Computer Supported Cooperative Work and Social Computing: 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer Singapore Pte. Limited
2023
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Communications in Computer and Information Science Series
v.1682 |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (683 Seiten) |
| ISBN: | 9789819923854 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV049876115 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 240919s2023 |||| o||u| ||||||eng d | ||
| 020 | |a 9789819923854 |9 978-981-9923-85-4 | ||
| 035 | |a (ZDB-30-PQE)EBC7248819 | ||
| 035 | |a (ZDB-30-PAD)EBC7248819 | ||
| 035 | |a (ZDB-89-EBL)EBL7248819 | ||
| 035 | |a (OCoLC)1388522472 | ||
| 035 | |a (DE-599)BVBBV049876115 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-2070s | ||
| 082 | 0 | |a 650.028546 | |
| 100 | 1 | |a Sun, Yuqing |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Computer Supported Cooperative Work and Social Computing |b 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. |
| 250 | |a 1st ed | ||
| 264 | 1 | |a Singapore |b Springer Singapore Pte. Limited |c 2023 | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 Online-Ressource (683 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Communications in Computer and Information Science Series |v v.1682 | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Crowd Intelligence and Crowd Cooperative Computing -- MatricEs: Matrices Embeddings for Link Prediction in Knowledge Graphs -- 1 Introduction -- 2 Definition -- 2.1 Problem Definition -- 2.2 Relation Patterns -- 2.3 Notations -- 3 Related Work -- 3.1 Transformation Based Models -- 3.2 Semantic Matching Models -- 4 Our MatricEs Approach -- 4.1 MatricEs -- 4.2 Overview of Our Learning Framework -- 4.3 Theoretical Analyses -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metric -- 5.3 Implementation Details -- 5.4 Experimental Results -- 5.5 Discussion -- 6 Conclusion -- References -- Learning User Embeddings Based on Long Short-Term User Group Modeling for Next-Item Recommendation -- 1 Introduction -- 2 Related Work -- 3 LSUG Model -- 3.1 Problem Formulation -- 3.2 Overview -- 3.3 General Embedding Construction -- 3.4 Context-Aware Input Embedding -- 3.5 Latent User Group Influence Modeling -- 3.6 Hybrid User Representation Modeling -- 3.7 Model Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison of Performance -- 4.3 Influence of Components -- 4.4 Influence of Hyper-Parameters -- 5 Conclusion -- References -- Context-Aware Quaternion Embedding for Knowledge Graph Completion -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Definition -- 3.2 Quaternion Background -- 4 Methodology -- 4.1 Context Sampling Module -- 4.2 Context Information Encoder Module -- 4.3 Quaternion Rotation Module -- 4.4 Scoring Function and Loss -- 5 Experiments and Results -- 5.1 Datasets -- 5.2 Baselines -- 5.3 Evaluation Protocol -- 5.4 Experimental Settings -- 5.5 Link Prediction -- 5.6 Number of Free Parameters Comparison -- 5.7 Ablation Study -- 5.8 Convergence Rate Comparison -- 5.9 Impact of Embedding Dimension -- 5.10 Multi-relation Analysis -- 6 Conclusion | |
| 505 | 8 | |a References -- Dependency-Based Task Assignment in Spatial Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Task Assignment Approach -- 4.1 Matching Pair Availability -- 4.2 Pruning Strategy -- 4.3 Task Matching Pairs Selection -- 4.4 The Greedy Approach -- 5 Experimental Study -- 5.1 Experimental Setting -- 5.2 Result Analysis -- 6 Conclusion -- References -- ICKG: An I Ching Knowledge Graph Tool Revealing Ancient Wisdom -- 1 Introduction -- 2 Related Work -- 3 I Ching Knowledge Graph -- 3.1 Key Entity Classes in the I Ching -- 3.2 The Relationship Among I Ching Entities -- 3.3 Knowledge Graph Storage and Mining of Linkages -- 4 ICKG Tool Development -- 5 Conclusion -- References -- Collaborative Analysis on Code Structure and Semantics -- 1 Introduction -- 2 Related Work -- 3 Collaborative Analysis on Code Structure and Semantics -- 3.1 Problem Definition -- 3.2 Framework -- 3.3 Function Call Graph Construction -- 3.4 The Self-encoder Based Structure Similarity -- 3.5 Function Semantics Similarity -- 3.6 Code Comparison Combining Code Structure and Function Semantics -- 4 Experiments and Analysis of Results -- 4.1 Dataset and Experimental Settings -- 4.2 Evaluation Metrics -- 4.3 Comparation Method -- 4.4 Performance Analysis -- 4.5 Ablation Experiment -- 4.6 Visualized Analysis of Function Call Structure -- 5 Conclusion -- References -- Temporal Planning-Based Choreography from Music -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Our Plan2Dance Approach -- 4.1 Step1: Dancing Domain Modeling -- 4.2 Step 2: Music Analysis -- 4.3 Step 3: Planning Problem Generation -- 5 Experiment -- 5.1 Motion Diversity -- 5.2 User Study -- 5.3 Running Time -- 6 Conclusion -- References -- An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 1 Introduction -- 2 Related Work | |
| 505 | 8 | |a 3 Preparatory Knowledge -- 3.1 RSVD+ Model -- 3.2 Adagrad Algorithm -- 3.3 Adaptive Parameter DBSCAN Algorithm -- 4 An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 4.1 Sparse Matrix Padding -- 4.2 Two-Phase Anomaly Data Detection -- 4.3 Predicted Missing Values -- 5 Experimental Evaluation -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baseline Methods -- 5.4 Performance Comparison -- 5.5 Impact of Matrix Density -- 5.6 Ablation Study -- 6 Conclusion -- References -- Effectiveness of Malicious Behavior and Its Impact on Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Effectiveness of Four Typical Malicious Behavior -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Experimental Results -- 6 Conclusion -- References -- Scene Adaptive Persistent Target Tracking and Attack Method Based on Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Visual Tracking Algorithm -- 2.2 Deep Reinforcement Learning -- 3 Follow Attack Model Based on Game Mechanism -- 3.1 Problem Description -- 3.2 Model Framework -- 3.3 Model Structure -- 4 Experimental Verification -- 4.1 Experimental Setup -- 4.2 Comparative Transfer Experiment -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- Research on Cost Control of Mobile Crowdsourcing Supporting Low Budget in Large Scale Environmental Information Monitoring -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 3.1 Definition -- 3.2 Assumptions -- 3.3 Problem Formulation -- 4 The Low Budget Model -- 4.1 The Sampling Matrix Generation Module -- 4.2 The Participant Employment Module -- 4.3 Data Recovery Module -- 5 Experiment Results -- 5.1 Data Sets -- 5.2 Experiment Results -- 6 Conclusion and Future Work -- References -- Question Answering System Based on University Knowledge Graph -- 1 Introduction | |
