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MARC Lista (tabellare)
Advanced Data Mining and Applications [[electronic resource] ] : 5th International Conference, ADMA 2009, Chengdu, China, August 17-19, 2009, Proceedings / / edited by Ronghuai Huang, Qiang Yang, Jian Pei, João Gama, Xiaofeng Meng, Xue Li
Advanced Data Mining and Applications [[electronic resource] ] : 5th International Conference, ADMA 2009, Chengdu, China, August 17-19, 2009, Proceedings / / edited by Ronghuai Huang, Qiang Yang, Jian Pei, João Gama, Xiaofeng Meng, Xue Li
Edizione [1st ed. 2009.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009
Descrizione fisica 1 online resource (XXI, 807 p.)
Disciplina 004n/a
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Data mining
Information storage and retrieval
Computers
Application software
Pattern recognition
Artificial intelligence
Data Mining and Knowledge Discovery
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl. Internet)
Pattern Recognition
Artificial Intelligence
Soggetto genere / forma Kongress.
Peking (2009)
ISBN 1-280-38316-X
9786613561084
3-642-03348-2
Classificazione DAT 600f
DAT 703f
DAT 825f
SS 4800
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Keynotes -- Regular Papers -- Short Papers.
Record Nr. UNISA-996465765803316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Advances in Data and Web Management [[electronic resource] ] : Joint International Conferences, APWeb/WAIM 2009, Suzhou, China, April 2-4, 2009, Proceedings / / edited by Qing Li, Ling Feng, Jian Pei, X. Sean Wang, Xiaofang Zhou, Qiaoming Zhu
Advances in Data and Web Management [[electronic resource] ] : Joint International Conferences, APWeb/WAIM 2009, Suzhou, China, April 2-4, 2009, Proceedings / / edited by Qing Li, Ling Feng, Jian Pei, X. Sean Wang, Xiaofang Zhou, Qiaoming Zhu
Edizione [1st ed. 2009.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009
Descrizione fisica 1 online resource (XXI, 685 p.)
Disciplina 004.6
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Computer communication systems
Data mining
Application software
Information storage and retrieval
Pattern recognition
Computers
Computer Communication Networks
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
Information Storage and Retrieval
Pattern Recognition
Information Systems and Communication Service
ISBN 3-642-00672-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Keynotes -- Distributed XML Processing -- Towards Multi-modal Extraction and Summarization of Conversations -- Query Processing I -- Simple but Effective Porn Query Recognition by k-NN with Semantic Similarity Measure -- Selective-NRA Algorithms for Top-k Queries -- Continuous K-Nearest Neighbor Query over Moving Objects in Road Networks -- Data Quality Aware Queries in Collaborative Information Systems -- Probabilistic Threshold Range Aggregate Query Processing over Uncertain Data -- Dynamic Data Migration Policies for Query-Intensive Distributed Data Environments -- Query Processing II -- C-Tree Indexing for Holistic Twig Joins -- Processing XML Keyword Search by Constructing Effective Structured Queries -- Representing Multiple Mappings between XML and Relational Schemas for Bi-directional Query Translation -- Finding Irredundant Contained Rewritings of Tree Pattern Queries Using Views -- Topic-Based Techniques -- IRank: A Term-Based Innovation Ranking System for Conferences and Scholars -- A Generalized Topic Modeling Approach for Maven Search -- Topic and Viewpoint Extraction for Diversity and Bias Analysis of News Contents -- Topic-Level Random Walk through Probabilistic Model -- Query-Focused Summarization by Combining Topic Model and Affinity Propagation -- Web Data Processing -- Rank Aggregation to Combine QoS in Web Search -- Personalized Delivery of On–Line Search Advertisement Based on User Interests -- Automatic Web Image Annotation via Web-Scale Image Semantic Space Learning -- Multidimensional Data Analysis -- Representing and Inferring Causalities among Classes of Multidimensional Data -- Efficient Incremental Computation of CUBE in Multiple Versions What-If Analysis -- Incremental Computation for MEDIAN Cubes in What-If Analysis -- The Tradeoff of Delta Table Merging and Re-writing Algorithms in What-If Analysis Application -- Stream Data Processing -- Efficiently Clustering Probabilistic Data Streams -- Detecting Abnormal Trend Evolution over Multiple Data Streams -- Mining Interventions from Parallel Event Sequences -- Effective Similarity Analysis over Event Streams Based on Sharing Extent -- Data Mining and Its Applications -- A Term-Based Driven Clustering Approach for Name Disambiguation -- Sentiment Clustering: A Novel Method to Explore in the Blogosphere -- Kernel-Based Transductive Learning with Nearest Neighbors -- An Improved Algorithm for Mining Non-Redundant Interacting Feature Subsets -- AutoPCS: A Phrase-Based Text Categorization System for Similar Texts -- A Probabilistic Approach for Mining Drifting User Interest -- Temporal Company Relation Mining from the Web -- Data Management Support to Advanced Applications -- StreetTiVo: Using a P2P XML Database System to Manage Multimedia Data in Your Living Room -- Qualitative Spatial Representation and Reasoning for Data Integration of Ocean Observational Systems -- Managing Hierarchical Information on Small Screens -- An Approach to Detect Collaborative Conflicts for Ontology Development -- Miscellaneous -- Using Link-Based Content Analysis to Measure Document Similarity Effectively -- Parallel Load Balancing Strategies for Tree-Structured Peer-to-Peer Networks -- A Verification Mechanism for Secured Message Processing in Business Collaboration -- Semantic Service Discovery by Consistency-Based Matchmaking -- A Fast Heuristic Algorithm for the Composite Web Service Selection -- Short Papers -- Intervention Events Detection and Prediction in Data Streams -- New Balanced Data Allocating and Online Migrating Algorithms in Database Cluster -- An Evaluation of Real-Time Transaction Services in Web Services E-Business Systems -- Formal Definition and Detection Algorithm for Passive Event in RFID Middleware -- Distributed Benchmarking of Relational Database Systems -- Optimizing Many-to-Many Data Aggregation in Wireless Sensor Networks -- Bag of Timestamps: A Simple and Efficient Bayesian Chronological Mining -- Efficient Hybrid Password-Based Authenticated Group Key Exchange -- Optimal K-Nearest-Neighbor Query in Data Grid -- A Routing Scheme with Localized Movement in Event-Driven Wireless Sensor Networks -- Short Text Clustering for Search Results -- Load Shedding for Shared Window Join over Real-Time Data Streams -- A Sliding-Window Approach for Finding Top-k Frequent Itemsets from Uncertain Streams -- A Novel ID-Based Anonymous Signcryption Scheme -- Pseudo Period Detection on Time Series Stream with Scale Smoothing -- An Affinity Path Based Wireless Broadcast Scheduling Approach for Multi-item Queries -- Lightweight Data Mining Based Database System Self-optimization: A Case Study -- Efficient Detection of Discords for Time Series Stream -- SLICE: A Novel Method to Find Local Linear Correlations by Constructing Hyperplanes -- Complete and Equivalent Query Rewriting Using Views -- Grubber: Allowing End-Users to Develop XML-Based Wrappers for Web Data Sources -- Performance Analysis of Group Handover Scheme for IEEE 802.16j-Enabled Vehicular Networks -- Directly Identify Unexpected Instances in the Test Set by Entropy Maximization -- Decentralized Orchestration of BPEL Processes with Execution Consistency -- Let Wiki Be More Powerful and Suitable for Enterprises with External Program -- DepRank: A Probabilistic Measure of Dependence via Heterogeneous Links.
Record Nr. UNISA-996465950003316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2009
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part VI
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part VI
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (329 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-66-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part VI -- Scientific Data -- FR3LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series -- 1 Introduction -- 2 Related Work -- 3 Problem Setup -- 4 Model Architecture -- 4.1 Temporal Contextual Consistency -- 4.2 Non-contrastive Representations Learning -- 4.3 Deterministic Forecasting -- 4.4 Probabilistic Forecasting -- 4.5 End-to-End Training -- 5 Experiments -- 5.1 Experimental Results -- 5.2 Visualization of Latent and Original Series Forecasts -- 5.3 Further Experimental Setup Details -- 6 Conclusion -- References -- Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations -- 1 Introduction -- 2 Preliminaries on Numerical Simulations -- 3 Identification and Analysis of Relevant Metadata -- 4 Efficient Metadata Capture for Parameter Optimization -- 4.1 Early Termination of Farming Runs -- 4.2 PROBE: Probing Specific Parameter Combinations -- 5 Experimental Evaluation -- 5.1 Quality of Parameter Optimization Using PROBE -- 5.2 Efficiency Evaluation -- 5.3 Reuse of Metadata Acquired Through PROBE -- 5.4 Generalization to Other Model Problems and Schemes -- 6 Conclusion and Outlook -- References -- Material Microstructure Design Using VAE-Regression with a Multimodal Prior -- 1 Introduction -- 2 Methodology -- 3 Related Work -- 4 Experimental Results -- 5 Summary and Conclusions -- References -- A Weighted Cross-Modal Feature Aggregation Network for Rumor Detection -- 1 Introduction -- 2 Related Work -- 2.1 Rumor Detection -- 2.2 Multimodal Alignment -- 3 Methodology -- 3.1 Overview of WCAN -- 3.2 Feature Extraction -- 3.3 Weighted Cross-Modal Aggregation Module -- 3.4 Multimodal Feature Fusion -- 3.5 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Ablation Experiment.
