检索结果-内蒙古大学图书馆

A Comprehensive Metadata Framework for Preservation and Accessibility of Digital News and Educational Resource Management

引用

SN computer Science 2025年第5期6.0卷 1-17页

作者： Khan, Muzammil Rani, Huma Ullah, Sana Rahman, Arif Ur Department of Computer and Software Technology University of Swat Alabad Swat KP Charbagh 19130 Pakistan Department of Computer Science Bahria University Shangrilla Rd E-8/1 Islamabad Islamabad 44000 Pakistan

Advanced and rapid digitalization brings complex challenges in managing massive digital collections, necessitating well-defined attributes to uniquely identify and efficiently access, preserve, and retrieve digital objects. Metadata plays a vital role, providing structure, accessibility, and transformation potential alongside content. This study aims to create a comprehensive metadata set capturing all essential attributes of digital objects. In particular, many critical metadata elements required for effective news article management are not readily available within the sources. This study introduces a “Digital News Stories Preservation (DNSP)” framework with twenty-eight metadata elements: sixteen explicit and twelve implicit. These elements are categorized as optional, repeatable, explicit, or implicit and support robust news preservation. Implicit metadata is extracted directly from the content, enabling advanced search and linkage between related news articles within the “Digital News Stories Archive (DNSA).” The experimental results indicate that 55% of explicit metadata items and 65% of implicit metadata items were present across selected news sources. The study compares the proposed metadata against five established standards, highlighting unique elements and offering insights for enhanced news management and retrieval. The study explores metadata adoption for educational digital resources, detailing mappings to educational objectives and evaluating each metadata element’s educational effectiveness. By embedding these elements in digital platforms, news sources can improve content management, accessibility, and educational applications, supporting both media and academic environments. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Content Management digital libraries Metadata Element set Metadata Standards News preservation framework

来源：评论

学校读者我要写书评

暂无评论

Clustered Reinforcement Learning

引用

Frontiers of computer Science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

来源：评论

学校读者我要写书评

暂无评论

Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

引用

computer Modeling in Engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

来源：评论

学校读者我要写书评

暂无评论

PerFedKG: two-stage information-loop federated knowledge graph for personalized privacy-preserving recommendation systems

引用

Science China(Information Sciences) 2025年第4期68卷 98-99页

作者： Fan WANG Xuyun ZHANG Weiming LIU Li LI Yuwen LIU Zhongyuan ZHANG Guanfeng LIU Shengye PANG Xiaolong XU Lianyong QI College of Computer Science and Technology Zhejiang University Department of Computing Macquarie University College of Computer Science and Technology China University of Petroleum (East China) School of Software Nanjing University of Information Science and Technology

Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Grey-box Adversarial Attack on Communication in Communicative Multi-agent Reinforcement Learning

引用

IEEE Transactions on Information Forensics and Security 2025年 20卷 4679-4693页

作者： Ma, Xiao Li, Wu-Jun Nanjing University National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China

Effective communication is a necessary condition for intelligent agents to collaborate in multi-agent environments. Although increasing attention has been paid to communicative multi-agent reinforcement learning (CMARL), the vulnerability of the communication mechanism in CMARL has not been well investigated, especially when there exist malicious agents that send adversarial communication messages to other regular agents. Existing works about adversarial communication in CMARL focus on black-box attack where the attacker cannot access any model within the multi-agent system (MAS). However, a more practical attack is grey-box attack, where the attacker has access to the models of their controlled agents. To the best of our knowledge, no research has been conducted to investigate grey-box attacks on communication in CMARL. In this paper, we propose the first grey-box attack method on communication in CMARL, which is called victim-simulation based adversarial attack (VSAA). At each timestep, the attacker simulates a victim attacked by other regular agents' communication messages and generates adversarial perturbations on its received communication messages. The attacker then sends the aggregation of these perturbations to the regular agents through communication messages, which will induce non-optimal actions of the regular agents and subsequently degrade the performance of the MAS. Experimental results on multiple tasks show that VSAA can effectively degrade the performance of the MAS. The findings in this paper will make researchers be aware of the grey-box attack in CMARL. © 2005-2012 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

EX-Gaze: High-frequency and Low-latency Gaze Tracking with Hybrid Event-frame Cameras for On-Device Extended Reality

引用

IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2299-2309页

作者： Chen, Ning Shen, Yiran Zhang, Tongyu Yang, Yanni Wen, Hongkai School of Software Shandong University China School of Computer Science and Technology Shandong University China Department of Computer Science University of Warwick United Kingdom

