检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Wang, Ziming Huang, Changwu Tang, Ke Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Data Science Lingnan University Hong Kong

Fairness in machine learning (ML) has garnered significant attention in recent years. While existing research has predominantly focused on the distributive fairness of ML models, there has been limited exploration of procedural fairness. This paper proposes a novel method to achieve procedural fairness during the model training phase. The effectiveness of the proposed method is validated through experiments conducted on one synthetic and six real-world datasets. Additionally, this work studies the relationship between procedural fairness and distributive fairness in ML models. On one hand, the impact of dataset bias and the procedural fairness of ML model on its distributive fairness is examined. The results highlight a significant influence of both dataset bias and procedural fairness on distributive fairness. On the other hand, the distinctions between optimizing procedural and distributive fairness metrics are analyzed. Experimental results demonstrate that optimizing procedural fairness metrics mitigates biases introduced or amplified by the decision-making process, thereby ensuring fairness in the decision-making process itself, as well as improving distributive fairness. In contrast, optimizing distributive fairness metrics encourages the ML model’s decision-making process to favor disadvantaged groups, counterbalancing the Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

COMMITSHIELD: Tracking Vulnerability Introduction and Fix in Version Control Systems

arXiv

引用

arXiv 2025年

作者： Wu, Zhaonan Zhao, Yanjie Wei, Chen Wan, Zirui Liu, Yue Wang, Haoyu Huazhong University of Science and Technology Wuhan China MYbank Ant Group Hangzhou China Monash University Melbourne Australia Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Version control systems are commonly used to manage open-source software, in which each commit may introduce new vulnerabilities or fix existing ones. Researchers have developed various tools for detecting vulnerabilities in code commits, but their performance is limited by factors such as neglecting descriptive data and challenges in accurately identifying vulnerability introductions. To overcome these limitations, we propose COMMITSHIELD, which combines the code analysis capabilities of static analysis tools with the natural language and code understanding capabilities of large language models (LLMs) to enhance the accuracy of vulnerability introduction and fix detection by generating precise descriptions and obtaining rich patch contexts. We evaluate COMMITSHIELD using the newly constructed vulnerability fix dataset, CommitVulFix, and a cleaned vulnerability introduction dataset. Experimental results indicate that COMMITSHIELD improves recall by 74%-77% over state-of-the-art methods in the vulnerability fix detection task, and its F1-score improves by 15%-27% in the vulnerability introduction detection task. Copyright © 2025, The Authors. All rights reserved.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Lyapunov-guided Deep Reinforcement Learning for Semantic-aware AoI Minimization in UAV-assisted Wireless Networks

引用

IEEE Transactions on Wireless Communications 2025年

作者： Long, Yusi Gong, Shimin Sun, Sumei Lee, Gary C.F Li, Lanhua Niyato, Dusit Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518000 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China A*STAR Singapore Nanyang Technological University College of Computing and Data Science Singapore

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic network where the ground users (GUs) periodically capture and upload the sensing information to a base station (BS) via UAVs’ relaying. Both the GUs and the UAVs can extract semantic information from large-size raw data and transmit it to the BS for recovery. Smaller-size semantic information reduces latency and improves information freshness, while larger-size semantic information enables more accurate data reconstruction at the BS, preserving the value of original information. We introduce a novel semantic-aware age-of-information (SAoI) metric to capture both information freshness and semantic importance, and then formulate a time-averaged SAoI minimization problem by jointly optimizing the UAV-GU association, the semantic extraction, and the UAVs’ trajectories. We decouple the original problem into a series of subproblems via the Lyapunov framework and then use hierarchical deep reinforcement learning (DRL) to solve each subproblem. Specifically, the UAV-GU association is determined by DRL, followed by the optimization module updating the semantic extraction strategy and UAVs’ deployment. Simulation results show that the hierarchical structure improves learning efficiency. Moreover, it achieves low AoI through semantic extraction while ensuring minimal loss of original information, outperforming the existing baselines. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

