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4th Asia-Pacific Conference on Communications Technology and computer science, ACCTCS 2024

作者： Deng, Peng Jia, Wenjun Yang, Nan Xiao, Liwen School of Computer Science and Software Engineering Southwest Petroleum University Nanchong China

ISBN: (纸本)9798350359985

Due to the aging of the world's population, the incidence of retinal diseases is on the rise. Machine learning is expected to have a crucial role in identifying retinal disease. Multiple medical institutions cooperate on retinal disease identification using machine learning, which can effectively break down data silos, but it is often accompanied by privacy concerns. Federated learning (FL) solves the privacy problem in the process of collaborative model training, where data of each medical institution does not leave the local to avoid data leakage. With a series of privacy laws coming into place, it is critical to give medical institutions participating in FL training the right to be forgotten. Federated unlearning (FU) allows a medical institution requesting to exit the cooperation to eliminate its contribution from the global model. We simulate the scenarios of cooperation among multiple medical institutions on retinal disease identification, using data augmentation to deal with an imbalanced dataset and backdoor attacks to evaluate the effects of FU. Experimental results show that FL and FU have potential for application in cooperation among multiple medical institutions on retinal disease identification. © 2024 IEEE.

关键词： Federated learning

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Invisible Fingerprint-based Copyright Protection for Generated Medical Image 2

Invisible Fingerprint-based Copyright Protection for Generat...

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2nd IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2024

作者： Yang, Nan Jia, Wenjun Deng, Peng Yang, Wen School of Computer Science and Software Engineering Southwest Petroleum University Nanchong China

ISBN: (纸本)9798350380958

In the recent years, the development of generative models has stimulated the health care progress, specially medical image generation. The synthetic medical images can be applied to several fields and have many utilization, such as data aug- mentation for model training. However, this might lead to the significant copyright issues. Firstly, the data for model training may be the privacy data belong to individuals. Secondly, owners of well-trained model may have burden computation cost. Both of these two stakeholders are not will to share model to others without any compensation or the risk of the image abuse. In order to protect the copyright of the generative model for medical image and reduce the risk of the image abuse, we propose Copyright Protection mechanism for Generated Medical Image (CP4GMI) by inducing invisible fingerprint. In other words, the generated medical image should have the unique fingerprint belong to the corresponding generative model, which can be examined when image abuse happens. Numerous experiments conducted on different medical image datasets shows that our approach can track the generative model in a high-accuracy level when the fingerprint in the generated image is mostly invisible. From the perspective of users and model inventors, we propose a copyright protection mechanism that introduces invisible fingerprints, which can be printed on the generated medical images, and can be used to protect the rights when the copyright is threatened. © 2024 IEEE.

关键词： Copyrights

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Renaissance of Fuzzy and Fast Matching Entity with DSHS Algorithm

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SN computer science 2024年第6期5卷 734页

作者： Kari, Venkatram Amalanathan, Geetha Mary School of Computer Science and Engineering Vellore Institute of Technology University

Entity matching is a crucial aspect of data management systems, requiring the identification of real-world entities from diverse expressions. Despite the human ability to recognize equivalences among entities, machines struggle due to variations in expression. This challenge underscores the necessity for a robust framework capable of matching entities based on their attributes. While previous studies have explored transformation techniques, NLP, and deep learning, they often fall short in accuracy and scalability or overlook internal constraints. Such shortcomings result in costly rework and compromised business decisions. To address these issues, proposed “Deep Semantic Homophonic Synonymy” (DSHS) algorithm, a hybrid approach integrating rule-based, NLP, and deep learning techniques. By employing semantic matching, rule-based preprocessing, NLP enrichment, phonetic analysis, abbreviation variations, and spelling corrections, the DSHS algorithm effectively overcomes entity matching challenges. This solution, comprising six modules including Preprocessing, Transformation, Translation, Word Embedding, Attribute Similarity, and Classification, outperforms existing EM models in terms of accuracy and scalability. This solution is a comprehensive entity matching, offering significant advancements in the field and providing a reliable framework for resolving complex matching requirements. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data pipeline Data sync up MDDR Message queue Schema registry

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Collaborative graph contrastive learning for recommendation

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Journal of Intelligent and Fuzzy Systems 2025年第1-2期48卷 33-46页

作者： Wei, Tao Yang, Changchun Zheng, Yanqi Zhang, Jingxue School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou University Jiangsu Changzhou China School of Microelectronics and Control Engineering Changzhou University Jiangsu Changzhou China

Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods. © 2025 – IOS Press. All rights reserved.

