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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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A Study of Machine Learning Model's Performance for Load Forecasting Based on XAI Techniques

A Study of Machine Learning Model's Performance for Load For...

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International Conference on Computer and Automation engineering, ICCAE

作者： Christopher M. Dean Xuzhe Ma Tong Ding Ke Xu Yujia Chen Huadong Mo School of Engineering & Technology University of New South Wales Canberra Australia Faculty of Engineering University of Sydney Sydney Australia School of Computer Science & Engineering University of New South Wales Sydney Australia Sydney Australia Faculty of engineering University of New South Wales Sydney Australia Yu Lu Textile Co. Ltd. Henan China School of Systems & Computing University of New South Wales Canberra Australia

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Accurate load forecasting is crucial for efficient management, planning, and operation of modern power systems, especially in an era of increasing electrical demand driven by technological advancements and environmental changes. This paper explores the development of hybrid Transformer-based models to improve load forecasting accuracy by capturing shortterm fluctuations and long-term dependencies in energy demand. Three hybrid models were developed, the RNR-Transformer, the GRU-Transformer, and the LSTM-Transformer, and compared with traditional hybrid models such as LSTM-GRU and GRURNN. Transformer models outperformed standard RNN-based architectures, with an MSE of 0.0066 and a Symmetric Mean Absolute Percentage Error (sMAPE) of 15.09%, highlighting its ability to predict energy demand with higher accuracy. Feature importance analysis, using Random Forest, Permutation Importance, and SHAP, highlighted temperature as the most influential variable affecting demand, followed by solar radiation and humidity. The implications of these findings are significant in this field, as hybrid models can enhance the adaptability of load forecasting. These can be implemented in different geographical regions and market conditions, improving energy management, reducing operational costs, and facilitating the integration of renewable energy sources into power grids.

关键词： Energy consumption Accuracy Recurrent neural networks Load forecasting Biological system modeling Predictive models Power system stability Transformers Random forests Load modeling

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FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image Classification

arXiv

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arXiv 2025年

作者： RahimiZadeh, Keyvan Taheri, Ahmad Baumbach, Jan Makarian, Esmael Dehghani, Abbas Ravaei, Bahman Javadi, Bahman Beheshti, Amin School of Computing Macquarie University SydneyNSW2109 Australia Institute of Computational Systems Biology University of Hamburg Notkestrasse 9 Hamburg22607 Germany Department of Mining Engineering Sahand University of Technology Tabriz513351996 Iran Department of Computer Engineering School of Engineering Yasouj University Yasouj*** Iran School of Computer Data and Mathematical Sciences Western Sydney University NSW2751 Australia

Lithology discrimination is a crucial activity in characterizing oil reservoirs, and processing lithology microscopic images is an essential technique for investigating fossils and minerals and geological assessment of shale oil exploration. In this way, Deep Learning (DL) technique is a powerful approach for building robust classifier models. However, there is still a considerable challenge to collect and produce a large dataset. Transfer-learning and data augmentation techniques have emerged as popular approaches to tackle this problem. Furthermore, due to different reasons, especially data privacy, individuals, organizations, and industry companies often are not willing to share their sensitive data and information. Federated Learning (FL) has emerged to train a highly accurate central model across multiple decentralized edge servers without transferring sensitive data, preserving sensitive data, and enhancing security. This study involves two phases;the first phase is to conduct Lithology microscopic image classification on a small dataset using transfer learning. In doing so, various pre-trained DL model architectures are comprehensively compared for the classification task. In the second phase, we formulated the classification task to a Federated Transfer Learning (FTL) scheme and proposed a Fine-Tuned Aggregation strategy for Federated Learning (FTA-FTL). In order to perform a comprehensive experimental study, several metrics such as accuracy, f1 score, precision, specificity, sensitivity (recall), and confusion matrix are taken into account. The results are in excellent agreement and confirm the efficiency of the proposed scheme, and show that the proposed FTA-FTL algorithm is capable enough to achieve approximately the same results obtained by the centralized implementation for Lithology microscopic images classification task. © 2025, CC BY.