4.4 Hyper-parameter Analysis -- 4.5 Visualization on the Representations -- 4.6 Case Study -- 5 Conclusions -- References -- Texts, Web, Social Network -- Quantifying Opinion Rejection: A Method to Detect Social Media Echo Chambers -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Echo Chamber Detection in Signed Networks -- 5 SEcho Method -- 5.1 SEcho Metric -- 5.2 Greedy Optimisation -- 6 Experiments -- 7 Conclusion -- References -- KiProL: A Knowledge-Injected Prompt Learning Framework for Language Generation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Statement -- 2.2 Knowledge-Injected Prompt Learning Generation -- 2.3 Training and Inference -- 3 Experiments -- 3.1 Datasets -- 3.2 Settings -- 3.3 Automatic Evaluation -- 3.4 Human Annotation -- 3.5 Ablation Study -- 3.6 In-Depth Analysis -- 3.7 Case Study -- 4 Conclusion -- References -- GViG: Generative Visual Grounding Using Prompt-Based Language Modeling for Visual Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Pix2Seq Framework -- 2.2 Prompt Tuning -- 3 Methodology -- 3.1 Prompt Tuning Module -- 3.2 VG Module -- 3.3 Conditional Trie-Based Search Algorithm (CTS) -- 4 Results -- 4.1 Dataset Description -- 4.2 Results on WSDM 2023 Toloka VQA Dataset Benchmark -- 5 Discussion -- 5.1 Prompt Study -- 5.2 Interpretable Attention -- 6 Conclusion -- References -- Aspect-Based Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition and Model Overview -- 3.2 Aspect Learning and Extraction -- 3.3 News Article Classification -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Evaluation -- 5 Analysing the Effect of Aspects Across Topics -- 6 Discussion and Future Work -- 7 Conclusion -- References -- DQAC: Detoxifying Query Auto-completion with Adapters -- 1 Introduction -- 2 Related Work -- 3 Methodology.
3.1 QDetoxify: Toxicity Classifier for Search Queries -- 3.2 The DQAC Model -- 4 Experimental Setup -- 5 Results and Analyses -- 6 Conclusions -- References -- Graph Neural Network Approach to Semantic Type Detection in Tables -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 GAIT -- 4.1 Single-Column Prediction -- 4.2 Graph-Based Prediction -- 4.3 Overall Prediction -- 5 Evaluation -- 5.1 Evaluation Method -- 5.2 Results -- 6 Conclusion -- References -- TCGNN: Text-Clustering Graph Neural Networks for Fake News Detection on Social Media -- 1 Introduction -- 2 Related Work -- 3 The Proposed TCGNN Method -- 3.1 Text-Clustering Graph Construction -- 3.2 Model Training -- 4 Experiments -- 5 Conclusions and Discussion -- References -- Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation and Motivation -- 3.2 Standalone ABSA Tasks -- 3.3 Adaptive Contextual Threshold Masking (ACTM) -- 3.4 Adaptive Attention Masking (AAM) -- 3.5 Adaptive Mask Over Masking (AMOM) -- 3.6 Training Procedure for ATE and ASC -- 4 Experiments and Results -- 5 Conclusion -- References -- An Automated Approach for Generating Conceptual Riddles -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Triples Creator -- 3.2 Properties Classifier -- 3.3 Generator -- 3.4 Validator -- 4 Evaluation and Results -- 5 Conclusion and Future Work -- References -- Time-Series and Streaming Data -- DiffFind: Discovering Differential Equations from Time Series -- 1 Introduction -- 2 Background and Related Work -- 2.1 Related Work -- 2.2 Background - Genetic Algorithms for Architecture Search -- 3 Proposed Method: DiffFind -- 4 Experiments -- 4.1 Q1 - DiffFind is Effective -- 4.2 Q2 - DiffFind is Explainable -- 4.3 Q3 - DiffFind is Scalable -- 5 Conclusions -- References.
DEAL: Data-Efficient Active Learning for Regression Under Drift -- 1 Introduction -- 2 Related Work -- 3 Problem Statement and Notation -- 4 Our Method: DEAL -- 4.1 The Adapted Stream-Based AL Cycle -- 4.2 Our Drift-Aware Estimation Model -- 5 Experimental Design -- 5.1 Baselines -- 5.2 Evaluation Data -- 5.3 Evaluation Metrics -- 6 Evaluation -- 6.1 Comparison of DEAL Against Baselines -- 6.2 Impact of the User-Required Error Threshold -- 7 Conclusion -- References -- Evolving Super Graph Neural Networks for Large-Scale Time-Series Forecasting -- 1 Introduction -- 2 Related Models -- 3 Evolving Super Graph Neural Networks -- 3.1 Preliminary Notations -- 3.2 Super Graph Construction -- 4 Diffusion on Evolving Super Graphs -- 4.1 Predictor -- 5 Experiments on Large-Scale Datasets -- 5.1 Forecasting Result and Analysis -- 5.2 Runtime and Space Usage Analysis -- 5.3 Ablation Study -- 6 Conclusion -- References -- Unlearnable Examples for Time Series -- 1 Introduction -- 2 Related Work -- 2.1 Data Poisoning -- 2.2 Adversarial Attack -- 2.3 Unlearnable Examples -- 3 Error-Minimizing Noise for Time Series -- 3.1 Objective -- 3.2 Threat Model -- 3.3 Challenges -- 3.4 Problem Formulation -- 3.5 A Straightforward Baseline Approach -- 3.6 Controllable Noise on Partial Time Series Samples -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Against Classification Models -- 4.3 Against Generative Models -- 5 Conclusion -- References -- Learning Disentangled Task-Related Representation for Time Series -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Overview -- 3.2 Task-Relevant Feature Disentangled -- 3.3 Task-Adaptive Augmentation Selection -- 4 Experiments and Discussions -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Analysis -- 4.3 Results on Classification Tasks -- 4.4 Results on Forecasting Tasks -- 4.5 Visualization Analysis.
5 Conclusion -- References -- A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Methodology -- 4.1 Feature Construction -- 4.2 Multi-view Representation -- 4.3 Multi-Encoder-Decoder Transformer (MEDT) Classification -- 5 Experiments -- 5.1 Experiments Using Multivariate Time Series Data Benchmarks -- 5.2 Experiment Using a Real-World Physical Activities Dataset -- 6 Conclusion -- References -- Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Key Concepts -- 3.2 Self-representation Learning in Time Series -- 3.3 Kernel Trick for Modeling Time Series -- 4 Proposed Method -- 4.1 Kernel Representation Learning: Modeling Regime Behavior -- 4.2 Forecasting -- 5 Experiments -- 5.1 Data -- 5.2 Experimental Setup and Evaluation -- 5.3 Regime Identification -- 5.4 Benchmark Comparison -- 5.5 Ablation Study -- 6 Conclusion -- References -- Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting -- 1 Introduction -- 2 Problem Statement -- 3 MLPs for Time Series Forecasting -- 3.1 Nlinear Model -- 4 Hyperparameters -- 4.1 Time Series Specific Configuration -- 4.2 Training Specific Configurations -- 4.3 TSBench-Metadataset -- 5 Experimental Setup -- 6 Results -- 7 Conclusion -- References -- Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification -- 1 Introduction -- 2 Related Work -- 2.1 Graph-Based Time Series Classification -- 2.2 Lower Bound of DTW -- 3 Problem Formulation -- 4 Methodology -- 4.1 Batch Sampling -- 4.2 LB_Keogh Graph Construction -- 4.3 Graph Convolution and Classification -- 4.4 Advantages of Our Model -- 5 Experimental Evaluation.
5.1 Comparing with 1NN-DTW.
Record Nr. UNISA-996594166503316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part III
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part III
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (448 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-59-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part III -- Interpretability and Explainability -- Neural Additive and Basis Models with Feature Selection and Interactions -- 1 Introduction -- 2 Generalized Additive Models (GAMs) -- 2.1 Neural Additive Model (NAM) -- 2.2 Neural Basis Model (NBM) -- 3 NAM and NBM with Feature Selection -- 3.1 Motivation -- 3.2 Model Architecture -- 3.3 Implementation Remark -- 4 Discussion of Model Complexities -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Baselines -- 5.3 Results -- 6 Conclusion -- References -- Random Mask Perturbation Based Explainable Method of Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Explainable Method -- 4.1 Node Importance Based on Fidelity -- 4.2 Explanation Sparsity -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Quantitative Experiments -- 5.3 Ablation Study -- 5.4 Use Case -- 6 Conclusion -- References -- RouteExplainer: An Explanation Framework for Vehicle Routing Problem -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework: RouteExplainer -- 3.1 Many-to-Many Edge Classifier -- 3.2 Counterfactual Explanation for VRP -- 4 Experiments -- 4.1 Quantitative Evaluation of the Edge Classifier -- 4.2 Qualitative Evaluation of Generated Explanations -- 5 Conclusion and Future Work -- References -- On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values -- 1 Introduction -- 2 Related Work -- 3 Outlier Detection Ensembles -- 4 The bagged Shapley Values -- 5 Theoretical Guarantees for the Approximation -- 6 Experiments -- 6.1 Quality of the Approximation -- 6.2 Effectiveness -- 6.3 Scalability -- 7 Conclusions -- References -- Interpreting Pretrained Language Models via Concept Bottlenecks -- 1 Introduction -- 2 Related Work -- 2.1 Interpreting Pretrained Language Models.
