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检索条件"任意字段=4th European Conference on Model Driven Architecture - Foundations and Applications"

共 96 条记录，以下是1-10 订阅

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8th Workshop on Software architecture Erosion and Architectural Consistency, SAEroCon 2021, 1st International Workshop on Software architecture and Machine Learning, SAML 2021, 4th International Workshop on Context-aware, Autonomous and Smart architecture, CASA 2021, 5th Workshop on Formal Approaches for Advanced Computing Systems, FAACS 2021, 2nd International Workshop on model-driven Engineering for Software architecture, MDE4SA 2021 and 1st International Workshop on Mining Software Repositories for Software architecture, MSR4SA 2021 which complemented 15th european conference on Software architecture, ECSA 2021

8th Workshop on Software Architecture Erosion and Architectu...

引用

8th Workshop on Software architecture Erosion and Architectural Consistency, SAEroCon 2021, 1st International Workshop on Software architecture and Machine Learning, SAML 2021, 4th International Workshop on Context-aware, Autonomous and Smart architecture, CASA 2021, 5th Workshop on Formal Approaches for Advanced Computing Systems, FAACS 2021, 2nd International Workshop on model-driven Engineering for Software architecture, MDE4SA 2021 and 1st International Workshop on Mining Software Repositories for Software architecture, MSR4SA 2021 which complemented 15th european conference on Software architecture, ECSA 2021

ISBN: (纸本)9783031151156

the proceedings contain 15 papers presendted at a virtual meeting. the special focus in this conference is on Software architecture Erosion and Architectural Consistency. the topics include: Interactive Elicitation of Resilience Scenarios Based on Hazard Analysis Techniques;Towards an Extensible Approach for Generative Microservice Development and Deployment Using LEMMA;applying Knowledge-driven architecture Composition with Gabble;architectural Optimization for Confidentiality Under Structural Uncertainty;foundations and Research Agenda for Simulation of Smart Ecosystems architectures;Blended Graphical and Textual modelling of UML-RT State-Machines: An Industrial Experience;toward Awareness Creation of Common Challenges Women are Facing in Academia: A Study from a German Perspective;mapping Source Code to Modular architectures Using Keywords;hierarchical Code-to-architecture Mapping;Building the MSR Tool Kaiaulu: Design Principles and Experiences;self-adaptive Machine Learning Systems: Research Challenges and Opportunities;behavioral Maps: Identifying Architectural Smells in Self-adaptive Systems at Runtime;an Architectural Approach for Enabling and Developing Cooperative Behaviour in Diverse Autonomous Robots.

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STAF-WS 2024 - Proceedings of the STAF 2024 Workshops: AgileMDE 2024, LLM4MDE 2024, and MeSS 2024, co-located with the International conference on Software Technologies: applications and foundations, STAF 2024

STAF-WS 2024 - Proceedings of the STAF 2024 Workshops: Agile...

引用

2024 Symposium on Software Technologies: applications and foundations, STAF 2024 Workshops: 2nd International Workshop on Agile model-driven Engineering, AgileMDE 2024, 1st Large Language models for model-driven Engineering Workshop, AgileMDE 2024 and 4th International Workshop on model-driven Engineering for Smart Internet-of-things Systems, MeSS 2024

the proceedings contain 8 papers. the topics discussed include: research directions for agile model-driven engineering;software modelling for sustainable software engineering;software language translation by example;towards agile collaborative multi-view modeling with inconsistency tolerance;model driven engineering in finance: bond valuation, from formulae to code;from image to UML: first results of image-based UML diagram generation using LLMs;LowcoBot: Towards chatting with low-code platforms;digital twins for IoT systems: exploiting synergies between industry and academia;and towards quantum-based graph matching for IoT systems.

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Enabling Reusable and Comparable xApps in the Machine Learning-driven Open RAN 25

Enabling Reusable and Comparable xApps in the Machine Learni...

