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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Kumari, K. Aruna Soujanya, Thota Alsalami, Zaid Rohini, I. Bala Dhandayuthapani, V. Srkr Engineering College Department of Cse Chinaamiram Bhimavaram India School of Agricultural Sciences Sr University Telangana Warangal India The Islamic University Najaf Iraq Malla Reddy Engineering College for Women Dept of Eee Telangana India College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Branch Department of It Oman

ISBN: (纸本)9798350382952

Load Balancing (LB) of the tasks on Cloud computing (CC) is the significant characteristics of distribution of the resources from data center. LB has to be taken out in such a way that every Virtual Machines (VMs) must have balanced the load to attain an optimal utilization of its capabilities. As an outcome, the LB of tasks on VM is examined as a significant thing of task scheduling in the cloud. The LB in cloud faced various challenges like fault tolerance as well as data security. In this research, Fire Hawk Optimization (FHO) algorithm is proposed for the dynamic LB in cloud environment. The aim of this research is to enhance the system performance and it considers the multi-objective functions named Makespan, Resource Utilization (RU), Response Time (RT), Degree of Imbalance as well as throughput. The experimental results proven that the proposed FHO-based LB had obtained the 2246, 6.7, 134.23, 141 and 5.5 in the number of tasks of 1500 using nakespan, RT, RU, DOI and throughput. These results show better enhancement when compared to the previous approaches like Particle Swarm Optimization (PSO), Q-learning Modified PSO (QMPSO) and Binary JAYA algorithm. © 2024 IEEE.

关键词： Cloud computing

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ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension 29

ET5: A Novel End-to-end Framework for Conversational Machine...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhang, Xiao Huang, Heyan Chi, Zewen Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China

Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning;(2) span extraction if required by the above decision;(3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Decision making

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Deploying Hybrid MAELM Approach for Human Emotion Detection Through Speech and Facial Expressions 3

Deploying Hybrid MAELM Approach for Human Emotion Detection ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Senthil Kumar, K. Rukmani Devi, S. Ranjan, Nidhi Rath, Gitika Indira, G. Nishant, Neerav SRM Institute of Science and Technology School of Computing Department of Networking and Communications College of Engineering and Technology Chennai India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India Computer Engineering Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai India Marri Laxman Reddy Institute of Technology and Management Department of CSE Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350343632

Emotion recognition is one of the most fascinating emerging fields of research. It's useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots' ability to see and communicate with one another. Human emotions can be recognized through both visual and audible cues. Facial expressions are one of the most accurate indicators of a person's emotional state. Data preprocessing, feature extraction, and model training are the first three steps of the proposed methodology. Image resizing, a median filter, noise removal, and histogram equalization are all components of preprocessing. Gaussian mixture models and the gray level co-occurrence matrix are being used for feature extraction. After the extraction of features, models with those features are trained using MA-ELM. The proposed method outperforms PCA and ELM, two of the most popular alternatives. © 2023 IEEE.

关键词： Gaussian Mixture Models(GMM) Gray Level Co-Occurrence Matrix (GLCM) Principal Component Analysis (PCA)

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Mitigating Poisoning Attacks in Federated Learning Through Deep One-Class Classification

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhang, Anqi Zhao, Ping Lu, Wenke Zhou, Yipeng Zhang, Wenqian Zhang, Guanglin Donghua University College of Information Science and Technology Shanghai201620 China Ministry of Education Engineering Research Center of Digitized Textile and Apparel Technology Shanghai201620 China Macquarie University School of Computing Faculty of Science and Engineering 2122 Australia