2.2 Learning from Noisy Labels -- 3 Enable Concept Bottlenecks for PLMs -- 3.1 Problem Setup -- 4 C3M: A General Framework for Learning CBE-PLMs -- 4.1 ChatGPT-Guided Concept Augmentation -- 4.2 Learning from Noisy Concept Labels -- 5 Experiments -- 6 Conclusion -- A Definitions of Training Strategies -- B Details of the Manual Concept Annotation for the IMDB Dataset -- C Implementation Detail -- D Parameters and Notations -- E Statistics of Data Splits -- F Statistics of Concepts in Transformed Datasets -- G More Results on Explainable Predictions -- H A Case Study on Test-Time Intervention -- I Examples of Querying ChatGPT -- References -- Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Jacobian Saliency Map (JSM) -- 3.2 Jacobian-Augmented Loss Function (JAL) -- 4 Experiments -- 4.1 Dataset -- 4.2 Preprocessing -- 4.3 Multimodal Classification -- 4.4 Performance Evaluation -- 5 Conclusion -- References -- Towards Nonparametric Topological Layers in Neural Networks -- 1 Introduction -- 1.1 Background -- 1.2 Motivation and Challenges -- 1.3 Contributions -- 2 Preliminaries and Related Work -- 2.1 Basics of Topology -- 2.2 Topological Neural Network -- 2.3 Functional Spaces for Machine Learning -- 3 Methodology -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Implementation -- 4.3 Overall Performance -- 4.4 Learning Rate -- 4.5 Temporal-Spatial Correlation -- 5 Conclusion -- References -- Online, Streaming, Distributed Algorithms -- Streaming Fair k-Center Clustering over Massive Dataset with Performance Guarantee -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Related Work -- 1.3 Our Contribution -- 2 A Two-Pass Algorithm with Approximation Ratio 3 -- 2.1 The -Independent Center Set -- 2.2 The Two-Pass Streaming Algorithm.
3 The Streaming Algorithm with an Approximation Ratio 7 -- 3.1 The Streaming Algorithm for Constructing 1 and 2 -- 3.2 Post-streaming Construction of Center Set C from 12 -- 4 Experimental Results -- 4.1 Experimental Setting -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- Projection-Free Bandit Convex Optimization over Strongly Convex Sets -- 1 Introduction -- 2 Related Work -- 2.1 Projection-Free OCO Algorithms -- 2.2 Bandit Convex Optimization -- 3 Main Results -- 3.1 Preliminaries -- 3.2 Our Proposed Algorithm -- 3.3 Theoretical Guarantees -- 4 Experiments -- 4.1 Problem Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Adaptive Prediction Interval for Data Stream Regression -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Adaptive Prediction Interval(AdaPI) -- 5 Experiments and Results -- 5.1 Comparison to Interval Forecast -- 5.2 Comparison Between MVE and AdaPI -- 6 Conclusions -- References -- Probabilistic Guarantees of Stochastic Recursive Gradient in Non-convex Finite Sum Problems -- 1 Introduction -- 1.1 Related Works -- 1.2 Our Contributions -- 1.3 Notation -- 2 Prob-SARAH Algorithm -- 3 Theoretical Results -- 3.1 Technical Assumptions -- 3.2 Main Results on Complexity -- 3.3 Proof Sketch -- 4 Numerical Experiments -- 4.1 Logistic Regression with Non-convex Regularization -- 4.2 Two-Layer Neural Network -- 5 Conclusion -- References -- Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Model Overview -- 3.2 Client Model Clustering -- 3.3 Dynamic Weighted Graph Construction -- 3.4 Knowledge Propagation and Aggregation -- 3.5 Precise Personalized Model Distribution -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 4.4 Case Study -- 4.5 Hyperparameter Study.
5 Conclusion -- References -- Unveiling Backdoor Risks Brought by Foundation Models in Heterogeneous Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Threat Model -- 3.2 FMs Empowered Backdoor Attacks to HFL -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Experimental Results -- 4.3 Homogeneous Setting Evaluation -- 4.4 Case Study: Attack Effectiveness v.s. Public Data Utilization Ratio -- 4.5 Hyper-Parameter Study: ASR v.s. Poisoning Ratio -- 5 Conclusion -- References -- Combating Quality Distortion in Federated Learning with Collaborative Data Selection -- 1 Introduction -- 2 Related Works -- 3 Proposal -- 3.1 Preliminaries -- 3.2 Design Principle -- 3.3 Collaborative Sample Selection (CSS) -- 4 Evaluation -- 4.1 Datasets and Experimental Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Probabilistic Models and Statistical Inference -- Neural Marked Hawkes Process for Limit Order Book Modeling -- 1 Introduction -- 2 Background -- 3 Neural Marked Hawkes Process -- 4 Related Work -- 5 Experiments -- 6 Conclusion -- References -- How Large Corpora Sizes Influence the Distribution of Low Frequency Text n-grams -- 1 Introduction -- 2 Background and Related Work -- 3 The Model -- 4 Results -- 4.1 The Corpora Collection -- 4.2 The Range of k Values for W(k,C -- L,n) Prediction -- 4.3 The Assessment Criteria and Parameter Estimation -- 4.4 Comparison with Other Models -- 4.5 Obtained Results -- 4.6 The Predictions with Growing Corpus Size -- 5 Conclusions -- References -- Meta-Reinforcement Learning Algorithm Based on Reward and Dynamic Inference -- 1 Introduction -- 2 Background -- 2.1 Meta-Reinforcement Learning -- 2.2 Context-Based Meta-Reinforcement Learning -- 2.3 Parametric Task Distributions -- 3 Problem Statement -- 4 Method -- 4.1 Reward and Dynamics Inference.
4.2 Meta-Reinforcement Learning Algorithm Based on Reward and Dynamics Inference Encoders -- 5 Experiment -- 5.1 Common MuJoCo Environments -- 5.2 Cartesian Product Combinations of Tasks with Different Goals and Dynamics -- 6 Discussion -- References -- Security and Privacy -- SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree -- 1 Introduction -- 2 Preliminaries -- 2.1 Gradient Boosting Decision Tree -- 2.2 Paillier Homomorphic Encryption -- 2.3 SecureBoost -- 2.4 Performance Bottlenecks Analysis for SecureBoost -- 3 Proposed SecureBoost+ Framework -- 3.1 Ciphertext Operation Optimization -- 3.2 Training Mechanism Optimization -- 4 Experiments -- 4.1 Setup -- 4.2 Ciphertext Operation Optimization Evaluation -- 4.3 Training Mechanism Optimization Evaluation -- 5 Conclusion -- References -- Construct a Secure CNN Against Gradient Inversion Attack -- 1 Introduction -- 2 Preliminary -- 2.1 Federated Learning -- 2.2 Gradient Inversion Attack -- 2.3 Recursive Gradient Attack on Privacy (R-GAP) -- 3 Secure Convolutional Neural Networks -- 4 Experiment -- 4.1 Quantitative Results -- 4.2 Quantitative Results -- 5 Related Work -- 6 Limitation and Conclusion -- References -- Backdoor Attack Against One-Class Sequential Anomaly Detection Models -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Deep One-Class Sequential Anomaly Detection -- 3.2 Mutual Information Maximization -- 4 Methodology -- 4.1 Threat Model -- 4.2 The Proposed Attack -- 4.3 Post-deployment Attack -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Conclusions -- References -- Semi-supervised and Unsupervised Learning -- DALLMi: Domain Adaption for LLM-Based Multi-label Classifier -- 1 Introduction -- 2 Language Model and Domain Adaptation -- 3 DALLMi -- 4 Experiments -- 5 Conclusion -- References.
Contrastive Learning for Unsupervised Sentence Embedding with False Negative Calibration.
Record Nr. UNISA-996594167903316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part I
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part I
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (406 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-42-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part I -- Anomaly and Outlier Detection -- Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Formulation -- 3.2 Proposed Architecture -- 3.3 Error-Restricted Probability (ERP) Loss -- 3.4 Anomaly Score -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 4.4 Overall Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Framework -- 3.1 Subgraph Sampling Based Data Augmentation -- 3.2 Context Matching Contrastive Learning -- 3.3 Link Prediction Contrastive Learning -- 3.4 Model Training and Anomaly Score Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Ablation Study -- 4.4 Parameter Study -- 5 Related Works -- 6 Conclusions -- References -- SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation: Frame-Level VAD -- 4 Proposal: SATJiP -- 4.1 Preliminary -- 4.2 Masked Temporal Jigsaw Puzzles (MTJiP) -- 5 Experiments -- 5.1 Datasets and Evaluation Metric -- 5.2 Implementation Details -- 5.3 Comparison in Detecting Accompanying Anomalies (AA) -- 5.4 Comparison in Detecting Diverse Video Anomalies -- 5.5 Ablation Study -- 5.6 VAD Examples -- 6 Conclusion -- References -- STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Prediction-Based Models -- 2.2 Reconstruction-Based Models.
