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arXiv 2024年

作者： Li, Zhilong Wu, Xiaohu Tang, Xiaoli He, Tiantian Ong, Yew-Soon Chen, Mengmeng Liu, Qiqi Lao, Qicheng Li, Xiaoxiao Yu, Han Beijing University of Posts and Telecommunications China College of Computing and Data Science Nanyang Technological University Singapore Singapore Westlake University China Department of Electrical & Computer Engineering The University of British Columbia Canada

There is growing research interest in measuring the statistical heterogeneity of clients’ local datasets. Such measurements are used to estimate the suitability for collaborative training of personalized federated learning (PFL) models. Currently, these research endeavors are taking place in silos and there is a lack of a unified benchmark to provide a fair and convenient comparison among various approaches in common settings. We aim to bridge this important gap in this paper. The proposed benchmarking framework currently includes six representative approaches. Extensive experiments have been conducted to compare these approaches under five standard non-IID FL settings, providing much needed insights into which approaches are advantageous under which settings. The proposed framework offers useful guidance on the suitability of various data divergence measures in FL systems. It is beneficial for keeping related research activities on the right track in terms of: (i) designing PFL schemes, (ii) selecting appropriate data heterogeneity evaluation approaches for specific FL application scenarios, and (iii) addressing fairness issues in collaborative model training. The code is available at https://***/Xiaoni-61/DH-Benchmark. © 2024, CC BY.

关键词： Federated learning

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Development of Advanced Noise Filtering Techniques for Medical Image Enhancement

Development of Advanced Noise Filtering Techniques for Medic...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Sumit Kushwaha K. Amuthachenthiru Geetha. K Jonnadula Narasimharao Dileep Kumar M Sai Sudha Gadde Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Faculty of Management Sciences Hensard University Bayelsa Nigeria Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Telangana India

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality.

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A Novel Multi-Scale Spectral-Guided Graph Attention Network for DeepFake Video Detection

A Novel Multi-Scale Spectral-Guided Graph Attention Network ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Muhammad Irfan Myung J. Lee Ashar Neyaz Daiki Nobayashi Department of Electrical Engineering City College of New York New York NY USA School of Computing and Data Science Wentworth Institute of Technology Boston MA USA Department of Electrical and Electronic Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The rapid advancement of deepfake technology presents significant challenges for video authenticity verification, necessitating robust detection mechanisms. This paper introduces a novel framework for deepfake video detection that integrates the Multi-Scale Spectral-Guided Graph Attention Network (MSG-GAT) with the Lightweight Assimilation-Elimination (Lite-ASEL) algorithm. By representing video frames as graphs, the framework effectively captures intricate pixel relationships, enabling the detection of subtle manipulations with enhanced precision. Additionally, the Lite-ASEL algorithm is employed for feature selection, balancing reduced computational complexity with high detection performance. Experimental results demonstrate the superiority of the proposed framework, achieving state-of-the-art performance with an AUC of 99.1 % and a detection accuracy of 99.3%. Furthermore, hyperparameter tuning confirms the framework's robustness and efficiency, consistently achieving an optimal objective score of 98.54%, validating its effectiveness for optimal feature selection and deepfake detection.

关键词： Deepfakes Accuracy Scalability Image edge detection Feature extraction Robustness Graph neural networks Synchronization Security Tuning

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ExClique: An Express Consensus Algorithm for High-Speed Transaction Process in Blockchains

arXiv

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arXiv 2025年

作者： Zhao, Chonghe Zhou, Yipeng Zhang, Shengli Sheng, Quan Z. Zhang, Yang Wen, Shiting College of Electronic and Information Engineering Shenzhen University Shenzhen China School of Computing Macquarie University Sydney Australia Anuradha and Vikas Sinha Department of Data Science University of North Texas DentonTX United States School of Computer and Data Engineering NingboTech University Ningbo China

Proof of Authority (PoA) plays a pivotal role in blockchains for reaching consensus. Clique, which selects consensus nodes to generate blocks with a pre-determined order, is the most popular implementation of PoA due to its low communication overhead and energy consumption. However, our study unveils that the speed to process transactions by Clique is severely restricted by 1) the long communication delay of full blocks (each containing a certain number of transactions) between consensus nodes;and 2) occurrences of no-turn blocks, generated by no-turn nodes if an in-turn block generation fails. Consequently, Clique struggles to support distributed applications requiring a high transaction processing speed, e.g., online gaming. To overcome this deficiency, we propose an Express Clique (ExClique) algorithm by improving Clique from two perspectives: compacting blocks for broadcasting to shorten communication delay and prohibiting the occurrences of no-turn blocks. For performance evaluation, we implement ExClique by modifying Geth of Ethereum, the software implementing Clique, and deploy a permissioned blockchain network by using container technology. The experimental results show that ExClique achieves a substantial enhancement in transactions per second (TPS). Specifically, it boosts TPS by 2.25× in a typical network with 21 consensus nodes and an impressive 7.01× in a large-scale network with 101 consensus nodes when compared to Clique. © 2025, CC BY.