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IEEE 25th International conference on High Performance Switching and Routing (HPSR)

作者： Herrera, Juan Luis Montebugnoli, Sofia Bellavista, Paolo Foschini, Luca Univ Bologna Dipartimento Informat Sci & Ingn Bologna Italy

ISBN: (纸本)9798350363869;9798350363852

the advent of the Open Radio Access Network (O-RAN) specifications for 5G and 6G Radio Access Networks (RANs) has brought forth a great interest in the use of machine learning to perform control and management tasks. the integration of machine learning in the O-RAN architecture is initially envisioned to be implemented through xApps, applications that act in a near-real timescale and that have machine learning models meant for specific tasks. However, the development of machine learning-based xApps presents challenges, as although the xApp architecture facilitates component reusability for the RAN, the state-of-the-art architectures for xApps themselves require the implementation of an ad-hoc xApp for each machine learning model. therefore, these architectures limit the reusability of the components of xApps as applications, even for xApps meant for the same purpose. To address these issues, we propose the Intelligent xApp architecture (IxAA), a software architecture to simplify the implementation of machine learning-based xApps with a focus on reuse, easing the comparison of machine learning models. As a proof of concept, we developed xAssessment, an xApp to evaluate the performance of data prediction models. Our evaluation shows the performance results of five machine learning models predicting three different RAN metrics through xAssessment in a simulated O-RAN testbed.

关键词： O-RAN xApps Machine Learning Software architecture

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Prediction-driven resource provisioning for serverless container runtimes

Prediction-driven resource provisioning for serverless conta...

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4th IEEE International conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Tomaras, Dimitrios Tsenos, Michail Kalogeraki, Vana Athens Univ Econ & Business Dept Informat Athens Greece

ISBN: (纸本)9798350337440

In recent years Serverless Computing has emerged as a compelling cloud based model for the development of a wide range of data-intensive applications. However, rapid container provisioning introduces non-trivial challenges for FaaS cloud providers, as (i) real-world FaaS workloads may exhibit highly dynamic request patterns, (ii) applications have service-level objectives (SLOs) that must be met, and (iii) container provisioning can be a costly process. In this paper, we present SLOPE, a prediction framework for serverless FaaS platforms to address the aforementioned challenges. Specifically, it trains a neural network model that utilizes knowledge from past runs in order to estimate the number of instances required to satisfy the invocation rate requirements of the serverless applications. In cases that a priori knowledge is not available, SLOPE makes predictions using a graph edit distance approach to capture the similarities among serverless applications. Our experimental results illustrate the efficiency and benefits of our approach, which can reduce the operating costs by 66.25% on average.

关键词： serverless neural networks resource provisioning

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Physics-Informed Neural Networks for Industrial applications: A Case Study in thermal Power Prediction of an AHU for a Topcoat Process 4th

Physics-Informed Neural Networks for Industrial Applications...

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4th International conference of the Italian Association of Design Methods and Tools for Industrial Engineering

作者： Ciampi, Francesco Giuseppe Rega, Andrea Diallo, thierno M. L. Patalano, Stanislao Univ Naples Federico II Dept Ind Engn I-80125 Naples Italy ISAE Supmeca Quartz Lab EA7393 F-93400 St Ouen France

ISBN: (纸本)9783031765964;9783031765971

modelling approaches for industry are typically classified into data-driven and physics-based models. the first type often struggles to interpret the complexities of physical systems and require enough data to capture patterns, while the second type depends on detailed information, which might be only partially available or subject to uncertainty. the recent methodology of Physics-Informed Neural Networks (PINNs) is investigated as a hybrid approach that integrates neural network flexibility with the definition of physical constraints. the work focuses first on a review of PINNs applications in industry, highlighting their advantages over traditional methods in terms of efficiency and versatility. then, methods and tools for PINN implementation are described and a general mathematical formulation is provided. In order to validate the model, a case study concerning the prediction of the thermal power of an industrial Air Handling Unit (AHU) is proposed, detailing the implementation process. the results show good performance compared to traditional data-driven techniques, highlighting the main advantages of this hybrid approach, such as the efficiency even with simple physical models and a smaller amount of training data. Future research directions include the implementation of variants of PINNs with different neural network models or different forms of integration of physical knowledge.

关键词： Industrial Engineering Energy Systems Prediction models Scientific Machine Learning Physics-Informed Neura Networks

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Enhancing the Assessment of Digital Archive Maturity using Enterprise architecture 17

Enhancing the Assessment of Digital Archive Maturity using E...

