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Privacy-Aware Edge Computation Offloading With Federated Learning in Healthcare Consumer Electronics System

IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Yiming Fang, Hao Yan, Zheng Qi, Lianyong Bilal, Muhammad Li, Yang Xu, Xiaolong Zhou, Xiaokang Nanjing University of Information Science and Technology School of Software Nanjing210044 China NanJing University of Information Science and Technology Reading Academy Nanjing210044 China Xidian University School of Cyber Engineering Shaanxi Xi'an710026 China College of computer science and technology Qingdao China Lancaster University School of Computing and Communications United Kingdom Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China Kansai University Faculty of Business and Data Science Japan

The continuous iteration of consumer electronics has significantly promoted the development of medical devices, which has enabled the collection of large amounts of heterogeneous medical data. These data are offloaded from local devices to cloud servers for processing using traditional methods, which results in high transmission latency and the risk of privacy leakage. Additionally, researchers have employed federated learning to protect data privacy, but this approach can lead to a straggler effect due to the limited computational and communication resources of terminal devices. To address these issues, a computation offloading framework is designed to optimize task and resource allocation, addressing multi-optimization problems and mitigating the straggler effect in federated learning. Moreover, a novel computation offloading method within a federated learning framework assisted by edge computing, named DRWB, is proposed. Specifically, we develop a deep reinforcement learning-based approach to transfer lagging training tasks to idle edge servers, enhancing data processing speed, minimizing transmission delays, and protecting user privacy. Extensive experimental results demonstrate that the DRWB method outperforms baseline methods, showcasing superior performance in handling heterogeneous medical data tasks. © 1975-2011 IEEE.

关键词： Electronic health record

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Enhanced Anomaly Detection in Embedded Payment Systems using Depthwise Separable CNN with Dandelion Optimizer

Enhanced Anomaly Detection in Embedded Payment Systems using...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Sevinthi Kali Sankar Nagarajan Rabail Adwani Kardeepa Ponnuchamy Harshal Shah Shashikala Gurpur Ramya Maranan Independent Researcher 12460 Lanthimos San Antonio TX USA MS in Data Science Cranston RI USA Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Gujarat Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Vimannagar Pune Maharashtra Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Due to the prevalence of online payments, it is essential to identify instances of payment fraud. Plans that break rules and regulations within the payment systems not only cost money but also erode customer confidence. Finding these fraudulent transactions can be challenging because of the complexity of fraud and the sheer number of transactions. In this study, a new deep learning technique for identifying online payment fraud is proposed. The classification phase, hybrid feature selection, and sophisticated preprocessing form its foundation. Starting with the IEEE CIS Fraud dataset and the PaySim Financial Credit dataset, the suggested approach is implemented. To ensure that all of the data sets have identical scales, a preprocessing step that uses Min-Max scaler normalization is utilized. The optimal qualities are picked using a hybrid approach that combines Gooseneck Barnacle Optimization (GBO) and the Artificial Hummingbird Algorithm (AHA). A depthwise separable convolutional neural network (DS-CNN) is then employed for classification, which facilitates the differentiation between fraudulent and non-fraudulent transactions. Additionally, to increase the detection rate, the Dandelion Optimizer (DO) optimizes the entire system. The aforementioned study indicates that the suggested approach has a high accuracy rate in detecting fraudulent transactions, which means it has almost infinite potential to speed up the entire process. In both datasets, the proposed method achieves an impressive 99% accuracy rate, proving its dependability and efficiency. This exceptional outcome shows how flexible the approach is to complex patterns and data variations, indicating its high level of reliability.

