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Communications and Information Technologies (GCCIT), Global Conference on

作者： Shrinivas Patil V.K. Kolekar K. Ranjith Singh Pratibha Sharma Hariharasudhan S Shubhi Goyal School of Management - PG Management JAIN Deemed to be University Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

In this article, we present a generic inference hybrid structure for Convolutional Recurrent Neural Network (conv-RNN) text semantic modelling, combining the strengths of convolutional and recurrent neural network structures in extracting distinct linguistic features. In addition to the conv-RNN-based sentence matching as well as classification models, we also offer a new sentence classification model as well as an attention-based response selection model with improved performance. Two difficult answer selection (AS) tasks & five standard datasets for sentence classification (SC) are used to verify the suggested models. As far as we are aware, these are the most comprehensive comparison findings in AS and SC to date. We demonstrate the superiority of conv-RNN over other state-of-the-art methods experimentally on a variety of hard tasks and benchmark datasets. Deep training-based architectures outperform ensemble learning methods and supervised training approaches for the sentiment analysis issue in educational data mining. Overall classification accuracy was 95.80% for long-term short-term memory networks using representations built upon the GloVe word-embedding method, making them the most efficient prediction arrangement.

关键词： Training Text mining Representation learning Sentiment analysis Recurrent neural networks Semantics Memory management Feature extraction Convolutional neural networks Standards

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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Optimizing Congestion Control in Wireless Sensor Networks Using a Trust-Based, Security-Aware Approach

Optimizing Congestion Control in Wireless Sensor Networks Us...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： S. Sathiya Naveena Abhinav Mishra Xavier V K P.R. Rege D. Anandhasilambarasan Divya Sharma Department of MBA Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Both random events and self-centred nodes may trigger network congestion. In Wireless Sensor Networks (WSNs), bad nodes may cause congestion-like conditions by discarding data packets or transmitting duplicate, fraudulent packets. So, congested mitigation strategies alone won't guarantee level delivery. Consequently, isolating these points from the rest of the network is an essential first step in preventing this kind of trouble. Current work on congestion management solutions relies on the unrealistic and mistaken premise that every node is genuine and behaves correctly. To address this, researchers have developed a Security-Aware Congestion Control (SACC) algorithm, which accounts for both the usual and unexpected causes of congestion and how malicious nodes might prevent packets from receiving their intended recipients. These networks parameters—nodes' spare energy, reservoir utilization, and delivery of packets ratio—have been chosen for the purpose of establishing trust indicators because of the critical roles they play in detecting malicious behaviour and avoiding overcrowding. The suggested method outperforms similar current systems in energy efficiency, throughput management, and security, all thanks to its robust ability to resist several internal assaults. The suggested technique, Security-Aware Congestion Control (SACC), demonstrates its remarkable efficacy in guaranteeing safe and effective data transfer in wireless sensor networks with an astounding 96% accuracy rate. SACC performs better than other models like Low-Energy Adaptive Clustering Hierarchy (LEACH), Trust-based Congestion Control and Energy Efficiency Routing (TCEER), Secure Quality of Experience Energy Efficient Routing (SQEER) indicating its capacity to manage congestion and lessen the effect of rogue nodes, providing a more dependable and secure solution.

关键词： Wireless sensor networks Resists Routing Throughput Reservoirs Energy efficiency Sensors Security Reliability Quality of experience

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Improved TF-IDF Algorithm Combined with Multiple Factors

Improved TF-IDF Algorithm Combined with Multiple Factors

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International Conference on Applied Machine Learning (ICAML)

作者： Zhe Zhang Zhifeng Wu College of computer technology and Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9781665421263

The TF-IDF algorithm is often used for the extraction of keywords of articles, but it only considers the information of word frequency, which limits the choice of keywords. In order to improve the efficiency of the algorithm, an improved algorithm (TF-IDFIS) has been presented,which adds the synonyms of keywords trained by word2vec model to the word vector composed of keywords. Then the improved algorithm is given different weights based on the part of speech and the location information(TF-IDFISW). In order to verify the effectiveness of the improved algorithm, experiments were conducted on a standard data set. The experimental results show that the accuracy of TF-IDFIS and TF-IDISW is greatly improved compared with the traditional method.

关键词： Machine learning algorithms Text categorization Machine learning Classification algorithms Data mining Standards

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Semantic Communication-Empowered Physical-layer Network Coding

Semantic Communication-Empowered Physical-layer Network Codi...

