检索结果-内蒙古大学图书馆

6th International Conference of Women in Data Science at Prince Sultan University, WiDS-PSU 2023

作者： Alhuqail, Sara Khalid Jamail, Nor Shahida Mohd Prince Sultan University College of Computer Science and Information Sciences Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9781665477239

Cyberattacks have increased. Security throughout software development reduces the program's attack surface. Despite several fixes. new research indicates software security weaknesses and hackers are rising. SSD security methods must be studied. 28 academic and government SSD approaches are examined in this study. Security is implemented throughout SDLC. We studied organizational. behavioral. legal. policy. and governance issues as well as SSD approaches. Method effectiveness strategies were investigated. We conclude with scientific problems. Our survey should help academics an institutions understand software security development (SSD). Nontechnical software security experts may benefit from our bridge. Our literature gaps may improve software security. Today's fast-paced. modern world requires software security for product success. CIA-classified data. software. and services (Confidentiality. Integrity. and Availability). © 2023 IEEE.

关键词： Life cycle

来源：评论

学校读者我要写书评

暂无评论

Research on key technologies of edge cache in virtual data space across WAN

引用

Frontiers of computer Science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and computer Network Information Center in Chinese Academy of Sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

来源：评论

学校读者我要写书评

暂无评论

A Hypergraph Transformer With Adaptive EdgeConv for Skeleton-based Action Recognition 7

A Hypergraph Transformer With Adaptive EdgeConv for Skeleton...

引用

7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhou, Fengyu Liu, Jun Zhang, Dazhi Huang, Zengxi School of Computer Science and Software Engineering Xihua University Chengdu China Sichuan Police College Criminal Science and Technolog Department Luzhou China

ISBN: (纸本)9798350350890

Skeleton-based action recognition is a crucial task in computer vision, aiming to perform pattern recognition on a skeleton sequence to identify the actions represented by the skeleton's semantics. However, current mainstream skeleton-based methods primarily rely on global edge information. Unimportant edges may influence this approach, limiting the model's performance in action recognition tasks. Additionally, because these methods treat all information equally, they may lead to resource wastage. This paper proposes a Hypergraph Transformer-based action recognition method using Adaptive EdgeConv Module(AEM) to address these issues effectively. In this method, we introduce Adaptive Edge Graph Convolution Networks(AEGCNs), which utilize the AEM to adaptive obtain edge sets and edge set weights, thereby adaptive identifying key edge sets. With this information, the model can dynamically adjust the importance of different edge sets based on various samples. This approach improves the accuracy of action recognition and reduces the loss of computational resources. Our experimental results on two challenging benchmark datasets (i.e., NTU RGB+D and NTU RGB+D 120) show that the proposed method is comparable to state-of-the-art methods. © 2024 IEEE.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

Deep-USIpred: identifying substrates of ubiquitin protein ligases E3 and deubiquitinases with pretrained protein embedding and bayesian neural network

Deep-USIpred: identifying substrates of ubiquitin protein li...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Jia Pan, Gui-Qing Li, Jian-Qiang Shang, Xue-Qun You, Zhu-Hong Huang, Yu-An Shenzhen University College of Computer Science and Software Engineering Shenzhen China Northwesterm Polytechnical University School of Computer Science Shaanxi China

ISBN: (纸本)9798350337488

Identifying the substrates of ubiquitin protein ligase (E3) and deubiquitinases (DUB) contributes to the discovery of potential therapeutic targets for diseases. However, experimental identification of E3/DUB-substrate interactions is costly and time-consuming. Current computational methods for predicting E3/DUB-substrate interactions rely heavily on specific domain knowledge and involve complex and diverse biological data processing. To address this challenge, we proposed a deep learning prediction model, named Deep-USIpred, which predicts E3/DUB-substrate interactions using protein sequences. The proposed Deep-USIpred model encodes protein sequences with a pretrained model and utilizes 1DCNN-BNN deep learning algorithm to make a robust prediction model. We evaluated the performance of the proposed model on real datasets, and our experimental results show that it can achieve excellent prediction performance on the tasks of ESI and DSI. Our proposed method provides a promising alternative for the prediction of E3/DUB-substrate interactions, which has the potential to accelerate drug discovery for various diseases. The source code and dataset are available at https://***/PGTSING/Deep-USIpred. © 2023 IEEE.

关键词： bayesian neural network deep learning deubiquitinases sequence-based prediction ubiquitin protein ligases E3

来源：评论

学校读者我要写书评

暂无评论

nPGSAO: A Hybrid Particle Swarm Optimization and Genetic Algorithm with Niching Technology for Edge Server Placement

引用

IEEE Internet of Things Journal 2025年第12期12卷 19370-19383页

作者： Wang, Bo Zhang, Zhifeng Song, Ying Chen, Ming Liu, Dongqing Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Computer School Beijing Information Science and Technology University Beijing China School of Mathematics and Computer Science Tongling University Tongling China National Engineering Laboratory for Internet Medical Systems and Applications the First Affiliated Hospital of Zhengzhou University Zhengzhou China

