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A Novel Intelligent Task Offloading Scheme for Multi-Controller Environment in Software Defined Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 20018-20029页

作者： Hussain, Mir Wajahat Sangaiah, Arun Kumar Reddy, K. Hemant Kumar Roy, Diptendu Sinha Alenazi, Mohammed J.F. Javvaji, Pavan Kumar Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Anekal562106 India International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Meghalaya 793003 India College of Computer and Information Sciences Department of Computer Engineering King Saud University Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing platform meets these stringent computation requirements, but it has a significant latency that fog computing effectively handles. Software Defined Network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network pre-requisites in an agile manner. Task Offloading (TO) is a crucial issue in Software Defined-IoV (SDIoV), particularly when the vehicle's resources are insufficient. Fog nodes schedule offloaded tasks;however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this paper introduces two proactive Intelligent Task Offloading (ITO' and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO' and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7-13.9)% more than the existing scheme. They also reduce average packet loss, delay, Round Trip Time (RTT), and offloaded node time selection by (45.35-21.3)%, (50-33.3)%, (53.9-9.9)% and (43.2-26.06)% which demonstrates the efficacy of the proposed scheme. © 2025 IEEE.

关键词： Fog computing

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Harmony in transcripts: a systematic literature review of transcriptome-wide association studies

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Journal of engineering and Applied science 2024年第1期71卷 1-15页

作者： Mashhour, Mahinaz A. Kandil, Ahmed Hisham AbdElwahed, Manal Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt School of Information Technology and Computer Science Nile University Sheikh Zayed City Egypt

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. © The Author(s) 2024.

关键词： Genome

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A Web Service Recommendation Method Based on Adaptive Gate Network and xDeepFM 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Tao, Zhi Cao, Buqing Ye, Hongfan Kang, Guosheng Peng, Zhenlian Wen, Yiping School of Computer Science and Engineering and Hunan Key Laboratory of Service Computing and New Software Service Technology Hunan University of Science and Technology Hunan Xiangtan China School of Computer and Artificial Intelligence Huaihua University Huaihua China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential increase in the number of Web services. To automatically generate or recommend a group of Web services according to the user's natural language requirements description, to build a Mashup application to meet the user's requirement is a hot topic in service computing. Some researchers enhance the quality of Web service recommendation by using auxiliary information into the recommendation system. However, they mainly focus on adding external information (e.g., pre-training of external corpora) to enhance semantic features, while some internal statistical features of the corpus such as word distribution on labels and frequency are not fully exploited. Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, the description document of Web services is taken as the basic corpus, the semantic and statistical information in the corpus are mined by utilizing the adaptive gate network, and the statistical features are encoded by a variational encoder. Secondly, the similarity between Web services is derived from the semantic features, at the same time the popularity and co-occurrence of Web services are calculated. Thirdly, the xDeepFM model is used to mine the explicit and implicit higher-order interactions in the sparse matrix which consists of the above information to recommend Web services for Mashup application. Finally, a multiple sets of experiments based on the dataset crawled from the ProgrammableWeb have been conducted to evaluate the proposed method and the experimental result shows that t

关键词： Web services

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Deep Boosting Robustness of DNN-Based Image Watermarking via Dbmark

Deep Boosting Robustness of DNN-Based Image Watermarking via...

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2023 International Conference on Culture-Oriented science and technology, CoST 2023

作者： Ye, Guanhui Gao, Jiashi Yin, Bo Xie, Wei Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Journalism and Communication Hunan University Changsha China

ISBN: (纸本)9798350357998

Image watermarking is a technique for hiding information into images that can withstand distortions while requiring the encoded image to be perceptually identical to the original image. Recent work based on deep neural networks (DNN) has achieved impressive progression in digital watermarking. Higher robustness under various distortions is the eternal pursuit of digital image watermarking approaches. In this paper, we propose DB MARK, a novel end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking. The key novelty is the synergy of invertible neural networks (INN) and effective watermark features generation. The framework generates watermark features with redundancy and error correction ability through the effective neural network based message processor, synergized with the powerful information embedding and extraction abilities of INN to achieve higher robustness and invisibility. The powerful learning ability of neural networks enables the message processor to adapt to various distortions. In addition, we propose to embed the watermark information in the discrete wavelet transform (DWT) domain and design low-low (LL) sub-band loss to enhance invisibility. Extensive experiment results demonstrate the superiority of the proposed framework compared with the state-of-the-art ones under various distortions such as dropout, cropout, crop, Gaussian filter, and JPEG compression. © 2023 IEEE.

关键词： Image watermarking

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Extended Multi-scale Feature Fusion and Balanced Generative Adversarial Network for Image Inpainting under Limited Data

Extended Multi-scale Feature Fusion and Balanced Generative ...

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2024 Workshop on Electronics Communication engineering, WECE 2024

作者： Xiao, Ting Sun, Minqian Chen, Haoyuan Li, Jiaqi Qiu, Yixing Wang, Zhe Lu, Dan Department of Computer Science and Engineering East China University of Science and Technology China School of Artificial Intelligence OPtics and ElectroNics Northwestern Polytechnical University China School of Computer Science and Technology Harbin Engineering University China

ISBN: (数字)9781510689145

ISBN: (纸本)9781510689138

Image inpainting has made remarkable progress in recent years due to the availability of sufficient training data. However, when the training data is insufficient, i.e., image inpainting in few-shot regimes, the performance of existing image inpainting methods degrades drastically. Two challenges impede image inpainting under few-shot regimes, adequate information and unstable training. To address the above issues, this paper proposes a new method for image inpainting under limited data. Specifically, an Extended Multi-scale Feature Fusion (EMFF) module is proposed to capture representative contextual information by progressively reducing the number of channels for features with increasing receptive fields, thereby utilizing effective information. Additionally, we leverage a multi-scale discriminator whose cross-layer features are mixed for supplementation to formulate a Balanced Generative Adversarial Network (BGAN) to stabilize the training dynamics. Comprehensive experiments demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Image fusion Image quality Feature fusion Education and training Image restoration Convolution Data modeling Image enhancement Gallium nitride Semantics

