2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)最新文献_第2页

Robust Machine Learning in Critical Care — Software Engineering and Medical Perspectives 重症监护中的鲁棒机器学习-软件工程和医学观点

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-15 DOI: 10.1109/WAIN52551.2021.00016

M. Staron, Helena Odenstedt Herg'es, S. Naredi, L. Block, Ali El-Merhi, Richard Vithal, M. Elam

{"title":"Robust Machine Learning in Critical Care — Software Engineering and Medical Perspectives","authors":"M. Staron, Helena Odenstedt Herg'es, S. Naredi, L. Block, Ali El-Merhi, Richard Vithal, M. Elam","doi":"10.1109/WAIN52551.2021.00016","DOIUrl":"https://doi.org/10.1109/WAIN52551.2021.00016","url":null,"abstract":"Using machine learning in clinical practice poses hard requirements on explainability, reliability, replicability and robustness of these systems. Therefore, developing reliable software for monitoring critically ill patients requires close collaboration between physicians and software engineers. However, these two different disciplines need to find own research perspectives in order to contribute to both the medical and the software engineering domain. In this paper, we address the problem of how to establish a collaboration where software engineering and medicine meets to design robust machine learning systems to be used in patient care. We describe how we designed software systems for monitoring patients under carotid endarterectomy, in particular focusing on the process of knowledge building in the research team. Our results show what to consider when setting up such a collaboration, how it develops over time and what kind of systems can be constructed based on it. We conclude that the main challenge is to find a good research team, where different competences are committed to a common goal.","PeriodicalId":224912,"journal":{"name":"2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129646369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Software Architecture for ML-based Systems: What Exists and What Lies Ahead 基于机器学习系统的软件架构:现有的和未来的

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-14 DOI: 10.1109/WAIN52551.2021.00026

H. Muccini, Karthik Vaidhyanathan

引用次数: 20

Concepts in Testing of Autonomous Systems: Academic Literature and Industry Practice 自主系统测试中的概念:学术文献和工业实践

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-12 DOI: 10.1109/WAIN52551.2021.00018

Qunying Song, Emelie Engström, P. Runeson

引用次数: 9

Towards Risk Modeling for Collaborative AI 面向协作人工智能的风险建模

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-12 DOI: 10.1109/WAIN52551.2021.00014

Matteo Camilli, M. Felderer, Andrea Giusti, D. Matt, A. Perini, B. Russo, A. Susi

{"title":"Towards Risk Modeling for Collaborative AI","authors":"Matteo Camilli, M. Felderer, Andrea Giusti, D. Matt, A. Perini, B. Russo, A. Susi","doi":"10.1109/WAIN52551.2021.00014","DOIUrl":"https://doi.org/10.1109/WAIN52551.2021.00014","url":null,"abstract":"Collaborative AI systems aim at working together with humans in a shared space to achieve a common goal. This setting imposes potentially hazardous circumstances due to contacts that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements domain specific standards and regulations is of greatest importance. Challenges associated with the achievement of this goal become even more severe when such systems rely on machine learning components rather than such as top-down rule-based AI. In this paper, we introduce a risk modeling approach tailored to Collaborative AI systems. The risk model includes goals, risk events and domain specific indicators that potentially expose humans to hazards. The risk model is then leveraged to drive assurance methods that feed in turn the risk model through insights extracted from run-time evidence. Our envisioned approach is described by means of a running example in the domain of Industry 4.0, where a robotic arm endowed with a visual perception component, implemented with machine learning, collaborates with a human operator for a production-relevant task.","PeriodicalId":224912,"journal":{"name":"2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)","volume":"54 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117353442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Challenges and Governance Solutions for Data Science Services based on Open Data and APIs 基于开放数据和api的数据科学服务的挑战和治理解决方案

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-12 DOI: 10.1109/WAIN52551.2021.00012

Juha-Pekka Joutsenlahti, Timo Lehtonen, M. Raatikainen, Elina Kettunen, T. Mikkonen

引用次数: 4

Systematic Mapping Study on the Machine Learning Lifecycle 机器学习生命周期的系统映射研究

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-11 DOI: 10.1109/WAIN52551.2021.00017

Yuanhao Xie, Luís Cruz, P. Heck, Jan S. Rellermeyer

引用次数: 6

Integration of Convolutional Neural Networks in Mobile Applications 卷积神经网络在移动应用中的集成

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-11 DOI: 10.1109/WAIN52551.2021.00010