关键词： Blockchain

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SR-BigGAN: lightweight image super-resolution with priors

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Machine Vision and Applications 2025年第4期36卷 1-18页

作者： Kumar, Deepak Srinivas Rao, Harshitha Kumar, Chetan Shao, Ming Department of Computer Science Worcester Polytechnic Institute Worcester USA Program of Data Science University of Massachusetts Dartmouth USA Old Dominion University - Thomas Jefferson National Accelerator Facility Joint Institute on Advanced Computing for Environmental Studies Portsmouth USA Miner School of Computer and Information Sciences University of Massachusetts Lowell Lowell USA

Recent progress in the field of high-fidelity image synthesis using GANs has shown appealing outcomes, which motivates a series of successful image super-resolution (SR) works. However, most GAN-based SR models apply plain GAN models and are cumbersome compared to CNN-based SR models. In addition, the very advantage of conditional GAN has not yet been explored under the context of SR. In this paper, we develop a lightweight SR-BigGAN with priors for single-image super-resolution (SISR). First, our new model is an extension of BigGAN tailored to deep SR pipeline, retaining both generator and discriminator architectures, but with modifications to accommodate SR tasks. Second, prior knowledge defined as class labels from the low-resolution images, is fully leveraged through the conditional generative model to refine the SR process. Third, the lightweight nature of the model is achieved through knowledge distillation, focusing on reduced computational complexity and memory usage, making it the first practice of this kind in GAN-based SISR modeling. Extensive experiments on DIV2K, Pascal, mini-ImageNet, and SR benchmarks including Set5 and Set14 in an attempt to compare the Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR) with the state-of-the-art models have shown appealing results. Our model achieves an average PSNR of 34.99 and SSIM of 0.791 across these datasets, demonstrating quantitative improvements over existing methods. The generated high-resolution images offer both perceptual enhancement and improved classification results. Additionally, explicit comparisons with GAN-based SR techniques such as ESRGAN and SRGAN highlight the superiority of our approach in both fidelity and efficiency. In particular, we achieve an average of $$\hbox{PSNR}=34.99$$ and $$\hbox{SSIM}=0.791$$ on several testing datasets.

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An Inconsistency-Based Hybrid Feature Selection Approach for Enhancing Medical Classification Modeling 2nd

An Inconsistency-Based Hybrid Feature Selection Approach for...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Zhao, Rong ALDharhani, Ghassan Saleh Ratnavelu, Kuru Thambiayya, Sathiyaraj Jasser, Muhammed Basheer Majeed, Anwar P. P. Abdul Luo, Yang Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Institute of Information Science and Technology Dalian University of Science and Technology Dalian China Faculty of Information Science and Technology UKM University Selangor Bangi43000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur56000 Malaysia Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Artificial Intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatment and life extension. A combination of classification and feature selection algorithms can effectively handle complex datasets in the medical field and improve the accuracy of disease diagnosis and treatment. In this paper, we propose a new hybrid feature selection algorithm that uses inconsistency metrics as a filter method for the first step, and then feeds the resulting dataset into a wrapper method, which ultimately results in a reduced dataset. The proposed HFSIM algorithm is tested on five datasets in the medical domain from Kaggle. The obtained feature subsets are verified on three classification algorithms, KNN, LR and RF of machine learning to validate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm obtains feature subsets with low dimensionality on most of the datasets and also has high classification accuracy after testing on the classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Hybrid Deep Learning Algorithm for Forecasting of Global Currency Market 2nd

A Hybrid Deep Learning Algorithm for Forecasting of Global C...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Yong, Yoke Leng Ajibade, Temiloluwa Iyanuoluwa Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Faculty of Computing and Digital Technology Help University Bukit Damansara Kuala Lumpur Malaysia Department of Business Management Scholar School System Birmingham United Kingdom Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The global currency market, which has millions of active participants and is the largest financial market in the world, has been greatly affected by the emergence of deep learning techniques. There is widespread recognition of deep learning’s effectiveness for price movement predictions, particularly in financial markets where its effectiveness has been shown. When compared to other machine learning methods, hybrid models like CNN-LSTM have shown to be the most effective and efficient. Nevertheless, there is a lack of thorough comparative research across deep learning hybrids, which makes it difficult to validate these claims empirically, especially in the diversified and extremely unpredictable FX market. To fill that void, this research builds a generic CNN-LSTM model to predict key currency pairs and compares it to another hybrid deep learning model. The objective is to evaluate and strengthen the reliability of deep learning techniques for FOREX forecasting. In order to maximize the generalizability of the model, this study mimics a categorization scenario using stringent cross-validation procedures. Important measures, including accuracy, precision, recall, and F1-score, were used to assess the models’ performance. The results show that there is a performance hierarchy, with CNN-LSTM, CNN-RNN. These findings highlight the need to evaluate deep learning models in different real-world contexts. In conclusion, this study highlights the difficulties of FOREX forecasting and the necessity for further evaluations of the predictive power of machine learning models in an ever-changing market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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An Intelligent Resource Scheduling Method with Edge Channel Deployment for BPM 19

