检索结果-内蒙古大学图书馆

Application Level Trust Authority (APPLETA) for Resource-Constrained Edge Devices in IoT and 6G

学校读者我要写书评

暂无评论

IEEE Transactions on Consumer Electronics 2025年

作者： Mahmood, Shahid Gohar, Moneeb Koh, Seok-Joo Tariq, Muhammad Usman Ghani, Anwar Department of Computer Science Bahria University Shangrilla Road E-8/1 Islamabad Pakistan School of Computer Science and Engineering Kyungpook National University Korea Republic of Marketing Operations Information Systems Abu Dhabi University United Arab Emirates Department of Computer Science International Islamic University Department of Computer Science International Islamic University Islamabad Pakistan Big Data Research Center Department of Computer Engineering Jeju National University Korea Republic of Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana010000 Kazakhstan

The rapid growth of the internet and edge devices has greatly increased the risk of cyber-attacks at the edge layer of computer networks. The rise of edge devices has driven extensive research into their cybersecurity challenges. These devices require robust protection to counter possible cyberattacks. Trust is a key security parameter used to determine the level of protection required for a device.. Trustworthiness gives confidence to other devices before they offer or request a service. The trust value indicates the probability of risk that a device may incur while engaging with another device. There are various solutions, but many fail due to the diversity of edge devices in the Internet of Things. In this paper, an efficient trust management framework, APPLETA, has been proposed. It computes the trust value based on routine application transactions and authentication behaviours without additional overhead. Simulation results show that the trust value is computed more efficiently, using only one application transaction trust parameter. APPLETA has demonstrated better results against good and bad-mouthing attacks with two parameters: application transaction and authentication. This scheme can effectively segregate malicious and harmless nodes. This segregation can further assist in the precise detection of malicious nodes. © 1975-2011 IEEE.

关键词： Cyber attacks

AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe Kim, Dong In Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates quantum computing capabilities into classical edge computing servers that are proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC system, where mobile devices can offload classical and quantum computation tasks to edge servers equipped with classical and quantum computers. We then formulate the hybrid classical-quantum computation offloading problem whose goal is to minimize system cost in terms of latency and energy consumption. To solve the offloading problem efficiently, we propose a hybrid discrete-continuous multi-agent reinforcement learning algorithm to learn long-term sustainable offloading and partitioning strategies. Finally, numerical results demonstrate that the proposed algorithm can reduce the MEQC system cost by up to 30% compared to existing baselines. © 2025, CC BY.

关键词： Qubits

Blockchain based electronic educational document management with role-based access control using machine learning model

学校读者我要写书评

暂无评论

Scientific Reports 2025年第1期15卷 1-19页

作者： Chinnasamy, P. Subashini, B. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Pandey, Binay Kumar Pandey, Digvijay Lelisho, Mesfin Esayas Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Engineering Srivilliputtur India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttrarakhand India Department of Technical Education (Government of Uttar Pradesh) Uttar Pradesh Kanpur India Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Ethiopia

The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%. © The Author(s) 2025.

关键词： Blockchain Electronic educational document management Remora swarm optimization Role-based access Temporal neural network

Machine learning for modelling unstructured grid data in computational physics: a review

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

Gru-Inc: An Inception-Attention Based Approach Using Gru for Human Activity Recognition

学校读者我要写书评

暂无评论

SSRN

SSRN 2022年

作者： Mim, Taima Rahman Amatullah, Maliha Afreen, Sadia Yousuf, Mohammad Abu Uddin, Shahadat Alyami, Salem A. Hasan, Khondokar Fida Moni, Mohammad Ali Information and Communication Technology Bangladesh University Of Professionals Mirpur Cantonment Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Savar Dhaka Bangladesh Complex Systems Research Group Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia Riyadh13318 Saudi Arabia Centre for Cyber Security Research & Innovation RMIT University 124 La Trobe Street MelbourneVIC3000 Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Human Activity Recognition is one of the most popular research fields nowadays, and many machine learning algorithms have been successfully implemented to achieve high-performance results. Although handcrafted feature extraction techniques were used in the past, ANN is now more popular. In this work, the GRU-INC model has been proposed, which is an Inception-Attention based approach using GRU that effectively makes use of the temporal and spatial information of the time-series data. The proposed model achieved an F1-score of 96.27%, 90.05%, 90.30%, 99.12% and 95.99% on the publicly available datasets such as UCI-HAR, OPPORTUNITY, PAMAP2, WISDM and Daphnet respectively. GRU along with Attention Mechanism was utilized for the temporal part and Inception module along with CBAM was exploited for the spatial part of the model. The proposed architecture was evaluated against the state-of-the-art as well as few existing works and it is proved that the GRU-INC model has a higher recognition rate as well as lower computational cost. © 2022, The Authors. All rights reserved.