关键词： Sensitive data

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AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses

arXiv

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arXiv 2025年

作者： Cai, Runze Janaka, Nuwan Kim, Hyeongcheol Chen, Yang Zhao, Shengdong Huang, Yun Hsu, David Synteraction Lab School of Computing National University of Singapore Singapore Synteraction Lab Smart Systems Institute National University of Singapore Singapore Synteraction Lab National University of Singapore Singapore College of Design and Engineering National University of Singapore Singapore Synteraction Lab School of Creative Media Department of Computer Science City University of Hong Kong Hong Kong China School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States School of Computing Smart Systems Institute National University of Singapore Singapore

Unlike the free exploration of childhood, the demands of daily life reduce our motivation to explore our surroundings, leading to missed opportunities for informal learning. Traditional tools for knowledge acquisition are reactive, relying on user initiative and limiting their ability to uncover hidden interests. Through formative studies, we introduce AiGet, a proactive AI assistant integrated with AR smart glasses, designed to seamlessly embed informal learning into low-demand daily activities (e.g., casual walking and shopping). AiGet analyzes real-time user gaze patterns, environmental context, and user profiles, leveraging large language models to deliver personalized, context-aware knowledge with low disruption to primary tasks. In-lab evaluations and real-world testing, including continued use over multiple days, demonstrate AiGet’s effectiveness in uncovering overlooked yet surprising interests, enhancing primary task enjoyment, reviving curiosity, and deepening connections with the environment. We further propose design guidelines for AI-assisted informal learning, focused on transforming everyday moments into enriching learning experiences. © 2025, CC BY.

关键词： Contrastive Learning

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AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses 25

AiGet: Transforming Everyday Moments into Hidden Knowledge D...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Runze Cai Nuwan Janaka Hyeongcheol Kim Yang Chen Shengdong Zhao Yun Huang David Hsu Synteraction Lab School of Computing National University of Singapore Singapore Singapore Smart Systems Institute Synteraction lab National University of Singapore Singapore Singapore Synteraction Lab National University of Singapore Singapore Singapore College of Design and Engineering National University of Singapore Singapore Singapore School of Creative Media & Department of Computer Science City University of Hong Kong Hong Kong China School of Information Sciences University of Illinois at Urbana-Champaign Champaign Illinois USA School of Computing Smart Systems Institute National University of Singapore Singapore Singapore

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Block-FeST: Blockchain-Enhanced Federated Sparse Transformers for Privacy-Preserving RES Forecasting in Internet of Vehicles systems

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IEEE Internet of Things Journal 2025年

作者： Hameed, Aroosa Danish, Syed Muhammad Ranjha, Ali Srivastava, Gautam Carleton University Department of Systems and Computer Engineering Ottawa Canada Algoma University School of Computer Science and Technology Brampton Canada University of the Fraser Valley School of Computing BC Canada Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

Internet of Vehicles (IoV) frameworks integrate smart vehicles, roads, network infrastructures, and users into one system, enhancing environmental awareness, increasing efficiency, and reducing accidents. Although IoV is widely adopted, it has resulted in an increase in global energy demand, necessitating more robust and reliable energy solutions. In order to meet this growing demand, both traditional and distributed energy generation technologies have been developed, particularly renewable energy sources (RES). In order to ensure seamless operation of smart vehicles and related infrastructure, integrating renewable energy into existing grid infrastructure is essential. This enables stable and efficient power supply to IoV systems, especially during time periods of high demand. As part of this shift, it is important to accurately forecast the energy generation load of individual prosumers—entities that both produce and consume energy—because of their intermittent and dynamic nature. Therefore, we propose Block-FeST, a blockchain-based Federated Learning (FL) framework designed to predict the energy generation patterns of RES prosumers while preserving their private and sensitive data. Within this Block-FeST framework, a Sparse Transformer model is used to forecast energy generation among prosumer clients. Additionally, blockchain technology is integrated into the Block-FeST framework to enable distributed aggregation and securely validate and record the local parameters shared by clients. The results indicate that Block-FeST is superior to the second-best baseline method, with improvements of 20.4% in the mean square error (MSE), 13.7% in mean absolute error (MAE) and 19.3% in root mean square error (RMSE) for a long sequence length of 128. © 2014 IEEE.

关键词： Federated learning

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File system performance comparison with Proxmox VE and KVM as type-1 Linux-based hypervisors

File system performance comparison with Proxmox VE and KVM a...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Borislav Đorđević Kristina Janjić Nenad Kraljević Nikola Davidović IMP Computer Systems Ltd. (IMP-CS) Institute Mihailo Pupin University of Belgrade Belgrade Serbia School of Electrical and Computing Engineering of Applied Studies Academy of Technical and Art Applied Studies Belgrade Serbia Faculty of Electrical Engineering University of East Sarajevo BIH

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper provides a comparative analysis of file system performance between type-1 Linux-based hypervisors. Proxmox VE and KVM were selected as representative examples of type-1 Linux-based hypervisors. Though both hypervisors initially appear to have similar Linux-based and QEMU-based architectures, significant differences become apparent upon closer examination. For our experiments, we utilized Filebench due to its exceptional flexibility and adaptability, which allow it to emulate practical applications within realistic server environments. CentOS 9 was chosen as the guest operating system, serving as a common example of Linux distributions. Tests were performed with one, two, three, and four virtual machines running concurrently. Furthermore, this paper includes a mathematical model of the type-1 virtual environment, supplemented by a real-world experiment that serves as a case study. The mathematical model is used to interpret the performance results obtained from the experiments.

