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Modeling Topic-Based Human Expertise for Crowd Entity Resolution

Journal of computer science & technology 2018年第6期33卷 1204-1218页

作者： Sai-Sai Gong Wei Hu Wei-Yi Ge Yu-Zhong Qu State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China Science and Technology on Information Systems Engineering Laboratory Nanjing 210007 China

Entity resolution （ER） aims to identify whether two entities in an ER task refer to the same real-world *** ER uses humans, in addition to machine algorithms, to obtain the truths of ER tasks. However, inaccurate orerroneous results are likely to be generated when humans give unreliable judgments. Previous studies have found thatcorrectly estimating human accuracy or expertise in crowd ER is crucial to truth inference. However, a large number ofthem assume that humans have consistent expertise over all the tasks, and ignore the fact that humans may have variedexpertise on different topics （e.g., music versus sport）. In this paper, we deal with crowd ER in the Semantic Web *** identify multiple topics of ER tasks and model human expertise on different topics. Furthermore, we leverage similartask clustering to enhance the topic modeling and expertise estimation. We propose a probabilistic graphical model thatcomputes ER task similarity, estimates human expertise, and infers the task truths in a unified framework. Our evaluationresults on real-world and synthetic datasets show that, compared with several state-of-the-art approaches, our proposedmodel achieves higher accuracy on the task truth inference and is more consistent with the human real expertise.

关键词： entity resolution crowdsourcing human expertise topic modeling task similarity

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Hierarchical Model Checking of SystemVerilog-Specified Asynchronous Circuits for Deadlock Detection

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2424-2437页

作者： Lu, Longlong Pan, Minxue Lu, Yifei Li, Xuandong Nanjing University State Key Laboratory for Novel Software Technology and the Software Institute Jiangsu Province Nanjing210093 China Nanjing University State Key Laboratory for Novel Software Technology and School of Computer Science Jiangsu Province Nanjing210023 China

Specifying channel-based asynchronous circuits in SystemVerilog is a promising alternative design paradigm to combine the advantages of asynchronous circuits and industrial EDA supports. However, communicating through channels can be error-prone, potentially introducing deadlocks that cannot be detected easily through simulation. In contrast, model checking can reliably identify deadlocks, but faces challenges related to scalability and modeling capability. This research proposes a novel model checking approach, named, to detect deadlocks of channel-based asynchronous circuits specified in SystemVerilog. To address the issue of modeling capability, extracts inter-module communication behavior from SystemVerilog circuit designs and builds models in Communication Protocols specifically designed for this purpose. Additionally, employs a novel hierarchical model checking algorithm that conducts localized verification of well-formed groups of the system from the bottom up, thus reducing the size of the checking problems and presenting the opportunity to parallelize the checking process. Extensive experimental evaluations confirm the efficiency of in publicly accessible and randomly synthesized large-scale asynchronous circuits. Remarkably, significant benefits of the hierarchical checking approach are demonstrated through an ablative experiment. © 1982-2012 IEEE.

关键词： Hierarchical systems

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Co-metric： a metric learning algorithm for data with multiple views

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Frontiers of computer science 2013年第3期7卷 359-369页

作者： Qiang QIAN Songcan CHEN Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China

We address the problem of metric learning for multi-view data. Many metric learning algorithms have been proposed, most of them focus just on single view circumstances, and only a few deal with multi-view data. In this paper, motivated by the co-training framework, we propose an algorithm-independent framework, named co-metric, to learn Mahalanobis metrics in multi-view settings. In its implementation, an off-the-shelf single-view metric learning algorithm is used to learn metrics in individual views of a few labeled examples. Then the most confidently-labeled examples chosen from the unlabeled set are used to guide the metric learning in the next loop. This procedure is repeated until some stop criteria are met. The framework can accommodate most existing metric learning algorithms whether types-of- side-information or example-labels are used. In addition it can naturally deal with semi-supervised circumstances under more than two views. Our comparative experiments demon- strate its competiveness and effectiveness.

关键词： multi-view learning metric learning algorithm- independent framework

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LimTDD: A Compact Decision Diagram Integrating Tensor and Local Invertible Map Representations

arXiv

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arXiv 2025年

作者： Hong, Xin Dai, Aochu Gao, Dingchao Li, Sanjiang Ji, Zhengfeng Ying, Mingsheng and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Computer Science and Technology Tsinghua University China Centre for Quantum Software and Information University of Technology Sydney Australia

Tensor Decision Diagrams (TDDs) provide an efficient structure for representing tensors by combining techniques from both tensor networks and decision diagrams, demonstrating competitive performance in quantum circuit simulation and verification. However, existing decision diagrams, including TDDs, fail to exploit isomorphisms within tensors, limiting their compression efficiency. This paper introduces Local Invertible Map Tensor Decision Diagrams (LimTDDs), an extension of TDD that integrates local invertible maps (LIMs) to achieve more compact representations. Unlike LIMDD, which applies Pauli operators to quantum states, LimTDD generalizes this approach using the XP-stabilizer group, enabling broader applicability. We develop efficient algorithms for normalization and key tensor operations, including slicing, addition, and contraction, essential for quantum circuit simulation and verification. Theoretical analysis shows that LimTDD surpasses TDD in compactness while maintaining its generality and offers exponential advantages over both TDD and LIMDD in the best-case scenarios. Experimental results validate these improvements, demonstrating LimTDD’s superior efficiency in quantum circuit simulation and functionality computation. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum electronics

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Infrastructure-Free Floor Localization Through Crowdsourcing

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Journal of computer science & technology 2015年第6期30卷 1249-1273页

作者：叶海波顾涛陶先平吕建 State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China Department of Computer Science Nanjing University Nanjing 210023 China School of Computer Science and Information Technology RMIT University Melbourne Victoria 3001 Australia

Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone＇s sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the eiTiciency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.

