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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated Learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and Communication-Efficient Personalized Federated Learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer Theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. Then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for Remote Sensing Scene Recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Han, Fangzhou Yu, Tianyi Zhang, Lamei Si, Lingyu Zhang, Yiqi Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio Visual Aerial Scene Recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results. © 2025 IEEE.

关键词： Audiovisual feature fusion Gated feature fusion Object-centric learning Slot attention

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MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

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Computers, Materials & Continua 2025年第2期82卷 2969-2984页

作者： Ruoxin Li Shaoxiong Wu Fengping Deng Zhongli Tian Hua Cai Xiang Li Xu Xu Qi Liu NARI-TECH Nanjing Control System Co. Ltd.Nanjing211106China School of Software Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044China

Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.

关键词： Electrical load forecasting cloud computing smart grid weather attributes energy consumption time-series analysis

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Optimizing Heart-Kidney Interaction for Cancer Detection through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwanj Othnman, Zalinda Yahya, Yazrina Nazri, Mohd Zakree Ahmad Goh, Say Leng Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran Iran Faculty of Information Science and Technology Center for Artificial Intelligence Technology Selangor Bangi43600 Malaysia Center for Software Technology and Management Faculty of Information Science and Technology UniversitiKebangsaan Malaysia Selangor Bangi43600 Malaysia Universiti Malaysia Sabah KampusAntarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical & Computer Engineering College ParkMD20742 United States Tehran University Science and Technology Park RayanMiniFab Tehran Iran

Decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization, using analysis of the available *** heart-kidney (HK) model proposed in this paper as a DSS simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA's local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm's effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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Leveraging large language model as news sentiment predictor in stock markets: a knowledge-enhanced strategy

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Discover Computing 2025年第1期28卷 1-36页

作者： Chen, Weisi Liu, Wulong Zheng, Jiaxin Zhang, Xu School of Software Engineering Xiamen University of Technology Xiamen China School of Computer and Information Engineering Xiamen University of Technology Xiamen China

In the fast-evolving artificial intelligence era, the intersection of natural language processing and financial analysis has attracted significant attention, primarily due to its potential to provide valuable insights into financial market behavior. Sentiment analysis of financial news articles is a crucial aspect of this intersection, providing cues about market sentiment that may affect stock price dynamics. Traditional sentiment analysis methods often rely on rules or machine learning algorithms trained on labeled datasets, but these methods face challenges in capturing the context within the text. This paper proposes a framework that incorporates prompt engineering strategies, including a novel Domain Knowledge Chain-of-Thought (DK-CoT) strategy, integrating domain-specific financial knowledge with chain-of-thought reasoning, designed to leverage and enhance the performance of large language models (LLMs) in financial news sentiment analysis. DK-CoT has been compared with various prompt engineering techniques, including zero-shot, few-shot, and chain-of-thought, as well as other benchmark models like BERT and RoBERTa. Through comprehensive experiments and evaluations, we introduce the weighted F1 score as a more practical metric, emphasizing the disproportionate impact of negative news on financial markets, which better reflects real-world financial dynamics, as negative sentiments often lead to more significant market reactions than positive or neutral sentiments. Experimental results have shown that DK-CoT adopted in an LLM called GLM is effective in improving the performance and reliability of financial news sentiment analysis. Our findings provide insights into optimal prompt designs and highlight the importance of incorporating financial knowledge to uplift LLM performance while reducing the need for extensive computational resources and fine-tuning.

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Joint-Wise Distributed Perception Graph Convolutional Network for Skeleton-Based Action Recognition

Joint-Wise Distributed Perception Graph Convolutional Networ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Geng, Qiang Chen, Zhaoyu Li, Xin Li, Yangyang Li, Xing College of Computer Science and Software Engineering Hohai University China College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University China

ISBN: (纸本)9798350368741

Recent studies have achieved remarkable results for action recognition with human skeletal data by utilizing graph convolutional models. Traditional approaches typically aggregate local spatio-temporal information bottom-up to form a single spatio-temporal global understanding. However, this method may fail to achieve multi-location perception for fine-grained action capture and may struggle to model subactions due to their varying durations. To solve these challenges, we design a Distributed Spatio-temporal Perception (DSP) module that innovatively treats each joint as an independent perception unit, performing joint-wise distributed multi-location perception in both spatial and temporal dimensions. In addition, we introduce an Anchor Pose-driven Subaction Encoding (APSE) module, which enhances informative clues for subaction reasoning through identifying the correlations between anchor pose and subactions formed by integrating distributed spatio-temporal perception features. Based on the above work, we propose a Joint-wise Distributed Perception Graph Convolutional Network (JDP-GCN). The experiments on three widely used datasets: NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA, demonstrate that our method achieves state-of-the-art performance. © 2025 IEEE.

