检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Aljedaani, Bakheet Ahmad, Aakash Fahmideh, Mahdi Khan, Arif Ali Shen, Jun Computer Science Department Aljumum University College Umm Alqura University Makkah Saudi Arabia School of Computing and Communications Lancaster University Leipzig Germany School of Business University of Southern Queensland QLD Australia M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Computing and Information Technology University of Wollongong Wollongong Australia

Context: Blockchain-based software (BBS) exploits the concepts and technologies popularized by cryptocurrencies like Bitcoin, offering decentralized transaction ledgers with immutable content for security-critical and transaction-critical systems. Recent research has explored the strategic benefits and technical limitations of BBS in various fields, including cybersecurity, healthcare, education, and financial technologies. Despite growing interest from academia and industry, there is a lack of empirical evidence, leading to an incomplete understanding of the processes, methods, and techniques necessary for systematic BBS ***: Existing research lacks a consolidated view, particularly empirically-driven guidelines based on published evidence and development practices. This study aims to address the gap by consolidating empirical evidence and development practices to derive or leverage existing processes, patterns, and models for designing, implementing, and validating BBS ***: Tied to this knowledge gap, we conducted a two-phase research project. First, a systematic literature review of 58 studies was performed to identify a development process comprising 23 tasks for BBS systems. Second, a survey of 102 blockchain practitioners from 35 countries across six continents was conducted to validate the BBS system development ***: Our results revealed a statistically significant difference (p-value © 2024, The Authors. All rights reserved.

关键词： Blockchain

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques

Enhancing Medical Image-Based Diagnostics Through the Applic...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Haewon Byeon Rajit Nair V. Mahalakshmi Mohammed I. Khalaf Bharat Kaushik Mohammad Shabaz Department of AI and Software Inje University Gimhae Republic of Korea School of Computing Science & Engineering VIT Bhopal University Bhopal India Department of Computer Science College of Engineering and computer science Jazan University Jazan Saudi Arabia Department of computer science Al Maarif University College Al Anbar Iraq Masters University of Northern Iowa Lowa USA Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques.

关键词： Fluctuations Reviews Hospitals Measurement uncertainty Surgery Medical services Predictive models

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Exploration and Sterilization Coronavirus in Air based on Unmanned Aerial Vehicles

Exploration and Sterilization Coronavirus in Air based on Un...

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Artificial Intelligence Science and Applications in Industry and Society (CAISAIS), International Conference on

作者： Gehad Abdullah Amran Shuang Wang Mohammed A. A. Al-qaness Ali A. AL-Bakhrani Khaled Hussain Mutmbak Mohammed Zaid Ghawy Department of Management Science and Engineering Dalian University of Technology Dalian China College of Software Engineering Northeastern University Shenyang China Emirates International University Sana'a Yemen College of Software Dalian University of Technology Dalian China College of Computing and Information Technology University of Tabuk Tabuk Saudi Arabia College of Cpmputer Science Thamar University Dhamar Yemen

In recent years, the academic and industrial science community has shown great interest in the Internet of Things and the decentralization of Blockchain networks, particularly in smart cities and emerging communities. Unmanned Aerial Vehicles (UAVs) have gained significant attention in military and commercial systems, serving as emergency transport during natural disasters and epidemics such as the Coronavirus pandemic. In such scenarios, the use of Ad-Hoc wireless networks to cover large areas can be more secure if implemented as a shared database, such as Blockchain. This paper suggests the Unmanned Aerial Vehicles as a solution for the location and sterilization of coronavirus contaminated areas. Thus, one of these aerial vehicles will locate the infected area and update its spatial data block. Also, the surrounded aerial vehicles comply with this modification and change their location information in the Blockchain to pursue the target location, and together start to spray sterilizer. To improve localization accuracy, a swarm intelligence algorithm is employed, while Blockchain technology serves as a decentralized database for clusterization of other UAVs. Through realistic studies and analyses of localization and clusterization, the proposed method proves effective in locating and sterilizing polluted air regions. Additionally, this database technique provides a higher level of security, making it challenging to attack. Ultimately, this research contributes to the advancement of emergency transport networks, Blockchain technology, and IoT services.

