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  • 1.
    Abghari, S.
    et al.
    Department of Computer Science, Blekinge Institute of Technology, Sweden.
    Boeva, V.
    Department of Computer Science, Blekinge Institute of Technology, Sweden.
    Brage, J.
    Noda Intelligent Systems Ab, Sweden.
    Johansson, C.
    Noda Intelligent Systems Ab, Sweden.
    Grahn, H.
    Department of Computer Science, Blekinge Institute of Technology, Sweden.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Higher order mining for monitoring district heating substations2019In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 382-391, article id 8964173Conference paper (Refereed)
    Abstract [en]

    We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. 

  • 2.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Gustafsson, Jörgen
    Ericsson AB.
    Shaikh, Junaid
    Ericsson AB.
    Outlier Detection for Video Session Data Using Sequential Pattern Mining2018In: ACM SIGKDD Workshop On Outlier Detection De-constructed, 2018Conference paper (Refereed)
    Abstract [en]

    The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.

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  • 3.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Ickin, Selim
    Ericsson, SWE.
    Gustafsson, Jörgen
    Ericsson, SWE.
    A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences2018In: The 17th IEEE International Conference on Machine Learning and Applications Special Session on Machine Learning Algorithms, Systems and Applications, IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

  • 4.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    García Martín, Eva
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Johansson, Christian
    NODA Intelligent Systems AB, Sweden.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Trend analysis to automatically identify heat program changes2017In: Energy Procedia, Elsevier, 2017, p. 407-415Conference paper (Refereed)
    Abstract [en]

    The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.

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  • 5.
    Abo Alsrour, Ammar
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Street-lights LED Lens Design Optimization using Machine Learning2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
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    fulltext
  • 6.
    Adlemo, Anders
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Hilletofth, Per
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Fuzzy logic based decision-support for reshoring decisions2018In: Proceedings of the 8th International Conference on Operations and Supply Chain Management, 2018Conference paper (Refereed)
  • 7.
    Adlemo, Anders
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. Jönköping University / School of Engineering.
    Tan, He
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Test case quality as perceived in Sweden2018In: Proceedings - International Conference on Software Engineering / [ed] Michael Unterkalmsteiner, ACM Digital Library, 2018, p. 9-12Conference paper (Refereed)
    Abstract [en]

    In order to reach an acceptable level of confidence in the quality of a software product, testing of the software is paramount. To obtain "good" quality software it is essential to rely on "good" test cases. To define the criteria for what make up for a "good" test case is not a trivial task. Over the past 15 years, a short list of publications have presented criteria for "good" test cases but without ranking them based on their importance. This paper presents a non-exhaustive and non-authoritative tentative list of 15 criteria and a ranking of their relative importance. A number of the criteria come from previous publications but also from discussions with our industrial partners. The ranking is based on results collected via a questionnaire that was sent out to a limited number of randomly chosen respondents in the Swedish software industry. This means that the results are more indicative than conclusive.

  • 8.
    Adlemo, Anders
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Hilletofth, Per
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Eriksson, David
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Knowledge intensive decision support for reshoring decisions2018In: Proceedings of the 30th Annual NOFOMA Conference: Relevant Logistics and Supply Chain Management Research, Kolding, 2018Conference paper (Refereed)
  • 9.
    Adlemo, Anders
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Hilletofth, Per
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Eriksson, David
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Reshoring decision support in a Swedish context2018Conference paper (Refereed)
    Abstract [en]

    This paper presents a decision-support system for reshoring decision-making based on fuzzy logic. The construction and functionality of the decision-support system are described, and the functionality is evaluated in a high cost environment exemplified through a Swedish context. Ten different reshoring scenarios, provided by Swedish reshoring experts, are entered into the decision-support system and the decision recommendations provided by the system are presented. The confidence that can be put on the recommendations is demonstrated by comparing them with those of the reshoring experts. The positive results obtained indicate that fuzzy logic is both feasible and that the quality of the results are sufficiently good for reshoring decision-making.

