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Semi-Supervised Gland Segmentation via Feature-Enhanced Contrastive Learning and Dual-Consistency Strategy

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4314-4324页

作者： Yu, Jiejiang Li, Bingbing Pan, Xipeng Shi, Zhenwei Wang, Huadeng Lan, Rushi Luo, Xiaonan Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Ganzhou Municipal Hospital Department of Pathology Ganzhou341000 China Southern Medical University Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China

In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gland with the background. To address this challenge, we propose a new semi-supervised method named DCCL-Seg for gland segmentation, which follows the teacher-student framework. Our approach can be divided into follows steps. First, we design a contrastive learning module to improve the ability of the student model's feature extractor to distinguish between gland and background features. Then, we introduce a Signed Distance Field (SDF) prediction task and employ dual-consistency strategy (across tasks and models) to better reinforce the learning of gland internal. Next, we proposed a pseudo label filtering and reweighting mechanism, which filters and reweights the pseudo labels generated by the teacher model based on confidence. However, even after reweighting, the pseudo labels may still be influenced by unreliable pixels. Finally, we further designed an assistant predictor to learn the reweighted pseudo labels, which do not interfere with the student model's predictor and ensure the reliability of the student model's predictions. Experimental results on the publicly available GlaS and CRAG datasets demonstrate that our method outperforms other semi-supervised medical image segmentation methods. © 2013 IEEE.

关键词： Contrastive Learning

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VHASR: A Multimodal Speech Recognition system With Vision Hotwords

VHASR: A Multimodal Speech Recognition System With Vision Ho...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hu, Jiliang Li, Zuchao Wang, Ping Ai, Haojun Zhang, Lefei Zhao, Hai Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9798891761643

The image-based multimodal automatic speech recognition (ASR) model enhances speech recognition performance by incorporating audio-related image. However, some works suggest that introducing image information to model does not help improving ASR performance. In this paper, we propose a novel approach effectively utilizing audio-related image information and set up VHASR, a multimodal speech recognition system that uses vision as hotwords to strengthen the model's speech recognition capability. Our system utilizes a dual-stream architecture, which firstly transcribes the text on the two streams separately, and then combines the outputs. We evaluate the proposed model on four datasets: Flickr8k, ADE20k, COCO, and OpenImages. The experimental results show that VHASR can effectively utilize key information in images to enhance the model's speech recognition ability. Its performance not only surpasses unimodal ASR, but also achieves SOTA among existing image-based multimodal ASR. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi-Scale Feature Extraction with Supervised Contrastive Learning for Vulnerability Detection 36

Multi-Scale Feature Extraction with Supervised Contrastive L...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhao, Yahui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

The capacity of Deep Learning to automatically learn features from source code has facilitated its extensive utilization in detecting software vulnerabilities. However, existing pre-trained models regard code snippets as token sequences, neglecting the inherent structure of the code. The utilization of graph-based code representations is constrained by the limitations of Graph Neural Networks, particularly the difficulty in capturing long-range dependencies, which renders it challenging to learn grammatical and semantic information from complex code graph representations. This paper proposes a novel multi-scale software vulnerability detection method based on supervised contrastive learning. The method integrates local features obtained from code paths with structural features extracted from the Code Property Graph by GraphTrans, thereby achieving a multi-scale feature representation of the source code. Additionally, a supervised contrastive loss function is employed during training in order to fully utilize label information and address the class imbalance problem. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods, achieving the highest accuracy, precision, and F1 on the real-world benchmark dataset CodeXGLUE for software vulnerability detection. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Contrastive Learning

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A Short-Term Electric Load Forecasting Method Based on the Fusion of Global and Local Features 9

A Short-Term Electric Load Forecasting Method Based on the F...

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Gu, Haozhan Tian, Hangwei Wang, Jingchun Li, Yumei Shandong University Of Arts Academy of Fine Arts China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Accurate short-term electric load forecasting is crucial for ensuring the stability and efficiency of the power grid. This study proposes an innovative dual-branch electric load forecasting model that enhances the accuracy of predictions by processing global and local features in parallel. Global features are extracted through the Savitzky-Golay filter and a linear model to capture the overall trends of the load data. Local features are identified using the Convolutional Self-Attention Module, which integrates the local perception capabilities of Convolutional neural network with the self-attention mechanism of Transformers. By employing a Weighted Feature Fusion Network, we effectively integrate global and local information to enhance the performance of load forecasting. Experimental results demonstrate that the proposed model significantly outperforms existing models in predictive performance. The model presented in this study offers a new perspective on electric load forecasting and has the potential to become an effective tool for power system planning and scheduling. © 2024 IEEE.

