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Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

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Intelligent Automation & Soft computing 2023年第8期37卷 2077-2101页

作者： Kuihua Huang Rui Li Wenyin Gong Weiwei Bian Rui Wang College of System Engineering National University of Defense TechnologyChangsha410073China School of Computer Science China University of GeosciencesWuhan430074China Equipment General Technology Laboratory Beijing Mechanical Equipment Research InstituteBeijing100854China

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce ***,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard *** numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

关键词： Distributed heterogeneous flow shop scheduling green scheduling SPEA2 competitive and cooperative

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Port-controllable routing of orbital angular momentum modes using a rotatable diffractive neural network

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 154-163页

作者： Junmin Liu Jiafu Chen Qingji Zeng Zemin Liang Xinping Wu Xin Zhao Jiangnan Xiao Huapeng Ye Ze Dong Dianyuan Fan Shuqing Chen Institute of Microscale Optoelectronics Shenzhen UniversityShenzhen 518060China College of New Materials and New Energies Shenzhen Technology UniversityShenzhen 518118China Terahertz Technology Innovation Research Institute Shanghai Key Laboratory of Modern Optical SystemTerahertz Science Cooperative Innovation CenterSchool of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology&Institute of Electronic Paper Displays South China Academy of Advanced OptoelectronicsSouth China Normal UniversityGuangzhou 510006China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China

Orbital angular momentum(OAM)modes provide an additional orthogonal physical dimension,offering transformative potential for enhancing optical communication *** significant progress in mode multiplexing,the development of robust communication networks faces persistent challenges,particularly in effectively routing and controlling these multiplexed channels among network *** tackle these dilemmas,we propose a rotatable diffractive neural network(R-DNN)strategy and demonstrate its capability for port-controllable OAM mode *** leveraging the correlation between the orthogonal evolution of OAM modes in free space and phase modulations during propagation,the R-DNN precisely shapes the spatial evolution of mode fields through multiple rotatable phase layers,enabling efficient routing to specific output *** approach exploits the interaction of secondary wavelets with the relative states of the rotatable layers,allowing on-demand control of mode evolution paths and enhancing routing *** a proof of concept,we developed a tri-functional router that successfully directs three OAM modes to individually controllable output *** router achieves an average intermode crosstalk of less than−16.4 dB across three functional states,one-dimensional,two-dimensional,and cross-connected switching,while supporting the routing of 5.85 Tbit/s quadrature phase-shift keying *** results highlight the R-DNN’s effectiveness in achieving precise and controllable OAM mode manipulation,paving the way for advanced applications in mode-multiplexed communication networks and beyond.

关键词： orbital angular momentum mode mode routing diffractive neural network

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MMSFD: Multi-grained and Multi-modal Fusion for Short Video Fake News Detection 7

MMSFD: Multi-grained and Multi-modal Fusion for Short Video ...

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7th International Conference on Data science and Information technology, DSIT 2024

作者： Ren, Shuai Liu, Yahui Zhu, Yaping Bing, Wanlong Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

Short video platforms have become an essential part of people's daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods. © 2024 IEEE.

关键词： Fake detection

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer systems science & Engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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StarMFND: Star Topology Multi-Domain Fake News Detection 7

StarMFND: Star Topology Multi-Domain Fake News Detection

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7th International Conference on Data science and Information technology, DSIT 2024

作者： Gu, Pengfei Chen, Yibing Bing, Wanlong Liu, Yahui Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection. © 2024 IEEE.

关键词： Fake News Detection Multi-Domain Learning Multi-Task Learning Social Media

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Method for Evaluating the Performance of Aircraft Collaborative Decision-Making Based on Dynamic Bayesian Network

Method for Evaluating the Performance of Aircraft Collaborat...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Zhang, Wenbo Yu, Boxiao Tong, Jiahui Li, Jing Beijing Electro-Mechanical Engineering Institute National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China

ISBN: (纸本)9798331527624

This research addresses the challenge of assessing equipment decision-making effectiveness in environments characterized by uncertainty and multiple influencing factors. We propose a novel approach involving the creation and optimization of a dynamic Bayesian network-based model tailored for equipment decision-making processes. The model facilitates a systematic evaluation of decision-making effectiveness by incorporating various factors, including equipment performance, environmental conditions, and enemy defensive capabilities. A comprehensive assessment system has been developed to simulate the penetration capabilities of different equipment through enemy defenses. Moreover, this study identifies potential errors in the effectiveness evaluation model by analyzing its performance in typical scenarios, pinpointing areas for enhancement. © 2024 IEEE.

关键词： Decision-making Dynamic Bayesian networks Equipment Simulation Uncertainty Management

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On the Outage of Far-Reach Communications via RIS-Empowered Satellites

On the Outage of Far-Reach Communications via RIS-Empowered ...

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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Wang, Fan Jiang, Ruihong Liu, Yuan Bao, Tingnan Peng, Mugen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Research Center for Space Computing System Zhejiang Laboratory Hangzhou311100 China School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada

ISBN: (纸本)9798350362312

The rising demand for reliable, high-bandwidth satellite communications in far-reach areas drives the integration of low earth orbit (LEO) satellites into future 6G networks. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) technology and rate splitting multiple access (RSMA) within LEO satellite systems to improve communication quality and system capacity. We derive closed-form expressions for outage probability (OP) and reliable throughput that validate the theoretical advantages of these technologies under various signal-to-noise ratio conditions. Numerical results demonstrate that, compared with traditional space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) approaches, our RSMA and RIS-integrated framework significantly can reduce OP and enhance reliable throughput with an appropriate power-splitting factor, showcasing its superiority in RIS-enabled satellite communication scenarios. © 2024 IEEE.

