi Jin, Michael Blumenstein 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVII, 563 p. 150 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v10412 311 $a3-319-63557-3 327 $aIntro -- Preface -- Organization -- Learning from Non-stationary Distributions (Invited Speech) -- Contents -- Text Mining and Document Analysis -- Learning Sparse Overcomplete Word Vectors Without Intermediate Dense Representations -- 1 Introduction -- 2 Related Work -- 3 Our Model -- 3.1 Parameter Estimation -- 3.2 Optimization Algorithm -- 4 Evaluation -- 4.1 Experimental Settings -- 4.2 Word Analogy -- 4.3 Word Similarity -- 4.4 Interpretability -- 5 Conclusion -- References -- A Study of Distributed Semantic Representations for Automated Essay Scoring -- 1 Introduction -- 2 Common Text Features -- 3 Semantic Representations for AES -- 3.1 Methods for Vector Representations -- 3.2 Semantic Features -- 4 Experimental Settings -- 4.1 Dataset -- 4.2 Evaluation Metrics and Learning Algorithms -- 5 Evaluation Design and Results -- 5.1 Evaluation Design -- 5.2 Evaluation Results -- 6 Conclusions and Future Work -- References -- Weakly Supervised Feature Compression Based Topic Model for Sentiment Classification -- 1 Introduction -- 2 Related Work -- 3 Hidden Topic Analysis Model -- 4 Weakly Supervised Feature Compression Based ELDA -- 5 Experiment Results -- 5.1 Data Preparation -- 5.2 Performance Evaluation -- 5.3 Results with Different Topics -- 5.4 Results with Different Gibbs Sampling Iterations -- 6 Conclusion -- References -- An Effective Gated and Attention-Based Neural Network Model for Fine-Grained Financial Target-Dependent Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 The Proposed Neural Network Model GABi-LSTM -- 3.1 Motivation -- 3.2 The Overview of Model Architecture -- 3.3 Word Representation with Gated Char- and Word- Embedding -- 3.4 Sentence Representation with Attention-Based Bi-LSTM -- 3.5 Linear Regression -- 3.6 Parameter Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Measure. 327 $a4.3 Experimental Results -- 4.4 Comparison with the State-of-the-Art Systems -- 4.5 Qualitative Visualization Analysis -- 5 Concluding Remarks -- References -- A Hidden Astroturfing Detection Approach Base on Emotion Analysis -- 1 Introduction -- 2 Related Work -- 3 Hidden Astroturfing Detection Method -- 3.1 Data Preparation -- 3.2 Pre-processing Operations -- 3.3 Bag-of-Word Module Construction -- 3.4 Emotion Mining and Analysis -- 3.5 Matching -- 3.6 Summary -- 4 Experiment and Evaluation -- 4.1 Experiment Setup -- 4.2 Evaluation Result -- 5 Conclusion -- References -- Leveraging Term Co-occurrence Distance and Strong Classification Features for Short Text Feature Selection -- Abstract -- 1 Introduction -- 2 Problem Preliminaries -- 2.1 Correlation of Two Terms in a Text -- 2.2 Expected Cross Entropy -- 3 The Proposed Approach -- 3.1 Terming Weighting Method Based on Co-occurrence Distance -- 3.2 Feature Dictionary Construction -- 4 Experiments and Results Analysis -- 4.1 Data Sets and Evaluation Metrics -- 4.2 Experimental Results and Analysis -- 4.2.1 Comparison of Feature Dictionaries -- 4.2.2 Effect of Variation of Dictionary Size for Short Text Classification -- 4.2.3 Classification Performance of Different Feature Selection Methods -- 5 Conclusions and Future Work -- Acknowledgement -- References -- Formal Semantics and Fuzzy Logic -- A Fuzzy Logic Based Policy Negotiation Model -- 1 Introduction -- 2 Preliminaries -- 3 Model Definition -- 4 Fuzzy Rules -- 5 Experiment -- 6 Related Work -- 7 Conclusions -- References -- f-ALC(D)-LTL: A Fuzzy Spatio-Temporal Description Logic -- 1 Introduction -- 2 Spatial Fuzzy Description Logic -- 3 Fuzzy Spatio-Temporal Description Logic f-ALC(D)-LTL -- 3.1 Syntax -- 3.2 Models -- 4 Hintikka Structures for f-ALC(D)-LTL -- 5 Reasoning in f-ALC(D)-LTL -- 5.1 Tableau Rules -- 5.2 Tableau Construction. 