  • 10.
    Ahlberg, Ernst
    et al.
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Winiwarter, Susanne
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Engkvist, Ola
    External Sciences, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Hammar, Oscar
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Bendtsen, Claus
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Cambridge, UK.
    Carlsson, Lars
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Using conformal prediction to prioritize compound synthesis in drug discovery2017In: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, Machine Learning Research , 2017, p. 174-184Conference paper (Refereed)
    Abstract [en]

    The choice of how much money and resources to spend to understand certain problems is of high interest in many areas. This work illustrates how computational models can be more tightly coupled with experiments to generate decision data at lower cost without reducing the quality of the decision. Several different strategies are explored to illustrate the trade off between lowering costs and quality in decisions.

    AUC is used as a performance metric and the number of objects that can be learnt from is constrained. Some of the strategies described reach AUC values over 0.9 and outperforms strategies that are more random. The strategies that use conformal predictor p-values show varying results, although some are top performing.

    The application studied is taken from the drug discovery process. In the early stages of this process compounds, that potentially could become marketed drugs, are being routinely tested in experimental assays to understand the distribution and interactions in humans.

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  • 11.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Modelling Network-based Defence: Success and Failure of an Enterprise Modelling Endeavour2010In: The Practice of Enterprise Modeling: Third IFIP WG 8.1 Working Conference / [ed] Patrick Van Bommel, Stijn Hoppenbrouwers, Sietse Overbeek, Erik Proper, Joseph Barjis, Berlin: Springer , 2010, p. 121-129Conference paper (Refereed)
    Abstract [en]

    Research projects have an inherent risk of failure, and learning howto cope with the risk is an important task for everyone involved. In order to doso it is necessary to share the knowledge of the experiences done during andafter the project. This paper investigates a recently completed enterprisemodeling research project and contributes with lessons learned andrecommendations for future enterprise modeling projects.

  • 12.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    The Practice of Competence Modelling2010In: The Practice of Enterprise Modeling: Third IFIP WG 8.1 Working Conference / [ed] Patrick Van Bommel, Stijn Hoppenbrouwers, Sietse Overbeek, Erik Proper, Joseph Barjis, Berlin: Springer , 2010, p. 106-120Conference paper (Refereed)
    Abstract [en]

    A clear understanding of the organizational competences of anenterprise and the underlying individual competences and the competencedevelopment needs has become more and more important for many industrialareas as a foundation for competence supply processes and adjustment tochanging market conditions. Competence modelling, i.e. the use of enterprisemodelling techniques for capturing existing and describing desiredorganisational and individual competences in enterprises, offers importantcontributions to this. In the last years, the authors of the paper have performed anumber of competence modelling cases, which revealed different characteristicsand resulted in lessons learned. This paper presents an examination of differentcharacteristics of competence modelling cases, and recommendations andlessons learned from these cases for the practice of competence modelling.

  • 13.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Towards Competence Modeling and Competence Matching for Network-based Defense2007In: Proc. The 3rd International Conference on Military Technology, MilTech3, June 2007, Stockholm, 2007Conference paper (Other academic)
  • 14.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Towards Competence Modeling and Competence Matching for Network-Based Defense2008In: Stockholm contributions in Military-Technology 2007 / [ed] M. Norsell, Stockholm: Swedish National Defence College , 2008, p. 9-22Chapter in book (Other academic)
  • 15.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Competence demand modeling: case study2009Report (Other academic)
  • 16.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Competence modeling and matching: frame concept2009Report (Other academic)
  • 17.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Modellering av 312. luftburna kompaniet: KOMO - Projektrapport2009Report (Other academic)
  • 18.
    Ali Fareedi, Abid
    et al.
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Modelling of the Ward Round Process in a Healthcare Unit2011In: The Practice of Enterprise Modeling: 4th IFIP WG 8.1 Working Conference, PoEM 2011 Oslo, Norway, November 2-3, 2011 Proceedings / [ed] Paul Johannesson, John Krogstie and Andreas L. Opdahl, Springer Berlin/Heidelberg, 2011, Vol. 92, p. 223-237Conference paper (Refereed)
    Abstract [en]

