检索结果-南通市图书馆

The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and engineering Southern Cross University the School of information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

Deep learning-based software engineering: progress,challenges, and opportunities

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Science China(information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software engineering Sun Yat-sen University School of Software Dalian University of Technology School of computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of computer Science and engineering Beihang University Institute of information engineering Chinese Academy of Sciences School of computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of computer and information Technology Beijing Jiaotong University School of computer Science and Technology Harbin Institute of Technology School of computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of computer and information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

Edge Coalition Construction Based on a Novel Market Equilibrium Price

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Communications 2025年第03期22卷 288-305页

作者： Wang Xiaolong Dang Jianwu Zhao Shuxu Wang Yangping Zhang Zhanping Hao Zhanjun School of Electronic and information engineering Lanzhou Jiaotong University Big Data Center of Gansu Province School of Mathematics and information engineering Longdong University College of computer Science and engineering Northwest Normal University

The emergence of multi-access edge computing(MEC) aims at extending cloud computing capabilities to the edge of the radio access network. As the large-scale internet of things(IoT) services are rapidly growing, a single edge infrastructure provider(EIP) may not be sufficient to handle the data traffic generated by these services. Most of the existing work addressed the computing resource shortage problem by optimizing tasks schedule, whereas others overcome such issue by placing computing resources on demand. However, when considering a multiple EIPs scenario, an urgent challenge is how to generate a coalition structure to maximize each EIP's gain with a suitable price for computing resource block corresponding to a container. To this end, we design a scheme of EIPs collaboration with a market price for containers under a scenario that considers a collection of service providers(SPs) with different budgets and several EIPs distributed in geographical ***, we bring in the net profit market price model to generate a more reasonable equilibrium price and select the optimal EIPs for each SP by a convex program. Then we use a mathematical model to maximize EIP's profits and form stable coalitions between EIPs by a distributed coalition formation algorithm. Numerical results demonstrate that our proposed collaborative scheme among EIPs enhances EIPs' gain and increases users' surplus.

关键词： coalition formation computing resource block market equilibrium multi-access edge computing surplus

维普期刊数据库

ICA-Net: improving class activation for weakly supervised semantic segmentation via joint contrastive and simulation learning

在线全文

学校读者我要写书评

暂无评论

Optoelectronics Letters 2025年第3期21卷 188-192页

作者： YE Zhuang LIU Ruyu SUN Bo School of information engineering Chengdu Aviation Vocational and Technical College School of computer Science and engineering Hangzhou Normal University Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences

In the field of optoelectronics, certain types of data may be difficult to accurately annotate, such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges. Weakly supervised learning can provide a more reliable approach in these situations. Current popular approaches mainly adopt the classification-based class activation maps (CAM) as initial pseudo labels to solve the task.

关键词： A

Key Agreement and Management Scheme Based on Blockchain for 5G-Enabled Vehicular networks

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

China Communications 2025年第03期22卷 270-287页

作者： Wang Zhihua Wang Shuaibo Wang Haofan Li Jiaze Yao Yizhe Wang Yongjian Yang Xiaolong School of computer and Communication engineering University of Science and Technology Beijing School of Cyber Science and engineering Zhengzhou University Lab of National computer network Emergency Response Technical Team/Coordination Center of China

5G technology has endowed mobile communication terminals with features such as ultrawideband access, low latency, and high reliability transmission, which can complete the network access and interconnection of a large number of devices, thus realizing richer application scenarios and constructing 5G-enabled vehicular networks. However, due to the vulnerability of wireless communication, vehicle privacy and communication security have become the key problems to be solved in vehicular networks. Moreover, the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable requirement. In order to achieve efficient and secure communication while protecting vehicle privacy, this paper proposes a lightweight key agreement and key update scheme for5G vehicular networks based on blockchain. Firstly,the key agreement is accomplished using certificateless public key cryptography, and based on the aggregate signature and the cooperation between the vehicle and the trusted authority, an efficient key updating method is proposed, which reduces the overhead and protects the privacy of the vehicle while ensuring the communication security. Secondly, by introducing blockchain and using smart contracts to load the vehicle public key table for key management, this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving vehicles. Finally, the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular networks. Performance analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.

关键词： blockchain certificateless public key cryptography 5G vehicular networks key agreement key management

维普期刊数据库

Multimodal Cross-Attention Mechanism-Based Algorithm for Elderly Behavior Monitoring and Recognition

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2025年第1期34卷 309-321页

作者： Hao Liu Zhiquan Feng Qingbei Guo School of information Science and engineering Unicersity of Jinan Shandong Provincial Key Laboratory of network Based Intelligent Computing Unicersity of Jinan

In contrast to the general population, behavior recognition among the elderly poses increased specificity and difficulty, rendering the reliability and usability aspects of safety monitoring systems for the elderly more challenging. Hence, this study proposes a multi-modal perception-based solution for an elderly safety monitoring recognition system. The proposed approach introduces a recognition algorithm based on multi-modal cross-attention mechanism, innovatively incorporating complex information such as scene context and voice to achieve more accurate behavior recognition. By fusing four modalities, namely image, skeleton, sensor data, and audio, we further enhance the accuracy of recognition. Additionally, we introduce a novel human-robot interaction mode, where the system associates directly recognized intentions with robotic actions without explicit commands, delivering a more natural and efficient elderly assistance paradigm. This mode not only elevates the level of safety monitoring for the elderly but also facilitates a more natural and efficient caregiving approach. Experimental results demonstrate significant improvement in recognition accuracy for 11 typical elderly behaviors compared to existing methods.

关键词： Privacy Accuracy Image recognition Speech recognition Robot sensing systems Skeleton Safety Reliability Older adults Monitoring

Attention Eraser and Quantitative Measures for Automated Bone Age Assessment

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

computers, Materials & Continua 2025年第1期82卷 627-644页

作者： Liuqiang Shu Lei Yu College of computer and information Science Chongqing Normal UniversityChongqing401331China

Bone age assessment(BAA)aims to determine whether a child’s growth and development are normal concerning their chronological *** predict bone age more accurately based on radiographs,and for the left-hand X-ray images of different races model can have better adaptability,we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for *** this study,a lightweight feature extractor(LFE)is designed to obtain the featuremaps fromradiographs,and amodule called attention erasermodule(AEM)is proposed to capture the fine-grained ***,the dimensional information of the metacarpal parts in the radiographs is measured to enhance the model’s generalization capability across images fromdifferent *** is trained and validated on the RSNA,RHPE,and digital hand atlas datasets,which include images from various racial *** model achieves a mean absolute error(MAE)of 4.42 months on the RSNA dataset and 15.98 months on the RHPE *** to ResNet50,InceptionV3,and several state-of-the-art methods,our proposed method shows statistically significant improvements(p<0.05),with a reduction in MAE by 0.2±0.02 years across different racial ***,t-tests on the features also confirm the statistical significance of our approach(p<0.05).

关键词： Bone age assessment attention eraser quantitative feature metacarpal bones

An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and information engineering Liaoning University of Technology Key Laboratory of Security for network and Data in Industrial Internet of Liaoning Province Faculty of information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection