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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Department of Computer Science and Software Engineering Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9783031496134

Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. With the increasing interest in interconnection networks, an extensive amount of research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this paper, we will mainly focus on determining the broadcast time and the optimal broadcasting scheme for graphs. Determination of the broadcast time of a node x in an arbitrary network G is known to be NP-hard. Polynomial time solutions are known only for a few classes of networks. There also exist various heuristic and approximation algorithms for different network topologies. In this paper, we will consider networks that can be represented as k-path graphs. We will present a polynomial time 2-approximation algorithm for the broadcast time problem in k-path graphs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Information dissemination

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Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications

IEEE Networking Letters

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IEEE Networking Letters 2022年第4期4卷 174-178页

作者： Gupta, Rishabh Saxena, Deepika Gupta, Ishu Makkar, Aaisha Singh, Ashutosh Kumar National Institute of Technology Department of Computer Applications Kurukshetra136119 India National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan University of Derby Department of Computer Science and Engineering DerbyDE22 1GB United Kingdom

This letter proposes a novel malicious user prediction model based on quantum machine learning that estimates the vicious entity present in the communication system precedently before allocating the data in the distributed environments. The proposed model scrutinizes the behavior of each user and estimates probable data breaches using a developed malicious user predictor unit. The model computes essential scores associated with each user request for the learning process of the prediction unit by generating training samples. The predictor unit exploits the computational and behavioral properties of Qubits and Quantum gates for the accurate prediction of the malicious user with high precision to grant access to non-malicious data requests only. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it significantly improves the security of the system up to 33.28%. © 2019 IEEE.

关键词： Forecasting

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Improving Performance of Vision Transformer on Skin Lesion Detection Leveraging Domain Associated Transfer Learning

Improving Performance of Vision Transformer on Skin Lesion D...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Rafi, Abdullah Hasan Sajjad Das, Arkadeep Reza, Md Tanzim Brac University Department Of Computer Science & Engineering kha-208 1 Bir Uttam Rafiqul Islam Ave Dhaka1212 Bangladesh

ISBN: (纸本)9798350371567

To prevent lethal consequences, identification and early detection of different skin lesions are required. However, lots of expertise and expenditure are needed to detect them in the initial stages. Therefore, the requirement of an automated system that can detect and classify the types of skin lesions from image data is necessary. The proposed research is an attempt to design such an automated system which is based on a recently published dataset namely Hospital Italiano de Buenos Aires - Skin Lesions Images (2019-2022), consisting of 1616 images in total. This research proposes a noble approach that uses transfer learning and image augmentation to classify different types of skin lesions from image data in the most accurate way. Initially the base vision transformer model (ViT) of Google was used on the dataset and the resultant accuracy was low (0.70) because of imbalanced class distribution and lack of samples. Afterward, the domain-associated transfer learning approach was used through the HAM10000 Dataset by training the base ViT model with it and the model with the best validation accuracy was saved. In the next step, fine-tuning was done on the pretrained model by the actual dataset again and the validation accuracy improved by 4% from 70% to 74%, with increased recall values for some of the classes. Thus, the proposed approach showcases the usefulness of using domain-associated transfer learning for skin lesion detection. © 2024 IEEE.

关键词： Image enhancement

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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CIOD: an intelligent class-incremental object detection system with nearest mean of exemplars

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10657-10671页

作者： Ren, Sheng He, Yan Wang, Xiaokang Guo, Kehua Barra, Silvio Li, Jianqi School of Computer and Electrical Engineering Hunan University of Arts and Science Dongting Avenue Hunan Changde415000 China School of Computer Science and Engineering Central South University Lushan South Road Hunan Changsha410083 China Department of Computer Science St. Francis Xavier University University Avenue 1-902-863-3300 AntigonishNS Canada Department University of Naples "Federico II" Via Claudio 21 Campania Naples80125 Italy

