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检索条件"任意字段=19th International Conference on Software Engineering and Knowledge Engineering"

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Building design automation: knowledge Fusion-based KBE system 28

Building design automation: Knowledge Fusion-based KBE syste...

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28th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Kaldheim, William Andersen Haaland Paaske, Haakon Valvik, Jens Emil Lobov, Andrei Norwegian University of Science and Technology Department of Mechanical and Industrial Engineering Trondheim Norway

ISBN: (纸本)9798350339918

Automation of engineering tasks requires the integration of multidisciplinary knowledge. that knowledge should ideally be expressed in a machine-processable format to enable computer automation of tasks. In this paper, we will seek to build a knowledge-based engineering (KBE) system for building design. We will illustrate how the knowledge of buildings can be integrated and extensively (re-)used in building design. the contribution of the paper is to demonstrate how to build such KBE systems following an object-oriented approach. We demonstrate a solution using Siemens NX CAD software and its APIs. However, the approach can also be followed with many other modern CAD tools having the relevant API support. © 2023 IEEE.

关键词： Product design

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Leveraging an Integrated Data Platform to Support Satellite Tradespace Analysis 57

Leveraging an Integrated Data Platform to Support Satellite ...

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57th IAA Symposium on Safety, Quality and knowledge Management in Space Activities at the 75th international Astronautical Congress, IAC 2024

作者： Gregory, Joe Iyer, Visalakshi Hoag, Lucy Zute, Alisha Marmar, Mike Cope, Brett Jones, Barry Salado, Alejandro Department of Systems and Industrial Engineering The University of Arizona 1127 E James E Rogers Way TucsonAZ85721-0020 United States Violet Labs Inc. San FranciscoCA United States

ISBN: (纸本)9798331312275

When developing hardware/software systems destined for a space environment, the low margin for error and high difficulty of meeting the requirements mean that data integrity must be guaranteed throughout the development process. this begins as early as ideation and design, when the designer(s) must navigate a large tradespace of candidate systems to identify the solution forward. Unfortunately, the software tools involved in space system design typically operate in isolation from one another, making this an inefficient and error-prone process. Using a digital engineering environment can address this challenge by integrating engineering data from disparate tools into a single shared database, structuring the data as entities, and enabling the definition of relations between these entities in a common environment. In turn, this would allow users to query this comprehensive dataset and generate reports containing the relevant information (e.g., project requirements, system architecture, hardware and software design, test campaigns, analyses) in a unified dashboard. In this paper, we present a hub-and-spoke architecture with Violet at the center. Violet can integrate data from multiple engineering tools and can generate a graph representation of this dataset in the Ontological Modeling Language (OML). By representing the dataset as a knowledge graph, users can leverage semantic web technologies to reason with, query and infer new information from this data. For this effort, the knowledge graph is structured according to the University of Arizona Ontology Stack (UAOS) to ensure its validity. the UAOS is a modular, multi-layered ontology stack based on the Basic Formal Ontology (BFO). these capabilities are particularly powerful when used to evaluate the consistency, completeness and correctness of a dataset. We apply this approach to the notional NoraSat: a low-Earth orbit (LEO) imaging satellite. We demonstrate how this approach can be applied to support tradespace anal

关键词： Budget control

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An industrial benchmark study of automated modal analysis using density-based clustering techniques 31

An industrial benchmark study of automated modal analysis us...

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31st international conference on Noise and Vibration engineering, ISMA 2024 and 10th international conference on Uncertainty in Structural Dynamics, USD 2024

作者： Tavares, A. Di Lorenzo, E. Cornelis, B. Manzato, S. Peeters, B. Desmet, W. Gryllias, K. Siemens Industry Software NV Interleuvenlaan 68 Leuven3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 Leuven3001 Belgium Flanders Make @ KU Leuven Belgium

ISBN: (纸本)9789082893175

Modal analysis has advanced as a vital technology for studying structural dynamics by capturing complex phenomena through modal parameters. However, manual pole selection from stabilization diagrams can be challenging, especially with complex datasets, necessitating automation for precision and repeatability. this study introduces three Automated Modal Analysis (AMA) methodologies using ML density-based clustering techniques (DBSCAN, OPTICS, HDBSCAN) along with domain knowledge. these methodologies are evaluated against manual selections by engineers with varying levels of expertise across eight industrial datasets, covering diverse structures like metallic specimens, motors, cars, a jet aircraft, and a wind turbine blade. Engineers' proficiency levels are categorized into novices, intermediates, and experts, depending on their expertise in the field. the paper provides a comprehensive comparative analysis, assessing metrics such as the number of selected modes, the quality of modal models, and alignment with expert results. © 2024 Proceedings of ISMA 2024 - international conference on Noise and Vibration engineering and USD 2024 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Modal analysis

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Spiking Neural Network Classification Method in Motor Imagery Brain-Computer Interface 19

Spiking Neural Network Classification Method in Motor Imager...

