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Working IEEE/IFIP Conference on software Architecture

作者： Céline Madeleine Aldenhoven Ralf Sascha Engelschall Software Engineering (Elite Graduate Program) Technical University of Munich Ludwig-Maximilian-University of Munich University of Augsburg Munich and Augsburg Germany msg Research msg Systems AG Ismaning Germany

The concept of beauty has fascinated humans since the beginning of their existence. To inspire research questions on the beauty of software architecture, we use the progress in other areas, like civil architecture and plastic surgery. Our most important findings are that practitioners view software architecture as beautiful if it reduces their effort to work with it and that the beauty of software architecture greatly impacts the happiness and motivation of the developers working on it. Overall, this research paper gives insights into the definition of beautiful software architecture in practice and explains its importance. Additionally, we propose ways to create beautiful software architecture and teach it. The focus on beauty in software architecture is essential in mid- to large-sized projects to increase the developers’ happiness and, therefore, the quality of the product.

关键词： Productivity software architecture Unified modeling language Surgery software Product design Quality assessment

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An Autonomous Mobile Robot Path Planner using Spline Curves and Differential Evolution

An Autonomous Mobile Robot Path Planner using Spline Curves ...

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2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023

作者： de Rezende Freitas, Elias José Cohen, Miri Weiss Guimarães, Frederico Gadelha Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Federal Institute of Minas Gerais IFMG Av. Antônio Carlos 6627 MG Belo Horizonte31270-901 Brazil Department of Software Engineering Braude College of Engineering Karmiel Israel Department of Electrical Engineering Universidade Federal de Minas Gerais UFMG MG Belo Horizonte Brazil

ISBN: (纸本)9798350348071

Robots that operate autonomously require a path planner that ensures the robot reaches the desired position in a safe manner. The robot will be able to achieve greater speeds along the path if the path is smooth, minimizing the effort of the controller. This work presents two new path planners, called DE-Spline and DE-NURBS. These planners provide a smooth path directly by the cost function optimization using the algorithm L-SHADE (Success-History based Adaptive of Differential Evolution using linear population size reduction algorithm) and two path’s parametrization: Ferguson cubic spline and NURBS. Additionally, it ensures that the robot reaches the target in the desired orientation. The planners are compared with a Vector Field-based planner, widely used in robot soccer. Based on the results, it has been found that both planners provide a smoother path along the shortest path, by accounting for the robot’s starting angle and its goal angle. Furthermore, DE-NURBS provides a greater degree of planning freedom than DE-Spline. © 2023 IEEE.

关键词： Motion planning

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Quantifying White Matter Hyperintensities: Predicting Periventricular Fazekas Scores with Uncertainty Estimation

Quantifying White Matter Hyperintensities: Predicting Perive...

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IEEE International Symposium on Biomedical Imaging

作者： Susanne Schmid Amirmohammed Shamaei Roberto Souza Richard Frayne Biomedical Engineering Graduate Program University of Calgary Hotchkiss Brain Institute University of Calgary Seaman Family MR Research Centre Foothills Medical Centre Calgary AB Canada Electrical and Software Engineering University of Calgary Radiology University of Calgary Clinical Neurosciences University of Calgary

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

White matter hyperintensities (WMH) are crucial markers in brain magnetic resonance (MR) images, often quantified using the Fazekas score. This study presents a deep learning model to predict Fazekas scores in the periventricular region from T1-weighted and FLAIR images. Our model achieved a Matthew correlation coefficient of 0.68 and F1 scores of 0.69, 0.88, 0.86, and 1.0 for Fazekas scores from 0 to 3, respectively, surpassing previous methods. We introduce a novel integration of t-distributed stochastic neighbor embedding (t-SNE) with uncertainty analysis using Monte Carlo dropout, offering insights into the model's decision-making. Results show effective class distinction, with increased uncertainty at class transitions indicating ambiguity, and confident misclassified cases suggesting overlapping features or label noise. These findings highlight that simple, well-tuned models, coupled with interpretability techniques, can provide robust predictions of WMH severity.

