Yu Gu

Buqiang Xu, Xin Dai, Zhenghao Liu et al.

Legal Judgment Prediction (LJP) is a fundamental task of legal artificial intelligence, aiming to automatically predict the judgment outcomes of legal cases. Existing LJP models primarily focus on identifying legal triggers within criminal fact descriptions by contrastively training language models. However, these LJP models overlook the importance of learning to effectively distinguish subtle differences among judgments, which is crucial for producing more accurate predictions. In this paper, we propose LegalDuet, which continuously pretrains language models to learn a more tailored embedding space for representing legal cases. Specifically, LegalDuet designs a dual-view mechanism to continuously pretrain language models: 1) Law Case Clustering retrieves similar cases as hard negatives and employs contrastive training to differentiate among confusing cases; 2) Legal Decision Matching aims to identify legal clues within criminal fact descriptions to align them with the chain of reasoning that contains the correct legal decision. Our experiments on the CAIL2018 dataset demonstrate the effectiveness of LegalDuet. Further analysis reveals that LegalDuet improves the ability of pretrained language models to distinguish confusing criminal charges by reducing prediction uncertainty and enhancing the separability of criminal charges. The experiments demonstrate that LegalDuet produces a more concentrated and distinguishable embedding space, effectively aligning criminal facts with corresponding legal decisions. The code is available at https://github.com/NEUIR/LegalDuet.

Le Xu, Chenxing Li, Yong Ren et al.

Current vision-guided audio captioning systems frequently fail to address audiovisual misalignment in real-world scenarios, such as dubbed content or off-screen sounds. To bridge this critical gap, we present an entropy-aware gated fusion framework that dynamically modulates visual information flow through cross-modal uncertainty quantification. Our novel approach employs attention entropy analysis in cross-attention layers to automatically identify and suppress misleading visual cues during modal fusion. Complementing this architecture, we develop a batch-wise audiovisual shuffling technique that generates synthetic mismatched training pairs, greatly enhancing model resilience against alignment noise. Evaluations on the AudioCaps benchmark demonstrate our system's superior performance over existing baselines, especially in mismatched modality scenarios. Furthermore, our solution demonstrates an approximately 6x improvement in inference speed compared to the baseline.

Yu Gu, Yantong Wang, Zhi Liu et al.

The ever accelerating process of urbanization urges more and more population into the swelling cities. While city residents are enjoying an entertaining life supported by advanced informatics techniques like 5G and cloud computing, the same technologies have also gradually deprived their sleep, which is crucial for their wellness. Therefore, sleep monitoring has drawn significant attention from both research and industry communities. In this article, we first review the sleep monitoring issue and point out three essential properties of an ideal sleep healthcare system, i.e., realtime guarding, fine-grained logging, and cost-effectiveness. Based on the analysis, we present SleepGuardian, a Radio Frequence (RF) based sleep healthcare system leveraging signal processing, edge computing and machine learning.SleepGuardian offers an offline sleep logging service and an online abnormality warning service. The offline service provides a fine-grained sleep log like timing and regularity of bed time, onset of sleep and night time awakenings. The online service keeps guarding the subject for any abnormal behaviors during sleep like intensive body twitches and a sudden seizure attack. Once an abnormality happens,it will automatically warn the designated contacts like a nearby emergency room or a closeby relative.We prototype SleepGuardian with low-cost WiFi devices and evaluate it in real scenarios. Experimental results demonstrate that SleepGuardian is very effective.