327 $a5.3 Tableau Elimination -- 5.4 Correctness -- 6 Conclusion and Future Work -- References -- R-Calculus for the Primitive Statements in Description Logic ALC -- 1 Introduction -- 2 Description Logic ALC -- 3 R-Calculus for Subset-Minimal Change -- 3.1 SDL: R-Calculus for a Statement -- 3.2 SDL: R-Calculus for a Set of Statements -- 4 Conclusions and Further Works -- References -- A Multi-objective Attribute Reduction Method in Decision-Theoretic Rough Set Model -- 1 Introduction -- 2 Preliminaries -- 2.1 Decision-Theoretic Rough Set Model -- 2.2 Three Kinds of Criteria in Decision-Theoretic Rough Set Model -- 3 Multi-objective Attribute Reduction in Decision-Theoretic Rough Set Model -- 3.1 Multi-objective Attribute Reduct -- 3.2 Multi-objective Attribute Reduction Algorithm -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Setting -- 4.3 Experimental Results -- 5 Conclusion -- References -- A Behavior-Based Method for Distinction of Flooding DDoS and Flash Crowds -- 1 Introduction -- 2 Proposed Method -- 3 Experiments -- 4 Conclusion -- References -- Knowledge Management -- Analyzing Customer's Product Preference Using Wireless Signals -- 1 Introduction -- 2 Channel State Information -- 3 Analyzing Customer's Product Preference Using CSI -- 3.1 CSI Preprocessing -- 3.2 Feature Extraction -- 3.3 Classification -- 4 Performance Evaluation -- 4.1 Experimental Methodology -- 4.2 Feasibility of Customer's Product Preference Analysis -- 5 Related Work -- 5.1 Device-Based Activity Sensing -- 5.2 Device-Free Activity Sensing Using WiFi -- 6 Conclusion -- References -- Improved Knowledge Base Completion by the Path-Augmented TransR Model -- 1 Introduction -- 2 Our Approach -- 2.1 Base Model: TransR -- 2.2 Path-Augmented TransR: PTransR -- 2.3 Training Details -- 3 Evaluation -- 3.1 Dataset -- 3.2 Experimental Settings -- 3.3 Overall Performance. 327 $a3.4 In-Depth Analysis and Discussion -- 4 Related Work -- 5 Conclusion -- References -- Balancing Between Cognitive and Semantic Acceptability of Arguments -- 1 Introduction -- 2 Preliminaries -- 3 Equilibrium-Based Resolutions -- 3.1 Semantic and Cognitive Acceptabilities -- 3.2 Satisfiability Resolution -- 3.3 Entailment Resolution -- 3.4 Semantic Equivalence Resolution -- 4 Generality and Applicability -- 4.1 Characterising Existence of Resolutions -- 4.2 Application Illustration in Online Forum -- 5 Conclusions and Discussion -- A Proofs -- References -- Discovery of Jump Breaks in Joint Volatility for Volume and Price of High-Frequency Trading Data in China -- Abstract -- 1 Introduction -- 2 Bivariate Normal Distribution -- 3 Realized Trading Volatility of Price and Volume -- 3.1 Price Volatility -- 3.2 Volume Volatility -- 3.3 Volatility Rate of Price and Volume of Realized Trading -- 4 The Jump Point Model for High-Frequency Trading Volatility Break -- 5 The Algorithm of Point-by-Point Test for Jump Critical Points -- 6 The Empirical Analysis of Jump Critical Points -- 7 Conclusion -- Acknowledgments -- References -- Device-Free Intruder Sensing Leveraging Fine-Grained Physical Layer Signatures -- 1 Introduction -- 2 Related Work -- 2.1 Gait Based Human Identification -- 2.2 WiFi Based Activity Recognition -- 3 System Design -- 3.1 Channel State Information Extration -- 3.2 Data Prepocessing -- 3.3 Step Analysis -- 3.4 Device-Free Intruder Sensing -- 4 Experimentation Evaluation -- 4.1 Equipment -- 4.2 Experimental Results -- 5 Conclusion -- References -- Understanding Knowledge Management in Agile Software Development Practice -- Abstract -- 1 Introduction -- 2 Background and Related Work -- 2.1 Knowledge Classifications -- 2.2 Prior Reviews on Knowledge Management in ASD -- 3 Review Method. 