10200nam 22004573 450 991088109740332120240818090313.03-031-68208-4(MiAaPQ)EBC31605055(Au-PeEL)EBL31605055(CKB)34039642300041(EXLCZ)993403964230004120240818d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierModeling Decisions for Artificial Intelligence 21st International Conference, MDAI 2024, Tokyo, Japan, August 27-31, 2024, Proceedings1st ed.Cham :Springer,2024.©2024.1 online resource (257 pages)Lecture Notes in Computer Science Series ;v.149863-031-68207-6 Intro -- Preface -- Organization -- Contents -- Invited Papers -- The Past and Future of Fuzzy Measures and Fuzzy Integrals -- 1 Introduction -- 2 On Prof. Sugeno's Doctoral Thesis ``Theory of Fuzzy Integrals and Its Applications'' (Yasuo Narukawa) -- 3 On MCDA Based on Fuzzy Measures and Its Integrals (Katsushige Fujimoto) -- 4 On Prof. Sugeno and the Choquet Integral (Vicenç Torra) -- 5 On the Three Questions Prof. Sugeno Asked Related to the Choquet Integral Computation (Zuzana Ontkovičová) -- 6 Prof. Sugeno at MDAI -- References -- Taste Media: Innovative Technology Transforms the Eating Experience -- 1 Introduction -- 2 FEELTECH Taste Sharing -- 3 Electric Salt -- 4 Norimaki Synthesizer -- 5 Taste Printer TTTV (Taste the TV) -- 6 Seasoning Appliance TTTV2 -- 7 Reproducing Differences in Production Regions and Varieties -- 8 Bottle Mounted Seasoner -- 9 Time Machine for Taste -- 10 Time Manipulating Seasoning -- 11 Video Images with Taste Information -- 12 Conclusion -- References -- Fuzzy Measures and Integrals -- An Axiomatic Definition of Non-discrete Möbius transform -- 1 Introduction -- 2 Möbius Base Function and Restrictions by Topological Assumptions -- 3 Axiomatic Definition of Möbius Transform -- 4 Möbius Transform with Bounded Variation -- 5 Conclusion -- References -- Fuzzy Rough Choquet Distances -- 1 Introduction -- 2 Preliminaries -- 2.1 Fuzzy Rough Set Theory -- 2.2 Fuzzy-Rough Attribute Measures -- 2.3 Choquet Integral -- 3 Fuzzy Rough Choquet Distances -- 3.1 Fuzzy Rough Choquet P-Distances -- 3.2 Choquet Distances for Classification -- 4 Conclusion and Future Research -- References -- Uncertainty in AI -- Entropies from f-Divergences -- 1 Introduction -- 2 f-Divergences -- 3 Majorization and Homogeneity -- 4 Entropies from f-Divergences -- 4.1 Cichocki and Amari ch5Cichocki10 Approach -- 4.2 Topsøe Approach ch5Topsoe2004.5 Conclusions and Future Work -- References -- Comparative Study of Methods for Estimating Interval Priority Weights Focusing on the Accuracy in Selecting the Best Alternative -- 1 Introduction -- 2 Interval Priority Weight Estimation Problem -- 3 Numerical Experiments -- 4 Results -- 5 Concluding Remarks -- References -- Clustering -- Sequential Cluster Extraction by Noise Clustering Based on Local Outlier Factor -- 1 Introduction -- 2 Preliminaries -- 2.1 Noise Clustering -- 2.2 Sequential Noise Clustering -- 2.3 Local Outlier Factor -- 3 Proposed Method -- 3.1 Noise Clustering Based on Local Outlier Factor -- 3.2 Sequential Cluster Extraction Based on NCLOF -- 4 Numerical Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Discussion -- 5 Conclusions -- References -- On Objective-Based Clustering from the Perspective of Transportation Problem -- 1 Introduction -- 2 Transportation Problem -- 3 Objective-Based Clustering -- 3.1 Hard c-Means and Fuzzy c-Means -- 3.2 Even-Sized Clustering and Fuzzy Even-Sized Clustering -- 4 Transportation Problem and Objective-Based Clustering -- 4.1 Relevance with HCM -- 4.2 Relevance with ECBO -- 4.3 Relevance with FCM -- 4.4 Relevance with FECBO -- 4.5 Relevance with Other Clustering Methods -- 5 Some Considerations on Clustering with Implications from the Transportation Problem -- 5.1 Supply and Demand -- 5.2 Proposal of Evaluation Function for Clustering -- 6 Conclusion -- References -- Data Science and Data Privacy -- Decision Tree Based Inference of Lightning Network Client Implementations -- 1 Introduction -- 2 Lightning Network Background -- 2.1 Gossip Protocol -- 2.2 Feature Flags -- 2.3 LN Clients -- 3 State of the Art -- 4 LN Client Inference Scenarios -- 4.1 Scenario 1: Traffic Based Inference -- 4.2 Scenario 2: BOLT #9 Features Based Inference -- 5 Experimental Setup.5.1 Regtest Network Environment -- 5.2 Mainnet Network Environment -- 5.3 Model Training and Testing -- 6 Evaluation of the Models -- 6.1 Regtest Network Environment -- 6.2 Mainnet Network Environment -- 7 Inferring the Distribution of LN Client Implementations -- 8 Conclusions and Further Research -- References -- nuggets: Data Pattern Extraction Framework in R -- 1 Introduction -- 2 Background Definitions -- 3 Pattern Searching Framework -- 4 Types of Patterns from Tabular Data -- 4.1 Implicative Association Rules -- 4.2 Conditional Correlations -- 4.3 Emerging Patterns -- 5 The Implementation in R -- 5.1 How to Obtain the nuggets package -- 5.2 Searching for Patterns -- 6 Example: Search for Implicative Association Rules -- 7 Technical Details -- 8 Conclusion -- References -- User-Centred Argumentation Analysis of Local Explanations in Explainable AI -- 1 Introduction -- 2 Explanation Generation and Expert User's Knowledge -- 2.1 Local Explanation Generation -- 2.2 Expert User's Knowledge Conjectures -- 3 Analogical Statements: Forms and Interpretations -- 3.1 The Notational Form -- 3.2 The Relational Form -- 4 Reconstructing Expert User's Post-Hoc Analogical Arguments -- 5 When Expert User's Arguments for Analogies Are Deductive -- 6 Faithful Feature-Attribution Methods Are Founded -- 7 Conclusion and Related Work -- References -- Revised Margin-Maximization Method for Fuzzy Nearest Prototype Classification -- 1 Introduction -- 2 Fuzzy Nearest Prototype Classification -- 3 Maximum-Margin Fuzzy Nearest Prototype Classifier -- 3.1 Margin-Maximization Problem and Reformulation -- 3.2 Initialization -- 3.3 Training Algorithm -- 4 Numerical Study -- 5 Concluding Remarks -- References -- Bistochastically Private Release of Data Streams with Zero Delay -- 1 Introduction -- 2 Background Elements -- 2.1 Randomized Response -- 2.2 Bistochastic Privacy.3 Bistochastically Private Continuous Publication of Data Streams -- 3.1 T-transforms and Elementary Plausible Deniability -- 3.2 Data Stream Anonymization Through Successive T-transformations -- 3.3 Application to Categorical Attributes -- 3.4 Application to Several Attributes -- 4 Empirical Illustrations -- 5 Conclusions and Future Work -- References -- Differentially Private Extreme Learning Machine -- 1 Introduction -- 2 Preliminaries -- 2.1 Extreme Learning Machine -- 2.2 Differential Privacy -- 3 Our DPELM Algorithm -- 4 Utility Evaluation for DPELM -- 5 Conclusion -- References -- Studying the Impact of Edge Privacy on Link Prediction in Temporal Graphs -- 1 Introduction -- 2 Related Work -- 3 Basic Definitions -- 3.1 Edge Privacy for Dynamic Graphs -- 3.2 Parallel Protection Mechanism -- 4 Experimental Evaluation -- 4.1 Experiment Description -- 4.2 Temporal Link Prediction -- 5 Conclusions -- References -- Dissimilar Similarities: Comparing Human and Statistical Similarity Evaluation in Medical AI -- 1 Introduction -- 2 The Importance of Similarity in ML Classification -- 3 The Similarity Assessment User Study -- 3.1 Methods -- 3.2 The User Study Results -- 4 Discussion -- 5 Conclusions -- References -- On the Necessity of Counterfeits and Deletions for Continuous Data Publishing -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Contribution and Plan of This Paper -- 2 Preliminary Definitions and Results -- 3 Clean Publication -- 3.1 Heuristic Algorithm -- 4 Clusters for the m-invariant Publication -- 4.1 Privacy Guarantee -- 4.2 Comparing Clustering Against Deletions -- 5 Conclusions -- References -- A Poisoning-Resilient LDP Schema Leveraging Oblivious Transfer with the Hadamard Transform -- 1 Introduction -- 2 Local Differential Privacy -- 2.1 Fundamental Definition -- 2.2 Count Mean Sketch -- 2.3 Hadamard Count Mean Sketch.2.4 1-out-of-2 Oblivious Transfer -- 2.5 Poisoning -- 3 Proposed Method -- 3.1 Threat Model -- 3.2 Secure OT-HCMS -- 4 Evaluation -- 4.1 Datasets -- 4.2 Methodology -- 4.3 Results -- 4.4 Discussion -- 5 Conclusions -- References -- Experimental Evaluation for Risk Assessment of Privacy Preserving Synthetic Data -- 1 Introduction -- 2 Related Works -- 2.1 TAPAS -- 3 Proposed Framework -- 4 Experimental Evaluation -- 4.1 Settings -- 4.2 Utility Evaluation -- 4.3 Evaluation of Effective Epsilon -- 4.4 Additional Experiments -- 4.5 Consideration -- 5 Conclusion -- References -- Transforming Stock Price Forecasting: Deep Learning Architectures and Strategic Feature Engineering -- 1 Introduction -- 2 Literature Review -- 2.1 Traditional Models -- 2.2 Deep Learning Integration -- 2.3 Feature Engineering in Model Development -- 3 Methodology -- 3.1 Data Collection -- 3.2 Feature Engineering -- 3.3 Model Selection and Evaluation Metrics -- 4 Experiments and Results -- 4.1 Experimental Process -- 4.2 Comparative Analysis: Statistical, Machine Learning, and Deep Learning Models -- 4.3 Advanced Deep Learning Models: An Inter-Comparison -- 4.4 Raw OHLCV Data Versus Enhanced Dataset -- 5 Discussion -- 5.1 Summary of Findings and Contributions -- 5.2 Limitations -- 5.3 Suggestions for Future Improvements -- 6 Conclusion -- References -- Author Index.Lecture Notes in Computer Science SeriesTorra Vicenç848974Narukawa Yasuo1255058Kikuchi Hiroaki1679931MiAaPQMiAaPQMiAaPQBOOK9910881097403321Modeling Decisions for Artificial Intelligence4207478UNINA