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arXiv 2022年

作者： Mei, Jingyi Xu, Ming Guan, Ji Deng, Yuxin Yu, Nengkun Shanghai Key Lab of Trustworthy Computing East China Normal University China Shanghai Key Lab of Trustworthy Computing & MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States

Verifying quantum systems has attracted a lot of interest in the last decades. In this paper, we study the quantitative model-checking of quantum continuous-time Markov chains (quantum CTMCs). The branching-time properties of quantum CTMCs are specified by continuous stochastic logic (CSL), which is famous for verifying real-time systems, including classical CTMCs. The core of checking the CSL formulas lies in tackling multiphase until formulas. We develop an algebraic method using proper projection, matrix exponentiation, and definite integration to symbolically calculate the probability measures of path formulas. Thus the decidability of CSL is established. To be efficient, numerical methods are incorporated to guarantee that the time complexity is polynomial in the encoding size of the input model and linear in the size of the input formula. A running example of Apollonian networks is further provided to demonstrate our method. © 2022, CC BY.

关键词： Stochastic systems

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An Intelligent Resource Scheduling Method with Edge Channel Deployment for BPM

An Intelligent Resource Scheduling Method with Edge Channel ...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Bowen Liu Wanchun Dou Xiaokang Zhou Xuyun Zhang Lianyong Qi Fei Dai Chaochao Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Data Science Shiga University Hikone Japan Department of Computing Macquarie University Sydney Australia College of Computer and Software China University of Petroleum (East China) Qingdao China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Zhejiang University Hangzhou China

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users’ status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method.

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Four-Valued Monitorability of ω -Regular Languages 22nd

Four-Valued Monitorability of ω -Regular Languages

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22nd International Conference on Formal Engineering Methods, ICFEM 2020

作者： Chen, Zhe Chen, Yunyun Hierons, Robert M. Wu, Yifan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shanghai Key Laboratory of Trustworthy Computing Shanghai China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9783030634056

The use of runtime verification has led to interest in deciding whether a property is monitorable: whether it is always possible for the satisfaction or violation of the property to be determined after a finite future continuation during system execution. However, classical two-valued monitorability suffers from two inherent limitations, which eventually increase runtime overhead. First, no information is available regarding whether only one verdict (satisfaction or violation) can be detected. Second, it does not tell us whether verdicts can be detected starting from the current monitor state during system execution. This paper proposes a new notion of four-valued monitorability for ω -languages and applies it at the state-level. Four-valued monitorability is more informative than two-valued monitorability as a property can be evaluated as a four-valued result, denoting that only satisfaction, only violation, or both are active for a monitorable property. We can also compute state-level weak monitorability, i.e., whether satisfaction or violation can be detected starting from a given state in a monitor, which enables state-level optimizations of monitoring algorithms. Based on a new six-valued semantics, we propose procedures for computing four-valued monitorability of ω -regular languages, both at the language-level and at the state-level. Experimental results show that our tool implementation Monic can correctly, and quickly, report both two-valued and four-valued monitorability. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis

arXiv

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arXiv 2023年

作者： Liu, Yaochen Zhang, Yazhou Song, Dawei School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Computing and Communications The Open University Walton Hall United Kingdom School of Nursing The Hong Kong Polytechnic University Hong Kong Ministry of Education China Artificial Intelligence Laboratory China Mobile Communication Group Tianjin Co. Ltd. China State Key Lab. for Novel Software Technology Nanjing University Nanjing China Zhengzhou University of Light Industry China

Sarcasm, sentiment, and emotion are three typical kinds of spontaneous affective responses of humans to external events and they are tightly intertwined with each other. Such events may be expressed in multiple modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal conversations. Joint analysis of humans’ multi-modal sarcasm, sentiment, and emotion is an important yet challenging topic, as it is a complex cognitive process involving both cross-modality interaction and cross-affection correlation. From the probability theory perspective, cross-affection correlation also means that the judgments on sarcasm, sentiment, and emotion are incompatible. However, this exposed phenomenon cannot be sufficiently modelled by classical probability theory due to its assumption of compatibility. Neither do the existing approaches take it into consideration. In view of the recent success of quantum probability (QP) in modeling human cognition, particularly contextual incompatible decision making, we take the first step towards introducing QP into joint multi-modal sarcasm, sentiment, and emotion analysis. Specifically, we propose a QUantum probabIlity driven multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET. Extensive experiments on two datasets and the results show that the effectiveness and advantages of QUIET in comparison with a wide range of the state-of-the-art baselines. We also show the great potential of QP in multi-affect analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

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Your transformer may not be as powerful as you expect 22

Your transformer may not be as powerful as you expect

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shengjie Luo Shanda Li Shuxin Zheng Tie-Yan Liu Liwei Wang Di He National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University and Zhejiang Lab Machine Learning Department School of Computer Science Carnegie Mellon University Microsoft Research National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University and Center for Data Science Peking University National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University

ISBN: (纸本)9781713871088

Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers is largely unexplored. In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any continuous sequence-to-sequence functions. One may naturally assume the answer is in the affirmative—RPE-based Transformers are universal function approximators. However, we present a negative result by showing there exist continuous sequence-to-sequence functions that RPE-based Transformers cannot approximate no matter how deep and wide the neural network is. One key reason lies in that most RPEs are placed in the softmax attention that always generates a right stochastic matrix. This restricts the network from capturing positional information in the RPEs and limits its capacity. To overcome the problem and make the model more powerful, we first present sufficient conditions for RPE-based Transformers to achieve universal function approximation. With the theoretical guidance, we develop a novel attention module, called Universal RPE-based (URPE) Attention, which satisfies the conditions. Therefore, the corresponding URPE-based Transformers become universal function approximators. Extensive experiments covering typical architectures and tasks demonstrate that our model is parameter-efficient and can achieve superior performance to strong baselines in a wide range of applications.

