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International Conference on Software Engineering (ICSE)

作者： Siyue Feng Wenqi Suo Yueming Wu Deqing Zou Yang Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab Jinyinhu Laboratory Wuhan China School of Cyber Science and Engineering HUST Wuhan China Nanyang Technological University Singapore School of Computer Science and Technology HUST Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings.

关键词： Codes Scalability Computational modeling Cloning Machine learning Detectors Feature extraction

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Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Wang, Shuo Song, Zhijian Academy for Engineering & Technology Fudan University Shanghai200433 China Digital Medical Research Center School of Basic Medical Science Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Shandong Computer Science Center China

Weakly supervised semantic segmentation (WSSS) with image-level labels intends to achieve dense tasks without laborious annotations. However, due to the ambiguous contexts and fuzzy regions, the performance of WSSS, especially the stages of generating Class Activation Maps (CAMs) and refining pseudo masks, widely suffers from ambiguity while being barely noticed by previous literature. In this work, we propose UniA, a unified single-staged WSSS framework, to efficiently tackle this issue from the perspective of uncertainty inference and affinity diversification, respectively. When activating class objects, we argue that the false activation stems from the bias to the ambiguous regions during the feature extraction. Therefore, we design a more robust feature representation with a probabilistic Gaussian distribution and introduce the uncertainty estimation to avoid the bias. A distribution loss is particularly proposed to supervise the process, which effectively captures the ambiguity and models the complex dependencies among features. When refining pseudo labels, we observe that the affinity from the prevailing refinement methods intends to be similar among ambiguities. To this end, an affinity diversification module is proposed to promote diversity among semantics. A mutual complementing refinement is proposed to initially rectify the ambiguous affinity with multiple inferred pseudo labels. More importantly, a contrastive affinity loss is further designed to diversify the relations among unrelated semantics, which reliably propagates the diversity into the whole feature representations and helps generate better pseudo masks. Extensive experiments are conducted on PASCAL VOC, MS COCO, and medical ACDC datasets, which validate the efficiency of UniA tackling ambiguity and the superiority over recent single-staged or even most multi-staged competitors. Codes will be publicly available here. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval 39

Learning Together Securely: Prototype-Based Federated Multi-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zuo, Ruifan Zheng, Chaoqun Zhu, Lei Lu, Wenpeng Xiang, Yuanyuan Li, Zhao Qu, Xiaofeng Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China School of Electronic and Information Engineering Tongji University China China Evay Info China School of Information Science and Engineering University of Jinan China

ISBN: (纸本)157735897X

With the proliferation of multi-modal data, safe and efficient multi-modal hashing retrieval has become a pressing research challenge, particularly due to concerns over data privacy during centralized processing. To address this, we propose Prototype-based Federated Multi-modal Hashing (PFMH), an innovative framework that seamlessly integrates federated learning with multi-modal hashing techniques. PFMH achieves fine-grained fusion of heterogeneous multi-modal data, enhancing retrieval accuracy while ensuring data privacy through prototype-based communication, thereby reducing communication costs and mitigating risks of data leakage. Furthermore, using a prototype completion strategy, PFMH tackles class imbalance and statistical heterogeneity in multi-modal data, improving model generalization and performance across diverse data distributions. Extensive experiments demonstrate the efficiency and effectiveness of PFMH within the federated learning framework, enabling distributed training for secure and precise multi-modal retrieval in real-world scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Non-salient region erasure for time series augmentation

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Frontiers of computer science 2022年第6期16卷 203-205页

作者： Pin LIU Xiaohui GUO Bin SHI Rui WANG Tianyu WO Xudong LIU State Key Lab of Software Development Environment(SKLSDE) Beihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data and Brain Computing(BDBC) Beijing100191China Hangzhou Innovation Institute Beihang UniversityHangzhou310052China School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an710049China

1 Introduction Time seriesaugmentationis an essential approachto solvethe overfitting problem on the time series classification(TSC)task[1,2].Although existing approaches perform better in mitigating this problem,none of them focus on protecting saliency regions on time *** key informative shapelets contained in these regions are the core basis for distinguishing categories(e.g.,upward spikes in ECG and high amplitude in Sensor).

