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arXiv 2023年

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023, CC BY-NC-ND.

关键词： Edge computing

Variability Modeling of Products, Processes, and Resources in Cyber-Physical Production systems engineering

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arXiv 2024年

作者： Meixner, Kristof Feichtinger, Kevin Fadhlillah, Hafiyyan Sayyid Greiner, Sandra Marcher, Hannes Rabiser, Rick Biffl, Stefan Christian Doppler Laboratory SQI TU Wien Favoritenstraße 9-11 Vienna1040 Austria Information Systems Engineering TU Wien Favoritenstraße 9-11 Vienna1040 Austria CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria Software Engineering Group Institute of Computer Science University of Bern Neubrückstraße 10 Bern3012 Switzerland LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Cyber-Physical Production systems (CPPSs), such as automated car manufacturing plants, execute a configurable sequence of production steps to manufacture products from a product portfolio. In CPPS engineering, domain experts start with manually determining feasible production step sequences and resources based on implicit knowledge. This process is hard to reproduce and highly inefficient. In this paper, we present the Extended Iterative Process Sequence Exploration (eIPSE) approach to derive variability models for products, processes, and resources from a domain-specific description. To automate the integrated exploration and configuration process for a CPPS, we provide a toolchain which automatically reduces the configuration space and allows to generate CPPS artifacts, such as control code for resources. We evaluate the approach with four real-world use cases, including the generation of control code artifacts, and an observational user study to collect feedback from engineers with different backgrounds. The results confirm the usefulness of the eIPSE approach and accompanying prototype to straightforwardly configure a desired CPPS. © 2024, CC BY.

关键词： Cyber Physical System

Exploiting crowd-based learning method in software engineering course 15

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Exploiting crowd-based learning method in software engineeri...

15th International Conference on Computer Science and Education, ICCSE 2020

作者： Mao, Xinjun Lu, Yao Yang, Yi National University of Defense Technology Key Lab. of Software Engineering for Complex Systems Changsha China National University of Defense Technology College of Computer Changsha China

ISBN: (纸本)9781728172675

software engineering is one of main major course for computer science. The main objective of this course is to improve the engineering capabilities of students to develop complex software systems. However, current course practice methods are teacher-centered and thus highly depend on the limited talents, experiences and efforts of teachers. Such methods greatly restrict students' scopes to gain necessary skills and artifacts, and to draw on successful experiences to address various personalized engineering issues and therefore enhance abilities to tackle engineering complexity. This paper presents a crowd-based learning method and introduces our exploration to use the method to reform the software engineering course practice. The essence of our exploration is to exploit the wisdoms and artifacts of crowds over Internet to support students to accomplish their course projects. The former is the engineering knowledges shared in question and answering (QA) platforms such as StackOverflow, and the latter is the open source software (OSS) hosted in OSS sites such as GitHub. We use the method in two separate course practices that aim to improve students' abilities to develop original, high-quality and large-scale software systems. The results of three-year explorations are positive and impressive and show that the crowd-based learning method is effective. © 2020 IEEE.

关键词： Open source software

CrowdSim: A hybrid simulation model for failure prediction in crowdsourced software development

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arXiv 2021年

作者： Saremi, Razieh Yang, Ye Vesonder, Gregg Ruhe, Guenther Zhang, He School of Systems and Enterprices Stevens Institute of Technology HobokenNJ United States Software Engineering Decision Support Lab University of Calgary Alberta Canada Software Institute Nanjing University Jiangsu China

A typical crowdsourcing software development (CSD) marketplace consists of a list of software tasks as service demands and a pool of freelancer developers as service suppliers. Highly dynamic and competitive CSD marketplaces may result in task failure due to unforeseen risks, such as increased competition over shared worker supply, or uncertainty associated with workers' experience and skills, and so on. To improve CSD effectiveness, it is essential to better understand and plan with respect to dynamic worker characteristics and risks associated with CSD processes. In this paper, we present a hybrid simulation model, CrowdSim, to forecast crowdsourcing task failure risk in competitive CSD platforms. CrowdSim is composed of three layered components: the macro-level reflects the overall crowdsourcing platform based on system dynamics, the meso-level represents the task life cycle based on discrete event simulation, and the micro-level models the crowd workers' decision-making processes based on agent-based simulation. CrowdSim is evaluated through three CSD decision scenarios to demonstrate its effectiveness, using a real-world historical dataset, and the results demonstrate CrowdSim's potential in empowering crowdsourcing managers to explore crowdsourcing outcomes with respect to different task scheduling options. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

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PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

Reinforcement-Learning Based Method for Accelerating Functional Coverage Closure of Traffic Light Controller Dynamic Digital Design

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Reinforcement-Learning Based Method for Accelerating Functio...

