Titlebook: Intelligent Information Processing XII; 13th IFIP TC 12 Inte Zhongzhi Shi,Jim Torresen,Shengxiang Yang Conference proceedings 2024 IFIP Int

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Dual Contrastive Learning for Anomaly Detection in Attributed Networks existing methods fail to capture the complexity of anomalous patterns at different levels with suitable supervision signals. To address this issue, we propose a novel dual contrastive self-supervised learning method for attributed network anomaly detection. Specifically, our approach relies on two

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Online Learning in Varying Feature Spaces with Informative Variationvary over time due to the emergence of new features and the vanishing of outdated features. This phenomenon is referred to as online learning with Varying Feature Space (VFS). Recently, there has been increasing attention towards exploring this online learning paradigm. However, none of the existing

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Towards a Flexible Accuracy-Oriented Deep Learning Module Inference Latency Prediction Framework foror improved task execution efficiency as well as decision-making quality. Due to memory constraints, models are commonly optimized using compression, pruning, and partitioning algorithms to become deployable onto resource-constrained devices. As the conditions in the computational platform change dy

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Table Orientation Classification Model Based on BERT and TCSMNormation. However, due to their diverse structures and open domains, employing computational methods for their automatic analysis remains a substantial challenge. Among these challenges, accurately classifying the forms of tables is fundamental for achieving deep comprehension and analysis, forming

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Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learningvarious domains such as gene expression analysis, healthcare, and traffic prediction. Structure learning of DBNs from data is a challenging endeavor, particularly for datasets with thousands of variables. Most current algorithms for DBN structure learning are adaptations from those used in static Ba

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Bayesian Personalized Sorting Based on Time Factors and Hot Recommendationsh considers the influence of time and incorporates hot recommendations. By extracting user behavior features, constructing an optimized BPR model, and processing recommendation results, we establish BPR-TH for realizing personalized online (or offline) recommendation of digital library information.

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Design and Implementation of Risk Control Model Based on Deep Ensemble Learning Algorithmthod. In this study, we propose a nested ensemble learning method. First, we employ the stacking framework for selective ensemble learning. Next, we integrate the stacked ensemble with bagging and boosting techniques to create a comprehensive stacked ensemble. We utilized both domestic and foreign o