Federated learning (FL) enables clients to learn a machine learning model collaboratively without sharing their private local data to the server. However, due to its distributed structure, FL is vulnerable to poisoning attacks where adversaries intentionally send the poisoned local model parameters to the server and further affect the behavior of the global model. Existing works on mitigating poisoning attacks in FL are difficult to accurately characterize data relationships of high-dimensional parameters. Moreover, they cannot guarantee Byzantine robustness when the majority of clients are compromised (Byzantine ratio) and clients’ local datasets are highly non-independent and identically distributed (non-IID). In this paper, we conduct a pioneering work to introduce deep one-class classification into mitigating poisoning attacks in FL, which can guarantee Byzantine robustness even under a Byzantine ratio and non-IID degree greater than 0.5 with the assistance of a root dataset. Our key idea is to adequately learn vector features of benign local model parameters using Deep Support Vector Data Description (Deep SVDD) and achieve the optimal classification by training a deep learning-based one-class classifier equipped with a proper decision boundary based on the root dataset. To further optimize the classifier, we employ a regularizer based on random noise injections, which can address the hypersphere collapse problem inherent in Deep SVDD. Exhaustive experiments on MNIST, F-MNIST, and CIFAR-10 demonstrate that compared with five typical Byzantine-robust methods, our defense strategy achieves excellent effectiveness in mitigating targeted/untargeted poisoning attacks and an adaptive attack in FL. Even with a similar but different root dataset, it still maintains a good Byzantine robustness. © 2015 IEEE.

关键词： Federated learning

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Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Con...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Huarong Shan, Jinjun Li, Xing Yu, Hongnian School of Internet of Things Engineering Jiangnan University Wuxi214122 China York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada School of Electrical Engineering and Intelligentization Dong-guan University of Technology Dongguan523000 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9781665410205

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches. © 2024 IEEE.

关键词： Sliding mode control

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An Analysis on the Power Consumption of Nodes in Ad hoc IEEE 802.11 Networks 3

An Analysis on the Power Consumption of Nodes in Ad hoc IEEE...

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Moyeenudin, H.M. Narender, M. Mukunthan, M.A. Raman, Kanimozhi Balasubramanian, D. Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai600117 India Tkr College of Engineering &technology Hyderabad India School of Computing Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai600062 India Kcg College of Technology Department of Mathematics Karapakkam Chennai600 097 India Jaya Engineering College Tamilnadu Thiruninravur602024 India

ISBN: (纸本)9798350322842

This article analyzes the power consumption of nodes in ad hoc IEEE 802.11 networks. The main objective of this analysis is to discover theoretic restrictions for the gains obtained in node lifetime through different energy conservation techniques found in the literature. The evaluation of energy consumption considers the characteristics of the medium access method and the interaction of the nodes in the packet forwarding process. The basic idea is to regulate the lifetime of a node based on its average consumption. Consumption is estimated based on the amount of time the node remnants transmitting, receiving or idle. The potential gains obtained by different techniques for saving or balancing energy consumption are then analysed. It is shown that the flow balancing technique can achieve a maximum gain for the node lifetime between 11% and 30%, depending on the network configuration. © 2023 IEEE.

关键词： Electric power utilization

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EV User’s Charging Behaviour and its Impact on the Power Grid – A Case Study in Singapore

EV User’s Charging Behaviour and its Impact on the Power Gr...

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IEEE Power India Conference

作者： Jiaxin Dong Siti Zuhairah Binte Borhan Anurag Sharma Dhivya Sampath Kumar Nandini Anant B. Sivaneasan Sanchari Deb Nosheen Khan Engineering Cluster Singapore Institute of Technology Singapore Electrical Power Engineering Newcastle University in Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore School of Engineering Newcastle University United Kingdom Newcastle University Business School Newcastle University United Kingdom

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

Singapore is increasingly embracing greener transportation options, marked by a growing number of Electric Vehicles (EVs) on its roads. Understanding the charging behavior of EV drivers is crucial to preparing Singapore’s power grid for the additional load. A survey was conducted to identify the factors influencing drivers' decisions to charge their vehicles. The collected data was analyzed using the statistical package for the social sciences (SPSS) to pinpoint dependent and independent variables affecting these decisions. Additionally, the study examined the impact of EV charging on the power grid and explored the potential of bi-directional chargers to mitigate peak load consumption. Survey findings reveal that cost-sensitive individuals tend to charge during off-peak hours (12 AM to 6 AM), while those less concerned with costs charge based on convenience and immediate need.