关键词： Self-supervised learning

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Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

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Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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PathActMarker:an R package for inferring pathway activity of complex diseases

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Frontiers of computer science 2025年第3期19卷 123-124页

作者： Xingyi LI Jun HAO Zhelin ZHAO Junming LI Xingyu LIAO Min LI Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi... 详细信息

关键词： pathway activity dysregulation complex diseases biological pathways disruption key biological pathwaysit identify dysfunctional pathways

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Event-based sliding mode control under denial-of-service attacks

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science China(Information sciences) 2022年第6期65卷 125-141页

作者： Yingxin TIAN Xiang LI Bo DONG Yabin GAO Ligang WU Department of Control Science and Engineering Harbin Institute of Technology School of Software Harbin Institute of Technology

This paper deals with the problem of estimator-based sliding mode control against denial-of-service(DoS) attacks and discrete events via a time-delay approach. A networked system is considered an uncertain dynamical system with matched and mismatched perturbations and exogenous disturbance in the network environment. A network-resource-aware event-triggering mechanism is designed with aperiodically releasing system measurements. Furthermore, to describe the DoS attack duration and inter-event time, a time-delay modeling approach considers the DOS attack duration and inter-event time as a “time delay” of the measurements between the sensor and controller over the network is proposed. Consequently, an intervaltime-delay system with uncertainties is formulated. A state-observer-based sliding mode controller, by which the ideal sliding mode can be achieved, is proposed against the DoS attacks. The resulting sliding motion is proved to be robust and stable with an Lgain performance. Finally, the effectiveness and applicability of the present sliding mode control are validated in a simulated pendulum system.

关键词： sliding mode control time delay uncertain system state observer event trigger

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Dual modality prompt learning for visual question-grounded answering in robotic surgery

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 316-328页

作者： Yue Zhang Wanshu Fan Peixi Peng Xin Yang Dongsheng Zhou Xiaopeng Wei National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622LiaoningChina School of Computer Science and Technology Dalian University of TechnologyDalian 116081LiaoningChina

With recent advancements in robotic surgery,notable strides have been made in visual question answering(VQA).Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the *** limitation restricts the interpretative capacity of the VQA models and their abil-ity to explore specific image *** address this issue,this study proposes a grounded VQA model for robotic surgery,capable of localizing a specific region during answer *** inspiration from prompt learning in language models,a dual-modality prompt model was developed to enhance precise multimodal information ***,two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model.A visual complementary prompter merges visual prompt knowl-edge with visual information features to guide accurate *** textual complementary prompter aligns vis-ual information with textual prompt knowledge and textual information,guiding textual information towards a more accurate inference of the ***,a multiple iterative fusion strategy was adopted for comprehensive answer reasoning,to ensure high-quality generation of textual and grounded *** experimental results vali-date the effectiveness of the model,demonstrating its superiority over existing methods on the EndoVis-18 and End-oVis-17 datasets.

关键词： Prompt learning Visual prompt Textual prompt Grounding-answering Visual question answering

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Research on lightweight pavement disease detection model based on YOLOv7

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10573-10589页

作者： Wang, Chishe Li, Jun Wang, Jie Zhao, Weikang School of Computer Science and Engineering Anhui University of Science and Technology Huainan China Jinling Institute of Technology Nanjing China

Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road disease detection model. Our approach involves integrating MobilieNetV3 as the backbone feature extraction network to reduce the network's parameters and computational requirements. Additionally, we introduce the BRA attention module into the spatial pyramid pooling module to eliminate redundant information and enhance the network's feature representation capability. Moreover, we utilize the F-ReLU activation function in the backbone network, expanding the convolutional layers' receptive field range. To optimize the model's boundary loss, we employ the Wise-IoU loss function, which places more emphasis on the quality of ordinary samples and enhances the overall performance and generalization ability of the network. Experimental results demonstrate that our improved detection algorithm achieves a higher recall rate and mean average precision (mAP) on the public dataset (RDD) and the NJdata dataset in Nanjing's urban area. Specifically, compared to YOLOv7, our model increases the recall rate and mAP on RDD by 3.3% and 2.6%, respectively. On the NJdata dataset, our model improves the recall rate and mAP by 1.9% and 1.3%, respectively. Furthermore, our model reduces parameter and computational requirements by 30% and 22.5%, respectively, striking a balance between detection accuracy and speed. In conclusion, our road disease detection model presents an effective solution to address the challenges associated with road disease detection in urban areas. It offers improved accuracy, efficiency, and generalization capabilities compared to existing models. © 2024 - IOS Press. All rights reserved.

关键词： Roads and streets

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Domain generalization with semi-supervised learning for people-centric activity recognition

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science China(Information sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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