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IEEE Conference on Wireless Communications and Networking

作者： Shuai Yang Haoyuan Pan Tse-Tin Chan Zhaorui Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China School of Science and Engineering Future Network of Intelligence Institute The Chinese University of Hong Kong (Shenzhen) Shenzhen China

In a two-way relay channel (TWRC), physical-layer network coding (PNC) doubles the system throughput by turning superimposed signals transmitted simultaneously by different end nodes into useful network-coded information (known as PNC de-coding). Prior works indicated that the PNC decoding performance is affected by the relative phase offset between the received signals from different nodes. In particular, some "bad" relative phase offsets could lead to huge performance degradation. Previous solutions to mitigate the relative phase offset effect were limited to the conventional bit-oriented communication paradigm, aiming at delivering a given information stream as quickly and reliably as possible. In contrast, this paper puts forth the first semantic communication-empowered PNC-enabled TWRC to address the relative phase offset issue, referred to as SC-PNC. Despite the bad relative phase offsets, SC-PNC directly extracts the semantic meaning of transmitted messages rather than ensuring accurate bit stream transmission. We jointly design deep neural network (DNN)-based transceivers at the end nodes and propose a semantic PNC decoder at the relay. Taking image delivery as an example, experimental results show that the SC-PNC TWRC achieves high and stable image reconstruction quality under different channel conditions and relative phase offsets, compared with the conventional bit-oriented counterparts.

关键词： Degradation Semantics Streaming media Network coding Turning Throughput Reliability engineering

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Quantifying the Benefits of Verifiable Security Schemes in Communications Software Design

Quantifying the Benefits of Verifiable Security Schemes in C...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Lakshya Swarup Richa Tiwari Dattatraya Babanrao Nalawade N. Thangarasu Rajat Saini A Keerthika Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The increasing use of communications software programs for replacing sensitive data requires cozy designs that prevent malicious actors from viewing or tampering with data. Verifiable security schemes offer an additional layer of assurance for communications software program designs; quantifying the benefits of such schemes is crucial for assessing their practicality. This work proposes a prevalent framework for quantifying the blessings of verifiable security schemes in communications software layout. The framework evaluates three factors: facts leakage, overhead price, and reliability. The usage of information theoretical metrics inclusive of Shannon entropy and mutual records quantifies facts leakage. Overhead value is evaluated at the protocol and machine tiers, taking account of metadata and associated computational prices. Reliability is classified by way of looking at device disruption and denial of carrier attack risks and is, for this reason, calculated on the grounds of gadget availability. A simulation-based evaluation is supplied to demonstrate the effectiveness of the proposed framework. The proposed framework offers a complete assessment of verifiable protection schemes, permitting selection makers to greater as it should be compare extraordinary device designs and pick out the most suitable one for their utility. This will, in turn, contribute to improving the safety and robustness of the verbal exchange software program layout.

关键词： Software design Protocols Layout Reliability theory Software Robustness Software reliability Security Protection Standards

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Multi Objective Optimization Based Land Cover Classification Using NSGA-II

Multi Objective Optimization Based Land Cover Classification...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ajay Kumar Singh Arpit Jain Divya Yogesh Kumar Sharma Senthil Athithan Savya Sachi University Institute of Engineering & Technology Chandigarh University Mohali Punjab India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science Shah Satnam Ji Girls' College Affiliated to Chaudhary Devi Lal University Sirsa Haryana India Department of Information Technology Lalit Narayan Mishra College of Business Management Muzaffarpur Bihar India

The current investigation makes use of a technique referred to as with multiple goals ounces-based land cover classification in order to identify the several forms of land use that are present in a certain area. For the purpose of forecasting the characteristics, the NSGA-II framework is going to be used, and it is going to offer accurate findings. The K-means Clustering method is applied for determining how the Land should be categorized. The tried and true strategy of Pareto Search was utilized for the land cover classification that was carried out in order to make comparisons more straightforward. The data revealed that NSGA-II attained an acceptable degree of performance when contrasted with the results of the Pareto Search.

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Secure Relay Selection for UAV-empowered Two Way Untrusted Relaying Networks

Secure Relay Selection for UAV-empowered Two Way Untrusted R...

