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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Fang, Zhizhou Zdun, Uwe University of Vienna Research Group Software Architecture Faculty of Computer Science Austria University of Vienna Research Group Software Architecture Faculty of Computer Science Austria

ISBN: (纸本)9798331527235

This study introduces a novel two-stage method, GPAction, for detecting environment drift in reinforcement learning settings. We first train a Gaussian process predicting the reinforcement learning agents' actions and then detect environment drifts by monitoring the mean squared error between the predicted actions of a Gaussian process action predictor and actual actions. Our proposed method is evaluated against three baselines across four environments with continuous action spaces. Results demonstrate the superior performance of GPAction in detecting environment drift. In an ablation study, by analyzing the plots and AUC values of the MSEs, we show that our method GPAction can provide more distinguishable monitoring metrics than the Gaussian process state predictor. © 2024 IEEE.

关键词： Adversarial machine learning

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AIS - A Metric for Assessing the Impact of an Influencer’s Twitter Activity on the Price of a Cryptocurrency 29th

AIS - A Metric for Assessing the Impact of an Influencer...

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29th International Conference on Cooperative Information Systems, CoopIS 2023

作者： Miller, Kevin Böhmer, Kristof Faculty of Computer Science Research Group Software Architecture University of Vienna Währinger Straße 29 Vienna Austria

ISBN: (纸本)9783031468452

Individual users on social media platforms like Twitter can significantly volatile assets, including cryptocurrencies. However, current research has overlooked this aspect, focusing on sentiment analysis that includes all posts from all users. Making it challenging to detect trends caused by individuals. To address this gap, we introduce the Asset Influence Score (AIS), a percentage-based metric that assesses the likelihood of a newly issued tweet aligning with periods of heightened trading activity. By analyzing price data and tweets concurrently, we identify correlations that enable to predict the likelihood of specific users’ tweets co-occurring with increased trading activity. Evaluating the AIS using a publicly available prototype and Twitter data from 2020 to 2023, we find that using the AIS as a buy signal outperforms buy-and-hold and technical trading strategies while maintaining high liquidity. Demonstrating the applicability of AIS in improving trading decisions and identifying key individuals on social media platforms. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Bridging the Gap Between MLOps and RLOps: An Industry 4.0 Case Study on Architectural Design Decisions in Practice 22

Bridging the Gap Between MLOps and RLOps: An Industry 4.0 Ca...

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22nd IEEE International Conference on software architecture, ICSA 2025

作者： Warnett, Stephen John Zdun, Uwe University of Vienna Research Group Software Architecture Faculty of Computer Science Vienna Austria University of Vienna UniVie Doctoral School Computer Science DoCS Faculty of Computer Science Vienna Austria

ISBN: (纸本)9798331520908

In the domain of Industry 4.0 Cyber-Physical Production Systems (CPPSs), Reinforcement Learning (RL) has gained momentum as an effective strategy for training intelligent agents in digital twins. Whilst the practice of Machine Learning Operations (MLOps) has become established as a holistic approach to automating workflows in supervised and unsupervised Machine Learning (ML), the extent to which MLOps practices are applicable to RL, particularly due to major differences between ML and RL concerning model deployment and model training, are not currently well-understood. The literature on RLOps as a paradigm is scarce. We tackle this open question by conducting an exploratory, qualitative, deductive-inductive industry case study on a CPPS, performing content analysis of CPPS artefacts, such as architectural schematics and source code, and understanding their relation to 22 known Architectural Design Decisions and 86 associated decision options through classification into four distinct emergent categories. Our findings help bridge the gap between MLOps and RLOps architectures, contributing novel insights into understanding the application of MLOps to RL and providing practical insights and inspiration for further research. © 2025 IEEE.

关键词： Reinforcement learning

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API Rate Limit Adoption – A pattern collection 28

API Rate Limit Adoption – A pattern collection

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Serbout, Souhaila El Malki, Amine Pautasso, Cesare Zdun, Uwe Software Institute USI Lugano Switzerland University of Vienna Faculty of Computer Science Software Architecture Research Group Doctoral School Computer Science Vienna Austria

ISBN: (纸本)9798400700408

The API Rate Limit pattern controls the rate at which clients make API requests by counting the number of requests in a specified time interval and reacting against abusive clients, in order to protect the limited resources of the API from exhaustion and denial of service attacks. This practice helps service providers to prevent abuse and ensure fair resource allocation, maintain system stability, monitor and control service availability, protect against DDoS attacks In this research paper, we have identified patterns covering the API Rate Limit pattern adoption starting from its documentation to its implementation. Our objective is to elucidate the trade-offs associated with different identified patterns and offer guidance to developers in making informed decisions when choosing the most suitable Rate Limit method, scope, and granularity for their service. By providing a comprehensive overview of how to adopt the Rate Limit pattern, this paper aims to enhance the understanding of how APIs can be designed to facilitate high scalability, security, reliability, and service availability. Furthermore, we present each pattern along with known uses observed in real-world APIs and technologies. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： System stability