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17th IFIP WG 8.1 Working conference on the Practice of Enterprise modeling Forum, 1st International Workshop on models for Simulation, M4S 2024, 2nd International Workshop on the foundations and applications of Capabilities in Enterprises, Transformations, and ESG Initiatives, FACETE 2024, Workshop on Advancing Enterprise modelling through Digital Transformation, FAIR Data Management, and Blockchain Integration, AEM 2024, Tools and Demos, PoEM-Companion 2024

作者： Cruz, Miguel Guerreiro, Sérgio Borbinha, José Proença, Diogo INESC-ID Rua Alves Redol 9 Lisbon1000-029 Portugal Instituto Superior Técnico Universidade de Lisboa Portugal

this paper addresses the problem of evaluating the maturity of a digital archive in terms of its digital preservation capabilities. the european eArchiving initiative supports this assessment by providing the key tools of: a maturity model (eACMM), a reference architecture (eRA), and a collection of real-world cases based on the eRA framework. To our knowledge, this is the first paper to integrate these artifacts for a comprehensive evaluation. the eACMM is represented by a set of detailed requirements classified by importance. the eRA is represented by an ArchiMate model, and the real-world cases are represented as ArchiMate eRA viewpoints. this paper details the process of how to assess the real-world cases against the eACMM and demonstrates the value of such viewpoints. One significant challenge addressed in the literature is the lack of effective view models for identifying gaps in reference architectures. Despite the systematic prescription offered by maturity models, they often lack the support of mechanisms that are necessary to fully realize the potential of diagnosing the current stage and identifying the next steps to increase maturity. this paper contributes with a solution implemented in a Enterprise architecture management tool where the output is the automatic creation of Enterprise architecture blueprints. Obtained results corroborate that our solution easily identifies the level of eACMM maturity in respect to eRA concepts, and also an immediate identification of actions that should be done to improve the eACMM level. © 2024 Copyright for this paper by its authors.

关键词： Blueprint Digital Archiving Maturity model Reference architecture Viewpoint

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Simplifying Differential Privacy for Non-Experts: the ENCRYPT Project Approach

Simplifying Differential Privacy for Non-Experts: The ENCRYP...

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4th IEEE Annual International conference on Cyber Security and Resilience (IEEE CSR)

作者： Erotokritou, Stelios Giannoulakis, Ioarmis Kafetzakis, Emmanouil Kaltakis, Konstantinos Eight Bells Ltd Nicosia Cyprus Eight Bells Ltd Athens Greece

ISBN: (纸本)9798350375367

the ENCRYPT project, funded under the Horizon Europe Framework, aims to advance privacy-preserving technologies across various sectors, ensuring robust data protection while maintaining utility. By providing users with core methodologies including the Fully Homomorphic Encryption, Trusted Execution Environments, Differential Privacy and advanced Hybrid Protection Services, ENCRYPT seeks to address the challenge of ensuring data privacy and utility, across federated data spaces within the EU. Differential privacy is an important approach within ENCRYPT, in protecting individual privacy in the growing landscape of digital data. In this paper, we provide an overview of fundamental concepts and present an overview of differential privacy foundations, examining its theoretical underpinnings and practical implementations. We also provide an insight into how it will be applied within the ENCRYPT project. Experiments carried out demonstrate that differential privacy can maintain high data accuracy despite the addition of noise, and we will describe how the ENCRYPT platform simplifies the use of this privacy-preserving technology for non-expert users by automating privacy parameter selection and model optimization. this approach enhances data security, efficiency and accessibility, helping to develop a more privacy-conscious environment for data analysis to carry out research and innovation in a secure and private manner. We will also explore potential future developments and applications of differential privacy within various industries and sectors.

关键词： Differential Privacy Data Security ENCRYPT Project Privacy-Preserving Technology Data Accuracy Automated model Optimization User-centric Privacy Solutions

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Data Information Interoperability model for IoT-enabled Smart Water Networks 16

Data Information Interoperability Model for IoT-enabled Smar...

