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Emerging Systems and Intelligent computing (ESIC), International Conference on

作者： Bishnu Pada Saha Satya Ranjan Dash Mainak Bandyopadhyay Paola Barra Vinh Truong Hoang School of Computer Engineering KIIT D.U. Bhubaneswar India School of Computer Applications KIIT D.U. Bhubaneswar India Department of Science and Technology University of Naples “Parthenope” Italy Faculty of Information Technology HCMC Open University Vietnam

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Attention mechanisms in vision applications of the computer world dynamically adjust feature weights to amplify the model’s capability to perceive information. This adaptive recalibration improves the overall effectiveness of the model. Spatial attention has seen widespread use to boost the efficiency of convolutional neural networks by enhancing their ability to model spatial information. This approach significantly improves CNNs’ spatial representation capabilities. Along this line of research, a novel binarized-attention module is drafted by taking spatial attention into consideration. Binary neural networks, which significantly reduce storage and computational requirements, are a promising approach for deploying deep models on resource-constrained devices. This technique offers efficiency in scenarios with limited resources. In this paper, we hereby implemented our algorithm of binarizing the spatial attention module and obtained diverse findings, demonstrating significant improvements in accuracies with fewer classes and degradation of accuracies with higher classes. Various experimental findings reveal that our technique delivers better accuracy with dataset having 10 classes but low accuracy with the one having 100 classes. Additionally, architectural optimizations, including the adoption of deeper networks like ResNet50, contribute to better handling of complex data patterns. While binarization offers computational efficiency, it often results in a trade-off with accuracy. However, selective binarization of specific network layers can mitigate this issue, maintaining a balance between performance and resource utilization. This study provides insights into the synergistic effects of these techniques, offering a pathway for developing efficient and accurate deep learning models suitable for deployment in resource-constrained environments.

关键词： Deep learning Adaptation models Visualization Attention mechanisms Accuracy Computational modeling Convolutional neural networks Resource management Optimization Residual neural networks

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Survey on Near-Space Information Networks: Channel Modeling, Transmission, and Networking Perspectives

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IEEE Communications Surveys and Tutorials 2025年

作者： Cao, Xianbin Su, Xiaoning Yang, Peng Gao, Yue Wu, Dapeng Oliver Quek, Tony Q. S. Beihang University School of Electronic Information Engineering Beijing100191 China Beihang University School of Electronic Information Engineering China Pengcheng Laboratory China State Key Laboratory of CNS/ATM Beijing100191 China Fudan University School of Computer Science Shanghai200433 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Near-space information networks (NSINs) composed of high-altitude platforms (HAPs) and high-and low-altitude unmanned aerial vehicles (UAVs) are a new regime for providing quick, robust, and cost-efficient sensing and communication services. Precipitated by innovations and breakthroughs in manufacturing, materials, communications, electronics, and control techniques, NSINs have been envisioned as an essential component of the emerging sixth-generation of mobile communication systems. This article reveals some critical issues needing to be tackled in NSINs through conducting experiments and discusses the latest advances in NSINs in the research areas of channel modeling, transmission, and networking from a forward-looking, comparative, and technical evolutionary perspective. In this article, we highlight the characteristics of NSINs and present the promising use cases of NSINs. The impact of airborne platforms’ unstable movements on the phase delays of onboard antenna arrays with diverse structures is mathematically analyzed. The recent advances in HAP and UAV channel modeling are elaborated on, along with the significant differences between HAP and UAV channel modeling. The promising techniques and communication protocols of the physical (PHY) layer, medium access control (MAC) layer, network layer, and transport layer of NSINs for achieving efficient transmission over NSINs are reviewed. A comprehensive review of the networking techniques of NSINs in network deployment, handoff management, and network management aspects is provided. Besides, experiments have been conducted using practical NSINs to verify the performance of some techniques. Finally, we outline some open issues and promising directions for NSINs deserved for future study and discuss the corresponding challenges. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Joint Optimization of Communication Latency and Platoon Control Based on Uplink RSMA for Future V2X Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Gang Hu, Jiewen Ma, Zheng Fan, Pingzhi Yu, F. Richard Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As an effective solution to address road congestion and improve traffic efficiency, vehicle platooning has received a lot of attention in recent years. However, vehicle-to-vehicle (V2V) communication latency can largely impact on the performance of vehicle platooning, which has not been well investigated. Rate-splitting multiple access (RSMA), as one of the promising technologies for 6 G, holds great potential for application in vehicle platooning to reduce the communication latency and thereby enhance platoon control performance. Motivated by the above considerations, platoon control and communication latency are jointly considered in this paper. We first introduce the communication latency into the vehicle dynamics model, and then derive a distributed model predictive control (DMPC)-based platoon control model. To reduce the communication latency, uplink RSMA is introduced for inter-platoon V2V communication. Since the latency minimization problem is non-convex and challenging to solve directly, we first employ a bisection method to give a latency upper bound and transform the initial problem into a non-convex feasibility problems in each iteration. Subsequently, through theoretical derivation, the bandwidth allocation factor is expressed in terms of transmission power, which reduces the number of optimization variables and transforms the problem into a convex problem. Then we can obtain the optimal solution by solving a convex problem in each iteration. Finally, the simulation results demonstrate that the proposed RSMA significantly outperforms traditional frequency division multiple access (FDMA) and non-orthogonal multiple access (NOMA), and can significantly reduce communication latency, enhance platoon control safety, improve driving comfort, and lower the performance requirement on vehicle maximum torque. © 2025 IEEE.

