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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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Malicious Clients and Contribution Co-Aware Federated Unlearning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Wang, Yang Li, Xue Chen, Siguang Nanjing University of Posts and Telecommunications Jiangsu Engineering Research Center of Communication and Network Technology Nanjing China Women’s Hospital of Nanjing Medical University Nanjing Women and Children’s Healthcare Hospital Department of Dermatology Nanjing China Hohai University College of Computer Science and Software Engineering Nanjing China

Existing federated unlearning methods to eliminate the negative impact of malicious clients on the global model are influenced by unreasonable assumptions (e.g., an auxiliary dataset) or fail to balance model performance and efficiency. To overcome these shortcomings, we propose a malicious clients and contribution co-aware federated unlearning (MCC-Fed) method. Specifically, we introduce a method for detecting malicious clients to reduce their impact on the global model. Next, we design a contribution-aware metric, which accurately quantifies the negative impact of malicious clients on the global calculating their historical contribution ratio. Then, based on this metric, we propose a novel federated unlearning method in which benign clients use the contribution-aware metric as a regularization term to unlearn the influence of malicious clients, and restoring model performance. Experimental results demonstrate that our method effectively addresses the issue of excessive unlearning during the unlearning process, improves the efficiency of performance recovery, and enhances robustness against malicious clients. © 2020 IEEE.

关键词： Federated learning federated unlearning malicious clients

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Two-Stream Architecture Using RGB-based ConvNet and Pose-based LSTM for Video Action Recognition

Two-Stream Architecture Using RGB-based ConvNet and Pose-bas...

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International Conference on Innovations in Information Technology

作者： Ching-Jung Huang Munkhjargal Gochoo Tan-Hsu Tan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Software Engineering Zayed Center for Health Sciences United Arab Emirates University Al Ain United Arab Emirates

Traditional methods for video recognition require hand-crafted features, which often involves offline pre-processing for real-world videos. In this study, we propose a conceptually simple framework that directly takes raw videos as an input source for activity recognition. Our framework consists of two streams, namely a spatial stream and a temporal stream. The spatial stream is trained on RepVGG-B0 ConvNet using cropped RGB features, while the temporal stream uses an attention-based Bi-directional Long Short-Term Memory (Bi-LSTM) network to learn posture vectors from human pose data obtained through Faster R-CNN pre-trained model. Our proposed method is evaluated on a standard video action recognition benchmark, MSR Daily Activity3D, and proves to be competitive with state-of-the-art action recognition methods. We achieve state-of-the-art performance on MSR Daily Activity3D with a precision and recall rate of 99.01% and 98.91%, respectively. Our results demonstrate the effectiveness of our approach in recognizing video actions.

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Predicting Mutation-Disease Associations Through Protein Interactions Via Deep Learning

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4512-4523页

作者： Xue Li Ben Cao Jianmin Wang Xiangyu Meng Shuang Wang Yu Huang Enrico Petretto Tao Song School of Computer Science and Technology China University of Petroleum (East China) Qingdao Shandong China Centre for Computational Biology Duke-NUS Medical School Singapore Singapore School of Computer Science and Technology Dalian University of Technology Dalian Liaoning China Department of Integrative Biotechnology Yonsei University Incheon South Korea National Engineering Research Center for Software Engineering Peking University Beijing China

Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the costs of wet experiments and constraints in sample types and scales. Therefore, this paper constructs a real-world mutation-induced disease dataset and proposes Capsule and Graph topology networks with Multi-head attention (CGM) to predict the mutation-disease associations. CGM can accurately predict protein mutation-disease associations, and to further elucidate the pathogenicity of protein mutations, we also verified that protein mutations lead to protein structural alterations by the model, which suggests that mutation-induced conformational changes may be an important pathogenic factor. Limited by the size of the mutated protein dataset, we also performed experiments on benchmark and imbalanced datasets, where CGM mined 22 unknown protein interaction pairs from the benchmark dataset, better illustrating the potential of CGM in predicting mutation-disease associations. In summary, this paper curates a real dataset. It proposes that CGM predicts protein mutations and disease associations, providing a novel tool for further understanding of biomolecular pathways and disease mechanisms.