    Information systems (IS) are nowadays extensively used to support all kinds of activities in healthcare organisations. Enterprise modelling can help to make the use of IS in healthcare more effective by providing process and domain models reflecting a particular healthcare unit. This paper proposes a model of the ward round process in a healthcare unit. The proposed model identifies the roles of medical professionals, tasks that can be performed according to the personnel’s competences, and activities that are carried out as part of the tasks to achieve goals of the ward round process. A formal approach has been used to implement the modelling results in the form of an ontology. Such formal ontologies can support improvement and development of IS in healthcare. We learned that modelling workshops are important for development of models that can be formalized in a machine-readable form.

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    Publisher's PDF
  • 19.
    Alirezaie, Marjan
    et al.
    Örebro University.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Blomqvist, Eva
    SICS - East Swedish ICT.
    SmartEnv as a Network of Ontology Patterns2018In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 9, no 6, p. 903-918Article in journal (Refereed)
    Abstract [en]

    In this article we outline the details of an ontology, called SmartEnv, proposed as a representational model to assist the development process of smart (i.e., sensorized) environments. The SmartEnv ontology is described in terms of its modules representing different aspects including physical and conceptual aspects of a smart environment. We propose the use of the Ontology Design Pattern (ODP) paradigm in order to modularize our proposed solution, while at the same time avoiding strong dependencies between the modules in order to manage the representational complexity of the ontology. The ODP paradigm and related methodologies enable incremental construction of ontologies by first creating and then linking small modules. Most modules (patterns) of the SmartEnv ontology are inspired by, and aligned with, the Semantic Sensor Network (SSN) ontology, however with extra interlinks to provide further precision and cover more representational aspects. The result is a network of 8 ontology patterns together forming a generic representation for a smart environment. The patterns have been submitted to the ODP portal and are available on-line at stable URIs.

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    fulltext
  • 20.
    Alirezaie, Marjan
    et al.
    Örebro Universitet.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). RISE SICS East AB, Linköping, Sweden.
    Blomqvist, Eva
    Linköpings Universitet.
    Nyström, Mikael
    Linköpings Universitet.
    Ivanova, Valentina
    Linköpings Universitet.
    SmartEnv Ontology in E-care@home2018In: SSN 2018 - Semantic Sensor Networks Workshop: Proceedings of the 9th International Semantic Sensor Networks Workshopco-located with 17th International Semantic Web Conference (ISWC 2018) / [ed] Maxime Lefrançois, Raúl Garcia Castro, Amélie Gyrard, Kerry Taylor, CEUR-WS , 2018, Vol. 2213, p. 72-79Conference paper (Refereed)
    Abstract [en]

    In this position paper we briefly introduce SmartEnv ontology which relies on SEmantic Sensor Network (SSN) ontology and is used to represent different aspects of smart and sensorized environments. We will also talk about E-carehome project aiming at providing an IoT-based health-care system for elderly people at their homes. Furthermore, we refer to the role of SmartEnv in Ecarehome and how it needs to be further extended to achieve semantic interoperability as one of the challenges in development of autonomous health care systems at home.

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    fulltext
  • 21.
    Alkhatib, A.
    et al.
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Boström, H.
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Ennadir, S.
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Approximating Score-based Explanation Techniques Using Conformal Regression2023In: Proceedings of Machine Learning Research / [ed] H. Papadopoulos, K. A. Nguyen, H. Boström, L. Carlsson, ML Research Press , 2023, Vol. 204, p. 450-469Conference paper (Refereed)
    Abstract [en]

