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A Tool for Automated Generation of Domain Specific Symbolic Models From Texts

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A Tool for Automated Generation of Domain Specific Symbolic ...

IEEE Annual Conference on Pervasive computing and Communications Workshops (PerCom)

作者： Teodor Stoev Tomasz Sosnowski Kristina Yordanova Institute for Data Science University of Greifswald Greifswald Germany Institute for Visual and Analytic Computing University of Rostock Rostock Germany

Recent studies in the area of pervasive computing have shown that it is possible to create systems which combine symbolic models incorporating domain knowledge and sensor observations in order to perform tasks such as activity recog-nition in health monitoring use-cases. Creating symbolic models manually can, however, be a very time consuming endeavour and in some cases it also requires experts in the domain of interest. In this work, we showcase a tool for automatic generation of symbolic models from texts written in natural language. The output of our tool is a model description in Planning Domain Definition Language (PDDL). We concentrate on instructional texts in the cooking domain (cooking recipes), but our tool can be also applied in other areas of interest.

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eDEM-CONNECT: An Ontology-Based Chatbot for Family Caregivers of People with Dementia 12th

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eDEM-CONNECT: An Ontology-Based Chatbot for Family Caregive...

12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Boiting, Maurice Tschorn, Niklas Suravee, Sumaiya Yordanova, Kristina Halek, Margareta Jagoda, Franziska A. Lüdtke, Stefan Burmann, Anja Fraunhofer Institute for Software and Systems Engineering ISST Dortmund44147 Germany Institute for Data Science Universität Greifswald Greifswald17489 Germany School of Nursing Science Faculty of Health Universiät Witten/Herdecke Witten58453 Germany Institute for Visual and Analytic Computing Universät Rostock Rostock18059 Germany

ISBN: (纸本)9783031606649

Home care of people with dementia (PwD) is mainly organized and carried out by non-professional family caregivers, who struggle to interpret the needs of PwD correctly and are confronted with the challenging behavior of their relatives. Although support services for family caregivers are widespread in Germany, they are rarely used due to the fact that information is poorly organized and relatives are faced with a flood of disorganized, outdated, and confusing content. Due to the technical development of chatbot technologies (ChatGPT), chatbots gain more and more relevance. Based on the new technological possibilities, we developed an online communication and service platform with an integrated chatbot within the eDEM-CONNECT project, with the aim of making structured and easily understandable information accessible for family caregivers. This work focuses on the development of a chatbot pipeline that has broad domain knowledge through a provided ontology on the topic of agitation of PwD. This allows the chatbot to provide relevant and peer-reviewed information to family members. In our approach, a patient history is first taken based on several diagnostic questions so that relevant information can be output in a later step. For this purpose, we demonstrate that agitations in natural language can be correctly recognized by the used BERT model and that our developed chatbot is able to select further diagnostic questions based on the predictions of a Markov logic network. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Neurodegenerative diseases

Site Choice in Recreational Fisheries: Towards an Agent-Based Approach 22

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Site Choice in Recreational Fisheries: Towards an Agent-Base...

Proceedings of the Winter Simulation Conference

作者： Kevin Haase Harry V. Strehlow Wolf-Christian Lewin Oliver Reinhardt Adelinde M. Uhrmacher Thünen Institut for Baltic Sea Fisheries Rostock GERMANY Institute for Visual and Analytic Computing University of Rostock Rostock GERMANY

The site choice decisions of recreational fishers (anglers) have important implications for fish stocks, fisheries management, and coastal economies by influencing catch rates and determining where economic values are created. However, the mechanisms of site choice are poorly understood. In this paper, we make the first steps toward applying agent-based modeling to improve the understanding of site choice decisions. Using an exploratory approach, we identified travel distance dependent on angler origin as a key element in site choice to rebuild travel patterns and distances. The 5-year average catches had a subdominant role for the travel patterns but could recreate the angler's distribution among the fishing locations realistically. Utility functions combined both factors, but further model development and more realistic angler agents and catch rates are required to understand anglers' site choices in more detail.

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Advanced Tutorial: Parallel and Distributed Methods for Scalable Discrete Simulation 22

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Advanced Tutorial: Parallel and Distributed Methods for Scal...

Proceedings of the Winter Simulation Conference

作者： Philipp Andelfinger Wentong Cai Institute for Visual and Analytic Computing University of Rostock Rostock GERMANY School of Computer Science and Engineering Nanyang Technological University Singapore SINGAPORE

Discrete simulation is an indispensable approach to investigate systems whose complexity prohibits analytical modeling and for which real-world experimentation is costly or dangerous. To keep pace with system models of increasing levels of detail and scope, a simulator's ability to make full use of the available hardware, typically through parallel and distributed simulation, is a vital concern. Building on well-studied synchronization algorithms, the field's focus has shifted towards aspects such as the avoidance of redundant computations in ensemble studies and the exploitation of heterogeneous hardware platforms. In this tutorial, we describe the fundamental notions of parallel and distributed simulation and summarize the main classes of synchronization algorithms as well their use when applied under the constraints of the domains of transportation and spiking neural networks. Current research directions and challenges are discussed in light of the tension between efficiency through specialization and wide applicability through generalization.

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A Generalized Network Generation Approach for Agent-Based Models

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A Generalized Network Generation Approach for Agent-Based Mo...

