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arXiv 2025年

作者： Zhou, Zhenhuan Zhang, Yuchen Xu, Ruihong Zhao, Xuansen Li, Tao College of Computer Science Nankai University Tianjin300350 China Key Laboratory of Data and Intelligent System Security Ministry of Education China Department of stomatology Tianjin Union Medical Center The First Affiliated Hospital of Nankai University Tianjin300121 China College of Mechanical Engineering Taiyuan University of Technology Shanxi 030000 China Haihe Lab of ITAI Tianjin300459 China

Deep learning (DL), a pivotal technology in artificial intelligence, has recently gained substantial traction in the domain of dental auxiliary diagnosis. However, its application has predominantly been confined to imaging modalities such as panoramic radiographs and Cone Beam Computed Tomography, with limited focus on auxiliary analysis specifically targeting Periapical Radiographs (PR). PR are the most extensively utilized imaging modality in endodontics and periodontics due to their capability to capture detailed local lesions at a low cost. Nevertheless, challenges such as resolution limitations and artifacts complicate the annotation and recognition of PR, leading to a scarcity of publicly available, large-scale, high-quality PR analysis datasets. This scarcity has somewhat impeded the advancement of DL applications in PR analysis. In this paper, we present PRAD-10K, a dataset for PR analysis. PRAD-10K comprises 10,000 clinical periapical radiograph images, with pixel-level annotations provided by professional dentists for nine distinct anatomical structures, lesions, and artificial restorations or medical devices, We also include classification labels for images with typical conditions or lesions. Furthermore, we introduce a DL network named PRNet to establish benchmarks for PR segmentation tasks. Experimental results demonstrate that PRNet surpasses previous state-of-the-art medical image segmentation models on the PRAD-10K dataset. The codes and dataset will be made publicly available. © 2025, CC BY-NC-ND.

关键词： Dental orthoses

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Verifying Diagnosability of Discrete Event System with Logical Formula

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Chinese Journal of Electronics 2020年第2期29卷 304-311页

作者： GENG Xuena OUYANG Dantong HAN Cheng College of Computer Science and Technology Changchun University of Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University

Diagnosability is an important property in the field of fault diagnosis. In this paper, a novel approach based on logical formula is proposed to verify diagnosability of Discrete event systems(DESs). CNFFSM is defined to represent a new model for DES. Each transition in DES can be described as a clause. According to CNF-FSM, we construct a CNF-diagnoser. Based on the resolution principle and CNF-diagnoser, an algorithm is presented to test whether the failure events can be detected or not in a finite number of observable *** algorithm can be applied in both off-line diagnosis and on-line diagnosis. Experimental results show that our algorithm can solve the diagnosability problem efficiently.

关键词： Fault diagnosis Diagnosability Discrete event system Finite state machine Conjunctive normal form

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Incomplete Multi-View Clustering via Multi-Level Contrastive Learning

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IEEE Transactions on knowledge and data engineering 2025年

作者： Yin, Jun Wang, Pei Sun, Shiliang Zheng, Zhonglong Shanghai Maritime University College of Information Engineering Shanghai201306 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science Shanghai200062 China Zhejiang Normal University School of Computer Science Zhejiang Jinhua321004 China

Although significant progress has been made in multi-view learning over the past few decades, it remains challenging, especially in the context of incomplete multi-view clustering, where modeling complex correlations among different views and handling missing data are key difficulties. In this paper, we propose a novel incomplete multi-view clustering network to address the aforementioned issue, named Incomplete Multi-view Clustering via Multi-level Contrastive Learning (IMC-MCL). Specifically, the proposed model aims to minimize the conditional entropy between views to recover missing data by dual prediction strategy. Moreover, the approach learns multi-level features, including latent, high-level and semantic features, with the goal of satisfying both reconstruction and consistency objectives in distinct feature spaces. Specifically, latent features are utilized to accomplish the reconstruction objective, while high-level features and semantic labels are employed to achieve the two consistency goals through contrastive learning. This framework enables the exploration of shared semantics within high-level features and achieves clustering assignment using semantic features. Extensive experiments have shown that the proposed approach outperforms other state-of-the-art incomplete multi-view clustering methods on seven challenging datasets. © 1989-2012 IEEE.

关键词： Sorting

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SheepDoctor: A knowledge graph enhanced large language model for sheep disease diagnosis

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Smart Agricultural Technology 2025年 11卷

作者： Xiong, Jiayi Zhou, Yong Tian, Fang Ni, Fuchuan Zhao, Liang College of Informatics Huazhong Agricultural University Hubei Wuhan 430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Hubei Wuhan 430070 China Key Laboratory of Smart Farming Technology for Agricultural Animals Ministry of Agriculture and Rural Affairs Hubei Wuhan 430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Hubei Wuhan 430070 China Jinchang General Animal Husbandry and Veterinary Station GanSu Jinchang 737100 China

