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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Xiong Wang Yuxin Chen Yuqing Li Xiaofei Liao Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients’ selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal ${{\mathcal{O}}}\left({{T^{ - 2/3}}}\right)$ convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques.

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Predicting visual acuity with machine learning in treated ocular trauma patients

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International Journal of Ophthalmology(English edition) 2023年第7期16卷 1005-1014页

作者： Zhi-Lu Zhou Yi-Fei Yan Jie-Min Chen Rui-Jue Liu Xiao-Ying Yu Meng Wang Hong-Xia Hao Dong-Mei Liu Qi Zhang Jie Wang Wen-Tao Xia Department of Forensic Medicine Guizhou Medical UniversityGuiyang 550009Guizhou ProvinceChina Shanghai Key Laboratory of Forensic Medicine Shanghai Forensic Service PlatformInstitute of Forensic ScienceMinistry of JusticeShanghai 200063China The SMART(Smart Medicine and AI-based Radiology Technology)Lab Shanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghai 200444China School of Communication and Information Engineering Shanghai UniversityShanghai 200444China Basic Medical College Jiamusi UniversityJiamusi 154007Heilongjiang ProvinceChina

AIM:To predict best-corrected visual acuity(BCVA)by machine learning in patients with ocular trauma who were treated for at least ***:The internal dataset consisted of 850 patients with 1589 eyes and an average age of *** initial visual acuity was 0.99 log *** test dataset consisted of 60 patients with 100 eyes collected while the model was *** different machine-learning algorithms(Extreme Gradient Boosting,support vector regression,Bayesian ridge,and random forest regressor)were used to predict BCVA,and four algorithms(Extreme Gradient Boosting,support vector machine,logistic regression,and random forest classifier)were used to classify BCVA in patients with ocular trauma after treatment for 6mo or *** features were obtained from outpatient records,and ocular parameters were extracted from optical coherence tomography images and fundus *** features were put into different machine-learning models,and the obtained predicted values were compared with the actual BCVA *** best-performing model and the best variable selected were further evaluated in the test ***:There was a significant correlation between the predicted and actual values[all Pearson correlation coefficient(PCC)>0.6].Considering only the data from the traumatic group(group A)into account,the lowest mean absolute error(MAE)and root mean square error(RMSE)were 0.30 and 0.40 log MAR,*** the traumatic and healthy groups(group B),the lowest MAE and RMSE were 0.20 and 0.33 log MAR,*** sensitivity was always higher than the specificity in group A,in contrast to the results in group *** classification accuracy and precision were above 0.80 in both *** MAE,RMSE,and PCC of the test dataset were 0.20,0.29,and 0.96,*** sensitivity,precision,specificity,and accuracy of the test dataset were 0.83,0.92,0.95,and 0.90,***:Predicting BCVA using machine-learning models in patients wit

关键词： ocular trauma predicting visiual acuity best-corrected visual acuity visual dysfunction machine learning

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Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases

arXiv

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arXiv 2023年

作者： Hu, Zheng Cai, Shi-Min Wang, Jun Zhou, Tao Compleχ Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Big Data Research Center University of Electronic Science and Technology of China Chengdu610054 China

The existing collaborative recommendation models that use multi-modal information emphasize the representation of users’ preferences but easily ignore the representation of users’ dislikes. Nevertheless, modelling users’ dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users’ dislikes should be integrated into the user modelling when we construct a collaborative recommendation model. In this paper, we propose a novel Collaborative Recommendation Model based on Multi-modal multi-view Attention Network (CRMMAN), in which the users are represented from both preference and dislike views. Specifically, the users’ historical interactions are divided into positive and negative interactions, used to model the user’s preference and dislike views, respectively. Furthermore, the semantic and structural information extracted from the scene is employed to enrich the item representation. We validate CRMMAN by designing contrast experiments based on two benchmark MovieLens-1M and Book-Crossing datasets. Movielens-1m has about a million ratings, and Book-Crossing has about 300,000 ratings. Compared with the state-of-the-art knowledge-graph-based and multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by 2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled experiments to explore the effects of multi-modal information and multi-view mechanism. The experimental results show that both of them enhance the model’s performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

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Modeling the Temporal Dynamics of EEG Signals in Selective Listening

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IEEE Transactions on Consumer Electronics 2025年

作者： Cai, Siqi Zhang, Ran Zhu, Hongxu Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore South China University of Technology Shien-Ming Wu School of Intelligent Engineering China Fano Hong Kong Hong Kong The Chinese University of Hon g Kong Shenzhen Research Institute of Big Data School of Data Science Shenzhen518172 China University of Bremen Machine Listening Lab Bremen28359 Germany