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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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Mutant reduction evaluation: What is there and what is missing?

arXiv

引用

arXiv 2021年

作者： Zhang, Peng Wang, Yang Liu, Xutong Li, Yanhui Yang, Yibao Wang, Ziyuan Zhou, Xiaoyu Chen, Lin Zhou, Yuming State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Computer Nanjing University of Posts and Telecommunication China School of Computer Science and Engineering Southeast University China

Background. Mutation testing is a commonly used fault injection technique for evaluating the effectiveness of a test suite. However, it is usually computationally expensive. Therefore, many mutation reduction strategies, which aim to reduce the number of mutants, have been proposed. Problem. It is important to measure the ability of a mutation reduction strategy to maintain test suite effectiveness evaluation. However, existing evaluation indicators are unable to measure the "order-preserving ability", i.e., to what extent the mutation score order among test suites is maintained before and after mutation reduction. As a result, misleading conclusions can be achieved when using existing indicators to evaluate the reduction effectiveness. Objective. We aim to propose evaluation indicators to measure the "order-preserving ability" of a mutation reduction strategy, which is important but missing in our community. Method. Given a test suite on a software Under Test (SUT) with a set of original mutants, we leverage the test suite to generate a group of test suites that have a partial order relationship in fault detecting potential. When evaluating a reduction strategy, we first construct two partial order relationships among the generated test suites in terms of mutation score, one with the original mutants and another with the reduced mutants. Then, we measure the extent to which the two partial order relationships are consistent. The more consistent the two partial order relationships are, the stronger the Order Preservation (OP) of the mutation reduction strategy is, and the more effective the reduction strategy is. Furthermore, we propose Effort-aware Relative Order Preservation (EROP) to measure how much gain a mutation reduction strategy can provide compared with a random reduction strategy. Result. The experimental results show that OP and EROP are able to efficiently measure the "order-preserving ability" of a mutation reduction strategy. As a result, they have a

关键词： software testing

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Cloud-based storage and computing for remote sensing big data:a technical review

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International Journal of Digital Earth 2022年第1期15卷 1417-1445页

作者： Chen Xu Xiaoping Du Xiangtao Fan Gregory Giuliani Zhongyang Hu Wei Wang Jie Liu Teng Wang Zhenzhen Yan Junjie Zhu Tianyang Jiang Huadong Guo Key Lab of Digital Earth Sciences Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China Institute for Environmental Sciences/GRID-Geneva University of GenevaGenevaSwitzerland Institute for Marine and Atmospheric research Utrecht (IMAU) Utrecht UniversityUtrechtThe Netherlands State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingPeople’s Republic of China Tianyi Cloud People’s Republic of China Telecom Co.LtdGuangzhouPeople’s Republic of China Department of Computer Science and Technology Tsinghua UniversityBeijingPeople's Republic of China

The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and *** the progress of computer technologies,conventional computing implementations have become technically inefficient for processing *** computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past *** study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer ***,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,*** storage,metadata management,data homogeneity,and computing ***,we introduce state-of-the-art cloud-based data management technologies for RSBD *** unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data *** data models are suggested,***,ARD,data cubes,and composite ***,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing ***,we categorize the architectures of mainstream RSBD *** research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

关键词： Remote sensing big data cloud computing data cube analysis ready data parallel computing data model

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When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey

arXiv

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arXiv 2025年

作者： Niu, Feifei Li, Chuanyi Liu, Kui Xia, Xin Lo, David State Key Laboratory for Novel Software Technology Software Institute Nanjing University Nanjing China Software Engineering Application Technology Lab Huawei Hangzhou China College of Computer Science and Technology Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model’s capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area. © 2025, CC BY.

关键词： Online searching

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FLBench: A Benchmark Suite for Federated Learning 1st

FLBench: A Benchmark Suite for Federated Learning

引用

1st BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020

作者： Liang, Yuan Guo, Yange Gong, Yanxia Luo, Chunjie Zhan, Jianfeng Huang, Yunyou Guangxi Key Lab of Multi -Source Information Mining & Security Department of Computer Science Guangxi Normal University Guilin541004 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China BenchCouncil R&D Lab - Guilin Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811611599

Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices–so-called an isolated data island–while keeping their data secure and private. Most existing federated learning benchmarks work manually splits commonly-used public datasets into partitions to simulate real-world isolated data island scenarios. Still, this simulation fails to capture real-world isolated data island’s intrinsic characteristics. This paper presents a federated learning (FL) benchmark suite named FLBench. FLBench contains three domains: medical, financial, and AIoT. By configuring various domains, FLBench is qualified to evaluate federated learning systems and algorithms’ essential aspects, like communication, scenario transformation, privacy-preserving, data distribution heterogeneity, and cooperation strategy. Hence, it becomes a promising platform for developing novel federated learning algorithms. Currently, FLBench is open-sourced and in fast-evolution. We package it as an automated deployment tool. The benchmark suite is available from https://***/***. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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