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检索条件"任意字段=22nd International Conference on Formal Methods and Models for System Design"

共 1415 条记录，以下是1-10 订阅

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Right or Wrong – Understanding How Users Write Software models in Alloy 22nd

Right or Wrong – Understanding How Users Write Software Mo...

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22nd international conference on Software Engineering and formal methods, SEFM 2024

作者： Jovanovic, Ana Sullivan, Allison The University of Texas at Arlington ArlingtonTX United States

ISBN: (纸本)9783031773815

Writing declarative models has numerous benefits, ranging from automated reasoning and correction of design-level properties before systems are built, to automated testing and debugging of their implementations after they are built. Alloy is a declarative modeling language that is well-suited for verifying system designs. A key strength of Alloy is its scenario-finding toolset, the Analyzer, which allows users to explore all valid scenarios that adhere to the model’s constraints up to a user-provided scope. However, even with visualized scenarios, it is difficult to write correct Alloy models. This paper presents an empirical study of over 93,000 models written mainly by users trying to learn Alloy to form a comprehensive series of observations to guide the growing body of debugging and educational efforts for Alloy model development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital elevation model

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2016 ACM/IEEE international conference on formal methods and models for system design, MEMOCODE 2016

2016 ACM/IEEE International Conference on Formal Methods and...

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14th ACM/IEEE international conference on formal methods and models for system design, MEMOCODE 2016

ISBN: (纸本)9781509027910

The proceedings contain 22 papers. The topics discussed include: a computer-algebraic approach to formal verification of data-centric low-level software;verifying the concentration property of permutation networks by bDDs;combining type-checking with model-checking for system verification;formal engineering frameworks in maritime domain awareness;towards integrating statistical model checking into property-based testing;and specification of precise timing in synchronous dataflow models.

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Revolutionizing Diabetes Prediction models: Leveraging Advanced Feature Reduction and Classification methods 22

Revolutionizing Diabetes Prediction Models: Leveraging Advan...

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22nd international Learning and Technology conference, L and T 2025

作者： Attiah, Ameera Alagha, Mawaddah Bukassim, Lujain El-Kafrawy, Passent College of Engineering Effat University Computer Science Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The global challenge of diabetes demands innovative approaches for early diagnosis and effective management. This paper investigates the integration of advanced dimensionality reduction and feature selection techniques to improve diabetes prediction models. Utilizing Principal Component Analysis (PCA) and the Artificial Bee Colony (ABC) algorithm, the study focuses on optimizing feature selection to enhance model accuracy, computational efficiency, and interpretability. Employing two diverse datasets - the Behavioral Risk Factor Surveillance system (BRFSS) and the Pima Indians Diabetes Dataset - the research evaluates the performance of Random Forest and Logistic Regression models. Results reveal significant improvements in predictive accuracy and computational efficiency, particularly with ABC-selected features, reducing feature dimensionality while maintaining high accuracy. This study underscores the potential of combining PCA and ABC to revolutionize predictive healthcare analytics, offering robust tools for diabetes management and prevention. © 2025 IEEE.

关键词： Dimensionality reduction

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Automated Integration of BIM and 3D GIS for Sustainable Infrastructure Management 22

Automated Integration of BIM and 3D GIS for Sustainable Infr...

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22nd international Learning and Technology conference, L and T 2025

作者： Zada, Yassmine Sebari, Imane Morel, Victor Pierrot, Amanda School of Geomatics and Surveying Engineering IAV Hassan II Department of Photogrammetry and Cartography Rabat Morocco Setec International Building Information Modeling Hub Paris France Setec International Geographic Information System Hub Paris France

ISBN: (纸本)9798331532215

In this paper, we present an automated integration approach for BIM and 3D GIS data about infrastructure projects to improve human-machine interaction with respect to sustainable project management. Based on the step-by-step process that starts with the preparation of data in BIM about buildings and complex infrastructures, CAD for the buried networks and terrain models, and BIM itself for buildings and complex infrastructures by generating IFC models. This is followed by a cleansing and optimisation step to find geometric conflicts. ETL processes automate the development of a unified spatial database integrating BIM and GIS data, without loss to geometric or attribute data. Automation tools are used to transfer data between BIM and GIS environments. This process enables the design of a 3D GIS application for 3D visualization and advanced spatial queries, such as efficient monitoring and decision making. From a case study in preliminary infrastructure projects, such as underground networks and building structure mapping, significant improvements in workflow efficiency were observed, improving by 30%, and data errors were reduced by 20% compared to manual methods. It is an essential gain for infrastructure integration and management, in particular within the complex environments involving smart cities. This approach contributes to sustainable infrastructure management through the use of global real-time views about projects. © 2025 IEEE.