| 505 | 8 | |a 2 Related Work -- 3 Preparation -- 3.1 University Knowledge Graph -- 3.2 Natural Language Question Dataset -- 4 Question Understanding and Knowledge Search -- 4.1 Named Entity Recognition -- 4.2 Feature Term Extraction and Word Vector Construction -- 4.3 Question Intent Recognition -- 4.4 Assembling Structured Query Statements -- 5 Implementation of Question Answering System -- 6 Conclusion -- References -- Deep Reinforcement Learning-Based Scheduling Algorithm for Service Differentiation in Cloud Business Process Management System -- 1 Introduction -- 2 Problem Description and System Design -- 2.1 Cloud BPMS Description -- 2.2 Problem Description -- 2.3 Scheduling System Design -- 3 Algorithm Design -- 3.1 Problem Modeling -- 3.2 DRL-Based Request Scheduler -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Experiment Result -- 5 Conclusion and Future Work -- References -- A Knowledge Tracing Model Based on Graph Attention Mechanism and Incorporating External Features -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Tracing -- 2.2 Graph Auto-encoder -- 2.3 Graph Attention Network -- 3 Problem Definition -- 4 Model -- 4.1 Sub-graph Building Layer -- 4.2 Sub-graph Embedding Layer -- 4.3 Predicted Layer -- 5 Experiments -- 5.1 Dataset -- 5.2 Evaluating Indicator -- 5.3 Baseline Methods -- 5.4 Experimental Setting -- 5.5 Overall Performance -- 5.6 Ablation Experiment -- 6 Conclusion -- References -- Crowd-Powered Source Searching in Complex Environments -- 1 Introduction -- 2 Related Work -- 2.1 Crowd-Powered Systems -- 2.2 Source Searching -- 3 Methods -- 3.1 Method Overview -- 3.2 Human-Machine Collaborative Tasks -- 3.3 Task Interface -- 4 User Study -- 4.1 Experimental Conditions -- 4.2 Experimental Environments -- 4.3 Measures -- 4.4 Procedure -- 5 Results -- 5.1 Participants -- 5.2 Source Searching Result -- 5.3 Usability | |
| 505 | 8 | |a 5.4 Cognitive Workload -- 6 Discussion -- 6.1 Implications for Designing Crowd-Powered Systems -- 6.2 Limitations and Future Work -- 7 Conclusions -- References -- Cooperative Evolutionary Computation and Human-Like Intelligent Collaboration -- Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment -- 1 Introduction -- 2 Related Work -- 2.1 Offloading Decisions and Resource Allocation -- 2.2 Privacy Protection -- 3 End-Edge-Cloud Computing Framework and Problem Modeling -- 3.1 End-Edge-Cloud Computing Framework -- 3.2 Problem Modeling -- 4 Task Offloading and Resource Allocation Algorithm with Privacy Awareness -- 4.1 Task Offloading Sequence Generation -- 4.2 Offloading Decision Adjustment -- 4.3 Communication Resource Allocation -- 4.4 Computing Resource Allocation -- 4.5 Scheduling Result Adjustment -- 5 Experiment and Performance Analysis -- 5.1 Parameter Calibration -- 5.2 Algorithm Comparison -- 6 Conclusion -- References -- A Classifier-Based Two-Stage Training Model for Few-Shot Segmentation -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Method -- 5 Experiments -- 6 Conclusion -- References -- EEG-Based Motor Imagery Classification with Deep Adversarial Learning -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 The Framework -- 3.2 Training Detail and Prediction -- 3.3 Optimization with Backpropagation -- 4 Experiment and Results -- 4.1 Data Description -- 4.2 Experimental Settings -- 4.3 Comparative Studies -- 4.4 Experimental Results Analysis -- 4.5 Algorithmic Properties -- 4.6 Visualization -- 5 Conclusion -- References -- Comparison Analysis on Techniques of Preprocessing Imbalanced Data for Symbolic Regression*-4pt -- 1 Introduction -- 2 Preprocessing Algorithms for Imbalanced Data Sets -- 2.1 Data Weighting -- 2.2 Data Compressing -- 2.3 Data Sampling | |
| 505 | 8 | |a 3 Experimental Studies | |
| 650 | 4 | |a Teams in the workplace-Data processing-Congresses | |
| 700 | 1 | |a Lu, Tun |e Sonstige |4 oth | |
| 700 | 1 | |a Guo, Yinzhang |e Sonstige |4 oth | |
| 700 | 1 | |a Song, Xiaoxia |e Sonstige |4 oth | |
| 700 | 1 | |a Fan, Hongfei |e Sonstige |4 oth | |
| 700 | 1 | |a Liu, Dongning |e Sonstige |4 oth | |
| 700 | 1 | |a Gao, Liping |e Sonstige |4 oth | |
| 700 | 1 | |a Du, Bowen |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Sun, Yuqing |t Computer Supported Cooperative Work and Social Computing |d Singapore : Springer Singapore Pte. Limited,c2023 |z 9789819923847 |
| 912 | |a ZDB-30-PQE | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035215565 | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=7248819 |l DE-2070s |p ZDB-30-PQE |q HWR_PDA_PQE |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1810687471469461504 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Sun, Yuqing |
| author_facet | Sun, Yuqing |
| author_role | aut |
| author_sort | Sun, Yuqing |
| author_variant | y s ys |
| building | Verbundindex |
| bvnumber | BV049876115 |
| collection | ZDB-30-PQE |
| contents | Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Crowd Intelligence and Crowd Cooperative Computing -- MatricEs: Matrices Embeddings for Link Prediction in Knowledge Graphs -- 1 Introduction -- 2 Definition -- 2.1 Problem Definition -- 2.2 Relation Patterns -- 2.3 Notations -- 3 Related Work -- 3.1 Transformation Based Models -- 3.2 Semantic Matching Models -- 4 Our MatricEs Approach -- 4.1 MatricEs -- 4.2 Overview of Our Learning Framework -- 4.3 Theoretical Analyses -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metric -- 5.3 Implementation Details -- 5.4 Experimental Results -- 5.5 Discussion -- 6 Conclusion -- References -- Learning User Embeddings Based on Long Short-Term User Group Modeling for Next-Item Recommendation -- 1 Introduction -- 2 Related Work -- 3 LSUG Model -- 3.1 Problem Formulation -- 3.2 Overview -- 3.3 General Embedding Construction -- 3.4 Context-Aware Input Embedding -- 3.5 Latent User Group Influence Modeling -- 3.6 Hybrid User Representation Modeling -- 3.7 Model Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison of Performance -- 4.3 Influence of Components -- 4.4 Influence of Hyper-Parameters -- 5 Conclusion -- References -- Context-Aware Quaternion Embedding for Knowledge Graph Completion -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Definition -- 3.2 Quaternion Background -- 4 Methodology -- 4.1 Context Sampling Module -- 4.2 Context Information Encoder Module -- 4.3 Quaternion Rotation Module -- 4.4 Scoring Function and Loss -- 5 Experiments and Results -- 5.1 Datasets -- 5.2 Baselines -- 5.3 Evaluation Protocol -- 5.4 Experimental Settings -- 5.5 Link Prediction -- 5.6 Number of Free Parameters Comparison -- 5.7 Ablation Study -- 5.8 Convergence Rate Comparison -- 5.9 Impact of Embedding Dimension -- 5.10 Multi-relation Analysis -- 6 Conclusion References -- Dependency-Based Task Assignment in Spatial Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Task Assignment Approach -- 4.1 Matching Pair Availability -- 4.2 Pruning Strategy -- 4.3 Task Matching Pairs Selection -- 4.4 