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

作者： Yu Chen Sheng Zhang Yuting Yan Yibo Jin Ning Chen Mingtao Ji Mingjun Xiao State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Computer Science and Technology University of Science and Technology of China P.R. China

Many crowdsourcing platforms are emerging, leveraging the resources of recruited workers to execute various outsourcing tasks, mainly for those computing-intensive video analytics with high quality requirements. Although the profit of each platform is strongly related to the quality of analytics feedback, due to the uncertainty on diverse performance of workers and the conflicts of interest over platforms, it is non-trivial to determine the dispatch of tasks with maximum benefits. In this paper, we design a decentralized mechanism for a Crowd of Crowdsourcing platforms, denoted as Crowd 2 , optimizing the worker selection to maximize the social welfare of these platforms in a long-term scope, under the consideration of both proportional fairness and dynamic flexibility. Concretely, we propose a video analytics dispatch algorithm based on multi-agent bandit, for which the more accurate profit estimates are attained via the decoupling of multi-knapsack based mapping problem. Via rigorous proofs, a sub-linear regret bound for social welfare of crowdsourcing profits is achieved while both fairness and flexibility are ensured. Extensive trace-driven experiments demonstrate that Crowd 2 improves the social welfare by 36.8%, compared with other alternatives.

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Emerging topic identification from app reviews via adaptive online biterm topic modeling

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Frontiers of Information technology & Electronic Engineering 2022年第5期23卷 678-691页

作者： Wan ZHOU Yong WANG Cuiyun GAO Fei YANG School of Information and Computer Anhui Polytechnic UniversityWuhu 241000China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210000China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen 518000China Zhejiang Lab Hangzhou 310000China

Emerging topics in app reviews highlight the topics(e.g.,software bugs)with which users are concerned during certain *** emerging topics accurately,and in a timely manner,could help developers more effectively update *** for identifying emerging topics in app reviews based on topic models or clustering methods have been proposed in the ***,the accuracy of emerging topic identification is reduced because reviews are short in length and offer limited *** solve this problem,an improved emerging topic identification(IETI)approach is proposed in this ***,we adopt natural language processing techniques to reduce noisy data,and identify emerging topics in app reviews using the adaptive online biterm topic *** we interpret the implicature of emerging topics through relevant phrases and *** adopt the official app changelogs as ground truth,and evaluate IETI in six common *** experimental results indicate that IETI is more accurate than the baseline in identifying emerging topics,with improvements in the F1 score of 0.126 for phrase labels and 0.061 for sentence ***,we release the codes of IETI on Github(https://***/wanizhou/IETI).

关键词： App reviews Emerging topic identification Topic model Natural language processing

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On the Noise Robustness of In-Context Learning for Text Generation

arXiv

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arXiv 2024年

作者： Gao, Hongfu Zhang, Feipeng Jiang, Wenyu Shu, Jun Zheng, Feng Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology China School of Economics and Finance Xi’an Jiaotong University China National Key Laboratory for Novel Software Technology Nanjing University China School of Mathematics and Statistics Xi’an Jiaotong University China Department of Computer Science and Engineering Southern University of Science and Technology China

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking © 2024, CC0.

关键词： Semantics

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What is Discussed about COVID-19:A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human labeling

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computers, Materials & Continua 2020年第9期64卷 1453-1471页

作者： Hengyang Lu Yutong Lou Bin Jin Ming Xu School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing210023China

Starting from late 2019,the new coronavirus disease(COVID-19)has become a global *** the development of online social media,people prefer to express their opinions and discuss the latest news *** have witnessed the positive influence of online social media,which helped citizens and governments track the development of this pandemic in *** is necessary to apply artificial intelligence(AI)techniques to online social media and automatically discover and track public opinions posted *** this paper,we take Sina Weibo,the most widely used online social media in China,for analysis and *** collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler,including texts and images posted by *** order to effectively discover what is being discussed about COVID-19 without human labeling,we propose a unified multi-modal framework,including an unsupervised short-text topic model to discover and track bursty topics,and a self-supervised model to learn image features so that we can retrieve related images about *** results have shown the effectiveness and superiority of the proposed models,and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.

关键词： COVID-19 public opinion microblog topic model self-supervised learning

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Dynamical Trust Construction Schema with Fuzzy Decision in P2P Systems

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Chinese Journal of Electronics 2023年第3期18卷 417-421页

作者： Yang Wang Ruchuan Wang Zhijie Han College of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer Anhui Normal University Wuhu China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The security problem for Peer-to-peer (P2P) systems has been gradually recognized as an important topic of P2P applications. Among existing solution for P2P security, how to establish trust relationship among peers is a key problem in demand. This paper proposes a novel dynamical trust construction schema based on fuzzy decision and Extended automated trust negotiation (EATN). The presented approach adopts fuzzy trust graph to describe the trust information in the P2P systems. Trust relationship among peers can be constructed by the combination of the fuzzy trust graph, fuzzy path search algorithm and EATN. The relevant application case indicates the schema is feasible. The simulations also show that the presented schema can improve the interactive success ratio by enhancing the trust relationship in the P2P systems.

关键词： Simulation Heuristic algorithms Education Telecommunication traffic Peer-to-peer computing Security Trust management

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

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

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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado 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

ISBN: (纸本)9798400702174

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. © 2024 ACM.

关键词： Open source software

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Comprehensive Semantic Repair of Obsolete GUI Test Scripts for Mobile Applications

Comprehensive Semantic Repair of Obsolete GUI Test Scripts f...

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International Conference on software Engineering (ICSE)

作者： Shaoheng Cao Minxue Pan Yu Pei Wenhua Yang Tian Zhang Linzhang Wang Xuandong Li State Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Computing The Hong Kong Polytechnic University Hong Kong China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Graphical User Interface (GUI) testing is one of the primary approaches for testing mobile apps. Test scripts serve as the main carrier of GUI testing, yet they are prone to obsolescence when the GUIs change with the apps' evolution. Existing repair approaches based on GUI layouts or images prove effective when the GUI changes between the base and updated versions are minor, however, they may struggle with substantial changes. In this paper, a novel approach named COSER is introduced as a solution to re-pairing broken scripts, which is capable of addressing larger GUI changes compared to existing methods. COSER incorporates both external semantic information from the GUI elements and internal semantic information from the source code to provide a unique and comprehensive solution. The efficacy of COSER was demonstrated through experiments conducted on 20 Android apps, resulting in superior performance when compared to the state-of-the-art tools METER and GUIDER. In addition, a tool that implements the COSER approach is available for practical use and future research.

关键词： Meters Source coding Semantics Layout Maintenance engineering Aging Mobile applications

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Graph-based Multi-view Clustering for Web services

Graph-based Multi-view Clustering for Web services

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wang Yang Yuan Zhenzhen Kang Guosheng Cao Buqing Liu JianXun Xiao Yong HNUST Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Computer Science and Engineering

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process.

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Towards Dynamic Evolutionary Analysis of ProgrammableWeb for API-Mashup Ecosystem

Towards Dynamic Evolutionary Analysis of ProgrammableWeb for...

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International Conference on computer Supported Cooperative Work in Design

作者： Yang Wang Jiayan Xiang Hangyu Cheng Wanjun Chen Yong Xiao Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline.

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