关键词： Deep learning Uncertainty Monte Carlo methods Noise Stochastic processes Magnetic resonance Estimation Predictive models Brain modeling White matter

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Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research

arXiv

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arXiv 2023年

作者： Tanyel, Toygar Ayvaz, Serkan Keserci, Bilgin Biomedical Engineering Graduate Program Istanbul Technical University Turkey Centre for Industrial Software Maersk Mc-kinney Moeller Institute University of Southern Denmark Denmark Department of Biomedical Engineering Yildiz Technical University Turkey

The field of explainability in artificial intelligence (AI) has witnessed a growing number of studies and increasing scholarly interest. However, the lack of human-friendly and individual interpretations in explaining the outcomes of machine learning algorithms has significantly hindered the acceptance of these methods by clinicians in their research and clinical practice. To address this issue, our study uses counterfactual explanations to explore the applicability of "what if?" scenarios in medical research. Our aim is to expand our understanding of magnetic resonance imaging (MRI) features used for diagnosing pediatric posterior fossa brain tumors beyond existing boundaries. In our case study, the proposed concept provides a novel way to examine alternative decision-making scenarios that offer personalized and context-specific insights, enabling the validation of predictions and clarification of variations under diverse circumstances. Additionally, we explore the potential use of counterfactuals for data augmentation and evaluate their feasibility as an alternative approach in our medical research case. The results demonstrate the promising potential of using counterfactual explanations to improve AI-driven methods in clinical research. © 2023, CC BY-NC-SA.

关键词： Pediatrics

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A Hybrid software Testing Recommendation System Based on Bugs and Tests Descriptions

A Hybrid Software Testing Recommendation System Based on Bug...

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International Conference on Computer Supported Cooperative Work in Design

作者： Bruno Kostiuk Gregory Moro Puppi Wanderley Cesar Augusto Tacla Antônio David Viniski Júlio Cesar Nievola Emerson Cabrera Paraiso Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná Curitiba Paraná Brazil Smart Infrastructure - Grid Software Siemens AG Curitiba Paraná Brazil Postgraduate Program in Electrical Engineering and Industrial Informatics (CPGEI-CT) Technological Federal University of Paraná Curitiba Brazil Polytechnic School Pontifícia Universidade Católica do Paraná Curitiba Paraná Brazil

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

During the software development cycle, there is often a need to run tests after correcting a bug. The tests to be executed are only a few times automated, demanding even more time from the tester. In addition to the testing time, there is also the difficulty in selecting the tests that should be executed at that moment, as depending on the size of the software, the number of tests reaches thousands. In this research, we propose a hybrid model for recommending manual tests, which, based on the bug description and test steps, selects the best tests to be carried out to check whether a given bug has been resolved. This work was developed using real-world data from software under development, which features manual user interface tests. During development, our application presented an accuracy rate of 60.82% through the Recall@20 metric using the training data, which was confirmed through the use of human users, where the system presented an accuracy rate of 59.70%.

关键词： software testing Measurement Accuracy Computer bugs Training data Manuals User interfaces software Recommender systems software development management

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Analyzing Morphological Changes in Zebrafish Embryos Exposed to Toxic Chemicals

Analyzing Morphological Changes in Zebrafish Embryos Exposed...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ambekar, Akhil Poschen, Carolin Lai, Chih Scholz, Stefan Teixido, Elisabet University of St. Thomas Graduate Program in Software St. PaulMN United States Helmholtz Centre for Environmental Research GmbH - UFZ Department of Bioanalytical Ecotoxicology Leipzig Germany University of Barcelona Department of Pharmacology Toxicology and Therapeutic Chemistry Barcelona Spain

ISBN: (纸本)9781665401265

Subtle morphological changes caused by toxic chemicals in different regions of organs in small zebrafish embryos can be difficult to detect by human eyes or even by traditional image processing tools. Without brute force testing arbitrary parameters and thresholds in various combinations of imaging algorithms, we present our systematic approach in which we first train convolutional neural networks (CNNs) to recognize which organs of which zebrafish embryos contain significant morphological differences so they can be reliably classified as being exposed to either the fresh water or the toxic water contaminated with chemicals. From the training results, the organ images in each identified image subset were then analyzed by the technique, occlusion sensitivity map, to allocate the exact locations and the scales of morphological changes caused by different chemicals. In the experiment section of this paper, we use multiple chemical exposure scenarios to demonstrate the morphological differences in organs that can be detected by our approach. © 2021 IEEE.

关键词： Chemicals

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A Model-Driven Approach for Interoperability Among SaaS and DaaS/DBaaS: The MIDAS Case

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SN Computer Science 2022年第4期3卷 1-18页

作者： Mane, Babacar Mascarenhas, Ana Patrícia F. Magalhães Quinteiro, Gustavo Maciel, Rita Suzana Pitangueira Claro, Daniela Barreiro Formalisms and Semantic Applications Research Group (FORMAS) Institute of Computing Federal University of Bahia Av. Milton Santos Ondina Bahia Salvador 40170-110 Brazil Models for Software Engineering (MAtisse) Department of Exact Sciences and Earth State University of Bahia 2555 Silveira Martins St. Cabula Bahia Salvador Brazil Post Graduate Program in Computing and Systems Salvador University 251 Dr. Jose Peroba St. Bahia Salvador Brazil Models for Software Engineering (MAtisse) Institute of Computing Federal University of Bahia Av. Milton Santos Ondina Bahia Salvador 40170-110 Brazil