关键词： Sensor series series

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Autoregressive Sequence Modeling for 3D Medical Image Representation 39

Autoregressive Sequence Modeling for 3D Medical Image Repres...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Siwen Wang, Churan Gao, Fei Su, Lixian Zhang, Fandong Wang, Yizhou Yu, Yizhou School of Computing and Data Science The University of Hong Kong Hong Kong Center on Frontiers of Computing Studies School of Computer Science Nat'l Eng. Research Center of Visual Technology Peking University China Deepwise AI Lab China State Key Lab of General Artificial Intelligence Inst. for Artificial Intelligence Peking University China

ISBN: (纸本)157735897X

Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse and comprehensive representations is particularly pronounced when considering the variability across different organs, diagnostic tasks, and imaging modalities. How to effectively interpret the intricate contextual information and extract meaningful insights from these images remains an open challenge to the community. While current self-supervised learning methods have shown potential, they often consider an image as a whole thereby overlooking the extensive, complex relationships among local regions from one or multiple images. In this work, we introduce a pioneering method for learning 3D medical image representations through an autoregressive pre-training framework. Our approach sequences various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence. By employing an autoregressive sequence modeling task, we predict the next visual token in the sequence, which allows our model to deeply understand and integrate the contextual information inherent in 3D medical images. Additionally, we implement a random startup strategy to avoid overestimating token relationships and to enhance the robustness of learning. The effectiveness of our approach is demonstrated by the superior performance over others on nine downstream tasks in public datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals

arXiv

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arXiv 2024年

作者： Hu, Qihan Wang, Daomiao Wu, Hong Liu, Jian Yang, Cuiwei Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai200093 China

The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging-typical health monitoring tasks-demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning less than 3% of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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SSI−FL: Self-sovereign identity based privacy-preserving federated learning

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Journal of Parallel and Distributed computing 2024年 191卷

作者： Haque, Rakib Ul Touhidul Hasan, A.S.M. Al-Hababi, Mohammed Ali Mohammed Zhang, Yuqing Xu, Dianxiang Department of Electrical & Computer Engineering University of Texas at San Antonio San Antonio United States School of Computer Science & Technology University of Chinese Academy of Sciences Beijing100049 China Division of Computing Analytics and Mathematics School of Science and Engineering University of Missouri-Kansas City Kansas CityMO64110 United States School of Computer Science & Technology University of Chinese Academy of Sciences Beijing101408 China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing100049 China

Traditional federated learning (FL) raises security and privacy concerns such as identity fraud, data poisoning attacks, membership inference attacks, and model inversion attacks. In the conventional FL, any entity can falsify its identity and initiate data poisoning attacks. Besides, adversaries (AD) holding the updated global model parameters can retrieve the plain text of the dataset by initiating membership inference attacks and model inversion attacks. To the best of our knowledge, this is the first work to propose a self-sovereign identity (SSI) and differential privacy (DP) based FL namely SSI−FL for addressing all the above issues. The first step in the SSI−FL framework involves establishing a secure connection based on blockchain-based SSI. This secure connection protects against unauthorized access attacks of any AD and ensures the transmitted data's authenticity, integrity, and availability. The second step applies DP to protect against model inversion attacks and membership inference attacks. The third step focuses on establishing FL with a novel hybrid deep learning to achieve better scores than conventional methods. The SSI−FL performance analysis is done based on security, formal, scalability, and score analysis. Moreover, the proposed method outperforms all the state-of-art techniques. © 2024

关键词： Blockchain

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Prediction of Echinococcosis Transmission in Qinghai Province Based on Machine Learning 20