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Static Analysis Techniques for Fixing software Defects in MPI-Based Parallel Programs

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computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Detecting Environment Drift in Reinforcement Learning Using a Gaussian Process

Detecting Environment Drift in Reinforcement Learning Using ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Zhizhou Fang Uwe Zdun Research Group Software Architecture Faculty of Computer Science Doctoral School Computer Science (DoCS) University of Vienna Austria Research Group Software Architecture Faculty of Computer Science University of Vienna Austria

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

关键词： Measurement Gaussian processes Reinforcement learning Artificial intelligence Monitoring

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Tool Support for Learning Architectural Guidance Models from Architectural Design Decision Models 28

Tool Support for Learning Architectural Guidance Models from...

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Amiri, Amirali Ntentos, Evangelos Zdun, Uwe Geiger, Sebastian University of Vienna Faculty of Computer Science Software Architecture Group Vienna Austria University of Vienna Doctoral School Computer Science Vienna Austria Siemens Vienna Austria

ISBN: (纸本)9798400700408

This paper presents an approach to architectural knowledge management that does not assume existing architectural design decisions or pattern applications are documented as architectural knowledge, but benefits from more existing data. We drew inspiration from manual qualitative research methods for mining patterns and architectural knowledge and created a guideline model of which the ADD models are instances. We evaluated our approach on 11 cases from the gray literature. We found that it can provide suitable recommendations after modeling only a single case and reaches theoretical saturation and recommendations with low to very low errors after only 6-8 cases. Our approach shows that creating a reusable architectural design space is possible based only on limited case data. Our approach not only provides a novel approach to architectural knowledge management but can also be used as a tool for pattern mining. © 2023 Copyright held by the owner/author(s).

关键词： Knowledge management

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Supporting Architectural Decision Making on Training Strategies in Reinforcement Learning architectures

Supporting Architectural Decision Making on Training Strateg...

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Working IEEE/IFIP Conference on software architecture

作者： Evangelos Ntentos Stephen John Warnett Uwe Zdun Faculty of Computer Science Research Group Software Architecture University of Vienna Vienna Austria

ISBN: (数字)9798350359169

ISBN: (纸本)9798350359176

In the dynamic landscape of artificial intelligence and machine learning, Reinforcement Learning (RL) has emerged as a powerful paradigm for training intelligent agents in sequential decision-making. As RL architectures progress in complexity, the need for informed decision-making regarding training strategies and related consequences on the software architecture becomes increasingly intricate. This work addresses this challenge by presenting the outcomes of a qualitative, in-depth study focused on best practices and patterns within training strategies for RL architectures, as articulated by practitioners. Leveraging a model-based qualitative research method, we introduce a formal architecture decision model to bridge the gap between scientific insights and practical implementation. We aim to enhance the understanding of practitioners' approaches in RL architecture. The paper analyzes 33 knowledge sources to discern established industrial practices, patterns, relationships, and decision drivers. Based on this knowledge, we introduce a formal Architectural Design Decision (ADD) model, encapsulating 6 decisions, 29 decision options, and 19 decision drivers, providing robust decision-making support for this critical facet of RL-based software architectures.

关键词： Training software architecture Decision making computer architecture Reinforcement learning Planning Complexity theory

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API Rate Limit Adoption -- A pattern collection 23

API Rate Limit Adoption -- A pattern collection

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Souhaila Serbout Amine El Malki Cesare Pautasso Uwe Zdun Software Institute Università della Svizzera italiana Switzerland Faculty of Computer Science Software Architecture Research Group and UniVie Doctoral School Computer Science DoCS University of Vienna Austria Faculty of Computer Science Software Architecture Research Group University of Vienna Austria

ISBN: (纸本)9798400700408

关键词： Application Programming Interfaces Pattern Language Rate Limit

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Tool Support for Learning Architectural Guidance Models from Architectural Design Decision Models 23

Tool Support for Learning Architectural Guidance Models from...

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Amirali Amiri Evangelos Ntentos Uwe Zdun Sebastian Geiger Faculty of Computer Science Software Architecture Research Group University of Vienna Austria Siemens Austria

ISBN: (纸本)9798400700408

关键词： Architectural Design Decisions Architectural Knowledge Management Design Patterns

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