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Enabling Data-Driven Nursing Innovations: User-centered Development of a Nursing Data Module

Current Directions in Biomedical engineering 2023年第1期9卷 339-342页

作者： Warnecke, Maren Holle, Daniela Burmann, Anja Fraunhofer Institute for Software and Systems Engineering ISST Speicherstrasse 6 Dortmund Germany HS Gesundheit Bochum Bochum Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany

The documentation landscape for nursing care data in Germany is predominantly heterogeneous and unstructured. Therefore, insightful methods such as Artificial Intelligence (AI) are difficult to implement. We propose a stepwiseapproach that identifies relevant information from nursing theory and practice and maps it to a standardized nursing core data set to enable data-based improvement of nursing care. This can be used for various use-cases in care, such as risk detection and prevention in diverse care contexts. Many care processes can benefit of a cross-facility and standardized repository and associated applications. We propose an approach that enables the use of AI while leveraging consensus and evidenced-based expert knowledge from nursing science. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Nursing

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Design Features for Data Trustee Selection in Data Spaces 13

Design Features for Data Trustee Selection in Data Spaces

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13th International Conference on Data Science, Technology and Applications, DATA 2024

作者： Steinert, Michael Tebernum, Daniel Hupperz, Marius Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany

ISBN: (纸本)9789897587078

As the world becomes increasingly digital, data is becoming a critical resource. When used effectively, it can lead to more accurate forecasts, process optimization, and the creation of innovative business models. The necessary data is often distributed across multiple organizations, and its full value can only be realized through shared collaboration. Data spaces provide organizations with a platform for sovereign and secure data sharing. To enable legally secure data sharing and ensure compliance with regulations, data trustees play a critical role as trusted intermediaries. However, choosing a suitable data trustee that meets the needs of the participants who want to share data with each other is difficult. Our study seeks to elucidate the process by which participants of a data space can choose an appropriate data trustee. To this purpose, we have implemented a whitelist approach. We report on the results of our design science research project, which includes design features to facilitate the integration of our whitelist approach into different data space instantiations. Potential shortcomings were identified and addressed during an expert workshop. By providing verified design knowledge, we help practitioners in the data space community to incorporate the concept of how to select the most appropriate data trustee. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Data consistency

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Phase Re-Service in Reinforcement Learning Traffic Signal Control 27

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Zhiyao Gunter, George Quinones-Grueiro, Marcos Zhang, Yuhang Barbour, William Biswas, Gautam Work, Daniel Institute for Software Integrated Systems Vanderbilt University Department of Civil and Environmental Engineering United States Institute for Software Integrated Systems Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798331505929

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation. © 2024 IEEE.

关键词： Markov processes

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Machine Learning-assisted Test Records Analysis during the Real-Time Validation of Automotive software systems based on HIL Simulation 58

Machine Learning-assisted Test Records Analysis during the R...

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Abboush, Mohammad Knieke, Christoph Rausch, Andreas Technische Universität Clausthal Institute for Software and Systems Engineering Germany

ISBN: (纸本)9780998133188

In the automotive industry, according to ISO 26262, comprehensive testing is conducted to ensure software systems quality over various phases of the V-model. However, at the system integration and testing phase, a significant amount of time series data is analyzed manually based on expert knowledge to identify the nature of the failure occurring, which is a costly process in terms of time, effort, and difficulty. This study proposes a novel Machine Learning (ML)-assisted failure analysis approach for the real-time validation of automotive software systems (ASSs). Specifically, based on a representative critical faults dataset, intelligent data-driven ML-based Fault Detection and Diagnosis (FDD) models are developed, including LSTM and Denoising Autoencoder (DAE) + k-means techniques for known and unknown sensor-related faults, respectively. The novelty of these models lies in their ability to identify single and simultaneous faults in noisy conditions. To assess the efficacy of the developed model, a dataset from a virtual test drive platform with a high-fidelity entire vehicle model was employed. The evaluation outcomes illustrate the superiority of the proposed model for known faults classification in comparison to other state-of-the-art methods, with an average F1-score of 91.85%. Furthermore, the integration of DAE with k-means exhibited a high clustering performance against noise with a low MSE and DB, i.e., 0.044 and 0.68, respectively. Consequently, the proposed approach facilitates the real-time validation of the ASSs in an efficient manner enhancing the safety and reliability. © 2025 IEEE Computer Society. All rights reserved.