The Greedy Approach -- 5 Experimental Study -- 5.1 Experimental Setting -- 5.2 Result Analysis -- 6 Conclusion -- References -- ICKG: An I Ching Knowledge Graph Tool Revealing Ancient Wisdom -- 1 Introduction -- 2 Related Work -- 3 I Ching Knowledge Graph -- 3.1 Key Entity Classes in the I Ching -- 3.2 The Relationship Among I Ching Entities -- 3.3 Knowledge Graph Storage and Mining of Linkages -- 4 ICKG Tool Development -- 5 Conclusion -- References -- Collaborative Analysis on Code Structure and Semantics -- 1 Introduction -- 2 Related Work -- 3 Collaborative Analysis on Code Structure and Semantics -- 3.1 Problem Definition -- 3.2 Framework -- 3.3 Function Call Graph Construction -- 3.4 The Self-encoder Based Structure Similarity -- 3.5 Function Semantics Similarity -- 3.6 Code Comparison Combining Code Structure and Function Semantics -- 4 Experiments and Analysis of Results -- 4.1 Dataset and Experimental Settings -- 4.2 Evaluation Metrics -- 4.3 Comparation Method -- 4.4 Performance Analysis -- 4.5 Ablation Experiment -- 4.6 Visualized Analysis of Function Call Structure -- 5 Conclusion -- References -- Temporal Planning-Based Choreography from Music -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Our Plan2Dance Approach -- 4.1 Step1: Dancing Domain Modeling -- 4.2 Step 2: Music Analysis -- 4.3 Step 3: Planning Problem Generation -- 5 Experiment -- 5.1 Motion Diversity -- 5.2 User Study -- 5.3 Running Time -- 6 Conclusion -- References -- An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 1 Introduction -- 2 Related Work 3 Preparatory Knowledge -- 3.1 RSVD+ Model -- 3.2 Adagrad Algorithm -- 3.3 Adaptive Parameter DBSCAN Algorithm -- 4 An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 4.1 Sparse Matrix Padding -- 4.2 Two-Phase Anomaly Data Detection -- 4.3 Predicted Missing Values -- 5 Experimental Evaluation -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baseline Methods -- 5.4 Performance Comparison -- 5.5 Impact of Matrix Density -- 5.6 Ablation Study -- 6 Conclusion -- References -- Effectiveness of Malicious Behavior and Its Impact on Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Effectiveness of Four Typical Malicious Behavior -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Experimental Results -- 6 Conclusion -- References -- Scene Adaptive Persistent Target Tracking and Attack Method Based on Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Visual Tracking Algorithm -- 2.2 Deep Reinforcement Learning -- 3 Follow Attack Model Based on Game Mechanism -- 3.1 Problem Description -- 3.2 Model Framework -- 3.3 Model Structure -- 4 Experimental Verification -- 4.1 Experimental Setup -- 4.2 Comparative Transfer Experiment -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- Research on Cost Control of Mobile Crowdsourcing Supporting Low Budget in Large Scale Environmental Information Monitoring -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 3.1 Definition -- 3.2 Assumptions -- 3.3 Problem Formulation -- 4 The Low Budget Model -- 4.1 The Sampling Matrix Generation Module -- 4.2 The Participant Employment Module -- 4.3 Data Recovery Module -- 5 Experiment Results -- 5.1 Data Sets -- 5.2 Experiment Results -- 6 Conclusion and Future Work -- References -- Question Answering System Based on University Knowledge Graph -- 1 Introduction 2 Related Work -- 3 Preparation -- 3.1 University Knowledge Graph -- 3.2 Natural Language Question Dataset -- 4 Question Understanding and Knowledge Search -- 4.1 Named Entity Recognition -- 4.2 Feature Term Extraction and Word Vector Construction -- 4.3 Question Intent Recognition -- 4.4 Assembling Structured Query Statements -- 5 Implementation of Question Answering System -- 6 Conclusion -- References -- Deep Reinforcement Learning-Based Scheduling Algorithm for Service Differentiation in Cloud Business Process Management System -- 1 Introduction -- 2 Problem Description and System Design -- 2.1 Cloud BPMS Description -- 2.2 Problem Description -- 2.3 Scheduling System Design -- 3 Algorithm Design -- 3.1 Problem Modeling -- 3.2 DRL-Based Request Scheduler -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Experiment Result -- 5 Conclusion and Future Work -- References -- A Knowledge Tracing Model Based on Graph Attention Mechanism and Incorporating External Features -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Tracing -- 2.2 Graph Auto-encoder -- 2.3 Graph Attention Network -- 3 Problem Definition -- 4 Model -- 4.1 Sub-graph Building Layer -- 4.2 Sub-graph Embedding Layer -- 4.3 Predicted Layer -- 5 Experiments -- 5.1 Dataset -- 5.2 Evaluating Indicator -- 5.3 Baseline Methods -- 5.4 Experimental Setting -- 5.5 Overall Performance -- 5.6 Ablation Experiment -- 6 Conclusion -- References -- Crowd-Powered Source Searching in Complex Environments -- 1 Introduction -- 2 Related Work -- 2.1 Crowd-Powered Systems -- 2.2 Source Searching -- 3 Methods -- 3.1 Method Overview -- 3.2 Human-Machine Collaborative Tasks -- 3.3 Task Interface -- 4 User Study -- 4.1 Experimental Conditions -- 4.2 Experimental Environments -- 4.3 Measures -- 4.4 Procedure -- 5 Results -- 5.1 Participants -- 5.2 Source Searching Result -- 5.3 Usability 5.4 Cognitive Workload -- 6 Discussion -- 6.1 Implications for Designing Crowd-Powered Systems -- 6.2 Limitations and Future Work -- 7 Conclusions -- References -- Cooperative Evolutionary Computation and Human-Like Intelligent Collaboration -- Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment -- 1 Introduction -- 2 Related Work -- 2.1 Offloading Decisions and Resource Allocation -- 2.2 Privacy Protection -- 3 End-Edge-Cloud Computing Framework and Problem Modeling -- 3.1 End-Edge-Cloud Computing Framework -- 3.2 Problem Modeling -- 4 Task Offloading and Resource Allocation Algorithm with Privacy Awareness -- 4.1 Task Offloading Sequence Generation -- 4.2 Offloading Decision Adjustment -- 4.3 Communication Resource Allocation -- 4.4 Computing Resource Allocation -- 4.5 Scheduling Result Adjustment -- 5 Experiment and Performance Analysis -- 5.1 Parameter Calibration -- 5.2 Algorithm Comparison -- 6 Conclusion -- References -- A Classifier-Based Two-Stage Training Model for Few-Shot Segmentation -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Method -- 5 Experiments -- 6 Conclusion -- References -- EEG-Based Motor Imagery Classification with Deep Adversarial Learning -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 The Framework -- 3.2 Training Detail and Prediction -- 3.3 Optimization with Backpropagation -- 4 Experiment and Results -- 4.1 Data Description -- 4.2 Experimental Settings -- 4.3 Comparative Studies -- 4.4 Experimental Results Analysis -- 4.5 Algorithmic Properties -- 4.6 Visualization -- 5 Conclusion -- References -- Comparison Analysis on Techniques of Preprocessing Imbalanced Data for Symbolic Regression*-4pt -- 1 Introduction -- 2 Preprocessing Algorithms for Imbalanced Data Sets -- 2.1 Data Weighting -- 2.2 Data Compressing -- 2.3 Data Sampling 3 Experimental Studies |