2021 Winter Simulation Conference, WSC 2021

作者： Hesz, Kristina Himmelspach, Jan Reinhardt, Oliver Uhrmacher, Adelinde M. NORDAKADEMIE Department of Computer Science Elmshorn25337 Germany Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

For their initialization, many agent-based models require a population which corresponds in its essential characteristics to the examined real population. It should reflect the real distribution of attributes of interest, e.g., age, gender, or income, as well as the social network between the agents. Since a disaggregated data set with all required information is rarely available, a synthetic population must be created. Methods that assign realistic attribute values to agents are well studied in the literature. In contrast, the generation of plausible social networks has been less extensively researched, but several comprehensive adhoc models have been developed. The focus of this work is to introduce a reusable, generalized approach for the generation of synthetic social networks. Symbolic regression is used to automatically train generation rules based on a network sample, instead of having to define rules a priori. Manually specified constraints are taken into account to avoid implausible relationships. © 2021 IEEE.

关键词： Autonomous agents

Multi-modal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds

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arXiv 2025年

作者： Jensen, Simon B. Oehmcke, Stefan Møgelmose, Andreas Madadi, Meysam Igel, Christian Escalera, Sergio Moeslund, Thomas B. Visual Analysis and Perception Laboratory Aalborg University Denmark Pioneer Centre for Artificial Intelligence Denmark Department of Computer Science Copenhagen University Denmark Institute for Visual & Analytic Computing Rostock University Germany University of Barcelona and Computer Vision Center Spain

Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos (12.5 cm resolution) and 3D airborne laser scanning (ALS) point clouds (8 points/m2) can enhance biodiversity assessment. We introduce the BioVista dataset, comprising 44 378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multi-modal fusion approaches for biodiversity potential classification. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving mean accuracies of 69.4% and 72.8%, respectively. We explore two fusion approaches: a confidence-based ensemble method and a feature-level concatenation strategy, with the latter achieving a mean accuracy of 75.5%. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in forest biodiversity assessment. © 2025, CC BY.

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Exploring transfer learning for Deep NLP systems on rarely annotated languages

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arXiv 2024年

作者： Yadav, Dipendra Strauß, Tobias Yordanova, Kristina University of Rostock Department of Computer Science and Electrical Engineering Institute of Visual & Analytic Computing Mobile Multimedia Information Systems Albert-Einstein-Str. 22 Rostock18059 Germany

Natural language processing has seen exponential growth since deep learning has been used for various tasks. Deep learning has replaced the old rule-based methods by providing better performance on almost all the tasks in NLP, as deep learning basically looks into the hidden patterns and structures in the data which were sometimes not detected and at other-times overlooked by the rule-based systems. The research and development in NLP have been however focused on some handful languages of the world given their financial importance and a huge number of speakers. This has left out most of the other languages unexplored. The other reason for the lack of research in these languages is the unavailability of properly annotated and the required amount of datasets which is very important for training deep learning neural networks. The situation looks grim. When we look at the world language tree, thankfully we see that most of the unexplored languages have other well-explored languages nearby which basically means that both the languages are kind of similar. So if we could somehow adapt research and development from one language to another, it could be really helpful for both the languages. The goal of this thesis work is to explore transfer learning for the task of Part-of-speech(POS) tagging between Hindi and Nepali languages which are part of the Indo-Aryan language family with very high similarity. We have tried to explore the possibility of jointly training a model for the task of POS tagging in both Hindi and Nepali language and see if it helps in improving the performance of the model. We also try to explore if multitask learning in the Hindi language can be helpful for the task of POS tagging with auxiliary tasks of the gender tagging and singularity/plurality tagging. The deep learning architecture used for this work is BLSTM-CNN-CRF. The model is trained with monolingual word embeddings, vector mapped embeddings, and also with jointly trained Hindi-Nepali word emb

关键词： Embeddings

visual analytics for Early Detection of Retinal Diseases

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arXiv 2022年

作者： Röhlig, Martin Stachs, Oliver Schumann, Heidrun Visual and Analytic Computing University of Rostock Germany Department of Ophthalmology Rostock University Medical Center Germany

Advances in optical coherence tomography (OCT) have enabled noninvasive imaging of substructures of the human retina with high spatial resolution. OCT examinations are now a standard procedure in clinics and an integral part of ophthalmic research. The interpretation of the OCT helps ophthalmologists understand the impact of various retinal and systemic diseases on the structure of the retina in a way not previously possible. In the early stages of retinal diseases, however, the identification and analysis of small and localized substructural changes in the retina remains a challenge. We present an overview of novel visual analytics approaches for the interactive exploration of early retinal changes in single and multiple patients, the comparison of the changes with normative data, and automated quantification and measurement of diagnosis-relevant information. We developed these approaches in close collaboration with ophthalmology researchers and industry experts from a leading OCT device manufacturer. As a result, they not only significantly reduced the time and effort required for OCT data analysis, especially in the context of cross-sectional studies, but have also led to several new discoveries published in biomedical journals. © 2022, CC BY.

关键词： visualization

Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs

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arXiv 2022年

作者： Felske, Timon Lüdtke, Stefan Bader, Sebastian Kirste, Thomas Institute of Visual & Analytic Computing University of Rostock Germany Institute for Enterprise Systems University of Mannheim Germany

We study sensor-based human activity recognition in manual work processes like assembly tasks. In such processes, the system states often have a rich structure, involving object properties and relations. Thus, estimating the hidden system state from sensor observations by recursive Bayesian filtering can be very challenging, due to the combinatorial explosion in the number of system states. To alleviate this problem, we propose an efficient Bayesian filtering model for such processes. In our approach, system states are represented by multi-hypergraphs, and the system dynamics is modeled by graph rewriting rules. We show a preliminary concept that allows to represent distributions over multi-hypergraphs more compactly than by full enumeration, and present an inference algorithm that works directly on this compact representation. We demonstrate the applicability of the algorithm on a real dataset. © 2022, CC BY.

关键词： Graph theory