Roger Creus Castanyer, Silverio Mart'inez-Fern'andez, Xavier Franch

{"title":"Integration of Convolutional Neural Networks in Mobile Applications","authors":"Roger Creus Castanyer, Silverio Mart'inez-Fern'andez, Xavier Franch","doi":"10.1109/WAIN52551.2021.00010","DOIUrl":"https://doi.org/10.1109/WAIN52551.2021.00010","url":null,"abstract":"When building Deep Learning (DL) models, data scientists and software engineers manage the trade-off between their accuracy, or any other suitable success criteria, and their complexity. In an environment with high computational power, a common practice is making the models go deeper by designing more sophisticated architectures. However, in the context of mobile devices, which possess less computational power, keeping complexity under control is a must. In this paper, we study the performance of a system that integrates a DL model as a trade-off between the accuracy and the complexity. At the same time, we relate the complexity to the efficiency of the system. With this, we present a practical study that aims to explore the challenges met when optimizing the performance of DL models becomes a requirement. Concretely, we aim to identify: (i) the most concerning challenges when deploying DL-based software in mobile applications; and (ii) the path for optimizing the performance trade-off. We obtain results that verify many of the identified challenges in the related work such as the availability of frameworks and the software-data dependency. We provide a documentation of our experience when facing the identified challenges together with the discussion of possible solutions to them. Additionally, we implement a solution to the sustainability of the DL models when deployed in order to reduce the severity of other identified challenges. Moreover, we relate the performance trade-off to a new defined challenge featuring the impact of the complexity in the obtained accuracy. Finally, we discuss and motivate future work that aims to provide solutions to the more open challenges found.","PeriodicalId":224912,"journal":{"name":"2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129656799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Data Collection and Utilization Framework for Edge AI Applications 边缘人工智能应用的数据收集和利用框架

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-11 DOI: 10.1109/WAIN52551.2021.00023

Hergys Rexha, S. Lafond

{"title":"Data Collection and Utilization Framework for Edge AI Applications","authors":"Hergys Rexha, S. Lafond","doi":"10.1109/WAIN52551.2021.00023","DOIUrl":"https://doi.org/10.1109/WAIN52551.2021.00023","url":null,"abstract":"As data being produced by IoT applications continues to explode, there is a growing need to bring computing power closer to the source of the data to meet the response-time, power dissipation and cost goals of performance-critical applications in various domains like Industrial Internet of Things (IIoT), Automated Driving, Medical Imaging or Surveillance among others. This paper proposes a data collection and utilization framework that allows runtime platform and application data to be sent to an edge and cloud system via data collection agents running close to the platform. Agents are connected to a cloud system able to train AI models to improve overall energy efficiency of an AI application executed on a edge platform. In the implementation part we show the benefits of FPGA-based platform for the task of object detection. Furthermore we show that it is feasible to collect relevant data from an FPGA platform, transmit the data to a cloud system for processing and receiving feedback actions to execute an edge AI application energy efficiently. As future work we foresee the possibility to train, deploy and continuously improve a base model able to efficiently adapt the execution of edge applications.","PeriodicalId":224912,"journal":{"name":"2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116206510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Linnaeus: A highly reusable and adaptable ML based log classification pipeline Linnaeus:一个高度可重用和适应性强的基于ML的日志分类管道

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-11 DOI: 10.1109/WAIN52551.2021.00008

Armin Catovic, Carolyn Cartwright, Yasmin Tesfaldet Gebreyesus, Simone Ferlin Oliveira

引用次数: 0

The Prevalence of Code Smells in Machine Learning projects 机器学习项目中代码气味的流行

2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN) Pub Date : 2021-03-06 DOI: 10.1109/WAIN52551.2021.00011

B. V. Oort, L. Cruz, M. Aniche, A. Deursen

{"title":"The Prevalence of Code Smells in Machine Learning projects","authors":"B. V. Oort, L. Cruz, M. Aniche, A. Deursen","doi":"10.1109/WAIN52551.2021.00011","DOIUrl":"https://doi.org/10.1109/WAIN52551.2021.00011","url":null,"abstract":"Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the current computer science landscape. Yet, there still exists a lack of software engineering experience and best practices in this field. One such best practice, static code analysis, can be used to find code smells, i.e., (potential) defects in the source code, refactoring opportunities, and violations of common coding standards. Our research set out to discover the most prevalent code smells in ML projects. We gathered a dataset of 74 open-source ML projects, installed their dependencies and ran Pylint on them. This resulted in a top 20 of all detected code smells, per category. Manual analysis of these smells mainly showed that code duplication is widespread and that the PEP8 convention for identifier naming style may not always be applicable to ML code due to its resemblance with mathematical notation. More interestingly, however, we found several major obstructions to the maintainability and reproducibility of ML projects, primarily related to the dependency management of Python projects. We also found that Pylint cannot reliably check for correct usage of imported dependencies, including prominent ML libraries such as PyTorch.","PeriodicalId":224912,"journal":{"name":"2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116327510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16