Traditional methods for diagnosing sheep diseases often fail to provide timely and accurate results, particularly in emergency situations, and public awareness of these diseases remains limited. This hinders effective prevention and control efforts. To address these challenges, a domain-specific large language model, SheepDoctor, was developed for sheep disease diagnosis. A comprehensive question-and-answer (Q&A) dataset was constructed using prompt techniques, resulting in 5987 samples covering 207 sheep diseases. This dataset included detailed symptom descriptions, treatments, and related information, which were also structured into a knowledge graph. The pre-trained LLaMA2–13B-Chinese model was fine-tuned using Low-Rank Adaptation (LoRA), with integration of the knowledge graph to enhance its diagnostic capabilities and response accuracy. Evaluation using BLEU, ROUGE, and BERTScore metrics demonstrates that SheepDoctor outperforms general-purpose models such as GPT-4o and Kimi on sheep-related diagnostic tasks. The proposed method exhibits strong domain expertise and holds significant potential for improving the efficiency and reliability of sheep disease diagnosis. © 2025

关键词： Intelligent diagnose knowledge graph Large language model Retrieval-augmented generation

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CLASCN： Candidate Network Selection for Efficient Top-κ keyword Queries over databases

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Journal of Computer Science & Technology 2007年第2期22卷 197-207页

作者：张俊彭朝晖王珊聂惠静 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China Computer Science and Technology College Dalian Maritime University Dalian 116026 China

keyword Search Over Relational databases （KSORD） enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN （Classification, Learning And Selection of Candidate Network） is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks （CNs） from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models （CNLMs）. The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used （NAU） queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.

关键词： relational database keyword search top-κ query candidate network

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Are Your Apps Accessible? A GCN-based Accessibility Checker for Low Vision Users

arXiv

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arXiv 2025年

作者： Zhang, Mengxi Liu, Huaxiao Song, Shenning Chen, Chunyang Huang, Pei Zhao, Jian College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China Department of Computer Science Technical University of Munich Heilbronn85354 Germany Department of Computer Science Stanford University CA94305 United States College of Computer Science Changchun University Jilin Changchun130012 China

Context: Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend. Objective: In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components. Method: ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues. Results: Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models. Conclusion: To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator

arXiv

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arXiv 2025年

作者： Wang, Bing Zhao, Bingrui Li, Ximing Li, Changchun Gao, Wanfu Wang, Shengsheng College of Computer Science and Technology Jilin University Jilin Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin University China

Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor Detection (RD) algorithms to automatically identify them using user comments as evidence. However, these RD methods often fail in the early stages of rumor propagation when only limited user comments are available, leading the community to focus on a more challenging topic named Rumor Early Detection (RED). Typically, existing RED methods learn from limited semantics in early comments. However, our preliminary experiment reveals that the RED models always perform best when the number of training and test comments is consistently extensive. This inspires us to address the RED issue by generating more human-like comments to support this hypothesis. To implement this idea, we tune a comment generator by simulating expert collaboration and controversy and propose a new RED framework named CameRed. Specifically, we integrate a mixture-of-expert structure into a generative language model and present a novel routing network for expert collaboration. Additionally, we synthesize a knowledgeable dataset and design an adversarial learning strategy to align the style of generated comments with real-world comments. We further integrate generated and original comments with a mutual controversy fusion module. Experimental results show that CameRed outperforms state-of-the-art RED baseline models and generation methods, demonstrating its effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Tweets

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A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

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Science China(Information Sciences) 2020年第12期63卷 266-268页

作者： Jing-Dong DIAO Jin GUO Changyin SUN School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education

Dear editor,With the rapid development of network and communication technology, group systems interrelated in terms of both time and space are commonly encountered, such as sensor networks, multi-agent systems, and sm... 详细信息

关键词： system identification event-triggered binary-valued observations packet loss convergence

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Leaving None Behind: data-Free Domain Incremental Learning for Major Depressive Disorder Detection

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IEEE Transactions on Affective Computing 2024年第02期16卷 758-770页

作者： Chen, Tao Guo, Yanrong Hao, Shijie Hong, Richang Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Hefei University of Technology Ministry of Education and School of Computer Science and Information Engineering Hefei230009 China

While deep learning techniques have shown promising performance in the Major Depressive Disorder (MDD) detection task, they still face limitations in real-world scenarios. Specifically, given the data scarcity, some efforts have resorted to aggregating data from different domains to expand the data volume. However, their effectiveness is currently limited by the domain gap and data privacy. Additionally, the class imbalance issue is particularly severe in our application, leading to biased classifying performance accordingly. To address these challenges, we propose data-Free Domain Incremental Learning for the MDD detection (DIL-MDD) task, accommodating multiple feature distributions by only accessing well-trained models from previous domains and the data in the current domain. Specifically, DIL-MDD consists of two key modules: Adaptive Class-tailored Threshold Learning (ACTL) and data-Free Domain Alignment (DFDA). The first module measures the discrepancy between the outputs of two sequential domains, based on which we learn a class-tailored threshold adaptively. Building on this, we differentiate between samples that either exhibit similarities or dissimilarities with the previous domain, where this similar sample set is identified to investigate the feature distribution of the historical data. The second module imposes an alignment constraint to narrow the gap between these two sample sets, thereby exploring the expertise of the previous domain. To validate the effectiveness of the proposed method, we conduct extensive experiments on the public MDD datasets, i.e., DAIC-WOZ, MODMA, and CMDC. We also apply our method to another mental health condition, Autism Spectrum Disorder (ASD), to further demonstrate its applicability. Finally, the ablation studies validate the superiority of the proposed modules. © 2010-2012 IEEE.

关键词： Adversarial machine learning

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