327 $a3.1 Planning the Review and Identifying Relevant Literature -- 3.2 Publication Selection -- 3.3 Data Extraction and Synthesis -- 4 Results -- 4.1 Agile Practices Supporting Knowledge Management -- 4.2 Knowledge Involved in Agile Practices -- 5 Discussion -- 5.1 Implications -- 5.2 Limitations -- 6 Conclusion -- Acknowledgement -- References -- Knowledge Integration -- Multi-view Unit Intact Space Learning -- 1 Introduction -- 2 The Proposed Model -- 2.1 Background -- 2.2 Multi-view Unit Intact Space Learning -- 3 Optimization -- 3.1 Update Latent Feature Vectors in Unit Intact Space -- 3.2 Update View Generation Matrices -- 3.3 Convergence Analysis -- 3.4 Complexity Analysis -- 4 Experiments -- 4.1 Datasets and Evaluation Measures -- 4.2 Parameter Analysis -- 4.3 Comparison Results -- 5 Conclusion -- References -- A Novel Blemish Detection Algorithm for Camera Quality Testing -- Abstract -- 1 Introduction -- 2 Related Works -- 2.1 Traditional Methods Based on Image Filtering -- 2.2 Image Size Reduction by Scaling -- 2.3 Median Filtering -- 2.4 Image Subtraction -- 2.5 Thresholding -- 3 Novel Filtering Method -- 3.1 Influences of Image Noises -- 3.2 Proposed Multi-directional Median Filter -- 3.3 Adaptive Threshold with Bias -- 4 Results and Discussion -- 4.1 Low Noise Samples -- 4.2 High Noise Samples -- 5 Conclusion -- Acknowledgments -- References -- Learning to Infer API Mappings from API Documents -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Overview -- 3.2 Understanding API Documents -- 3.3 Computing Similarity Between APIs -- 4 Evaluation -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Results -- 5 Conclusion -- References -- Super-Resolution for Images with Barrel Lens Distortions -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Pretreatment Process -- 3.2 Training Stage -- 3.3 Testing Stage. 327 $a4 Experiments. 330 $aThis book constitutes the refereed proceedings of the 10th International Conference on Knowledge Science, Engineering and Management, KSEM 2017, held in Melbourne, Australia, in August 2017. The 35 revised full papers and 12 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: text mining and document analysis; formal semantics and fuzzy logic; knowledge management; knowledge integration; knowledge retrieval; recommendation algorithms and systems; knowledge engineering; and knowledge representation and reasoning. 410 0$aLecture Notes in Artificial Intelligence ;$v10412 606 $aArtificial intelligence 606 $aData mining 606 $aApplication software 606 $aInformation storage and retrieval 606 $aOptical data processing 606 $aComputer communication systems 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aComputer Applications$3https://scigraph.springernature.com/ontologies/product-market-codes/I23001 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aApplication software. 615 0$aInformation storage and retrieval. 615 0$aOptical data processing. 615 0$aComputer communication systems. 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputer Applications. 615 24$aInformation Storage and Retrieval. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputer Communication Networks. 676 $a006.33 702 $aLi$b Gang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGe$b Yong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhang$b Zili$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJin$b Zhi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBlumenstein$b Michael$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466472203316 996 $aKnowledge Science, Engineering and Management$9772454 997 $aUNISA LEADER 01356nam 2200373Ia 450 001 996387119003316 005 20221108043737.0 035 $a(CKB)1000000000614201 035 $a(EEBO)2240860933 035 $a(OCoLC)12936900 035 $a(EXLCZ)991000000000614201 100 $a19851220d1692 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 12$aA vision concerning the mischievous seperation [sic] among Friends in Old England$b[electronic resource] 210 $aPhiladelphia $cPrinted and sold by Will. 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