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R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm

arXiv

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arXiv 2024年

作者： Tahernezhad-Javazm, Farajollah Rankin, Debbie Bois, Naomi Du Smith, Alice E. Coyle, Damien Intelligent Systems Research Center School of Computing Engineering and Intelligent Systems Ulster University LondonderryBT48 7JL United Kingdom The Bath Institute for Augmented Human University of Bath BathBA2 7AY United Kingdom The Department of Industrial and Systems Engineering Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

Choosing an appropriate optimization algorithm is essential to achieving success in optimization challenges. Here we present a new evolutionary algorithm structure that utilizes a reinforcement learning-based agent aimed at addressing these issues. The agent employs a double deep q-network to choose a specific evolutionary operator based on feedback it receives from the environment during optimization. The algorithm’s structure contains five single-objective evolutionary algorithm operators. This single-objective structure is transformed into a multi-objective one using the R2 indicator. This indicator serves two purposes within our structure: first, it renders the algorithm multi-objective, and second, provides a means to evaluate each algorithm’s performance in each generation to facilitate constructing the reinforcement learning-based reward function. The proposed R2-reinforcement learning multi-objective evolutionary algorithm (R2-RLMOEA) is compared with six other multiobjective algorithms that are based on R2 indicators. These six algorithms include the operators used in R2-RLMOEA as well as an R2 indicator-based algorithm that randomly selects operators during optimization. We benchmark performance using the CEC09 functions, with performance measured by inverted generational distance and spacing. The R2-RLMOEA algorithm outperforms all other algorithms with strong statistical significance (p © 2024, CC BY.

关键词： Reinforcement learning

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Position: Topological Deep Learning is the New Frontier for Relational Learning 41

Position: Topological Deep Learning is the New Frontier for ...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Birdal, Tolga Bronstein, Michael Carlsson, Gunnar Curry, Justin Gao, Yue Hajij, Mustafa Kwitt, Roland Liò, Pietro Di Lorenzo, Paolo Maroulas, Vasileios Miolane, Nina Nasrin, Farzana Ramamurthy, Karthikeyan Natesan Rieck, Bastian Scardapane, Simone Schaub, Michael T. Veličković, Petar Wang, Bei Wang, Yusu Wei, Guo-Wei Zamzmi, Ghada Department of Mathematics The University of Manchester Manchester United Kingdom Department of Computing Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Mathematics Stanford University Stanford United States BlueLightAI Inc. United States University at Albany New York United States School of Software Tsinghua University Beijing China University of San Francisco San Francisco United States Department of Artificial Intelligence and Human Interfaces University of Salzburg Austria Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Mathematics University of Tennessee Knoxville United States Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara United States Department of Mathematics University of Hawai'i at Mānoa HI United States IBM Corporation New York United States Helmholtz Munich Munich Germany Technical University of Munich Munich Germany RWTH Aachen University Aachen Germany Google Deep-Mind United Kingdom School of Computing University of Utah Utah United States Computer Science and Engineering Department University of California San Diego San Diego United States Department of Mathematics Michigan State University East LansingMI United States University of South Florida Florida United States

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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ADAPTIVE INCENTIVE FOR CROSS-SILO FEDERATED LEARNING: A MULTI-AGENT REINFORCEMENT LEARNING APPROACH

arXiv

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arXiv 2023年

作者： Yuan, Shijing Liu, Hongze Lv, Hongtao Feng, Zhanbo Li, Jie Chen, Hongyang Wu, Chentao Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China School of Software Shandong University Jinan China Research Center for Graph Computing Zhejiang Lab China

Cross-silo federated learning (FL) is a typical FL that enables organizations (e.g., financial or medical entities) to train global models on isolated data. Reasonable incentive is key to encouraging organizations to contribute data. However, existing works on incentivizing cross-silo FL lack consideration of the environmental dynamics (e.g., precision of the trained global model and data owned by uncertain clients during the training processes). Moreover, most of them assume that organizations share private information, which is unrealistic. To overcome these limitations, we propose a novel adaptive mechanism for cross-silo FL, towards incentivizing organizations to contribute data to maximize their long-term payoffs in a real dynamic training environment. The mechanism is based on multi-agent reinforcement learning, which learns near-optimal data contribution strategy from the history of potential games without organizations’ private information. Experiments demonstrate that our mechanism achieves adaptive incentive and effectively improves the long-term payoffs for organizations. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Adaptive Access Control Policies for IoT Deployments 16

