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arXiv 2024年

作者： Chen, Hao Bei, Yuanchen Huang, Wenbing Chen, Shengyuan Huang, Feiran Huang, Xiao Faculty of Data Science City University of Macau China College of Computer Science and Technology Zhejiang University Hangzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong College of Information Science and Technology Guangzhou China

Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powerful modeling capability of graph neural networks for user-item interaction graphs, graph-based CF models have gained increasing attention. They encode each user/item and its subgraph into a single super vector by combining graph embeddings after each graph convolution. However, each hop of the neighbor in the user-item subgraphs carries a specific semantic meaning. Encoding all subgraph information into single vectors and inferring user-item relations with dot products can weaken the semantic information between user and item subgraphs, thus leaving untapped potential. Exploiting this untapped potential provides insight into improving performance for existing recommendation models. To this end, we propose the Graph Cross-correlated Network for Recommendation (GCR), which serves as a general recommendation paradigm that explicitly considers correlations between user/item subgraphs. GCR first introduces the Plain Graph Representation (PGR) to extract information directly from each hop of neighbors into corresponding PGR vectors. Then, GCR develops Cross-Correlated Aggregation (CCA) to construct possible cross-correlated terms between PGR vectors of user/item subgraphs. Finally, GCR comprehensively incorporates the cross-correlated terms for recommendations. Experimental results show that GCR outperforms state-of-the-art models on both interaction prediction and click-through rate prediction tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Collaborative filtering

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Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing

arXiv

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arXiv 2024年

作者： Hackstein, Jakob Sumbul, Gencer Clasen, Kai Norman Demir, Begüm Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10623 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10623 Germany Sion1950 Switzerland

Self-supervised learning through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) image representation learning, and thus embodies a significant potential for content-based image retrieval (CBIR) from ever-growing RS image archives. However, the existing MAE based CBIR studies in RS assume that the considered RS images are acquired by a single image sensor, and thus are only suitable for uni-modal CBIR problems. The effectiveness of MAEs for cross-sensor CBIR, which aims to search semantically similar images across different image modalities, has not been explored yet. In this paper, we take the first step to explore the effectiveness of MAEs for sensor-agnostic CBIR in RS. To this end, we present a systematic overview on the possible adaptations of the vanilla MAE to exploit masked image modeling on multi-sensor RS image archives (denoted as cross-sensor masked autoencoders [CSMAEs]) in the context of CBIR. Based on different adjustments applied to the vanilla MAE, we introduce different CSMAE models. We also provide an extensive experimental analysis of these CSMAE models. We finally derive a guideline to exploit masked image modeling for uni-modal and cross-modal CBIR problems in RS. The code of this work is publicly available at https://***/jakhac/CSMAE. © 2024, CC BY.

关键词： Self-supervised learning

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An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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Smart water quality analysis using IoT and big data analytics: A review 2

Smart water quality analysis using IoT and big data analytic...

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2nd IEEE International Conference on Electronic Engineering, ICEEM 2021

作者： Hemdan, Ezz El-Din Essa, Youssef M. El-Sayed, Ayman Shouman, Marwa Moustafa, Abdullah N. Menofia University Computer Science and Engineering Department Faculty of Electronic Engineering Menouf Egypt Principle Data and Knowledge. Bayer Berlin Germany

ISBN: (纸本)9781665418423

In recent times, water quality monitoring, observing, and testing have become a significant research study with the advancement on the Internet of Things, and Big data analytics research ideas. The relationship between water demand and supply is very crucial for every country and is likewise a complex challenge to satisfy this requirement around the world. Water is compulsory for human survival being on the earth. Therefore, for survival, the preservation and taking care of the existing water resource are also equally substantial. Moreover, for a healthier society, access to clean and safe water resources is also imperative. To recognize the water quality effects and provide an automated water quality mentoring and a testing system can support in guaranteeing the safety of the water. This paper presents a study on water quality analysis using IoT and Big data analytics. This can help in developing an agile environment that can handle the massive flow of water big data generated by the smart sensors spread everywhere around us. © 2021 IEEE.