An Intelligent Resource Scheduling Method with Edge Channel ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Bowen Dou, Wanchun Zhou, Xiaokang Zhang, Xuyun Qi, Lianyong Dai, Fei Chen, Chaochao Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shiga University Faculty of Data Science Hikone Japan Macquarie University Department of Computing Sydney Australia College of Computer and Software Qingdao China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350346558

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users' status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method. © 2022 IEEE.

关键词： Cost effectiveness

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Iterative Feature Space Optimization through Incremental Adaptive Evaluation

arXiv

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arXiv 2025年

作者： Wu, Yanping Huang, Yanyong Chen, Zhengzhang Yao, Zijun Fu, Yanjie Liu, Kunpeng Luo, Xiao Wang, Dongjie University of Kansas Department of Electrical Engineering and Computer Science United States Southwestern University of Finance and Economics Department of Statistics China NEC Laboratories America Data Science and System Security United States Arizona State University Department of Computing and Augmented Intelligence United States Portland State University Department of Computer Science United States University of California Los Angeles Department of Computer Science United States

Iterative feature space optimization involves systematically evaluating and adjusting the feature space to improve downstream task performance. However, existing works suffer from three key limitations: 1) overlooking differences among data samples leads to evaluation bias;2) tailoring feature spaces to specific machine learning models results in overfitting and poor generalization;3) requiring the evaluator to be retrained from scratch during each optimization iteration significantly reduces the overall efficiency of the optimization process. To bridge these gaps, we propose a gEneralized Adaptive feature Space Evaluator (EASE) to efficiently produce optimal and generalized feature spaces. This framework consists of two key components: Feature-Sample Subspace Generator and Contextual Attention Evaluator. The first component aims to decouple the information distribution within the feature space to mitigate evaluation bias. To achieve this, we first identify features most relevant to prediction tasks and samples most challenging for evaluation based on feedback from the subsequent evaluator. These identified feature and samples are then used to construct feature subspaces for next optimization iteration. This decoupling strategy makes the evaluator consistently target the most challenging aspects of the feature space. The second component intends to incrementally capture evolving patterns of the feature space for efficient evaluation. We propose a weighted-sharing multi-head attention mechanism to encode key characteristics of the feature space into an embedding vector for evaluation. Moreover, the evaluator is updated incrementally, retaining prior evaluation knowledge while incorporating new insights, as consecutive feature spaces during the optimization process share partial information. Extensive experiments on fourteen real-world datasets demonstrate the effectiveness of the proposed framework. Our code and data are publicly available Copyright © 2025, The Autho

关键词： Vector spaces

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Assistive Tool for Emotion and Importance Quadrant LEIQ™ 10th

Assistive Tool for Emotion and Importance Quadrant LEIQ™

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10th International Conference on Kansei Engineering and Emotion Research, KEER 2024

作者： Mustafa, Nur Batrisyia Damia Hamidi, Saidatul Rahah Hussein, Surya Sumarni Shuhidan, Shuhaida Mohamed College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Center for Research in Data Science Computer and Information Sciences Department Universiti Teknologi PETRONAS Perak Malaysia

ISBN: (纸本)9789819798896

In today's rapidly growing digital world, effective and user-centered experiences rely on integrating emotions into the design of digital products. This study focuses on investigating the LEIQ™ paradigm and creating computer-aided assistive tools for enhancing the evaluation of emotion-driven user experience (UX) in web-based solutions. Given the increasing incorporation of technology into our everyday routines, it is critical to understand the impact of emotions on user interactions. Emotional experiences play a crucial role in the efficacy of online design and development, as they have a substantial impact on user engagement, contentment, and product loyalty. The study acknowledges the limitations of existing methods for evaluating emotions, which include challenges in managing and analyzing the large datasets generated by frameworks like LEIQ™. Thus, this paper aims to develop a prototype assistive tool specifically designed for LEIQ™ emotion measurement, with the intention of addressing these shortcomings. This advancement reduces the need for a human workforce by providing the opportunity to quickly and accurately acquire information using automated technology. Potential gains for LEIQ™ practitioners, IT artifact designers, developers, and product organizations are envisaged by the research. The ensuing solution, which goes beyond LEIQ™ data consolidation, has the potential to completely alter emotional UX research by going deeper and encompassing all areas of LEIQ™ data processing. Finally, the assistive tool's usefulness may improve by making data interpretation in the digital age easier across a range of prospects and disciplines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Assistive Tool Emotional UX Lokman's Emotion and Importance Quadrant (LEIQ)™

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