检索结果-内蒙古大学图书馆

Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

引用

Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

来源：评论

学校读者我要写书评

暂无评论

Priority Based Cyber Incident Handling: Work in Progress 11

Priority Based Cyber Incident Handling: Work in Progress

引用

11th International Conference on Electrical engineering, computer Science and informatics, EECSI 2024

作者： Mulyati Stiawan, Deris Widhiarso, Wijang Budiarto, Rahmat Multi Data Palembang University & Faculty of Engineering Universitas Sriwijaya Department of Information System Palembang Indonesia Sriwijaya University Faculty of Computer Science Palembang Indonesia Multi Data Palembang University Department of Informatics Palembang Indonesia College of Computer Science & IT Albaha University Saudi Arabia

ISBN: (纸本)9798350355314

Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by adopting the NIST 2.0 framework and utilizing machine learning classification algorithms. This method enables organizations to quickly and accurately identify and respond to incidents based on threat levels and impact, focusing resources on the most critical incidents. The research employs the Support Vector Machine (SVM) classification algorithm to determine incident priorities. The study is still developing for handling cyber incidents, followed by testing and evaluating the system prototype within organizations. Initial results indicate that the priority-based approach using the SVM classification algorithm achieves a True Positive (TP) Rate of 0.999 and a False Positive (FP) Rate of 0.008, with a correlation of 98.88%, resulting in excellent performance in identifying positive cases and a low error rate. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Optimizing Symbol Visibility Through Displacement 19

Optimizing Symbol Visibility Through Displacement

引用

19th Scandinavian Symposium on Algorithm Theory, SWAT 2024

作者： Gärtner, Bernd Kalani, Vishwas Reddy, Meghana M. Meulemans, Wouter Speckmann, Bettina Stojaković, Miloš Department of Computer Science ETH Zürich Switzerland Department of Computer Science and Engineering I.I.T. Delhi India Department of Mathematics and Computer Science TU Eindhoven Netherlands Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Serbia

ISBN: (纸本)9783959773188

In information visualization, the position of symbols often encodes associated data values. When visualizing data elements with both a numerical and a categorical dimension, positioning in the categorical axis admits some flexibility. This flexibility can be exploited to reduce symbol overlap, and thereby increase legibility. In this paper we initialize the algorithmic study of optimizing symbol legibility via a limited displacement of the symbols. Specifically, we consider unit square symbols that need to be placed at specified y-coordinates. We optimize the drawing order of the symbols as well as their x-displacement, constrained within a rectangular container, to maximize the minimum visible perimeter over all squares. If the container has width and height at most 2, there is a point that stabs all squares. In this case, we prove that a staircase layout is arbitrarily close to optimality and can be computed in O(n log n) time. If the width is at most 2, there is a vertical line that stabs all squares, and in this case, we give a 2-approximation algorithm (assuming fixed container height) that runs in O(n log n) time. As a minimum visible perimeter of 2 is always trivially achievable, we measure this approximation with respect to the visible perimeter exceeding 2. We show that, despite its simplicity, the algorithm gives asymptotically optimal results for certain instances. © Bernd Gärtner, Vishwas Kalani, Meghana M. Reddy, Wouter Meulemans, Bettina Speckmann, and Miloš Stojaković;licensed under Creative Commons License CC-BY 4.0.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

Lightweight Deep Learning for Human Activity Recognition in Ambient Assisted Living

Lightweight Deep Learning for Human Activity Recognition in ...