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Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Kan Yu Zhiyong Feng Lin Yang Dianxia Chen Dong Li Xiaoshan Ma The School of Computer Science and Engineering Macau University of Science and Technology Taipa P. R. China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing P.R. China School of Computer Science and Engineering Macau University of Science and Technolog China The National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute AMS PLA Beijing P.R. China The Education Tianjin Ren’ai College Tianjin P.R. China The School of Computer Science and Engineering Macau University of Science and Technology P. R. China The Academy of Military Sciences of PLA Beijing P.R. China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

In this paper, we examine the security aspects of two-way untrusted relaying networks (TWURNs) through the lens of physical layer security (PLS). These networks involve the exchange of confidential information between two multiple-antenna base stations (BSs) with the aid of unmanned aerial vehicle (UAV)-enabled untrusted relays equipped with a single antenna. Given the shortcomings observed in existing single untrusted relay selection methods, we introduce an enhanced approach grounded in the min-min criterion (MMC) based on received signal strength. Additionally, we present a novel closed-form expression for the secrecy outage probability (SOP), a fundamental metric within the domain of PLS, derived from the cumulative distribution function (CDF) and probability density function (PDF) of received signal strengths. Through simulations, we substantiate the validity of our analytical findings, demonstrating the superior efficacy of the proposed MMC compared to prevailing schemes.

关键词： Measurement Probability density function Probability Physical layer security Minimax techniques Power system reliability Relays

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TokenHPE: Learning Orientation Tokens for Efficient Head Pose Estimation via Transformers

TokenHPE: Learning Orientation Tokens for Efficient Head Pos...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Cheng Zhang Hai Liu Yongjian Deng Bochen Xie Youfu Li National Engineering Research Center for E-Learning Central China Normal University Wuhan China College of Computer Science Beijing University of Technology Beijing China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China Department of Mechanical Engineering City University of Hong Kong Kowloon Hong Kong China

Head pose estimation (HPE) has been widely used in the fields of human machine interaction, self-driving, and attention estimation. However, existing methods cannot deal with extreme head pose randomness and serious occlusions. To address these challenges, we identify three cues from head images, namely, neighborhood similarities, significant facial changes, and critical minority relationships. To leverage the observed findings, we propose a novel critical minority relationship-aware method based on the Transformer architecture in which the facial part relationships can be learned. Specifically, we design several orientation tokens to explicitly encode the basic orientation regions. Meanwhile, a novel token guide multiloss function is designed to guide the orientation tokens as they learn the desired regional similarities and relationships. We evaluate the proposed method on three challenging benchmark HPE datasets. Experiments show that our method achieves better performance compared with state-of-the-art methods. Our code is publicly available at https://***/zc2023/TokenHPE.

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Deep Learning Detection of Defect-Free Bangles: Machine Vision Approach for Quality Assurance in Bangle Manufacturing

Deep Learning Detection of Defect-Free Bangles: Machine Visi...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Helcy D. Alon Alvin Sarraga Alon Ryan S. Evangelista Michael Angelo Delos Santos Ligayo Ryan Reyes Michelle C. Reyes Jovencio Merin Yolanda D. Austria College of Engineering Batangas State University - TNEU Batangas City Philippines Digital Transformation Center Batangas State University - TNEU Batangas City Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Engineering Quezon City University Quezon City Philippines Dept. of Electrical Engineering Technological University of the Philippines Manila Philippines General Education Department FEU Nicanor Reyes Medical Foundation Quezon City Philippines Dept of Computer Engineering Adamson University Manila Philippines

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

This study explores the application of deep learning and machine vision methods to identify defect-free, circular bangles. The main objective is to improve quality control in the manufacturing of bangles. The study introduces a new model that achieves a remarkable mean Average Precision (mAP) score of 99.1%, showcasing its exceptional accuracy and efficiency in the identification and categorization of bangles. The model successfully differentiates between “good,” “defect,” and “broken” bangles, as evidenced by thorough testing and analysis. During the testing phase, the model regularly achieved a 99% average detection rate for recognizing “good” bangles, hence confirming its capacity to properly detect perfect bangles. Moreover, the model demonstrated remarkable competence in detecting “defect” bangles, achieving detection rates ranging from 98% to 97%, thereby emphasizing its efficacy in recognizing flawed goods. During the analysis phase, the model consistently and precisely recognized “defect” and “broken” bangles, achieving an average detection rate of 98%. This confirms its capability to effectively classify various types of bangles. The model's high mean average precision (mAP) value, along with its exceptional testing and analysis results, highlights its promise as a reliable and effective solution for quality control in bangle manufacturing applications. This study highlights the substantial advantages of utilizing machine vision and deep learning for accurate quality assurance in bangle manufacturing.

关键词： Deep learning Fault diagnosis Analytical models Accuracy Quality assurance Machine vision Quality control

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