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International Journal of Digital Earth 2022年第1期15卷 503-526页

作者： Saeed Safikhani Stephan Keller Gerald Schweiger Johanna Pirker Institute of Interactive Systems and Data Science Graz University of TechnologyGrazAustria Institute of Software Technology Graz University of TechnologyGrazAustria

The field of architecture,engineering,and construction(AEC)is continually striving to use resources efficiently and manage complex *** more than ever,there is a strong need for digital transformation in *** improvement in the accessibility of consumer-based head-mounted displays(HMD)is attracting different entertainment and research fields to immersive virtual reality(VR)*** Information Modeling(BIM)is known as a promising technology in *** full potential of BIM is not yet employed to empower this field,however,and this could be a result of some barriers still to be surmounted by BIM in both technological and management *** of these barriers is the communication and collaboration between design,construction,operation,and maintenance *** can fill this gap by providing additional capabilities for BIM which either were not available before or were not possible to employ in practical *** this paper,we systematically review recent research around the application of VR in BIM and discuss the results using the PRISMA *** discuss the most commonly used technologies,software,and evaluation methods and the various applications of VR in the reviewed ***,we extend the discussion by summarizing the potential future work in this area.

关键词： Building information modeling architecture engineering construction virtual reality BIM VR

Concentrating on the Impact: Consequence-based Explanations in Recommender systems 10

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Concentrating on the Impact: Consequence-based Explanations ...

10th Joint Workshop on Interfaces and Human Decision Making for Recommender systems, IntRS 2023

作者： Lubos, Sebastian Tran, Thi Ngoc Trang Erdeniz, Seda Polat Mansi, Merfat El Felfernig, Alexander Wundara, Manfred Leitner, Gerhard Institute of Software Technology Graz University of Technology Inffeldgasse 16b Graz8010 Austria Magistrat Villach Rathausplatz 1 Villach9500 Austria Interactive Systems Group University of Klagenfurt Universitätsstrasse 65-67 Klagenfurt am Wörthersee9020 Austria

Recommender systems assist users in decision-making, where the presentation of recommended items and their explanations are critical factors for enhancing the overall user experience. Although various methods for generating explanations have been proposed, there is still room for improvement, particularly for users who lack expertise in a specific item domain. In this study, we introduce the novel concept of consequence-based explanations, a type of explanation that emphasizes the individual impact of consuming a recommended item on the user, which makes the effect of following recommendations clearer. We conducted an online user study to examine our assumption about the appreciation of consequence-based explanations and their impacts on different explanation aims in recommender systems. Our findings highlight the importance of consequence-based explanations, which were well-received by users and effectively improved user satisfaction in recommender systems. These results provide valuable insights for designing engaging explanations that can enhance the overall user experience in decision-making. © 2023 Copyright for this paper by its authors.

关键词： Decision making

A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature 23

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A Data-driven Analysis of a Cloud Data Center: Statistical C...

Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing

作者： Shashikant Ilager Adel N Toosi Mayank Raj Jha Ivona Brandic Rajkumar Buyya Institute of Information Systems Engineering Vienna University of Technology Vienna Austria Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne Victoria Australia Università degli Studi dell'Aquila L'Aquila Itlay Vienna University of Technology Vienna Austria The University of Melbourne Melbourne Australia

ISBN: (纸本)9798400702341

To efficiently manage large-scale cloud data centers, it is critical to understand data centers' workload, energy, and thermal characteristics and their impact on the system through data-driven analysis. However, most publicly available traces solely focuses on application workloads ignoring energy and thermal aspects, forcing existing studies to rely on inaccurate and unrealistic analytical or simulation models. In this paper, we present a comprehensive data-driven analysis of a production cloud data center. We monitor and collect the physical machine-level metrics such as resource utilization, energy, and temperature for up to nine months, with a system size of over 26000 CPU cores hosting, on average 1300 virtual machines. We perform a systematic statistical analysis to characterize the monitored data, and study their distributions, variations, trends, and inter-dependencies. We also develop data-driven models to predict resource usage and energy consumption of a physical machine, and demonstrate the usefulness of our dataset through this use case. Our study reveals interesting insights into the energy and thermal phenomena of a data center. The outcome of this analysis helps to increase infrastructure efficiency and long-term strategic planning and improve key business KPIs. The ope-sourced dataset and artefacts enables to investigate new optimization approaches and use cases by researchers.

