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An Approach for Evaluating the Potential Impact of Anti-Patterns on Microservices Performance

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An Approach for Evaluating the Potential Impact of Anti-Patt...

IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Raghad Matar Jasmin Jahić Department of Architecture-Centric Engineering Fraunhofer Institute for Experimental Software Engineering Kaiserslautern Germany Department of Computer Science and Technology University of Cambridge Cambridge UK

The microservices architectural style has been increasingly adopted in recent years due to its advantageous characteristics. While there exist design patterns for microservices that are theoretically beneficial for ensuring performance (and anti-patterns that should be avoided), they do not always lead to the expected impact. Some designs are solutions for achieving quality properties other than performance, and while they might be categorized as anti-patterns in theory, their influence on system performance can be minimal, depending on the context. Architects find it hard to reason about these trade-offs and the impact that design decisions will have on performance before implementing them. To solve this problem, in this paper, we propose an approach that enables rapid evaluation of high-level architectural models by combining both static and dynamic analysis. The static analysis identifies the design anti-patterns that are known to hinder system performance and guides the architect in reasoning about these design decisions. The approach then generates source code for the system under study based on the architectural design model for a subsequent dynamic analysis to assess whether the statically detected anti-patterns do indeed have a negative effect on the performance of the analyzed system.

关键词： Analytical models software architecture System performance Source coding Microservice architectures Static analysis Cognition

Improving the Educational Value of Operating Systems

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Improving the Educational Value of Operating Systems

Roedunet International Conference (RoEduNet)

作者： Cezar Sava Răzvan Deaconescu Martin Kröning Antonello Monti Dinu Turcanu Computer Science and Engineering Department POLITEHNICA of Bucharest Bucharest Romania Institute for Automation of Complex Power Systems RWTH Aachen University Aachen Germany Fraunhofer Center Digital Energy Fraunhofer FIT Aachen Germany Department of Software Engineering and Automatics Technical University of Moldova Chişinău Moldova

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

关键词： Adaptive systems Operating systems Education Focusing Transforms Information age

Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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Advances in Manufacturing 2021年第3期9卷 388-402页

作者： Long-Hua Xu Chuan-Zhen Huang Jia-Hui Niu Jun Wang Han-Lian Liu Xiao-Dan Wang Center for Advanced Jet Engineering Technologies Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China School of Mechanical and Manufacturing Engineering The University of New South Wales(UNSW)SydneyNSW2052Australia Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface ***,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining ***,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface *** the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership *** membership functions are varied based on the machining *** inputs of the NANFIS model are cutting parameters,and the outputs are the machining *** optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS ***,the IPSO algorithm as a learning algorithm is used to train the NANFIS *** proposed intelligent system is applied to the high-speed milling process of compacted graphite *** experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,*** NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent *** on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling *** is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

关键词： Novel adaptive neuro-fuzzy inference system(NANFIS)model Improved particle swarm optimization(IPSO)algorithm Energy consumption Surface roughness Multiobjective optimization

Breamy: An augmented reality mHealth prototype for surgical decision-making in breast cancer

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arXiv 2023年

作者： Najafi, Niki Addie, Miranda Meterissian, Sarkis Kersten-Oertel, Marta Applied Perception Lab Department of Computer Science and Software Engineering Concordia University Québec Canada Experimental Surgery Mcgill University Québec Canada Breast Center McGill University Health Centre Québec Canada Department of Surgery Faculty of Medicine McGill University Québec Canada

In 2020, according to WHO, breast cancer affected 2.3 million women worldwide, resulting in 685,000 fatalities. By the end of the year, approximately 7.8 million women worldwide had survived their breast cancer making it the most widespread form of cancer globally. Surgical treatment decisions, including choosing between oncoplastic options, often require quick decision-making within an 8-week time frame. However, many women lack the necessary knowledge and preparation for making such complex informed decisions. Anxiety and unsatisfactory outcomes can result from inadequate decision-making processes, leading to complications and the need for revision surgeries. Shared decision-making and personalized decision aids have shown positive effects on patient satisfaction and treatment outcomes. This paper introduces Breamy, a prototype mobile health (mHealth) application that utilizes augmented reality (AR) technology to assist breast cancer patients in making informed decisions. The app provides 3D visualizations of different oncoplastic procedures, aiming to improve confidence in surgical decision-making, reduce decisional regret, and enhance patient well-being after surgery. To determine the perception of the usefulness of Breamy, we collected data from 166 participants through an online survey. The results suggest that Breamy has the potential to reduce patient's anxiety levels and assist them during the decision-making process. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

Offsite Evaluation of Localization Systems: Criteria, Systems, and Results From IPIN 2021 and 2022 Competitions

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IEEE Journal on Indoor and Seamless Positioning and Navigati...

IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital Healthcare Systems

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Qiao, Sibo Guo, Qiang Shi, Fengdong Wang, Min Zhu, Haohao Khan, Fazlullah Rodrigues, Joel J.P.C. Lyu, Zhihan School of Software Tiangong University Tianjin300387 China School of Artificial Intelligence Tiangong University Tianjin300387 China National Demonstration Center for Experimental Engineering Training Education Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China School of Control Sciences and Engineering Tiangong University Tianjin300387 China School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China Higher School of Technology Amazonas State University Amazonas Manaus69050-010 Brazil Department of Game Design Faculty of Arts Uppsala University Uppsala Sweden

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems. © 2013 IEEE All rights reserved.