关键词： Decision making

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IoT-enabled Student Attendance system using RISC-V based Embedded system 22

IoT-enabled Student Attendance System using RISC-V based Emb...

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22nd international Learning and Technology conference, L and T 2025

作者： Khan, Khadija Alzahrani, Nada Abed, Reham Razi, Iman Sofia Dhaou, Imed Ben Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah22246-4872 Saudi Arabia

ISBN: (纸本)9798331532215

Inefficiencies, cumbersome processes, and a significant risk of errors, delays, and misuse often plague conventional methods for tracking student attendance. To address these challenges, this paper introduces a smart monitoring system driven by the Artificial Intelligence of Things (AIoT), specifically designed for resource-limited embedded processors. This research examines a real-time facial recognition-based student attendance tracking system implemented on an edge device. Additionally, it assesses the effectiveness of ARM and RISC-V microprocessors as the foundation for a reliable edge computing platform. The system employs Python's Dlib library and the ResNet model for effective facial recognition. Comparisons between the Raspberry Pi Model 4 B and the RISC-V StarFive VisionFive2 are made to determine the optimal platform for extensive deployments. The proposed edge system emphasizes security, automation, and reliability, facilitating real-time, precise attendance tracking and significantly improving operational efficiency for educators and administrators. The solution using the RISC-V processor is at least 10 times more energy-efficient than the competing solution based on the RISC processor instruction set architecture. © 2025 IEEE.

关键词： Students

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Do Large Language models Contain Software Architectural Knowledge? : An Exploratory Case Study with GPT 22

Do Large Language Models Contain Software Architectural Know...

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22nd IEEE international conference on Software Architecture, ICSA 2025

作者： Soliman, Mohamed Keim, Jan Universität Paderborn Heinz Nixdorf Institut Germany Germany

ISBN: (纸本)9798331520908

Architectural knowledge (AK) of existing systems is essential for software engineers to make design decisions. Recently, Large Language models (LLMs) trained on large-scale datasets, including software repositories, have shown promise in embedding knowledge and answering questions. However, LLMs have not been evaluated for their abilities to answer questions about AK, leaving doubts about their accuracy. This paper assesses GPT, a leading LLM, by evaluating its responses' accuracy, quality, and trustworthiness on the AK of the large-scale open-source system HDFS. We conducted an exploratory case study with 14 software engineers who posed questions to GPT and compared its responses to a predefined ground truth. The engineers rated GPT's answers with moderate quality and trustworthiness. Our findings on GPT's accuracy indicates moderate recall but lower precision, especially in identifying quality attribute solutions and design rationales. These results suggest that while GPT and similar models can provide initial insights into AK, expert validation remains necessary for reliability. This study underscores LLMs' potential and limitations to discover software AK. © 2025 IEEE.

关键词： Open source software

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Hierarchical Learning of Generative Automaton models from Sequential Data 22nd

Hierarchical Learning of Generative Automaton Models from ...

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22nd international conference on Software Engineering and formal methods, SEFM 2024

作者： von Berg, Benjamin Aichernig, Bernhard K. Rindler, Maximilian Štern, Darko Tappler, Martin Institute of Software Technology Graz University of Technology Graz Austria TU Wien Vienna Austria AVL List GmbH Graz Austria

ISBN: (纸本)9783031773815

Passive automata learning is a method for inferring automaton models from a given collection of observations of system behavior (traces). It has been applied to reactive systems with probabilistic behavior. In particular, IOAlergia is a well known algorithm for inferring models in the form of deterministically labeled Markov decision processes from system traces. The quality of the resulting model depends heavily on the provided data set and suffers if data is scarce. However, in many cases additional knowledge about the system is available. This work aims to incorporate knowledge about system modes into the learning process in order to improve model quality for low-data scenarios. This is done by splitting the traces according to system modes, learning individual models for each mode and combining those models into one model with sub-regions corresponding to individual system modes. In our evaluation on artificial models, our method outperforms the baseline in at least 90 % of cases for all considered metrics. This method was developed to learn generative models of human driving behavior. Data from recorded test drives on highways was used to learn a hierarchical stochastic model of typical acceleration behavior of human drivers. In the automotive industry, such models make the simulations of driving emissions more realistic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Using Large Language models to Generate Process Knowledge from Enterprise Content 22nd

Using Large Language Models to Generate Process Knowledge fr...