    Score-based explainable machine-learning techniques are often used to understand the logic behind black-box models. However, such explanation techniques are often computationally expensive, which limits their application in time-critical contexts. Therefore, we propose and investigate the use of computationally less costly regression models for approximating the output of score-based explanation techniques, such as SHAP. Moreover, validity guarantees for the approximated values are provided by the employed inductive conformal prediction framework. We propose several non-conformity measures designed to take the difficulty of approximating the explanations into account while keeping the computational cost low. We present results from a large-scale empirical investigation, in which the approximate explanations generated by our proposed models are evaluated with respect to efficiency (interval size). The results indicate that the proposed method can significantly improve execution time compared to the fast version of SHAP, TreeSHAP. The results also suggest that the proposed method can produce tight intervals, while providing validity guarantees. Moreover, the proposed approach allows for comparing explanations of different approximation methods and selecting a method based on how informative (tight) are the predicted intervals.

  • 22. Alkhatib, Amr
    et al.
    Boström, Henrik
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Assessing Explanation Quality by Venn Prediction2022In: Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications: Volume 179: Conformal and Probabilistic Prediction with Applications, 24-26 August 2022, Brighton, UK / [ed] U. Johansson, H. Boström, K. A. Nguyen, Z. Luo & L. Carlsson, ML Research Press , 2022, Vol. 179, p. 42-54Conference paper (Refereed)
    Abstract [en]

    Rules output by explainable machine learning techniques naturally come with a degree of uncertainty, as the complex functionality of the underlying black-box model often can be difficult to approximate by a single, interpretable rule. However, the uncertainty of these approximations is not properly quantified by current explanatory techniques. The use of Venn prediction is here proposed and investigated as a means to quantify the uncertainty of the explanations and thereby also allow for competing explanation techniques to be evaluated with respect to their relative uncertainty. A number of metrics of rule explanation quality based on uncertainty are proposed and discussed, including metrics that capture the tendency of the explanations to predict the correct outcome of a black-box model on new instances, how informative (tight) the produced intervals are, and how certain a rule is when predicting one class. An empirical investigation is presented, in which explanations produced by the state-of-the-art technique Anchors are compared to explanatory rules obtained from association rule mining. The results suggest that the association rule mining approach may provide explanations with less uncertainty towards the correct label, as predicted by the black-box model, compared to Anchors. The results also show that the explanatory rules obtained through association rule mining result in tighter intervals and are closer to either one or zero compared to Anchors, i.e., they are more certain towards a specific class label.

  • 23.
    Angelova, Milena
    et al.
    Technical University of sofia, BUL.
    Vishnu Manasa, Devagiri
    Blekinge Tekniska Högskola, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datavetenskap.
    Linde, Peter
    Blekinge Tekniska Högskola, Biblioteket.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datavetenskap.
    An Expertise Recommender System based on Data from an Institutional Repository (DiVA)2019In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan & Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
    Abstract [en]

    Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

  • 24.
    Angelova, Milena
    et al.
    Technical University of Sofia-branch Plovdiv, BUL.
    Vishnu Manasa, Devagiri
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Linde, Peter
    Blekinge Tekniska Högskola, Biblioteket.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    An Expertise Recommender SystemBased on Data from an Institutional Repository (DiVA)2018In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing, 2018Conference paper (Refereed)
    Abstract [en]

    Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors inacademy.

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  • 25. Annavarjula, Vaishnavi
    et al.
    Mbiydzenyu, Gideon
    Riveiro, Maria
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Implicit user data in fashion recommendation systems2020In: Developments of artificial intelligence technologies in computation and robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020) / [ed] Zhong Li, Chunrong Yuan, Jie Lu & Etienne E. Kerre, World Scientific, 2020, p. 614-621Conference paper (Refereed)
    Abstract [en]

    Recommendation systems in fashion are used to provide recommendations to users on clothing items, matching styles, and size or fit. These recommendations are generated based on user actions such as ratings, reviews or general interaction with a seller. There is an increased adoption of implicit feedback in models aimed at providing recommendations in fashion. This paper aims to understand the nature of implicit user feedback in fashion recommendation systems by following guidelines to group user actions. Categories of user actions that characterize implicit feedback are examination, retention, reference, and annotation. Each category describes a specific set of actions a user takes. It is observed that fashion recommendations using implicit user feedback mostly rely on retention as a user action to provide recommendations.