2.3 Transformers for Time Series Analysis -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overall Architecture -- 3.3 Data Preprocessing -- 3.4 Decomposition Block -- 3.5 Local-Transformer Encoder and Decoder -- 3.6 Loss Function and Anomaly Score -- 4 Experiments -- 5 Conclusion -- References -- TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 TOPOMA: Our Proposed Anomaly Detector -- 3.1 Problem Formulation -- 3.2 Moving Average of Orthogonal Projection Operators -- 3.3 Adaptive Choice of Anomaly Score Thresholds -- 3.4 Complexity Analysis -- 4 Results and Discussion -- 4.1 Synthetic Data -- 4.2 Real-World Data -- 5 Conclusion -- References -- Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Robust Hybrid Error with MD in Latent Space -- 3.2 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Ablation Study -- 5 Hyperparameter Sensitivity -- 6 Conclusion -- References -- SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection -- 1 Introduction -- 2 Proposed Model for Multi-view Anomaly Detection -- 2.1 The SeeM Model and Its Inference -- 2.2 Complexity Analysis -- 2.3 Anomaly Score -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Multi-view Anomaly Detection Performance -- 3.3 Latent Dimension Analysis -- 3.4 Non-linear Projections -- 3.5 A Use Case with Real-World Multi-view Data -- 4 Related Work -- 5 Conclusion -- References -- Classification -- QWalkVec: Node Embedding by Quantum Walk -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Quantum Walks on Graphs -- 3 Related Works -- 3.1 Problems -- 4 Proposed Method: QWalkVec -- 4.1 Algorithm -- 5 Evaluations.
5.1 Experimental Settings and Dataset -- 5.2 Overall Results -- 6 Conclusion -- References -- Human-Driven Active Verification for Efficient and Trustworthy Graph Classification -- 1 Introduction -- 2 Related Work -- 2.1 Human-in-the-loop Machine Learning -- 2.2 Deep Learning for Case-Based Reasoning -- 2.3 Interpretable Graph Neural Networks -- 3 Methodology -- 3.1 Problem Formulation and Framework Overview -- 3.2 Human-Compatible Representation Learning -- 3.3 Interpretable Predictor -- 3.4 Prediction Explanation -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Implementations and Configurations -- 4.3 Predictive Performance Comparison -- 4.4 Benefits of Human-AI Interactions -- 4.5 User Perception of Prediction Explanations -- 4.6 Is Instance-Level Feedback Helpful in Any Cases? -- 5 Discussions of Fairness and Ethical Issues -- 6 Conclusion and Future Work -- References -- SASBO: Sparse Attack via Stochastic Binary Optimization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Sparse Adversarial Attack via Stochastic Binary Optimization -- 4 Experiments and Results -- 4.1 Non-targeted Attack -- 4.2 Targeted Attack -- 4.3 Visualization -- 5 Conclusion -- References -- LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Definition -- 3.2 Overall Framework -- 3.3 Utterance Encoder -- 3.4 Malevolence Shift Detection -- 3.5 Hierarchy-Aware Label Encoder -- 3.6 Malevolence Detection in Dialogues -- 3.7 Multi-task Learning -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Compared Baselines -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Study -- 4.6 Analysis of Malevolence Shift Detection -- 4.7 Case Study -- 4.8 Analysis of LLMs -- 5 Conclusion -- References.
Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features -- 1 Introduction -- 2 Preliminary -- 2.1 Knowledge Graph -- 2.2 Knowledge Graph Completion -- 2.3 Dynamic Graph Attention Variant GATv2 -- 3 Methodology -- 3.1 Structural Local Contexts Aggregation -- 3.2 High-Order Connected Contexts Aggregation -- 3.3 Decoder -- 4 Experiment -- 4.1 Datasets and Metrics -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusions -- References -- Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations -- 1 Introduction -- 2 Related Works -- 2.1 Multi-label Recognition with Full Annotations -- 2.2 Multi-label Recognition with Limited Annotations -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Ambiguity-Aware Instance Weighting -- 3.3 Total Training Loss -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Ablation Studies -- 4.4 Model Analysis -- 5 Conclusion -- References -- Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification -- 1 Introduction -- 1.1 Node Classification -- 1.2 Graph Neural Network (GNN) -- 1.3 Chaotic Neural Oscillator (CNO) -- 2 Methodology -- 3 Experiment -- 3.1 Datasets -- 3.2 Settings and Baselines -- 3.3 Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Adversarial Learning of Group and Individual Fair Representations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Problem Statement -- 4.2 Model -- 4.3 Theoretical Properties of Loss Functions -- 4.4 Optimization with Focal Loss -- 5 Experiments and Analysis -- 6 Conclusion -- References -- Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage -- 1 Introduction -- 2 Methodology -- 2.1 Data Partitioning -- 2.2 Classification and Evaluation -- 3 Experimental Study -- 3.1 Datasets -- 3.2 Results.
4 Discussion and Recommendations -- 5 Conclusions and Future Work -- References -- Clustering -- Clustering-Friendly Representation Learning for Enhancing Salient Features -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 The Framework of cIDFD -- 3.2 Loss for Background Feature Extraction -- 3.3 Loss for Target Feature Extraction -- 3.4 Two-Stage Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Comparison with Conventional Methods -- 4.3 Representation Distribution -- 4.4 Similarity Distribution -- 5 Conclusion -- References -- ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Contribution -- 2 Related Work -- 3 Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning (ImMC-CSFL) -- 3.1 Deep Feature Extraction Module -- 3.2 Common Information Learning Module -- 3.3 Specific Information Learning Module -- 3.4 Deep Multi-view Clustering Based on Common-Specific Feature Learning -- 4 Experiment -- 4.1 Experimental Datasets and Evaluation Criteria -- 4.2 Methods of Comparison -- 4.3 Experimental Results -- 5 Summary -- References -- Multivariate Beta Mixture Model: Probabilistic Clustering with Flexible Cluster Shapes -- 1 Introduction -- 2 Multivariate Beta Mixture Model -- 2.1 Multivariate Beta Distribution -- 2.2 MBMM Density Function and Generative Process -- 2.3 Parameter Learning for the MBMM -- 2.4 The Similarity Score Between Data Points -- 3 Experiments -- 3.1 Comparisons on the Synthetic Datasets -- 3.2 Comparison on the Real Datasets -- 3.3 Distance Between Data Points -- 4 Related Work -- 5 Discussion -- References -- AutoClues: Exploring Clustering Pipelines via AutoML and Diversification -- 1 Introduction -- 2 Related Works -- 3 AutoClues -- 3.1 Formalization -- 3.2 Implementation.
4 Benchmark Generation and Empirical Evaluation.
Record Nr. UNISA-996594167803316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part IV
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part IV
Autore Yang De-Nian
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer Singapore Pte. Limited, , 2024
Descrizione fisica 1 online resource (380 pages)
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Computer Science Series
ISBN 981-9722-38-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996601563903316
Yang De-Nian  
Singapore : , : Springer Singapore Pte. Limited, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part I / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part I / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Autore Yang De-Nian
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (406 pages)
Disciplina 006.3
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Algorithms
Education - Data processing
Computer science - Mathematics
Signal processing
Computer networks
Artificial Intelligence
Design and Analysis of Algorithms
Computers and Education
Mathematics of Computing
Signal, Speech and Image Processing
Computer Communication Networks
ISBN 981-9722-42-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part I -- Anomaly and Outlier Detection -- Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Formulation -- 3.2 Proposed Architecture -- 3.3 Error-Restricted Probability (ERP) Loss -- 3.4 Anomaly Score -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 4.4 Overall Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Framework -- 3.1 Subgraph Sampling Based Data Augmentation -- 3.2 Context Matching Contrastive Learning -- 3.3 Link Prediction Contrastive Learning -- 3.4 Model Training and Anomaly Score Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Ablation Study -- 4.4 Parameter Study -- 5 Related Works -- 6 Conclusions -- References -- SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation: Frame-Level VAD -- 4 Proposal: SATJiP -- 4.1 Preliminary -- 4.2 Masked Temporal Jigsaw Puzzles (MTJiP) -- 5 Experiments -- 5.1 Datasets and Evaluation Metric -- 5.2 Implementation Details -- 5.3 Comparison in Detecting Accompanying Anomalies (AA) -- 5.4 Comparison in Detecting Diverse Video Anomalies -- 5.5 Ablation Study -- 5.6 VAD Examples -- 6 Conclusion -- References -- STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Prediction-Based Models -- 2.2 Reconstruction-Based Models.
