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18th ACM/IEEE International Symposium on Empirical Software engineering and Measurement, ESEM 2024

作者： Deng, Yang Wang, Bangchao Zou, Zhiyuan Ye, Luyao School of Computer Science and Artificial Intelligence Wuhan Textile University China Engineering Research Center of Hubei Province for Clothing Information Wuhan Textile University China

ISBN: (纸本)9798400710476

In recent years, Prompt Learning, based on pre-training, prompting, and prediction, has achieved significant success in natural language processing (NLP). The current issue-commit link recovery (ILR) method converts the ILR into a classification task using pre-trained language models (PLMs) and dedicated neural networks. However, due to inconsistencies between the ILR task and PLMs, these methods not fully leverage the semantic information in PLMs. To imitate the above problem, we make the first trial of the new paradigm to propose a Multi-template prompt learning method with adversarial training for issue-commit link recovery (PromptLink), which transforms the ILR task into a cloze task through the template. Specifically, a Multi-template PromptLink is designed to enhance the generalisation capability by integrating various templates and adopting adversarial training to mitigate the model overfitting. Experiments are conducted on six open-source projects and comprehensively evaluated across six commonly measures. The results show that PromptLink achieves an average F1 of 96.10%, Precision of 96.49%, Recall of 95.92%, MCC of 94.04%, AUC of 96.05%, and ACC of 98.15%, significantly outperforming existing state-of-the-art methods on all measures. Overall, PromptLink not only enhances performance and generalisation but also emerges new ideas and methods for future research. The source code of PromptLink is available at https://***/s/6130d42ff464c579cdec. © 2024 ACM.

关键词： Adversarial machine learning

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Soft Global-Local Representation Learning and Labeling for Cross-Domain Facial Expression Recognition 10

Soft Global-Local Representation Learning and Labeling for C...

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10th International Conference on computer and Communications, ICCC 2024

作者： Lin, Jiyuan He, Wu Zheng, Huicheng School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Center Technology Desay Sv Automotive Co. Ltd Shenzhen China

Cross-Domain Facial Expression Recognition (CD-FER) is concentrated on learning transferable knowledge to understand human emotion states automatically in the cross-domain scenario and has achieved much progress in the past decade. Most works extract both global and local features and adopt adversariallearning mechanism to learn domain-invariant features, which takes facial landmarks as prior knowledge and overlooks the unreliability, resulting in suboptimal performance. To solve this limitation, we propose a Soft Global-Local Representation Learning and Labeling (SGLRLL) framework for reliable and effective unsupervised facial expression domain adaptation. Specifically, instead of extracting local features with unreliable prior facial landmarks, we propose a Local Clue Searching (LCS) module to look up the local regions adaptively and extract the local features in a soft manner. We also propose a Joint Soft Pseudo Label Generator (JSPLG) based on learned Joint Gaussian distributions for assigning pseudo labels and weights to the features of unlabeled samples from the target domain, which considers the correlation between global-local learning and achieves a trade-off between the quality and quantity of pseudo labels. The cross-dataset results demonstrate that the proposed framework outperforms existing competing methods while requiring fewer parameters and enabling faster inference. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Expression Image Recognition and Classification Based on Multi stimulus Perception Deep Learning in Intelligent Learning Environment 24

Research on Expression Image Recognition and Classification ...

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5th International Conference on computer Information and Big Data Applications, CIBDA 2024

作者： Zhao, Li He, Xin Li, Shuangshuang Liu, Hai National Engineering Research Center for E-Learning Central China Normal University Wuhan China School of computer science Wuhan Donghu University Wuhan China

ISBN: (纸本)9798400718106

While current deep learning-based methods for image emotion classification have achieved superior performance, most approaches primarily extract generic features directly from the entire emotional image, without considering the unique process of emotional arousal in human cognition. Addressing this issue and inspired by the"stimulus-organism-response" psychological model, this paper introduces a multi-incentive perception network to simulate the emotional arousal process, aiming to tackle the challenge of abstraction in image emotion *** from relevant psychological studies, the paper selects color, objects, and faces as emotional stimuli and designs three specific networks to selectively extract emotional features from different stimuli. Additionally, the paper proposes a hierarchical cross-entropy loss to distinguish between simple negative samples and challenging negative samples. The performance of this method consistently surpasses existing approaches on commonly used datasets for image emotion classification, and visual results also demonstrate the importance of various incentives in the emotional arousal process. © 2024 ACM.

关键词： Image recognition

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

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computers, Materials & Continua 2023年第1期74卷 1279-1302页

作者： Yu Fan Jingbing Li Uzair Aslam Bhatti Chunyan Shao Cheng Gong Jieren Cheng Yenwei Chen School of Information and Communication Engineering Hainan UniversityHaikou570100China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570100China School of Computer Science and Technology Hainan UniversityHaikou570100China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570100China TJ-YZ School of Network Science Haikou University of EconomicsHaikou571127China

Medical images are a critical component of the diagnostic process for *** the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical *** watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient *** addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are *** paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned ***,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution ***,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical *** last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third *** encrypted watermarks and the original image features are performed logical operations to realize the embedding of *** the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed *** NC values for all the images are more than 90%accurate which shows the robustness of the *** experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

关键词： Inception V3 multi-watermarking DCT watermark encryption robustness

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Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

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Journal of computer science & Technology 2023年第1期38卷 196-210页

作者：张照博钟芷漫袁平鹏金海 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

关键词： natural language processing(NLP) domain entity linking computational linguistics word sense disambiguation knowledge graph