关键词： Deep learning Feature fusion Load prediction Time series data analysis

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Vulnerability Detection Based on Pre-trained Code Language Model and Convolutional Neural Network 36

Vulnerability Detection Based on Pre-trained Code Language M...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Liao, Tingfeng Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

Software vulnerabilities damage the reliability of software systems. Recently, many methods based on deep learning have been proposed for vulnerability detection by learning features from code sequences or various property graphs. However, sequence-based methods frequently fail to consider the structural information of the code, whereas graph-based methods often encounter difficulties in capturing long-distance contextual semantic information. To overcome these limitations, we propose PTCN, a novel vulnerability detection method that integrates Pre-Trained code language model and Convolutional Neural Network (CNN). First, the code tokens sequence and the propagation chains are constructed from the source code in preparation for the subsequent feature extraction. Next, GraphCodeBERT, a graph-based pre-trained code language model, is introduced as a means of learning both the semantic and structural information contained in the code tokens sequence and the propagation chains. Then, a CNN architecture is designed to effectively extract the crucial vulnerability features from the hidden states of the last Transformer layer of GraphCodeBERT for vulnerability detection. The experiments on the public benchmark dataset REVEAL indicate that PTCN outperforms the state-of-the-art methods with respect to the Accuracy and F1-score metrics by 3.41%-15.80% and 22.68%-104.27%, respectively. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Convolutional neural networks

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A Novel Probabilistic Saturating Counter Design for Secure Branch Predictor

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Journal of computer science & technology 2021年第5期36卷 1022-1036页

作者： Lu-Tan Zhao Rui Hou Kai Wang Yu-Lan Su Pei-Nan Li Dan Meng State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences Beijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100093China Department of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China

In a modern processor,branch prediction is crucial in effectively exploiting the instruction-level parallelism for high-performance ***,recently exposed vulnerabilities reveal the urgency to improve the security of branch *** vital cause of the branch predictor vulnerabilities is that the update strategy of the saturating counter is *** a fundamental building block in a modern branch predictor,previous studies have paid too much attention to the performance and hardware cost and ignored the security of saturating *** leaves attackers with the opportunities to perform side-channel attacks on the branch *** paper focuses on the saturating counter to explore a secure and lightweight design to mitigate branch predictor side-channel *** of applying the isolation mechanism to branch predictor resources,we propose a novel probabilistic saturating counter design to confuse the attacker's perception of the victim's *** changes the conventional deterministic state transition function to a probabilistic state transition *** a branch is committed,the conventional saturating counter needs to be updated about whether the prediction results are correct or *** for the probabilistic saturating counter,the branch predictor determines whether the update is performed based on the update *** probabilistic saturating counter dramatically reduces the ability of the attacker to spy the saturating counter's *** analyses using a cycle-accurate simulator suggest that the proposed mechanism incurs 2.4%performance overhead and hardware cost while providing strong protection.

关键词： branch predictor side-channel attack saturating counter

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Fake News Detection Across Multiple Domains Using Fuzzy Association Rules 24th

Fake News Detection Across Multiple Domains Using Fuzzy Asso...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Xiaofeng Zhang, Weisha Huang, Yuanyuan Lu, Hongyu Lu, Jiazhong School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China School of Foreign Languages University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615247

The detection of fake news is not confined to a single domain, and due to domain transfer, the reliance on detection methods specific to a single domain becomes inadequate when dealing with multi-domain issues. When a multi-domain detection occurs, labels from one domain focus exclusively on the news characteristics of that particular domain, ignoring the possibility that part of the news may include functions from more than one domain. To address these challenges, we propose a multi-domain fake news detection model based on fuzzy rules, incorporating a fuzzy mechanism and an optimization module for fuzzy association rules. In this model, we first introduce a fuzzy mechanism that applies fuzzy processing to domains through neural networks, constructing fuzzy domain labels for each news domain. Subsequently, we incorporate fuzzy association rules to extract a broader range of sample features, thereby enhancing detection effectiveness. Experiments conducted on a fake news dataset demonstrate that our model achieves commendable detection performance with a relatively low training cost, reaching an F1 score of over 97.13% for sample detection. This model successfully overcomes the limitations of single-domain detection in fake news identification and improves the precision of feature extraction through fuzzy rules, showing superior performance in handling multi-domain fake news detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： fake news detection fuzzy rules multi-domain social media

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The Power of Bamboo: On the Post-Compromise security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed system security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed system security Symposium, NDSS 2023. All Rights Reserved.

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Rolling Horizon Co-evolution for Snake AI Competition 13th

Rolling Horizon Co-evolution for Snake AI Competition

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13th IFIP TC 12 International Conference on Intelligent information Processing, IIP 2024

作者： Li, Hui Zhou, Jiayi Zhang, Qingquan Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031578076

The Snake game, a classic in the gaming world, gains new dimensions with the Snake AI competition, where two players controlled by AI algorithms can now compete simultaneously in the same game session. This competition holds significance in advancing our understanding of artificial intelligence (AI) algorithms. In the 2020 and 2021 Snake AI competitions, popular algorithms, using graph-based search or heuristic strategies, demonstrate competitive performance, such as the A* algorithm, Monte Carlo Tree Search (MCTS). Contrary to these heuristic approaches, the Rolling Horizon Co-evolution Algorithm (RHCA), characterised by its core principles of rolling horizon evaluation and co-evolution, maintains two populations, one for each player, to co-evolve with each other without reliance on heuristics. RHCA has been verified its effectiveness in a two-player spaceship game. In this paper, we extend the RHCA application to the two-player Snake AI game, comparing it with other state-of-the-art methods. Additionally, we introduce various obstacles to create different complex scenarios, ensuring a comprehensive analysis. Experimental results reveal RHCA’s superior and stable performance, especially in resource-constrained and complex scenarios. Furthermore, an analysis of RHCA’s behaviours across maps with diverse obstacle scenarios highlights its ability to make intelligent decisions in competing with state-of-the-art methods. © IFIP International Federation for information Processing 2024.

关键词： Trees (mathematics)

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