The integration of gaze/eye tracking into virtual and augmented reality devices has unlocked new possibilities, offering a novel human-computer interaction (HCI) modality for on-device extended reality (XR). Emerging applications in XR, such as low-effort user authentication, mental health diagnosis, and foveated rendering, demand real-time eye tracking at high frequencies, a capability that current solutions struggle to deliver. To address this challenge, we present EX-Gaze, an event-based real-time eye tracking system designed for on-device extended reality. EX-Gaze achieves a high tracking frequency of 2KHz, providing decent accuracy and low tracking latency. The exceptional tracking frequency of EX-Gaze is achieved through the use of event cameras, cutting-edge, bio-inspired vision hardware that delivers event-stream output at high temporal resolution. We have developed a lightweight tracking framework that enables real-time pupil region localization and tracking on mobile devices. To effectively leverage the sparse nature of event-streams, we introduce the sparse event-patch representation and the corresponding sparse event patches transformer as key components to reduce computational time. Implemented on Jetson Orin Nano, a low-cost, small-sized mobile device with hybrid GPU and CPU components capable of parallel processing of multiple deep neural networks, EX-Gaze maximizes the computation power of Jetson Orin Nano through sophisticated computation scheduling and offloading between GPUs and CPUs. This enables EX-Gaze to achieve real-time tracking at 2KHz without accumulating latency. Evaluation on public datasets demonstrates that EX-Gaze outperforms other event-based eye tracking methods by striking the best balance between accuracy and efficiency on mobile devices. These results highlight EX-Gaze’s potential as a groundbreaking technology to support XR applications that require high-frequency and real-time eye tracking. The code is available at https://gith

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Events and findings of the Purdue university Calumet computer Information technology & Graphics (CIT & G) department teaching partnership with the College of software Engineering of Southeast university in Nanjing China 09

Events and findings of the Purdue University Calumet Compute...

引用

10th ACM Special Interest Group for Information technology Education, SIGITE 2009

作者： Winer, Charles Wu, Hanqian Department of Computer Information Technology and Graphics Purdue University Calumet Hammond IN United States College of Software Engineering Southeast University Nanjing China

ISBN: (纸本)9781605587653

In this poster session we are reporting on the results of two, three-week summer graduate teaching experiences that took place in Nanjing, China over a two-year period. A faculty exchange program was entered into between Southeast university of Nanjing China and Purdue university Calumet of Hammond, Indiana, USA. One of the goals of the exchange program was to expose Chinese students to the instructional methods employed by United States Universities. By understanding the cultural differences and utilizing various teaching methodologies employed by American teachers, the faculty and students involved in these three-week classroom intensive training courses were able to adapt and successfully complete the graduate level material that was presented.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

An aspect-based sentiment analysis model for Arabic game reviews based on hybrid transformers models

引用

Neural Computing and Applications 2025年 1-23页

作者： Hammad, Mahmoud AbuEnnab, Noor Al-Refai, Mohammed IT Department Ajman University Ajman United Arab Emirates Software Engineering Department Jordan University of Science and Technology Irbid Jordan Computer Science Department Jordan University of Science and Technology Irbid Jordan

Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) technique to determine the various sentiments of a customer in a single comment regarding different aspects. The increasing online data content generated by interested customers and reviewers motivated researchers and data scientists to conduct ABSA. ABSA has become increasingly popular in recent years due to its versatility in e-commerce, social media, and customer feedback analysis. However, ABSA faces several significant challenges, including determining the aspects and their sentiment polarities (positive, negative, or neutral) in a given text. Moreover, ABSA faces particular challenges in non-English languages such as Arabic due to the lack of resources and mature models. Typically, ABSA tackles one or more of the ABSA research tasks: (T1) aspect term extraction, (T2) aspect term polarity, (T3) aspect category identification, and (T4) aspect category polarity. To identify the aspects and their corresponding sentiment polarities in a given text, accurate and efficient NLP techniques are required. Despite growing interest in Arabic ABSA, the lack of annotated datasets and pre-trained models has hindered its development. In this research, we have collected a dataset of Arabic game reviews and annotated them using three annotators, and then we trained an ABSA deep learning model based on the BERT pre-trained model combined with zero-shot learning (ZSL) to tackle all the four aforementioned tasks. Our best performing model achieved a high accuracy on all four tasks with an accuracy of 91.61% on T1, 90.99% on T2, 79.08% on T3, and 88.17% on T4. Finally, we compared our model’s accuracy with the state-of-the-art Arabic-based ABSA models on different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

A survey on cross-user federated recommendation

引用

Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

来源：评论

学校读者我要写书评

暂无评论

Minimization of the Training Makespan in Hybrid Federated Split Learning

引用

IEEE Transactions on Mobile Computing 2025年第6期24卷 5400-5417页

作者： Tirana, Joana Tsigkari, Dimitra Iosifidis, George Chatzopoulos, Dimitris University College Dublin School of Computer Science Ireland Telefónica Research Spain Delft University of Technology Department of Software Technology Delft2628 Netherlands

Parallel Split Learning (SL) allows resource-constrained devices that cannot participate in Federated Learning (FL) to train deep neural networks (NNs) by splitting the NN model into parts. In particular, such devices (clients) may offload the processing task of the largest model part to a computationally powerful helper, and multiple helpers may be employed and work in parallel. In hybrid federated and split learning (HFSL), on the other hand, devices can participate in the training process through any of the two protocols (SL and FL), depending on the system's characteristics. This could considerably reduce the maximum training time over all clients (makespan), especially in highly heterogeneous scenarios. In this paper, we study the joint problem of the training protocol selection, client-helper assignments, and scheduling decisions, to minimize the training makespan. We prove this problem is NP-hard and propose two solution methods: one based on the decomposition of the problem by leveraging its inherent symmetry, and a second fully scalable one. Through numerical evaluations using our testbed's measurements, we build a solution strategy comprising these methods. Moreover, this strategy finds a near-optimal solution and achieves a shorter makespan than the baseline schemes by up to 71%. © 2025 IEEE.

关键词： Federated 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：