关键词： mobile phone localization floor localization crowdsourcing mobile phone sensing

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STAR-RIS and UAV Combination in MEC Networks: Simultaneous Task Offloading and Communications

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IEEE Transactions on Communications 2025年

作者： Xiao, Han Hu, Xiaoyan Wang, Wenjie Su, Zhou Wong, Kai-Kit Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China University College London Department of Electronic and Electrical Engineering Torrington Place LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering Essex United Kingdom

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks. © 1972-2012 IEEE.

关键词： Convex optimization

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Task assignment for social-oriented crowdsourcing

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Frontiers of computer science 2021年第2期15卷 39-49页

作者： Gang WU Zhiyong CHEN Jia LIU Donghong HAN Baiyou QIAO School of Computer Science and Engineering Northeastern UniversityShenyang110004China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing210023China

Crowdsourcing has become an efficient measure to solve machine-hard problems by embracing group wisdom,in which tasks are disseminated and assigned to a group of workers in the way of open *** social relationships formed during this process may in turn contribute to the completion of future *** this sense,it is necessary to take social factors into consideration in the research of ***,there is little work on the interactions between social relationships and crowdsourcing *** this paper,we propose to study such interactions in those social-oriented crowdsourcing systems from the perspective of task assignment.A prototype system is built to help users publish,assign,accept,and accomplish location-based crowdsourcing tasks as well as promoting the development and utilization of social relationships during the ***,in order to exploit the potential relationships between crowdsourcing workers and tasks,we propose a“worker-task”accuracy estimation algorithm based on a graph model that joints the factorized matrixes of both the user social networks and the history“worker-task”*** the worker-task accuracy estimation matrix,a group of optimal worker candidates is efficiently chosen for a task,and a greedy task assignment algorithm is proposed to further the matching of worker-task pairs among multiple crowdsourcing tasks so as to maximize the overall *** with the similarity based task assignment algorithm,experimental results show that the average recommendation success rate increased by 3.67%;the average task completion rate increased by 6.17%;the number of new friends added per week increased from 7.4 to 10.5;and the average task acceptance time decreased by 8.5 seconds.

关键词： crowdsourcing social networks task assignment

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Pairwise tagging framework for end-to-end emotion-cause pair extraction

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Frontiers of computer science 2023年第2期17卷 111-120页

作者： Zhen WU Xinyu DAI Rui XIA National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210023China

Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and *** works adopt pipelined approaches or multi-task learning to address the ECPE ***,the pipelined approaches easily suffer from error propagation in real-world *** multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction *** address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging *** prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging *** this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause *** validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark *** experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.

关键词： emotion-cause pair extraction pairwise tagging framework end-to-end neural network

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Automatic Detection and Repair Recommendation for Missing Checks

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Journal of computer science & technology 2019年第5期34卷 972-992页

作者： Ling-Yun Situ Lin-Zhang Wang Yang Liu Bing Mao Xuan-Dong Li State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore

Missing checks for untrusted inputs used in security-sensitive operations is one of the major causes of various vulnerabilities. Efficiently detecting and repairing missing checks are essential for prognosticating potential vulnerabilities and improving code reliability. We propose a systematic static analysis approach to detect missing checks for manipulable data used in security-sensitive operations of C/C++ programs and recommend repair references. First, customized securitysensitive operations are located by lightweight static analysis. Then, the assailability of sensitive data used in securitysensitive operations is determined via taint analysis. And, the existence and the risk degree of missing checks are assessed. Finally, the repair references for high-risk missing checks are recommended. We implemented the approach into an automated and cross-platform tool named Vanguard based on Clang/LLVM 3.6.0. Large-scale experimental evaluation on open-source projects has shown its effectiveness and efficiency. Furthermore, Vanguard has helped us uncover five known vulnerabilities and 12 new bugs.

关键词： static analysis missing check vulnerability detection repair recommendation

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Meaningful Update and Repair of Markov Decision Processes for Self-Adaptive Systems

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Journal of computer science & technology 2022年第1期37卷 106-127页

作者： Wen-Hua Yang Min-Xue Pan Yu Zhou Zhi-Qiu Huang College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing UniversityNanjing 210023China

Self-adaptive systems are able to adjust their behaviour in response to environmental condition changes and are widely deployed as *** as a promising way to handle the ever-growing complexity of software systems,they have seen an increasing level of interest and are covering a variety of applications,e.g.,autonomous car systems and adaptive network *** approaches for the construction of self-adaptive systems have been developed,and probabilistic models,such as Markov decision processes(MDPs),are one of the ***,the majority of them do not deal with the problems of the underlying MDP being obsolete under new environments or unsatisfactory to the given *** results in the generated policies from such MDP failing to guide the self-adaptive system to run correctly and meet *** this article,we propose a systematic approach to updating an obsolete MDP by exploring new states and transitions and removing obsolete ones,and repairing an unsatisfactory MDP by adjusting its structure in a more meaningful way rather than arbitrarily changing the transition probabilities to values not in line with *** results show that the MDPs updated and repaired by our approach are more competent in guiding the self-adaptive systems’correct running compared with the original ones.

关键词： self-adaptive system Markov decision process model repair

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