High-Rate Quasi-Dyadic Quantum LDPC Codes
本文提出基于准二元矩阵设计高码率量子LDPC码，通过构造具有至少6围长的Tanner图，提高了代码在不同噪声模型下的逻辑错误率性能。
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Leveraging contextual events on structure-aware next activity prediction
本文通过引入基于实例图的方法并结合上下文信息，利用图神经网络改进了流程中下一个活动的预测性能。
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Theia: Large-Scale Multimodal Captioning and Automated Validation of the Incidents1M Dataset for Data-Free Distillation
This study addresses the scarcity of high-quality, large-scale multimodal datasets with semantically aligned image-text pairs in disaster response, which hinders data-free knowledge distillation for vision-language models. Building upon the purely visual Incidents1M dataset, the authors leverage the Qwen3.5 series of large language models to generate image captions and introduce an innovative image-blinded LLM-as-a-Judge automated verification mechanism. This approach simulates the modality gap faced by student models during distillation, effectively identifying and suppressing annotation inconsistencies and false positives inherent in human labeling. The method produces high-fidelity descriptions for 100,000 images, achieving a semantic consistency score of 78.65 on 173,179 annotated pairs and a verification accuracy of 77.6%, substantially enhancing the semantic reliability of data-free distillation.
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A forgery attack on the Block.co blockchain-based digital credential certification system
This study addresses a critical vulnerability in existing blockchain-based academic credential verification systems, such as Block.co, which lack a trustworthy mechanism to securely bind an educational institution’s real-world identity to its digital counterpart. The authors present the first practical forgery attack against such systems, exploiting the disconnect between blockchain immutability and identity validation logic to generate fraudulent credentials that are erroneously validated as legitimate. This work exposes a fundamental flaw in decentralized credentialing models—namely, the insecure identity-binding process—and demonstrates that this weakness introduces systemic security risks across similar platforms. The proposed attack methodology is generalizable, highlighting a pervasive threat that undermines the integrity of blockchain-issued digital credentials and offering crucial insights for the secure design of future systems.
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A Two-Stage Decision Support System for Sustainability-Aware Long Short Portfolio Optimization
This study addresses the challenge of constructing long–short portfolios that simultaneously achieve sustainability and superior risk-adjusted returns by integrating environmental, social, and governance (ESG) factors in a market-regime-adaptive manner. The authors propose a two-stage decision framework: first, assets are selected for long and short positions using a hybrid approach combining TODIMSort multi-criteria classification with MEREC-based weighting; second, a non-convex optimization model maximizing the Omega ratio is formulated and solved via a novel particle swarm optimization algorithm featuring operator self-adaptation and constraint-projection repair mechanisms. Empirical analysis on 421 constituents of the STOXX Europe 600 index demonstrates that the proposed ESG-enhanced strategy significantly outperforms both conventional non-ESG approaches and the market-capitalization-weighted benchmark, delivering higher risk-adjusted returns while advancing sustainable investment objectives.
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High-Rate Quasi-Dyadic Quantum LDPC Codes
本文提出基于准二元矩阵设计高码率量子LDPC码，通过构造具有至少6围长的Tanner图，提高了代码在不同噪声模型下的逻辑错误率性能。
0 citationsRead paper
Leveraging contextual events on structure-aware next activity prediction
本文通过引入基于实例图的方法并结合上下文信息，利用图神经网络改进了流程中下一个活动的预测性能。
0 citationsRead paper
Theia: Large-Scale Multimodal Captioning and Automated Validation of the Incidents1M Dataset for Data-Free Distillation
This study addresses the scarcity of high-quality, large-scale multimodal datasets with semantically aligned image-text pairs in disaster response, which hinders data-free knowledge distillation for vision-language models. Building upon the purely visual Incidents1M dataset, the authors leverage the Qwen3.5 series of large language models to generate image captions and introduce an innovative image-blinded LLM-as-a-Judge automated verification mechanism. This approach simulates the modality gap faced by student models during distillation, effectively identifying and suppressing annotation inconsistencies and false positives inherent in human labeling. The method produces high-fidelity descriptions for 100,000 images, achieving a semantic consistency score of 78.65 on 173,179 annotated pairs and a verification accuracy of 77.6%, substantially enhancing the semantic reliability of data-free distillation.
0 citationsRead paper
A forgery attack on the Block.co blockchain-based digital credential certification system
This study addresses a critical vulnerability in existing blockchain-based academic credential verification systems, such as Block.co, which lack a trustworthy mechanism to securely bind an educational institution’s real-world identity to its digital counterpart. The authors present the first practical forgery attack against such systems, exploiting the disconnect between blockchain immutability and identity validation logic to generate fraudulent credentials that are erroneously validated as legitimate. This work exposes a fundamental flaw in decentralized credentialing models—namely, the insecure identity-binding process—and demonstrates that this weakness introduces systemic security risks across similar platforms. The proposed attack methodology is generalizable, highlighting a pervasive threat that undermines the integrity of blockchain-issued digital credentials and offering crucial insights for the secure design of future systems.
0 citationsRead paper
A Two-Stage Decision Support System for Sustainability-Aware Long Short Portfolio Optimization
This study addresses the challenge of constructing long–short portfolios that simultaneously achieve sustainability and superior risk-adjusted returns by integrating environmental, social, and governance (ESG) factors in a market-regime-adaptive manner. The authors propose a two-stage decision framework: first, assets are selected for long and short positions using a hybrid approach combining TODIMSort multi-criteria classification with MEREC-based weighting; second, a non-convex optimization model maximizing the Omega ratio is formulated and solved via a novel particle swarm optimization algorithm featuring operator self-adaptation and constraint-projection repair mechanisms. Empirical analysis on 421 constituents of the STOXX Europe 600 index demonstrates that the proposed ESG-enhanced strategy significantly outperforms both conventional non-ESG approaches and the market-capitalization-weighted benchmark, delivering higher risk-adjusted returns while advancing sustainable investment objectives.
0 citationsRead paper