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16th IEEE International conference on Semantic Computing (ICSC)

作者： Singh, Mandeep Wu, Wenyan Rizou, Stamatia Vakaj, Edlira Birmingham City Univ Birmingham B4 7XG W Midlands England SingularLogic Kifisia 14564 Greece

ISBN: (纸本)9781665434188

Syntactic and semantic interoperability is a fundamental requirement for the success of the Internet of things (IoT)-enabled Smart Water Networks (SWNs). Still, whilst consuming publicly accessible IoT data, the syntactic and semantic representation of the collected data poses challenges for the success of pervasive and ubiquitous sensing in the water domain. Challenges include the heterogeneity of data representation formats, semantic models, and the adoption of domain-specific standards and ontologies. these challenges emphasise the requirement for enhanced interoperability in SWNs. To address this, we propose a Data and Information Interoperability model (DIIM) by combining the Semantic Web technologies, widely known for overcoming interoperability issues, and model-driven architecture (MDA) approach. DIIM facilitates syntactic interoperability by serialization conversion and adoption of domain-specific standards as well as semantic interoperability of metadata by aligning the semantic models of IoT and Smart Water Network (SWN) applications. Furthermore, it automatically creates an ontology as a semantic model if it is missing and adds references to existing domain-specific ontologies as annotation in their models. We evaluate DIIM methodology by applying it to a real-world use case of IoT-enabled applications for water quality monitoring.

关键词： Data Interoperability Syntactic harmonization Semantic model Generation and Alignment Smart Water Networks IoT/WoT Ontology Representation Standard Water Quality Monitoring

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Detecting Suspicious Player Behavior in Web3 games: A Data-driven Analysis of Bot Accounts 24

Detecting Suspicious Player Behavior in Web3 games: A Data-D...

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4th International conference on Information Technology for Social Good (GoodIT)

作者： Guidi, Barbara Michienzi, Andrea Ricci, Laura Univ Pisa Dept Comp Sci Pisa Italy

ISBN: (纸本)9798400710940

Blockchain fuelled the innovation of numerous application fields. In particular, Web3 applications benefit the most because blockchain can be used to implement a rewarding system for users that contribute the most, thus increasing the overall social good provided by these platforms. One of the sectors that has benefited most from blockchain technology is the gaming sector through the so-called Play-to-Earn (P2E) model. the P2E Blockchain Video Games allow players to earn rewards in the form of tokens or NFTs, by having an impact on the social good. Unfortunately, bot accounts could exploit these platforms, which defeats the purpose of having a reward system because they invalidate the social good introduced by the rewards. In this paper, we provide an analysis geared towards detecting suspicious behaviour in P2E blockchain-based games by exploiting Gods Unchained as a case study. Using the game's official APIs, we download 12 months' worth of players' activity. Analysing the data, we detect two groups of players with abnormal activity. Additionally, analysing the players' graph, we find communities made of the best players with similar activity. Lastly, we observe that users with suspicious behaviour belong to these communities.

关键词： Blockchain Web3 Play-2-Earn Non-Fungible Tokens Bots

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Taurus: A Data Plane architecture for Per-Packet ML 27

Taurus: A Data Plane Architecture for Per-Packet ML

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27th ACM International conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Swamy, Tushar Rucker, Alexander Shahbaz, Muhammad Gaur, Ishan Olukotun, Kunle Stanford Univ Stanford CA 94305 USA Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450392051

Emerging applications-cloud computing, the internet of things, and augmented/virtual reality-demand responsive, secure, and scalable datacenter networks. these networks currently implement simple, per-packet, data-plane heuristics (e.g., ECMP and sketches) under a slow, millisecond-latency control plane that runs data-driven performance and security policies. However, to meet applications' service-level objectives (SLOs) in a modern data center, networks must bridge the gap between line-rate, per-packet execution and complex decision making. In this work, we present the design and implementation of Taurus, a data plane for line-rate inference. Taurus adds custom hardware based on a flexible, parallel-patterns (MapReduce) abstraction to programmable network devices, such as switches and NICs;this new hardware uses pipelined SIMD parallelism to enable per-packet MapReduce operations (e.g., inference). Our evaluation of a Taurus switch ASIC-supporting several real-world models-shows that Taurus operates orders of magnitude faster than a server-based control plane while increasing area by 3.8% and latency for linerate ML models by up to 221 ns. Furthermore, our Taurus FPGA prototype achieves full model accuracy and detects two orders of magnitude more events than a state-of-the-art control-plane anomaly-detection system.

关键词： Per-packet ML Self-driving Networks MapReduce P4 Spatial

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