关键词： data centers workload characterization energy and thermal analysis workload and energy forecasting

Touching the Moon: Leveraging Passive Haptics, Embodiment and Presence for Operational Assessments in Virtual Reality

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arXiv 2024年

作者： Dufresne, Florian Nilsson, Tommy Gorisse, Geoffrey Guerra, Enrico Zenner, André Christmann, Olivier Bensch, Leonie Callus, Nikolai Anton Cowley, Aidan Arts et Métiers Institute of Technology Changé France European Space Agency Cologne Germany University Duisburg-Essen Duisburg Germany Saarland University DFKI Saarbrücken Germany Institute for Software Technology Software for Space Systems and Interactive Visualization Cologne Germany

Space agencies are in the process of drawing up carefully thought-out Concepts of Operations (ConOps) for future human missions on the Moon. These are typically assessed and validated through costly and logistically demanding analogue field studies. While interactive simulations in Virtual Reality (VR) offer a comparatively cost-effective alternative, they have faced criticism for lacking the fidelity of real-world deployments. This paper explores the applicability of passive haptic interfaces in bridging the gap between simulated and real-world ConOps assessments. Leveraging passive haptic props (equipment mockup and astronaut gloves), we virtually recreated the Apollo 12 mission procedure and assessed it with experienced astronauts and other space experts. Quantitative and qualitative findings indicate that haptics increased presence and embodiment, thus improving perceived simulation fidelity and validity of user reflections. We conclude by discussing the potential role of passive haptic modalities in facilitating early-stage ConOps assessments for human endeavours on the Moon and *** Codes 93B51, 97M50 Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual reality

DiLaAg - digitalisation and innovation laboratory in agricultural sciences 10

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DiLaAg - digitalisation and innovation laboratory in agricul...

10th European Conference on Precision Livestock Farming, ECPLF 2022

作者： Pichlbauer, B. Chapa-Gonzalez, J.-M. Purcell, W. Mallinger, K. Koppensteiner, L. Kitzler, F. Pejakovic, V. Medel-Jimenez, F.J. Krexner, T. Iwersen, M. Neubauer, T. Neugschwandtner, R. Bauer, A. Kral, I. Motsch, V. Korth, F. Drillich, M. Rauber, A. Tjoa, A.M. Kaul, H.-P. Gronauer, A. Clinical Unit for Herd Health Management in Ruminants University Clinic for Ruminants Department for Farm Animals and Veterinary Public Health University of Veterinary Medicine Vienna Austria Information and Software Engineering Group Institute of Information Systems Engineering University of Technology Vienna Austria Institute of Agronomy Department of Crop Sciences University of Natural Resources and Life Sciences Vienna Austria Institute of Agricultural Engineering Department of Sustainable Agricultural Systems University of Natural Resources and Life Sciences Vienna Austria

ISBN: (纸本)9788396536006

Sustainable production and development in livestock sectors are critical to meeting the growing requirements placed on it, increased production, efficiency and reduced labour, along with the various political declarations and societal claims. Digitalisation is increasingly seen as a prerequisite to achieving the goal of sustainable agriculture and reduced labour. Therefore, it is the aim of the DiLaAg project to bring together scientists from different fields of expertise to work interdisciplinary on the complex topic of digitalisation in agriculture. In the DiLaAg consortium, PhD students and their supervisors from the University of Natural Resources and Life Sciences, vienna (BOKU), the University of technology vienna (TU) and the University of Veterinary Medicine vienna (VETMED) build the scientific core. The project was initiated and is funded by the Federal State of Lower Austria and the private foundation ‘Forum Morgen’. The PhD students work in the fields of robotics, image-based plant classification, agronomic analyses of crops, impact of technologies, digital twinning, livestock management on pasture and sustainability assessment as well as on interdisciplinary topics such as computer vision, application of digital sensor technologies, machine learning, complexity science, remote sensing, data integration, precision agriculture and precision livestock farming. Apart from this scientific nucleus, the DiLaAg consortium aims to act as a platform for innovation and exchange, by continuously expanding its network and collaborations in order to continue working on current challenges in agriculture. © ECPLF 2022. All rights reserved.

关键词： Sustainable development

Intersectional inequalities in social networks

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arXiv 2024年

作者： Martin-Gutierrez, Samuel Cartier van Dissel, Mauritz N. Karimi, Fariba Network Inequality Group Complexity Science Hub Josefstaedter Str. 39 Vienna1080 Austria Institute of Interactive Systems and Data Science Graz University of Technology Sandgasse 36 Graz8010 Austria

Social networks are shaped by complex, intersecting identities that drive our connection preferences. These preferences weave networks where certain groups hold privileged positions, while others become marginalized. While previous research has examined the impact of single-dimensional identities on inequalities of social capital, social disparities accumulate nonlinearly, further harming individuals at the intersection of multiple disadvantaged groups. However, how multidimensional connection preferences affect network dynamics and in what forms they amplify or attenuate inequalities remains unclear. In this work, we systematically analyze the impact of multidimensionality on social capital inequalities through the lens of intersectionality. To this end, we operationalize several notions of intersectional inequality in networks. Using a network model, we reveal how attribute correlation (or consolidation) combined with biased multidimensional preferences lead to the emergence of counterintuitive patterns of inequality that are unobservable in one-dimensional systems. We calibrate the model with real-world high school friendship data and derive analytical closed-form expressions for the predicted inequalities, finding that the model’s predictions match the observed data with remarkable accuracy. These findings hold significant implications for addressing social disparities and inform strategies for creating more equitable networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

Federated Learning Solution Blueprints for Use Cases Surveyed in Austrian Industries

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Federated Learning Solution Blueprints for Use Cases Surveye...