| ctrlnum | (ZDB-30-PQE)EBC7248819 (ZDB-30-PAD)EBC7248819 (ZDB-89-EBL)EBL7248819 (OCoLC)1388522472 (DE-599)BVBBV049876115 |
| dewey-full | 650.028546 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 650 - Management and auxiliary services |
| dewey-raw | 650.028546 |
| dewey-search | 650.028546 |
| dewey-sort | 3650.028546 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nmm a2200000zcb4500</leader><controlfield tag="001">BV049876115</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">240919s2023 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9789819923854</subfield><subfield code="9">978-981-9923-85-4</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC7248819</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC7248819</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL7248819</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1388522472</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049876115</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-2070s</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">650.028546</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Yuqing</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computer Supported Cooperative Work and Social Computing</subfield><subfield code="b">17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Singapore</subfield><subfield code="b">Springer Singapore Pte. Limited</subfield><subfield code="c">2023</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (683 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Communications in Computer and Information Science Series</subfield><subfield code="v">v.1682</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Crowd Intelligence and Crowd Cooperative Computing -- MatricEs: Matrices Embeddings for Link Prediction in Knowledge Graphs -- 1 Introduction -- 2 Definition -- 2.1 Problem Definition -- 2.2 Relation Patterns -- 2.3 Notations -- 3 Related Work -- 3.1 Transformation Based Models -- 3.2 Semantic Matching Models -- 4 Our MatricEs Approach -- 4.1 MatricEs -- 4.2 Overview of Our Learning Framework -- 4.3 Theoretical Analyses -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metric -- 5.3 Implementation Details -- 5.4 Experimental Results -- 5.5 Discussion -- 6 Conclusion -- References -- Learning User Embeddings Based on Long Short-Term User Group Modeling for Next-Item Recommendation -- 1 Introduction -- 2 Related Work -- 3 LSUG Model -- 3.1 Problem Formulation -- 3.2 Overview -- 3.3 General Embedding Construction -- 3.4 Context-Aware Input Embedding -- 3.5 Latent User Group Influence Modeling -- 3.6 Hybrid User Representation Modeling -- 3.7 Model Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison of Performance -- 4.3 Influence of Components -- 4.4 Influence of Hyper-Parameters -- 5 Conclusion -- References -- Context-Aware Quaternion Embedding for Knowledge Graph Completion -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Definition -- 3.2 Quaternion Background -- 4 Methodology -- 4.1 Context Sampling Module -- 4.2 Context Information Encoder Module -- 4.3 Quaternion Rotation Module -- 4.4 Scoring Function and Loss -- 5 Experiments and Results -- 5.1 Datasets -- 5.2 Baselines -- 5.3 Evaluation Protocol -- 5.4 Experimental Settings -- 5.5 Link Prediction -- 5.6 Number of Free Parameters Comparison -- 5.7 Ablation Study -- 5.8 Convergence Rate Comparison -- 5.9 Impact of Embedding Dimension -- 5.10 Multi-relation Analysis -- 6 Conclusion</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">References -- Dependency-Based Task Assignment in Spatial Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Task Assignment Approach -- 4.1 Matching Pair Availability -- 4.2 Pruning Strategy -- 4.3 Task Matching Pairs Selection -- 4.4 The Greedy Approach -- 5 Experimental Study -- 5.1 Experimental Setting -- 5.2 Result Analysis -- 6 Conclusion -- References -- ICKG: An I Ching Knowledge Graph Tool Revealing Ancient Wisdom -- 1 Introduction -- 2 Related Work -- 3 I Ching Knowledge Graph -- 3.1 Key Entity Classes in the I Ching -- 3.2 The Relationship Among I Ching Entities -- 3.3 Knowledge Graph Storage and Mining of Linkages -- 4 ICKG Tool Development -- 5 Conclusion -- References -- Collaborative Analysis on Code Structure and Semantics -- 1 Introduction -- 2 Related Work -- 3 Collaborative Analysis on Code Structure and Semantics -- 3.1 Problem Definition -- 3.2 Framework -- 3.3 Function Call Graph Construction -- 3.4 The Self-encoder Based Structure Similarity -- 3.5 Function Semantics Similarity -- 3.6 Code Comparison Combining Code Structure and Function Semantics -- 4 Experiments and Analysis of Results -- 4.1 Dataset and Experimental Settings -- 4.2 Evaluation Metrics -- 4.3 Comparation Method -- 4.4 Performance Analysis -- 4.5 Ablation Experiment -- 4.6 Visualized Analysis of Function Call Structure -- 5 Conclusion -- References -- Temporal Planning-Based Choreography from Music -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Our Plan2Dance Approach -- 4.1 Step1: Dancing Domain Modeling -- 4.2 Step 2: Music Analysis -- 4.3 Step 3: Planning Problem Generation -- 5 Experiment -- 5.1 Motion Diversity -- 5.2 User Study -- 5.3 Running Time -- 6 Conclusion -- References -- An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 1 Introduction -- 2 Related Work</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3 Preparatory Knowledge -- 3.1 RSVD+ Model -- 3.2 Adagrad Algorithm -- 3.3 Adaptive Parameter DBSCAN Algorithm -- 4 An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 4.1 Sparse Matrix Padding -- 4.2 Two-Phase Anomaly Data Detection -- 4.3 Predicted Missing Values -- 5 Experimental Evaluation -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baseline Methods -- 5.4 Performance Comparison -- 5.5 Impact of Matrix Density -- 5.6 Ablation Study -- 6 Conclusion -- References -- Effectiveness of Malicious Behavior and Its Impact on Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Effectiveness of Four Typical Malicious Behavior -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Experimental Results -- 6 Conclusion -- References -- Scene Adaptive Persistent Target Tracking and Attack Method Based on Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Visual Tracking Algorithm -- 2.2 Deep Reinforcement Learning -- 3 Follow Attack Model Based on Game Mechanism -- 3.1 Problem Description -- 3.2 Model Framework -- 3.3 Model Structure -- 4 Experimental Verification -- 4.1 Experimental Setup -- 4.2 Comparative Transfer Experiment -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- Research on Cost Control of Mobile Crowdsourcing Supporting Low Budget in Large Scale Environmental