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

作者： Mingyu Liu Yijie Wang Xiaohui Zhou Yongjun Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors.

关键词： Learning systems Time-frequency analysis Fast Fourier transforms Time series analysis Signal processing Feature extraction Entropy Iterative methods Speech processing Anomaly detection

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Open source oriented cross-platform survey

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Information and Software Technology 2025年 182卷

作者： Yao, Simeng Zhang, Xunhui Zhang, Yang Wang, Tao College of Computer Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex & Critical Software Environment Changsha410073 China National Key Laboratory of Parallel and Distributed Computing Changsha410073 China

Context: Open-source software development has become a widely adopted approach to software creation. However, developers’ activities extend beyond social coding platforms (e.g., GitHub), encompassing social Q&A platforms (e.g., StackOverflow) and social media platforms (e.g., Twitter). Therefore, cross-platform research is essential for a deeper understanding of the nature of software development activities. Objective: This paper focuses on open-source platforms and systematically summarizes relevant cross-platform research. It aims to assess the current state of cross-platform research and provide insights into the challenges and future developments in this field. Method: This paper reviews 69 cross-platform research papers related to open-source software from 2013 to 2024, with a focus on several key areas, including platform interconnections, research themes, experimental design methods, challenges and research opportunities. Results: Through the analysis of 69 papers, we found that cross-platform research primarily involves platforms such as social coding, social Q&A, and social media. Researchers typically rely on information traces, including user personal info, technical info, project/post/bug report metadata, interaction info, to facilitate connections between platforms. Cross-platform research in the open-source domain mainly focuses on problem classification and feature extraction. The predominant research methods include data-driven approaches, qualitative studies, modeling and machine learning, and tool development and implementation. Despite these advancements, common challenges remain, such as subjective evaluation bias in manual data classification, insufficient data source coverage, and inaccurate data recognition. Future research opportunities may focus on increasing the diversity of data sources, improving data recognition accuracy, optimizing data classification methods, and clarifying user skill requirements. Conclusions: Based on our findings, w

关键词： Data accuracy

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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

作者： Yan Jia Yuqing Cheng Kele Xu Yong Dou Peng Qiao Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.

关键词： Training Ultrasonic imaging Tongue Self-supervised learning Data augmentation Data models Cognition Data mining Noise measurement Signal to noise ratio

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

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

作者： Yizhou Li Yijie Wang Hongzuo Xu Xiaohui Zhou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab (IGDL) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors.

关键词： Time series analysis Semantics Neural networks Training data Manuals Signal processing Data models Speech processing Anomaly detection Testing

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SMDPG: Identifying Metabolite-Disease Associations via Optimized Negative Sampling and Sparse Graph Convolutional Network

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 672-683页

作者： Yiran Huang Qiulong Pu Wei Lan Cuiyu Huang Cheng Zhong School of Computer Electronics and Information Guangxi University Nanning China Guangxi Universities Key Laboratory of Parallel and Distributed Computing and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China College of Chemistry Nankai University Tianjin China

Identifying disease-associated metabolites could provide critical clues for the diagnosis and treatment of diseases. Although computational approaches have been proposed to predict disease-associated metabolites by training models using positive and negative samples, few efforts have paid attention to optimize the reliability of negative samples, which could possibly improve the prediction accuracy of model. In this work, we propose a novel method called SMDPG to leverage optimized negative sampling and sparse graph convolutional network to predict metabolite-disease associations. In SMDPG, we first build a metabolite-disease bipartite graph based on less similar metabolites and diseases and propose a negative sampling method to select reliable negative metabolite-disease samples from the bipartite graph to reduce the effect of noisy samples. Then a homogeneous metabolite-disease pair graph is constructed based on the selected negative samples and known metabolite-disease associations. Further, an edge sparseness operation is designed to simplify the connection of the homogeneous graph. Finally, the simplified homogeneous graph is fed into the graph convolutional network to predict metabolite-disease associations. Experimental results show that SMDPG can more accurately predicts metabolite-disease associations as compared to the existing methods. Moreover, the study case demonstrates that SMDPG is an effective framework for identifying potential metabolite-disease associations.