Object detection has been widely used in intelligent video surveillance, robot navigation, industrial detection, and other fields. Object detection can effectively reduce the consumption of human capital and has important practical significance. However, the existing object detection methods lack adaptability to the application environment and need to obtain all images classes at one time to train the model in a static setting, and do not support the incremental learning. Therefore, we propose a novel Class-Incremental Object Detection (CIOD) framework based on deep learning. CIOD divides object detection into two stages: object candidate box generation and selection. In the first stage, we improved the traditional OpenCV cascaded classifier to adapt to class-incremental learning while maintaining accuracy. In the second stage, we use example sets and prototype vectors to construct a class increment-based classifier to identify the generated object candidate box. We verify the effectiveness of the proposed method in terms of object detection effect, efficiency, and memory capacity. Experimental results show that CIOD can detect object in a class-incremental manner and can control the memory capacity not to increase with the number of newly increased classes. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Intelligent systems

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CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID-19 Detection in CT-Scans 4th

CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Das, Ankit Banik, Debapriya Roy, Kaushiki Chan, Gordon K. Bhattacharjee, Debotosh Department of Computer Science and Engineering Amity University Kolkata700135 India Department of Computer Science and Engineering ICFAI University Tripura799210 India Institute of Engineering and Management Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Oncology and the Department of Experimental Oncology Cross Cancer Institute University of Alberta EdmontonABT6G 1Z2 Canada Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

ISBN: (纸本)9789819726103

The coronavirus disease, COVID-19, that grasped the earth since 2019, has taken a serious toll on humanity. COVID-19 cases were spiking exponentially in densely populated regions, and the health infrastructure was challenged daily. As such, Artificial Intelligence (AI) had to step in to improvise medical imaging applications. One such use case was the classification of computerized Tomography (CT) Scan images into COVID-infected and un-infected categories. Thus it would effectively classify a patient being either COVID Positive or Negative. computer Vision (CV) algorithms manifest the potential to solve such image classification problems. In retrospect, deep learning-based architectures have been very productive. However, Vision Transformers (ViT) have taken over CV since its inception. In this study, we propose CoViT-Net: A pre-trained hybrid Vision Transformer architecture for detecting COVID-19 in CT scan images. Our proposed CoViT-Net has a unique architecture incorporating a ViT-S/16 feature extractor, pre-trained on the ImageNet21k dataset, and a modified MLP block with dense layers on top. CoViT-Net has thereby achieved a state-of-the-art accuracy score of 0.9638. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Efficient Path and Action Planning Method for Multi-Agent Pickup and Delivery Tasks under Environmental Constraints

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SN computer science 2023年第1期4卷 83页

作者： Yamauchi, Tomoki Miyashita, Yuki Sugawara, Toshiharu Department of Computer Science and Communications Engineering Waseda University 3-4-1 Okubo Tokyo Shinjuku-ku 1698555 Japan

We propose a method called path and action planning with orientation (PAPO) that efficiently generates collision-free paths to satisfy environmental constraints, such as restricted path width and node size, for the multi-agent pickup and delivery in non-uniform environment (N-MAPD) problem. The MAPD problem, wherein multiple agents repeatedly pick up and carry materials without collisions, has attracted considerable attention;however, conventional MAPD algorithms assume a specially designed environment and thus use simple, uniform models with few environmental constraints. Such conventional algorithms cannot be applied to realistic applications where agents need to move in more complex and restricted environments. For example, the actions and orientations of agents are strictly restricted by the sizes of agents and carrying materials and the width of the passages at a construction site and a disaster area. In our N-MAPD formulation, which is an extension of the MAPD problem to apply to non-uniform environments with constraints, PAPO considers not only the path to the destination but also the agents’ direction, orientation, and timing of rotation. It is costly to consider all these factors, especially when the number of nodes is large. Our method can efficiently generate acceptable plans by exploring the search space via path planning, action planning, and conflict resolution in a phased manner. We experimentally evaluated the performance of PAPO by comparing it with our previous method, which is the preliminary version of PAPO, the baseline method in a centralized approach, and fundamental meta-heuristic algorithms. Finally, we demonstrate that PAPO can efficiently generate sub-optimal paths for N-MAPD instances. © 2022, The Author(s).

