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software Engineering: software Engineering Education and Training (ICSE-SEET), IEEE/ACM International Conference on

作者： Hao He Minghui Zhou Qingye Wang Jingyue Li School of Computer Science Peking University China Key Laboratory of High Confidence Software Technologies Ministry of Education China Department of Computer Science Norwegian University of Science and Technology Norway

Without newcomers, open source software (OSS) projects are hardly sustainable. Yet, newcomers face a steep learning curve during OSS onboarding in which they must overcome a multitude of technical, social, and knowledge barriers. To ease the onboarding process, OSS communities are utilizing mentoring, task recommendation (e.g., “good first issues”), and engagement programs (e.g., Google Summer of Code). However, newcomers must first cultivate their motivation for OSS contribution and learn the necessary preliminaries before they can take advantage of these mechanisms. We believe this gap can be filled by a dedicated, practice-oriented OSS onboarding course. In this paper, we present our experience of teaching an OSS onboarding course at Peking University. The course contains a series of lectures, labs, and invited talks to prepare students with the required skills and motivate them to contribute to OSS. In addition, students are required to complete a semester-long course project in which they plan and make actual contributions to OSS projects. They can either contribute to some recommended OSS projects with dedicated mentors, or contribute to any OSS project they prefer. Finally, 16 out of the 19 enrolled students have successfully contributed to OSS projects, and five have retained. However, the onboarding trajectories, final contributions, and retention outcomes differ vastly between the two groups of students with different course project choices, yielding lessons for software engineering education.

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

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

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Irfan Ahmed Sadia Naseer Umm - e - Habiba Jabar Mahmood Shehzad Ashraf Chaudhry Khadija Batool Department of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi U.A.E International Institute of Science Arts and Technology (IISAT) Gujranwala Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

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.

关键词： Frequency modulation Costs Automated teller machine software systems software reliability Security Formal specifications Stakeholders Testing software engineering

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On the noise robustness of in-context learning for text generation 24

On the noise robustness of in-context learning for text gene...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

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

ISBN: (纸本)9798331314385

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

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Location-Based Service Recommendation for Cold-Start in Mobile Edge Computing 17th

Location-Based Service Recommendation for Cold-Start in Mobi...

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17th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2020

作者： Yu, Mengshan Fan, Guisheng Yu, Huiqun Chen, Liang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030794774

With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Service recommendation in such a distributed environment is a hot issue. However, the cold-start problem for service recommendation in mobile edge computing is still urgent to be solved. In this paper, we propose a service recommendation method based on collaborative filtering (CF) and user location, by comprehensively considering the characteristic of services at the edge and mobility of users. In detail, we first synthesize the service characteristics of each dimension through multidimensional weighting method. We further introduce the idea of Inverse_CF_Rec to the traditional CF and then predict the lost QoS value to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS value and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on collaborative filtering (MDICF) outperforms Inverse_CF_Rec in terms of the accuracy of recommendation. © 2021, IFIP International Federation for Information Processing.

关键词： Location based services

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

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IEEE Transactions on Vehicular technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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SymPhase: Phase Symbolization for Fast Simulation of Stabilizer Circuits

arXiv

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arXiv 2023年

作者： Fang, Wang Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

This paper proposes an efficient stabilizer circuit simulation algorithm that only traverses the circuit forward once. We introduce phase symbolization into stabilizer generators, which allows possible Pauli faults in the circuit to be accumulated explicitly as symbolic expressions in the phases of stabilizer generators. This way, the measurement outcomes are also symbolic expressions, and we can sample them by substituting the symbolic variables with concrete values, without traversing the circuit repeatedly. We show how to integrate symbolic phases into the stabilizer tableau and maintain them efficiently using bit-vector encoding. A new data layout of the stabilizer tableau in memory is proposed, which improves the performance of our algorithm (and other stabilizer simulation algorithms based on the stabilizer tableau). We implement our algorithm and data layout in a Julia package named ***, and compare it with Stim, the state-of-the-art simulator, on several benchmarks. We show that *** has superior performance in terms of sampling time, which is crucial for generating a large number of samples for further analysis. © 2023, CC BY.