Prediction of Echinococcosis Transmission in Qinghai Provinc...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Liu, Junyi Zhang, Yu-An Xiao, Jian Yao, Tian Guo, Zhihong Song, Rende School of Computer Technology and Application Qinghai university Xining Qinghai810016 China Qinghai university Qinghai Provincial Laboratory for Intelligent Computing and Application Xining Qinghai810016 China Academy of Animal Science and Veterinary Medicine Qinghai university Xining Qinghai810016 China Yushu Prefecture Animal Disease Prevention and Control Center Yushu Qinghai815099 China

ISBN: (纸本)9798350356328

This article collects data on echinococcosis infection in the population, dogs, livestock, and rodents of Qinghai Province through literature review. Four traditional machine learning methods are used to predict the positive rates of the population, dogs, livestock, and rodents in the next three years based on the collected disease rate data from the past six years (2016-2021). In addition, to expand the dataset of the affected population in Qinghai Province, the annual population disease rate and total number of people in Qinghai Province are analyzed to obtain the total number of people affected each year. The total number of people affected each year is dispersed to each day of the year by using Normal distribution to expand the dataset, and an improved LSTM model is used to fit the expanded dataset. After conducting experimental simulations on the prevalence data, it reveals that the prevalence of echinococcosis in the population, dogs, livestock, and rodents in Qinghai Province has been decreasing year by year since 2022. After conducting experiments on the expanded dataset of affected populations, it shows that the fitting results of the improved LSTM model were 8.7% lower than those of the original LSTM model in terms of MSE. Using the improved LSTM model to predict the daily prevalence of echinococcosis in subsequent years, it reveals that the annual prevalence of echinococcosis in the region is below 0.06 %, indicating that the overall trend of echinococcosis in Qinghai Province is controllable, and the measures taken by Qinghai Province to prevent and treat echinococcosis are effective. © 2024 IEEE.

关键词： Livestock

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Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving

arXiv

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arXiv 2023年

作者： Mei, Jilin Zhou, Junbao Hu, Yu Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China

In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmentation methods focus on camera data, and most of them only predict the novel classes without considering the base classes. This setting cannot be directly applied to autonomous driving due to safety concerns. Thus, we propose a few-shot 3D LiDAR semantic segmentation method that predicts both novel and base classes simultaneously. Our method tries to solve the background ambiguity problem in generalized few-shot semantic segmentation. We first review the original cross-entropy and knowledge distillation losses, then propose a new loss function that incorporates the background information to achieve 3D LiDAR few-shot semantic segmentation. Extensive experiments on SemanticKITTI demonstrate the effectiveness of our method. © 2023, CC BY-NC-ND.

关键词： Semantic Segmentation

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IntNet: A novel framework using reconstructed mutation samples and a biologically informed neural network for pathway analysis

IntNet: A novel framework using reconstructed mutation sampl...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Li Zhou Jie Li Weilong Tan School of Computer Science and Technology Harbin Institute of Technology Harbin China College of Computing Harbin Institute of Technology National Key Laboratory of Smart Farming Technology and Systems Harbin China Center for Medicines Nanjing Bioengineering (Gene) Technology Center for Medicines Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Most pathway analysis methods based on simple nucleotide variations overlook mutations occurring in genes outside the pathway, fail to address mutation heterogeneity with targeted measures, and do not consider the functional relationships among gene components within biomolecular networks when modeling the contributions of biomarkers to pathways. So, we proposed a novel framework, named IntNet, for pathway analysis based on simple nucleotide variations. The core components of IntNet are sample reconstruction (SC) and a new biologically informed neural network model (BINN). To address the difficulty of extracting effective features caused by the mutation heterogeneity, IntNet reconstructs mutation samples based on the hypothesis that mutations in patients with the same phenotype may cluster in similar network regions. BINN uses the human gene network and expanded biological pathways to construct its neural network architecture. BINN implements mutation signaling propagation along the human gene network and integration within the pathways. IntNet's performance was validated using multiple cancer datasets. Experimental results demonstrated that IntNet could identify more accurate and reliable pathways.

关键词： Analytical models Accuracy Phenotypes Biological system modeling Biomarkers Feature extraction Reliability Biological neural networks Nuclear measurements Cancer

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