关键词： Machine learning

Gaze-music Media Transformation by Similarity of Impression Words

学校读者我要写书评

暂无评论

Gaze-music Media Transformation by Similarity of Impression ...

International Electronics Symposium (IES)

作者： Abdullah Iskandar Takafumi Nakanishi Achmad Basuki Ryotaro Okada Takashi Kitagawa Department of Creative Multimedia Technology Electronics Engineering Polytechnic Institute of Surabaya Surabaya Indonesia Department of Data Science Musashino University Tokyo Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728195308

ISBN: (纸本)9781728195643

In this paper, we present gaze-music transformation realization method by realizing gaze emotion detection system and interconnecting the detection system and an automatic music media creation system. This method automatically creates a music media contents by a detected emotion from human gaze. When it is possible to realize a system that acquires human gaze data from a camera device, detects emotions from the data, and creates media content in response to those emotions, it will contribute to creative activities corresponding to human emotion. Our method connects a gaze emotion detection system and an automatic music creation system by the similarity of words that represent emotions and impressions. It is possible to compare emotions by comparing the emotions expressed by each different vocabulary using the similarity of words.

关键词： Media Eyelids Pupils Feature extraction Histograms Cameras Emotion recognition

Confidence-Aware Learning for Deep Neural Networks

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Moon, Jooyoung Kim, Jihyo Shin, Younghak Hwang, Sangheum Department of Data Science Seoul National University of Science and Technology Seoul Korea Republic of LG CNS Seoul Korea Republic of Department of Industrial & Information Systems Engineering Seoul National University of Science and Technology Seoul Korea Republic of

Despite the power of deep neural networks for a wide range of tasks, an overconfident prediction issue has limited their practical use in many safety-critical applications. Many recent works have been proposed to mitigate this issue, but most of them require either additional computational costs in training and/or inference phases or customized architectures to output confidence estimates separately. In this paper, we propose a method of training deep neural networks with a novel loss function, named Correctness Ranking Loss, which regularizes class probabilities explicitly to be better confidence estimates in terms of ordinal ranking according to confidence. The proposed method is easy to implement and can be applied to the existing architectures without any modification. Also, it has almost the same computational costs for training as conventional deep classifiers and outputs reliable predictions by a single inference. Extensive experimental results on classification benchmark datasets indicate that the proposed method helps networks to produce well-ranked confidence estimates. We also demonstrate that it is effective for the tasks closely related to confidence estimation, out-of-distribution detection and active learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Your Face Mirrors Your Deepest Beliefs-Predicting Personality and Morals through Facial Emotion Recognition

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Gloor, Peter A. Colladon, Andrea Fronzetti Altuntas, Erkin Cetinkaya, Cengiz Kaiser, Maximilian F. Ripperger, Lukas Schaefer, Tim MIT Center for Collective Intelligence CambridgeMA02142 United States Department of Engineering University of Perugia Perugia06123 Italy Galaxyadvisors AG Aarau5000 Switzerland Department of Data Science Lucerne University of Applied Sciences and Arts Lucerne6002 Switzerland Department of Information Systems University of Cologne Cologne50923 Germany

Can we really"read the mind in the eyes"? Moreover, can AI assist us in this task? This paper answers these two questions by introducing a machine learning system that predicts personality characteristics of individuals on the basis of their face. It does so by tracking the emotional response of the individual’s face through facial emotion recognition (FER) while watching a series of 15 short videos of different genres. To calibrate the system, we invited 85 people to watch the videos, while their emotional responses were analyzed through their facial expression. At the same time, these individuals also took four well-validated surveys of personality characteristics and moral values: the revised NEO FFI personality inventory, the Haidt moral foundations test, the Schwartz personal value system, and the domain-specific risk-taking scale (DOSPERT). We found that personality characteristics and moral values of an individual can be predicted through their emotional response to the videos as shown in their face, with an accuracy of up to 86% using gradient-boosted trees. We also found that different personality characteristics are better predicted by different videos, in other words, there is no single video that will provide accurate predictions for all personality characteristics, but it is the response to the mix of different videos that allows for accurate prediction. © 2021, CC BY.

关键词： Forecasting