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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Xiao, Mingjun Xu, Yin Zhou, Jinrui Wu, Jie Zhang, Sheng Zheng, Jun University of Science and Technology of China School of Computer Science and Technology China Suzhou Institute for Advanced Research University of Science and Technology of China China Temple Universityy Department of Computer and Information Sciences United States Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI. © 2023 IEEE.

关键词： Optimal systems

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On interleaving space exploration of multi-threaded programs

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Frontiers of computer science 2021年第4期15卷 17-28页

作者： Dongjie CHEN Yanyan JIANG Chang XU Xiaoxing MA State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China

Exploring the interleaving space of a multithreaded program to efficiently detect concurrency bugs is important but also difficult because of the astronomically many thread *** paper presents a novel framework to decompose a thread schedule generator that explores the interleaving space into the composition of a basic generator and its extension under the“small interleaving hypothesis”.Under this framework,we in-depth analyzed research work on interleaving space exploration,illustrated how to design an effective schedule generator,and shed light on future research opportunities.

关键词： survey testing concurrency bugs interleaving space

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Verifying Contextual Refinement with Ownership Transfer

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Journal of computer science & technology 2021年第6期36卷 1342-1366页

作者： Zhao-Hui Li Xin-Yu Feng School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

Contextual refinement is a compositional approach to compositional verification of concurrent *** has been much work designing program logics to prove the contextual refinement between the object implementation and its abstract ***,these program logics for contextual refinement verification cannot support objects with resource ownership transfer,which is a common pattern in many concurrent objects,such as the memory management module in OS kernels,which transfers the allocated memory block between the object and *** this paper,we propose a new approach to give abstract and implementation independent specifications to concurrent objects with ownership *** also design a program logic to verify contextual refinement of concurrent objects *** abstract *** have successfully applied our logic to verifying an implementation of the memory management module,where the implementation is an appropriately simplified version of the original version from a real-world preemptive OS kernel.

关键词： contextual refinement program logic concurrent object ownership transfer verification

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RAT: A Refactoring-Aware Tool for Tracking Code History 24

RAT: A Refactoring-Aware Tool for Tracking Code History

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46th International Conference on software Engineering: Companion, ICSE-Companion 2024

作者： Niu, Feifei Shao, Junqian Xu, Chaofan Mayr-Dorn, Christoph Assunção, Wesley K. G. Huang, Liguo Li, Chuanyi Ge, Jidong Luo, Bin Egyed, Alexander State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina United States Department of Computer Science and Engineering Southern Methodist University DallasTX United States

ISBN: (纸本)9798400705021

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT-Demo. Demo Video: https://***/VI-xwUaIPp4. © 2024 IEEE computer Society. All rights reserved.

关键词： Rats

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第07期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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Formal Model of ATM Based on Object Constraint Language (OCL) 10

Formal Model of ATM Based on Object Constraint Language (OCL...

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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Ahmed, Irfan Naseer, Sadia Umm - E - Habiba Mahmood, Jabar Chaudhry, Shehzad Ashraf Batool, Khadija University of Sialkot Department of Computer Science Sialkot Pakistan School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University State Key Laboratory of Blockchain Hangzhou China College of Engineering Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates Gujranwala Pakistan

ISBN: (纸本)9798331532895

Formal methods (FM) are innovative methods which employ mathematical notations to specify explicit and precise requirements. This paper presents a formal model of an automated teller machine (ATM) using Object Constraint Language (OCL) to illustrate the benefits of employing FM to develop commercial applications. FM provide a mathematically rigorous framework for specifying, developing, and verifying software systems. It enhances reliability, robustness, and security. FM plays a crucial role in the context of ATM, which requires high levels of trust and correctness due to their financial and security-critical nature. Using OCL, we demonstrate how formal specifications can be applied to define an ATM system's precise behavior and constraints. It allows for the early detection and correction of errors, reducing development costs and improving overall system quality. This study argues that adopting FM in commercial applications not only boosts the reliability and security of the systems but also increases customer satisfaction and trust. Results of ATM development under OCL have been compared with traditional methods (TM), showing reductions such as 51 % in development time, 53 % in cost, 94 % in ambiguity and 97% in quality and customer trust. © 2024 IEEE.

关键词： Customer satisfaction

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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

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18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Network security

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?-net: Dual supervised medical image segmentation with multi-dimensional self-attention and diversely-connected multi-scale convolution

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NEUROCOMPUTING 2022年 500卷 177-190页

作者： Xu, Zhenghua Liu, Shijie Yuan, Di Wang, Lei Chen, Junyang Lukasiewicz, Thomas Fu, Zhigang Zhang, Rui State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology China Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health Hebei University of Technology China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science University of Oxford United Kingdom Department of Health Management Center 983 Hospital of Joint Logistics Support Force China www.ruizhang.info

Although U-Net and its variants have achieved some great successes in medical image segmentation tasks, their segmentation performances for small objects are still unsatisfactory. Therefore, in this work, a new deep model, co-Net, is proposed to achieve more accurate medical image segmentations. The advancements of co-Net are mainly threefold: First, it incorporates an additional expansive path into U-Net to import an extra supervision signal and obtain a more effective and robust image segmentation by dual supervision. Then, a multi-dimensional self-attention mechanism is further developed to highlight salient features and suppress irrelevant ones consecutively in both spatial and channel dimensions. Finally, to reduce semantic disparity between the feature maps of the contracting and expansive paths, we further propose to integrate diversely-connected multi-scale convolution blocks into the skip connections, where several multi-scale convolutional operations are connected in both series and parallel. Extensive experimental results on three abdominal CT segmentation tasks show that (i) co-Net greatly outperforms the state-of-the-art image segmentation methods in medical image segmentation tasks;(ii) the proposed three advancements are all effective and essential for co-Net to achieve the superior performances;and (iii) the proposed multi-dimensional self-attention (resp., diversely-connected multi scale convolution) is more effective than the state-of-the-art attention mechanisms (resp., multi-scale solutions) for medical image segmentations. The code will be released online after this paper is formally accepted. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Medical image segmentation Dual supervision Multi-dimensional self-attention Diversely-connected multi-scale convolution

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Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

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Journal of computer science & technology 2022年第1期37卷 182-206页

作者： Zi-Jie Huang Zhi-Qing Shao Gui-Sheng Fan Hui-Qun Yu Xing-Guang Yang Kang Yang Department of Computer Science and Engineering East China University of Science and TechnologyShanghai 200237China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai 200237China Shanghai Engineering Research Center of Smart Energy Shanghai 200237China

Community smells are sub-optimal developer community structures that hinder *** studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community ***,refactoring smells also requires bottom-up effort from every ***,supportive measures and guidelines for them are not available at a fine-grained *** recent work revealed developers'personalities and working states could influence community smells'emergence and variation,we build prediction models with experience,sentiment,and development process features of developers considering three smells including Organizational Silo,Lone Wolf,and Bottleneck,as well as two related classes including smelly developer and smelly *** predict the five classes in the individual granularity,and we also generate forecasts for the number of smelly developers in the community *** proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project,time-wise,and cross-project prediction,and mean R2 performance of 0.68 in community-wide Smelly Developer *** also exploit SHAP(SHapley Additive exPlanations)to assess feature importance to explain our *** conclusion,we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities,and we recommend community shepherds to use the forecasting model for refactoring planning.

关键词： community smell developer sentiment socio-technical analysis empirical software engineering

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