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Models and algorithms for determining the probability of occurrence of a critical combination of events for three-element minimum sections in intelligent decision support systems 4

Models and algorithms for determining the probability of occ...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Kolomiitsev, Oleksii Osiievskyi, Serhii Zakharchenko, Iryna Rybalchenko, Alina Tretiak, Viacheslav Heiko, Hennadii Computer Engineering and Programming Department National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Mathematics and Software of ACS Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

A method for determining the probability of occurrence of a critical combination of events for the three-element minimum cross-sections of attributes of the characteristics of the software quality model of intelligent... 详细信息

ISBN: (纸本)9798350395532

关键词： Decision support systems

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An operation-based versioning approach for synchronous and asynchronous collaboration in graphical modeling tools 9

An operation-based versioning approach for synchronous and a...

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STAF 2021 Workshop: 9th International Workshop on Bidirectional Transformations, Joint Workshop on Foundations and Practice of Visual Modeling and Data for Model-Driven Engineering, International Workshop on MDE for Smart IoT Systems, 4th International Workshop on (Meta) Modeling for Healthcare Systems, and 20th International Workshop on OCL and Textual Modeling, STAF-WS 2021

作者： Pietron, Jakob Füg, Fabian Tichy, Matthias Institute of Software Engineering and Programming Languages Ulm University Ulm Germany

In the domain of Model-driven Engineering (MDE), modeling of software and technical systems is often a collaborative and interactive activity performed by several people. However, existing tools do not offer sufficient collaboration features as reported by studies conducted with industrial practitioners. In this paper, we introduce a new operation-based approach enabling both synchronous and asynchronous collaboration in graphical modeling tools. The presented approach is capable of conflict detection, resolving, branching, and merging. Furthermore, we demonstrate how a seamless transition between both collaboration modes can be ensured. We define user performed edit operations, such as adding a new block or changing a property’s value, as first-class citizens. Edit operations do not have to be atomic and can result in multiple atomic operations which are finally applied to the local model. Both kinds of operations are persisted in a sequential history and they are also distributed to other connected clients through a central server, which ensures a global unified history across all clients. Due to the sequential history consisting of operations and the change information they contain, we can apply a conflict detection method that is able to narrow down conflicts to the minimal possible set of individual conflicting operations for manual resolution while automatically merging the remaining changes. © 2021 Copyright (c) 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Merging

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Predicting the Performance of ATL Model Transformations 23

Predicting the Performance of ATL Model Transformations

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14th Annual ACM/SPEC International Conference on Performance Engineering, ICPE 2023

作者： Groner, Raffaela Bellmann, Peter Höppner, Stefan Thiam, Patrick Schwenker, Friedhelm Tichy, Matthias Institute of Software Engineering and Programming Languages Ulm University Ulm Germany Institute of Neural Information Processing Ulm University Ulm Germany Institute of Medical Systems Biology Ulm University Ulm Germany

ISBN: (纸本)9798400700682

Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. Typically, developers create their transformations based on small input models which systematically cover the language of the input models. This makes it difficult for the developers to estimate how the transformations would perform for a large and diverse set of input models. Hence, developers would benefit from an approach for predicting the performance of model transformations based on just abstract characteristics of input models. Regression approaches based on machine learning lend themselves well to such predictions. However, it is currently unknown, whether and which regression approach is suitable in this context as well as how a model should be abstractly characterized for this purpose. We conducted several experiments to analyze how well different machine learning methods predict the execution time of model transformations defined in the Atlas Transformation Language (ATL) transformations for distinct sets of model characteristics. As possible methods, we have investigated linear regression, random forests and support vector regression using a radial basis function kernel. The results of our experiments show that support vector regression is the best choice in terms of usability and prediction accuracy for the model transformation modules covered in our experiments and is thus suited for a prediction approach. In addition, simple model characterizations based only on the number of model elements, the number of references, and the number of attributes are a suitable way to easily describe a model and to achieve decent prediction accuracy. © 2023 Owner/Author.

关键词： Forecasting

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Academic Fraud Detection in Online Exams with DNN's Multilayer Model

Academic Fraud Detection in Online Exams with DNN's Multilay...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Bahaddin Erdem Murat Karabatak Department of Computer Programming Bitlis Eren University Bitlis Turkey Department of Software Engineering Firat University Elaziğ Turkey

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This study aims to reveal the best deep learning models that are improved and optimized by predicting undesirable behavior patterns using a dataset consisting of artificial and real exam data of students taking online distance education courses in an online environment through the distance education system. Using online exam data of 129 students, the researchers conducted analysis with two different scenarios to determine the best prediction performance through regression and classification models. The model we proposed was determined as a four-layer DNN with 80.4% test performance in detecting students who “cheated” from undesirable behavior patterns, which was performed with K-10, K-5 and K-3 cross-validation. The results prove that students' online distance education exam data can be easily applied to the DNN model. The models presented in the study provide a roadmap for educational institutions to evaluate their online examination practices and develop more effective strategies for academic honesty.

