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Diversified and compatible web APIs recommendation based on game theory in IoT

Digital Communications and Networks 2024年第4期10卷 1198-1209页

作者： Wenwen Gong Huiping Wu Xiaokang Wang Xuyun Zhang Yawei Wang Yifei Chen Mohammad R.Khosravi College of Information and Electrical Engineering China Agricultural UniversityBeijing100000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Department of Computer Science St.Francis Xavier UniversityAntigonishNSCanada Department of Computing Macquarie UniversitySydneyNSW2122Australia Department of Computer Engineering Persian Gulf UniversityBushehr***Iran Department of Electrical and Electronic Engineering Shiraz University of TechnologyShiraz71557-13876Iran

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

关键词： Internet of things Web APIs recommendation Game theory Diversity and compatibility

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Voltage imbalance mitigation in an active distribution network using decentralized current control

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Protection and control of Modern Power Systems 2023年第2期8卷 68-84页

作者： Ahmed M.M.Nour Ahmed A.Helal Magdi M.El-Saadawi Ahmed Y.Hatata Alexandria Higher Institute of Engineering and Technology(AIET) AlexandriaEgypt Department of Electrical and Control Engineering Arab Academy for Science and Technology and Maritime TransportAlexandriaEgypt Department of Electrical Engineering Faculty of EngineeringMansoura UniversityMansouraEgypt Department of Electrical Engineering College of EngineeringShaqra UniversityShaqraKingdom of Saudi Arabia

Voltage imbalance(VI)is caused by the difference in connected single-phase load or generation in a low voltage distribution network(DN).VI increase in a smart distribution grid is due to the current practice of increasing single-phase distributed generators such as photovoltaic(PV)*** paper proposes a decentralized control method to mitigate VI using distributed batteries included in smart grid interfaced residential PV *** mitigate VI using the batteries in this way,five challenges must be overcome,i.e.,equalizing all battery stress currents within the DN,mitigating VI in abnormal conditions such as signal loss among bus controllers,being immune from the distorted feedback measurements,minimizing the steady-state error at different loads,and overcoming the insufficient number or capacity of the distributed batteries at the same *** fuzzy logic controllers(FLC)are proposed at each bus to overcome these five tasks based on a decentralized control *** proposed decentralized control based on FLC is compared with centralized control based on a PI *** proposed control method is tested and verified using simulations in the MATLAB/Simulink software,and the results validate the ability of the scheme to alleviate VI on a smart distribution network under both normal and abnormal conditions.

关键词： Fuzzy controller Distributed batteries Decentralized control Voltage imbalance Distributed PV

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Knowledge-Empowered,Collaborative,and Co-Evolving AI Models:The Post-LLM Roadmap

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工程（英文） 2025年第1期44卷 87-100页

作者： Fei Wu Tao Shen Thomas Bäck Jingyuan Chen Gang Huang Yaochu Jin Kun Kuang Mengze Li Cewu Lu Jiaxu Miao Yongwei Wang Ying Wei Fan Wu Junchi Yan Hongxia Yang Yi Yang Shengyu Zhang Zhou Zhao Yueting Zhuang Yunhe Pan College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Leiden Institute of Advanced Computer Science Leiden UniversityLeiden 2333 CCNetherlands College of Electrical Engineering Zhejiang UniversityHangzhou 310027China School of Engineering Westlake UniversityHangzhou 310024China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Cyber Science and Technology Sun Yat-Sen UniversityShenzhen 518107China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.

关键词： Artificial intelligence Large language models Knowledge empowerment Model collaboration Model co-evolution

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REAL-TIME PSYCHOACOUSTIC FREQUENCY MASKING COMPENSATION FOR AUDIO SIGNALS WITH OVERLAPPING SPECTRA 21

REAL-TIME PSYCHOACOUSTIC FREQUENCY MASKING COMPENSATION FOR ...

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21st Sound and Music Computing Conference, SMC 2024

作者： Presti, Giorgio Degiorgi, Nicola Fresia, Amedeo Servetti, Antonio Department of Computer Science University of Milan Italy Control and Computer Engineering Department Politecnico di Torino Italy

ISBN: (纸本)9789893520758

This work presents a prototype for the automatic and real-time psychoacoustic frequency masking effect compensation. This tool is intended to handle audio signals with overlapping spectra coming from two different mono or stereo sources in order to emphasise those frequencies of a main source that the second one masks. The goal is achieved using a dynamic equaliser controlled by a function of the differences of the input spectra psychoacoustic models. The tool has been prototyped as a standard Virtual Studio Technology (VST3) plugin and its effectiveness has been tested with a user study carried out in the context of music production. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

关键词： Psychoacoustic

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A Graph-Assisted Digital-Twin-Driven Multiagent Shared Offloading for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17349-17363页

作者： Zahangir Alam, Md Rahman, Suryaia Asif Bin Khaled, Md Islam, Ashraful Jamalipour, Abbas Independent University Bangladesh Department of Computer Science and Engineering Center for Computational and Data Sciences Dhaka1229 Bangladesh The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability. © 2014 IEEE.