  • 26. Aralikatte, Rahul
    et al.
    Murrieta Bello, Héctor Ricardo
    de Lhoneux, Miryam
    Hershcovich, Daniel
    Bollmann, Marcel
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Søgaard, Anders
    How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task2021In: Proceedings of the 8th Workshop on Asian Translation (WAT2021), Association for Computational Linguistics, 2021, p. 205-211Conference paper (Refereed)
    Abstract [en]

    This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization. We train and evaluate large multilingual translation models using a single GPU for a maximum of 100 hours and get within 4-5 BLEU points of the top submission on the leaderboard. We also benchmark standard baselines on the PMI corpus and re-discover well-known shortcomings of translation systems and metrics.

  • 27.
    Arvidsson, Simon
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing.
    Gabrielsson, Patrick
    Jönköping University, School of Engineering, JTH, Department of Computing.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Texture Mapping of Flags onto Polandball Characters using Convolutional Neural Nets2021In: 2021 International Joint Conference on Neural Networks (IJCNN), 2021, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Polandball comics are hand-drawn satirical content that portray personified countries in a unique style. Although certain parts of these comics, such as ball outlines, are easy to draw, some country flags are complex and require time, effort, and skill to depict correctly. Convolutional Neural Networks have shown success in image synthesis tasks but lack the ability to rescale and rotate images for texture mapping. The domain of Virtual Try-On Networks has made great progress in networks that can handle spatially invariant transforms. We show that similar methods can be used in another domain dependent on texture mapping, namely generating valid, rule-abiding Poland-ball characters given an outline and a country flag. To evaluate our method we make use of the Fréchet Inception Distance where we achieved a score of 34.9. Multiple configurations of the model were evaluated to show that all modules used in the model contribute to the achieved performance. The main contributions in this paper are: a model that can be used by Polandball artists to aid in comic creation and a dataset with over 40,000 labeled Polandball characters for computer vision tasks.

  • 28.
    Arvidsson, Simon
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Gullstrand, Marcus
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Riveiro, Maria
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Sensor fusion and convolutional neural networks for indoor occupancy prediction using multiple low-cost low-resolution heat sensor data2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 4, p. 1-21, article id 1036Article in journal (Refereed)
    Abstract [en]

    Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.

  • 29.
    Aussenac-Gilles, Nathalie
    et al.
    IRIT-CNRS Toulouse, France.
    Hahmann, TorstenUniversity of Maine, USA.Galton, AntonyUniversity of Exeter, UK.Hedblom, Maria M.Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Formal ontology in information systems: Proceedings of the 13th International Conference (FOIS 2023)2023Conference proceedings (editor) (Refereed)
    Abstract [en]

    FOIS is the flagship conference of the International Association for Ontology and its Applications, a non-profit organization which promotes interdisciplinary research and international collaboration at the intersection of philosophical ontology, linguistics, logic, cognitive science, and computer science.

    This book presents the papers delivered at FOIS 2023, the 13th edition of the Formal Ontology in Information Systems conference. The event was held as a sequentially-hybrid event, face-to-face in Sherbrooke, Canada, from 17 to 20 July 2023, and online from 18 to 20 September 2023. In total, 62 articles from 19 different countries were submitted, out of which 25 were accepted for inclusion in the conference and for publication; corresponding to an acceptance rate of 40 percent.

    The contributions are separated into the book’s three sections: (1) Foundational ontological issues; (2) Methodological issues around the development, alignment, verification and use of ontologies; and (3) Domain ontologies and ontology-based applications. In these sections, ontological aspects from a wide variety of fields are covered, primarily from various engineering domains including cybersecurity, manufacturing, petroleum engineering, and robotics, but also extending to the humanities, social sciences, medicine, and dentistry. A noticeable trend among the contributions in this edition of the conference is the recognition that improving the tools to analyze, align, and improve ontologies is of paramount importance in continuing to advance the field of formal ontology.

