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arXiv 2023年

作者： Chen, Jinbiao Wang, Jiahai Zhang, Zizhen Cao, Zhiguang Ye, Te Chen, Siyuan School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training;meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

arXiv

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arXiv 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin School of Automation Guangdong University of Technology Guangzhou510006 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 2024, CC BY.

关键词： Latent semantic analysis

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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Qais Al-Na'amneh Mohammad Aljaidi Hasan Gharaibeh Ahmad Nasayreh Rabia Emhamed Al Mamlook Sattam Almatarneh Dalia Alzu'bi Abla Suliman Husien Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University of Zawia Tripoli Libia Dep. of Data Science and Artificial Intelligence Zarqa University Zarqa Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A “spoofing attack” refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems.

关键词： Electric potential Accuracy Law Machine learning Learning (artificial intelligence) Feature extraction Internet of Things

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Supervised Machine Learning Strategy for detection of covid19 patients

Supervised Machine Learning Strategy for detection of covid1...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： I. S. Hephzi Punithavathi K. Deepa Cheruku Poorna Venkata Srinivasa Rao S. Raja Gopal P. Rajasekar Ashok Kumar Department of Artificial Intelligence Vidya Jyothi Institute of Technology Hyderabad Telagana India Department of Computer Science and Artificial Intelligence SR University Kavaraipettai Hasanparthy Telangana India Department of Information Technology Laki Reddy Balireddy College of Engineering Mylavaram Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Computer Science Banasthali Vidyapith Rajasthan India

The current severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) public health catastrophe, both human lives have been lost and the economy has disrupted severely the current scenario. In this paper, we develop a detection module using a series of steps that involves pre-processing, feature extraction and detection of covid-19 patients based on the images collected from the computerized tomography (CT) images. The images are initially pre-processed and then the features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then finally classified using back propagation neural network (BPNN). The simulation is conducted to test the efficacy of the model against various CT image datasets of numerous patients. The results of simulation shows that the proposed method achieves higher detection rate, and reduced mean average percentage error (MAPE) than other existing methodologies.

关键词： COVID-19 Training Support vector machines Computed tomography Computational modeling Predictive models Sensitivity and specificity

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Efficient Sampling Algorithms for Approximate Motif Counting in Temporal Graph Streams

arXiv

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arXiv 2022年

作者： Wang, Jingjing Wang, Yanhao Jiang, Wenjun Li, Yuchen Tan, Kian-Lee Department of Mathematics and Computer Science Changsha University Changsha China School of Data Science and Engineering East China Normal University Shanghai China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Computing and Information Systems Singapore Management University Singapore Singapore School of Computing National University of Singapore Singapore Singapore

A great variety of complex systems ranging from user interactions in communication networks to transactions in financial markets can be modeled as temporal graphs, which consist of a set of vertices and a series of timestamped and directed edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs which take into account edge orderings and durations in addition to topologies. Counting the number of occurrences of temporal motifs is a fundamental problem for temporal network analysis. However, existing methods either cannot support temporal motifs or suffer from performance issues. Moreover, they cannot work in the streaming model where edges in a temporal graph are observed incrementally over time. In this paper, we focus on approximate temporal motif counting via random sampling. We first propose two sampling algorithms for approximate temporal motif counting in the offline setting, where the whole temporal graph is available in advance and can be kept in main memory. The first is a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif and the second is an improved edge-wedge sampling (EWS) algorithm that hybridizes edge sampling with wedge sampling for counting temporal motifs with 3 vertices and 3 edges. Furthermore, we propose two algorithms to count temporal motifs incrementally in temporal graph streams by extending the ES and EWS algorithms, which are referred to as the SES and SEWS algorithms, respectively. We provide comprehensive analyses of the theoretical bounds and complexities of our proposed algorithms. Finally, we perform extensive experimental evaluation of our proposed algorithms on several real-world temporal graphs. The results show that ES and EWS have higher efficiency, better accuracy, and greater scalability than state-of-the-art sampling methods for temporal motif counting in the offline setting. What is more, SES and SEWS further achieve up to three orders of

关键词： Graphic methods

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Permutation polynomials x~(2k+1+3)+ax2k+2+bx over F_(22k) and their differential uniformity

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science China(information sciences) 2020年第10期63卷 255-257页