关键词： software testing

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Towards Feature-based Versioning for Musicological Research 24

Towards Feature-based Versioning for Musicological Research

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18th International Working Conference on Variability Modelling of software-Intensive systems, VaMoS 2024

作者： Grünbacher, Paul Neuwirth, Markus Institute of Software Systems Engineering Johannes Kepler University Linz Linz4040 Austria

ISBN: (纸本)9798400708770

This paper discusses the management of revisions and variants of musical works for the context of musicological research. Domain-specific languages (DSLs) are a fundamental tool in music notation and analysis, as they enable the notation of music and also support music analysis when investigating particular structural properties of melody, harmony, rhythm, or form. However, the fields of music philology and music analysis still lack a systematic approach for uniformly managing revisions and variants of musical compositions. This research-in-progress paper proposes the use of feature-based versioning to streamline the management of revisions and variants in music artifacts. We introduce an illustrative example and present research challenges regarding variability and feature-based versioning for musicological research. We present a preliminary approach which involves mapping features to specific parts of musical works and musical analyses, thereby facilitating the composition of new variants based on selected features, a prerequisite for enhanced research in music philology and music analysis. © 2024 Owner/Author.

关键词： Music

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GoLive A modular Mixed Reality Simulation for Semantic Plug and Play 23

GoLive A modular Mixed Reality Simulation for Semantic Plug ...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Wanninger, Constantin Badem, Thomas Schorner, Martin Eymueller, Christian Poeppel, Alexander Reif, Wolfgang Institute for Software and Systems Engineering University of Augsburg Augsburg Germany

ISBN: (纸本)9788993215274

In the field of mobile robotics, the interaction of robots with their environment plays a key role. Simulations serve to simulate a part of the environment so that the partly complex hardware is not damaged in extensive field tests. However, the transition from simulation to reality can be difficult. This paper presents a modular simulation and visualization with mixed reality mechanisms that can be reconfigured at runtime using a new concept called MR devices. A MR device is a physical device that registers with the simulation through self-description mechanisms and can interact with real objects. MR devices themselves can be used as tangible user interfaces in mixed reality visualization, e.g. to influence the composition of the environment. This concept enables the stepwise transition from simulation to reality. The advantages of these intermediate steps are presented by means of three scenarios, on which drones, sensors, infrastructure and also gas emitters are used. GoLive builds on the self-description mechanisms resulting from the own preliminary work: Semantic Plug and Play, in which capabilities and properties can be described by Semantic Web mechanisms and linked to the execution logic of the hardware at runtime. © 2023 ICROS.

关键词： Robotics

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An adaptive synthetic sampling and batch generation-oriented hybrid approach for addressing class imbalance problem in software defect prediction

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Soft Computing 2024年第23期28卷 13595-13614页

作者： Taskeen, Anam Khan, Saif Ur Rehman Mashkoor, Atif Islamabad45550 Pakistan Islamabad45550 Pakistan Institute of Software Systems Engineering Johannes Kepler University Altenbergerstraße 69 Linz4040 Austria

Learning classifiers with uneven class distribution datasets poses a significant challenge in software defect prediction. This problem arises when the number of samples representing one class is significantly smaller than the others, leading to weak classification performance, particularly for minority class instances. Traditional classification models assuming equal class instances can result in low prediction accuracy and decision-making precision for minority class instances, raising concerns about identifying such instances accurately. To overcome this issue, this research proposes a hybrid technique that combines the Adaptive Synthetic Sampling (ADASYN) approach with a batch generator named the HADAB technique. ADASYN generates synthetic samples for the minority class, balancing the dataset and improving prediction accuracy. Conversely, the batch generator feeds data to the model in batches, enhancing training efficiency. The Multi-Layer Perceptron (MLP) serves as the base classifier in this study. The proposed HADAB technique significantly improves prediction accuracy and training efficiency without requiring additional parameter tuning, algorithm modification, or increasing complexity. We validate the performance of HADAB using publicly available NASA datasets encompassing diverse types. The results demonstrate the superiority of HADAB over traditional prediction accuracy methods. In conclusion, the proposed HADAB technique offers a practical and effective solution for handling class imbalance in software defect prediction, leading to improved prediction accuracy. © The Author(s) 2024.

关键词： Prediction models

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Brick by Brick - Findings of an of-the-Shelf Automation System Application for Building Block Component Group Disassembly 9

Brick by Brick - Findings of an of-the-Shelf Automation Syst...

