EmoPhone: A Multi-Wave Dataset for In-the-Wild Mobile and Wearable Affect Sensing
研究通过构建一个包含智能手机、可穿戴设备和密集体验采样标签的多波数据集,采用不同方法解决情感感知中的用户间和跨时间泛化问题。
研究通过构建一个包含智能手机、可穿戴设备和密集体验采样标签的多波数据集,采用不同方法解决情感感知中的用户间和跨时间泛化问题。
研究通过创建PetQA基准,使用多种模型评测方法解决兽医知识和临床推理在大语言模型中的应用问题。
为解决纵向放射学报告生成中的任务干扰和错误追溯问题,STRIVE通过多代理结构化时间推理与集成验证方法,提高报告的准确性和一致性。
This work addresses the inefficiency of differential fault analysis (DFA) by proposing the first DFA framework based on mixed-integer linear programming (MILP). For the first time, MILP is employed to systematically search for differential trails with a unique solution, combined with bit-level single-bit flip fault modeling to optimize both the location and number of injected faults. The approach enables attacks on deeper-round implementations and allows theoretical computation of the minimal number of faults required to recover the secret key. When applied to the DEFAULT block cipher, the method uniquely recovers the full key with only three faults in the sixth-to-last round and two faults each in the seventh- and eighth-to-last rounds, significantly outperforming existing DFA results and effectively breaking the cipher’s claimed DFA resistance.
Historical documents are often rendered partially illegible due to physical degradation, posing significant challenges—particularly in recovering proper nouns that rely heavily on external contextual knowledge. This work proposes a novel framework that integrates implicit knowledge from large language models with explicit historical context retrieved from external knowledge bases, leveraging retrieval-augmented generation (RAG) for the joint restoration of both general characters and named entities. By incorporating context-aware reasoning to effectively fuse domain-specific knowledge, the method substantially outperforms existing baselines on Korean historical documents, achieving marked improvements in both character-level and named entity recovery accuracy. The approach has also been endorsed by domain experts as a practical tool for historical text analysis.
研究通过构建一个包含智能手机、可穿戴设备和密集体验采样标签的多波数据集,采用不同方法解决情感感知中的用户间和跨时间泛化问题。
研究通过创建PetQA基准,使用多种模型评测方法解决兽医知识和临床推理在大语言模型中的应用问题。
为解决纵向放射学报告生成中的任务干扰和错误追溯问题,STRIVE通过多代理结构化时间推理与集成验证方法,提高报告的准确性和一致性。
This work addresses the inefficiency of differential fault analysis (DFA) by proposing the first DFA framework based on mixed-integer linear programming (MILP). For the first time, MILP is employed to systematically search for differential trails with a unique solution, combined with bit-level single-bit flip fault modeling to optimize both the location and number of injected faults. The approach enables attacks on deeper-round implementations and allows theoretical computation of the minimal number of faults required to recover the secret key. When applied to the DEFAULT block cipher, the method uniquely recovers the full key with only three faults in the sixth-to-last round and two faults each in the seventh- and eighth-to-last rounds, significantly outperforming existing DFA results and effectively breaking the cipher’s claimed DFA resistance.
Historical documents are often rendered partially illegible due to physical degradation, posing significant challenges—particularly in recovering proper nouns that rely heavily on external contextual knowledge. This work proposes a novel framework that integrates implicit knowledge from large language models with explicit historical context retrieved from external knowledge bases, leveraging retrieval-augmented generation (RAG) for the joint restoration of both general characters and named entities. By incorporating context-aware reasoning to effectively fuse domain-specific knowledge, the method substantially outperforms existing baselines on Korean historical documents, achieving marked improvements in both character-level and named entity recovery accuracy. The approach has also been endorsed by domain experts as a practical tool for historical text analysis.