In this paper, we focus on the edge server placement problem (ESPP). ESPP is to decide positions (edge stations) where purchased edge servers (ESs) are placed when a service provider builds or upgrades its edge computing solution. The decision of ESPP largely determines the overall service quality and resource efficiency. We aim at providing ESPP solutions for optimizing the service quality in terms of the system response time, given a set of purchased ESs. To achieve this aim, we first deduce the optimal request dispatch for each edge station based on queue theory and Binomial theorem. Then, based on the deduced result, we formulate ESPP into a discrete nonlinear constrained optimization model. Because ESPP is NP-hard and generally multi-modality, we designed a hybrid metaheuristic algorithm to combine advantages of particle swarm optimization (PSO) and genetic algorithm (GA) as well as the niching technology, which is named nPGSAO. The basic idea of nPGSAO is grouping similar individuals into a niche and finding the niche best. Then, nPGSAO evolves each individual by integrating the swarm intelligence of PSO into the evolutionary process of GA based on its personal and niching best. To evaluate the performance of nPGSAO, we conducted extensive simulated experiments. The results show that nPGSAO achieves 5.78%–40.4% better response time with varied ESPP scale, compared to 11 up-to-date algorithms, and has a linear time complexity with the ESPP scale. These confirm the superior performance and scalability of nPGSAO. © 2014 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

引用

ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

来源：评论

学校读者我要写书评

暂无评论

Semantic Correspondence with Peripheral Position Coding 13

Semantic Correspondence with Peripheral Position Coding

引用

13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Chen, Jinjian Li, Zuoyong Lai, Taotao Xie, Huosheng College of Computer and Data Science Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350319156

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

MG-CR: Factor Memory Network and Graph Neural Network Based Personalized Course Recommendation 28th

MG-CR: Factor Memory Network and Graph Neural Network Based...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Yun Yu, Minghe Sun, Jintong Zhang, Tiancheng Yu, Ge School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China

ISBN: (纸本)9783031306716

Course recommendations in universities select the most suitable courses for students according to their interests and academic requirements. However, existing works often focus on modeling course selection history, without paying sufficient attention to rich features contained in students’ personal information and courses’ teaching attributes, and various relationships between them. In order to realize the personalization and dynamics of course recommendation, we consider students and courses as two types of nodes to construct a heterogeneous information network (HIN), and propose a factor Memory network and Graph neural network based personalized Course Recommendation (MG-CR) on top of HIN. MG-CR captures multiple features of students and courses simultaneously in the input module, thereby maximizing the retention of personalized information. The feature transfer module optimizes course features by considering various types of relationships between courses in the HIN, and together with the input module, it ensures high recommendation accuracy in the cold-start scenario. The updating module explicitly stores the student’s interest level for each factor in the state matrix, which is then used to dynamically recommend courses for students in the predicting module. Experimental results show that MG-CR provides accurate, dynamic and personalized recommendations, and has superior performance in the cold-start scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Combining Strategy and Roles to Formally Analyze Multi-Agent Reinforcement Learning 3

Combining Strategy and Roles to Formally Analyze Multi-Agent...

引用

3rd International Conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Wang, Xiaoyan Zhang, Yujuan Li, Bing Jilin University College of Computer Science and Technology Changchun China Jilin University Software Engineering Changchun China

ISBN: (纸本)9798350316674

A novel semantics for MARL called neural concurrent game structure (NCGS) is introduced, which extends CGS with neural network and roles where the agents are implemented via feed-forward ReLU neural networks. To formally verify concrete NCGS systems and reduce the complexity, multi-role strategy logic(mrSL) was put forward. mrSL is suitable for the properties of the NCGS, regardless of which agent is responsible for the specific task. Parameterized model checking(PMC) is used to solve the NCGS verification problem against mrSL. Finally an algorithm for MILP-based verification process is introduced, and report the experimental results. © 2023 IEEE.

关键词： CGS MARL MILP mrSL PMC

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection and classification of Scoliosis from Spine X-rays using Transfer Learning 2

Automatic Detection and classification of Scoliosis from Spi...

引用

2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Amin, Arslan Abbas, Moneeb Salam, Anum Abdul College of Electrical and Mechanical Engineering Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Scoliosis is a typical spinal disease that causes the spine to curve. Early treatment during the formation of the spine can greatly reduce the chances of health issues. Diagnosis of scoliosis relies on X-ray imaging, using X-ray images to diagnose lumbar, cervical, and thoracic spinal structures have traditionally proven difficult and time-consuming. Many clinical applications of spinal imaging require the accurate and robust identification of vertebrae from medical images. This paper presents an automated approach using deep learning to detect the spine's curvature using its spinal column. Models of deep learning could be used to assist with the increasing volume of medical imaging data as well as provide initial interpretation of images gathered in primary care. Deep learning algorithms are a quicker and more efficient alternative to manual X-ray investigation for scoliosis detection. X-ray images of spine curvature are used to detect and classify scoliosis using a pre-trained EfficientNet model. In the first stage, the model was evaluated without augmentation, in which we achieved an accuracy of 78 %. In the second step, we augment the training data by using machine learning techniques, and after that, we achieved an accuracy of 86 %. Our findings show that the proposed automatic scoliosis identification method can accurately detect and classify spine curvature in X-ray images. © 2022 IEEE.

关键词： Diagnosis

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：