检索结果-内蒙古大学图书馆

International Conference on computer Supported Cooperative Work in Design

作者： Zhuo Wang Hongtu Zhang Jingdong Guo Laile Xi Sidy Tambadou Fang Zuo Hong Li Yan Hu School Of Software Henan University Kaifeng China Institute of Information Engineering Chinese Academy of Sciences Beijing China Henan International Joint Laboratory ofIntelligent Network Theory and Key Technology Henan University School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Collaborative systems frequently depend on various software components, like third-party libraries (TPLs), to execute their functions and expedite the development of the system. The security of an entire collaboration system can be compromised by a TPL that is vulnerable, particularly in an industrial setting. Unfortunately, current TPL detection tools encounter difficulties in precisely identifying version levels and exhibit inefficiency in detecting TPLs on a large *** address these challenges, we recommend JHunter, a precise and efficient tool for detecting TPL version details. Our approach involves introducing a novel concept called the attribute class dependency graph (ACDG) as a feature at the package level for TPLs. We then utilise a graph neural network-based method to compare the similarity of ACDGs and identify a list of candidate TPLs. Later, we use more detailed class-level features, such as Control Flow Graphs (CFGs), and constant features to determine version-specific information. We collected 19,095 different versions of TPLs from Maven to build our feature database. Our analysis demonstrates the effectiveness of JHunter on a real-world dataset, achieving F1 scores of 99.34% and 97.28% at the library and version levels, respectively, surpassing previous state-of-the-art (SOTA) results.

关键词： Java Federated learning Collaborative software Collaboration Reliability engineering Libraries software

来源：评论

学校读者我要写书评

暂无评论

Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts

arXiv

引用

arXiv 2025年

作者： Huang, Weipeng Li, Qin Xiao, Yang Qiao, Cheng Cai, Tie Liang, Junwei Hurley, Neil J. Piao, Guangyuan School of Computer Science and Software Engineering Shenzhen Institute of Information Technology Guangdong China State Key Lab of Integrated Service Networks School of Cyber Engineering Xidian University Shaanxi China Cyberspace Institute of Advanced Technology Guangzhou University Guangdong China School of Computer Science University College Dublin Dublin Ireland

Noise in data appears to be inevitable in most realworld machine learning applications and would cause severe overfitting problems. Not only can data features contain noise, but labels are also prone to be noisy due to human input. In this paper, rather than noisy label learning in multiclass classifications, we instead focus on the less explored area of noisy label learning for multilabel classifications. Specifically, we investigate the post-correction of predictions generated from classifiers learned with noisy labels. The reasons are two-fold. Firstly, this approach can directly work with the trained models to save computational resources. Secondly, it could be applied on top of other noisy label correction techniques to achieve further improvements. To handle this problem, we appeal to deep generative approaches that are possible for uncertainty estimation. Our model posits that label noise arises from a stochastic shift in the latent variable, providing a more robust and beneficial means for noisy learning. We develop both unsupervised and semi-supervised learning methods for our model. The extensive empirical study presents solid evidence to that our approach is able to consistently improve the independent models and performs better than a number of existing methods across various noisy label settings. Moreover, a comprehensive empirical analysis of the proposed method is carried out to validate its robustness, including sensitivity analysis and an ablation study, among other elements. © 2025, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

African Vultures Based Feature Selection with Multi-modal Deep Learning for Automatic Seizure Prediction

African Vultures Based Feature Selection with Multi-modal De...

引用

Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Mounika Nallur Sandhya M Zabiha Khan Mohan B R C P Nayana S A Rajashekhar Software Engineer Masters in Computer Information Systems Murray State University USA Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The superiority of life for people with epilepsy can be greatly improved with the assistance of accurate seizure prediction and early warning. An automatic prediction model is required to procedure the EEG signals and account for the leads optimization problematic, as opposed to the majority of hand-designed prediction approaches. In this research, we put forth a fully automated model for seizure prediction using Channel and Spatial attention (CASA). The first step in the feature extraction practice is to pre-process the raw EEG signals. Large amounts of computation can be saved by adding more features to the system, but finding the right ones can be tricky. The African vulture optimization algorithm's (AVOA) strong capacity to break out of local optima is what makes this procedure possible. CASA saved the raw EEG data's temporal and geographical details. Automatic optimization of EEG full-lead data was completed with channel attention (CA), leading to an increase in the accuracy of predictions. The aforementioned adaptive learning of feature parameters was accomplished via spatial attention (SA). When all else fails, a fully associated layer is used to make the seizure forecast. The suggested algorithm is tested on the Freiburg EEG database, and the results reveal that the AVOA-based system performs admirably when it comes to predicting seizures.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advancing Medical Innovation through Blockchain-Secured Federated Learning for Smart Health

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Khan, Salabat Khan, Mansoor Khan, Muhammad Asghar Wang, Lu Wu, Kaishun Qilu Institute of Technology School of Computer and Information Engineering Jinan China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China Prince Mohammad Bin Fahd University Department of Electrical Engineering P.O. Box 1664 Al-Khobar31952 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen China Thrust Guangzhou China

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

引用

IEEJ Transactions on Electronics, information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The institute of Electrical Engineers of Japan.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Exploration of Efficient Neural Network for Path Optimization Method 39

Exploration of Efficient Neural Network for Path Optimizatio...