关键词： Distributed Perception Graph Convolutional Network Human Action Recognition Skeleton Modality

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Ethiopic Cryptography: An Efficient Hybrid Encryption Algorithm for Reducing Cybersecurity Attack 11th

Ethiopic Cryptography: An Efficient Hybrid Encryption Algori...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Akele, Tadesse Birara Bahiru, Tadesse Kebede Molla, Tibebu Legesse Fentie, Shambel Dessale Belay, Tadesse Destaw Marye, Almaz Yemataw Getaneh, Mersha Derese Department of Information System Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Information Technology Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Deparment of Software Engineering Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Computer Science Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia

ISBN: (纸本)9783031641503

Cryptography is an essential tool in ensuring the security of data during network communication. Network and service providers prioritize data security to ensure secure data exchanges. Ethiopic cryptography is a unique encryption method that utilizes the Amharic language, prevalent in Ethiopia. Unlike Latin alphabets, Amharic consists of 287 characters, making decryption challenging. The encryption process involves converting the text to Amharic and then mapping it to a pair of randomly chosen two-bit English alphabets, creating an intermediate cipher. The Ethiopic cryptography algorithm employs a hybrid approach, using the RSA algorithm for secure key exchange and the Advanced Encryption Standard algorithm for text confidentiality. The algorithm comprises three phases—translation, mapping, and encryption—contributing to its effectiveness in securing data over the network. Decryption requires familiarity with the Amharic language and mapping details. The algorithm demonstrates a stronger avalanche effect (97%) in comparison with Blowfish and DES. The algorithm is proposed and evaluated using the design science research methodology, and its performance is validated through simulation using MATLAB and BunnaScript tools, considering factors like the avalanche effect, time complexity, and memory consumption. The evaluation shows that the suggested solution operates faster, requires lesser time for data encryption, and uses less memory than other methods in use. Overall, the proposed Ethiopic cryptography algorithm proves to be an efficient and effective method for securing data during network communication, offering stronger text confidentiality and an improved avalanche effect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATLAB

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APTNet: Adaptive Partial Transformer Network for Infrared Small Target Detection

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IEEE Sensors Journal 2025年第10期25卷 17960-17974页

作者： Zhang, Yingmei Bao, Wangtao Wan, Weiguo Xiao, Qin Tang, Yingjun Zou, Xueting Huang, Liu Zhong, Kaichen Lan, Yuhao Jiangxi University of Finance and Economics School of Software and Internet of Things Engineering Nanchang330032 China CGN Begood Technology co. Ltd Nanchang330032 China Jiangxi University of Finance and Economics School of Information Management and Mathematics Nanchang330032 China

Infrared small target detection (IRSTD) plays a vital role in various applications such as surveillance and rescue missions. However, existing methods struggle to effectively integrate contextual information for small targets, with background noise and limited long-distance dependency learning further constraining detection performance. To address these issues, this paper proposes a novel adaptive partial transformer network based on asymmetric U-Net architecture, namely APTNet, which enhances contextual information integration and accurately detects small objects in complex scenes, improving detection performance. Specifically, a Double Residual Attention Block (DRAB) is designed to enhance the contrast of dim targets within complex backgrounds through channel and spatial attention mechanisms, thereby improving discrimination capability for small targets. Furthermore, leveraging the context pooling connections and DRAB output features, an Adaptive Partial Transformer Block is proposed. This block first proposes an adaptive channel splitting factor, achieving contextual information integration and long-distance dependency learning with low computational overhead. Extensive experiments on two public datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art IRSTD methods in terms of intersection over union (IoU), probability of detection (Pd), and false alarm rate (Fa). The implementation of our code can be accessed at https://***/Wangtao-Bao/APTNet. © 2025 IEEE.

关键词： HTTP

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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

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IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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Zero Trust Networks: Evolution and Application from Concept to Practice

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Computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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