Adaptive Access Control Policies for IoT Deployments

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16th IEEE International Wireless Communications and Mobile computing Conference, IWCMC 2020

作者： Alkhresheh, Ashraf Elgazzar, Khalid Hassanein, Hossam S. School of Computing Queen's University KingstonON Canada Department of Electrical Computer and Software Engineering OshawaON Canada

ISBN: (纸本)9781728131290

In the era of the Internet of Things (IoT), it has become possible for a set of smart devices to collaborate autonomously and communicate seamlessly to achieve complex tasks that require a high degree of intelligence. Unlike traditional internet devices, a compromised IoT device can cause real-world damages. The severity of these damages increases dangerously in sensitive contexts especially when these devices are controlled by system insiders. Detecting abnormal access behaviors in such environments is quite challenging, due to frequent changes in the access contexts under which the IoT device can be accessed. In this paper, we propose an adaptive access control policy framework that dynamically refines the system access policies in response to changes in the device-to-device access behavior. We apply supervised machine learning to model and classify the device access behavior based on a real-life data set. We provide a use case scenario of a door locking system to validate our work. Results show that our framework provides improved security, dynamic adaptability and sufficient scalability to the target application domain. © 2020 IEEE.

关键词： Internet of things

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Manjula R Jagadeesan S Venkadeshan Ramalingam Vetriselvi T Johnpeter T Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Information Technology Department University of Technology and Applied Sciences Sultanate of Oman Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.

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ETHACA - A Container-Based Cyber Range for Research and Education

ETHACA - A Container-Based Cyber Range for Research and Educ...

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IEEE International Workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Nestoras Chouliaras Ioanna Kantzavelou Grammati Pantziou Leandros Maglaras Panagiota Chouliara-Sidera Antonis Adamakos Panagiotis Chalatsakos Department of Informatics and Computer Engineering University of West Attica Athens Greece School of Computing Edinburgh Napier University Edinburgh UK Department of Computer Science and Biomedical Infromatics University of Thessaly Lamia Greece Senior Software Engineer at *** Athens Greece Faculty of Pure and Applied Sciences Open University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences.

关键词： Training Production systems Scalability Conferences Computational modeling Computer architecture Fourth Industrial Revolution Complexity theory Cyberattack Industrial Internet of Things

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Impact of E-Learning System User Interface Design on User Satisfaction 9

Impact of E-Learning System User Interface Design on User Sa...

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9th Edition of IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2021

作者： Senevirathne, Gayathri Manathunga, Kalpani Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665432405

Interface design is a mandatory aspect influencing the success of an e-Learning system implementation. User interface (UI) design of e-learning is a point of interaction between user and computer software. Users prefer more attractive and simpler interface designs rather than dull or complex designs. This study aims to outline the impact of UI design on the satisfaction of learners. Specifically, this study will be evaluating different user interaction design strategies such as ease of navigation, ease of resource discoverability, ease of configuring integrated tools etc. in e-learning platforms such as learning management systems, and massive open online courses (MOOCs). Further, this research aims to find answers for the challenges and issues faced by students and teachers when using e-learning platforms. A comprehensive questionnaire was distributed among teachers and students. Collected data was analyzed to get an idea about main interface design problems that frustrate the learners and teachers and distract them from educational tasks. Using this statistical analysis results, a model is proposed indicating success factors and failure factors that may affect to e-learning system interface designing. Moreover, this research also results in a set of guidelines or suggestions that can be followed to improve UI designing in e-learning platforms. Finally, an initial prototype implementation capable of recommending suggestions intelligently for e-learning platform designers and users is proposed after modelling the user satisfaction factors. © 2021 IEEE.

关键词： User interfaces

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