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IEEE Conference on Decision and Control

作者： Zhuangzhuang Ma Bowen Li Jinliang Shao Yuhua Cheng Wei Xing Zheng School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

This paper investigates the bipartite flocking behavior of multi-agent systems with coopetition interactions, where communications between agents are described by signed digraphs. The scenario with switching topologies due to the movement of agents, and time delays caused by the limited data transmission capability, is considered comprehensively. Nonlinear weight functions are designed to describe the relationship between the communication distance of agents and the coopetition degree in real biological networks. A distributed update rule based on the neighbors' information and the designed weight functions is proposed. By the aid of the graph theory and sub-stochastic matrix properties, the effectiveness of the proposed update rule is proved theoretically, and the algebraic conditions for achieving the bipartite flocking behavior are obtained. Finally, the theoretical results are verified by numerical simulations.

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Method for creating privacy-preserving information using Probabilistic Latent Semantic Analysis

Method for creating privacy-preserving information using Pro...

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IEEE International Conference on Big data

作者： Yuki Sugawara Eiichi Sakurai Yoichi Motomura Yukihiko Okada Kai Tanabe Akiko Tsukao Shinya Kuno National Institute of Advanced Industrial Science and Technology Koto Japan University of Tsukuba Tsukuba Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Koto Japan Institute of Systems and Information Engineering Center for Artificial Intelligence Research University of Tsukuba Tsukuba Japan Faculty of Health and Sport Sciences R&D Center for Smart Wellness City Policies University of Tsukuba Tsukuba Japan Tsukuba Wellness Research Co. Ltd Kashiwa Japan

In recent years, rapid digitization and technological advances have enabled governments and companies to utilize various types of personal information, including environment, medical, and lifestyle. Privacy protection is an important issue in the utilization of personal information, and anonymization methods such as k-anonymization and 1-diversity have been proposed in the past. However, to utilize data effectively, it is also important not to lose the usefulness of the information. Privacy protection and preserving the usefulness of information are in a trade-off relationship, and balancing these two aspects has been a difficult problem. Recently, microaggregation using clustering algorithms has been attracting attention as an anonymization method that ensures the usefulness of the data while protecting privacy. In this study, we proposed a new microaggregation method based on iterative processing using probabilistic latent semantic analysis and evaluated the proposed method based on the use case. The use case is a classification of social isolation and an analysis of the relationship with physical frailty risk using the classified groups. Compared to microaggregation using quasi-identifiers and microaggregation based on a conventional clustering algorithm, the proposed method was able to find social isolation characteristics obtained in the raw data with higher accuracy. In addition, in a more detailed analysis focusing on a population with certain characteristics, the proposed method was able to replicate the analysis results obtained using the raw data and to make appropriate assessments of the health risks.

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A Successful Spam Detection Technique for Industrial IoT Devices based on Machine Learning Techniques

A Successful Spam Detection Technique for Industrial IoT Dev...

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Applied artificial intelligence and Computing (ICAAIC), International Conference on

作者： B N Manjunatha Madhavarapu Chandan Sreekanth Kottu Sherin Rappai Hema Priya K Kishan Singh Rawat Swagata Sarkar Department of Computer Science and Engineering R L Jalappa Institute of Technology Bangalore Karnataka India Department of Computer science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Information Technology St Peter's Engineering College Medchal-Malkajgiri Telangana India Department of Computer Science Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science and Engineering Panimalar Institute of Technology Chennai Tamil Nadu India Department of Civil Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Tamil Nadu India

This research study discusses about the detection of botnets that use the Internet Relay Chat protocol. This application protocol, popularly used by programs in large chat rooms is the predominant protocol among botnet Command and Control Servers, although its use has declined in recent years. This study analyzed the use of Machine Learning techniques for the detection of Botnets based on the IRC protocol in a network, through the analysis of bidirectional flows. Similar research studies are becoming increasingly relevant to the use of networked devices that can be integrated into botnets by further increasing the power of their attacks. As a result, it is possible to conclude that this research study accomplished its objectives by investigating the application and efficiency of different algorithms, as well as the usage of characteristics employed in previous studies and explores new possibilities. According to the results, the tested classifiers were effective in completing their tasks and achieving high levels of accuracy. The best results were obtained by the Tree-based approaches., followed by the Support Vector Machine methods, and finally by the Naive Bayes method.

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To What Extent Do Disadvantaged Neighborhoods Mediate Social Assistance Dependency? Evidence from Sweden

arXiv

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arXiv 2022年

作者： Lin, Cheng Daoud, Adel Brandén, Maria Institute for Analytical Sociology Department of Management and Engineering Linköping University Norrköping Sweden The Division of Data Science and Artificial Intelligence for the Social Sciences Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Sociology Stockholm University Stockholm Sweden

Occasional social assistance prevents individuals from a range of social ills, particularly unemployment and poverty. It remains unclear, however, how and to what extent continued reliance on social assistance leads to individuals becoming trapped in social assistance dependency. In this paper, we build on the theory of cumulative disadvantage and examine whether the accumulated use of social assistance over the life course is associated with an increased risk of future social assistance recipiency. We also analyze the extent to which living in disadvantaged neighborhoods constitutes an important mechanism in the explanation of this association. 17-year Swedish register data allow us to apply causal mediation analysis, and thereby quantify the extent to which the likelihood of ending up in social assistance dependency is affected by residing in disadvantaged neighborhoods. Our findings show the accumulation of social assistance over the studied period is associated with a more than the four-fold increase on a risk ratio scale for future social assistance recipiency, compared to never having received social assistance during the period examined. Our results suggest that the indirect effect of disadvantaged neighborhoods is weak to moderate. Therefore, social assistance dependency may be a multilevel process. © 2022, CC BY.

