检索结果-内蒙古大学图书馆

Intelligent Computing and Robotics (ICICR), International Conference on

作者： Yayue Tan Yaqi Zhang Hui Liu Yongqing Tang College of Computer and Information Science School of Software Southwest University Chongqing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350372274

ISBN: (纸本)9798350372281

Anomaly detection stands as a critical element in securing space information networks (SINs). This paper delves into the realm of anomaly detection within dynamic networks, shedding light on established methodologies. The focus lies on scrutinizing various anomaly detection strategies in space information networks, conducting a comprehensive assessment of their practical applications, advantages, and drawbacks. Moreover, it introduces a novel anomaly detection approach driven by deep learning principles. This innovative method merges graph convolution techniques with a network security-oriented knowledge graph, presenting a cutting-edge solution. As a final contribution, the paper extends its purview to the future landscape of anomaly detection within space information networks, offering valuable insights into potential advancements and forthcoming trends in this domain. This holistic investigation seeks to enhance the understanding of anomaly detection paradigms within the unique context of space information networks, aiming to fortify their security measures and operational resilience.

关键词： Knowledge engineering Convolution Network topology Knowledge graphs Market research computer security Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids

引用

计算机科学 2022年第6期49卷 44-54页

作者： Ran WANG Jiang-tian NIE Yang ZHANG Kun ZHU College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106China School of Computer Science and Engineering Nanyang

As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-demand ***-ever,existing literatures try to adjust customers’load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption *** this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the *** approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical *** on the extracted information,the residential appliances can be scheduled effectively and ***,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning *** simulation experiments are conducted with real-world *** results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio(PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR *** the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.

关键词： Demand response(DR) Customer clustering Deep learning 6G-enabled industrial Internet of things(IIOT) Smart srid(SG)

来源：评论

学校读者我要写书评

暂无评论

ATL Model Checking for Analysis of Ethereum Smart Contracts

引用

Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 2006-2014页

作者： Nam, Wonhong Kil, Hyunyoung Dept. of Software Korea Aerospace University Korea Republic of Dept. of Computer Science and Engineering Konkuk University Korea Republic of

A blockchain is a growing list of cryptographically secured blocks to maintain shared data on decentralized systems, in order to archive transactions between untrusted participants. Smart contracts are computer programs that automatically execute legal events according to the terms of contracts. Although the Ethereum blockchain has been successfully applied to a number of interesting applications, there have been several events that subtle flaws in smart contracts induce a huge amount of financial loss such as the DAO attack. Accordingly, to analyze smart contracts, we propose a novel formal verification technique to employ ATL (Alternating-time Temporal Logic) model checking. Our methodology represents the interaction between users and smart contracts with a two-player game and verify game properties by using MCMAS that is an efficient ATL model checker. © The Korean Institute of Electrical Engineers

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

PC-BNA: Parallel Convolution Binary Neural Network Accelerator 24

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

引用

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Liu, Haihui Zhang, Yuang Zheng, Xiangwei Yu, Mengxin An, Baoxu Liu, Xiaojie School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798350319934

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications. © 2022 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Application Deployment in Mobile Edge Computing Environment Based on Microservice Chain 25

Application Deployment in Mobile Edge Computing Environment ...

引用

25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Li, Haiyan Tang, Bing Xu, Wei Guo, Feiyan Zhang, Xiaoyuan Hunan University of Science and Technology School of Computer Science and Engineering Hunan Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9781665405270

Mobile edge computing (MEC) has become an extremely hot topic in recent years. Mobile edge cloud relies on storage and computing resources on network edge to provide users with delay-sensitive services. However, the transmission delay among microservices and the load of the servers tend to increase due to improper service placement and unreasonable resource allocation under MEC. In this paper, an edge service placement strategy based on an improved fast non-domination sorted genetic algorithm is proposed. First, a microservice placement optimization model is built with the goal of minimizing the average transmission delay and the load balance degree. Then, a genetic algorithm-based microservice placement approach called GA-MSP using improved NSGA-II is studied under the premise that a single service instance is deployed only on one container. The experiments show that the proposed GA-MSP approach is able to achieve low delay and load balance effectively, and ultimately deploy services based on the resulting sets after convergence, which outperforms several other existing representative methods. © 2022 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Categorization of User Experience and Usability Issues (UXUIs) and their Prioritization w.r.t Categorization for Improving the UXU of Interactive System (IS) 3

Categorization of User Experience and Usability Issues (UXUI...