关键词： Surveys Vehicle-to-grid Renewable energy sources Costs Power system dynamics Bidirectional control Electric vehicle charging Power grids Timing Vehicle dynamics

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Deep Learning ResNet-50 Framework for Automatic Early Breast Cancer Diagnosis

Deep Learning ResNet-50 Framework for Automatic Early Breast...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Ashwini, A. Suji, M. Angel Merlin Prathaban, Banu Priya Shahila, D. Ferlin Deva Jency Rubia, J. Kumar, Narla Vinod Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Department Of Ece Chennai India St. Xavier's Catholic College Of Engineering Department of CSE Kanyakumari Nagercoil India School Of Computing Srm Institute Of Science And Technology Department Of Networking And Communication Kattankulathur Chennai India

ISBN: (纸本)9798350352689

The most serious disease that affect the women worldwide is breast cancer. Early identification of breast cancer contributes to lower death rates. Through the process of histopathological examination, a pathologist looks at the tissues' microscopic features to find different malignant patterns. However, the process of manual histological analysis is challenging, unreliable, and time-consuming. Therefore, in order to determine the malignancy, a computer-aided system created to analyze these images must take these two magnifications into account completely. As a result, this research suggests a distinct deep neural algorithms for the categorization of breast cancer histological images, designed to imitate a pathologist. ResNet-50 focuses on possible locations that are relevant for the precise classification of tumor images by utilizing global and local information in an integrated manner. Preprocessing of the images with nuclei is therefore regarded as the first stage in this procedure. To get an equalized preprocessing result, the input images are subjected to Color normalization. After additional processing, these pictures are divided into two group called benign and malignant. The efficacy of the suggested models in improving nuclei is demonstrated by experimental evaluation on both public and private datasets. The experimental evaluation shows the best result with the proposed research technique. © 2024 IEEE.

关键词： Diseases

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Research on the Co-Creation Construction Model of Digital Education Resources in Internet+ Era 23

Research on the Co-Creation Construction Model of Digital Ed...

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7th International Conference on Education and Multimedia technology, ICEMT 2023

作者： Chen, Ruxian Li, Xufeng Bian, Buxing Wang, Jie Cai, Ruiqi Zhang, Jing Department of Computer Science Software Engineering Institute of Guangzhou China Wushen Banner High School Nei Monggol China Leshan Normal University Teaching Departmen China Institute of Computing Science and Technology Guangzhou University China

ISBN: (纸本)9798400709142

With the development of Internet+, an open and innovative educational ecosystem is forming. The combination of Internet and education can greatly promote the integration and utilization of high-quality education resources. Firstly, we analyze the main problems in the construction of digital education resources. Secondly, we explore the co-creation construction model of high-quality mathematical resources among government, enterprises and schools, including the main elements of the model, the connection between the elements and the related mechanism support. Finally, it takes NetPad as an example to carry out practical research on the co-creation of digital education resource in the field of basic education, improving the construction and service level of high-quality digital education resources. © 2023 ACM.

关键词： E-learning

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Lite-ODNet: A Lightweight Object Detection Network

Lite-ODNet: A Lightweight Object Detection Network

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Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR), International Conference on

作者： Wenbang Peng Li Zhang Liaoying Zhao Xiaorun Li School of Computing Science and Technology Hangzhou Dianzi University Hangzhou China Department of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331534035

ISBN: (纸本)9798331534042

Most existing object detection models generally have a large number of parameters and high hardware requirements. How to balance high detection accuracy with low computational demand? The study presents Lite-ODNet, a Lightweight Object Detection Network, for efficient object detection. More precisely, it presents a network that delivers excellent detection performance while utilizing fewer hardware resources. Lite-ODNet is composed of a designed Enhanced ShuffleNetV2 (E-ShuffleNetV2), an Efficient Feature Pyramid Network (EffiFPN) with structural re-parameterization and a Simplified Head (S-Head). The model is validated after being tested on VOC07+12, VisDrone2019, and WiderPerson datasets. The findings demonstrate that the proposed method outperforms current leading algorithms, achieving the highest performance in terms of #Param., mAP, FLOPs, and FPS. Finally, this proposed method also reaches an equivalent performance to existing methods in the challenging small objects detection task.

关键词： Human computer interaction Accuracy Computational modeling Object detection Hardware Human activity recognition Artificial intelligence Robots

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