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19th international conference on Natural Computation, Fuzzy Systems and knowledge Discovery, ICNC-FSKD 2023

作者： Tan, Ping Xiao, Han Zou, Han Liu, Kun Shen, Yi Chen, Chao Hunan University of Technology and Business School of Intelligent Engineering and Intelligent Manufacturing Changsha410205 China Hunan Xiaodian Automobile Service Co. Ltd Changsha China

ISBN: (纸本)9798350304398

Electroencephalogram (EEG) Brain-computer interface (BCI) classification research has become a hot field recently. this paper proposes spiking neural network (SNN) classification method of motor imagery EEG signals. Firstly, the raw EEG signal is preprocessed by band-pass filtering, and encoded into spike signals by Ben's Spike encoding algorithm. then, convolution layers and pooling layer is designed in SNN to take advantage of the characteristics of EEG signal. Finally, the SNN is employed to classify the spike signals. It is worth noting that the activation function in SNN is different from the convolutional neural network. It simulates biological encode information of brain in the form of spikes by the LIF model. By simulating the change process of membrane potential, it realizes the transmission of intercellular spikes and the active release of membrane potential without input, which makes the signal processing closer to the neuron cell membrane potential processing mechanism. On the other hand, since spike signals can weaken the negative effect of noise in the superposition process, spikes input can reduce the influence of noise in the network. the BCI Competition IV 2a datasets are used in the experiments, and the classification results show that the proposed classification method achieves the average classification accuracy of 82.08%. © 2023 IEEE.

关键词： Electroencephalography

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Conflict-aware Inference of Python Compatible Runtime Environments with Domain knowledge Graph 22

Conflict-aware Inference of Python Compatible Runtime Enviro...

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ACM/IEEE 44th international conference on software engineering (ICSE)

作者： Cheng, Wei Zhu, Xiangrong Hu, Wei Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781450392211

Code sharing and reuse is a widespread use practice in software engineering. Although a vast amount of open-source Python code is accessible on many online platforms, programmers often find it difficult to restore a successful runtime environment. Previous studies validated automatic inference of Python dependencies using pre-built knowledge bases. However, these studies do not cover sufficient knowledge to accurately match the Python code and also ignore the potential conflicts between their inferred dependencies, thus resulting in a low success rate of inference. In this paper, we propose PyCRE, a new approach to automatically inferring Python compatible runtime environments with domain knowledge graph (KG). Specifically, we design a domain-specific ontology for Python third-party packages and construct KGs for over 10,000 popular packages in Python 2 and Python 3. PyCRE discovers candidate libraries by measuring the matching degree between the known libraries and the third-party resources used in target code. For the NP-complete problem of dependency solving, we propose a heuristic graph traversal algorithm to efficiently guarantee the compatibility between packages. PyCRE achieves superior performance on a real-world dataset and efficiently resolves nearly half more import errors than previous methods.

关键词： Python Runtime environment inference knowledge graph Conflict resolution Dependency solving Configuration management

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Respond to Change or Die: An Educational Scrum Simulation for Distributed Teams 44

Respond to Change or Die: An Educational Scrum Simulation fo...

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ACM/IEEE 44th international conference on software engineering - software engineering Education and Training (ICSE-SEET)

作者： Christensen, Emily Laue Paasivaara, Maria IT Univ Copenhagen Copenhagen Denmark LUT Univ Lahti Finland

ISBN: (纸本)9781665495929

Scrum is best learned by doing, e.g. through simulation, as it is simple to understand but difficult to master. During the Covid-19 pandemic, we could not use the traditional face-to-face Scrum Lego simulation game, but had to utilize something workable in an online environment. In this paper we present an online Scrum simulation for distributed teams that was created in a multiplayer game "Don't Starve Together" (DST) through iterative reflective work and analysis. We ran the simulation with 25 Scrum teams on four different courses with participants from eight universities located in three countries. We collected feedback from 244 participants by analysing 196 student learning diaries and 84 student evaluation surveys, and by running a retrospective with 19 industry participants. the participants' feedback was highly positive. the main reported learning outcomes were communication, estimation, Scrum in practice, communication and collaboration with industrial Product Owners, Scrum events, work organisation, teamwork and prioritisation.

关键词： software engineering education Scrum simulation play

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Randomized Differential Testing of RDF Stores 23

Randomized Differential Testing of RDF Stores

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Yang, Rui Zheng, Yingying Tang, Lei Dou, Wensheng Wang, Wei Wei, Jun Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Chinese Acad Sci Nanjing Coll Nanjing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350322637

As a special kind of graph database systems, RDF stores have been widely used in many applications, e.g., knowledge graphs and semantic web. RDF stores utilize SPARQL as their standardized query language to store and retrieve RDF graphs. Incorrect implementations of RDF stores can introduce logic bugs that cause RDF stores to return incorrect query results. these logic bugs can lead to severe consequences and are likely to go unnoticed by developers. However, no available tools can detect logic bugs in RDF stores. In this paper, we propose RD2, a Randomized Differential testing approach of RDF stores, to reveal discrepancies among RDF stores, which indicate potential logic bugs in RDF stores. the core idea of RD2 is to build an equivalent RDF graph for multiple RDF stores, and verify whether they can return the same query result for a given SPARQL query. Guided by the SPARQL syntax and the generated RDF graph, we automatically generate syntactically valid SPARQL queries, which can return non-empty query results with high probability. We further unify the formats of SPARQL query results from different RDF stores and find discrepancies among them. We evaluate RD2 on three popular and widely-used RDF stores. In total, we have detected 5 logic bugs in them. A video demonstration of RD2 is available at https://***/da7XlsdbRR4.