引用

39th International Symposium on Lattice Field theory, LATTICE 2022

作者： Namekawa, Yusuke Kashiwa, Kouji Matsuda, Hidefumi Ohnishi, Akira Takase, Hayato Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan Department of Physics Faculty of Science Kyoto University Kyoto606-8502 Japan Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan

We present our attempts to control the sign problem by the path optimization method with an emphasis on the efficiency of the neural network. We found a gauge invariant neural network is successful in the 2-dimensional U(1) gauge theory with a complex coupling. We also investigate the possibility of improvement in the learning process. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Quantum theory

来源：评论

学校读者我要写书评

暂无评论

UAV Swarm Network Coverage with Rechargeable Deployments on Buildings 19th

UAV Swarm Network Coverage with Rechargeable Deployments on...

引用

19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Hu, Yibo Zhou, Yu Zhao, Yingchao Zhang, Xiao College of Computer Science South-Central Minzu University Wuhan430074 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Sciences Caritas Institute of Higher Education Hong Kong China

ISBN: (纸本)9789819623754

Due to their exceptional mobility, simplified deployment process, and excellent line-of-sight communication links, Unmanned Aerial Vehicles (UAVs) are capable of delivering highly dependable and cost-effective communication services to ground users in emergency regions via their miniaturized base stations. When it comes to energy-efficient deployment and long-lasting service, this paper evaluates the possibility of placing rechargeable devices atop high-rise buildings in urban environments as potential sites for UAV deployment. For vast urban areas, characterized by an extensive number of buildings, this paper endeavors to minimize the energy consumption of the UAV swarm system by discretizing the transmission power of the UAVs. The optimal locations for deploying UAV base stations are determined by the Dijkstra Algorithm on the constructed directed weighted graphs (DWG-DA), ensuring uninterrupted network coverage in linear areas like urban streets or roads. The superiority of the DWG-DA algorithm is demonstrated through simulation experiments and algorithm comparison tests, taking into account various distribution patterns of UAVs and buildings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Expert System for Kubernetes Cluster Autoscaling and Resource Management

Expert System for Kubernetes Cluster Autoscaling and Resourc...

引用

Advancements in Computing (ICAC), International Conference on

作者： Lasal Sandeepa Hettiarachchi Senura Vihan Jayadeva Rusiru Abhisheak Vikum Bandara Dilmi Palliyaguruge Udara Srimath S.Samaratunge Arachchillage Dharshana Kasthurirathna Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398106

The importance of orchestration tools such as Kubernetes has become paramount with the popularity of software architectural styles such as microservices. Furthermore, advancements in containerization technologies such as Docker has also played a vital role when it comes to advancements in the field of DevOps, enabling developers and system engineers to deploy are manage applications much more effectively. However, infrastructure configuration and management of resources are still challenging due to the disjointed nature of the infrastructure and resource management tools’ failure to comprehend the deployed applications and create a holistic view of the services. This is partly due to the extensive knowledge required to operate these tools or due to the inability to perform specific tasks. As a result, multiple tools and platforms need to conFigure together to automate the deployment, monitoring and management processes to provide the optimal deployment strategy for the applications. In response to this issue, this research proposes an expert system that creates a centralized approach to cluster autoscaling and resource management, which also provides an automated low-latency container management system and resiliency evaluation for dynamic systems. Furthermore, the time series load prediction is done using a BiLSTM and periodically creates an optimized autoscaling policy for cluster performance, thus creating a seamless pipeline from deployment, monitoring scaling, and troubleshooting of distributed applications based on Kubernetes.

关键词： Time series analysis Pipelines Microservice architectures software Resource management Expert systems Task analysis

来源：评论

学校读者我要写书评

暂无评论

Developing mapping and allotment in volunteer cloud systems using reliability profile algorithms in a virtual machine 2

Developing mapping and allotment in volunteer cloud systems ...

引用

2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Jeyalaksshmi, S. Nidhya, M.S. Suseendran, G. Pal, Souvik Akila, D. Information Technology Vels Institute of Science Technology Advanced Studies Chennai India Periyar Maniammai Institute of Science Technology Dept of Software Engineering Vallam Thanjavur India Global Institute of Management and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9781728194912

While the placement of the Virtual computer was It remains an open challenge for Volunteer Cloud Computing, which reveals many divergent gaps in conventional cloud computing contexts, and has been thoroughly studied. Features, including sporadic usability of nodes and Infrastructure that's inefficient. In this article, we are modeling the Virtual In Volunteer Cloud Computing, computer positioning dilemma As a multi-dimensional, constrained 0-1 knapsack issue and Built algorithms to satisfy the basic aims and shortcomings of Volunteer Cloud Computing. The proof of a dedicated Cloud Computing Volunteer, The competitive success outcomes of these test beds are illustrated With algorithm. © 2021 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education

Enhancing Early Childhood Learning using Image Processing fo...

引用

Advancements in Computing (ICAC), International Conference on

作者： R.G.V Saranga A.H.N. Jayasinghe U.A.S. Divyantha S.W.L.P de Silva N.H.P. Ravi Supunya Swarnakantha P. K. Suriya Kumari Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children.

关键词：

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：