Video Anomaly Detection (VAD) faces persistent challenges, including annotated data availability, contextual dependency, and elevated false alarm rates. While Self-Supervised Learning (SSL) effectively mitigates label scarcity, the current Self-Supervised Multi-Task Learning (SSMTL) framework encounters two primary challenges. The first challenge pertains to the susceptibility of Conv3D-based encoders to overfitting. This study investigated several overfitting mitigation strategies integrated into the encoder architecture to address this issue. The second challenge concerns the quadratic computational costs inherent in Transformer-based encoders. As a solution, this study introduces Vision Mamba (ViM), leveraging a Selective State Space Model to capture long-range temporal dependencies with linear computational complexity. This efficiency is theoretically substantiated by an asymptotic time complexity analysis, demonstrating ViM’s superiority over Vision Transformers (ViT) regarding data depth dimensions and architectural depth layers. The comprehensive experiments yielded two key findings. First, the empirical results on UCSD Ped2 demonstrate that a 0.3 dropout rate provides superior stability for mitigating overfitting in Conv3D baselines compared to standard regularization. Second, evaluations on a theft-focused UCF-Crime subset confirm ViM as the most lightweight architecture, reducing Floating Point Operations (FLOPs) by a factor of 2-4 relative to alternatives. In terms of performance, ViM outperformed the Conv3D baseline (avg. +0.012) and rival VideoSwin (avg. -0.017), although it trails ViT in the AUC-ROC, Precision, and Recall metrics. Finally, this study validates the ViM as a highly efficient solution that balances computational feasibility with good performance in detecting complex criminal activities at the frame level.
Rahman Indra Kesuma, M. L. Khodra, B. R. Trilaksono· IEEE Access· 0 citations
This paper presents a systematic literature review (SLR) on the development of a cybersecure and privacy-aware governance framework for Face Verification ID in Digital Public Infrastructure (DPI). The review responds to a growing need for digital public services that are secure, interoperable, scalable, and socially trusted, particularly where biometric identity verification is integrated with national electronic identity systems such as NFC-based e-ID. Following a PRISMA-oriented protocol, 181 records were identified from IEEE Xplore, ScienceDirect, and Scopus, comprising 59, 54, and 68 articles respectively. After duplicate removal, title-abstract screening, full-text eligibility assessment, and quality assessment using seven criteria, 36 studies were included for final synthesis. The results show that the literature has developed strong technical components in self-sovereign identity, e-KYC, blockchain-enabled digital credentials, privacy-preserving biometrics, face presentation attack detection, zero-trust security, and public trust in police use of facial recognition. However, these components are still fragmented. Very few studies combine NFC e-ID assurance, face verification, liveness detection, privacy-by-design, cybersecurity risk management, interoperability governance, and law-enforcement accountability within a single DPI-oriented framework. The discussion therefore proposes a layered governance architecture that integrates identity assurance, biometric verification, cyber defence, privacy governance, interoperability orchestration, and accountability mechanisms. The review contributes a consolidated taxonomy, research gap analysis, and evaluation agenda for future DPI implementations toward a trusted and secure digital society.
Eko Wahyu Bintoro, B. R. Trilaksono, S. Supangkat· IEEE Access· 0 citations
This research addresses the common challenge of a lack of context in Question and Answer (QA) datasets in digital education, which limits the reasoning potential of Large Language Models (LLMs). To address this, we optimize an automated retrieval-based dataset generation system that systematically enriches QA pairs with relevant pedagogical context from authoritative digital textbooks. This study conducts a comparative analysis of two major text chunking strategies: sentence chunking and recursive chunking. Although these pipelines are designed for general education applications, they are evaluated here through a case study of Indonesian elementary education materials. To ensure the highest reliability, the workflow performance is measured against a ground truth dataset of 978 entries, manually curated and validated by education experts to ensure pedagogical accuracy, and 781 entries from other subjects. Quantitative evaluation using BERTScore shows that recursive chunking achieves a superior F1 score of 0.748 compared to 0.737 for sentence chunking, with peak performance observed on upper elementary school materials (Grades 5 and 6). These findings were corroborated by the final verification phase through User Acceptance Testing (UAT) with an elementary school educator, where recursive chunking achieved a 'Relevant' score of 22 compared to 17 for sentence chunking. A key contribution of this study is the development and validation of a standardized, automated workflow by experts that effectively overcomes the barriers of manual dataset construction for domain-specific tasks, providing a semantically robust foundation for context-aware educational AI.
Viny Christanti Mawardi, Ayu Purwarianti, B. R. Trilaksono et al.· International Conference on...· 0 citations
This study establishes that fault discrimination relies on transient impulse morphology rather than bearing characteristic frequencies, a finding invisible to feature-level XAI, and introduces a multi-resolution diagnostic framework bridging deep learning accuracy with physically interpretable vibration analysis for trustworthy deployment in safety-critical industrial environments.
T. Suharto, Kadarsah Suryadi, B. Iskandar et al.· Emerging Science Journal· 0 citations
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