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Tensor-Based Factorial Hidden Markov Model for Cyber-Physical-Social Services

IEEE Transactions on Services Computing 2025年第3期18卷 1825-1837页

作者： Lu, Zhixing Yang, Laurence T. Azman, Azreen Zhang, Shunli Zhou, Fang Hainan University School of Computer Science and Technology Hainan China Universiti Putra Malaysia Faculty of Computer Science and Information Technology Serdang Malaysia Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China Huazhong University of Science and Technology School of Computer Science and Technology Huazhong China St. Francis Xavier University Department of Computer Science Canada

With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score. © 2008-2012 IEEE.

关键词： Viterbi algorithm

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Identify information sources with different start times in complex networks based on sparse observers

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Chinese Physics B 2024年第11期33卷 467-479页

作者： Yuan-Zhang Deng Zhao-Long Hu Feilong Lin Chang-Bing Tang Hui Wang Yi-Zhen Huang School of Computer Science and Technology Zhejiang Normal UniversityJinhua 321004China School of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua 321004China School of Information Engineering Jinhua PolytechnicJinhua 321016China

The dissemination of information across various locations is an ubiquitous occurrence,however,prevalent methodologies for multi-source identification frequently overlook the fact that sources may initiate dissemination at distinct initial *** there are many research results of multi-source identification,the challenge of locating sources with varying initiation times using a limited subset of observational nodes remains *** this study,we provide the backward spread tree theorem and source centrality theorem,and develop a backward spread centrality algorithm to identify all the information sources that trigger the spread at different start *** proposed algorithm does not require prior knowledge of the number of sources,however,it can estimate both the initial spread moment and the spread *** core concept of this algorithm involves inferring suspected sources through source centrality theorem and locating the source from the suspected sources with linear *** experiments from synthetic and real network simulation corroborate the superiority of our method in terms of both efficacy and ***,we find that our method maintains robustness irrespective of the number of sources and the average degree of *** with classical and state-of-the art source identification methods,our method generally improves the AUROC value by 0.1 to 0.2.

关键词： complex networks information spread source identification backward spread centricity

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Trust management in online computing environment: a complete review

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 491-545页

作者： Singh, Ashish Chatterjee, Kakali School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering National Institute of Technology Bihar Patna800005 India

Nowadays, trust management plays a significant role in different applications like commercial applications, Internet of Things (IoT) based applications, social networking applications, cloud computing-based applications, sensor networks, online services, and many more. Thus, it is an important research area for industry and engineering research communities. The concept of trust is mainly derived from long-term sociological and psychological human behaviour. It is used for decision-making purposes in different contexts and perspectives. Many growing research studies address the development of trust computation models using different approaches in different areas, such as cloud computing, P2P networks, online social network, etc. Still, most of the prior work does not provide a comprehensive systematic review of different trust management systems nor provide sufficient details about the computation process. In this paper, we have discussed a systematic review of the existing trust management systems by indicating the limitation of previous works. A systematic review started with research question identification, followed by searching and filtration of relevant works, and ended with a comparison and final report. A detailed comparative study of trust computational models will help understand the security and privacy aspects of trust. This survey provides a detailed study of each trust taxonomy and computation model in different online computing environments. The survey result finds several trust computation techniques. The results also say that trust in the online computing environment provides greater security and protection against several threats and attacks. Despite the potential benefits of trust, the results also included challenges and open research issues that are still opportunities for new researchers. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Cloud computing

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Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

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computer Modeling in engineering & sciences 2024年第8期140卷 1997-2017页

作者： Yunye Wan Peng Chen Yunni Xia Yong Ma Dongge Zhu Xu Wang Hui Liu Weiling Li Xianhua Niu Lei Xu Yumin Dong College of Computer Science Chongqing UniversityChongqing400044China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer and Information Engineering Jiangxi Normal UniversityNanchang330022China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd.Yinchuan750002China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100083China School of Computer Science and Technology Dongguan University of TechnologyDongguan523808China School of Emergency Management Xihua UniversityChengdu610039China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Mobile edge computing content caching system average cost deep reinforcement learning collaborative mechanism

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Text Representation Based on WT-GloVe Word Vector Weighting Model

Text Representation Based on WT-GloVe Word Vector Weighting ...

