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Developer Role Evolution in Open Source software Ecosystem： An Explanatory Study on GNOME

Journal of computer Science & Technology 2017年第2期32卷 396-414页

作者： Can Cheng Bing Li Zeng-Yang Li Yu-Qi Zhao Feng-Ling Liao International School of Software Wuhan University Wuhan 430072 China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China Complex Networks Research Center Wuhan University Wuhan 430072 China

An open source software （OSS） ecosystem refers to an OSS development community composed of many software projects and developers contributing to these projects. The projects and developers co-evolve in an ecosystem. To keep healthy evolution of such OSS ecosystems, there is a need of attracting and retaining developers, particularly project leaders and core developers who have major impact on the project and the whole team. Therefore, it is important to figure out the factors that influence developers＇ chance to evolve into project leaders and core developers. To identify such factors, we conducted a case study on the GNOME ecosystem. First, we collected indicators reflecting developers＇ subjective willingness to contribute to the project and the project environment that they stay in. Second, we calculated such indicators based on the GNOME dataset. Then, we fitted logistic regression models by taking as independent variables the resulting indicators after eliminating the most collinear ones, and taking as a dependent variable the future developer role （the core developer or project leader）. The results showed that part of such indicators （e.g., the total number of projects that a developer joined） of subjective willingness and project environment significantly influenced the developers＇ chance to evolve into core developers and project leaders. With different validation methods, our obtained model performs well on predicting developmental core developers, resulting in stable prediction performance （0.770, F-value）.

关键词： developer role evolution open source software ecosystem project environment subjective willingness

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Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein

arXiv

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arXiv 2025年

作者： Guo, Xiaotong Yang, Deqian Wang, Dan Zhao, Haochen Li, Yuan Sui, Zhilin Zhou, Tao Zhang, Lijun Meng, Yanda State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Thoracic Surgery National Clinical Research Center for Cancer Cancer Hospital Shenzhen Hospital China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China R&D Center Guangxi Huayi Artificial Intelligence Medical Technology Co. Ltd. China School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Exeter Exeter United Kingdom Department of Cardiovascular & Metabolic Medicine University of Liverpool Liverpool United Kingdom Guangzhou Jiayi Software Technology Co. Ltd. China

Accurate segmentation of pulmonary structures is crucial in clinical diagnosis, disease study, and treatment planning. Significant progress has been made in deep learning-based segmentation techniques, but most require much labeled data for training. Consequently, developing precise segmentation methods that demand fewer labeled datasets is paramount in medical image analysis. The emergence of pre-trained vision-language foundation models, such as CLIP, recently opened the door for universal computer vision tasks. Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data. However, exploring these models for pulmonary artery-vein segmentation is still limited. This paper proposes a novel framework called Language-guided self-adaptive Cross-Attention Fusion Framework. Our method adopts pre-trained CLIP as a strong feature extractor for generating the segmentation of 3D CT scans, while adaptively aggregating the cross-modality of text and image representations. We propose a s pecially designed adapter module to fine-tune pre-trained CLIP with a self-adaptive learning strategy to effectively fuse the two modalities of embeddings. We extensively validate our method on a local dataset, which is the largest pulmonary artery-vein CT dataset to date and consists of 718 labeled data in total. The experiments show that our method outperformed other state-of-the-art methods by a large margin. Our data and code will be made publicly available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Differential function analysis: identifying structure and activation variations in dysregulated pathways

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Science China(Information Sciences) 2017年第1期60卷 116-131页

作者： Chuanchao ZHANG Juan LIU Qianqian SHI Tao ZENG Luonan CHEN State Key Laboratory of Software Engineering School of Computer Wuhan University Key Laboratory of Systems Biology Innovation Center for Cell Signaling Network Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences

complex diseases are generally caused by the dysregulation of biological functions rather than individual molecules. Hence, a major challenge of the systematical study on complex diseases is how to capture the differentially regulated biological functions, e.g., pathways. The traditional differential expression analysis(DEA) usually considers the changed expression values of genes rather than functions. Meanwhile, the conventional function-based analysis(e.g., PEA: pathway enrichment analysis) mainly considers the varying activation of functions but disregards the structure change of genetic elements of functions. To achieve precision medicine against complex diseases, it is necessary to distinguish both the changes of functions and their elements from heterogeneous dysregulated pathways during the disease development and progression. In this work, in contrast to the traditional DEA, we developed a new computational framework, namely differential function analysis(DFA), to identify the changes of element-structure and expression-activation of biological functions, based on comparative non-negative matrix factorization(cN MF). To validate the effectiveness of our method, we tested DFA on various datasets, which shows that DFA is able to effectively recover the differential element-structure and differential activation-score of pre-set functional groups. In particular, the analysis of DFA on human gastric cancer dataset, not only capture the changed network-structure of pathways associated with gastric cancer, but also detect the differential activations of these pathways(i.e., significantly discriminating normal samples and disease samples), which is more effective than the state-of-the-art methods, such as GSVA and Pathifier. Totally,DFA is a general framework to capture the systematical changes of genes, networks and functions of complex diseases, which not only provides the new insight on the simultaneous alterations of pathway genes and pathway activations, but a

关键词： complex disease biological function non-negative matrix factorization network structure function activation

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High Quality Audio Object Coding Framework Based on Non-Negative Matrix Factorization

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China Communications 2017年第9期14卷 32-41页

作者： Tingzhao Wu Ruimin Hu Xiaochen Wang Shanfa Ke Jinshan Wang National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Collaborative Innovation Center of Geospatial Technology

Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization(NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.

关键词： object-based audio coding non-negative matrix factorization audio scenecoding

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MULTI-GRANULARITY EVOLUTION ANALYSIS OF software USING complex network THEORY

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Journal of Systems Science & complexity 2011年第6期24卷 1068-1082页

作者： Weifeng PAN Bing LI Yutao MA Jing LIU State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China School of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou 310018 China School of Computer Wuhan University Wuhan 430072 China Complex Networks Research Center Wuhan University Wuhan 430072 China

software systems are a typical kind of man-made complex systems. Understanding their evolutions can lead to better software engineering practices. In this paper, the authors use complex network theory as a tool to analyze the evolution of object-oriented （OO） software from a multi-granularity perspective. First, a multi-granularity software networks model is proposed to represent the topological structures of a multi-version software system from three levels of granularity. Then, some parameters widely used in complex network theory are applied to characterize the software networks. By tracing the parameters＇ values in consecutive software systems, we have a better understanding about software evolution. A case study is conducted on an open source OO project, Azureus, as an example to illustrate our approach, and some underlying evolution characteristics are uncovered. These results provide a different dimension to our understanding of software evolutions and also are very useful for the design and development of OO software systems.

关键词： complex networks multi-granularity software evolution software system.

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Verify All Traffic: Towards Zero-Trust In-network Intrusion Detection against Multipath Routing

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2155-2171页

作者： Zhao, Ziming Li, Zhaoxuan Xie, Xiaofei Liu, Zhipeng Li, Tingting Yu, Jiongchi Zhang, Fan Chen, Binbin School of Software Technology Zhejiang University Ningbo315100 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Scientific and Technological Innovation Center 311200 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province 310027 China Jiaxing Research Institute Zhejiang University 314000 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China Singapore State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security UCAS Beijing100049 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Advanced Digital Sciences Center Singapore138632 Singapore Singapore University of Technology and Design SingaporeB96049 Singapore

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multi-path routing phenomenon that exists in practice. In this paper, we reveal practical challenges in the context of enabling line-speed model inference in the network dataplane. Furthermore, we propose FCPlane, the forwarding and computing integrated dataplane for zero-trust intrusion detection that aims to enable efficient load balancing while providing reliable traffic analysis results, even against multipath routing. The core idea is to reconcile forwarding and computation to the flowlet level, for which a tailor-made Markov chain model is designed. Based on two public traffic datasets, we evaluate seven state-of-the-art in-network traffic analysis models deployed in four types of topologies (three with multipath routing and one without) to explore performance impact and demonstrate the effectiveness of our proposal. © 1983-2012 IEEE.

关键词： network intrusion

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From model to implementation: a network algorithm programming language

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Science China(Information Sciences) 2020年第7期63卷 205-221页