Cloud Platforms are heterogeneous, and users may face interoperability issues migrating applications or exchanging data among distinct clouds due, for instance, to the lack of standards solutions. Several solutions have been proposed to overcome lock-in situations, and middleware platforms are one of them. A semantic interoperability solution named middleware for Data as a Service (DaaS)/Database as a Service (DBaaS) and software as a Service (SaaS)—MIDAS has been developed to overcome this lock-in issue. It is an intermediate communication layer to retrieve data from DaaS or DBaaS through a Structured Query Language (SQL) or a Not Only SQL (NoSQL) created at the SaaS level. MIDAS is a platform for software execution, but software development needs support for its entire life cycle. Therefore, we propose the Model drIven Approach for MIDAS (MIAMI), which enables the specification of platform-independent middleware models and their use to generate code on different cloud platforms. MIAMI comprises a Domain-Specific Modeling Language (DSML) that enables middleware models and a transformation specification, which defines how these models can be converted to code. MIAMI offers a strategy for MIDAS specification and code generation phases to help middleware developers’ activities. MIAMI was applied to code generation specifications in Cloud Foundry, Amazon Web Services, OpenShift, and Heroku providers to evaluate our approach. This specification shows MIAMI’s feasibility and points out that MDD is a promising approach to improving cloud interoperability solutions. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing Interoperability Metamodel Model-driven development

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AI-Empowered Data Offloading in MEC-Enabled IoV Networks

arXiv

引用

arXiv 2022年

作者： Fontes, Afonso de Ribeiro, Igor L. Muhammad, Khan Gandomi, Amir H. Gay, Gregory de Albuquerque, Victor Hugo C. Chalmers The University of Gothenburg Gothenburg Sweden University of Fortaleza CE Fortaleza Brazil Department of Software Sejong University Seoul Korea Republic of Faculty of Engineering & Information Technology University of Technology Sydney Australia Graduate Program on Teleinformatics Engineering Federal University of Ceará Fortaleza CE Fortaleza Brazil Graduate Program on Electrical Engineering Federal University of Ceará CE Fortaleza Brazil

Advancements in smart vehicle design have enabled the creation of Internet of Vehicle (IoV) technologies that can utilize the information provided by various sensors and wireless communication to perform complex functionality. Many of these functionalities rely on high computational power and low latency. Mobile Edge Computing (MEC) technologies have been proposed as a way to meet these requirements, as their proximity and decentralization provide unique benefits for networks like real-time communication, higher throughput, and flexibility. Diverse challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network. This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit. Afterward, this article discusses challenges and future perspectives for IoV technologies. © 2022, CC BY-SA.

关键词： Mobile edge computing

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Extracting Semantics of Predicates from Millions of Bio-Medical Abstracts for Inferencing New Biological Key Events and Relationships

Extracting Semantics of Predicates from Millions of Bio-Medi...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Lai, Chih Martinovic-Weigelt, Dalma Filippo, Aline Serrao De Kramer, Stefan Poschen, Carolin University of St. Thomas Graduate Program in Software St. PaulMN United States University of St. Thomas Department of Biology St. PaulMN United States Helmholtz Centre ForEnvironmental Research GmbH UFZ Department of Molecular Systems Biology Permoserstraße 15 Leipzig04318 Germany

ISBN: (纸本)9781665401265

Adverse outcome pathways (AOP) structure toxicological knowledge as sequential, directed chains of key events (KE) that culminate in adverse outcomes (AOs). AOP development is a laborious process that involves extensive knowledge mining and could be improved via use of machine / deep learning. In this paper, we present an artificial intelligence system that can accelerate putative AOP development process by inferencing new AOP modules based on the knowledge learned from 16-million pre-parsed PubMed abstracts. Each AOP modules is represented as a triplet that consists of antecedent and consequent biological entities connected by a relation. Users can also investigate specific types of antecedent, consequent, and relations by specifying macro/microtemplates using the MeSH semantic type hierarchy. We also provide visualizations to illustrate the hidden semantics that our system can extract from input triplets. © 2021 IEEE.

关键词： Abstracting

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An Autonomous Mobile Robot Path Planner using Spline Curves and Differential Evolution

An Autonomous Mobile Robot Path Planner using Spline Curves ...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Elias José de Rezende Freitas Miri Weiss Cohen Frederico Gadelha Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Av. Antônio Carlos 6627 Belo Horizonte MG Brazil Federal Institute of Minas Gerais IFMG Department of Software Engineering Braude College of Engineering Karmiel Israel Department of Electrical Engineering Machine Intelligence and Data Science Laboratory (MINDS) Universidade Federal de Minas Gerais UFMG Belo Horizonte MG Brazil

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