2.3 Transformers for Time Series Analysis -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overall Architecture -- 3.3 Data Preprocessing -- 3.4 Decomposition Block -- 3.5 Local-Transformer Encoder and Decoder -- 3.6 Loss Function and Anomaly Score -- 4 Experiments -- 5 Conclusion -- References -- TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 TOPOMA: Our Proposed Anomaly Detector -- 3.1 Problem Formulation -- 3.2 Moving Average of Orthogonal Projection Operators -- 3.3 Adaptive Choice of Anomaly Score Thresholds -- 3.4 Complexity Analysis -- 4 Results and Discussion -- 4.1 Synthetic Data -- 4.2 Real-World Data -- 5 Conclusion -- References -- Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Robust Hybrid Error with MD in Latent Space -- 3.2 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Ablation Study -- 5 Hyperparameter Sensitivity -- 6 Conclusion -- References -- SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection -- 1 Introduction -- 2 Proposed Model for Multi-view Anomaly Detection -- 2.1 The SeeM Model and Its Inference -- 2.2 Complexity Analysis -- 2.3 Anomaly Score -- 3 Experiments -- 3.1 Datasets and Baselines -- 3.2 Multi-view Anomaly Detection Performance -- 3.3 Latent Dimension Analysis -- 3.4 Non-linear Projections -- 3.5 A Use Case with Real-World Multi-view Data -- 4 Related Work -- 5 Conclusion -- References -- Classification -- QWalkVec: Node Embedding by Quantum Walk -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Quantum Walks on Graphs -- 3 Related Works -- 3.1 Problems -- 4 Proposed Method: QWalkVec -- 4.1 Algorithm -- 5 Evaluations.
5.1 Experimental Settings and Dataset -- 5.2 Overall Results -- 6 Conclusion -- References -- Human-Driven Active Verification for Efficient and Trustworthy Graph Classification -- 1 Introduction -- 2 Related Work -- 2.1 Human-in-the-loop Machine Learning -- 2.2 Deep Learning for Case-Based Reasoning -- 2.3 Interpretable Graph Neural Networks -- 3 Methodology -- 3.1 Problem Formulation and Framework Overview -- 3.2 Human-Compatible Representation Learning -- 3.3 Interpretable Predictor -- 3.4 Prediction Explanation -- 4 Experiments -- 4.1 Datasets and Baselines -- 4.2 Implementations and Configurations -- 4.3 Predictive Performance Comparison -- 4.4 Benefits of Human-AI Interactions -- 4.5 User Perception of Prediction Explanations -- 4.6 Is Instance-Level Feedback Helpful in Any Cases? -- 5 Discussions of Fairness and Ethical Issues -- 6 Conclusion and Future Work -- References -- SASBO: Sparse Attack via Stochastic Binary Optimization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Definition -- 3.2 Sparse Adversarial Attack via Stochastic Binary Optimization -- 4 Experiments and Results -- 4.1 Non-targeted Attack -- 4.2 Targeted Attack -- 4.3 Visualization -- 5 Conclusion -- References -- LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Definition -- 3.2 Overall Framework -- 3.3 Utterance Encoder -- 3.4 Malevolence Shift Detection -- 3.5 Hierarchy-Aware Label Encoder -- 3.6 Malevolence Detection in Dialogues -- 3.7 Multi-task Learning -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Compared Baselines -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Study -- 4.6 Analysis of Malevolence Shift Detection -- 4.7 Case Study -- 4.8 Analysis of LLMs -- 5 Conclusion -- References.
Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features -- 1 Introduction -- 2 Preliminary -- 2.1 Knowledge Graph -- 2.2 Knowledge Graph Completion -- 2.3 Dynamic Graph Attention Variant GATv2 -- 3 Methodology -- 3.1 Structural Local Contexts Aggregation -- 3.2 High-Order Connected Contexts Aggregation -- 3.3 Decoder -- 4 Experiment -- 4.1 Datasets and Metrics -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusions -- References -- Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations -- 1 Introduction -- 2 Related Works -- 2.1 Multi-label Recognition with Full Annotations -- 2.2 Multi-label Recognition with Limited Annotations -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Ambiguity-Aware Instance Weighting -- 3.3 Total Training Loss -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Ablation Studies -- 4.4 Model Analysis -- 5 Conclusion -- References -- Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification -- 1 Introduction -- 1.1 Node Classification -- 1.2 Graph Neural Network (GNN) -- 1.3 Chaotic Neural Oscillator (CNO) -- 2 Methodology -- 3 Experiment -- 3.1 Datasets -- 3.2 Settings and Baselines -- 3.3 Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Adversarial Learning of Group and Individual Fair Representations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Problem Statement -- 4.2 Model -- 4.3 Theoretical Properties of Loss Functions -- 4.4 Optimization with Focal Loss -- 5 Experiments and Analysis -- 6 Conclusion -- References -- Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage -- 1 Introduction -- 2 Methodology -- 2.1 Data Partitioning -- 2.2 Classification and Evaluation -- 3 Experimental Study -- 3.1 Datasets -- 3.2 Results.
4 Discussion and Recommendations -- 5 Conclusions and Future Work -- References -- Clustering -- Clustering-Friendly Representation Learning for Enhancing Salient Features -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 The Framework of cIDFD -- 3.2 Loss for Background Feature Extraction -- 3.3 Loss for Target Feature Extraction -- 3.4 Two-Stage Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Comparison with Conventional Methods -- 4.3 Representation Distribution -- 4.4 Similarity Distribution -- 5 Conclusion -- References -- ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning -- 1 Introduction -- 1.1 Motivation -- 1.2 Contribution -- 2 Related Work -- 3 Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning (ImMC-CSFL) -- 3.1 Deep Feature Extraction Module -- 3.2 Common Information Learning Module -- 3.3 Specific Information Learning Module -- 3.4 Deep Multi-view Clustering Based on Common-Specific Feature Learning -- 4 Experiment -- 4.1 Experimental Datasets and Evaluation Criteria -- 4.2 Methods of Comparison -- 4.3 Experimental Results -- 5 Summary -- References -- Multivariate Beta Mixture Model: Probabilistic Clustering with Flexible Cluster Shapes -- 1 Introduction -- 2 Multivariate Beta Mixture Model -- 2.1 Multivariate Beta Distribution -- 2.2 MBMM Density Function and Generative Process -- 2.3 Parameter Learning for the MBMM -- 2.4 The Similarity Score Between Data Points -- 3 Experiments -- 3.1 Comparisons on the Synthetic Datasets -- 3.2 Comparison on the Real Datasets -- 3.3 Distance Between Data Points -- 4 Related Work -- 5 Discussion -- References -- AutoClues: Exploring Clustering Pipelines via AutoML and Diversification -- 1 Introduction -- 2 Related Works -- 3 AutoClues -- 3.1 Formalization -- 3.2 Implementation.
4 Benchmark Generation and Empirical Evaluation.
Record Nr. UNINA-9910851982903321
Yang De-Nian  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part VI / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part VI / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Autore Yang De-Nian
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (329 pages)
Disciplina 006.3
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Algorithms
Education - Data processing
Computer science - Mathematics
Signal processing
Computer networks
Artificial Intelligence
Design and Analysis of Algorithms
Computers and Education
Mathematics of Computing
Signal, Speech and Image Processing
Computer Communication Networks
ISBN 981-9722-66-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part VI -- Scientific Data -- FR3LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series -- 1 Introduction -- 2 Related Work -- 3 Problem Setup -- 4 Model Architecture -- 4.1 Temporal Contextual Consistency -- 4.2 Non-contrastive Representations Learning -- 4.3 Deterministic Forecasting -- 4.4 Probabilistic Forecasting -- 4.5 End-to-End Training -- 5 Experiments -- 5.1 Experimental Results -- 5.2 Visualization of Latent and Original Series Forecasts -- 5.3 Further Experimental Setup Details -- 6 Conclusion -- References -- Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations -- 1 Introduction -- 2 Preliminaries on Numerical Simulations -- 3 Identification and Analysis of Relevant Metadata -- 4 Efficient Metadata Capture for Parameter Optimization -- 4.1 Early Termination of Farming Runs -- 4.2 PROBE: Probing Specific Parameter Combinations -- 5 Experimental Evaluation -- 5.1 Quality of Parameter Optimization Using PROBE -- 5.2 Efficiency Evaluation -- 5.3 Reuse of Metadata Acquired Through PROBE -- 5.4 Generalization to Other Model Problems and Schemes -- 6 Conclusion and Outlook -- References -- Material Microstructure Design Using VAE-Regression with a Multimodal Prior -- 1 Introduction -- 2 Methodology -- 3 Related Work -- 4 Experimental Results -- 5 Summary and Conclusions -- References -- A Weighted Cross-Modal Feature Aggregation Network for Rumor Detection -- 1 Introduction -- 2 Related Work -- 2.1 Rumor Detection -- 2.2 Multimodal Alignment -- 3 Methodology -- 3.1 Overview of WCAN -- 3.2 Feature Extraction -- 3.3 Weighted Cross-Modal Aggregation Module -- 3.4 Multimodal Feature Fusion -- 3.5 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Ablation Experiment.