来源：评论

学校读者我要写书评

暂无评论

Summarizing Large-Scale database Schema Using Community Detection

引用

Journal of Computer Science & Technology 2012年第3期27卷 515-526页

作者：王雪周烜王珊 School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China

Schema summarization on large-scale databases is a challenge. In a typical large database schema, a great proportion of the tables are closely connected through a few high degree tables. It is thus difficult to separate these tables into clusters that represent different topics. Moreover, as a schema can be very big, the schema summary needs to be structured into multiple levels, to further improve the usability. In this paper, we introduce a new schema summarization approach utilizing the techniques of community detection in social networks. Our approach contains three steps. First, we use a community detection algorithm to divide a database schema into subject groups, each representing a specific subject. Second, we cluster the subject groups into abstract domains to form a multi-level navigation structure. Third, we discover representative tables in each cluster to label the schema summary. We evaluate our approach on Freebase, a real world large-scale database. The results show that our approach can identify subject groups precisely. The generated abstract schema layers are very helpful for users to explore database.

关键词： schema summarization large scale community detection

来源：评论

学校读者我要写书评

暂无评论

Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation

arXiv

引用

arXiv 2025年

作者： Lyu, Shen-Huan He, Yi-Xiao Wang, Yanyan Qu, Zhihao Tang, Bin Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China

Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been proven to have a stronger representation ability than DT, as it provides a way to create shallower tree structures while still approximating complex decision boundaries. However, its learning efficiency is still insufficient, since the linear projections cannot be transmitted to the child nodes, resulting in a waste of model parameters. In this work, we propose an enhanced ODT method with Feature Concatenation (FC-ODT), which enables in-model feature transformation to transmit the projections along the decision paths. Theoretically, we prove that our method enjoys a faster consistency rate w.r.t. the tree depth, indicating that our method possesses a significant advantage in generalization performance, especially for shallow trees. Experiments show that FC-ODT can outperform the other state-of-the-art decision trees with a limited tree depth. Copyright © 2025, The Authors. All rights reserved.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

引用

IEEE Transactions on Multimedia 2025年

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

Accelerating exploitation and integration of global renewable energy

引用

Innovation 2025年

作者： Wang, Jianxiao Chen, Xinjiang Zhuang, Minghao Li, Yan Ruan, Ziwen Wang, Yuhan Zhang, Ning Song, Jie He, Kebin Lu, Xi National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China State Key Laboratory of Nutrient Use and Management College of Resources and Environmental Sciences China Agricultural University Beijing100193 China College of Environmental Science and Engineering Tongji University Shanghai200092 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Key Lab of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

来源：评论

学校读者我要写书评

暂无评论

Utilizing semantically enhanced self-supervised graph convolution and multi-head attention fusion for herb recommendation

引用

Artificial Intelligence in Medicine 2025年 164卷 103112-103112页

作者： Tang, Xianlun Tang, Yuze Liu, Xinran Zhang, Haochuan Dang, Xiaoyuan Wang, Ying Xu, Zihui Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing400065 China School of Intelligent Engineering Chongqing College of Mobile Communication Chongqing401520 China Big Data and Internet of Things School Chongqing Vocational Institute of Engineering Chongqing402260 China Xinqiao Hospital Army Medical University Chongqing400037 China

Traditional Chinese herbal medicine has long been recognized as an effective natural therapy. Recently, the development of recommendation systems for herbs has garnered widespread academic attention, as these systems significantly impact the application of traditional Chinese medicine. However, existing herb recommendation systems are limited by data sparsity, insufficient correlation between prescriptions, and inadequate representation of symptoms and herb characteristics. To address these issues, this paper introduces an approach to herb recommendation based on semantically enhanced self-supervised graph convolution and multi-head attention fusion (BSGAM). This method involves efficient embedding of entities following fine-tuning of BERT;leveraging the attributes of herbs to optimize feature representation through a residual graph convolution network and self-supervised learning;and ultimately employing a multi-head attention mechanism for feature integration and recommendation. Experiments conducted on a publicly available traditional Chinese medicine prescription dataset demonstrate that our method achieves improvements of 6.80%, 7.46%, and 6.60% in F1-Score@5, F1-Score@10, and F1-Score@20, respectively, compared to baseline methods. These results confirm the effectiveness of our approach in enhancing the accuracy of herb recommendations. © 2025 Elsevier B.V.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：