关键词： Environmental constraints Multi-agent path finding Multi-agent pickup and delivery problem Multi-agent planning Robot planning

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Single-Phase Direct PWM Bipolar Buck–Boost AC–AC Converter Without Commutation Problem

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2023年第6期11卷 5940-5953页

作者： Ahmed, Hafiz Furqan Ali Khan, Ashraf Abdoli, Iman Zaabi, Omar Al the Department of Electrical Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan the Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X7 Canada the Department of Electrical Engineering Shahid Beheshti University Tehran19839-69411 Iran Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

— The direct pulsewidth modulation (PWM) ac–ac converters are seeing rapid development due to their single-stage power conversion with reduced footprints, due to the elimination of intermediate dc-link capacitor. However, they only provide unipolar voltage gain and fixed frequency operation and use the bidirectional voltage-blocking devices without natural inductor current free-wheeling path, restricting their practical applications. This article proposes a direct PWM bipolar and step-variable frequency buck–boost ac–ac converter. The proposed converter is fabricated by connecting input–output LC filters and a small value film capacitor to six unidirectional voltage-blocking devices, which are arranged as a three-leg inverter module. It can provide noninverting and inverting buck–boost voltage operations with a symmetric bipolar voltage gain of ±d/(1 − d) and can also change the frequency of output voltage in discrete steps, same as that of single-phase matrix converters (MCs). It has no open-circuit risk of inductor due to the presence of natural current free-wheeling path during PWM dead time. Therefore, it is unaffected by the commutation problem of conventional direct ac–ac converters. The proposed converter can realize high-quality input and output due to the presence of second-order input–output filters with continuous input–output currents. A detailed analysis is provided based on proposed switch modulation strategies and confirmed through extensive experimental tests on a laboratory-fabricated hardware. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Matrix converters

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A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 462-473页

作者： Bingyun Li Qinmin Yang Innocent Kamwa IEEE the College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Huzhou Institute of Zhejiang University China the Department of Electrical and Computer Engineering Laval UniversityQuebec CityQCG1V 0A6Canada

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this *** this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg *** holds a large-scale ESS that is shared among users in the form of energy *** sells energy to users and sets the selling price *** maximizes its profit through optimal pricing and ESS *** purchase some energy from operator for the reduction of their demand charges after operator's selling price is *** game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization *** upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'*** bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing *** studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

关键词： Bi-level optimization demand charge energy storage system(ESS)sharing energy transaction mathematical program with equilibrium constraints(MPEC) stackelberg game

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Time-Division Online Speculative Inference for Cost-Effective Intelligent Internet of Things 10

Time-Division Online Speculative Inference for Cost-Effectiv...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Du, Yao Wang, Zehua Leung, Cyril Leung, Victor C.M. The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9798350373011

Intelligent Internet of Things (IIoT), a network paradigm, is an interconnection of intelligent edge devices, empowered by machine learning models. The recent emergence of large language models (LLMs) opens a new path towards IIoT. Although device models are generally lightweight and suitable for edge devices with limited processing power, their performance is less impressive compared to LLMs running on large servers. Server models offer superior performance with the cost of significant computational resources for model inference. To realize cost-effective IIoT, we introduce a novel collaborative inference framework: the online speculative inference enabled by device collaboration and time divisions. We aim to find the minimum number of servers required for the immediate processing of arrived inference requests. To solve the problem, we propose an evolving directed acyclic graph along with a Proof-of-Deletion mechanism for cost reduction and privacy protection. Based on computer simulations using heterogeneous LLMs, it is found that time-division online speculative inference is a promising approach towards cost-effective IIoT. © 2024 IEEE.

关键词： Adversarial machine learning

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