引用

Multimedia University engineering Conference (MECON)

作者： Ahsanul Bari Fahmid Al Farid Md. Tanjil Sarker Sarina Mansor Hezerul Abdul Karim Md Roman Bhuiyan Hasanul Bannah Faculty of Engineering Multimedia University Cyberjaya Malaysia Faculty of Computer Science and Informatics Berlin School of Business and Informatics Berlin Germany

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This Lightweight Deep Learning (LDL) for Multi-View Human Activity Recognition in Ambient Assisted Living Systems can significantly improve the conditions of daily activities for people living with the elderly, disabled, or simply needing assistance. Human Activity Recognition (HAR) is essential for these systems; currently, the task of identifying the wide array of human activities remains complicated. This study introduces an improved deep learning solution for recognizing human activity from multiple viewpoints in AAL environments. By utilizing skeleton data from various perspectives, such as the RHM-HAR-SK dataset, we demonstrate useful improvements in accuracy for activity recognition when compared with single-view methods. Our LeNet and Vision Transformer models have been carefully designed to excel with skeletal data, delivering outstanding performance while keeping computational requirements to a minimum. In this research study also highlights the benefits of multi-view human activity recognition in ambient assisted living and emphasizes the effectiveness of efficient deep learning models in enabling assistive technologies that are real-time and resource friendly. These advancements support more independence and safety by supporting the development of AAL systems that are more sensitive and intelligent.

关键词： Deep learning Ambient assisted living Computational modeling Soft sensors Assistive technologies Transformers Data models Skeleton Human activity recognition Older adults

来源：评论

学校读者我要写书评

暂无评论

Blockchain and Artificial Intelligence: Enhancing Security and Efficiency in Metaverse Payment Transactions 6th

Blockchain and Artificial Intelligence: Enhancing Security a...

引用

6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Rajawat, Anand Singh Goyal, S.B. Shaari, Rosnizah Bt Verma, Chaman Singh, Pradeep Kumar School of Computer Sciences and Engineering Sandip University Nashik Nashik India City University Petaling Jaya Malaysia City University Petaling Jaya46100 Malaysia Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest1053 Hungary Department of Computer Science and Engineering Central University of Jammu Jammu India

ISBN: (纸本)9789819778614

Blockchain and artificial intelligence (AI) are two revolutionary technologies that are significantly impacting various sectors, including the metaverse. The integration of these technologies is particularly influential in enhancing security and efficiency in metaverse payment transactions. Blockchain, with its decentralized and immutable ledger, provides a secure and transparent way to record transactions. This characteristic is crucial in the metaverse, where digital assets and virtual currencies are frequently exchanged. By leveraging blockchain, metaverse platforms can ensure the integrity and traceability of transactions, reducing the risk of fraud and unauthorized access. On the other hand, AI contributes to the efficiency and intelligence of payment systems. Through machine learning algorithms and data analytics, AI can detect and prevent fraudulent activities, provide personalized recommendations, and automate complex transaction processes. This not only improves the user experience but also enhances the overall security of the system by identifying and mitigating risks in real time. The convergence of blockchain and AI in the metaverse enables the development of more sophisticated and secure payment solutions. These technologies work together to ensure that transactions are not only fast and efficient but also robust against various security threats. This synergy is essential for building trust in the digital economy of the metaverse, encouraging more users and businesses to engage in this emerging virtual space. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification 2nd

Grasshopper Optimization Algorithm Based Spam Detection Syst...

引用

2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Ghanem, Waheed A. H. M. Abdullahi, Akibu Mahmoud Nasser, Abdullah B. Saleh, Sami Abdulla Mohsen Arshad, Humaira Omolara, Abiodun Esther Abiodun, Oludare Isaac Ghetas, Mohamed Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Faculty of Education Saber University of Lahej Lahej Yemen Faculty of Engineering University of Aden Aden Yemen Faculty of Education Aden University of Aden Aden Yemen Faculty of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology and Innovation University of Vaasa Vaasa Finland School of Electrical and Electronic Engineering Universiti Sains Malaysia Nilai Malaysia Department of Computer Science Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Abuja Abuja Nigeria Department of Computer Science Bingham University Nasarawa Nigeria Faculty of Computer Science Nahda University New Beni Seuf City Egypt

ISBN: (纸本)9783031252730

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Advancements in Multi-View Human Activity Recognition for Ambient Assisted Living

Advancements in Multi-View Human Activity Recognition for Am...