关键词： Timing circuits

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Detecting Violations of Differential Privacy for Quantum Algorithms

arXiv

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arXiv 2023年

作者： Guan, Ji Fang, Wang Huang, Mingyu Ying, Mingsheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring. The concern about privacy and other ethical issues in quantum computing naturally rises up. In this paper, we define a formal framework for detecting violations of differential privacy for quantum algorithms. A detection algorithm is developed to verify whether a (noisy) quantum algorithm is differentially private and automatically generates bugging information when the violation of differential privacy is reported. The information consists of a pair of quantum states that violate the privacy, to illustrate the cause of the violation. Our algorithm is equipped with Tensor Networks, a highly efficient data structure, and executed both on TensorFlow Quantum and TorchQuantum which are the quantum extensions of famous machine learning platforms - TensorFlow and PyTorch, respectively. The effectiveness and efficiency of our algorithm are confirmed by the experimental results of almost all types of quantum algorithms already implemented on realistic quantum computers, including quantum supremacy algorithms (beyond the capability of classical algorithms), quantum machine learning models, quantum approximate optimization algorithms, and variational quantum eigensolvers with up to 21 quantum bits. © 2023, CC BY.

关键词： Quantum noise

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Nerftap: Enhancing Transferability of Adversarial Patches on Face Recognition Using Neural Radiance Fields

SSRN

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SSRN 2024年

作者： Liu, Xiaoliang Shen, Furao Han, Feng Zhao, Jian Nie, Changhai National Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns. Existing research primarily focuses on transferability to different FR models, overlooking the direct transferability to victim's face images, which is a practical threat in real-world scenarios. In this study, we propose a novel adversarial attack method that considers both the transferability to the FR model and the victim's face image, called NeRFTAP. Leveraging Neural Radiance Fields (NeRF)-based 3D Generative Adversarial Network (GAN), we generate new view face images for the source and target subjects to enhance transferability of adversarial patches. We introduce a style consistency loss to ensure the visual similarity between the adversarial UV map and the target UV map under a 0-1 mask, enhancing the effectiveness and naturalness of the generated adversarial face images. Extensive experiments and evaluations on various FR models demonstrate the superiority of our approach over existing attack techniques. Our work provides valuable insights for enhancing the robustness of FR systems in practical adversarial settings. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Heuristic Elastic Scaling for Kubernetes Heterogeneous Microservices 21

Heuristic Elastic Scaling for Kubernetes Heterogeneous Micro...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Song He, Sheng Ning, Yishuang Gao, Xiang Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen518057 China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou30012 China

ISBN: (纸本)9798350365221

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions;second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost;and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms. © 2024 IEEE.

关键词： Cloud platforms

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SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework

arXiv

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arXiv 2025年

作者： Peng, Yubo Xiang, Luping Yang, Kun Jiang, Feibo Wang, Kezhi Wu, Dapeng Oliver State Key Laboratory of Novel Software Technology Nanjing University Nanjing China Suzhou China School of Information Science and Engineering Hunan Normal University Changsha China Department of Computer Science Brunel University London United Kingdom Department of Computer Science City University of Hong Kong Hong Kong

Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users’ diverse demands. To overcome these challenges, we propose a semantic-driven integrated multimodal sensing and communication (SIMAC) framework. This framework leverages a joint source-channel coding architecture to achieve simultaneous sensing decoding and transmission of sensing results. Specifically, SIMAC first introduces a multimodal semantic fusion (MSF) network, which employs two extractors to extract semantic information from radar signals and images, respectively. MSF then applies cross-attention mechanisms to fuse these unimodal features and generate multimodal semantic representations. Secondly, we present a large language model (LLM)-based semantic encoder (LSE), where relevant communication parameters and multimodal semantics are mapped into a unified latent space and input to the LLM, enabling channel-adaptive semantic encoding. Thirdly, a task-oriented sensing semantic decoder (SSD) is proposed, in which different decoded heads are designed according to the specific needs of tasks. Simultaneously, a multi-task learning strategy is introduced to train the SIMAC framework, achieving diverse sensing services. Finally, experimental simulations demonstrate that the proposed framework achieves diverse sensing services and higher accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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