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IEEE International Conference on Systems, Man and Cybernetics

作者： Sevil Zanjani Miyandoab Shahryar Rahnamayan Azam Asilian Bidgoli Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada

A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, feature selection should aim at both minimizing the number of selected features and maximizing the accuracy of classification, or any other task. However, this crucial task is computationally highly demanding on many real-world datasets and requires a very efficient algorithm to reach a set of optimal features with a limited number of fitness evaluations. For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems. To the best of our knowledge, the proposed algorithm in this paper is the first multi-objective coordinate search algorithm. In this method, we generate new individuals by flipping a variable of the candidate solutions on the Pareto front. This enables us to investigate the effectiveness of each feature in the corresponding subset. In fact, this strategy can play the role of crossover and mutation operators to generate distinct subsets of features. The reported results indicate the significant superiority of our method over NSGA-II, on five real-world large-scale datasets, particularly when the computing budget is limited. Moreover, this simple hyper-parameter-free algorithm can solve feature selection much faster and more efficiently than NSGA-II.

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A Pairwise Surrogate Model using GNN for Evolutionary Optimization

A Pairwise Surrogate Model using GNN for Evolutionary Optimi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Vida Gharavian Shahryar Rahnamayan Azam Asilian Bidgoli Masoud Makrehchi Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada

Optimization problems widely arise in various science and engineering fields and can be computationally expensive in many real-world applications. Evaluation of the fitness function to assess a candidate solution is the main operation in all optimization procedures which can be heavily compute-intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to consequently reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison despite the fact that most of the surrogate models try to predict the exact fitness value. The proposed idea can significantly help the optimizer to reach better results in a shorter period of time. It seems comparing two candidate solutions for a greedy selection is much easier than approximating fitness values for both. We demonstrated Graph Neural Network (GNN) for this purpose to be trained on a limited number of pairwise ranks and then utilized to compare a pair of candidate solutions. In order to examine the efficacy of our model, we utilized different well-known single-objective optimization benchmarks in dimensions 10,20, and 30. Moreover, the results of the learning-based evaluation are compared with the results from the real fitness evaluation. The results, assessed in terms of the number of fitness calls and the best-found solution, showed that the proposed method is able to decrease the computing cost of fitness evaluation significantly while we achieve a comparable solution. Our model can be tested with any optimization algorithm which employs a comparison-based mechanism among its candidate solutions.

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Quan Zhang Lei Wang Vishal M. Patel Xiaohua Xie Jianhuang Lai School of Computer Science and Engineering Sun Yat-Sen University China Department of Electrical and Computer Engineering Johns Hopkins University USA Pazhou Lab (HuangPu) Guangdong China Guangdong Province Key Laboratory of Information Security Technology Guangdong China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： computer vision Cameras Transformers Pattern recognition Computational complexity Identification of persons

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Neuromorphic Auditory Perception by Neural Spiketrum

arXiv

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arXiv 2023年

作者： Tang, Huajin Gu, Pengjie Wijekoon, Jayawan Alsakkal, M.H.D. Anas Wang, Ziming Shen, Jiangrong Yan, Rui The State Key Lab of Brain-Machine Intelligence College of Computer Science Zhejiang University Hangzhou310027 China The College of Computer Science Zhejiang University Hangzhou310027 China The Department of Electrical & Electronic Engineering University of Manchester United Kingdom The College of Computer Science Zhejiang University of Technology Hangzhou310014 China

Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intelligence, it needs to solve the challenges of the neuromorphic hardware architecture design of biological neural substrate and the hardware amicable algorithms with spike-based encoding and learning. Here we introduce a neural spike coding model termed spiketrum, to characterize and transform the time-varying analog signals, typically auditory signals, into computationally efficient spatiotemporal spike patterns. It minimizes the information loss occurring at the analog-to-spike transformation and possesses informational robustness to neural fluctuations and spike losses. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks. We further investigate the algorithm-hardware co-designs through a neuromorphic cochlear prototype which demonstrates that our approach can provide a systematic solution for spike-based artificial intelligence by fully exploiting its advantages with spike-based computation. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural networks

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SmartTrap: An On-Field Insect Monitoring System Empowered by Edge Computing Capabilities