关键词： Diseases Feature extraction Semantics Databases Reliability Graph convolutional networks Training Sparse matrices Predictive models Bipartite graph

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Social media in Git Hub: the role of @-mention in assisting software development

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Science China(Information Sciences) 2017年第3期60卷 50-67页

作者： Yang ZHANG Huaimin WANG Gang YIN Tao WANG Yue YU Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology

Recently, many researches propose that social media tools can promote the collaboration among developers, which are beneficial to the software development. Nevertheless, there is little empirical evidence to confirm that using @-mention has indeed a beneficial impact on the issues in Git Hub. In order to begin investigating such claim, we examine data from two large and successful projects hosted on Git Hub, the Ruby on Rails and the Angular JS. By using qualitative and quantitative analysis, we give an in-depth understanding on how @-mention is used in the issues and the role of @-mention in assisting software development. Our statistical results indicate that, @-mention attracts more participants and tends to be used in the difficult issues. @-mention favors the solving process of issues by enlarging the visibility of issues and facilitating the developers' collaboration. Our study also build an @-network based on the @-mention database we *** the @-network, we investigate its evolution over time and prove that we certainly have the potential to mine the relationships and characteristics of developers by exploiting the knowledge from the @-network.

关键词： issues social media @-mention Git Hub software development

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Why API documentation is insufficient for developers: an empirical study

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Science China(Information Sciences) 2021年第1期64卷 248-250页

作者： Qiang FAN Yue YU Tao WANG Gang YIN Huaimin WANG Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology

Dear editor,Application programming interface (API) documentation plays an important role in software development and reuse [1] for both API maintainers and API *** documentation helps developers understand and reuse codes effectively [2] and focus their time on desired interfaces and functions instead of the entire system [3].Most high-quality open source projects maintain complete and informative official *** documentation typically conveys detailed specifications,such as class/inter face hierarchies and method descriptions,which can be of great benefit to developers [4].However,despite its authoritativeness and thoroughness,single-sourced official documentation does not always meet the developers'requirements [5].

关键词： Information Systems and Communication Service

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Auxo: an architecture-centric framework supporting the online tuning of software adaptivity

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Science China(Information Sciences) 2015年第9期58卷 31-45页

作者： WANG HuaiMin DING Bo SHI DianXi CAO JianNong Alvin T.S.Chan National Key Laboratory of Parallel and Distributed Processing College of ComputerNational University of Defense Technology Department of Computing Hong Kong Polytechnic University

Adaptivity is the capacity of software to adjust itself to changes in its environment. A common approach to achieving adaptivity is to introduce dedicated code during software development stage. However,since those code fragments are designed a priori, self-adaptive software cannot handle situations adequately when the contextual changes go beyond those that are originally anticipated. In this case, the original builtin adaptivity should be tuned. For example, new code should be added to provide the capacity to sense the unexpected environment or to replace outdated adaptation decision logic. The technical challenges in this process, especially that of tuning software adaptivity at runtime, cannot be understated. In this paper,we propose an architecture-centric application framework for self-adaptive software named Auxo. Similar to existing work, our framework supports the development and running of self-adaptive software. Furthermore,our framework supports the tuning of software adaptivity without requiring the running self-adaptive software to be terminated. In short, the architecture style that we are introducing can encapsulate not only general functional logic but also the concerns in the self-adaptation loop(such as sensing, decision, and execution)as architecture elements. As a result, a third party, potentially the operator or an augmented software entity equipped with explicit domain knowledge, is able to dynamically and flexibly adjust the self-adaptation concerns through modifying the runtime software architecture. To truly exercise, validate, and evaluate our approach,we describe a self-adaptive application that was deployed on the framework, and conducted several experiments involving self-adaptation and the online tuning of software adaptivity.

关键词： software architecture self-adaptive software architecture style application framework software adaptation

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Growing construction and adaptive evolution of complex software systems

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Science China(Information Sciences) 2016年第5期59卷 5-7页

作者： Huaimin WANG Bo DING National Key Laboratory of Parallel and Distributed Processing College of ComputerNational University of Defense Technology

distributed software systems are becoming more and more complex *** is easy to find a huge amount of computing nodes in a nationwide or global information *** example,We Chat(Wei Xin),a well-known mobile application in China,has reached a record of 650 million monthly active users in the third quarter of *** the same time,researchers are starting to talk about software systems which have billions of lines of codes[1]or can last one hundred years.

关键词： Growing construction and adaptive evolution of complex software systems

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Efficient Multi-Tenant Virtual Machine Allocation in Cloud Data Centers

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Tsinghua Science and Technology 2015年第1期20卷 81-89页

作者： Jiaxin Li Dongsheng Li Yuming Ye Xicheng Lu the National Key Laboratory of Parallel and Distributed Processing (PDL) College of Computer National University of Defense Technology

Virtual Machine（VM） allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant＇s VMs may considerably impact the tenant＇s Quality of Service（Qo S）. In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs＇ network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem（LP-MKP）. The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants＇ overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.

关键词： virtual machine allocation cloud data center multiple tenants multiple knapsack problem

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