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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Trapezin, Andrey Filianin, Ivan ITMO University Faculty of Programming and Infocommunications St. Petersburg Russia ITMO University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia

ISBN: (纸本)9798331511241

The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed to automate the delivery of machine learning models to production by integrating MLflow for model tracking and Seldon Core for model serving. The developed operator allows data scientists to deploy models while maintaining the familiar MLflow environment. The operator's automatic deployment triggers upon tagging models in MLflow, greatly simplifying engineers' tasks and minimizing the need for manual infrastructure configuration. By automating configuration tasks and optimizing deployment workflows, the solution achieves a 40-50% reduction in model time to deployment (TTD) metric compared to manual processes and decreases error rates from 15% to around 3%. The practical relevance of the work is that it simplifies collaboration between data and infrastructure teams by providing a unified deployment framework, resulting in faster, more reliable, and automated integration of machine learning models into an organisation's business processes. © 2025 IEEE.

关键词： Engineering education

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Analysis of recurrent neural networks via property-directed verification of surrogate models

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INTERNATIONAL JOURNAL ON software TOOLS FOR TECHNOLOGY TRANSFER 2023年第3期25卷 341-354页

作者： Khmelnitsky, Igor Neider, Daniel Roy, Rajarshi Xie, Xuan Barbot, Benoit Bollig, Benedikt Finkel, Alain Haddad, Serge Leucker, Martin Ye, Lina Univ Paris Saclay LMF ENS Paris Saclay CNRS Gif Sur Yvette France INRIA Paris France Max Planck Inst Software Syst Kaiserslautern Germany Univ Paris Est Creteil Creteil France Univ Lubeck Inst Software Engn & Programming Languages Lubeck Germany Univ Paris Saclay Cent Supelec Gif Sur Yvette France Inst Univ France Paris France Carl von Ossietzky Univ Oldenburg Safety & Explainabil Learning Syst Grp Oldenburg Germany Univ Alberta Edmonton AB Canada

This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a given RNN using active automata learning. This model may then be analyzed using model checking as a verification technique. The term property-directed reflects the idea that our procedure is guided and controlled by the given property rather than performing the two steps separately. We show that this not only allows us to discover small counterexamples fast, but also to generalize them by pumping toward faulty flows hinting at the underlying error in the RNN. We also show that our method can be efficiently used for adversarial robustness certification of RNNs.

关键词： Explainable AI Neural network verification Active learning

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ARCADE-Adversarially Robust Cost-Sensitive Anomaly Detection in Blockchain Using Explainable Artificial Intelligence

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ELECTRONICS 2025年第8期14卷 1648-1648页

作者： Kamran, Muhammad Rehan, Muhammad Maaz Nisar, Wasif Rehan, Muhammad Waqas COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah 47040 Pakistan Air Univ Islamabad Dept Comp Sci Aerosp & Aviat Campus Kamra Attock 43600 Pakistan Univ Lancashire Univ Cent Lancashire Sch Engn & Comp Dept Comp Sci Preston PR1 2HE England Univ Lubeck Inst Software Engn & Programming Languages ISP D-23562 Lubeck Germany

Blockchain technology is increasingly being adopted across critical domains, such as healthcare and finance, yet it remains susceptible to anomalies and malicious attacks. Hence, robust anomaly detection is essential in these decentralized systems to maintain integrity, trust, and reliability. However, anomaly detection is still challenging due to data imbalances, adversarial resilience, and the lack of explanation in existing approaches. This work presents ARCADE, a novel approach for adversarially resilient anomaly detection in blockchain networks that leverages an optimized cost-sensitive stacking ensemble learning combined with explainable artificial intelligence (XAI) techniques. Firstly, the proposed approach uses cost-sensitive learning to address the data imbalance problem by optimizing class weights that are integrated with stacking ensemble learning to enhance detection accuracy. Secondly, along with this, newly engineered features are employed to strengthen the resilience of the model against malicious perturbations. Lastly, XAI techniques are applied to provide comprehensive insights and explanations for model prediction. To evaluate ARCADE, the Ethereum network transactions dataset is utilized to ensure a realistic case study. The experimental results show the superiority of the ARCADE in several aspects, achieving a high accuracy of 99.65%;strong resilience against adversarial perturbations, achieving an accuracy of 99.38% for low-intensity attacks, 91.04% for moderate attacks, and over 78% for extreme attacks;and surpassing existing techniques while also providing explainability for domain users.

