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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Subbulakshmi, G. Sujatha, S. Saraswathi, V. Hemamlini, Ranganathan Rani St Joseph's College of Engineering and Technology Department of Artificial Intelligence and Data Science Engineering Chennai India Bit Campus Anna University Department of Computer Appication Tiruchirappalli India Chennai Institute of Technology Department of Computer Science Engineering Chennai India St. Peter's Institute of Higher Education and Research Department of Electrical and Elctronics Engineering Chennai India

ISBN: (纸本)9798331510022

The rapid evolution of solar energy in industrial sectors has sparked a growing interest in renewable energy, particularly within the context of smart grids and systems. The effective detection and management of energies within photovoltaic (PV) systems stand as crucial tasks in this landscape. This abstract introduces a proposed model aimed at exploring the integration of blockchain networks within large-scale solar energy infrastructures. The primary objective is to establish a framework that enables the distribution of solar energy in a decentralized manner to end-users while ensuring secure energy transactions between producers and consumers. Blockchain technology serves as the cornerstone of this model, encompassing intelligent techniques to address energy mitigation challenges within the solar energy network. By leveraging blockchain, the detection of energies within solar power plants becomes shared knowledge accessible to all users connected to the solar power network. The research within this framework emphasizes the exploration and application of diverse Deep Learning (DL) architectures, including autoencoders, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Deep Stacking Networks (DSNs), and Deep Belief Networks (DBNs). Furthermore, this research extends beyond conventional machine learning approaches, incorporating knowledge-driven methodologies. It incorporates generative models such as adversarial networks and transfer learning, elevating the scope of machine learning in optimizing energy detection, distribution, and consumption within the block chain-based solar energy infrastructure. This interdisciplinary approach signifies a significant stride in fortifying renewable energy systems. By amalgamating blockchain's decentralized nature with sophisticated DL architectures and knowledge-driven models, it establishes a resilient and efficient renewable energy infrastructure capable of meeting escalating demands for sustainabl

关键词： Adversarial machine learning

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A First Look at LLM-powered Smartphones

A First Look at LLM-powered Smartphones

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IEEE/ACM International Conference on Automated Software Engineering - Workshops (ASE Workshops)

作者： Liangxuan Wu Yanjie Zhao Chao Wang Tianming Liu Haoyu Wang Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology

ISBN: (数字)9798400712494

ISBN: (纸本)9798331523169

The integration of Large Language Models (LLMs) into edge devices such as smartphones represents a significant leap in mobile technology, promising enhanced user experiences and novel functionalities. This paper presents a first look at LLM-powered smartphones, addressing four key aspects: the current market landscape, core functions enabled by integrated LLMs, potential security risks, and user perceptions. The findings reveal a rapidly evolving market with major manufacturers competing to integrate LLMs, innovative features that improve user interaction, significant security challenges, and mixed user perceptions that balance enthusiasm for new capabilities with privacy concerns. This study contributes to understanding LLM integration in mobile devices and its implications for users, manufacturers, and the broader technological landscape.

关键词： Industries Privacy Technological innovation Ethics Large language models Conferences User experience Security Smart phones Software engineering

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Enhancing Micro-Expression Recognition with Broadbent Attention Mechanism: A High-Performance Approach for Emotion Detection 21

Enhancing Micro-Expression Recognition with Broadbent Attent...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ashraf, Imran Kehkashan, Tanzila Zaman, Muhammad Akhunzada, Adnan Talib, Hafiz Khizer Bin Cao, Yanlong University of Lahore Faculty of Computer Science Lahore Pakistan Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia COMSATS University Islamabad Department of Computer Science Islamabad Pakistan University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350378078

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and Stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with Stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Requirements Prediction for Medical Products Based on Named Entity Recognition 3

Requirements Prediction for Medical Products Based on Named ...