IEEE Conference on Commerce and Enterprise Computing, CEC

作者： Thomas Blumauer-Hiessl Angela Fessl Gert Breitfuss Daniel Schall Stefan Schulte TU Wien and Siemens Technology Vienna Austria Know-Center GmbH Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria Know-Center GmbH Graz Austria Siemens Technology Vienna Austria Christian Doppler Laboratory for Blockchain Technologies for the Internet of Things Institute for Data Engineering TU Hamburg Hamburg Germany

ISBN: (数字)9798331529093

ISBN: (纸本)9798331529109

Federated Learning (FL) holds immense potential for transforming the industrial landscape by leveraging distributed data to solve Artificial Intelligence (AI) use cases with collectively trained models in a privacy-preserving way. In this regard, Industrial FL (IFL) arose as a collaborative approach for training AI models between multiple industry partners and devices without the need to share the actual training data. However, despite its promising prospects, the transition and successful implementation of FL in practice is currently lagging behind, posing challenges for industrial companies. To address this, it is of crucial relevance to analyze different business types and involved stakeholders to be able to design FL-based solutions tailored to the industries needs. This paper presents the results of 13 semi-structured interviews conducted in Austrian industries, involving 11 companies from different domains. We identify AI applications, pain points, and attitudes towards AI and FL. Based on the interviews, three industry personas are derived, namely, service business, production optimization, and complex product and project business. To address the needs of these personas, three collaborative FL solution blueprints are proposed. The blueprints include system architectures, implementation steps, and collaboration modes for the involved parties. The blueprints are discussed based on dimensions such as FL paradigm, collaboration mode, key benefits, main addressed needs, and challenges.

关键词： Industries Pain Collaboration systems architecture Production Companies Artificial intelligence Interviews Optimization Business

The hidden architecture of connections: How do multidimensional identities shape our social networks?

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arXiv 2024年

Our multidimensional identities determine how we interact with each other, shaping social networks through group-based connection preferences. While interactions along single dimensions have been extensively studied, the dynamics driving multidimensional connection preferences remain largely unexplored. In this work, we develop a network model of multidimensional social interactions to tackle two crucial questions: What is the structure of our latent connection preferences, and how do we integrate information from our multidimensional identities to connect with others? To answer these questions, we systematically model different latent preference structures and preference aggregation mechanisms. Then, we compare them using Bayesian model selection by fitting empirical data from high school friendship networks. We find that a simple latent preference model consistently outperforms more complex alternatives. The calibrated model provides robust measures of latent connection preferences in real-world networks, bringing insights into how one- and multidimensional groups interact. Finally, we develop natural operationalizations of dimension salience, revealing which aspects of identity are most relevant for individuals when forming connections. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

The dynamics of diversity on corporate boards

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arXiv 2024年

作者： Raddant, Matthias Karimi, Fariba Graz University of Technology Institute of Software Technology Inffeldgasse 16b/II Graz8010 Austria University for Continuing Education Krems Department for Knowledge and Communication Management Dr.-Karl-Dorrek-Straße 30 Krems3500 Austria Graz University of Technology Institute of Interactive Systems and Data Science Sandgasse 36/III Graz8010 Austria Complexity Science Hub Vienna Josefstädter Straße 39 Vienna1080 Austria

Diversity in leadership positions, including corporate boards, is an important aspect of equality. It is important because it is the key to better decision-making and innovation, and above all, it paves the way for future generations to participate and shape our society. Many studies emphasize the importance of the visibility of role models and the effect that connectivity has on the success of minorities in leadership. However, the connectivity of firms, the dynamics of the adoption of minorities into leadership positions, and the long-term effects in terms of group dynamics and visibility are not well understood. Here, we present a model that shows how these effects work together in a dynamic model that is calibrated with empirical data of firm and board networks. We show that homophily - the appointment of minorities is influenced by the presence of minorities in a board and its neighboring entities - is an important effect shaping the trajectory towards equality. We further show how perception biases and feedback related to the visibility of minority members influence the dynamic. We find that reaching equality can be sped up or slowed down depending on the distribution of minorities in central firms. These insights bear significant implications for policy-making geared towards fostering equality and diversity within corporate boards. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

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Federated Learning for Predictive Maintenance and Quality In...

Prognostics and System Health Management Conference (PHM)

作者： Viktorija Pruckovskaja Axel Weissenfeld Clemens Heistracher Anita Graser Julia Kafka Peter Leputsch Daniel Schall Jana Kemnitz Technische Universität Wien Vienna Austria Data Science & Artificial Intelligence AIT Austrian Institute of Technology Vienna Austria Innovation Software Stiwa Automation Attnang-Puchheim Austria Distributed AI Systems Research Siemens Technology Vienna Austria

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting.

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