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2017 ISWC Posters and Demonstrations and Industry Tracks, ISWC-P and D-Industry 2017

作者： Hilal, Median Schuetz, Christoph G. Schre, Michael Department of Business Informatics - Data and Knowledge Engineering Johannes Kepler University Linz Austria

Exploiting Resource Description Framework (RDF) data for Online Analytical Processing (OLAP), especially Linked Open data (LOD), could allow analysts to obtain interesting insights. To conduct OLAP analysis over RDF data, analysts should know the specific semantics, structure, and querying mechanisms of such data. Furthermore, these data should ideally adhere to a multidimensional structure to be accessible to OLAP. In this demo paper, we present an OLAP endpoint that allows casual analysts to perform self-service OLAP analysis over RDF datasets. Specifically, analysts can instantiate semantic web analysis graphs, which are predefined models of the analysis processes. Semantic web analysis graphs are built on top of multidimensional structures that can be superimposed over arbitrary RDF datasets.

关键词： Resource Description Framework (RDF)

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Towards contextualized rule repositories for the semantic web

Towards contextualized rule repositories for the semantic we...

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2017 Joint Web Stream Processing Workshop and the 2nd International Workshop on Ontology Modularity, Contextuality, and Evolution, WSP+WOMoCoE 2017

作者： Burgstaller, Felix Schuetz, Christoph G. Neumayr, Bernd Steiner, Dieter Schrefl, Michael Department for Business Informatics - Data and Knowledge Engineering Johannes Kepler University Linz Austria

Central to the semantic web are ontologies: shared conceptualizations of domains of interest expressed in an ontology language such as OWL. Rule languages complement ontology languages. For large heterogeneous bodies of knowledge on the semantic web, contextualized knowledge repositories facilitate the organization of ontological concepts. In this paper we propose a similar mechanism - contextualized rule repositories - for organizing and executing large sets of context-specific rules. We investigate feasibility of a SPARQL Inferencing Notation (SPIN) based implementation using a real-world use case from the aeronautical domain. Furthermore, we compare SPIN-based contextualized rule repositories to a context-unaware SPIN implementation.

关键词： Ontology

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PRE-ACT: Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification

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Journal of Cancer Policy 2025年 43卷

作者： Yuqin Liang Paolo Bajardi Guido Bologna Francesco Bonchi Gabriella Cortellessa Andre Dekker Francesca Fracasso Manuela Joore Nikos Paragios Tim Rattay Sofia Rivera Cheryl Roumen Johan van Soest Alberto Traverso Karolien Verhoeven Iordanis Koutsopoulos Christopher J. Talbot Department of Radiation Oncology (Maastro) GROW School for Oncology and Reproduction Maastricht University Medical Centre Maastricht Netherlands CENTAI Institute Torino Italy University of Applied Sciences and Arts of Western Switzerland HES-SO Geneva Switzerland Consiglio Nazionale delle Ricerche (CNR) ISTC Roma Italy Maastricht University Maastricht Netherlands Therapanacea Paris France Leicester Cancer Research Centre University of Leicester Leicester United Kingdom UNICANCER Paris France Institut Gustave Roussy Paris France Medical Data Works Maastricht Netherlands Brightlands Institute for Smart Society (BISS) Faculty of Science and Engineering Maastricht University Heerlen the Netherlands Athens University of Economics and Business Department of Informatics Athens Greece Department of Genetics Genomics & Cancer Sciences University of Leicester Leicester United Kingdom

PRE-ACT leverages Artificial Intelligence (AI) to predict radiotherapy side effects in breast cancer patients, integrating complex imaging and genomics data. This Horizon Europe project aims to enhance shared decision-making by providing explainable predictions and tailored interventions, advancing personalised radiotherapy and improving patient outcomes.

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Innovative informatics methods for process mining in health care

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Journal of Biomedical informatics 2020年 109卷 103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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DeepRuby: Extending ruby with dual deep instantiation

DeepRuby: Extending ruby with dual deep instantiation

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2017 MODELS Satellite Event: Workshops (ModComp, ME, EXE, COMMitMDE, MRT, MULTI, GEMOC, MoDeVVa, MDETools, FlexMDE, MDEbug), Posters, Doctoral Symposium, Educator Symposium, ACM Student Research Competition, and Tools and Demonstrations, MODEL-SSE 2017

作者： Neumayr, Bernd Schuetz, Christoph G. Horner, Christian Schrefl, Michael Department of Business Informatics – Data Knowledge Engineering Johannes Kepler University Linz Linz Austria

Clabjects, the central construct of multi-level modeling, overcome the strict separation of class and object in conceptual modeling. Ruby, a dynamic object-oriented programming language, similarly treats classes as objects and thus appears as a natural candidate for implementing clabject-based modeling constructs. In this paper we introduce DeepRuby, a Ruby implementation of the core constructs of Dual Deep Instantiation: clabject hierarchies and attributes with separate source potency and target potency. DeepRuby represents clabjects at two layers: the clabject layer and the clabject facet layer. At the clabject facet layer, a clabject with maximum source potency i-1 and maximum target potency j-1 is represented by a matrix of i × j clabject facets organized using Ruby’s superclass and eigenclass constructs. Clabject facets can easily be extended with behavior implemented in custom methods.