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2024 International Conference on Intelligent Computing and Robotics, ICICR 2024

作者： Xue, Cheng Fei, Rong Yao, Quanzhu School of Computer Science and Engineering Xi'An University of Technology Xi'an China School of Computer Science and Technology Xi'An University of Technology Xi'an China

ISBN: (纸本)9798350372274

The text representation issue in text classification is thoroughly examined in this paper, with a focus on handling massive volumes of text data. The paper suggests the word vector weighting model based on WT-GloVe in response to the issues with excessive sparsity and subpar classification performance that classic neural network text representation models frequently confront. To find important information in the text, the model first extracts features from the text using a feature weighting approach based on word spacing and inter-class contribution. This work then presents a filtering technique that tries to enhance the training quality and relevance of word vectors in order to address the issue of irrelevant words that may be introduced by the GloVe model in word vector training. This work effectively builds a weighted word vector model that can precisely capture the value of feature words and rich semantic information by combining the feature weighting technique with irrelevant word screening. According to experimental results, the model performs better in numerous text classification assignments when compared to existing models, demonstrating its efficacy in enhancing text representation performance. The paper enriches the field of text representation by offering a fresh approach and aiding in the advancement ofNatural Language Processing technology. ©2024 IEEE.

关键词： Semantics

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Res-SRNN: Residual Spiking Recurrent Neural Network for Efficient Video Recognition

Res-SRNN: Residual Spiking Recurrent Neural Network for Effi...

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Zhang, Ya Luo, Xiaoling Sun, Qian Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

ISBN: (纸本)9798331504755

Action recognition in videos is a critical task in computer vision, with a wide range of applications including security surveillance, behavior analysis, and cooperative control. Despite significant advancements in action recognition accuracy achieved by existing video recognition methods, they commonly suffer from high computational complexity and substantial consumption of memory and computational resources. To address the urgent demand for concise, energy-efficient, and high-performance video recognition methods, we propose a residual spiking recurrent neural network (Res-SRNN). Firstly, we design the residual convolution module based on 2D convolution operations to effectively extract spatial information from input video frames. Moreover, drawing inspiration from the recurrent connections in the visual cortex, we incorporate the microscale spiking mechanism of the brain and put forward the spiking recurrent neural network (SRNN). Our SRNN not only excels at capturing complex temporal dependencies but also conserves computational energy costs. Experimental results demonstrate that our proposed Res-SRNN method significantly outperforms most comparative methods on benchmark datasets, achieving the best balance between parameter efficiency and performance. © 2024 IEEE.

关键词： Recurrent neural networks

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A two-dimensional MoS_(2) array based on artificial neural network learning for high-quality imaging

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Nano Research 2023年第7期16卷 10139-10147页

作者： Long Chen Siyuan Chen Jinchao Wu Luhua Chen Shuai Yang Jian Chu Chengming Jiang Sheng Bi Jinhui Song School of Mechanical Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Southwest Petroleum UniversityChengdu 610500China

As the basis of machine vision,the biomimetic image sensing devices are the eyes of artificial *** recent years,with the development of two-dimensional(2D)materials,many new optoelectronic devices are developed for their outstanding ***,there are still little sensing arrays based on 2D materials with high imaging quality,due to the poor uniformity of pixels caused by material defects and fabrication ***,we propose a 2D MoS_(2)sensing array based on artificial neural network(ANN)*** equipping the MoS_(2)sensing array with a“brain”(ANN),the imaging quality can be effectively *** the test,the relative standard deviation(RSD)between pixels decreased from about 34.3%to 6.2%and 5.49%after adjustment by the back propagation(BP)and Elman neural networks,*** peak signal to noise ratio(PSNR)and structural similarity(SSIM)of the image are improved by about 2.5 times,which realizes the re-recognition of the distorted *** provides a feasible approach for the application of 2D sensing array by integrating ANN to achieve high quality imaging.

