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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Li, Li El Rami, Jean-Pierre S. Taylor, Adrian Rao, James Hailing Kunz, Thomas Defence Research & Development Canada Ottawa Canada Queen's University Dept of Electrical and Computer Engineering Kingston Canada Carleton University Dept. of Systems & Computer Engineering Ottawa Canada

ISBN: (纸本)9798350320282

This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment, namely the Cyber Gym for Intelligent Learning (CyGIL), is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S. From preliminary experimental results, CyGIL-S can train agents in minutes compared with the days required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly to CyGIL-E, showing full decision proficiency in the emulated 'real' network. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real in leveraging RL agents in real-world cyber networks. © 2022 IEEE.

关键词： Reinforcement learning

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Multi-stage biomedical feature selection extraction algorithm for cancer detection

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SN Applied Sciences 2023年第5期5卷 131页

作者： Keshta, Ismail Deshpande, Pallavi Sagar Shabaz, Mohammad Soni, Mukesh Bhadla, Mohit kumar Muhammed, Yasser Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Engineering Pune India Arba Minch University Arba Minch Ethiopia Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali140413 India Department of Information Technology Ahmedabad Institute of Technology Ahmedabad India College of Technical Engineering Al-Farahidi University Baghdad Iraq

Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained;in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced. © 2023, The Author(s).

关键词： Feature Selection

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An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1115-1138页

作者： Bingjie Li Guohua Wu Yongming He Mingfeng Fan Witold Pedrycz School of Traffic and Transportation Engineering Central South UniversityChangsha 410075China College of Systems Engineering National University of Defense TechnologyChangsha 410073China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada the Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia the Systems Research Institute Polish Academy of SciencesWarsaw 01447Poland IEEE

The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its *** existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing *** solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the *** paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step *** performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO ***,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms.

关键词： End-to-end approaches learning-based optimization(LBO)algorithms reinforcement learning step-by-step approaches vehicle routing problem(VRP)

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Converting Vocal Performances into Sheet Music Leveraging Large Language Models 24

Converting Vocal Performances into Sheet Music Leveraging La...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Jiang, Jinjing Teo, Nicole Pen, Haibo Ho, Seng-Beng Wang, Zhaoxia Mastercard Research and Development Singapore Singapore Singapore Management University School of Computing and Information Systems Singapore Singapore Tianjin University China School of Electrical and Information Engineering China AI Institute Global 139955 Singapore

ISBN: (纸本)9798331530631

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly in generating vocal melodies and simulating singing voices. While NLP techniques have been successfully used to analyze vocal performance data, providing insights into performance quality and style, the automatic transcription of vocal performances into sheet music remains a complex challenge. Existing tools for manual transcription are often insufficient due to the intricate dynamics of vocal expression. This study addresses the challenge of automating the conversion of vocal performances into sheet music using a combination of novel techniques, including large language models (LLMs). We present a method that successfully translates vocal audio input into display-ready sheet music. Our findings highlight the strengths and limitations of various approaches, particularly in the transcription of a cappella performances into notes and lyrics. This research contributes to the expanding field of NLP-driven music analysis and showcases the potential of these models to revolutionize vocal transcription in the future. © 2024 IEEE.

关键词： Natural language processing systems

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Spatial Attention-Enhanced Encoder-Decoder Network for Accurate Segmentation of the Prostate's Transition Zone 46

Spatial Attention-Enhanced Encoder-Decoder Network for Accur...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos S. Kalantzopoulos, Charalampos Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Biomedical Research Institute Forth Ioannina Greece Computational Biomedicine Laboratory Forth Heraklion Greece University of Ioannina Biomedical Research Institute Unit of Medical Technology and Intelligent Information Systems Forth Ioannina Greece

ISBN: (纸本)9798350371499

Accurate segmentation of the prostate and its substructures consist the most important component for reliable localization and characterization of prostate cancer. In this study a Spatial Attention Residual U-Net (Spatial ResU-Net) deep learning (DL) network is proposed for segmenting the transitional zone of the prostate, by leveraging the learning capacity of spatial attention modules and residual connections. Spatial attention modules efficiently extract features in intra-channel manner and boost the performance of encoder and decoder while residual connections facilitate the information flow within the different network's levels. The proposed model was compared against 8 state-of-the-art DL segmentation models demonstrating a superior performance. The improvement in terms of Sensitivity, Dice Score, Hausdorff distance and Average surface distance was at least 1%, 1%, 0.05 mm and 0.09 mm, respectively. © 2024 IEEE.

关键词： Urology

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction systems in High-Speed Trains

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Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax

Representation Learning on Graphs to Identifying Circular Tr...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Mehta, Priya Bhargava, Sanat Kumar, Sandeep Kumar, Ravi Babu, Ch. Sobhan Welingkar Institute of Management Development and Research Mumbai India Indian Institute of Technology Hyderabad Department of Computer Science and Engineering Sangareddy India

ISBN: (纸本)9781665480451

Circular trading is a form of tax evasion in Goods and Services Tax where a group of fraudulent taxpayers (traders) aims to mask illegal transactions by superimposing several fictitious transactions ( where no value is added to the goods or service) among themselves in a short period. Due to the vast database of taxpayers, it is infeasible for authorities to manually identify groups of circular traders and the illegitimate transactions they are involved in. This work uses big data analytics and graph representation learning techniques to propose a framework to identify communities of circular traders and isolate the illegitimate transactions in the respective communities. Our approach is tested on real-life data provided by the Department of Commercial Taxes, Government of Telangana, India, where we uncovered several communities of circular traders. © 2022 IEEE.

关键词： Taxation

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research on Tumor Images Segmentation based on Improved Convolutional Neural Network 24

Research on Tumor Images Segmentation based on Improved Conv...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud

Enhancement to Training of Bidirectional GAN : An Approach t...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Mehta, Priya Kumar, Sandeep Kumar, Ravi Babu, Ch. Sobhan Welingkar Institute of Management Development and Research Mumbai India Indian Institute of Technology Hyderabad Department of Computer Science and Engineering Sangareddy India

ISBN: (纸本)9781665480451

Outlier detection is a challenging activity. Several machine learning techniques are proposed in the literature for outlier detection. In this article, we propose a new training approach for bidirectional GAN (BiGAN) to detect outliers. To validate the proposed approach, we train a BiGAN with the proposed training approach to detect taxpayers, who are manipulating their tax returns. For each taxpayer, we derive six correlation parameters and three ratio parameters from tax returns submitted by him/her. We train a BiGAN with the proposed training approach on this nine-dimensional derived ground-truth data set. Next, we generate the latent representation of this data set using the encoder (encode this data set using the encoder) and regenerate this data set using the generator (decode back using the generator) by giving this latent representation as the input. For each taxpayer, compute the cosine similarity between his/her ground-truth data and regenerated data. Taxpayers with lower cosine similarity measures are potential return manipulators. We applied our method to analyze the iron and steel taxpayer's data set provided by the Commercial Taxes Department, Government of Telangana, India. © 2022 IEEE.

关键词： Taxation

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