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Optimized Design of a Haptic Unit for Vibrotactile Amplitude Modulation

Optimized Design of a Haptic Unit for Vibrotactile Amplitude...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Huang, Jingchen Fang, Yun Guo, Weichao Qiao, Zhijian Sheng, Xinjun School of Mechanical Engineering Meta Robotics Institute Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Communicating information to users is a crucial aspect of human-machine interaction. Vibrotactile feedback encodes information into spatiotemporal vibrations, enabling users to perceive tactile sensations. It offers advantages such as lightweight, wearability, and high stability, with broad applications in sensory substitution, virtual reality, education, and healthcare. However, existing haptic unit designs lack amplitude modulation capabilities, which limits their applications. This paper proposed an optimized design of the haptic unit from the perspective of vibration amplitude modulation. A modified elastic model was developed to describe the propagation and attenuation mechanisms of vibration in the skin. Based on the model, two types of hierarchical architectural design were proposed. The design incorporated various materials arranged in multiple layers to amplify or attenuate the vibration amplitude as it traveled through the structure. An experimental platform was built to evaluate the performance of the optimized design. © 2024 IEEE.

关键词： Virtual reality

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WebGL vs. WebGPU: A Performance Analysis for Web 3.0 5

WebGL vs. WebGPU: A Performance Analysis for Web 3.0

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Chickerur, Satyadhyan Balannavar, Sankalp Hongekar, Pranali Prerna, Aditi Jituri, Soumya Center for Artificial Intelligence Research KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

This study investigates web 3.0 heterogeneous computing with webGL, webGPU, and IPFS. The primary focus is on the benefits of utilising these technologies to enhance the functionality and performance of web 3.0 applications. The study investigates web 3.0 as it currently exists and the constraints that developers face due to graphic, computational, and storage capabilities. According to the findings, incorporating webGL and webGPU can considerably increase user experience, speed, efficacy, and decentralization. Finally, this study summarizes the importance of continuing research in this subject, particularly with relation to platform interoperability and the future prospects of heterogeneous computing on web 3.0 via graphical APIs. © 2024 The Authors. Published by Elsevier B.V.

关键词： Rendering (computer graphics)

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FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation 40

FedCross: Towards Accurate Federated Learning via Multi-Mode...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Hu, Ming Zhou, Peiheng Yue, Zhihao Ling, Zhiwei Huang, Yihao Li, Anran Liu, Yang Lian, Xiang Chen, Mingsong School of Computer Science and Engineering Nanyang Technological University Singapore East China Normal University MoE Engineering Research Center of SW/HW Co-Design Tech. and App. China Kent State University Department of Computer Science OH United States

ISBN: (纸本)9798350317152

As a promising distributed machine learning paradigm, Federated Learning (FL) has attracted increasing attention to deal with data silo problems without compromising user privacy. By adopting the classic one-to-multi training scheme (i.e., FedAvg), where the cloud server dispatches one single global model to multiple involved clients, conventional FL methods can achieve collaborative model training without data sharing. However, since only one global model cannot always accommodate all the incompatible convergence directions of local models, existing FL approaches greatly suffer from inferior classification accuracy. To address this issue, we present an efficient FL framework named FedCross, which uses a novel multi-to-multi FL training scheme based on our proposed multi-model cross-aggregation approach. Unlike traditional FL methods, in each round of FL training, FedCross uses multiple middleware models to conduct weighted fusion individually. Since the middleware models used by FedCross can quickly converge into the same flat valley in terms of loss landscapes, the generated global model can achieve a well-generalization. Experimental results on various well-known datasets show that, compared with state-of-the-art FL methods, Fed Cross can significantly improve FL accuracy within both IID and non-IID scenarios without causing additional communication overhead. © 2024 IEEE.

关键词： Middleware

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Assessing the Robustness of Deep Learning-Assisted Pathological Image Analysis Under Practical Variables of Imaging System 48

Assessing the Robustness of Deep Learning-Assisted Pathologi...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sun, Yuxuan Zhu, Chenglu Zhang, Yunlong Li, Honglin Chen, Pingyi Yang, Lin Zhejiang University College of Computer Science and Technology China Westlake University Research Center for Industries of the Future School of Engineering China

ISBN: (纸本)9781728163277

With the advancement of deep learning, computer-assisted clinical diagnosis, such as liquid-based cervical cytology, has attracted more attention. However, the fragile robustness of deep learning models has a non-negligible impact on their classification accuracy and reliability. To be more specific, various scanner parameters will be used depending on the pathologist's preferences during the clinical diagnosis process (e.g., field source brightness, contrast, saturation, etc.), and this variation will lead to the unstable performance of the model. In this paper, we construct an evaluation pathway to assess the stability and consistency of deep learning models under various customized scanner parameters. Specifically, a multi-scanned dataset consists of 4200 whole slide images (WSIs) is generated by scanning 200 stained slices using various scanner parameters. Moreover, we conducted a large number of experiments to investigate the robustness of numerous models, including convolution-based and transformer-based models concerning various scanner parameter settings. Furthermore, we introduce several indicators to analyze the prediction accuracy, consistency and robustness of the model on the constructed dataset. The experimental results indicate that the deep learning models are sensitive to luminance-related scanner parameters. In addition, transformer-based models have better robustness than traditional convolutional neural networks. Our code has been made available1 © 2023 IEEE.

关键词： Cervical cytology images Deep learning Robustness Scanner parameters

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