关键词： Accuracy Prevention and mitigation Feature extraction Regulation Fraud Complexity theory Classification algorithms Timing Convolutional neural networks Reliability

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

arXiv

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arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Encoding Variables, Evaluation Criteria and Evaluation Methods for data Physicalizations: A Review

arXiv

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arXiv 2023年

作者： Ranasinghe, Champika Degbelo, Auriol Data Management & Biometrics Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede7500 AE Netherlands Geoinformatics Faculty of Environmental Sciences TU Dresden Dresden01062 Germany

data physicalisations, or physical visualisations, represent data physically, using variable properties of physical media. As an emerging area, data physicalisation research needs conceptual foundations to support thinking about and designing new physical representations of data and evaluating them. Yet, it remains unclear at the moment (i) what encoding variables are at the designer's disposal during the creation of physicalisations, (ii) what evaluation criteria could be useful, and (iii) what methods can be used to evaluate physicalisations. This article addresses these three questions through a narrative review and a systematic review. The narrative review draws on the literature from Information Visualisation, HCI and Cartography to provide a holistic view of encoding variables for data. The systematic review looks closely into the evaluation criteria and methods that can be used to evaluate data physicalisations. Both reviews offer a conceptual framework for researchers and designers interested in designing and evaluating data physicalisations. The framework can be used as a common vocabulary to describe physicalisations and to identify design opportunities. We also proposed a seven-stage model for designing and evaluating physical data representations. The model can be used to guide the design of physicalisations and ideate along the stages identified. The evaluation criteria and methods extracted during the work can inform the assessment of existing and future data physicalisation artefacts. © 2023, CC BY.

关键词： Encoding (symbols)

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PolyLite: A Lightweight Transformer With Multi-Scale Attention for Colorectal Polyp Segmentation

PolyLite: A Lightweight Transformer With Multi-Scale Attenti...

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International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then Faculty of Engineering Computing and Science Swinburne University of Technology (Sarawak Campus) Sarawak Malaysia Department of Artificial Intelligence and Data Science Coimbatore Institute of Technology Tamil Nadu India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

The urgent need for early detection and intervention in colorectal cancer highlights the importance of automating colorectal polyp detection. Despite the use of deep learning techniques for polyp detection from medical images, these methods often struggle with issues such as skewed data distribution and class imbalances. In this study, we introduce PolyLite, a lightweight transformer-based model designed to balance performance and efficiency. PolyLite features a custom SegFormer encoder that takes advantage of its lightweight design, and a decoder that uses a multi-scale attention network (MA-NET) to achieve an optimal balance between accuracy and efficiency. To improve accuracy and reduce false negatives, we incorporate a reverse attention layer that effectively counteracts input mask attention during training. This refocuses the model's efforts to better learn non-polyp regions. PolyLite undergoes a two-stage training process, beginning with pre-training on the ImageNet dataset and then fine-tuning on the Kvasir-SEG dataset. We conduct rigorous evaluations on three complex datasets: CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB. Empirical results demonstrate PolyLite's superiority over existing techniques, as evidenced by its performance in terms of Intersection over Union and Dice scores. This validates its effectiveness and scalability in polyp segmentation, making complex and computationally intensive architectures unnecessary.

关键词：

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Network-Aware Asynchronous Distributed ADMM Algorithm for Peer-to-Peer Energy Trading

arXiv

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arXiv 2023年

作者： Yang, Zeyu Wang, Hao Department of Computer Science Rice University HoustonTX77005 United States Department of Data Science and Artificial Intelligence Faculty of Information Technology the Monash Energy Institute Monash University MelbourneVIC3800 Australia

The increasing uptake of distributed energy resources (DERs) in smart home prosumers calls for distributed energy management strategies, and the advances in information and communications technology enable peer-to-peer (P2P) energy trading and transactive energy management. Many works attempted to solve the transactive energy management problem using distributed optimization to preserve the privacy of DERs’ operations. But such distributed optimization requires information exchange among prosumers, often via synchronous communications, which can be unrealistic in practice. This paper addresses a transactive energy trading problem for multiple smart home prosumers with rooftop solar, battery storage, and controllable load, such as heating, ventilation, and air-conditioning (HVAC) units, considering practical communication conditions. We formulate a network-aware energy trading optimization problem, in which a local network operator manages the network constraints supporting bidirectional energy flows. We develop an asynchronous distributed alternating direction method of multipliers (ADMM) algorithm to solve the problem under asynchronous communications, allowing communication delay and indicating a higher potential for real-world applications. We validate our design by simulations using real-world data. The results demonstrate the convergence of our developed asynchronous distributed ADMM algorithm and show that energy trading reduces the energy cost for smart home prosumers. © 2023, CC BY.