4.4 Hyper-parameter Analysis -- 4.5 Visualization on the Representations -- 4.6 Case Study -- 5 Conclusions -- References -- Texts, Web, Social Network -- Quantifying Opinion Rejection: A Method to Detect Social Media Echo Chambers -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Echo Chamber Detection in Signed Networks -- 5 SEcho Method -- 5.1 SEcho Metric -- 5.2 Greedy Optimisation -- 6 Experiments -- 7 Conclusion -- References -- KiProL: A Knowledge-Injected Prompt Learning Framework for Language Generation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Statement -- 2.2 Knowledge-Injected Prompt Learning Generation -- 2.3 Training and Inference -- 3 Experiments -- 3.1 Datasets -- 3.2 Settings -- 3.3 Automatic Evaluation -- 3.4 Human Annotation -- 3.5 Ablation Study -- 3.6 In-Depth Analysis -- 3.7 Case Study -- 4 Conclusion -- References -- GViG: Generative Visual Grounding Using Prompt-Based Language Modeling for Visual Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Pix2Seq Framework -- 2.2 Prompt Tuning -- 3 Methodology -- 3.1 Prompt Tuning Module -- 3.2 VG Module -- 3.3 Conditional Trie-Based Search Algorithm (CTS) -- 4 Results -- 4.1 Dataset Description -- 4.2 Results on WSDM 2023 Toloka VQA Dataset Benchmark -- 5 Discussion -- 5.1 Prompt Study -- 5.2 Interpretable Attention -- 6 Conclusion -- References -- Aspect-Based Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition and Model Overview -- 3.2 Aspect Learning and Extraction -- 3.3 News Article Classification -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Evaluation -- 5 Analysing the Effect of Aspects Across Topics -- 6 Discussion and Future Work -- 7 Conclusion -- References -- DQAC: Detoxifying Query Auto-completion with Adapters -- 1 Introduction -- 2 Related Work -- 3 Methodology.
3.1 QDetoxify: Toxicity Classifier for Search Queries -- 3.2 The DQAC Model -- 4 Experimental Setup -- 5 Results and Analyses -- 6 Conclusions -- References -- Graph Neural Network Approach to Semantic Type Detection in Tables -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 GAIT -- 4.1 Single-Column Prediction -- 4.2 Graph-Based Prediction -- 4.3 Overall Prediction -- 5 Evaluation -- 5.1 Evaluation Method -- 5.2 Results -- 6 Conclusion -- References -- TCGNN: Text-Clustering Graph Neural Networks for Fake News Detection on Social Media -- 1 Introduction -- 2 Related Work -- 3 The Proposed TCGNN Method -- 3.1 Text-Clustering Graph Construction -- 3.2 Model Training -- 4 Experiments -- 5 Conclusions and Discussion -- References -- Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation and Motivation -- 3.2 Standalone ABSA Tasks -- 3.3 Adaptive Contextual Threshold Masking (ACTM) -- 3.4 Adaptive Attention Masking (AAM) -- 3.5 Adaptive Mask Over Masking (AMOM) -- 3.6 Training Procedure for ATE and ASC -- 4 Experiments and Results -- 5 Conclusion -- References -- An Automated Approach for Generating Conceptual Riddles -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Triples Creator -- 3.2 Properties Classifier -- 3.3 Generator -- 3.4 Validator -- 4 Evaluation and Results -- 5 Conclusion and Future Work -- References -- Time-Series and Streaming Data -- DiffFind: Discovering Differential Equations from Time Series -- 1 Introduction -- 2 Background and Related Work -- 2.1 Related Work -- 2.2 Background - Genetic Algorithms for Architecture Search -- 3 Proposed Method: DiffFind -- 4 Experiments -- 4.1 Q1 - DiffFind is Effective -- 4.2 Q2 - DiffFind is Explainable -- 4.3 Q3 - DiffFind is Scalable -- 5 Conclusions -- References.
DEAL: Data-Efficient Active Learning for Regression Under Drift -- 1 Introduction -- 2 Related Work -- 3 Problem Statement and Notation -- 4 Our Method: DEAL -- 4.1 The Adapted Stream-Based AL Cycle -- 4.2 Our Drift-Aware Estimation Model -- 5 Experimental Design -- 5.1 Baselines -- 5.2 Evaluation Data -- 5.3 Evaluation Metrics -- 6 Evaluation -- 6.1 Comparison of DEAL Against Baselines -- 6.2 Impact of the User-Required Error Threshold -- 7 Conclusion -- References -- Evolving Super Graph Neural Networks for Large-Scale Time-Series Forecasting -- 1 Introduction -- 2 Related Models -- 3 Evolving Super Graph Neural Networks -- 3.1 Preliminary Notations -- 3.2 Super Graph Construction -- 4 Diffusion on Evolving Super Graphs -- 4.1 Predictor -- 5 Experiments on Large-Scale Datasets -- 5.1 Forecasting Result and Analysis -- 5.2 Runtime and Space Usage Analysis -- 5.3 Ablation Study -- 6 Conclusion -- References -- Unlearnable Examples for Time Series -- 1 Introduction -- 2 Related Work -- 2.1 Data Poisoning -- 2.2 Adversarial Attack -- 2.3 Unlearnable Examples -- 3 Error-Minimizing Noise for Time Series -- 3.1 Objective -- 3.2 Threat Model -- 3.3 Challenges -- 3.4 Problem Formulation -- 3.5 A Straightforward Baseline Approach -- 3.6 Controllable Noise on Partial Time Series Samples -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Against Classification Models -- 4.3 Against Generative Models -- 5 Conclusion -- References -- Learning Disentangled Task-Related Representation for Time Series -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Overview -- 3.2 Task-Relevant Feature Disentangled -- 3.3 Task-Adaptive Augmentation Selection -- 4 Experiments and Discussions -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Analysis -- 4.3 Results on Classification Tasks -- 4.4 Results on Forecasting Tasks -- 4.5 Visualization Analysis.
5 Conclusion -- References -- A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Methodology -- 4.1 Feature Construction -- 4.2 Multi-view Representation -- 4.3 Multi-Encoder-Decoder Transformer (MEDT) Classification -- 5 Experiments -- 5.1 Experiments Using Multivariate Time Series Data Benchmarks -- 5.2 Experiment Using a Real-World Physical Activities Dataset -- 6 Conclusion -- References -- Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Key Concepts -- 3.2 Self-representation Learning in Time Series -- 3.3 Kernel Trick for Modeling Time Series -- 4 Proposed Method -- 4.1 Kernel Representation Learning: Modeling Regime Behavior -- 4.2 Forecasting -- 5 Experiments -- 5.1 Data -- 5.2 Experimental Setup and Evaluation -- 5.3 Regime Identification -- 5.4 Benchmark Comparison -- 5.5 Ablation Study -- 6 Conclusion -- References -- Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting -- 1 Introduction -- 2 Problem Statement -- 3 MLPs for Time Series Forecasting -- 3.1 Nlinear Model -- 4 Hyperparameters -- 4.1 Time Series Specific Configuration -- 4.2 Training Specific Configurations -- 4.3 TSBench-Metadataset -- 5 Experimental Setup -- 6 Results -- 7 Conclusion -- References -- Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification -- 1 Introduction -- 2 Related Work -- 2.1 Graph-Based Time Series Classification -- 2.2 Lower Bound of DTW -- 3 Problem Formulation -- 4 Methodology -- 4.1 Batch Sampling -- 4.2 LB_Keogh Graph Construction -- 4.3 Graph Convolution and Classification -- 4.4 Advantages of Our Model -- 5 Experimental Evaluation.
5.1 Comparing with 1NN-DTW.
Record Nr. UNINA-9910851993803321
Yang De-Nian  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
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Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part IV / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part IV / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Autore Yang De-Nian
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (380 pages)
Disciplina 006.3
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Algorithms
Education - Data processing
Computer science - Mathematics
Signal processing
Computer networks
Artificial Intelligence
Design and Analysis of Algorithms
Computers and Education
Mathematics of Computing
Signal, Speech and Image Processing
Computer Communication Networks
ISBN 981-9722-38-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part IV -- Financial Data -- Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal -- 1 Introduction -- 2 Preliminaries -- 3 Approach -- 3.1 Graph Construction -- 3.2 Transaction Encoding -- 3.3 Neighbor Aggregator -- 3.4 Community Aggregator -- 3.5 Dynamic Adaptor -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Overall Performance -- 4.3 Ablation Study -- 5 Conclusion -- References -- Multi-time Window Ensemble and Maximization of Expected Return for Stock Movement Prediction -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Problem Formulation -- 3.2 Multi-time Window Ensemble Classifier -- 3.3 Base Learner -- 3.4 Proposed Loss Function for Base Learner -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Predictive Performance Comparison -- 4.3 Trading Performance Comparison -- 4.4 Ablation Study -- 4.5 Visualization of Proposed Loss Function -- 5 Conclusion -- References -- MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading -- 1 Introduction -- 2 Problem Formulation -- 3 Methodology -- 3.1 Imitation Learning -- 3.2 Pretrain Module -- 3.3 Multiple Actors -- 3.4 Optimal Transport Regularization -- 4 Experiments -- 4.1 Dataset -- 4.2 Baselines, Evaluation Metrics and Hyperparameters -- 4.3 Experimental Results -- 4.4 Ablation Study -- 5 Related Work -- 6 Conclusion -- References -- Agent-Based Simulation of Decision-Making Under Uncertainty to Study Financial Precarity -- 1 Introduction -- 2 Background: Modeling Consumption -- 2.1 Capturing Uncertainty -- 3 The Framework: Introducing Real Constraints -- 3.1 Background: Modeling Ruin -- 3.2 Our New Model -- 4 Simulation Study: Precarity -- 4.1 Long Term Precarity -- 4.2 Factors Contributing to Precarity.