关键词： data Analytics

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A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables

arXiv

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arXiv 2023年

作者： Jain, Pranay Ding, Cheng Rudin, Cynthia Hu, Xiao Department of Computer Science Duke University United States Department of Biomedical Engineering Georgia Institute of Technology Emory University United States Center for Data Science Nell Hodgson Woodruff School of Nursing Associated Faculty of Biomedical Informatics School of Medicine Associated Faculty of Computer Science College of Arts and Sciences Emory University United States

Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG signals collected from such devices are susceptible to corruption from noise and motion artifacts, which cause errors in heart rate estimation. Typical denoising approaches filter or reconstruct the signal in ways that eliminate much of the morphological information, even from the clean parts of the signal that would be useful to preserve. In this work, we develop an algorithm for denoising PPG signals that reconstructs the corrupted parts of the signal, while preserving the clean parts of the PPG signal. Our novel framework relies on self-supervised training, where we leverage a large database of clean PPG signals to train a denoising autoencoder. As we show, our reconstructed signals provide better estimates of heart rate from PPG signals than the leading heart rate estimation methods. Further experiments show significant improvement in Heart Rate Variability (HRV) estimation from PPG signals using our algorithm. We conclude that our algorithm denoises PPG signals in a way that can improve downstream analysis of many different health metrics from wearable devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Photoplethysmography

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Automated Detection of Alzheimer’s Disease Using EfficientNet-B3 Architecture with Transfer Learning

Automated Detection of Alzheimer’s Disease Using EfficientN...

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Information Technology (ACIT)

作者： Ahmad Aburomman Suhaila Abuowaida Adai Al-Momani Esraa Al Henawi Esraa Abu Elsoud Zaher Salah Haneen Alzoubi Huah Yong Chan Mohammed Khouj Department of Data Science and AI Zarqa University Zarqa Jordan Department of Computer Science AI Al-Bayt University Mafraq Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Gelugor Pulau Pinang Malaysia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

In order to bring forward expansion in both the accuracy and the processing speed of the detection of Alzheimer’s disease, an automated approach for Alzheimer’s disease diagnosis with passable EfficientNet-B3 neural network and transfer learning techniques is provided in this work. After direct fitness on a large number of annotated brain images, the model of EfficientNet-B3 is further trained, in an effort to obtain worse depicted quality connected with Alzheimer’s Disease. To hasten the process of training and to make the model fit for this specific task, transfer learning was employed. The results of these experiments prove the efficiency of our approach to the diagnostic Alzheimer’s disease compared to the most advanced and regular deep learning methods. Thus, the model may also withstand the test amidst diverse clinical populations by performing well in dementia classification with precision and recall performance coupled with over 99% accuracy. These findings demonstrate how the EfficientNet-B3 architecture and transfer learning enhance generalization across.

关键词： Training Deep learning Accuracy Transfer learning Brain modeling Medical diagnosis Alzheimer's disease Information technology Biological neural networks

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Quantile-based Clustering for Functional data via Modelling Functional Principal Components Scores

Quantile-based Clustering for Functional Data via Modelling ...

引用

2022 International Conference on Applied Mathematics and Digital Simulation, AMDS 2022

作者： Hael, Mohanned A. Qiang Ma, Hai Al-Kuhali, Hamas A. Rizk, Zeinab School of Statistics Jiangxi University of Finance and Economics Nanchang330013 China Department of Data Science and Information Technology Taiz University Taiz9674 Yemen School of Computer & Artificial Intelligence Wuhan University of Technology Wuhan430070 China Faculty of Commerce Damietta University Damietta El-Gadeeda City34511 Egypt