关键词： Energy management

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

引用

IEEE Transactions on Information Forensics and Security 2025年 20卷 5888-5903页

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Qilu University of Technology Shandong Academy of Sciences Faculty of Computer Science and Technology Jinan 250014 China National University of Singapore School of Computing 21 Lower Kent Ridge Rd 119077 Singapore Great Bay University School of Computing and Information Technology Dongguan 523000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Qilu University of Technology (Shandong Academy of Sciences) Shandong Artificial Intelligence Institute Jinan 250014 China Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin 300384 China Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Shenzhen 518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. © 2005-2012 IEEE.

关键词： CLIP Diffusion face forgery detection Image-noise fusion Sample pair attention Transformer

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Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications

arXiv

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arXiv 2024年

作者： Zhang, Yichi Zhang, Yuchen Zhu, Lipeng Xiao, Sa Tang, Wanbin Eldar, Yonina C. Zhang, Rui National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of subarrays, under the constraints of unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function/constraints, as well as the highly coupled variables, the formulated problem is challenging to solve. By employing fractional programming, we develop an alternating optimization framework to solve the problem via a combination of Lagrange multipliers, penalty method, and gradient descent. Numerical results reveal that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to its fixed-position antenna (FPA) counterpart. Moreover, with sufficiently large movable regions, the proposed scheme with sub-connected MA arrays even outperforms the fully-connected FPA array. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting

arXiv

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arXiv 2024年

作者： Bilal, Iman Munire Fang, Zheng Arana-Catania, Miguel van Lier, Felix-Anselm Velarde, Juliana Outes Bregazzi, Harry Carter, Eleanor Airoldi, Mara Procter, Rob Department of Computer Science University of Warwick and Alan Turing Institute for Data Science and AI Coventry United Kingdom Faculty of Engineering and Applied Sciences Cranfield University United Kingdom Government Outcomes Lab Blavatnik School of Government University of Oxford Oxford United Kingdom

As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language processing (NLP) tools. In particular, we focus on automating stages within the systematic reviewing process that are time-intensive and repetitive for human annotators and which lend themselves to immediate scalability through tools such as information retrieval and summarisation guided by expert advice. The article concludes with a summary of lessons learnt regarding the integrated approach towards systematic reviews and future directions for improvement, including explainability. © 2024, CC BY.

关键词： Adversarial machine learning

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FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion

arXiv

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arXiv 2024年

作者： Wu, Xiaofeng Bojkovic, Velibor Gu, Bin Suo, Kun Zou, Kai Faculty of Data Science City University of Macau China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Computer Science Kennesaw State University GA United States ProtagoLabs Inc.

Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes. However, this potential comes with inherent challenges in directly training SNNs through spatio-temporal backpropagation - stemming from the temporal dynamics of spiking neurons and their discrete signal processing - which necessitates alternative ways of training, most notably through ANN-SNN conversion. In this work, we introduce a lightweight Forward Temporal Bias Correction (FTBC) technique, aimed at enhancing conversion accuracy without the computational overhead. We ground our method on provided theoretical findings that through proper temporal bias calibration the expected error of ANN-SNN conversion can be reduced to be zero after each time step. We further propose a heuristic algorithm for finding the temporal bias only in the forward pass, thus eliminating the computational burden of backpropagation and we evaluate our method on CIFAR-10/100 and ImageNet datasets, achieving a notable increase in accuracy on all datasets. Codes are released at a GitHub repository. Copyright © 2024, The Authors. All rights reserved.

关键词： Heuristic algorithms

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