关键词： Deep learning Analytical models Digital forensics Predictive models Nonhomogeneous media Data models Educational courses Fraud Security

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Medical Data Analysis Using AutoML Frameworks

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第7期19卷 4515-4522页

作者： Shin, Seunghun Park, Dongyoung Ji, Suhwan Joo, Gihun Im, Hyeonseung Kangwon Natl Univ Interdisciplinary Grad Program Med Bigdata Converg Major AI & Software Chunchon 24341 South Korea Yonsei Univ Theory Computat Lab Seoul 03722 South Korea Kangwon Natl Univ Programming Language & Machine Learning Lab Chunchon 24341 South Korea Kangwon Natl Univ Dept Comp Sci & Engn Chunchon 24341 South Korea

Recently, there has been a growing interest in applying machine learning (ML) and deep learning to medical big data and smart healthcare. However, it can be challenging to possess both domain knowledge of medical data and expertise in ML necessary for conducting research in this area. In this regard, an automated machine learning (AutoML) framework can be a solution, which automates an ML pipeline-building process from data cleansing and preprocessing to model building, optimization, and performance evaluation. Since AutoML can reduce domain dependency in ML and quickly build high-performance models, it has a high potential for use in the medical and healthcare fields. Therefore, in this paper, we aim to validate the effectiveness of various AutoML frameworks using structured and unstructured medical data such as electronic medical records, medical images, and signal data. More specifically, we compare the performance of representative AutoML frameworks and simple handcrafted models in terms of various metrics such as the area under the curve, accuracy, and F1 score. Our experimental results show that AutoML effectively handles structured and unstructured data in general, but it still needs to be further improved to deal with data that is imbalanced or requires special preprocessing, such as signal data.

关键词： AutoML Machine learning Medical data Medical imaging Biosignals

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Enhanced performance prediction of ATL model transformations

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PERFORMANCE EVALUATION 2024年 164卷

作者： Groner, Raffaela Bellmann, Peter Hoeppner, Stefan Thiam, Patrick Schwenker, Friedhelm Kestler, Hans A. Tichy, Matthias Ulm Univ Inst Software Engn & Programming Languages Albert Einstein Allee 11 D-89069 Ulm Baden Wurttembe Germany Ulm Univ Inst Neural Informat Proc Albert Einstein Allee 11 D-89069 Ulm Baden Wurttembe Germany Ulm Univ Inst Med Syst Biol Albert Einstein Allee 11 D-89069 Ulm Baden Wurttembe Germany

Model transformation languages are domain-specific languages used to define transformations of models. These transformations consist of the translation from one modeling formalism into another or just the updating of a given model. Such transformations are often described declaratively and are often implemented based on very small models that cover the language of the input model. As a result, transformation developers are often unable to assess the time required to transform a larger model. Hence, we propose a prediction approach based on machine learning which uses a set of model characteristics as input and provides a prediction of the execution time of a transformation defined in the Atlas Transformation Language (ATL). In our previous work (Groner et al., 2023), we already showed that support vector regression in combination with a model characterization based on the number of model elements, the number of references, and the number of attributes is the best choice in terms of usability and prediction accuracy for the transformations considered in our experiments. A major weakness of our previous approach is that it fails to predict the performance of transformations that also transform attribute values of arbitrary length, such as string values. Therefore, we investigate in this work whether an extension of our feature sets that describes the average size of string attributes can help to overcome this weakness. Our results show that the random forest approach in combination with model characterizations based on the number of model elements, the number of references, the number of attributes, and the average size of string attributes filtered by the 85th percentile of their variance is the best choice in terms of the simple way to describe a model and the quality of the obtained prediction. With this combination, we obtained a mean absolute percentage error (MAPE) of 5.07% over all modules and a MAPE of 4.82% over all modules excluding the transformation for wh

关键词： Performance prediction ATL Model transformation Linear regression Random forests Support vector regression

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Optimistic Versioning for Conflict-tolerant Collaborative Blended Modeling 10

Optimistic Versioning for Conflict-tolerant Collaborative Bl...