关键词： Stochastic systems

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Machine learning-driven optimization of plasma-catalytic dry reforming of methane

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Journal of Energy Chemistry 2024年第9期96卷 153-163页

作者： Yuxiang Cai Danhua Mei Yanzhen Chen Annemie Bogaerts Xin Tu Department of Electrical Engineering and Electronics University of LiverpoolLiverpool L693GJUK Research Group PLASMANT Department of ChemistryUniversity of AntwerpUniversiteitsplein 1BE-2610 Wilrijk-AntwerpBelgium College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816JiangsuChina

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental *** address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML *** ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM *** model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM *** found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/***,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM *** results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction *** hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

关键词： Plasma catalysis Machine learning Process optimization Dry reforming of methane Syngas production

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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NCFS:new chaotic fuzzy system as a general function approximator

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Journal of control and Decision 2023年第4期10卷 514-528页

作者： Hamid Abbasi Mahdi Yaghoobi Arash Sharifi Mohammad Teshnehlab Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehranIran Department of Electrical and Computer Engineering Mashhad BranchIslamic Azad UniversityMashhadIran Department of Electrical and Computer Engineering K.N.T University of TechnologyTehranIran

Conventional fuzzy systems(type-1 and type 2)are universal *** goal of this paper is to design and implement a new chaotic fuzzy system(NCFS)based on the Lee oscil-lator for function approximation and chaotic *** incorporates fuzzy reasoning of the fuzzy systems,self-adaptation of the neural networks,and chaotic signal generation in a unique *** features enable the structure to handle uncertainties by generating new information or by chaotic search among prior *** fusion of chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the NCFS more capable of confronting nonlinear *** on the GFA and Stone-Weierstrass theorems,we show that the proposed model has the function approximation *** NCFS perfor-mance is investigated by applying it to the problem of chaotic *** results are demonstrated to ilustrate the concept of function approximation.

关键词： Chaotic fuzzy system function approximation chaotic neural network oscillatory neuron

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A hybrid feature weighting and selection-based strategy to classify the high-dimensional and imbalanced medical data

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Neural Computing and Applications 2024年第20期36卷 12299-12316页

作者： Singh, Harpreet Kaur, Manpreet Singh, Birmohan Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Sangrur148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal Sangrur148106 India

Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from the patient information which creates an imbalance in class distribution as the number of normal persons is more than the number of patients and contains a large number of features to represent a sample. It tends to the machine learning algorithms biased toward the majority class which degrades their classification performance for minority class samples and increases the computation overhead. Therefore, oversampling, feature selection and feature weighting-based four strategies are proposed to deal with the problems of class imbalance and high dimensionality. The key idea behind the proposed strategies is to generate a balanced sample space along with the optimal weighted feature space of the most relevant and discriminative features. The Synthetic Minority Oversampling Technique is utilized to generate the synthetic minority class samples and reduce the bias toward the majority class. An Improved Elephant Herding Optimization algorithm is applied to select the optimal features and weights for reducing the computation overhead and improving the interpretation ability of the learning algorithms by providing weights to relevant features. In addition, thirteen methods are developed from the proposed strategies to deal with the problems of high-dimensionality and imbalanced data. The optimized k-Nearest Neighbor (k-NN) learning algorithm is utilized to perform classification. The performance of the proposed methods is evaluated and compared for sixteen high-dimensional imbalanced medical datasets. Further, Freidman’s mean rank test is applied to show the statistical difference between the proposed methods. Experimental and statistical results show that the proposed Feature Weighting followed by the Feature Selection (FW–FS) method performed significantly b

关键词： Feature Selection

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An efficient weighted partial MaxSAT encoding for scheduling in overloaded real-time systems

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International Journal of computers and Applications 2024年第3期46卷 175-183页

作者： Pourhashem Kallehbasti, Mohammad Mehdi Pirgazi, Jamshid Ghanbari Sorkhi, Ali Kermani, Ali Department of Electrical and Computer Engineering University of Science and Technology of Mazandaran P.O. Box 48518-78195 Behshahr Iran

Scheduling tasks in overloaded real-time systems is a challenging problem that has received a significant amount of attention in recent years. The processor is overloaded with more tasks than its capacity, resulting in missed deadlines and degraded system performance. Therefore, scheduling algorithms play a critical role in ensuring that high-priority tasks are completed on time while minimizing the impact of incomplete lower-priority tasks. This paper proposes an efficient Weighted Partial MaxSAT(WPMS) encoding that returns an optimal solution in which the total weight of incomplete tasks is minimized in a single machine environment. To assess the efficiency of our proposed formulation, a comparative analysis is conducted alongside the state-of-the-art encoding. By examining the solving 75 distinct problems, it becomes evident that the WPMS encoding proposed herein exhibits a considerable advantage in terms of both time and memory efficiency. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Real time systems

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