关键词： Proteins Diseases Protein engineering Predictive models Cancer Electronic mail Heart Deep learning Bioinformatics Tumors

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FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction

arXiv

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arXiv 2023年

作者： Peng, Tianshuo Li, Zuchao Zhang, Lefei Du, Bo Zhao, Hai National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Luojia Laboratory Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Universal Information Extraction (UIE) has been introduced as a unified framework for various Information Extraction (IE) tasks and has achieved widespread success. Despite this, UIE models have limitations. For example, they rely heavily on span boundaries in the data during training, which does not reflect the reality of span annotation challenges. Slight adjustments to positions can also meet requirements. Additionally, UIE models lack attention to the limited span length feature in IE. To address these deficiencies, we propose the Fuzzy Span Universal Information Extraction (FSUIE) framework. Specifically, our contribution consists of two concepts: fuzzy span loss and fuzzy span attention. Our experimental results on a series of main IE tasks show significant improvement compared to the baseline, especially in terms of fast convergence and strong performance with small amounts of data and training epochs. These results demonstrate the effectiveness and generalization of FSUIE in different tasks, settings, and scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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A New Identity-Based Encryption Scheme with Accountable Authority Based on SM9 11

A New Identity-Based Encryption Scheme with Accountable Auth...

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11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Wang, Ke Zhao, Yuan Luo, Song Guan, Zhi MoE Beijing China Department of Computer Science and Technology EECS Peking University Beijing China School of Computer Science and Engineering Chongqing University of Technology Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9781643682402

Accountable authority identity-based encryption (A-IBE) is an extension of identity-based encryption (IBE) in which private key's source can be traced, i.e., whether the key comes from a private key generator or a user. SM9 is an official cryptography standard of China which defines a practical IBE scheme. In this paper, we construct a practical A-IBE scheme from the SM9-IBE scheme. Our A-IBE scheme has public traceability and is proven secure if the based SM9-IBE scheme is secure. Compared with other A-IBE schemes, our A-IBE scheme has better efficiency in encryption and decryption. © 2022 The authors and IOS Press.

关键词： Cryptography

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Simple In-place Data Augmentation for Surveillance Object Detection

Simple In-place Data Augmentation for Surveillance Object De...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Munkh-Erdene Otgonbold Ganzorig Batnasan Munkhjargal Gochoo Department of Computer Science and Software Engineering United Arab Emirates University UAE Department of Electronics Mongolian University of Science and Technology Mongolia Emirates Center for Mobility Research United Arab Emirates University UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.

关键词： Image segmentation Surveillance Scalability Conferences Object detection Data augmentation Cameras

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Bilateral Contract for Load Frequency and Renewable Energy Sources Using Advanced Controller

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computers, Materials & Continua 2022年第11期73卷 3165-3180页

作者： Krishan Arora Gyanendra Prasad Joshi Mahmoud Ragab Muhyaddin Rawa Ahmad H.Milyani Romany F.Mansour Eunmok Yang School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwara144001PunjabIndia School of Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia King Abdullah City for Atomic and Renewable Energy(K.A.CARE) RiyadhSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoul02707Korea

Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated ***,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation *** regular active load and inefficiency of best measures among varied associates is a huge *** sudden load deviation will give rise to immediate amendment in frequency and tie-line power *** is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the ***,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power *** the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks *** proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test *** experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s *** model can also be leveraged to examine the decision boundaries of the Bilateral case.

关键词： Bilateral contract load frequency control optimization harris hawks optimizer

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Application of Data Science and Machine Learning in the Prediction of College Dropout: A Data-Driven Predictive Approach

Application of Data Science and Machine Learning in the Pred...

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International Conference on software Process Improvement (CIMPS)

作者： Axel Frederick Félix Jiménez Vania Stephany Sanchéz Lee Isaul Ibarra Belmonte Ezra Federico Parra González Engineering in Computer Systems National Polytechnic Institute Zacatecas México Software Engineer Center for Research in Mathematics Zacatecas México Computer Science Department Center for Research in Mathematics Zacatecas México

ISBN: (数字)9798350358568

ISBN: (纸本)9798350358575

This paper presents a study on the prediction of student graduation or failure using two predictive models: K-Nearest Neighbors (KNN) and a forward sequential Artificial Neural Network (ANN). The models, built with a well-chosen set of independent variables, were assessed using metrics like precision and accuracy. The results obtained revealed that both the KNN model and the sequential forward ANN model achieved high efficiency in predicting student graduation or failure, achieving accuracies of 0.9133% (K=3) and 0.9312% (after 50 epochs), respectively. Providing a valuable tool to identify early on students at risk of not graduating and to take preventive measures to improve their academic performance. Comparisons with related research showed consistent outcomes, underscoring the credibility and importance of the employed predictive models.

关键词： Measurement Artificial neural networks Machine learning Predictive models Data science software

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification

A Novel Attention-based Explainable Deep Learning Framework ...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Gladys W. Muoka Ding Yi Chiagoziem Chima Ukwuoma Moindi Duke Martin Ahmet Arif Aydin Mugahed A. Al-Antari School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China Department of Computer Science Unicaf University Lilongwe Lilongwe Malawi Department of Computer Engineering Faculty of Engineering İnönü University Turkey Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul Korea

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study’s primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics.

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