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3rd International Conference on Electronic Information Engineering and Computer science, EIECS 2023

作者： Ruan, Peiguang Liu, Ruanchong Guo, Bo Liu, Zhepeng Zhang, Chengying Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China Guangdong Pharmaceutical University The First Clinical Medicine School Guangzhou China College of Science and Technology School of Finance and Trade Zhuhai Zhuhai China Shantou University College of Engineering Shantou China Xihua University School of Management Chengdu China

ISBN: (纸本)9798350316674

Medical product demand forecasting is a necessary condition for building a medical intelligent system, and is an important means to alleviate the lack of medical resources, and the form of medical product demand is mainly unstructured text, including the number of medicines, medical record texts. These medical texts contain a large number of medical terms related to the patient health situation and medical terms are the most direct embodiment of medical knowledge. However, existing methods for requirements are concentrated on the neural network or machine learning algorithms, which ignores the tremendous information in the medical terms. In this work, we utilize the named entity recognition method to predict the demand of the medical products. Efficiently identifying these medical terms from medical texts is a key factor in ensuring the high performance of medical intelligence systems. Therefore, the goal of the medical named entity recognition task is to extract medically significant symptoms, medical history, diseases, etc. from medical texts and realize the downstream prediction task. From our extensive experiment simulation and comparison with existing prediction methods, we can conclude the proposed method can achieve the precise prediction accuracy with reasonable computation costs. © 2023 IEEE.

关键词： Computation Costs Medical Product Named Entity Recognition Requirement Prediction

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Controller Optimization of an Automated Force Calibration Device Based on an Improved Particle Swarm Algorithm

Controller Optimization of an Automated Force Calibration De...

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Sensors and Information technology (ICSI), International Conference on

作者： Jiming Jiang Xiang Dong Qiang Li Huibin Cao School of Electrical Engineering and Automation Anhui University Hefei China Department of Computer Science and Technology College of Big Data and Internet Shenzhen Technology University Shenzhen China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei Anhui China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

This paper presents a PID controller optimization method based on an Improved Particle Swarm Optimization (IPSO) algorithm, aimed at enhancing the design and performance of automated force calibration devices. Traditional force calibration systems often suffer from limitations in automation and force application accuracy, which this method seeks to address. The IPSO algorithm incorporates logistic chaotic mapping to initialize particle positions and enhances the standard particle swarm algorithm through nonlinear decreasing weights and adaptive learning factors, resulting in improved global search capabilities and faster local convergence. Additionally, the objective function is refined by normalizing and weighting the integral of time-multiplied absolute error (ITAE) and the overshoot, thereby further enhancing the force application accuracy of the PID controller. Simulation results demonstrate that the IPSO algorithm offers significant advantages in terms of settling time, overshoot, and ITAE error when compared to traditional PID tuning methods (e.g., Ziegler-Nichols) and other optimization algorithms (e.g., GA, GWO). Experimental results validate the effectiveness and practicality of the IPSO algorithm, providing an efficient solution for the automation and high-precision control of force sensor calibration systems.

关键词： Performance evaluation Accuracy Automation Force Optimization methods Systems modeling Calibration Particle swarm optimization Tuning Standards

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Product Quality Enhancement in Social Media using Emotion-Enhanced Bert (EE-BERT) Model

Product Quality Enhancement in Social Media using Emotion-En...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： V S Raj Kumar T Kumaresan V Mythily N Raghavendran Department of Artificial Intelligence and Data science Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Artificial Intelligence and Data science RMK college of Engineering and Technology Chennai India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Twitter plays an important role in understanding the consumer sentiment about the products. The advanced analytics and Natural Language Processing (NLP) are used to extract actionable insights from this data and useful for a business to improve features and address issues on time for better customer satisfaction and brand loyalty. Most of the current models suffer from a lack of effective ways to handle subtle sentiment analysis because of a lack of contextual understanding and proper feature extraction. Most traditional models cannot combine and optimize features that lead to obtaining sentiments. This leads to overfitting and problems caused by local minima. The proposed Emotion-Enhanced BERT (EE-BERT) model is an extension of the standard BERT to learn contextual embeddings in user sentiment. In this work, data preprocessing includes text cleaning, tokenization, dealing with emojis and emoticons, and lemmatization with the best quality data. The proposed work is basically a dual-stream architecture extended with butterfly optimizer. First, the advanced tokenization and semantic understanding techniques is used for extracting textual features. Second, the emotion-enhanced features extracted through emotion-hash vectorization to enhance the model’s contextual awareness in social media data. This provides a unified framework that combines the feature extraction methods to enhance the accuracy in sentiment analysis. It demonstrated with an impressive accuracy of ${9 8. 2 \%}$ on sentiment in posts and comments with 0.15 losses. The proposed work is free from overfitting and local minima in product quality enhancement.

关键词： Accuracy Social networking (online) Computational modeling Blogs Feature extraction Product design Encoding data models Quality assessment Context modeling

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation 41

Reducing Fine-Tuning Memory Overhead by Approximate and Memo...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Yuchen Shi, Yingdong Wang, Cheems Zhen, Xiantong Shi, Yuxuan Xu, Jun School of Statistics and Data Science Nankai University Tianjin China School of Information Science and Technology ShanghaiTech University Shanghai China Department of Automation Tsinghua University Peking China Central Research Institute United Imaging Healthcare Co. Ltd. China

Fine-tuning pretrained large models to downstream tasks is an important problem, which however suffers from huge memory overhead due to large-scale parameters. This work strives to reduce memory overhead in fine-tuning from perspectives of activation function and layer normalization. To this end, we propose the Approximate Backpropagation (Approx-BP) theory, which provides the theoretical feasibility of decoupling the forward and backward passes. We apply our Approx-BP theory to backpropagation training and derive memory-efficient alternatives of GELU and SiLU activation functions, which use derivative functions of ReLUs in the backward pass while keeping their forward pass unchanged. In addition, we introduce a Memory-Sharing Backpropagation strategy, which enables the activation memory to be shared by two adjacent layers, thereby removing activation memory usage redundancy. Our method neither induces extra computation nor reduces training efficiency. We conduct extensive experiments with pretrained vision and language models, and the results demonstrate that our proposal can reduce up to ∼30% of the peak memory usage. Our code is released at github. Copyright 2024 by the author(s)

关键词： Visual languages

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Superradiant growth anomaly magnification in evolution of vector bosonic condensates bounded by a Kerr black hole with near-horizon reflection

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 62-81页

作者： Nayun Jia Yin-Da Guo Gui-Rong Liang Zhan-Feng Mai Xin Zhang Department of Physics Southern University of Science and TechnologyShenzhen 518055China Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Institute of Frontier and Interdisciplinary Science Key Laboratory of Particle Physics and Particle Irradiation(Ministry of Education)Shandong UniversityQingdao 266237China School of Materials Science and Physics China University of Mining and TechnologyXuzhou 221116China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Ultralight vector particles can form evolving condensates around a Kerr black hole(BH)due to superradiant *** study the effect of near-horizon reflection on the evolution of this system:by matching three pieces of asymptotic expansions of the Proca equation in Kerr metric and considering the leading order in the electric mode,we present explicit analytical expressions for the corrected spectrum and the superradiant instability ***,in high-spin BH cases,we identify an anomalous situation where the superradiance rate is temporarily increased by the reflection parameter R,which also occurs in the scalar scenario,but is largely magnified in vector condensates due to a faster growth rate in dominant *** point out that the condition for the growth anomaly in the adiabatic case is that information carried per particle exceeds a certain value δ1/δN>2πκ_(B)√(1+R)/(1-R).We further construct several featured quantities to illustrate it,and formalize the anomalyinduced gravitational wave strain deformation.

关键词： superradiance ultralight boson black hole gravitational wave near-horizon reflection

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FedEAT: A Robustness Optimization Framework for Federated LLMs

arXiv

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arXiv 2025年

作者： Pang, Yahao Wu, Xingyuan Zhang, Xiaojin Chen, Wei Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Wuhan China

Significant advancements have been made by Large Language Models (LLMs) in the domains of natural language understanding and automated content creation. However, they still face persistent problems, including substantial computational costs and inadequate availability of training data. The combination of Federated Learning (FL) and LLMs (federated LLMs) offers a solution by leveraging distributed data while protecting privacy, which positions it as an ideal choice for sensitive domains. However, Federated LLMs still suffer from robustness challenges, including data heterogeneity, malicious clients, and adversarial attacks, which greatly hinder their applications. We first introduce the robustness problems in federated LLMs, to address these challenges, we propose FedEAT (Federated Embedding space Adversarial Training), a novel framework that applies adversarial training in the embedding space of client LLM and employs a robust aggregation approach, specifically geometric median aggregation, to enhance the robustness of Federated LLMs. Our experiments demonstrate that FedEAT effectively improves the robustness of Federated LLMs with minimal performance loss. © 2025, CC BY.

关键词： Federated learning

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