Information Monitoring -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 3.1 Definition -- 3.2 Assumptions -- 3.3 Problem Formulation -- 4 The Low Budget Model -- 4.1 The Sampling Matrix Generation Module -- 4.2 The Participant Employment Module -- 4.3 Data Recovery Module -- 5 Experiment Results -- 5.1 Data Sets -- 5.2 Experiment Results -- 6 Conclusion and Future Work -- References -- Question Answering System Based on University Knowledge Graph -- 1 Introduction</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2 Related Work -- 3 Preparation -- 3.1 University Knowledge Graph -- 3.2 Natural Language Question Dataset -- 4 Question Understanding and Knowledge Search -- 4.1 Named Entity Recognition -- 4.2 Feature Term Extraction and Word Vector Construction -- 4.3 Question Intent Recognition -- 4.4 Assembling Structured Query Statements -- 5 Implementation of Question Answering System -- 6 Conclusion -- References -- Deep Reinforcement Learning-Based Scheduling Algorithm for Service Differentiation in Cloud Business Process Management System -- 1 Introduction -- 2 Problem Description and System Design -- 2.1 Cloud BPMS Description -- 2.2 Problem Description -- 2.3 Scheduling System Design -- 3 Algorithm Design -- 3.1 Problem Modeling -- 3.2 DRL-Based Request Scheduler -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Experiment Result -- 5 Conclusion and Future Work -- References -- A Knowledge Tracing Model Based on Graph Attention Mechanism and Incorporating External Features -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Tracing -- 2.2 Graph Auto-encoder -- 2.3 Graph Attention Network -- 3 Problem Definition -- 4 Model -- 4.1 Sub-graph Building Layer -- 4.2 Sub-graph Embedding Layer -- 4.3 Predicted Layer -- 5 Experiments -- 5.1 Dataset -- 5.2 Evaluating Indicator -- 5.3 Baseline Methods -- 5.4 Experimental Setting -- 5.5 Overall Performance -- 5.6 Ablation Experiment -- 6 Conclusion -- References -- Crowd-Powered Source Searching in Complex Environments -- 1 Introduction -- 2 Related Work -- 2.1 Crowd-Powered Systems -- 2.2 Source Searching -- 3 Methods -- 3.1 Method Overview -- 3.2 Human-Machine Collaborative Tasks -- 3.3 Task Interface -- 4 User Study -- 4.1 Experimental Conditions -- 4.2 Experimental Environments -- 4.3 Measures -- 4.4 Procedure -- 5 Results -- 5.1 Participants -- 5.2 Source Searching Result -- 5.3 Usability</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.4 Cognitive Workload -- 6 Discussion -- 6.1 Implications for Designing Crowd-Powered Systems -- 6.2 Limitations and Future Work -- 7 Conclusions -- References -- Cooperative Evolutionary Computation and Human-Like Intelligent Collaboration -- Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment -- 1 Introduction -- 2 Related Work -- 2.1 Offloading Decisions and Resource Allocation -- 2.2 Privacy Protection -- 3 End-Edge-Cloud Computing Framework and Problem Modeling -- 3.1 End-Edge-Cloud Computing Framework -- 3.2 Problem Modeling -- 4 Task Offloading and Resource Allocation Algorithm with Privacy Awareness -- 4.1 Task Offloading Sequence Generation -- 4.2 Offloading Decision Adjustment -- 4.3 Communication Resource Allocation -- 4.4 Computing Resource Allocation -- 4.5 Scheduling Result Adjustment -- 5 Experiment and Performance Analysis -- 5.1 Parameter Calibration -- 5.2 Algorithm Comparison -- 6 Conclusion -- References -- A Classifier-Based Two-Stage Training Model for Few-Shot Segmentation -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Method -- 5 Experiments -- 6 Conclusion -- References -- EEG-Based Motor Imagery Classification with Deep Adversarial Learning -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 The Framework -- 3.2 Training Detail and Prediction -- 3.3 Optimization with Backpropagation -- 4 Experiment and Results -- 4.1 Data Description -- 4.2 Experimental Settings -- 4.3 Comparative Studies -- 4.4 Experimental Results Analysis -- 4.5 Algorithmic Properties -- 4.6 Visualization -- 5 Conclusion -- References -- Comparison Analysis on Techniques of Preprocessing Imbalanced Data for Symbolic Regression*-4pt -- 1 Introduction -- 2 Preprocessing Algorithms for Imbalanced Data Sets -- 2.1 Data Weighting -- 2.2 Data Compressing -- 2.3 Data Sampling</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3 Experimental Studies</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Teams in the workplace-Data processing-Congresses</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Tun</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Yinzhang</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Song, Xiaoxia</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fan, Hongfei</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Dongning</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Liping</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Bowen</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Sun, Yuqing</subfield><subfield code="t">Computer Supported Cooperative Work and Social Computing</subfield><subfield code="d">Singapore : Springer Singapore Pte. Limited,c2023</subfield><subfield code="z">9789819923847</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035215565</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=7248819</subfield><subfield code="l">DE-2070s</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV049876115 |
| illustrated | Not Illustrated |
| indexdate | 2024-09-20T04:22:16Z |
| institution | BVB |
| isbn | 9789819923854 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035215565 |
| oclc_num | 1388522472 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (683 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer Singapore Pte. Limited |
| record_format | marc |
| series2 | Communications in Computer and Information Science Series |
| spelling | Sun, Yuqing Verfasser aut Computer Supported Cooperative Work and Social Computing 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. 