引用

3rd International Conference on Communication Technologies, ComTech 2021

作者： Farooqui, Tayba Rana, Tauseef Maqbool, Ayesha MCS Department of Computer Software Engineering Islamabad Pakistan NBC Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665419017

software user experience (UX) and usability (UXU) plays a vital role in the quality users perceived. The existence of user experience and usability issues (UXUIs) affects the software quality perceived by users. A variety of classification of UXUIs for improving the UXU of IS indicates that there is still no consensus in the respected area. The purpose of this paper is to present the findings of a literature review, with the aim of demonstrating all relevant UXUIs classification that have been introduced so far. This comprehensive view, represented by a knowledge map with time-frame, on the one hand, provides an in-depth understanding of the observed evolution and, on the other, serves as a guide for researchers, experts, and interactive application designers and developers as they work toward designing, developing, and evaluating interactive systems (IS) and its associated interfaces. This, in turn, may assist with sustained engagement among users. © 2021 IEEE.

关键词： computer software selection and evaluation

来源：评论

学校读者我要写书评

暂无评论

Joint Energy and Computation Workload Management for Geo-distributed Data Centers

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

The efficiency of using PPDIOO Methodology to Design Graduation Projects for Network Department Students

The efficiency of using PPDIOO Methodology to Design Graduat...

引用

International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Abdelsalam Saleh Elrashdi Salem Khiralla Alferjani Rami Radwan Omar Fatma Mohammed Hasan Department of Computer Networks College of Computer Technology Benghazi Libya Department of Software Engineering College of Computer Technology Benghazi Libya Department of Computer Networks Libyan International Medical University Benghazi Libya

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

In higher education institutions graduation project students, selecting an appropriate methodology is one of the most challenging and complex choices. Furthermore, some students haven’t experience and knowledge in the methods that should be used to complete their graduation projects. This paper proposes and highlights one of the most important methodologies used recently in the design of computer network called PPDIOO model (Prepare, Plan, Design, Implement, Operate, and Optimize). This methodology is a clear strategy that engineers, and computer network specialists must follow in order to design an integrated computer network. The main contribution of this paper is to assist students in networking departments with their graduation projects, ensuring they meet their requirements accurately and without errors. For supervisors and students who used PPDIOO methodology, questionnaires have been published to assess the quality and efficacy of this methodology. Positive feedback was received regarding the effectiveness of all the students who used the model, and it was highly satisfying.

关键词： Computational modeling Image processing Design methodology Education Buildings computer networks Task analysis

来源：评论

学校读者我要写书评

暂无评论

A New Flow-Based Approach for Enhancing Botnet Detection Efficiency Using Convolutional Neural Networks and Long Short-Term Memory

引用

Knowledge and Information Systems 2025年 1-32页

作者： Asadi, Mehdi Heidari, Arash Jafari Navimipour, Nima Department of Computer Engineering Islamic Azad University Khameneh Branch Khameneh Iran Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Science and Engineering Qatar University Doha Qatar Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan

Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method’s propensity for delivering a promising performance in the face of these adversarial challenges.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analyzing emotions in online classes: Unveiling insights through topic modeling, statistical analysis, and random walk techniques

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 221-236页

作者： Abdellaoui, Benyoussef Remaida, Ahmed Sabri, Zineb Abdellaoui, Mohammed El Hafidy, Abderrahim El Bouzekri El Idrissi, Younes Moumen, Aniss National School of Applied Sciences Engineering Sciences Laboratory Ibn Tofaïl University Kenitra Morocco Faculty of Sciences Laboratory of Computer Science Ibn Tofaïl University Kenitra Morocco LPHE-modeling and Simulation Faculty of Sciences Mohammed V University Rabat Morocco Software Project Management Research Team ENSIAS Mohammed V University Rabat Morocco

High dropout rates globally perpetuate educational disparities with various underlying causes. Despite numerous strategies to address this issue, more attention should be given to understanding and addressing student emotions during classes. This lack of focus adversely affects learner engagement and retention rates. While previous studies on online learning have primarily emphasized the effectiveness of technology, infrastructure, cognition, motivation, and economic benefits, there is still a gap in understanding the emotional aspects of distance learning. First, this study addresses this gap by employing thematic modeling and utilizing non-negative matrix factorization (NMF) for emotion recognition through students’ deep learning techniques and facial emotion recognition (FER). Second, statistical analysis of these findings further augments the depth of the study. Finally, the research proposes a mathematical model based on the random walk of emotional state transitions. The findings of this study underscore the importance of considering emotions in distance learning environments and their significant impact on student's academic performance and satisfaction. By acknowledging and addressing these emotional factors, educators can enhance learner engagement, promote positive emotions, mitigate negative emotions during online learning, and ultimately improve the effectiveness of online courses. © 2024 The Authors

关键词： Emotion Recognition

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：