关键词： blockchain anomaly detection stacking cost sensitive adversarial robustness XAI

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FreeCHR: An Algebraic Framework for CHR-Embeddings

arXiv

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arXiv 2023年

作者： Rechenberger, Sascha Frühwirth, Thom Institute for Software Engineering and Programming Languages Ulm University Albert-Einstein-Allee 11 Ulm89069 Germany

We introduce the framework FreeCHR which formalizes the embedding of Constraint Handling Rules (CHR) into a host language, using the concept of initial algebra semantics from category theory, to establish a high-level implementation scheme for CHR as well as a common formalization for both theory and practice. We propose a lifting of the syntax of CHR via an endofunctor in the category Set and a lifting of the very abstract operational semantics of CHR into FreeCHR, using the free algebra, generated by the endofunctor and give proofs for soundness and completeness w.r.t. their original definition. © 2023, CC BY.

关键词： Semantics

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Research and Development of Kubernetes Operator for Machine Learning Pipelines

Research and Development of Kubernetes Operator for Machine ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Andrey Trapezin Ivan Filianin Faculty of Programming and Infocommunications ITMO University St. Petersburg Russia Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

关键词： Production systems Error analysis Machine learning Manuals Tagging Data models Reliability Research and development Load modeling Resilience

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Analysis of static plantar pressure data with capsule networks: Diagnosing ataxia in MS patients with a deep learning-based approach

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MULTIPLE SCLEROSIS AND RELATED DISORDERS 2024年 83卷 105465-105465页

作者： Danaci, Cagla Baydogan, Merve Parlak Tuncer, Seda Arslan Firat Univ Inst Sci Dept Software Engn Elazig Turkiye Sivas Republ Univ Fac Technol Dept Software Engn Sivas Turkiye Firat Univ Vocat Sch Tech Sci Dept Comp Programming Elazig Turkiye Firat Univ Fac Engn Dept Software Engn Elazig Turkiye

In this study, it was aimed to detect ataxia in patients with Multiple Sclerosis (MS) by utilizing static plantar pressure data and capsule networks (CapsNet), one of the deep learning (DL) architectures. CapsNet is also equipped with a robust dynamic routing mechanism that determines the output of the next capsule. MS is a chronic nervous system disease that shows its effect in the central nervous system and manifests itself with attacks. One of the most common and challenging symptoms of MS is known as ataxia. Ataxia causes loss of control of limb muscle tone or gait disorders, leading to loss of balance and coordination. The diagnosis of ataxia in MS is applied employing the standard Expanded Disability Status Scale (EDSS) score. However, due to reasons such as physician misconception, diagnosis differences among physicians, and incorrect patient information, more unbiased solutions are required for the diagnosis. The results included Sensitivity at 96.34 % +/- 1.71, Specificity at 98.11 % +/- 2.04, Precision at 98.08 % +/- 2.16, and Accuracy at 97.13 % +/- 0.33. The main motivation of the study is to show that these deep learning methods can successfully detect ataxia in MS patients using static plantar pressure data. The high-performance measurements of sensitivity, specificity, precision and accuracy emphasize that the proposed system can be an effective tool in clinical practice. In addition, it was concluded that the proposed autonomous system would be a support mechanism to assist the physician in the detection of ataxia in patients with MS.

关键词： Ataxia DL Multiple sclerosis Static plantar pressure EDSS

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The Impact of Model Transformation Language Features on Quality Properties of MTLs: A Study Protocol

arXiv

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arXiv 2022年

作者： Höppner, Stefan Tichy, Matthias Ulm University Institute of Software Engineering and Programming Languages Ulm Germany

Background: Dedicated model transformation languages are claimed to provide many benefits over the use of general purpose languages for developing model transformations. However, the actual advantages and disadvantages associated with the use of model transformation languages are poorly understood empirically. There is little knowledge over what advantages and disadvantages hold in which cases and where they originate from. In a prior interview study, we elicited expert opinions on what advantages result from what factors surrounding model transformation languages as well as a number of moderating factors that moderate the influence. Objective: We aim to quantitatively asses the interview results to confirm or reject the influences and moderation effects posed by different factors and to gain insights into how valuable different factors are to the discussion. Method: We gather data on the factors and quality attributes using an online survey. To analyse the data and examine the hypothesised influences and moderations we use universal structure modelling based on a structural equation model. Universal structure modelling will produce significance values and path coefficients for each hypothesised and modelled interdependence between factors and quality attributes that can be used to confirm or reject correlation and to weigh the strength of influence present. Limitations: Due to the complexity and abstractness of the concepts under investigation, a measurement via reflective or formative indicators is not possible. Instead participants are queried about their assessment of concepts through a single item question. We further assume that positive and negative effects of a feature are more prominent if the feature is used more frequently. © 2022, CC BY.