作者： Jie PENG Lijing ZHENG Chunsheng WU Haibin KAN Mathematics and Science College Shanghai Normal University School of Mathematics and Physics University of South China Department of Mathematics Lianyungang Normal University Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Fudan-Zhongan Joint Laboratory of Blockchain and Information Security Shanghai Blockchain Engineering Research Center Shanghai Institute for Advanced Communication and Data Science Shanghai Institute of Intelligent Electronics & Systems

Dear editor,A polynomial f（x）∈Fq[x] is called a permutation polynomial （PP） over the finite field Fq if the associated mapping f:c （?） f（c） from Fq to itself is bijective.A PP f is called a complete permutation polynomial if f（x）+x is a *** over finite fields of even characteristic have wide applications,including cryptography,coding theory,and communication *** many block ciphers with substitution-permutation network structure,the substitution box is usually a PP over F_(22k)for some positive integer *** resist the differential attacks,the differential uniformity of this

关键词： Finite fields Permutation polynomial Binomial Trinomial Differential uniformity

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Real-Valued Group Testing for Quantitative Molecular Assays 26th

Real-Valued Group Testing for Quantitative Molecular Assays

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26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Saeedi, Seyran Serrano, Myrna Yang, Dennis G. Brooks, J. Paul Buck, Gregory A. Arodz, Tomasz Department of Computer Science College of Engineering Virginia Commonwealth University RichmondVA United States Department of Microbiology and Immunology School of Medicine Virginia Commonwealth University RichmondVA United States Department of Mathematics College of Arts and Sciences Drexel University PhiladelphiaPA United States Department of Information Systems School of Business Virginia Commonwealth University RichmondVA United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States Center for Microbiome Engineering and Data Analysis Virginia Commonwealth University RichmondVA United States

Combinatorial group testing and compressed sensing both focus on recovering a sparse vector of dimensionality n from a much smaller number m © 2022, The Author(s), under exclusive license to Springer Nature Switz... 详细信息

ISBN: (纸本)9783031047480

关键词： SARS

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The intersection of video capsule endoscopy and artificial intelligence: Addressing unique challenges using machine learning

arXiv

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arXiv 2023年

作者： Guleria, Shan Schwartz, Benjamin Sharma, Yash Fernandes, Philip Jablonski, James Adewole, Sodiq Srivastava, Sanjana Rhoads, Fisher Porter, Michael Yeghyayan, Michelle Hyatt, Dylan Copland, Andrew Ehsan, Lubaina Brown, Donald Syed, Sana Rush University Medical Center Department of Internal Medicine ChicagoIL60607 United States University of Virginia Systems and Information Engineering CharlottesvilleVA22903 United States University of Virginia Department of Pediatrics CharlottesvilleVA22903 United States University of Virginia Data Science Institute CharlottesvilleVA22903 United States

Introduction: Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process. Methods: An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning. Results: When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well. Discussion: Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved. © 2023, CC BY.

关键词： Endoscopy

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Research on recognition method of liner wear stage based on operation and vibration information of semi-autogenous mil

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Minerals engineering 2025年 232卷

作者： TianQing Li Dakuo He Shuiqing Yu College of Information Science and Engineering Northeastern University Shenyang 110819 China College of Information Science and Engineering National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang 110819 China

The wear stage of the liner is of great significance to the production and maintenance of the mill. However, it is difficult to monitor the wear stage of the liner in real time. Therefore, this study developed a method to recognize the wear stage of semi-autogenous(SAG) mill liner on line. The pulp density is an important characteristic variable for reflecting liner wear stage. In view of the difficulty of on-line detection, this paper presents a method to calculate the pulp density of SAG mill under the condition that the product fineness index is qualified and the filling level of the internal grinding medium(steel ball) is stable. On account of the characteristics of nonlinear and non-stable significant changes in mill vibration information, this paper uses mode decomposition method(EMD, VMD) and principal component analysis (PCA) method to screen and reduce for the important vibration modal characteristics. Combined with the important operating parameters of the mill (feed ore and water), multiple groups of deep learning algorithms based on the recognition model of liner wear stage are established. In response to the actual need to recognize the stage of severe liner wear, an accurate recognition method of critical interval of liner wear stage is proposed to achieve accurate recognition of severe wear stage of liner based on the performance analysis results of several liner wear stage recognition models.

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