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STAF 2022 Workshop: 10th International Workshop on Bidirectional Transformations, BX 2022, 2nd International Workshop on Foundations and Practice of Visual Modeling, FPVM 2022 and 2nd International Workshop on MDE for Smart IoT Systems, MeSS 2022

作者： Exelmans, Joeri Pietron, Jakob Raschke, Alexander Vangheluwe, Hans Tichy, Matthias Department of Computer Science University of Antwerp - Flanders Make Antwerp Belgium Institute of Software Engineering and Programming Languages Ulm University Ulm Germany

Optimistic versioning is a key component in supporting collaborative workflows. Text-based versioning has been widely adopted for versioning code, but in model-driven engineering, dealing with visual concrete syntaxes, new methods are required. In the case of blended modeling, a mixture of both textual and visual syntaxes, concurrently editable and synchronizable, introduces additional challenges. We propose a type of operation-based versioning to record not only user edits, but also bi-directional change propagations between concrete and abstract syntax. This way we can support blended modeling with layout continuity, and flexible handling of missing information (e. g., layout information) when rendering changes from abstract to concrete syntax. In addition, the proposed versioning approach enables collaborative conflict resolution by allowing partial conflict resolution, thus deferring a final resolution. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Abstracting

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WIP: An Engaging Undergraduate Intro to Model Checking in software Engineering Using TLA+

arXiv

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arXiv 2024年

作者： Läufer, Konstantin Mertin, Gunda Thiruvathukal, George K. Software and Systems Laboratory Department of Computer Science Loyola University Chicago United States Institute for Software Engineering and Programming Languages University of Lübeck Germany

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one’s undergraduate studies, (3) demonstrate how to address successive "failures" through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and

关键词： Students

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A Survey on the Relevance of the Performance of Model Transformations

A Survey on the Relevance of the Performance of Model Transf...

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Fachtagung des GI-Fachbereichs softwaretechnik, software Engineering 2022 - Conference of the GI software Engineering Section, software Engineering 2022

作者： Groner, Raffaela Juhnke, Katharina Höppner, Stefan Tichy, Matthias Becker, Steffen Vijayshree, Vijayshree Frank, Sebastian Ulm University Institute for Software Engineering and Programming Languages James-Franck-Ring UlmD-89069 Germany University of Stuttgart Institute for Software Engineering Universitätsstraße StuttgartD-70569 Germany

ISBN: (纸本)9783885797142

When we are confronted with performance issues in a general-purpose language, like Java, it is a given to us that we have various tools and techniques at our disposal to help us. But is such support also needed when using model transformation languages? To address this question, we conducted a quantitative online survey as part of a mixed methods study with 84 respondents to our questionnaire. Our results show that a certain performance is desired but not always achieved. The developers would like to improve the performance, but they lack insights on how a transformation is performed. As a first step to mitigate this issue, we compiled a list of information regarding the models used, the transformations applied and their execution deemed to be helpful by the participants. Additionally, we used hypotheses tests to investigate possible influencing factors that cause participants to try to improve the performance of transformations. The main relevant factors found in our study are the satisfaction with the execution time, the size of the models used, the relevance of whether a certain execution time is not exceeded in the average case, and the knowledge of how a transformation engine executes a transformation. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Surveys

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Model-Based Generation of Attack-Fault Trees 42nd

Model-Based Generation of Attack-Fault Trees

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42nd International Conference on Computer Safety, Reliability and Security (SAFECOMP)

作者： Groner, Raffaela Witte, Thomas Raschke, Alexander Hirn, Sophie Pekaric, Irdin Frick, Markus Tichy, Matthias Felderer, Michael Ulm Univ Inst Software Engn & Programming Languages Ulm Germany Univ Innsbruck Dept Comp Sci Innsbruck Austria Univ Liechtenstein Dept Informat Syst & Comp Sci Vaduz Liechtenstein German Aerosp Ctr DLR Inst Software Technol Cologne Germany Univ Cologne Dept Math & Comp Sci Cologne Germany

ISBN: (纸本)9783031409226;9783031409233

Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a combination of dynamic Fault Trees and Attack Trees and can be used to model and model-check a holistic view on both safety and security. Manually creating a complete AFT for the whole system is, however, a daunting task. It needs to span multiple abstraction layers, e.g., abstract application architecture and data flow as well as system and library dependencies that are affected by various vulnerabilities. We present an AFT generation tool-chain that facilitates this task using partial Fault and Attack Trees that are either manually created or mined from vulnerability databases. We semi-automatically create two system models that provide the necessary information to automatically combine these partial Fault and Attack Trees into complete AFTs using graph transformation rules.

关键词： AFT CPS safety analysis security analysis

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