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9th International Conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Briechle, Dominique Geger, Tobias Schartow, Lena Rausch, Andreas Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld Germany

ISBN: (纸本)9798331509262

Automation is one of the key drivers in today's global economy. It ensures the conduction of a manifold of standardized processes which helps tackle the decreasing amount of skilled workers in certain areas as well as reducing cost. Although automation is broadly used nowadays, the overall applicability of automated processes is often limited to use cases with predefined process steps of minor flexibility. However, new and upcoming sustainable industries that are focusing on product life-cycle prolonging (e.g. repair, refurbish, and remanufacture (3Rs)) options to enable Circular Economy-friendly treatment, require a high amount of adaptability in automated systems to tackle the high variability of product compositions in disassembly scenarios. The goal of the conducted case study is therefore to investigate the possibilities of a nowadays available of-the-shelf automation system regarding its functions and mechanisms towards enabling automated adaptability by using disassembly scenarios of component groups consisting of different building blocks as an example scenario and the concluding future research areas. The paper, therefore, investigates the different methods offered by the default system and applies them to various levels of disassembly scenarios. The conducted study helps to clarify the capabilities of the system's functionalities in the overall goal to enhance adaptability in disassembly processes. © 2025 IEEE.

关键词： Automation Circular Economy Cobots Computer Vision Disassembly

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Flexible assembly planning for self-organizing production cells 6

Flexible assembly planning for self-organizing production ce...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Lehner, Christian Kosak, Oliver Ponsar, Hella Reif, Wolfgang Institute for Software & Systems Engineering University of Augsburg Universitätsstraße 2 Augsburg86159 Germany

In the era of Industry 4.0 and individualized mass customization, the demand for adaptable manufacturing systems is paramount. Therefore, assembly plans used in such systems must also allow a high degree of flexibility to adapt to changing requirements and conditions. At the same time, they must still guarantee the successful execution of the assembly process. In this paper, we present a novel approach to modeling assembly plans in the context of an ongoing, larger project in which we intend to implement flexible manufacturing in a real production cell. In our assembly plans, we specifically emphasize flexibility regarding the order of assembly steps, the allocation of appropriate teams of robots to execute these steps with applicable parts, and the ability to respond to failures in the system. We present a comprehensible modeling of flexible assembly plans, accompanied by the semantics of Petri nets, illustrated by example, and comparatively evaluated against other approaches. © 2024 The Authors. Published by Elsevier B.V.

关键词： Flexible manufacturing systems

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Reliable and Content-specific Support for Keyword Selection through AI and Statistics Characterising Educational Content with Large Language Models & Agreement Analyses

Reliable and Content-specific Support for Keyword Selection ...

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作者： Strube, Tom Nowak, Tom Pokotylo, Mariia Kuhlenkötter, Bernd Fraunhofer Institute for Software and Systems Engineering Dortmund Germany Ruhr University Bochum Bochum Germany

Due to the recent popularity and availability of Large Language Models (LLMs), creators of educational materials can more efficiently extract keywords for use in personalised learning recommendations than ever before. However, due to the LLMs' probabalistic nature, the automation of the otherwise labour-intense keyword extraction inherits the risk of biased and non-explainable results. In this research, we present an original framework to enhance keyword selection based on content title and description through a novel, reliability-sensitive, keyword selection algorithm. For this, we collected 38 potential keywords (together with their definitions) for five topics on dementia care from previous studies, together with two contents per topic. To assess the new method's support in extracting keywords, we then prompted 5 human experts and 3 LLMs (using Retrieval Augmented Generation (RAG) for the keyword definitions) to select keywords to include and exclude for each content. Using Krippendorf's a metric, we then were able to adapt to the present agreement, and to reliably select keyword sets for inclusion and exclusion for each content individually. Last, we compared these LLM-based keyword sets with those selected by humans to assess the impact of the adaptive keyword selection algorithm. Overall, the results suggest that LLMs generally struggle with the task (66% of extraction attempts either contained hallucinated or did not return any keywords), and topic-wise internal agreement is low (a=0.59 (0.42) for model 3 (using RAG) on average;α=0.68 for human raters). Due to this, the reliable keyword selection resulted in a median set of 6|27 keywords for inclusion|exclusion per topic, with many of those keywords being within the benchmark keyword sets selected by human raters. To conclude, this approach shows effective in adapting to different levels of agreement in extracting keywords. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Reliability analysis

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