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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Voelz, Alexander Kiesenhofer, Daniel Karagiannis, Dimitris University of Vienna Faculty of Computer Science Doctoral School Computer Science Vienna Austria University of Vienna Faculty of Computer Science Research Group Knowledge Engineering Vienna Austria

ISBN: (纸本)9798350373011

The increasing complexity of designing, deploying, and maintaining Cyber-Physical Systems (CPS), particularly those incorporating multiple interacting robots, presents significant challenges regarding programming and system integration. Existing methods and tools that aim to decrease the complexity of setting up such systems through modeling-based approaches often focus on single-robot interaction and rely on manual data entries, thus limiting their scalability and applicability to more intricate multi-robot environments. This contribution introduces the Robot ion Method (RobAM), a conceptual modeling method developed to decrease the design complexity of CPS through additional layers of abstraction. By utilizing conceptual modeling, capabilities of humanoid robots can be leveraged for the design of multi-robot interactions. Thereby, our approach enhances the modeling of complex CPS as well as the subsequent generation of code to enable efficient deployments. Through an experimental scenario, we demonstrate how RobAM simplifies the development process of CPS, paving the way for advancements regarding the integration of AI-supported technologies, such as Large Language Models, to ultimately generate contextually relevant semantics for future system design. © 2024 IEEE.

关键词： Multipurpose robots

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Digital Transformation through Conceptual Modeling: The NEMO Summer School Use Case

Digital Transformation through Conceptual Modeling: The NEMO...

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Modellierung 2024

作者： Völz, Alexander Vaidian, Iulia University of Vienna Faculty of Computer Science Research Group Knowledge Engineering Währinger Straße 29 Vienna1090 Austria OMiLAB@University of Vienna Faculty of Computer Science Research Group Knowledge Engineering Währinger Straße 29 Vienna1090 Austria

ISBN: (纸本)9783885797425

In the digital age, achieving a balance between human creative thinking and technology capabilities is crucial. Recognizing the potential of such collaborations, OMiLAB (Open Model Initiative Laboratory) developed a conceptual framework for establishing experimental innovation spaces in which skills to advance human-machine interaction can be taught and applied. The resulting Digital Innovation Environment incorporates both business and engineering perspectives, emphasizing the importance of interdisciplinary settings. Conceptual models and Digital Twins play a pivotal role within the environment, seamlessly bridging business strategies with cyber-physical systems. This paper offers a comprehensive understanding of the OMiLAB network, highlighting its alignment with the principles of a Community of Practice and emphasizing the knowledge exchange, exemplified by the NEMO Summer School Series. We present insights, best practices, and educational paradigms vital for navigating the digital transformation landscape. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Community of Practice Conceptual Modeling Digital Innovation Environment knowledge Transfer NEMO Summer School Series OMiLAB

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Drawing Order Diagrams Through Two-Dimension Extension

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Journal of Graph Algorithms and Applications 2023年第9期27卷 783-802页

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Knowledge & Data Engineering Group Research Center for Information System Design & Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order diagrams provide an important tool for understanding and thus discovering knowledge in such data. Easily readable drawings of such order diagrams are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work, we present the novel algorithm DimDraw to decompose an ordered set (e.g., a concept hierarchy) in linear orders and to produce a corresponding order diagram. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of its transitive incompatibility graph. To evaluate the quality of the algorithm, a user study was conducted where generated drawings were compared with ones from state-of-the-art drawing algorithms. © 2023 Brown University. All rights reserved.

关键词： Set theory

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How Trees and Traps Become Part of the Language Vocabulary - Postulating the Need for a New Meta-Concept for Composite Elements 17

How Trees and Traps Become Part of the Language Vocabulary -...

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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： Döller, Victoria Research Group Knowledge Engineering Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science DoCS University of Vienna Vienna Austria

This paper discusses the need for a new meta-concept termed compound type, which represents composite structures in models that cannot be captured by traditional object types. Compound types allow for the expression of overt concepts that emerge from the mere coexistence of other modeling elements. This can be, in particular, seen in the example of trees that are instantly perceived by humans as a unit. The constituting nodes and edges are subordinate to this structure. The pervasiveness and relevance of compound types are illustrated in several more examples from various modeling languages and domains, including process modeling, Petri Nets, and ER diagrams, where concepts like processes, traps or ERD constructs are essential but not directly represented in the language's metamodel. By introducing the meta-concept of compound types on the meta2level, the paper aims to extend the expressiveness and functionality of modeling languages, ensuring they can handle these complex structures. This allows for "speaking" about compound instances within the vocabulary of the language, making them available to actors not exhibiting human cognition, i.e. machines. An attempt is made to neatly integrate the new meta-concept in the meta2model and put it in context to the established meta-concepts. In the end, composite types are determined as a means of operationalizing the model. © 2024 Copyright for this paper by its authors.