关键词： two-dimensional MoS_(2) sensing array artificial neural network individual difference imaging quality

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Intelligent Carpooling - Machine Learning Route and Pickup Optimisation 5

Intelligent Carpooling - Machine Learning Route and Pickup O...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Goojar, Arunya Verma, Parth Singh, Nitin Debnath, Shubhradeep Tyagi, Neha Computer Science and Engineering Amity School of Engineering and Technology UP India

ISBN: (纸本)9798350383522

In order to encourage carpooling between workplace personnel and college students, this research article gives a unique method that makes use of machine getting to know and synthetic intelligence (AI) algorithms. The file contends that although carpooling remains an underused mode of transportation, it has the ability to significantly lessen visitors' congestion and air pollution in metropolitan areas. The look at shows a brand-new web tool that compares commuter routes for drivers and passengers while taking vicinity, vacation spot, and preference into attention. Users might also chat with each other and prepare excursions at the utility's user-pleasant platform. The effectiveness of the advised software in encouraging carpooling and decreasing traffic congestion and air pollution is classified within the article. The studies' conclusions offer insightful information on how carpooling can be used to alleviate urban transportation issues. © 2024 IEEE.

关键词： AI Algorithms Air Pollution Carpooling Sustainable Transportation Traffic Congestion

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Review the Deep Learning Strategies for Enhancing the Security of Body Area Networks Against Adversarial Attack

Review the Deep Learning Strategies for Enhancing the Securi...

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Nagdiya, Jagrati Goyal, Rajeev Amity School of Engineering and Technology Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350388800

Thereis an urgent need for strong security measures to protect Body Area Networks (BANs) from malicious threats, as their use in health monitoring and telemedicine continues to grow. To improve BAN security, this research presents a new method that makes use of deep learning approaches. Data breaches, unauthorised access, and other cyberattacks that target the particular weaknesses of BANs are among the many cybersecurity threats that our approach uses sophisticated neural network designs to detect and mitigate. Typical network traffic events and simulated adversarial attacks are both included in the extensive dataset that our deep learning model is trained on. The model can then learn to identify suspicious behaviour by observing complicated patterns and outliers. The design of the device is such that it can run smoothly on very little power while still achieving excellent efficiency. © 2024 IEEE.

关键词： Adversarial machine learning

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Prevention of Attacks in Vehicular Adhoc Networks

Prevention of Attacks in Vehicular Adhoc Networks

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Anjali, Thuvva Goyal, Rajeev G.n, Balaji Amity University Computer Science and Engineering Amity School of Engineering and Technology Gwalior India Vellore Institute of Technology Computer Science and Engineering School of Cse Vellore India

ISBN: (纸本)9798350348460

Over the past years, numerous methods have been developed to identify anomalies in traditional VANETs networks. A survey of VANET anomaly detection and mitigation methods is presented in this analysis. This survey looks into a variety of VANET research projects, attack types, anomaly detection methods, and mitigation strategies. VANETs provide wireless communication among vehicles as well as between vehicles and infrastructure. Its major objective is to improve road safety, comfort, and convenience. VANET varies from ad hoc networks due to its specific features. Several methods for identifying anomalies in conventional VANET networks have been developed over time. This work examines VANET anomaly detection and prevention technologies. Various VANET research studies, different types of attacks, anomaly detection, and VANET protection measures. The current investigation seeks to address various sorts of vehicle network assaults as well as existing security methods for coping with these attacks. This work will help academics understand the taxonomy of automotive network security vulnerabilities, which can subsequently be researched further to develop unique solutions. © 2024 IEEE.

关键词： Identity Based Signature Intelligent Transport System Intrusion Detection System Vehicle to Vehicle Vehicular Adhoc Network

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