关键词： Satellite communication systems

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Container-based data-intensive application scheduling in hybrid cloud-edge collaborative environment

Container-based data-intensive application scheduling in hyb...

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作者： Tang, Bing Luo, Jincheng Zhang, Jiaming School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangdong Guangzhou China School of Information Engineering Wuhan University of Technology Hubei Wuhan China

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. It can bring convenience to system deployment, operation, and maintenance. This article considers the implementation of AI-based IoT applications based on Docker and container technology in a cloud-edge collaborative environment. Three typical data-intensive applications, preprocessing, training, and inference in machine learning, are deployed using Docker containers. For these three types of tasks, this article proposes a container-based data-intensive application scheduling framework. Using an attribute-based data-driven method, a new scheduling approach considering multiple weighting factors is proposed, which is called DICS-OPT. The cloud nodes and edge nodes are scored and sorted uniformly. The container is scheduled to the node with the highest score, and then the task data is transmitted to the node to execute the task. NSGA-III-based genetic algorithm has been proposed to search for the optimal weighting factors. This article introduces the implementation of the prototype system and builds a cloud-edge collaborative testbed consisting of Raspberry Pis and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing approaches. Compared with existing approaches, DICS-OPT improves the average edge resource utilization by 10.05% to 69.04%, and saves the cloud resource cost by 16.02% to 36.68%. © 2023 John Wiley & Sons Ltd.

关键词： Edge computing

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A Blackbox Fuzzing Based on Automated State Machine Extraction 8

A Blackbox Fuzzing Based on Automated State Machine Extracti...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Zhang, Zhaowei Yu, Wenjing Wang, Zibin Xiang, Youlin National Key Laboratory of Science and Technology on Information System Security Beijing China School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Shanghai China

ISBN: (纸本)9798350331035

Currently, protocol fuzzing techniques mainly employ two approaches: greybox fuzzing based on mutation and blackbox fuzzing based on generation. Greybox fuzzing techniques use message exchanges between the protocol server and actual clients as seeds, and generate test cases through mutation. Although this approach can provide coverage information of the SUT's code and state space through instrumentation and feedback, its drawback lies in the relatively random mutation strategy, which makes it challenging to validate the SUT's message verification process. This paper addresses this limitation by utilizing artificial intelligence techniques to extract protocol state machines. It aims to overcome the reliance on manual work in blackbox fuzzing based on generation and leverage its advantages to generate more effective fuzzing test cases. The study utilizes Prompt-Learning technology to analyze the semantic information in protocol RFC documents, obtain corresponding intermediate representations, and extract protocol state machines from these representations. Taking the BGP protocol as an experimental subject, the experimental results demonstrate a certain level of accuracy in the obtained protocol state machines and the ability to generate test cases from these state machines, thereby enhancing the automation level of protocol fuzzing. © 2023 IEEE.

关键词： fuzzing network protocols network security protocol testing state machine extraction

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Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track

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science China(Information sciences) 2023年第5期66卷 196-214页

作者： Yu WANG Jiaojiao YAN Wei HUANG Leszek RUTKOWSKI Jinde CAO School of Mathematics Southeast University Jiangsu Provincial Key Laboratory of Networked Collective Intelligence Southeast University Intelligent Transportation System Research Center Southeast University Systems Research Institute of the Polish Academy of Sciences Institute of Computer Science AGH University of Science and Technology Information Technology Institute University of Social Sciences Yonsei Frontier Lab Yonsei University

Rutting deformation is typical damage to asphalt pavement and can be quantitatively described as the time-varying mechanical deformation feature of the asphalt mixture during the creep process. First,a nonlinear viscoelastic creep model is established based on the Caputo variable-order fractional *** fractional order in this model is not fixed and is defined as a time-varying function. The time-dependent mechanical properties of asphalt mixtures are efectively reflected. Based on the modified Burgers model, the variable-order function is revised to a linear form. Second, the variable-order fractional Burgers rutting model is obtained by fitting the test data of the RIOHTrack full-scale track. The Levenberg-Marquardt optimization algorithm is used to analyze the test data and the corresponding parameters to obtain more accurate and applicable variable-order fractional rutting mechanics and empirical models for diferent asphalt pavement structures. Finally, according to the classification of the pavement structure, structures III and VI are taken as examples. The rutting prediction and comparison results of several representative pavement structures are obtained to verify that the proposed rutting prediction model can also accurately predict all road sections of RIOHTrack. The new model is also compared with the rutting model of the mechanistic-empirical pavement design guide, the fractional-order Burgers rutting model, and the modified Burgers rutting model. The fitting results are evaluated using the determination coefficient R2. The results show that the variable-order fractional Burgers rutting model proposed in this study exhibits a better fit and is more applicable to describe the rutting deformation of asphalt pavement.

关键词： RIOHTrack full-scale track Caputo fractional derivative variable-order fractional modified Burgers model rutting prediction

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