引用

Multimedia University engineering Conference (MECON)

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This Ambient Assisted Living (AAL) uses technology to improve the well-being, autonomy, and security of seniors and disabled individuals. AAL services depend on Human Activity Recognition to detect human behaviors from sensor data. Compared to single-view models, multi-view HAR, which aggregates data from multiple sensors, provides improved accuracy and reliable detection. In this review, we assess recent progress in multi-view HAR for AAL, focusing on the value of lightweight deep learning models. We cover the development of HAR systems, as well as the emergence of multi-view datasets and highly efficient deep learning models that are explicitly designed for AAL scenarios. Furthermore, we address problems such as data synchronization, data protection, and adaptability, as well as possible remedies like sensor fusion and transfer learning. As a result, we also demonstrate the potential of multi-view HAR to increase AAL services and outlining future research trends in providing adaptive, efficient, and privacy-aware systems.

关键词： Deep learning Adaptation models Ambient assisted living Accuracy Reviews Transfer learning Sensor fusion Human activity recognition Synchronization Security

来源：评论

学校读者我要写书评

暂无评论

Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

引用

computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

来源：评论

学校读者我要写书评

暂无评论

Navigating online learning challenges and opportunities: Insights from small group of lecturers during pandemic

引用

Procedia computer Science 2024年 234卷 1164-1174页

作者： Ahmad Faza Harry Budi Santoso Panca O. Hadi Putra Universitas Multimeia Nusantara Faculty of Engineering and Informatics Tangerang 15810 Indonesia Universitas Indonesia Faculty of Computer Science Depok 16424 Indonesia

The implementation of e-learning platforms in higher education offers opportunities to enhance education quality and increase access to flexible learning. However, challenges arise, including limited guidance for students, high dropout rates, and obstacles faced by instructors. This qualitative case study explores the challenges and opportunities of online learning from instructors' perspectives. Data was collected through a survey administered to 23 instructors, and thematic analysis was conducted to identify the themes of challenges and opportunities. Findings reveal challenges in interaction, practical session delivery, monitoring, assessment, and identity verification. Institutional support challenges include insufficient training and socialization. Individual Challenges encompass digital skills, health concerns, and fatigue. Technological challenges involve availability, integration, capacity, and network-related issues. Despite these challenges, online learning provides opportunities for time effectiveness, flexibility, personal development, innovative teaching methods, and collaboration with external experts.

关键词： e-learning platforms online learning instructors' perspectives

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of an Artificial Intelligence Nanosystem with Biological Internet of Nano Things

引用

computer Modeling in engineering & Sciences 2022年第10期133卷 111-131页

作者： Saied M.Abd El-atty Nancy A.Arafa Atef Abouelazm Osama Alfarraj Konstantinos A.Lizos Farid Shawki Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway

Artificial intelligence(AI)has recently been used in nanomedical applications,in which implanted intelligent nanosystems inside the human body were used to diagnose and treat a variety of ailments with the help of the Internet of biological Nano Things(IoBNT).Biological circuit engineering or nanomaterial-based architectures can be used to approach the *** nanomedical applications,the blood vascular medium serves as a communication channel,demonstrating a molecular communication system based on flow and *** paper presents a performance study of the channel capacity for flow-based-diffusive molecular communication nanosystems that takes into account the ligand-receptor binding *** earlier studies,we take into account the effects of biological physical characteristics such as blood pressure,blood viscosity,and vascular diameter on channel ***,in terms of drug transmission error probability,the inter-symbol interference(ISI)phenomenon is applied to the proposed *** numerical results show that the proposed AI nanosystems-based IoBNT technology can be successfully implemented in future nanomedicine.

关键词： Nanosystem AI Internet of biological Nano Things(IoBNT) channel capacity

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：