SmartTrap: An On-Field Insect Monitoring System Empowered by...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Quan M. Nguyen Duy A. Pham Dong T. Pham An D. Le Nguyen Q. H. Vo Hien B. Vo School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam Joint Lab Artificial Intelligence and Data Science Universität Osnabrück Osnabrück Germany Graduate School of Bioresource and Bioenvironmental Sciences Kyushu University Fukuoka Japan Department of Electrical and Computer Engineering University of California San Diego San Diego USA Department of Electrical and Computer Engineering Vietnamese-German University Binh Duong Vietnam

Fruit flies pose a significant threat to fruit yields, necessitating immediate detection solutions for effective pest management. In this study, we present our approach using YOLOv7 and the Jetson Nano 4GB for rapid and accurate fruit fly detection. Our methodology attains an Average Precision (AP) of 0.993 at an Intersection over Union threshold (IoU) of 0.75, facilitating almost instantaneous enumeration of ensnared fruit flies in field conditions, thereby augmenting the efficacy of pest management practices. Concurrently, the system transmits this data to either a local or cloud-based server, assisting end-users in accurately identifying regions with elevated fruit fly densities to strategically administer pesticides. This targeted approach has significant implications for sustainable agriculture as it reduces the need for widespread spraying. Moreover, our versatile method adapts for detecting other pests like aphids or beetles, serving as a valuable tool in agriculture and beyond.

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Expression of Concern for: Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

Expression of Concern for: Medical Image Fusion Solid Works ...

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： Rakhi Mutha Devendra Nath Pathak Vandana Ahuja Prabhakar Ramesh Bhandari Pankaj Rahi Udit Mamodiya Department of Computer Application Lovely Professional University Punjab India Department of Computer Science and Engineering Poornima College of Engineering Jaipur Rajasthan India Department of Computer Science and Engineering Swami Vivekanand Institute of Engineering and Technology Punjab INDIA Department of Microbiology Sevadal Mahila Mahavidyalaya Umred Road Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur Rajasthan India Department of Electrical Engineering Coordinator at PIET AICTE IDEA LAB Poornima Institute of Engineering and Technology Jaipur Rajasthan India

ISBN: (数字)9781665491990

ISBN: (纸本)9781665492003

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.

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Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

Medical Image Fusion Solid Works Supporting Multiple Techniq...

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

关键词： Technological innovation Three-dimensional displays Image resolution Deformation Transforms Solids Object recognition

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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

arXiv

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arXiv 2022年

作者： Rauniyar, Ashish Haileselassie, Desta Jha, Debesh Håkegård, Jan Erik Bagci, Ulas Rawat, Danda B. Vlassov, Vladimir SINTEF Digital Trondheim7034 Norway Department of Electrical Engineering and Computer Science Howard University WashingtonDC United States Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University United States Department of Computer Science School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

With the advent of the Internet of Things (IoT), Artificial intelligence (AI), Machine Learning (ML), and Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable FL models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifyin

关键词： Medical applications

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Perceptual attacks of no-reference image quality models with human-in-the-loop 22

Perceptual attacks of no-reference image quality models with...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Weixia Zhang Dingquan Li Xiongkuo Min Guangtao Zhai Guodong Guo Xiaokang Yang Kede Ma MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Network Intelligence Research Department Peng Cheng Laboratory Department of Computer Science and Electrical Engineering West Virginia University Department of Computer Science City University of Hong Kong

ISBN: (纸本)9781713871088

No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA models are extensively studied in computational vision, and are widely used for performance evaluation and perceptual optimization of man-made vision systems. Here we make one of the first attempts to examine the perceptual robustness of NR-IQA models. Under a Lagrangian formulation, we identify insightful connections of the proposed perceptual attack to previous beautiful ideas in computer vision and machine learning. We test one knowledge- driven and three data-driven NR-IQA methods under four full-reference IQA models (as approximations to human perception of just-noticeable differences). Through carefully designed psychophysical experiments, we find that all four NR- IQA models are vulnerable to the proposed perceptual attack. More interestingly, we observe that the generated counterexamples are not transferable, manifesting themselves as distinct design flows of respective NR-IQA methods.

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