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22nd international conference on Business Process Management (BPM)

作者： Franzoi, Sandro Delwaulle, Maxime Dyong, Julian Schaffner, Jan Burger, Mara vom Brocke, Jan Univ Munster D-48149 Munster Germany ERCIS European Res Ctr Informat Syst D-48149 Munster Germany

ISBN: (纸本)9783031786655;9783031786662

Large language models (LLMs) have disrupted knowledge work in many application areas. Accordingly, the Business Process Management (BPM) community has started to explore how LLMs can be leveraged, resulting in a variety of promising research directions across the BPM lifecycle. Despite rapid adoption in practice and strong research interest, however, little is known about the actual design of BPM systems that leverage LLMs in organizational contexts. In this paper, we report on design science-based research in collaboration with a large multinational company to design a BPM system that leverages LLMs for process knowledge extraction from diverse enterprise content. Based on the development of our prototype, we observe that LLMs provide the means to organize and generate process knowledge independent of specific forms of representation. We present a conceptual framework that describes the role of LLMs in generating process knowledge from diverse input formats and, in turn, making it available in diverse output formats via prompting, resulting in representation-agnostic process knowledge. We also highlight implications of our study for BPM research and practice.

关键词： Large Language models Process Knowledge Generative Artificial Intelligence Business Process Management

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Composing Run-Time Variability models 22nd

Composing Run-Time Variability Models

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22nd international conference on Software Engineering and formal methods, SEFM 2024

作者： Farhat, Salman Bliudze, Simon Duchien, Laurence Kouchnarenko, Olga Univ. Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL Lille59000 France Université de Franche-Comté CNRS Institut FEMTO-ST Besançon25000 France

ISBN: (纸本)9783031773815

The sheer complexity of modern systems requires compositional approaches to variability modelling. To manage the variability of large systems’ architecture, feature models are widely used at design-time, with several operators defined to allow their composition. However, complex systems’ architectures may evolve at run-time by acquiring new features and functionalities while respecting new constraints. To address this challenge, this paper defines composition operators for component-based run-time variability models that not only encode these feature model composition operators, but also ensure safe run-time reconfiguration. To prove the correctness and compositionality properties, we propose a novel multi-stepUP-bisimulation equivalence and use it to show that the component-based run-time variability models preserve the semantics of the composed feature models. In addition, reachability results permit safe reconfiguration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Large scale systems

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Integrated QoS- and Vulnerability-Driven Self-adaptation for Microservices Applications 22nd

Integrated QoS- and Vulnerability-Driven Self-adaptation for...

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22nd international conference on Service Oriented Computing

作者： Camilli, Matteo Luccioletti, Fabio Mirandola, Raffaela Scandurra, Patrizia Politecn Milan Milan Italy Karlsruhe Inst Technol KIT Karlsruhe Germany Univ Bergamo Bergamo Italy

ISBN: (纸本)9789819608072;9789819608089

Prioritizing security concerns in modern (micro)service-based applications is paramount to protecting sensitive data and maintaining end-user trust. Self-adaptation can strengthen security measures at runtime by autonomously adjusting the configuration and behavior of the managed system with limited, or even without, human intervention. In this paper, we present AQUA, a novel approach to orchestrate microservices jointly considering Quality of Service (QoS) and vulnerabilities. The framework maintains an architectural model of the system at runtime expressed through a Discrete-Time Markov Chain (DTMC). Probabilistic model checking is then used to evaluate and compare alternative DTMCs to identify the adaptation actions that reduce security threats (reducing the attack surface preventively) while increasing the delivered QoS (availability and response time). We evaluate the cost-effectiveness of AQUA using a microservice application benchmark. We show that the framework outperforms existing baseline methods by consistently planning better adaptation decisions that consider QoS and security aspects. However, this comes with higher computational costs, which increase linearly with the problem size.

关键词： Microservices security self-adaptation Markov models

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