关键词： RDF store differential testing SPARQL

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LLMs for Intelligent software Testing: A Comparative Study 24

LLMs for Intelligent Software Testing: A Comparative Study

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7th international conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Boukhlif, Mohamed Kharmoum, Nassim Hanine, Mohamed Lti Laboratory National School of Applied Sciences Chouaib Doukkali University El Jadida Morocco Ipss Team Faculty of Sciences Mohammed v University in Rabat Rabat Morocco

ISBN: (纸本)9798400709296

the need for effective and timely testing processes has become critical in the constantly changing field of software development. Large Language Models (LLMs) have demonstrated promise in automating test case creation, defect detection, and other software testing tasks through the use of the capabilities of machine/deep learning and natural language processing. this work explores the field of intelligent software testing, with a focus on the use of LLMs in this context. the purpose of this comparative study is to assess the corpus of research in the field in terms of used LLMs, how to interact with them, the use of fine-tuning, and prompt engineering, and explore the different technologies and testing types automated using LLMs. the findings of this study not only contribute to the growing body of knowledge on intelligent software testing but also guide fellow researchers and industry engineers in selecting the most suitable LLM for their specific testing needs. © 2024 ACM.

关键词： Contrastive Learning

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Enhancing Manufacturing Efficiency through Integration of MBSE and Capella in the Digital thread 19thth

Enhancing Manufacturing Efficiency Through Integration of MB...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

作者： Burgstaller, Michael Schichl, thomas Jungwirth, Mario Research Group Smart Mechatronics Engineering University of Applied Sciences Upper Austria Wels Campus Wels Austria University of Applied Sciences Upper Austria Wels Campus Wels Austria

ISBN: (纸本)9783031829512

the advancement of digitalization in manufacturing has brought about a significant transformation in the industry. the digital thread, a key component this for transformation is defined as the use of digital tools and representations for design, evaluation, and life cycle management. this paper presents a project focused on enhancing the digital thread of a manufacturing line by integrating Model-Based Systems engineering (MBSE) and Capella. It automates part identification, handling, and transportation in a small-scale manufacturing line, utilizing data from programs like Siemens NX in Capella to generate assembly code for manufacturing. the integration of various software tools like Capella, Siemens NX, and Mechatronics Concept Designer (MCD), along with hardware components like robots and Automated Guided Vehicles (AGVs), is crucial for optimizing manufacturing processes. the physical key components include cameras, sensors, and the UR10 robotic arm, demonstrating a use case of a flexible transport system (FTS) with a gripper and conveyor belt for pick-and-place tasks. the anticipated outcome is improved communication in the digital thread and increased production efficiency through automated processes, reduced manual intervention, and a more agile response to design changes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Automatic guided vehicles

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Coverage Guided Fault Injection for Cloud Systems 23

Coverage Guided Fault Injection for Cloud Systems

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Gao, Yu Dou, Wensheng Wang, Dong Feng, Wenhan Wei, Jun Zhong, Hua Huang, Tao Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Chinese Acad Sci Nanjing Coll Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China

ISBN: (纸本)9781665457019

To support high reliability and availability, modern cloud systems are designed to be resilient to node crashes and reboots. that is, a cloud system should gracefully recover from node crashes/reboots and continue to function. However, node crashes/reboots that occur under special timing can trigger crash recovery bugs that lie in incorrect crash recovery protocols and their implementations. To ensure that a cloud system is free from crash recovery bugs, some fault injection approaches have been proposed to test whether a cloud system can correctly recover from various crash scenarios. these approaches are not effective in exploring the huge crash scenario space without developers' knowledge. In this paper, we propose CrashFuzz, a fault injection testing approach that can effectively test crash recovery behaviors and reveal crash recovery bugs in cloud systems. CrashFuzz mutates the combinations of possible node crashes and reboots according to runtime feedbacks, and prioritizes the combinations that are prone to increase code coverage and trigger crash recovery bugs for smart exploration. We have implemented CrashFuzz and evaluated it on three popular open-source cloud systems, i.e., ZooKeeper, HDFS and HBase. CrashFuzz has detected 4 unknown bugs and 1 known bug. Compared with other fault injection approaches, CrashFuzz can detect more crash recovery bugs and achieve higher code coverage.

关键词： cloud system crash recovery bug fault injection bug detection fuzzing

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