| Autore |
Hameurlain Abdelkader
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Berlin, Heidelberg : , : Springer Berlin / Heidelberg, , 2024
|
| Descrizione fisica |
1 online resource (162 pages)
|
| Altri autori (Persone) |
TjoaA. Min
AkbariniaReza
BonifatiAngela
|
| Collana |
Lecture Notes in Computer Science Series
|
| ISBN |
9783662696033
9783662696026
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Multi-objective Test Recommendation for Adaptive Learning -- 1 Introduction -- 2 Model and Problem -- 2.1 Expected Performance, Aptitude, and Gap -- 2.2 The AdUp Problem -- 3 Solutions -- 3.1 Multi-Objective Optimization (MOO) -- 3.2 Multi-Armed Bandits Algorithm (MAB) -- 4 Experiments -- 4.1 Datasets -- 4.2 Skill Update and Mastery Achievement -- 4.3 Research Questions -- 4.4 Experimental Settings -- 4.5 RQ1: Impact of Optimizing All Dimensions -- 4.6 RQ2: Impact of Changing the Settings of the Skill Update -- 4.7 RQ3: Impact of Changing the Learner Simulation Model -- 4.8 RQ4: Impact of the Meta-strategy -- 4.9 RQ5. The Quality of the Generated Test Batches -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Handling Dropouts in Federating Learning with Personal Data Management Systems -- 1 Introduction -- 2 Related Work -- 2.1 Differential Privacy-Based Secure Aggregation -- 2.2 Anonymity-Based Secure Aggregation -- 2.3 Trusted Execution Environment-Based Secure Aggregation -- 2.4 Cryptography-Based Secure Aggregation -- 2.5 Conclusion -- 3 System Overview and Threat Model -- 3.1 System Architecture -- 3.2 Threat Model -- 4 Straw-Man Protocol -- 4.1 Background -- 4.2 Straw-Man Protocol in an Ideal World -- 4.3 Analysis and Problem Formulation -- 5 Handling Dropouts -- 5.1 Dropout Model and Detection -- 5.2 Replacing (or not) a Dropped Out Node -- 5.3 Ensuring Validity -- 5.4 Ensuring Termination -- 6 Aggregation Strategies -- 6.1 LowCost -- 6.2 HighCpl -- 6.3 Sync& -- Prune -- 6.4 Hybrid -- 6.5 Summary -- 7 Performance Evaluation -- 7.1 Experimental Platform -- 7.2 Evaluation Metrics -- 7.3 Experimental and Security Parameters -- 7.4 Performance with Varying Dropout Rate -- 7.5 Scalability and Security -- 7.6 Best Fit Strategy -- 8 Conclusion -- References.
ANTM: Aligned Neural Topic Models for Exploring Evolving Topics -- 1 Introduction -- 2 Related Work -- 3 ANTM -- 3.1 Contextual Embedding Layer (CEL) -- 3.2 Aligned Clustering Layer (ACL) -- 3.3 Representation Layer -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Models -- 4.3 Evaluation Metrics -- 4.4 Experimental Setup -- 4.5 Results -- 5 Conclusion -- References -- A Data-Driven Model Selection Approach to Spatio-Temporal Prediction -- 1 Introduction -- 2 Problem Formulation -- 3 Our Proposal -- 3.1 Domain Partitioning -- 3.2 Model Representative Construction -- 3.3 Model Selection for Model Composition -- 3.4 Spatio-Temporal Predictive Query Processing -- 4 Experiments and Results -- 4.1 Domain Partitioning Evaluation -- 4.2 Predictive Quality of Models at Representatives -- 4.3 Processing Spatio-Temporal Predictive Queries -- 5 Related Works -- 6 Conclusions and Future Works -- References -- Optimistic Data Generation for JSON Schema -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 The JSONData Model -- 3.2 Overview of JSON Schema -- 3.3 Syntax and Semantics of JSON Schema -- 4 The Pre-processing Phase -- 4.1 Reference Expansion -- 4.2 Schema Normalization -- 5 The Generation Phase -- 5.1 Generation of Basic Types -- 5.2 Object Types -- 5.3 Array Types -- 6 Experiments -- 6.1 Comparative Experiments -- 6.2 Multiple Instance Generation Experiments -- 6.3 uniqueItems Experiments -- 7 Conclusion -- References -- Author Index.
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| Record Nr. | UNINA-9910874673203321 |
Info
Proceedings of the 8th annual ACM International Workshop on Web Information and Data Management