Clustering tasks of functional data arise naturally in many applications, and efficient classification approaches are needed to find groups. The current paper combines the quantile-based model with the principal component analysis of functional data (FPCA). In our proposed procedures, the projection of functional data is first approximated based on (rotated) FPCA. The quantile-based model is then implemented on the space of rotated scores to identify the potential features of underlying clusters. The proposed method overcomes the limitation of using direct basis function expansion such as Fourier, B-spline, or linear fitting, besides representing a nonparametric clustering alternative based on a quantile approach. The proposed method's performance has been evaluated in a comprehensive simulation study and afterward compared with existing functional and non-functional clustering methods. The simulation study results showed that the proposed method performs well in terms of correct classification rate and computing time average. Finally, a real-world application concerning temporal wind speed data has been analyzed to demonstrate the proposed method's advantages and usefulness. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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Machine Unlearning Through Fine-Grained Model Parameters Perturbation

arXiv

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arXiv 2024年

作者： Zuo, Zhiwei Tang, Zhuo Li, Kenli Datta, Anwitaman College of Computer Science and Electronic Engineering Hunan University China College of Computing and Data Science Nanyang Technological University Singapore Shenzhen Research Institute Hunan University China Faculty of Computing Engineering & Media De Montfort University Leicester United Kingdom

Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while keeping the computational costs tractable. However, commonly used training data are independent and identically distributed, for inexact machine unlearning, current metrics are inadequate in quantifying unlearning degree that occurs after unlearning. To address this quantification issue, we introduce SPD-GAN, which subtly perturbs data distribution targeted for unlearning. Then, we evaluate unlearning degree by measuring the performance difference of the models on the perturbed unlearning data before and after unlearning. Furthermore, to demonstrate efficacy, we tackle the challenge of evaluating machine unlearning by assessing model generalization across unlearning and remaining data. To better assess the unlearning effect and model generalization, we propose novel metrics, namely, the forgetting rate and memory retention rate. By implementing these innovative techniques and metrics, we achieve computationally efficacious privacy protection in machine learning applications without significant sacrifice of model performance. A by-product of our work is a novel method for evaluating and quantifying unlearning degree. © 2024, CC BY.

关键词： Differential privacy

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Protecting Consumer Electronics Human-computer Interactive Verification Security via Anomaly-Aware Reconstruction-Guided Forgery Localization

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Jin, Zehui Zeng, Yangyan Liu, Tenghao Liu, Xinyue Leng, Biao Zhou, Xiaokang Beihang University School of Computer Science and Engineering Beijing100191 China Xiangjiang Laboratory Changsha410205 China Kansai University Faculty of Business Data Science Osaka565-8535 Japan RIKEN RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan

With the popularity of computer vision, many consumer electronic applications now require users to interactively authenticate their identities through cameras while handling business. However, numerous spoofing technologies were proposed in recent years, which enables attackers to perform manipulation during verification. Such manipulations can easily bypass the human-computer-interaction verification, allowing attackers to impersonate others to commit crimes or fraud. Diffusion, as the SOTA generative model, can flexibly reprint image content according to prompt guidance, evading traditional forgery detection techniques. Therefore, there is an urgent need for countermeasures on diffusion manipulation and protects data security of consumer electronics applications. However, existing studies suffer from overfitting specific forgery traces in the training dataset with limited ability on unseen diffusions. In this paper, we propose a novel diffusion-manipulated forgery localization framework leveraging Anomaly-aware Image Reconstruction Deviation(AIRD). Specifically, we train a reconstruction model capable of recovering detailed texture of authentic images, regarding various spoof traces as uniform anomalies and conducts segmentation according to reconstruction deviation. In this case, the proposed method avoids overfitting specific generators, thus achieving better generalization performance in cross-domain scenarios. Extensive experiments on the multiple datasets showcase our effectiveness in extracting general forgery artifacts facing unseen scenarios and diffusion models. © 1975-2011 IEEE.

关键词： Authentication

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