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Survey on deep learning in multimodal medical imaging for cancer detection

arXiv

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arXiv 2023年

作者： Tian, Yan Xu, Zhaocheng Ma, Yujun Ding, Weiping Wang, Ruili Gao, Zhihong Cheng, Guohua He, Linyang Zhao, Xuran School of Computer Science and Technology Zhejiang Gongshang University Hangzhou310018 China School of Mathematical and Computational Sciences Massey University Auckland0632 New Zealand School of Information Science and Technology Nantong University Nantong226019 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325000 China Jianpei Technology Co. Ltd Hangzhou311200 China

The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer vision

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WaterTS: Integrating Enhanced Transformer, Sliding Block, and Channel Independence for Long-term Water Quality Prediction

WaterTS: Integrating Enhanced Transformer, Sliding Block, an...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Jing Bi Lifeng Xu Ziqi Wang Haitao Yuan Shichao Chen Mu Gu MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and Beijing Huairou Academy of Parallel Sensing Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Nowadays, the deterioration of water resources leads to negative ecological impacts. To effectively inhibit the deterioration of water resources, a water quality prediction model based on enhanced transformer, sliding block, and channel independence (WaterTS) is proposed by comprehensively analyzing the indicators of water resources and making long-term predictions of the dissolved oxygen index. WaterTS adopts a sliding block method to extract the short-term temporal features of the water quality series and combine them with channel independence to make independent predictions of multi-featured data. Moreover, it upgrades the internal encoder structure of the transformer and improves the attention mechanism to Probsparse-attention and Auto-Correlation to speed up the prediction speed. Furthermore, Post LayerNormal is adjusted to Pre LayerNormal, which makes the training gradient more stable and enhances the accuracy of predictions. Experiments are conducted using real-world water environment data, and comparison results with state-of-the-art prediction models show that the WaterTS achieves accurate predictions on both short-term and long-term water quality data.

关键词： Training Accuracy Biological system modeling Time series analysis Water quality Predictive models Transformers Feature extraction Real-time systems Water resources

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Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

arXiv

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arXiv 2024年

作者： Shokry, Ahmed Gomaa, Walid Zaenker, Tobias Dawood, Murad Menon, Rohit Maged, Shady A. Awad, Mohammed I. Bennewitz, Maren Humanoid Robots Lab Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany Cyber Physical Systems Lab Egypt Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt Mechatronics Department Ain Shams University Cairo Egypt

Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task parameters, such as the hole position and orientation, resulting from sensor noise. Although context-based meta reinforcement learning (RL) methods have been previously presented to adapt to unknown task parameters in PiH assembly tasks, the performance depends on a sample-inefficient procedure or human demonstrations. Thus, to enhance the applicability of meta RL in real-world PiH assembly tasks, we propose to train the agent to use information from the robot’s forward kinematics and an uncalibrated camera. Furthermore, we improve the performance by efficiently adapting the meta-trained agent to use data from force/torque sensor. Finally, we propose an adaptation procedure for out-of-distribution tasks whose parameters are different from the training tasks. Experiments on simulated and real robots prove that our modifications enhance the sample efficiency during meta training, real-world adaptation performance, and generalization of the context-based meta RL agent in PiH assembly tasks compared to previous approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Proceedings of the Fortieth Conference on Uncertainty in artificial intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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Sentinel Rover: Cutting-Edge Wireless Mine Detection and Alert System for High-Risk Terrains

Sentinel Rover: Cutting-Edge Wireless Mine Detection and Ale...

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Self Sustainable artificial intelligence Systems (ICSSAS), International Conference on

作者： R. Hemalatha K. Sangeethalakshmi M. Venkatesan D. Anitha C. Srinivasan Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

The “Sentinel Rover (SR)” is a state-of-the-art, wirelessly operated land rover intended to revolutionize mine clearance operations in high-risk terrains. The rover, equipped with cutting-edge sensor technology, seeks to detect and warn of planted land explosives, mitigating dangers to human personnel. It can transverse rocky terrains and withstand potential explosives due to its robust chassis and agile mobility. The rover's sensor suite includes metal detectors, ground-penetrating radar, thermal imaging, acoustic and optical sensors, ensuring comprehensive mine detection. data processing and fusion methods increase precision and decrease false positives. When the system detects a potential land mine, it transmits alerts to remote operators or command centers via various communication technologies. With safety mechanisms to prevent accidental detonations and non-ignitable components, the SR operates with the highest dependability and durability. While the automaton is vital, human expertise is essential for mission planning and decision-making. The SR provides a safer and more effective method for addressing land mine hazards.

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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