关键词： Information leakage

Technical debt and agile software development practices and processes: An industry practitioner survey

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arXiv 2021年

作者： Holvitie, Johannes Licorish, Sherlock A. Spínola, Rodrigo O. Hyrynsalmi, Sami MacDonell, Stephen G. Mendes, Thiago S. Buchan, Jim Leppänen, Ville Turku Centre for Computer Science Software Development Laboratory Turku Finland University of Turku Department of Information Technology Turku Finland University of Otago Department of Information Science Otago Dunedin New Zealand Salvador University Graduate Program in Systems and Computer Bahia Salvador Brazil Federal University of Bahia Fraunhofer Project Center for Software and System Engineering Salvador Brazil Tampere University of Technology Pori Campus Pori Finland Federal Institute of Bahia Information Technology Department Bahia Santo Amaro Brazil Auckland University of Technology School of Engineering Computer and Mathematical Sciences Auckland New Zealand

Context: Contemporary software development is typically conducted in dynamic, resource-scarce environments that are prone to the accumulation of technical debt. While this general phenomenon is acknowledged, what remains unknown is how technical debt specifically manifests in and affects software processes, and how the software development techniques employed accommodate or mitigate the presence of this debt. Objectives: We sought to draw on practitioner insights and experiences in order to classify the effects of agile method use on technical debt management, given the popularity and perceived success of agile methods. We explore the breadth of practitioners' knowledge about technical debt;how technical debt is manifested across the software process;and the perceived effects of common agile software development practices and processes on technical debt. In doing so, we address a research gap in technical debt knowledge and provide novel and actionable managerial recommendations. Method: We designed, tested and executed a multi-national survey questionnaire to address our objectives, receiving 184 responses from practitioners in Brazil, Finland, and New Zealand. Results: Our findings indicate that: 1) Practitioners are aware of technical debt, although, there was under utilization of the concept, 2) Technical debt commonly resides in legacy systems, however, concrete instances of technical debt are hard to conceptualize which makes it problematic to manage, 3) Queried agile practices and processes help to reduce technical debt;in particular, techniques that verify and maintain the structure and clarity of implemented artifacts (e.g., Coding standards and Refactoring) positively affect technical debt management. Conclusions: The fact that technical debt instances tend to have characteristics in common means that a systematic approach to its management is feasible. However, notwithstanding the positive effects of some agile practices on technical debt management, comp

关键词： Legacy systems

Adoption and suitability of software development methods and practices

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arXiv 2021年

作者： Licorish, Sherlock A. Holvitie, Johannes Hyrynsalmi, Sami Leppänen, Ville Spínola, Rodrigo O. Mendes, Thiago S. MacDonell, Stephen G. Buchan, Jim Department of Information Science University of Otago PO Box 56 Dunedin9054 New Zealand Department of Information Technology University of Turku TurkuFI-20014 Finland Fraunhofer Project Center for Software and System Engineering at UFBA Salvador University Salvador Brazil School of Computer and Mathematical Sciences Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand

In seeking to complement consultants' and tool vendors' reports, there has been an increasing academic focus on understanding the adoption and use of software development methods and practices. We surveyed practitioners working in Brazil, Finland, and New Zealand in a transnational study to contribute to these efforts. Among our findings we observed that most of the 184 practitioners in our sample focused on a small portfolio of projects that were of short duration. In addition, Scrum and Kanban were used most;however, some practitioners also used conventional methods. Coding Standards, Simple Design and Refactoring were used most by practitioners, and these practices were held to be largely suitable for project and process management. Our evidence points to the need to properly understand and support a wide range of software methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

Safeml: Safety monitoring of machine learning classifiers through statistical difference measure

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arXiv 2020年

作者： Aslansefat, Koorosh Sorokos, Ioannis Whiting, Declan Kolagari, Ramin Tavakoli Papadopoulos, Yiannis Department of Computer Science University of Hull Kingston upon HullHU6 7RX United Kingdom Fraunhofer Institute for Experimental Software Engineering IESE Fraunhofer-Gesellschaft Fraunhofer-Platz 1 Kaiserslautern67663 Germany Department of Computer Science University of Hull APD Communications Kingston upon HullHU6 7RX United Kingdom Faculty of Computer Science Nuremberg Tech Keßlerplatz 12 Nürnberg90489 Germany

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems. Especially the interaction between safety and security is a central challenge, as security violations can lead to compromised safety. The contribution of this paper to addressing both safety and security within a single concept of protection applicable during the operation of ML systems is active monitoring of the behavior and the operational context of the data-driven system based on distance measures of the Empirical Cumulative Distribution Function (ECDF). We investigate abstract datasets (XOR, Spiral, Circle) and current security-specific datasets for intrusion detection (CICIDS2017) of simulated network traffic, using statistical distance measures including the Kolmogorov-Smirnov, Kuiper, Anderson-Darling, Wasserstein and mixed Wasserstein-Anderson-Darling measures. Our preliminary findings indicate that there is a meaningful correlation between ML decisions and the ECDF-based distances measures of the input features. Thus, they can provide a confidence level that can be used for a) analyzing the applicability of the ML system in a given field (safety/security) and b) analyzing if the field data was maliciously manipulated1. Copyright © 2020, The Authors. All rights reserved.

关键词： Intrusion detection