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Reactive Power Optimization of AC-DC Hybrid System Based on Improved Whale Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2084-2098页

作者： Huang, Xin-Yu Liang, Quan Chen, Xian-Fu Zhan, Chuang-Tao School of Electronic Electrical Engineering Physics Fujian University of Technology Fuzhou350118 China School of Computer Science Mathematics Fujian University of Technology Fuzhou350118 China

With the continuous construction of HVDC, it has gradually become dis-tances and large capacity transmission, as well as one of the main technologies of regional power grid interconnection. The scale of AC-DC hybrid grid continues to grow, bringing new challenges to reactive power balance and voltage stability of power grid. An improved whale optimization algorithm is proposed to solve the reactive power optimization problem of AC-DC hybrid systems. By introducing local neighborhood search mechanism, adaptive threshold and adaptive inertia weight, the local search accuracy, global search ability and convergence speed of the algorithm are improved. The reactive power optimization model is established with the total network loss and node voltage offset as the objective function, and the solution steps are given. An IEEE 22-node system with DC transmission lines is used as an example to simulate the proposed algorithm, and compared with the traditional whale optimization algorithm and genetic algorithm, the results show that the proposed algorithm has good convergence and stability, and can obtain better reactive power optimization effect. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Optimization algorithms

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Feature selection for determining input parameters in antenna modeling

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Journal of Systems engineering and Electronics 2025年第1期36卷 15-23页

作者： LIU Zhixian SHAO Wei CHENG Xi OU Haiyan DING Xiao School of Physics University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer and Information Engineering Xinjiang Agricultural UniversityUrumqi 830052China

In this paper,a feature selection method for determining input parameters in antenna modeling is *** antenna modeling,the input feature of artificial neural network(ANN)is geometric *** selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)*** information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear *** EM response range is utilized to evaluate the *** wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is *** the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly *** modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed *** number of input parameters decreases from8 to *** testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.

关键词： antenna modeling artificial neural network(ANN) feature selection maximal information coefficient(MIC)

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AI-Generated Content-as-a-Service in IoMT-Based Smart Homes: Personalizing Patient Care With Human Digital Twins

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Akram, Junaid Aamir, Misha Raut, Roshani Anaissi, Ali Jhaveri, Rutvij H. Akram, Awais School of Computer Science The University of Sydney Camperdown NSW Australia Department of Information Technology Pimpri Chinchwad College of Engineering Savitribai Phule Pune University Pune India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University India COMSATS University Islamabad Vehari Pakistan

We introduce the AI-Generated Optimal Decision (AIGOD) algorithm and the Deep Diffusion Soft Actor-Critic (DDSAC) framework, marking a significant advancement in integrating Human Digital Twins (HDTs) with AI-Generated Content (AIGC) within IoMT-based smart homes. Our innovative AI-Generated Content-as-a-Service (AIGCaaS) architecture, optimized for IoMT environments, leverages network edge servers to enhance the selection of AI-Generated Content Service Providers (AISPs) tailored to the unique characteristics of individual HDTs. Extensive experiments demonstrate DDSAC’s HDT-centric approach outperforms traditional Deep Reinforcement Learning algorithms, offering optimal AIGC services for diverse healthcare needs. Specifically, DDSAC achieved a 20% improvement in task completion rates and a 15% increase in overall utility compared to existing methods. These findings highlight the potential of HDTs in personalized healthcare by simulating and predicting patient-specific medical outcomes, leading to proactive and timely interventions. This integration facilitates personalized healthcare, establishing a new standard for patient-centric care in smart home environments. By leveraging cutting-edge AI techniques, our research significantly contributes to the fields of IoMT and AIGC, paving the way for smarter and more responsive healthcare services. IEEE

关键词： Real time systems

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Mitigating Cross-Site Request Forgery Vulnerabilities: An Examination of Prevention Systems 18

Mitigating Cross-Site Request Forgery Vulnerabilities: An Ex...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Singh, Yuvraj Goel, Paranjay Aggarwal, Shubhani Chaudhary, Rajat Budhiraja, Ishan Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Punjab India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350391725

Cross-Site Request Forgery (CSRF) remains a pervasive vulnerability in web applications, appearing regularly in the OWASP Top 10 lists, posing significant threats to user data and system integrity despite the existence of numerous rectification methodologies. This study undertakes a thorough, comprehensive analysis of the present environment of CSRF prevention systems, investigating their operational mechanisms, effectiveness, and associated limitations. Hence, this paper explores the role of CSRF tokens in the prevention of cyberattacks, including the detection of token absence and the implementation of token-based countermeasures to mitigate CSRF risks. The system model utilizing simple JavaScript and Flask Applications that aim to utilize the token-based countermeasure to identify the existence of tokens and generate and append them to requests, which ensures security of user data and system integrity. © 2024 IEEE.

关键词： CSRF attacks CSRF tokens Mitigation Prevention

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Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

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Chinese Physics B 2023年第10期32卷 273-282页

作者：马璐陈梅辉何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health *** physiological complexity of the cardiac system is related to age,disease,*** the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time *** propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological *** obtained results suggest that these two network measures show distinct changes between different *** is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac ***,the complexity of the cardiac system is *** that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed *** of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two ***,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

关键词： electrocardiogram signals multivariate recurrence networks cross-clustering coefficient entropy multiscale analysis

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