Human brain possesses an extraordinary ability to attend to a specific sound source in a multi-talk, noisy environment such as a cocktail party. Auditory attention detection (AAD) aims to automatically identify such attentive neural activity from brain signals, such as electroencephalography (EEG). Given the dynamic and nonlinear nature of EEG signals, we propose a spiking long short-term memory (LSTM) network to capture the temporal features from EEG data. Additionally, we introduce a spiking temporal attention mechanism that dynamically assigns differentiated weights, thereby enhancing the representation of EEG features. We evaluate our proposed spiking temporal LSTM model, named ST-LSTM, on a widely used AAD dataset through a wide range of experiments. The experiments demonstrate that ST-LSTM outperforms other competing models, especially in low-latency settings. Moreover, with low power consumption, ST-LSTM offers a practical solution for edge computing implementations such as neuro-steered hearing aids, and other portable brain-computer interfaces. © 1975-2011 IEEE.

关键词： Hearing aids

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Marvin Borsdorf Zexu Pan Pascal Himmelmann Haizhou Li Tanja Schultz Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time.

关键词： Training Measurement databases Signal processing data models Blind source separation Robustness Acoustics Speech processing

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Deep Learning-based Risk Prediction Model for Recurrence-free Survival in Patients with Hepatocellular Carcinoma Using Multi-phase CT Image 11

Deep Learning-based Risk Prediction Model for Recurrence-fre...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Wang, Weibin Wang, Fang Yang, Yunjun Li, Yinhao Liu, Jing Han, Xianhua Lin, Lanfen Tong, Ruofeng Hu, Hongjie Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Kusatsu Japan Zhejiang University Department of Radiology Sir Run Run Shaw Hospital Hangzhou China Wenzhou Medical University Department of Radiology the First Affiliated Hospital Wenzhou China Ritsumeikan University College of Information Science and Engineering Kusatsu Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Information Science and Engineering Yamaguchi University Yamaguchi Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9781665492324

Risk prediction for recurrence is a critical task for patients with hepatocellular carcinoma (HCC). Effective prediction can evaluate treatment options and guide personalized medicine. Traditional approaches use clinical data to construct cox-regression models to predict the risk of recurrence. Recently, prognosis analysis based on image information of patient often has better performance compared with traditional approaches. In particular, deep learning has demonstrated its superiority in medical image processing. In this paper, we propose a deep learning-based model to predict the risk of recurrence. We collected 292 patients with HCC from two independent centers, which were divided into a training set, an internal validation set, and an external validation set. Our models were compared with traditional clinical models and better performance was obtained. Our proposed method achieves a C-index performance of 0.627 and 0.630 for the internal validation set and external validation set, respectively. © 2022 IEEE.

关键词： Forecasting

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

arXiv

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 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 China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin 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 School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action Recognition

arXiv

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arXiv 2024年

作者： Lin, Kun-Yu Ding, Henghui Zhou, Jiaming Tang, Yu-Ming Peng, Yi-Xing Zhao, Zhilin Loy, Chen Change Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China S-Lab Nanyang Technological University Singapore Institute of Big Data Fudan University China AI Thrust Hong Kong University of Science and Technology Guangzhou China Data Science Lab School of Computing & DataX Research Centre Macquarie University Australia

Building upon the impressive success of CLIP (Contrastive Language-Image Pretraining), recent pioneer works have proposed to adapt the powerful CLIP to video data, leading to efficient and effective video learners for open-vocabulary action recognition. Inspired by that humans perform actions in diverse environments, our work delves into an intriguing question: Can CLIP-based video learners effectively generalize to video domains they have not encountered during training? To answer this, we establish a CROSS-domain Open-Vocabulary Action recognition benchmark named XOV-Action, and conduct a comprehensive evaluation of five state-of-the-art CLIP-based video learners under various types of domain gaps. The evaluation demonstrates that previous methods exhibit limited action recognition performance in unseen video domains, revealing potential challenges of the cross-domain open-vocabulary action recognition task. In this paper, we focus on one critical challenge of the task, namely scene bias, and accordingly contribute a novel scene-aware video-text alignment method. Our key idea is to distinguish video representations apart from scene-encoded text representations, aiming to learn scene-agnostic video representations for recognizing actions across domains. Extensive experiments demonstrate the effectiveness of our method. The benchmark and code will be available at https://***/KunyuLin/XOV-Action/. Copyright © 2024, The Authors. All rights reserved.

关键词： Video recording

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Validating SMT Solvers via Skeleton Enumeration Empowered by Historical Bug-Triggering Inputs

Validating SMT Solvers via Skeleton Enumeration Empowered by...

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International Conference on Software engineering (ICSE)

作者： Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Huazhong University of Science and Technology China

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.

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