关键词： Object oriented programming

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An Integrated AI-Enabled System Using One Class Twin Cross Learning (OCT-X) for Early Gastric Cancer Detection

arXiv

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arXiv 2025年

作者： Liu, Xian-Xian Wei, Yuanyuan Xu, Mingkun Guo, Yongze Zhang, Hongwei Dong, Huicong Song, Qun Zhao, Qi Luo, Wei Tien, Feng Gao, Juntao Fong, Simon Department of Computer and Information Science University of Macau 999078 China Department of Biomedical Engineering The Chinese University of Hong Kong Shatin999077 Hong Kong Department of Neurology David Geffen School of Medicine University of California Los AngelesCA90095 United States Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai519031 China Department of Precision Instrument Tsinghua University Beijing100084 China Department of Gastroenterology Affiliated Hospital of Hebei University of Engineering Handan China Institute of Artificial Intelligence Chongqing Technology and Business University CO Chongqing400067 China Cancer Centre Institute of Translational Medicine Faculty of Health Sciences University of Macau Taipa519000 China MoE Frontiers Science Center for Precision Oncology University of Macau Taipa519000 China Institute of Clinical Medicine The First People’s Hospital of Foshan Guangzhou1528000 China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Hebei Province Handan056038 China Tsinghua University Beijing China

Early detection of gastric cancer, a leading cause of cancer-related mortality worldwide, remains hampered by the limitations of current diagnostic technologies, leading to high rates of misdiagnosis and missed diagnoses. To address these challenges, we propose an integrated system that synergizes advanced hardware and software technologies to balance speed-accuracy. Our study introduces the One Class Twin Cross Learning (OCT-X) algorithm. Leveraging a novel fast double-threshold grid search strategy (FDT-GS) and a patch-based deep fully convolutional network, OCT-X maximizes diagnostic accuracy through real-time data processing and seamless lesion surveillance. The hardware component includes an all-in-one point-of-care testing (POCT) device with high-resolution imaging sensors, real-time data processing, and wireless connectivity, facilitated by the NI CompactDAQ and LabVIEW software. Our integrated system achieved an unprecedented diagnostic accuracy of 99.70%, significantly outperforming existing models by up to 4.47%, and demonstrated a 10% improvement in multirate adaptability. These findings underscore the potential of OCT-X as well as the integrated system in clinical diagnostics, offering a path toward more accurate, efficient, and less invasive early gastric cancer detection. Future research will explore broader applications, further advancing oncological diagnostics. Code is available at https://***/ liu37972/*** © 2025, CC BY-NC-ND.

关键词： Computer hardware description languages

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Taking the pulse of COVID-19:a spatiotemporal perspective

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International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： Chaowei Yang Dexuan Sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding NSF Spatiotemporal Innovation Center George Mason Univ.FairfaxVAUSA Department of Geography and GeoInformation Science George Mason Univ.FairfaxVAUSA Department of Geographical Sciences University of MarylandCollege ParkMDUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of Geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

关键词： Big data Earth system emergency geospatial sciences epidemics applications

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Author Correction: General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

npj Women's Health

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npj Women's Health 2024年第1期2卷 1页

作者： Jamison H. Burks Patrick Kasl Benjamin L. Smarr Lauryn Keeler Bruce Severine Soltani Varun Viswanath Wendy Hartogensis Frederick M. Hecht Ashley E. Mason Stephan Dilchert Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Shu Chien-Gene Lay Department of Bioengineering University of California San Diego San Diego CA USA Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA UC San Diego Health Department of Biomedical Informatics University of California San Diego San Diego CA USA Bioinformatics and Systems Biology University of California San Diego San Diego CA USA Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA Osher Center for Integrative Health University of California San Francisco San Francisco CA USA Department of Management Zicklin School of Business Baruch College The City University of New York New York NY USA San Diego Supercomputer Center University of California San Diego San Diego CA USA

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General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

npj Women's Health

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npj Women's Health 2024年第1期2卷 1-11页

In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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