关键词： Surveys

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Dedicated Model Transformation Languages vs. General-purpose Languages: A Historical Perspective on ATL vs. Java 9th

Dedicated Model Transformation Languages vs. General-purpose...

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9th International Conference on Model-Driven Engineering and software Development (MODELSWARD)

作者： Goetz, Stefan Tichy, Matthias Kehrer, Timo Ulm Univ Inst Software Engn & Programming Languages Ulm Germany Humboldt Univ Dept Comp Sci Berlin Germany

ISBN: (纸本)9789897584879

Model transformations are among the key concepts of model-driven engineering (MDE), and dedicated model transformation languages (MTLs) emerged with the popularity of the MDE paradigm about 15 to 20 years ago. MTLs claim to increase the ease of development of model transformations by abstracting from recurring transformation aspects and hiding complex semantics behind a simple yet intuitive syntax. Nonetheless, MTLs are rarely adopted in practice, there is still no empirical evidence for this claim, and the argument of abstraction deserves a fresh look in the light of modern general-purpose languages (GPLs) which have undergone a significant evolution in the last two decades. In this paper, we report about a study in which we compare the complexity of model transformations written in three different languages, namely (i) the Atlas Transformation Language (ATL), (ii) Java SE5, and (iii) Java SE14;the Java transformations are derived from an ATL specification using a translation schema we developed in terms of our study. In a nutshell, we found that some of the new features in Java SE14 compared to Java SE5 help to significantly reduce the complexity of transformations written in Java. At the same time, however, the relative amount of complexity that stems from aspects that ATL can hide from the developer stays about the same. Based on these results, we indicate potential avenues for future research on the comparison of MTLs and GPLs in a model transformation context.

关键词： Model Transformation Languages ATL Java Complexity

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Effectiveness of Performance Visualizations for Declarative Model Transformations

Effectiveness of Performance Visualizations for Declarative ...

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IEEE International Workshop on Visualizing software for Understanding and Analysis (VISSOFT)

作者： Raffaela Groner Matthias Tichy Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden Institute of Software Engineering and Programming Languages Ulm University Ulm Germany

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

Several profilers for general-purpose languages like Java offer visualizations to support users in understanding the execution of a program and identifying the causes of a performance issue. Unfortunately, these performance visualizations are difficult to reuse in profilers for declarative model transformations since they cannot display transformation-specific information. For example, a profiler for a declarative model transformation language must provide information on the traversal of the input model since it impacts the performance but is hidden from the developer. Moreover, the respective visualization must scale for input models that consist of several thousand model elements. Hence, we developed performance visualizations for the declarative model transformation language Henshin that provide insights into the transformation execution. Subsequently, we performed a mixed methods study with 18 Henshin novices to evaluate the effectiveness of our visualizations. In our study, the participants tried to improve the execution performance of four transformations by performing a root cause analysis using our visualizations. The results of our study show that depending on the task, between 16 and 18 participants understood the execution of a transformation correctly based on our visualizations. Moreover, between 12 and 18 participants proposed effective optimizations using our visualizations.

关键词： Visualization Root cause analysis Java Color software Encoding Data mining Optimization

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ADF- BDD: An ADF Solver Based on Binary Decision Diagrams 9

ADF- BDD: An ADF Solver Based on Binary Decision Diagrams

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9th International Conference on Computational Models of Argument (COMMA)

作者： Ellmauthaler, Stefan Gaggl, Sarah A. Rusovac, Dominik Wallner, Johannes P. Tech Univ Dresden Cfaed Knowledge Based Syst Grp Dresden Germany Tech Univ Dresden Log Programming & Argumentat Grp Dresden Germany Graz Univ Technol Inst Software Technol Graz Austria

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