1st ed Singapore Springer Singapore Pte. Limited 2023 ©2023 1 Online-Ressource (683 Seiten) txt rdacontent c rdamedia cr rdacarrier Communications in Computer and Information Science Series v.1682 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Crowd Intelligence and Crowd Cooperative Computing -- MatricEs: Matrices Embeddings for Link Prediction in Knowledge Graphs -- 1 Introduction -- 2 Definition -- 2.1 Problem Definition -- 2.2 Relation Patterns -- 2.3 Notations -- 3 Related Work -- 3.1 Transformation Based Models -- 3.2 Semantic Matching Models -- 4 Our MatricEs Approach -- 4.1 MatricEs -- 4.2 Overview of Our Learning Framework -- 4.3 Theoretical Analyses -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metric -- 5.3 Implementation Details -- 5.4 Experimental Results -- 5.5 Discussion -- 6 Conclusion -- References -- Learning User Embeddings Based on Long Short-Term User Group Modeling for Next-Item Recommendation -- 1 Introduction -- 2 Related Work -- 3 LSUG Model -- 3.1 Problem Formulation -- 3.2 Overview -- 3.3 General Embedding Construction -- 3.4 Context-Aware Input Embedding -- 3.5 Latent User Group Influence Modeling -- 3.6 Hybrid User Representation Modeling -- 3.7 Model Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison of Performance -- 4.3 Influence of Components -- 4.4 Influence of Hyper-Parameters -- 5 Conclusion -- References -- Context-Aware Quaternion Embedding for Knowledge Graph Completion -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Definition -- 3.2 Quaternion Background -- 4 Methodology -- 4.1 Context Sampling Module -- 4.2 Context Information Encoder Module -- 4.3 Quaternion Rotation Module -- 4.4 Scoring Function and Loss -- 5 Experiments and Results -- 5.1 Datasets -- 5.2 Baselines -- 5.3 Evaluation Protocol -- 5.4 Experimental Settings -- 5.5 Link Prediction -- 5.6 Number of Free Parameters Comparison -- 5.7 Ablation Study -- 5.8 Convergence Rate Comparison -- 5.9 Impact of Embedding Dimension -- 5.10 Multi-relation Analysis -- 6 Conclusion References -- Dependency-Based Task Assignment in Spatial Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Task Assignment Approach -- 4.1 Matching Pair Availability -- 4.2 Pruning Strategy -- 4.3 Task Matching Pairs Selection -- 4.4 The Greedy Approach -- 5 Experimental Study -- 5.1 Experimental Setting -- 5.2 Result Analysis -- 6 Conclusion -- References -- ICKG: An I Ching Knowledge Graph Tool Revealing Ancient Wisdom -- 1 Introduction -- 2 Related Work -- 3 I Ching Knowledge Graph -- 3.1 Key Entity Classes in the I Ching -- 3.2 The Relationship Among I Ching Entities -- 3.3 Knowledge Graph Storage and Mining of Linkages -- 4 ICKG Tool Development -- 5 Conclusion -- References -- Collaborative Analysis on Code Structure and Semantics -- 1 Introduction -- 2 Related Work -- 3 Collaborative Analysis on Code Structure and Semantics -- 3.1 Problem Definition -- 3.2 Framework -- 3.3 Function Call Graph Construction -- 3.4 The Self-encoder Based Structure Similarity -- 3.5 Function Semantics Similarity -- 3.6 Code Comparison Combining Code Structure and Function Semantics -- 4 Experiments and Analysis of Results -- 4.1 Dataset and Experimental Settings -- 4.2 Evaluation Metrics -- 4.3 Comparation Method -- 4.4 Performance Analysis -- 4.5 Ablation Experiment -- 4.6 Visualized Analysis of Function Call Structure -- 5 Conclusion -- References -- Temporal Planning-Based Choreography from Music -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Our Plan2Dance Approach -- 4.1 Step1: Dancing Domain Modeling -- 4.2 Step 2: Music Analysis -- 4.3 Step 3: Planning Problem Generation -- 5 Experiment -- 5.1 Motion Diversity -- 5.2 User Study -- 5.3 Running Time -- 6 Conclusion -- References -- An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 1 Introduction -- 2 Related Work 3 Preparatory Knowledge -- 3.1 RSVD+ Model -- 3.2 Adagrad Algorithm -- 3.3 Adaptive Parameter DBSCAN Algorithm -- 4 An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 4.1 Sparse Matrix Padding -- 4.2 Two-Phase Anomaly Data Detection -- 4.3 Predicted Missing Values -- 5 Experimental Evaluation -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baseline Methods -- 5.4 Performance Comparison -- 5.5 Impact of Matrix Density -- 5.6 Ablation Study -- 6 Conclusion -- References -- Effectiveness of Malicious Behavior and Its Impact on Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Effectiveness of Four Typical Malicious Behavior -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Experimental Results -- 6 Conclusion -- References -- Scene Adaptive Persistent Target Tracking and Attack Method Based on Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Visual Tracking Algorithm -- 2.2 Deep Reinforcement Learning -- 3 Follow Attack Model Based on Game Mechanism -- 3.1 Problem Description -- 3.2 Model Framework -- 3.3 Model Structure -- 4 Experimental Verification -- 4.1 Experimental Setup -- 4.2 Comparative Transfer Experiment -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- Research on Cost Control of Mobile Crowdsourcing Supporting Low Budget in Large Scale Environmental Information Monitoring -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 3.1 Definition -- 3.2 Assumptions -- 3.3 Problem Formulation -- 4 The Low Budget Model -- 4.1 The Sampling Matrix Generation Module -- 4.2 The Participant Employment Module -- 4.3 Data Recovery Module -- 5 Experiment Results -- 5.1 Data Sets -- 5.2 Experiment Results -- 6 Conclusion and Future Work -- References -- Question Answering System Based on University Knowledge Graph -- 1 Introduction 2 Related Work -- 3 Preparation -- 3.1 University Knowledge Graph -- 3.2 Natural Language Question Dataset -- 4 Question Understanding and Knowledge Search -- 4.1 Named Entity Recognition -- 4.2 Feature Term Extraction and Word Vector Construction -- 4.3 Question Intent Recognition -- 4.4 Assembling Structured Query Statements -- 5 Implementation of Question Answering System -- 6 Conclusion -- References -- Deep Reinforcement Learning-Based Scheduling Algorithm for Service Differentiation in Cloud Business Process Management System -- 1 Introduction -- 2 Problem Description and System Design -- 2.1 Cloud BPMS Description -- 2.2 Problem Description -- 2.3 Scheduling System Design -- 3 Algorithm Design -- 3.1 Problem Modeling -- 3.2 DRL-Based Request Scheduler -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Experiment Result -- 5 Conclusion and Future Work -- References -- A Knowledge Tracing Model Based on Graph Attention Mechanism and Incorporating External Features -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Tracing -- 2.2 Graph Auto-encoder -- 2.3 Graph Attention Network -- 3 Problem Definition -- 4 Model -- 4.1 Sub-graph Building Layer -- 4.2 Sub-graph Embedding Layer -- 4.3 Predicted Layer -- 5 Experiments -- 5.1 Dataset -- 5.2 Evaluating Indicator -- 5.3 Baseline Methods -- 5.4 Experimental Setting -- 5.5 Overall Performance -- 5.6 Ablation Experiment -- 6 Conclusion -- References -- Crowd-Powered Source Searching in Complex Environments -- 1 Introduction -- 2 Related Work -- 2.1 Crowd-Powered Systems -- 2.2 Source Searching -- 3 Methods -- 3.1 Method Overview -- 3.2 Human-Machine Collaborative Tasks -- 3.3 Task Interface -- 4 User Study -- 4.1 Experimental Conditions -- 4.2 Experimental Environments -- 4.3 Measures -- 4.4 Procedure -- 5 Results -- 5.1 Participants -- 5.2 Source Searching Result -- 5.3 Usability 5.4 Cognitive Workload -- 6 Discussion -- 6.1 Implications for Designing Crowd-Powered Systems -- 6.2 Limitations and Future Work -- 7 Conclusions -- References -- Cooperative Evolutionary Computation and Human-Like Intelligent Collaboration -- Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment -- 1 Introduction -- 2 Related Work -- 2.1 Offloading Decisions and Resource Allocation -- 2.2 Privacy Protection -- 3 End-Edge-Cloud Computing Framework and Problem Modeling -- 3.1 End-Edge-Cloud Computing Framework -- 3.2 Problem Modeling -- 4 Task Offloading and Resource Allocation Algorithm with Privacy Awareness -- 4.1 Task Offloading Sequence Generation -- 4.2 Offloading Decision Adjustment -- 4.3 Communication Resource Allocation -- 4.4 Computing Resource Allocation -- 4.5 Scheduling Result Adjustment -- 5 Experiment and Performance Analysis -- 5.1 Parameter Calibration -- 5.2 Algorithm Comparison -- 6 Conclusion -- References -- A Classifier-Based Two-Stage Training Model for Few-Shot Segmentation -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Method -- 5 Experiments -- 6 Conclusion -- References -- EEG-Based Motor Imagery Classification with Deep Adversarial Learning -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 The Framework -- 3.2 Training Detail and Prediction -- 3.3 Optimization with Backpropagation -- 4 Experiment and Results -- 4.1 Data Description -- 4.2 Experimental Settings -- 4.3 Comparative Studies -- 4.4 Experimental Results Analysis -- 4.5 Algorithmic Properties -- 4.6 Visualization -- 5 Conclusion -- References -- Comparison Analysis on Techniques of Preprocessing Imbalanced Data for Symbolic Regression*-4pt -- 1 Introduction -- 2 Preprocessing Algorithms for Imbalanced Data Sets -- 2.1 Data Weighting -- 2.2 Data Compressing -- 2.3 Data Sampling 3 Experimental Studies Teams in the workplace-Data processing-Congresses Lu, Tun Sonstige oth Guo, Yinzhang Sonstige oth Song, Xiaoxia Sonstige oth Fan, Hongfei Sonstige oth Liu, Dongning Sonstige oth Gao, Liping Sonstige oth Du, Bowen Sonstige oth Erscheint auch als Druck-Ausgabe Sun, Yuqing Computer Supported Cooperative Work and Social Computing Singapore : Springer Singapore Pte. Limited,c2023 9789819923847 |
| spellingShingle | Sun, Yuqing Computer Supported Cooperative Work and Social Computing 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Crowd Intelligence and Crowd Cooperative Computing -- MatricEs: Matrices Embeddings for Link Prediction in Knowledge Graphs -- 1 Introduction -- 2 Definition -- 2.1 Problem Definition -- 2.2 Relation Patterns -- 2.3 Notations -- 3 Related Work -- 3.1 Transformation Based Models -- 3.2 Semantic Matching Models -- 4 Our MatricEs Approach -- 4.1 MatricEs -- 4.2 Overview of Our Learning Framework -- 4.3 Theoretical Analyses -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metric -- 5.3 Implementation Details -- 5.4 Experimental Results -- 5.5 Discussion -- 6 Conclusion -- References -- Learning User Embeddings Based on Long Short-Term User Group Modeling for Next-Item Recommendation -- 1 Introduction -- 2 Related Work -- 3 LSUG Model -- 3.1 Problem Formulation -- 3.2 Overview -- 3.3 General Embedding Construction -- 3.4 Context-Aware Input Embedding -- 3.5 Latent User Group Influence Modeling -- 3.6 Hybrid User Representation Modeling -- 3.7 Model Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison of Performance -- 4.3 Influence of Components -- 4.4 Influence of Hyper-Parameters -- 5 Conclusion -- References -- Context-Aware Quaternion Embedding for Knowledge Graph Completion -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Definition -- 3.2 Quaternion Background -- 4 Methodology -- 4.1 Context Sampling Module -- 4.2 Context Information Encoder Module -- 4.3 Quaternion Rotation Module -- 4.4 Scoring Function and Loss -- 5 Experiments and Results -- 5.1 Datasets -- 5.2 Baselines -- 5.3 Evaluation Protocol -- 5.4 Experimental Settings -- 5.5 Link Prediction -- 5.6 Number of Free Parameters Comparison -- 5.7 Ablation Study -- 5.8 Convergence Rate Comparison -- 5.9 Impact of Embedding Dimension -- 5.10 Multi-relation Analysis -- 6 Conclusion References -- Dependency-Based Task Assignment in Spatial Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Task Assignment Approach -- 4.1 Matching Pair Availability -- 4.2 Pruning Strategy -- 4.3 Task Matching Pairs Selection -- 4.4 The Greedy Approach -- 5 Experimental Study -- 5.1 Experimental Setting -- 5.2 Result Analysis -- 6 Conclusion -- References -- ICKG: An I Ching Knowledge Graph Tool Revealing Ancient Wisdom -- 1 Introduction -- 2 Related Work -- 3 I Ching Knowledge Graph -- 3.1 Key Entity Classes in the I Ching -- 3.2 The Relationship Among I Ching Entities -- 3.3 Knowledge Graph Storage and Mining of Linkages -- 4 ICKG Tool Development -- 5 Conclusion -- References -- Collaborative Analysis on Code Structure and Semantics -- 1 Introduction -- 2 Related Work -- 3 Collaborative Analysis on Code Structure and Semantics -- 3.1 Problem Definition -- 3.2 Framework -- 3.3 Function Call Graph Construction -- 3.4 The Self-encoder Based Structure Similarity -- 3.5 Function Semantics Similarity -- 3.6 Code Comparison Combining Code Structure and Function Semantics -- 4 Experiments and Analysis of Results -- 4.1 Dataset and Experimental Settings -- 4.2 Evaluation Metrics -- 4.3 Comparation Method -- 4.4 Performance Analysis -- 4.5 Ablation Experiment -- 4.6 Visualized Analysis of Function Call Structure -- 5 Conclusion -- References -- Temporal Planning-Based Choreography from Music -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Our Plan2Dance Approach -- 4.1 Step1: Dancing Domain Modeling -- 4.2 Step 2: Music Analysis -- 4.3 Step 3: Planning Problem Generation -- 5 Experiment -- 5.1 Motion Diversity -- 5.2 User Study -- 5.3 Running Time -- 6 Conclusion -- References -- An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 1 Introduction -- 2 Related Work 3 Preparatory Knowledge -- 3.1 RSVD+ Model -- 3.2 Adagrad Algorithm -- 3.3 Adaptive Parameter DBSCAN Algorithm -- 4 An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method -- 4.1 Sparse Matrix Padding -- 4.2 Two-Phase Anomaly Data Detection -- 4.3 Predicted Missing Values -- 5 Experimental Evaluation -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Baseline Methods -- 5.4 Performance Comparison -- 5.5 Impact of Matrix Density -- 5.6 Ablation Study -- 6 Conclusion -- References -- Effectiveness of Malicious Behavior and Its Impact on Crowdsourcing -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Effectiveness of Four Typical Malicious Behavior -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Experimental Results -- 6 Conclusion -- References -- Scene Adaptive Persistent Target Tracking and Attack Method Based on Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Visual Tracking Algorithm -- 2.2 Deep Reinforcement Learning -- 3 Follow Attack Model Based on Game Mechanism -- 3.1 Problem Description -- 3.2 Model Framework -- 3.3 Model Structure -- 4 Experimental Verification -- 4.1 Experimental Setup -- 4.2 Comparative Transfer Experiment -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- Research