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Titlebook: Deep Learning Theory and Applications; 4th International Co Donatello Conte,Ana Fred,Carlo Sansone Conference proceedings 2023 The Editor(s

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31#
發(fā)表于 2025-3-26 21:54:22 | 只看該作者
32#
發(fā)表于 2025-3-27 03:23:36 | 只看該作者
,Synthetic Network Traffic Data Generation and?Classification of?Advanced Persistent Threat Samples:metrics indicate successful generation and detection with an accuracy of 99.97% a recall rate of 99.94%, and 100% precision. Further results show a 99.97% . score for detecting APT samples in the synthetic data, and a Receiver Operator Characteristic Area Under the Curve (ROC_AUC) value of 1.0, indi
33#
發(fā)表于 2025-3-27 07:39:39 | 只看該作者
34#
發(fā)表于 2025-3-27 12:59:16 | 只看該作者
35#
發(fā)表于 2025-3-27 15:35:40 | 只看該作者
,Research Data Reusability with?Content-Based Recommender System,te that the developed prototype content-based recommender system effectively provides relevant recommendations for research data repositories. The evaluation of the system using standard evaluation metrics shows that the system achieves an accuracy of 79% in recommending relevant items. Additionally
36#
發(fā)表于 2025-3-27 20:54:04 | 只看該作者
,MSDeepNet: A Novel Multi-stream Deep Neural Network for?Real-World Anomaly Detection in?Surveillanction module (WS-TAM). The features extracted from the individual streams are fed to train the modified MIL classifier by employing a novel temporal loss function. Finally, a fuzzy fusion method is used to aggregate the anomaly detection scores. To validate the performance of the proposed method, com
37#
發(fā)表于 2025-3-28 00:31:55 | 只看該作者
,Explaining Relation Classification Models with?Semantic Extents,ng both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the
38#
發(fā)表于 2025-3-28 05:16:48 | 只看該作者
39#
發(fā)表于 2025-3-28 08:31:17 | 只看該作者
ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can
40#
發(fā)表于 2025-3-28 12:48:13 | 只看該作者
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