关键词： Traffic congestion

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RLG-RAG: Guiding the Knowledge Retrieval and Evaluation in Retrieval-Augmented Generation Framework by Reasoning Logic 25

RLG-RAG: Guiding the Knowledge Retrieval and Evaluation in R...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Kehan Xu Kun Zhang Wei Huang Jingyuan Li Yuanzhuo Wang School of Information Science and Engineering Yanshan University Qinhuangdao China Pattern Recognition Center Tencent WeChat AI Beijing China School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China

ISBN: (纸本)9798400713316

The knowledge retrieval, integration, and evaluation processes in the RAG method lack the guidance of reasoning logic, leading to ongoing challenges in maintaining factual consistency. To address these issues, this paper proposes the RLG-RAG framework, which constructs a reasoning graph based on user queries to guide the knowledge retrieval, integration, and evaluation processes. By fully representing the reasoning logic of RAG, RLG-RAG dynamically models and integrates knowledge relationships during retrieval and defines a precise scope of relevant knowledge through sufficiency evaluation. This reduces inference-irrelevant knowledge that large language models may obtain. Experimental analyses on accuracy, factual consistency, and robustness demonstrate that RLG-RAG resists interference and provides accurate, factually consistent answers. The project URL is https://***/10.5281/zenodo.14852250.

关键词： knowledge evaluation

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Rate Outage Constrained Energy Efficiency Under MISO Interference Channels

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IEEE Transactions on Wireless Communications 2025年

作者： Lu, Yang Chi, Chong-Yung Ai, Bo Zhong, Zhangdui Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operationand also with the School of Computer Science and Technology Beijing100044 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China

This paper investigates the rate outage constrained (ROC) energy efficiency (EE) under multiple-input single-output (MISO) interference channels, where only channel distribution information (CDI) is available at base stations (BSs). An EE maximization problem is formulated under the constraints of tolerable rate outage probability of each user and the power budget of each BS. Due to the computationally intractable form of the considered problem, two equivalent formulations are derived and four algorithms are proposed, where two are based on the block successive upper bound minimization (BSUM) method and the other two are based on the successive convex approximation (SCA) method. Further, the philosophies behind the BSUM-based algorithms and the SCA-based algorithms are analyzed. Numerical results demonstrate the efficacy of the proposed algorithms, while the BSUM-based algorithms are much more computationally efficient than all the state-of-the-art algorithms, as long as all the coupling requirements on quality of service (like ROC transmission) can be fused into EE. Besides, the impacts of power budget, circuit power and tolerable outage probability on the ROC EE are revealed and discussed. © 2025 IEEE. All rights reserved.

关键词： Budget control

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Cutting Force Prediction in Drilling Operations Using Temporal Convolutional Networks (TCN) and Spindle Speed Data

Cutting Force Prediction in Drilling Operations Using Tempor...

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Automation and Computation (AUTOCOM), International Conference on

作者： Dilli Ganesh V Mohsin Ikram Saif O. Husain Jayanti Ballabh T. Mohanraj K Aanandha Saravanan Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences- (SIMATS) Chennai India School of Agriculture Uttaranchal University Dehradun India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of ECE Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The prediction of forces in drilling is vital for enhancement of machining process, reduction of tool wear rates, and quality improvement of the final product. The present work introduces a new method for force estimation based on Temporal Convolution Networks (TCNs) and trained with spindle speed data. TCNs take advantage of the ability to process sequential data that perform well in capturing short-term and long-term dependencies, and hence are used in time-series analysis to analyze machining environments that are ever changing. The process is real-time acquisition of spindle speed and cutting force signals, filtering of signals to remove noise, and feature extraction for training of the models. To make correct predictions while making efficient computations, a TCN architecture that has been specifically customized with causal and dilated convolutions is used. The proposed model has MAE = 2.15 N and 98.7 % inclusive squared coefficient of determination, which is significantly better than traditional machine learning models like ANN, SVM, and LSTM and faster on inference time. Indeed, all of the present model's validation has been done across different spindle speeds and for different types of materials. Also, the TCN model implementation into real-time monitoring allows optimizing the modelling parameters in proximity to changing conditions, which is a feature of industry 4.0. It also strongly supports the practice of sustainable manufacturing, which in turn improves the efficiency of machining. High value is credited to the potential of TCNs as a transformative tool in intelligent machining systems.