作者： Jian WANG Jie AN Mingshuai CHEN Naijun ZHAN Lulin WANG Miaomiao ZHANG Ting GAN State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Engineering Tongji University Network Integration Technology Research Department Huawei Technologies Co. Ltd. School of Computer Science Wuhan University

software-defined networking(SDN) is a revolutionary technology that facilitates network management and enables programmatically efficient network configuration, thereby improving network performance and flexibility. However, as the application programming interfaces(APIs) of SDN are low-level or functionality-restricted, SDN programmers cannot easily keep pace with the ever-changing devices, topologies, and demands of SDN. By deriving motivation from industry practice, we define a novel network algorithm programming language(NAPL) that enhances the SDN framework with a rapid programming flow from topology-based network models to C++ implementations, thus bridging the gap between the limited capability of existing SDN APIs and the reality of practical network management. In contrast to several state-of-the-art languages, NAPL provides a range of critical high-level network programming features:(1) topology-based network modeling and visualization;(2) fast abstraction and expansion of network devices and constraints;(3) a declarative paradigm for the fast design of forwarding policies;(4) a built-in library for complex algorithm implementation;(5) full compatibility with C++ programming; and(6) userfriendly debugging support when compiling NAPL into highly readable C++ codes. The expressiveness and performance of NAPL are demonstrated in various industrial scenarios originating from practical network management.

关键词： software-defined networking(SDN) network algorithm programming language(NAPL) network abstraction

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Action Recognition with Temporal Scale-Invariant Deep Learning Framework

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China Communications 2017年第2期14卷 163-172页

作者： Huafeng Chen Jun Chen Ruimin Hu Chen Chen Zhongyuan Wang State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China National Engineering Research Center for Multimedia Software Computer School of Wuhan University Wuhan 430072 China Center for Research in Computer Vision University of Central Florida Orlando FL 32816 USA

Recognizing actions according to video features is an important problem in a wide scope of applications. In this paper, we propose a temporal *** deep learning framework for action recognition, which is robust to the change of action speed. Specifically, a video is firstly split into several *** clips and a keyframe is selected from each *** clip. The spatial and motion features of the keyframe are extracted separately by two Convolutional Neural networks(CNN) and combined in the convolutional fusion layer for learning the relationship between the features. Then, Long Short Term Memory(LSTM) networks are applied to the fused features to formulate *** temporal clues. Finally, the action prediction scores of the LSTM network are combined by linear weighted summation. Extensive experiments are conducted on two popular and challenging benchmarks, namely, the UCF.101 and the HMDB51 Human Actions. On both benchmarks, our framework achieves superior results over the *** methods by 93.7% on UCF.101 and 69.5% on HMDB51, respectively.

关键词： action recognition CNN LSTM

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Big data in smart cities

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Science China(Information Sciences) 2015年第10期58卷 179-190页

作者： LI DeRen CAO JianJun YAO Yuan State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Collaborative Innovation Center of Geospatial Technology Geomatics Center of Gansu Province National Engineering Research Center for Multimedia Software Computer School Wuhan University

In this paper, we discuss the concept of the smart city, summarize its development, analyze the motivation and goals of building smart cities in China, and illustrate the supporting technologies of the smart city. Next, we propose a smart city infrastructure that is based on the concept of the digital city and integrates the Internet of Things and cloud computing technologies, thereby achieving automatic control and intelligence services for people and logistics in physical cities. We analyze the big data of smart cities according to ubiquitous sensor networks. Facing the problems and challenges caused by big data of smart cities, we propose a strategy to handle big data that focuses on cloud computing and data mining. In addition, we present a basic framework for cloud computing platforms and propose the establishment of an operating center for smart cities. In closing,we discuss the bright prospects of smart cities.

关键词： smart city big data digital city Internet of Things cloud computing data mining intelligence service

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An ontology-based approach for semantic web service searching 1

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4th International Conference on Human-Centric Computing, HumanCom'11 and the 6th International Conference on Embedded and Multimedia Computing, EMC'11

作者： Shen, Shuijing Li, Bing Shao, Bo Li, Dingwei State Key Laboratory of Software Engineering Wuhan University 430072 Wuhan China School of Computer Science and Complex Network Research Center Wuhan University 430205 Wuhan China

ISBN: (数字)9789400721050

ISBN: (纸本)9789400721043

Web service is released, combined and invoked through the web. With the continuous growth of web services, finding the web service to meet user requirement has become a challenging problem. The current web service discovery technology such as UDDI which provides keyword-based search is unable to fulfill the user's demand of semantic search. In view of that web services have some functional and non-functional information described by natural language, and the sparsity of user requirement description, we put forward a semantic web service discovery method depending on a concept network which is built on the domain ontology and local ontology in registration model. This method fully uses the registered users' knowledge of web service to build conceptual network and processes the user's search text to mine the true intentions, thus enabling more effective service discovery. © 2011 Springer Science+Business Media B.V.

关键词： Ontology

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