5 Simulation Study: Interventions -- 6 Related Work -- 7 Conclusions -- References -- Information Retrieval and Search -- Semantic Completion: Enhancing Image-Text Retrieval with Information Extraction and Compression -- 1 Introduction -- 2 Related Work -- 2.1 Dual-Stream Structure -- 2.2 Single-Stream Structure -- 3 Methodology -- 3.1 Overview -- 3.2 Information Extraction and Compression (IEC) -- 3.3 Training Tasks -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Comparison to Baseline -- 4.3 Ablation Study -- 5 Conclusion -- References -- Fast Edit Distance Prediction for All Pairs of Sequences in Very Large NGS Datasets -- 1 Introduction -- 2 Related Works -- 3 Basic Concepts -- 3.1 Preliminary -- 3.2 Key Ideas -- 3.3 Selecting the Reference Sequences -- 4 Predicting the Edit Distance -- 5 Edit Distance Prediction from Non-matching Sub-sequences -- 5.1 Differences of Distances with Respect to Pairs of Letters -- 5.2 Computational Complexity -- 6 Results and Discussions -- 6.1 Datasets -- 6.2 Selecting K-Mer Length -- 6.3 Comparison of the Proposed Strategies -- 6.4 Edit Distance Prediction -- 6.5 Hierarchical Clustering -- 7 Conclusion -- References -- MixCL: Mixed Contrastive Learning for Relation Extraction -- 1 Introduction -- 2 Mixed Contrastive Learning -- 2.1 Neural Relation Extraction Baseline Model -- 2.2 Entity Centralized Contrastive Learning for Relation Extraction -- 2.3 Mixed Contrastive Learning for Relation Extraction -- 3 Experiments -- 3.1 Datasets and Comparison Models -- 3.2 Main Results -- 4 Analysis -- 4.1 Metric of Quality of Negative Examples -- 4.2 Why Does MixCL Work? -- 5 Related Work -- 5.1 Neural Relation Extraction -- 5.2 Contrastive Learning -- 6 Conclusion -- References -- Decomposing Relational Triple Extraction with Large Language Models for Better Generalization on Unseen Data -- 1 Introduction.
2 Methodology -- 2.1 Problem Formalization -- 2.2 Sub-task 1: Relation Extraction -- 2.3 Sub-task 2: Entity Extraction -- 2.4 Sub-task 3: Triple Filtering -- 3 Experiments -- 3.1 Datasets -- 3.2 Compared Methods and Evaluation Metrics -- 3.3 Overall Performance -- 3.4 Model Generalization Evaluation -- 3.5 Ablation Studies -- 4 Related Work -- 5 Conclusion -- References -- Multi-Query Person Search with Transformers -- 1 Introduction -- 2 Method -- 2.1 Multi-Query Decoder -- 2.2 Detection and Identification Losses -- 3 Experiments -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Comparison to the State-of-the-Art Approaches -- 3.5 Model Analysis -- 4 Conclusion -- References -- BioReX: Biomarker Information Extraction Inspired by Aspect-Based Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Biomarker Extraction (BE) -- 3.2 Result Association (RA) -- 4 Experiments -- 4.1 Experiment Setting -- 4.2 Results and Analysis -- 5 Conclusions -- References -- IR Embedding Fairness Inspection via Contrastive Learning and Human-AI Collaborative Intelligence -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Model Overview -- 3.2 GP Based Contrastive Learning -- 4 Experiments and Deployment -- 4.1 Implementation -- 4.2 Offline Evaluation -- 4.3 Online A/B Testing -- 5 Conclusion -- References -- SemPool: Simple, Robust, and Interpretable KG Pooling for Enhancing Language Models -- 1 Introduction -- 2 Related Work -- 3 Problem Statement and Preliminaries -- 4 Empirical Findings on Robustness -- 5 SemPool: Semantic Graph Pooling -- 5.1 KG Initialization -- 5.2 Pooling -- 5.3 KG Grounding -- 6 Experimental Setting -- 7 Results -- 7.1 Main Results -- 7.2 Ablation Studies and Analysis -- 8 Conclusions -- References -- Medical and Biological Data.
Spatial Gene Expression Prediction Using Multi-Neighborhood Network with Reconstructing Attention -- 1 Introduction -- 2 Related Work -- 2.1 Gene Expression Prediction -- 2.2 Vision Transformer (ViT) -- 3 Method -- 3.1 Dual-Scale Attention -- 3.2 Reconstructing Attention -- 3.3 Transformer Block -- 4 Experiment -- 4.1 Datasets -- 4.2 Experiment Setup -- 5 Results and Discussion -- 5.1 Baseline Experiments for Multi-Neighborhood Network -- 5.2 Influence of Number of Neighborhoods -- 5.3 Influence of Attention Mechanisms -- 6 Conclusion -- References -- APFL: Active-Passive Forgery Localization for Medical Images -- 1 Introduction -- 2 Related Work -- 2.1 Active Forgery Localization -- 2.2 Passive Forgery Localization -- 3 Methodology -- 3.1 Active Fuzzy Localization with Reversible Watermarking -- 3.2 Passive Precise Localization with Lightweight KDU-Net -- 4 Experiments -- 4.1 Settings -- 4.2 Comparison Results -- 4.3 Robustness Evaluation -- 4.4 Hyperparameter Evaluation -- 4.5 Ablation Study -- 5 Conclusion -- References -- A Universal Non-parametric Approach for Improved Molecular Sequence Analysis -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Compression Methods -- 3.2 Problem Formulation -- 3.3 Our Algorithm -- 3.4 Distance Matrix Symmetry -- 3.5 Kernel Matrix Computation -- 3.6 Experimental Setup -- 3.7 Justification of Employing the Kernel Matrix -- 4 Results and Discussion -- 5 Conclusion -- References -- Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data -- 1 Introduction -- 2 Related Work -- 3 Methodology: NeuroGNN -- 3.1 Node Features Generation -- 3.2 Adjacency Matrix Generation -- 3.3 Prediction Using the Generated NeuroGraph -- 3.4 Pretraining -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 4.3 Ablation Study -- 4.4 Analysis of Graph Embeddings.
4.5 Handling Scarce Training Data -- 5 Conclusion -- References -- A Novel Population Graph Neural Network Based on Functional Connectivity for Mental Disorders Detection -- 1 Introduction -- 2 Methods -- 2.1 Brain Connectomic Graph -- 2.2 Heterogeneous Population Graph -- 3 Dataset and Experimental Evaluation -- 3.1 Dataset and Experimental Setup -- 3.2 Cross-Validation -- 3.3 Ablation Experiment and Parameter Sensitivity Analysis -- 3.4 Interpretability Analysis -- 4 Conclusion -- References -- Weighted Chaos Game Representation for Molecular Sequence Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Vision DL Models -- 4 Experimental Evaluation -- 4.1 Baselines Models: -- 5 Results and Discussion -- 6 Conclusion -- References -- Robust Influence-Based Training Methods for Noisy Brain MRI -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Influence Function on Single Validation Point -- 3.2 Influence Function on Validation Group -- 4 Influence-Based Sample Reweighing -- 4.1 Framework -- 4.2 Calculating Sample Weights -- 5 Influence-Based Sample Perturbation -- 5.1 Framework -- 5.2 Selecting Influential Samples -- 5.3 Adding Influence-Based Perturbation -- 6 Experiments -- 6.1 Evaluation Setup -- 6.2 Proposed Methods and Baselines -- 6.3 Results -- 7 Conclusion -- References -- Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations -- 1 Introduction -- 2 Problem Definition -- 3 The Co-ReaSON Approach -- 3.1 Data Preprocessing -- 3.2 Feature Extraction -- 3.3 Co-ReaSON Predictive Model -- 4 Evaluation Setup -- 5 Evaluation Results -- 6 Related Work -- 7 Conclusion -- References -- A Data-Driven Approach for Building a Cardiovascular Disease Risk Prediction System -- 1 Introduction -- 2 The Proposed AutoML System -- 3 Experiment -- 3.1 The Dataset.