on Cost Control of Mobile Crowdsourcing Supporting Low Budget in Large Scale Environmental Information Monitoring -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 3.1 Definition -- 3.2 Assumptions -- 3.3 Problem Formulation -- 4 The Low Budget Model -- 4.1 The Sampling Matrix Generation Module -- 4.2 The Participant Employment Module -- 4.3 Data Recovery Module -- 5 Experiment Results -- 5.1 Data Sets -- 5.2 Experiment Results -- 6 Conclusion and Future Work -- References -- Question Answering System Based on University Knowledge Graph -- 1 Introduction 2 Related Work -- 3 Preparation -- 3.1 University Knowledge Graph -- 3.2 Natural Language Question Dataset -- 4 Question Understanding and Knowledge Search -- 4.1 Named Entity Recognition -- 4.2 Feature Term Extraction and Word Vector Construction -- 4.3 Question Intent Recognition -- 4.4 Assembling Structured Query Statements -- 5 Implementation of Question Answering System -- 6 Conclusion -- References -- Deep Reinforcement Learning-Based Scheduling Algorithm for Service Differentiation in Cloud Business Process Management System -- 1 Introduction -- 2 Problem Description and System Design -- 2.1 Cloud BPMS Description -- 2.2 Problem Description -- 2.3 Scheduling System Design -- 3 Algorithm Design -- 3.1 Problem Modeling -- 3.2 DRL-Based Request Scheduler -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Experiment Result -- 5 Conclusion and Future Work -- References -- A Knowledge Tracing Model Based on Graph Attention Mechanism and Incorporating External Features -- 1 Introduction -- 2 Related Work -- 2.1 Knowledge Tracing -- 2.2 Graph Auto-encoder -- 2.3 Graph Attention Network -- 3 Problem Definition -- 4 Model -- 4.1 Sub-graph Building Layer -- 4.2 Sub-graph Embedding Layer -- 4.3 Predicted Layer -- 5 Experiments -- 5.1 Dataset -- 5.2 Evaluating Indicator -- 5.3 Baseline Methods -- 5.4 Experimental Setting -- 5.5 Overall Performance -- 5.6 Ablation Experiment -- 6 Conclusion -- References -- Crowd-Powered Source Searching in Complex Environments -- 1 Introduction -- 2 Related Work -- 2.1 Crowd-Powered Systems -- 2.2 Source Searching -- 3 Methods -- 3.1 Method Overview -- 3.2 Human-Machine Collaborative Tasks -- 3.3 Task Interface -- 4 User Study -- 4.1 Experimental Conditions -- 4.2 Experimental Environments -- 4.3 Measures -- 4.4 Procedure -- 5 Results -- 5.1 Participants -- 5.2 Source Searching Result -- 5.3 Usability 5.4 Cognitive Workload -- 6 Discussion -- 6.1 Implications for Designing Crowd-Powered Systems -- 6.2 Limitations and Future Work -- 7 Conclusions -- References -- Cooperative Evolutionary Computation and Human-Like Intelligent Collaboration -- Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment -- 1 Introduction -- 2 Related Work -- 2.1 Offloading Decisions and Resource Allocation -- 2.2 Privacy Protection -- 3 End-Edge-Cloud Computing Framework and Problem Modeling -- 3.1 End-Edge-Cloud Computing Framework -- 3.2 Problem Modeling -- 4 Task Offloading and Resource Allocation Algorithm with Privacy Awareness -- 4.1 Task Offloading Sequence Generation -- 4.2 Offloading Decision Adjustment -- 4.3 Communication Resource Allocation -- 4.4 Computing Resource Allocation -- 4.5 Scheduling Result Adjustment -- 5 Experiment and Performance Analysis -- 5.1 Parameter Calibration -- 5.2 Algorithm Comparison -- 6 Conclusion -- References -- A Classifier-Based Two-Stage Training Model for Few-Shot Segmentation -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Method -- 5 Experiments -- 6 Conclusion -- References -- EEG-Based Motor Imagery Classification with Deep Adversarial Learning -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 The Framework -- 3.2 Training Detail and Prediction -- 3.3 Optimization with Backpropagation -- 4 Experiment and Results -- 4.1 Data Description -- 4.2 Experimental Settings -- 4.3 Comparative Studies -- 4.4 Experimental Results Analysis -- 4.5 Algorithmic Properties -- 4.6 Visualization -- 5 Conclusion -- References -- Comparison Analysis on Techniques of Preprocessing Imbalanced Data for Symbolic Regression*-4pt -- 1 Introduction -- 2 Preprocessing Algorithms for Imbalanced Data Sets -- 2.1 Data Weighting -- 2.2 Data Compressing -- 2.3 Data Sampling 3 Experimental Studies Teams in the workplace-Data processing-Congresses |
| title | Computer Supported Cooperative Work and Social Computing 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. |
| title_auth | Computer Supported Cooperative Work and Social Computing 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. |
| title_exact_search | Computer Supported Cooperative Work and Social Computing 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. |
| title_full | Computer Supported Cooperative Work and Social Computing 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. |
| title_fullStr | Computer Supported Cooperative Work and Social Computing 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. |
| title_full_unstemmed | Computer Supported Cooperative Work and Social Computing 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. |
| title_short | Computer Supported Cooperative Work and Social Computing |
| title_sort | computer supported cooperative work and social computing 17th ccf conference chinesecscw 2022 taiyuan china november 25 27 2022 revised selected papers part ii |
| title_sub | 17th CCF Conference, ChineseCSCW 2022, Taiyuan, China, November 25-27, 2022, Revised Selected Papers, Part II. |
| topic | Teams in the workplace-Data processing-Congresses |
| topic_facet | Teams in the workplace-Data processing-Congresses |
| work_keys_str_mv | AT sunyuqing computersupportedcooperativeworkandsocialcomputing17thccfconferencechinesecscw2022taiyuanchinanovember25272022revisedselectedpaperspartii AT lutun computersupportedcooperativeworkandsocialcomputing17thccfconferencechinesecscw2022taiyuanchinanovember25272022revisedselectedpaperspartii AT guoyinzhang computersupportedcooperativeworkandsocialcomputing17thccfconferencechinesecscw2022taiyuanchinanovember25272022revisedselectedpaperspartii AT songxiaoxia computersupportedcooperativeworkandsocialcomputing17thccfconferencechinesecscw2022taiyuanchinanovember25272022revisedselectedpaperspartii AT fanhongfei computersupportedcooperativeworkandsocialcomputing17thccfconferencechinesecscw2022taiyuanchinanovember25272022revisedselectedpaperspartii AT liudongning computersupportedcooperativeworkandsocialcomputing17thccfconferencechinesecscw2022taiyuanchinanovember25272022revisedselectedpaperspartii AT gaoliping computersupportedcooperativeworkandsocialcomputing17thccfconferencechinesecscw2022taiyuanchinanovember25272022revisedselectedpaperspartii AT dubowen computersupportedcooperativeworkandsocialcomputing17thccfconferencechinesecscw2022taiyuanchinanovember25272022revisedselectedpaperspartii |