International Conference on Computer Theory and Applications (ICCTA)

作者： Youstina M. Halim Khaled A. Ismail Mohamed A. Abd El Ghany Sameh A. Ibrahim Youssef M. Halim Mixel Egypt. Electronics and Communications Engineering Dept. Faculty of Engineering Ain Shams University Cairo Egypt GSI Helmholtz Center for Heavy-Ion Research GmbH (GSI) Germany Electronics Department German University in Cairo Egypt. Integrated Electronic Systems Lab TU Darmstadt Germany Electronics and Communications Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Engineering and Software Systems Dept. Faculty of Engineering Ain Shams University Cairo Egypt

Functional verification of digital designs is an increasingly complex and time-consuming endeavor. One of the major challenges in functional verification is achieving functional coverage closure in a timely manner. Verification experts have moved from using directed stimulus, towards constrained random stimulus and finally, towards using machine learning to direct stimulus. In this work, reinforcement learning (RL) was used, incorporating three different kinds of RL agents to direct stimulus generation for a traffic light controller system. Two of the agents are based on Q-learning (DQN and SARSA), and one is based on Monte-Carlo method (CEM). The maximum reduction in simulation time needed to achieve a targeted percent of coverage was 97%. The average reduction of simulation time across all testcases and agents used was 70%. The solution is highly reusable, the architecture was integrated with different agents and testcases.

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JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer

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arXiv 2024年

作者： Guan, ZhiHao Yang, Jia-Qi Yang, Yang Zhu, Hengshu Li, Wenjie Xiong, Hui the Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Career Science Lab BOSS Zhipin Beijing100028 China the Hong Kong Polytechnic University Hong Kong the Artificial Intelligence Thrust The Hong Kong University of Science and Technology Guangzhou China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user’s skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： User profile

On the Secrecy Capacity of $\mathcal{F}$ Composite Fading Channels with Multiple Eavesdroppers

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On the Secrecy Capacity of $\mathcal{F}$ Composite Fading Ch...

IEEE International Conference on Communications (ICC)

作者： Seong Ki Yoo Simon L. Cotton Lei Zhang Jae Seung Song Imran S. Ansari Paschalis C. Sofotasios Centre for Future Transport and Cities Coventry University Coventry UK Centre for Wireless Innovation ECIT Institute Queen's University Belfast Belfast UK Software Engineering and Security Lab. Sejong University Seoul South Korea James Watt School of Engineering University of Glasgow Glasgow UK EECS Department Center for Cyber-Physical Systems Khalifa University Abu Dhabi UAE Department of Electrical Engineering Tampere University Tampere Finland

This contribution quantifies the achievable physical layer security of wireless transmission over realistic composite fading conditions in the presence of multiple eavesdroppers (Eves). To this end, exact closed-form analytic expressions are derived for the achievable secure outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) for the case of $\mathcal{F}$ composite fading channels. The derived expressions are tractable and their validity is justified through comparisons with respective computer simulation results. In addition, they allow for the development of useful theoretical and practical insights on the effect of the presence of numerous eavesdroppers under different multipath fading and shadowing conditions, as these are encountered in realistic wireless communication scenarios.

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Formation control of multiple nonholonomic vehicles with local measurements in 3D space

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Formation control of multiple nonholonomic vehicles with loc...

IEEE Conference on Decision and Control

作者： Shuai Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China

ISBN: (纸本)9781665436601

We study the formation control problem for multiple nonholonomic vehicles in 3D space, where vehicles are required to maintain a prescribed pattern and rotate around a target in the target plane, with the direction of rotation along with the normal vector of the target plane. Unlike the most existing works that study formation problems of agents modeled by mass-point in a 2D plane, control of multiple nonholonomic vehicles in 3D space means a grand challenge since both the space and dynamics are complex. We give explicit formulations of the problem and vehicles’ dynamics. Then, control laws to solve the problem are proposed and further transformed into the body-fixed frames of vehicles to show that they can be easily implemented using only local measurements and the direction of gravity. Theoretical analysis is presented and simulations show the effectiveness of the proposed laws.

关键词： Space vehicles Solid modeling Analytical models Three-dimensional displays Conferences Aerospace electronics Extraterrestrial measurements

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

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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