关键词： Virtual machine monitors File systems Linux Random access memory Virtual environments Solids Mathematical models Virtual machines Hardware Servers

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A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Data

arXiv

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arXiv 2025年

作者： Baes, Naomi Merx, Raphaël Haslam, Nick Vylomova, Ekaterina Dubossarsky, Haim Melbourne School of Psychological Sciences The University of Melbourne Australia School of Computing and Information Systems The University of Melbourne Australia School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom The Alan Turing Institute London United Kingdom Language Technology Lab University of Cambridge United Kingdom

Lexical Semantic Change (LSC) offers insights into cultural and social dynamics. Yet, the validity of methods for measuring kinds of LSC has yet to be established due to the absence of historical benchmark datasets. To address this gap, we develop a novel three-stage evaluation framework that involves: 1) creating a scalable, domain-general methodology for generating synthetic datasets that simulate theory-driven LSC across time, leveraging In-Context Learning and a lexical database;2) using these datasets to evaluate the effectiveness of various methods;and 3) assessing their suitability for specific dimensions and domains. We apply this framework to simulate changes across key dimensions of LSC (SIB: Sentiment, Intensity, and Breadth) using examples from psychology, and evaluate the sensitivity of selected methods to detect these artificially induced changes. Our findings support the utility of the synthetic data approach, validate the efficacy of tailored methods for detecting synthetic changes in SIB, and reveal that a state-of-the-art LSC model faces challenges in detecting affective dimensions of LSC. This framework provides a valuable tool for dimension- and domain-specific benchmarking and evaluation of LSC methods, with particular benefits for the social sciences. © 2025, CC BY-NC-SA.

关键词： Latent semantic analysis

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Esc-Cafnet: A Multimodal Brain Tumor Segmentation Network Based on Enhanced Separable Convolution and Context-Aware Fusion

SSRN

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SSRN 2025年

作者： Zhao, Mengyao Tang, Chaosheng Dai, Chendi Sun, Junding Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

(Aim) Brain tumors pose a significant threat to human health and overall well-being. Identifying tumor locations is crucial for image-guided radiotherapy, effective treatment planning, and preventing other disorders. Nevertheless, notable difficulties exist because of (i) the diverse shapes of organs and tumor tissue structures, (ii) low contrast between adjacent organs among patients, and (iii) high imaging similarity in magnetic resonance (MR). (Method) We present an efficient ESC-CAFNet segmentation approach based on deep learning to address these issues. Our approach utilizes 3D Depth-wise separable convolution in the encoder to extract the brain tumor's volumetric feature map from MR images. Further, we introduce an Enhanced Separable Convolution Module (ESCM) that (i) focuses on significant features and (ii) reduces parameter weights of irrelevant features in the feature map. We then feed the output feature map of the encoder to the Context-Aware Fusion Module (CAFM) at the network's bottleneck. By strengthening the relationship between the query matrix and the key-value matrix at the pixel level, our approach reduces the computational complexity of self-attention from quadratic to linear calculation. Additionally, detailed feature information on the lesion is obtained through multi-scale features. Finally, Deep Supervision is employed to facilitate the learning of dense feature representations from MR *** the proposed ESC-CAFNet can accurately locate organs and refine the boundary of tumor regions. (Results) The proposed ESC-CAFNet approach achieved Dice scores of 91.01%, 80.56%, and 83.51%, and Hausdorff Distances (95th percentile) of 5.04, 4.13, 6.32 for the WT, ET, and TC based on Testing set of BRATS 2019, respectively. Meanwhile, our ESC-CAFNet achieved the best average Dice score of 85.62% in the testing set of BRATS 2020. (Conclusion) The accurate segmentation and lightweight design of our ESC-CAFNet provide valuable guidance for clinicians in di

关键词： Magnetic resonance imaging

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Aspect category and sentiment polarity detection using a permutation language-based transformer model

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Procedia Computer Science 2025年 258卷 4179-4189页

作者： Ashok Kumar Jayaraman Tina Ester Trueman Gayathri Ananthakrishnan Abirami Murugappan Erik Cambria Manikandan G Satanik Mitra HCLTech Engineering and R&D Services Chennai Tamil Nadu India Department of Computer Science University of the People California USA School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. Recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result.

关键词： Aspect-based sentiment analysis permutation language model deep learning transformers multiclass classification

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