关键词： Drilling Support vector machines Convolution Computational modeling Force Machine learning Real-time systems Data models Convolutional neural networks Long short term memory

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Correction: Divine Religions algorithm: a novel social-inspired metaheuristic algorithm for engineering and continuous optimization problems

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Cluster computing 2025年第5期28卷 1-2页

作者： Mozhdehi, Ali Toufanzadeh Khodadadi, Nima Aboutalebi, Mohaddeseh El-kenawy, El-Sayed M. Hussien, Abdelazim G. Zhao, Weiguo Nadimi-Shahraki, Mohammad H. Mirjalili, Seyedali Faculty of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Civil Architectural and Environmental Engineering University of Miami Coral Gables USA Department of Computer Engineering Azad University of Lahijan Lahijan Iran School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic Isa Town Bahrain Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer and Information Science Linköping University Linköping Sweden Faculty of Science Fayoum University Fayoum Egypt School of Water Conservancy and Hydropower Handan China Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley Australia University Research and Innovation Center (EKIK) Obuda University Budapest Hungary

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Utilizing Transforming Portfolio Management Through Automation Using Advanced Deep Reinforcement Learning Algorithms for Optimized Investment Strategies

Utilizing Transforming Portfolio Management Through Automati...

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Automation and Computation (AUTOCOM), International Conference on

作者： D David Winster Praveenraj Hemalatha D Mohammed Al-Farouni S. Priya B. Arunkumar Prakhar Mittal School of Business and Management CHRIST University Bangalore India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq KCG College of Technology Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of IT Atricure Mason Ohio

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

This paper focuses on the future possibility of enhancing the applications of DRL in autonomously managing a portfolio for better investment plans. Having used past financial data and a highly developed case of DRL, the proposed system shows better performance compared to conventional investment strategies and indices. This process includes data gathering from the financial databases, the steps of preprocessing and feature extraction, and the use of the DQN structure. After that, the system's training and validation are done by a finite portion of real-world data and a large number of synthesized data to improve stability. The result shows that the new method achieves superior cumulative return, Sharpe ratio, maximum drawdown, and annualized volatility; therefore, it suggests that the proposed system can flexibly predict the fluctuating stock market trends and make appropriate investment decisions. Thus, the present research adds importance to the use of DRL in improving return potential and risk management in portfolio management. Thus, this study adds to the existing literature and practice by allowing for the automation of the optimization and testing for investment solutions at a larger scale, while opening up opportunities for future developments in the application of financial technology and investment tools.

关键词： Training Automation Machine learning algorithms Stability criteria Deep reinforcement learning Risk management Stock markets Portfolios Optimization Investment

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Development of Lightweight and Cheaper 5G Mobile Communication System to Analyze the Performance of Espresso Ciphers and Grain Family 2nd

Development of Lightweight and Cheaper 5G Mobile Communicati...

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2nd International Conference on Cyber Warfare, Security and Space computing, SpacSec 2024

作者： Kamalanathan, C. Balamurugan, J. Sharma, Neelam Reddy, A. Basi Selvan, R. Senthamil Department of Electrical Electronics and Communication Engineering GITAM Deemed to Be University Bengaluru Campus Karnataka Bengaluru India Department of Master of Business Administration St.Joseph’s College of Engineering OMR Tamilnadu Chennai India Department of Physical Education Lovely Professional University Punjab India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Annamacharaya Institute of Technology and Sciences Andhra Pradesh Tirupati India

ISBN: (纸本)9783031734939

A significant difficulty in a resource-constrained setting is keeping an optimal sense of balance among safety and other presentation parameters such as memorial need, energy requirement and throughput. In its most recent study on lightweight cryptography, the National Institute of Standards and Technology (NIST) recommended symmetric cyphers for devices with little resources. This work presents statistical security studies of six modern stream ciphers: Espresso, Grain v1, Sprout, Fruit, Plantlet, and Lizard. The analyses were conducted using the following tests: avalanche, randomness, structure, and autocorrelation. After porting the improved code to a microcontroller in low-cost, that is the ATmega 328P, they have also conducted a thorough performance study of these ciphers. A device’s suitability for use in an IoT-based network is a deciding factor in its selection. According to statistical security, performance measurements, and comparative analysis, the chosen ciphers are good choices for situations with limited resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 5G mobile communication systems

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Designing Quantum-Safe Lattice-Based Multi-Authority CP-ABE Scheme for Blockchain-Enabled IoT-Based Consumer Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Bagchi, Prithwi Bisht, Abhishek Das, Ashok Kumar Saxena, Neetesh Hossain, M. Shamim International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Cardiff University School of Computer Science and Informatics CardiffCF24 4AG United Kingdom King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh13273 Saudi Arabia

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications. © 2025 IEEE.

关键词： Quantum cryptography

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