关键词： Modeling languages

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Uncertainty propagation in the internet of things

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Discover Internet of Things 2024年第1期4卷 1-20页

作者： Pal, Shantanu Khalifa, Sara Miller, Dimity Dedeoglu, Volkan Dorri, Ali Ramachandran, Gowri Moghadam, Peyman Kusy, Brano Jurdak, Raja The School of Information Technology Faculty of Science Engineering and Built Environment Deakin University MelbourneVIC 3125 Australia The School of Information Systems Faculty of Science Queensland University of Technology BrisbaneQLD4000 Australia The School of Electrical Engineering and Robotics Faculty of Engineering Queensland University of Technology BrisbaneQLD4000 Australia The Distributed Sensing Systems Group Data61 CSIRO PullenvaleQLD 4069 Australia The School of Computer Science Faculty of Science Queensland University of Technology BrisbaneQLD 4000 Australia The CSIRO Robotics Data61 CSIRO PullenvaleQLD 4069 Australia

The Internet of Things (IoT) detects context through sensors capturing data from dynamic physical environments, in order to inform automation decisions within cyber physical systems (CPS). Diverse types of uncertainty in the IoT pipeline can propagate within and across nodes, involving complex interactions with security, privacy, and trust that remain largely unexplored. This paper conducts an in-depth analysis of the types of uncertainties in IoT and how they propagate within IoT nodes and networks. We consider adversarial uncertainty in the context of distributed IoT networks to capture perturbations due to malicious actors in the network that can influence IoT security, privacy and trust. We examine the propagation of adversarial uncertainty and well-known uncertainty types, namely aleatoric and epistemic uncertainty, within the five distinct stages of sensing, communication, storage, processing and decision-making in an IoT pipeline, across network layer boundaries, and across nodes within an IoT network. Using this mapping, we analyse the interactions between the uncertainty types and their propagation, and security, privacy, and trust in IoT. Based on this analysis, we discuss guidelines and considerations for mitigating uncertainty in IoT through a smart grid use case study. © The Author(s) 2024.

关键词： Differential privacy

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Towards A knowledge Graph-based Exploratory Search for Privacy engineering 8

Towards A Knowledge Graph-based Exploratory Search for Priva...

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8th International Workshop on the Visualization and Interaction for Ontologies, Linked data and knowledge Graphs, VOILA! 2023

作者： Herwanto, Guntur Budi Ekaputra, Fajar J. Piroi, Florina Sabou, Marta Faculty of Computer Science University of Vienna Austria Department of Computer Science and Electronics Universitas Gadjah Mada Indonesia Institute of Information Systems Engineering Faculty of Informatics TU Wien Austria Institute of Data Process and Knowledge Management WU Wien Austria

The concept of privacy-by-design has gained considerable attention in the wake of legal requirements that software systems should fulfill. The interconnected knowledge on privacy concepts, legal requirements, and software development artifacts required to implement this concept can be a major burden for organizations. To address this challenge, we propose a knowledge graph-based Exploratory Search system to help organizations complete privacy engineering tasks. We identify major requirements of such systems and develop an Exploratory Search prototype built on privacy engineering knowledge that integrates different information sources. While still preliminary, this work serves as a foundation for future research on integrating privacy knowledge into software development and demonstrates the potential of Exploratory Search Systems to support the cognitive process in privacy tasks such as privacy threat identification. © 2023 Copyright for this paper by its authors.

关键词： knowledge graph

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API 4

HardnessTesterV: A Web Machine Learning application for Vick...

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4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Olanipekun, Ayorinde Tayo Mashao, Daniel Data Science Across Disciplines Research Group Institute for the Future of Knowledge Faculty of Engineering and the Built Environment University of Johannesburg Auckland Park South Africa Faculty of Engineering and the Built Environment University of Johannesburg Auckland Park South Africa

ISBN: (纸本)9798350369694

This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design. © 2023 IEEE.

关键词： Machine learning

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