3.2 Evaluation Metrics.
Record Nr. UNINA-9910855370003321
Yang De-Nian  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part V / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part V / / edited by De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Autore Yang De-Nian
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (431 pages)
Disciplina 006.3
Altri autori (Persone) XieXing
TsengVincent S
PeiJian
HuangJen-Wei
LinJerry Chun-Wei
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Algorithms
Education - Data processing
Computer science - Mathematics
Signal processing
Computer networks
Artificial Intelligence
Design and Analysis of Algorithms
Computers and Education
Mathematics of Computing
Signal, Speech and Image Processing
Computer Communication Networks
ISBN 981-9722-62-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part V -- Multimedia and Multimodal Data -- Re-thinking Human Activity Recognition with Hierarchy-Aware Label Relationship Modeling -- 1 Introduction -- 2 Related Work -- 2.1 Human Activity Recognition (HAR) -- 2.2 Hierarchical Label Modeling -- 3 Problem Formulation -- 4 Our Proposals -- 4.1 Hierarchy-Aware Label Encoding -- 4.2 Activity Data Encoding -- 4.3 Label-Data Joint Embedding Learning -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Ablation Study -- 6 Discussions and Conclusion -- References -- Geometrically-Aware Dual Transformer Encoding Visual and Textual Features for Image Captioning -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Features Extractor -- 3.2 Caption Generator -- 3.3 Attention Block -- 3.4 Training and Objectives -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Experiment Result -- 5 Conclusions -- References -- MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection -- 1 Introduction -- 2 Related Work -- 3 MHDF Model -- 3.1 Model Overview -- 3.2 Multi-source Heterogeneous Data Amplification -- 3.3 News Textual Feature Fusion -- 3.4 News Visual Feature Fusion -- 3.5 Sentiment Feature Extractor -- 3.6 Feature Integration Classifier -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparison -- 4.4 Ablation Experiments and Validity Verification -- 4.5 Conclusions -- References -- Accurate Semi-supervised Automatic Speech Recognition via Multi-hypotheses-Based Curriculum Learning -- 1 Introduction -- 2 Related Works -- 2.1 Automatic Speech Recognition Methods -- 2.2 Connectionist Temporal Classification (CTC) Loss -- 3 Proposed Method -- 3.1 Multiple Hypotheses for Unlabeled Instances.
3.2 Training ASR Model with Multiple Hypotheses -- 3.3 Curriculum Learning -- 3.4 Theoretical Analysis -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Transcription Performance (Q1) -- 4.3 Speed of Convergence (Q2) -- 4.4 Ablation Study (Q3) -- 5 Conclusions -- References -- MM-PhyQA: Multimodal Physics Question-Answering with Multi-image CoT Prompting -- 1 Introduction -- 2 Related Works -- 2.1 Available Datasets -- 2.2 Large Multimodal Models and Chain-of-Thought -- 3 Novel Dataset -- 3.1 Original Dataset Creation -- 3.2 Data Augmentation Procedure -- 3.3 Chain of Thought Variant -- 3.4 MM-PhyQA Dataset Topics -- 4 Methodology -- 4.1 Multi-image Chain-of-Thought (MI-CoT) -- 5 Experiments -- 5.1 Models -- 6 Results and Discussion -- 6.1 Model Performance -- 6.2 Zero Shot Prompting Vs Supervised Fine-Tuning -- 6.3 Effect of Chain of Thought Prompting -- 6.4 Error Analysis -- 7 Conclusion -- References -- Adversarial Text Purification: A Large Language Model Approach for Defense -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Large Language Models -- 3.2 Adversarial Text Purification -- 4 LLM-Guided Adversarial Text Purification -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Results and Discussion -- 6 Conclusion -- References -- lil'HDoC: An Algorithm for Good Arm Identification Under Small Threshold Gap -- 1 Introduction -- 2 Background -- 2.1 Good Arm Identification -- 3 Problem Setting -- 4 Preliminary -- 5 Algorithm -- 5.1 Correctness of lil'HDoC -- 5.2 First Arms Sampling Complexity -- 5.3 Total Sample Complexity -- 6 Experiment -- 6.1 Dataset -- 6.2 Baseline -- 6.3 Results -- 7 Conclusion -- References -- Recommender Systems -- ScaleViz: Scaling Visualization Recommendation Models on Large Data -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation -- 4 Proposed Solution -- 4.1 Cost Profiling -- 4.2 RL Agent.
5 Evaluations -- 5.1 Experimental Setup -- 5.2 Speed-Up in Visualization Generation -- 5.3 Budget vs. Error Trade-Off -- 5.4 Need for Dataset-Specific Feature Selection -- 5.5 Scalability with Increasing Data Size -- 6 Conclusion -- References -- Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation -- 1 Introduction -- 2 Related Work and Problem Formulation -- 2.1 Problem Formulation -- 3 Methodology -- 3.1 Overview -- 3.2 Statistical Model in CFLS -- 3.3 Optimization -- 3.4 Prediction -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Performance Comparisons -- 4.4 Influence of Different Cold-Start Levels -- 4.5 Diversity, Interpretability and User Controllability -- 5 Conclusions -- References -- On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Statement -- 4 Variants of a Session-Based Recommender System -- 4.1 Quality Metrics -- 5 Experiments and Evaluation -- 5.1 Selection of Real-World Datasets -- 5.2 Task Definition and Parameter Configuration -- 5.3 Evaluation of Experimental Results -- 6 Conclusion and Future Work -- References -- HMAR: Hierarchical Masked Attention for Multi-behaviour Recommendation -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 HMAR -- 2.3 Multi-task Learning -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Evaluation Protocol -- 3.3 Model Performance (RQ1) -- 3.4 Effect of Auxiliary Behaviors and Individual Model Components (RQ2 & -- RQ3) -- 4 Related Work -- 5 Conclusion -- References -- Residual Spatio-Temporal Collaborative Networks for Next POI Recommendation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Problem Formulation -- 3.2 Long-Term Dependence Module -- 3.3 Short-Term Dependence Module -- 3.4 Sample Balancer.
4 Experiments -- 4.1 Experimental Settings -- 4.2 Recommendation Performance -- 4.3 Ablation Study -- 5 Conclusions -- References -- Conditional Denoising Diffusion for Sequential Recommendation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Stepwise Diffuser -- 3.2 Sequence Encoder -- 3.3 Cross-Attentive Conditional Denoising Decoder -- 3.4 Optimization -- 4 Experiments -- 4.1 Plateau of Ranking Prediction -- 4.2 Overall Experiments -- 4.3 Ablation Study -- 4.4 Hyperparameter Sensitivity -- 4.5 Case Study for Stepwise Generation -- 5 Conclusion -- References -- UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering -- 1 Introduction -- 2 Related Work -- 2.1 Collaborative Filtering -- 2.2 Explainable and Transparent Recommender Models -- 2.3 The Prototype-Based Collaborative Filtering -- 3 Methodology -- 3.1 User-Item Prototypes Connections Matrix Factorization (UIPC-MF) -- 3.2 Loss Function -- 4 Experiments and Discussion -- 4.1 Evaluation Metrics -- 4.2 Baseline Models -- 4.3 Training Details -- 4.4 Evaluation Results -- 4.5 Explaining UIPC-MF Recommendations -- 4.6 The Impact of L1-Norm in Reduction of Learning Bias -- 5 Conclusion -- References -- Towards Multi-subsession Conversational Recommendation -- 1 Introduction -- 2 Related Works -- 3 MSMCR Scenario -- 3.1 Definition -- 3.2 General Framework -- 4 Methodology -- 4.1 Context-Aware Recommendation -- 4.2 Policy Learning -- 4.3 Model Training -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Overall Performance -- 5.3 Further Experiments -- 6 Conclusion -- References -- False Negative Sample Aware Negative Sampling for Recommendation -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 Methodology -- 4.1 False Negatives Identification -- 4.2 False Negatives Elimination -- 5 Experiment -- 5.1 Experiment Settings.
5.2 Performance Comparison -- 5.3 Study of EDNS -- 6 Conclusion -- References -- Multi-sourced Integrated Ranking with Exposure Fairness -- 1 Introduction -- 2 Problem Formulation -- 3 Proposed Model -- 3.1 Input Layer -- 3.2 Dual RNN Module -- 3.3 Multi-task Module -- 3.4 Model Training -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Baselines -- 4.3 Model Selection -- 4.4 Performance Comparison -- 4.5 Ablation Study -- 4.6 Online A/B Testing -- 5 Conclusion -- References -- Soft Contrastive Learning for Implicit Feedback Recommendations -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notations -- 3.2 The SCLRec Framework -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Overall Performance (RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Robustness to Interaction Noises (RQ3) -- 5 Conclusion -- References -- Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation -- 1 Introduction -- 2 Problem Formulation -- 3 Approach -- 3.1 Dual-Graph Construction -- 3.2 Dual-Graph Network for Member Preference -- 3.3 Dual-View Fusion for Group Preference -- 3.4 Group Recommendation and Model Training -- 4 Experiments -- 4.1 Experimental Dataset and Setup -- 4.2 Experimental Results and Analysis -- 4.3 Parameter Sensitivity -- 5 Related Works -- 6 Conclusion and Future Work -- References -- TripleS: A Subsidy-Supported Storage for Electricity with Self-financing Management System -- 1 Introduction -- 2 Literature Review -- 2.1 Electricity Subsidy and Operating Reserve -- 2.2 Electricity Management System -- 2.3 Electricity Storage -- 3 Problem Definition and Simulation Environment -- 4 Proposed TripleS -- 5 Experimental Results -- 5.1 Performance Evaluation -- 5.2 Performance Evaluation Under MS Attack -- 5.3 Influence of Self-discharge -- 6 Conclusion -- References -- Spatio-temporal Data.
Mask Adaptive Spatial-Temporal Recurrent Neural Network for Traffic Forecasting.
Record Nr. UNINA-9910851987103321
Yang De-Nian  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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