    The book will be of interest to all formal and applied ontology researchers, and to those who use formal ontologies and information systems as part of their work.

  • 30.
    Bae, Juhee
    et al.
    University of Skövde, Skövde, Sweden.
    Helldin, Tove
    University of Skövde, Skövde, Sweden.
    Riveiro, Maria
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Nowaczyk, Sławomir
    University of Halmstad, Halmstad, Sweden.
    Bouguelia, Mohamed-Rafik
    University of Halmstad, Halmstad, Sweden.
    Falkman, Göran
    University of Skövde, Skövde, Sweden.
    Interactive Clustering: A Comprehensive Review2020In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 53, no 1, p. 1-39, article id 1Article, review/survey (Refereed)
    Abstract [en]

    In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

  • 31.
    Beauxis-Aussalet, Emma
    et al.
    Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
    Behrisch, Michael
    Utrecht University, Utrecht, The Netherlands.
    Borgo, Rita
    King’s College London, London, United Kingdom.
    Chau, Duen Horng
    Georgia Tech, Atlanta, GA, USA.
    Collins, Christopher
    Ontario Tech University, Ontario, Canada.
    Ebert, David
    University of Oklahoma, Norman, OK, USA.
    El-Assady, Mennatallah
    University of Konstanz, Konstanz, Germany.
    Endert, Alex
    Georgia Tech, Atlanta, GA, USA.
    Keim, Daniel A.
    University of Konstanz, Konstanz, Germany.
    Kohlhammer, Jörn
    Fraunhofer IGD, Darmstadt, Germany.
    Oelke, Daniela
    Offenburg University, Offenburg, Germany.
    Peltonen, Jaakko
    Tampere University, Tampere, Finland.
    Riveiro, Maria
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Schreck, Tobias
    Graz University of Technology, Graz, Austria.
    Strobelt, Hendrik
    IBM Research, Cambridge, MA, USA.
    van Wijk, Jarke J
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Rhyne, Theresa-Marie
    The Role of Interactive Visualization in Fostering Trust in AI2021In: IEEE Computer Graphics and Applications, ISSN 0272-1716, E-ISSN 1558-1756, Vol. 41, no 6, p. 7-12Article in journal (Refereed)
    Abstract [en]

    The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.

  • 32.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science; STLab, ISTC-CNR.
    Gangemi, AldoSTLab, ISTC-CNR.Hammar, KarlJönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).Suárez-Figueroa, Mari CarmenOntology Engineering Group, Universidad Politécnica de Madrid.
    WOP 2012: Proceedings of the 3rd Workshop on Ontology Patterns2012Conference proceedings (editor) (Refereed)
  • 33.
    Blomqvist, Eva
    et al.
    Linköping University, Sweden.
    Hahmann, TorstenUniversity of Maine, USA.Hammar, KarlJönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).Hitzler, PascalKansas State University, USA.Hoekstra, RinkeElsevier B.V., Netherlands.Mutharaju, RaghavaIIIT-Delhi, India.Poveda-Villalón, MaríaUniversidad Politécnica de Madrid, Spain.Shimizu, CoganKansas State University, USA .Skjæveland, Martin G.University of Oslo, Norway.Solanki, MonikaUniversity of Oxford, UK.Svátek, VojtěchPrague University of Economics and Business, Czech Republic.Zhou, LuKansas State University, USA.
    Advances in Pattern-Based Ontology Engineering2021Collection (editor) (Refereed)
    Abstract [en]

    Ontologies are the corner stone of data modeling and knowledge representation, and engineering an ontology is a complex task in which domain knowledge, ontological accuracy and computational properties need to be carefully balanced. As with any engineering task, the identification and documentation of common patterns is important, and Ontology Design Patterns (ODPs) provide ontology designers with a strong connection to requirements and a better communication of their semantic content and intent.

    This book, Advances in Pattern-Based Ontology Engineering, contains 23 extended versions of selected papers presented at the annual Workshop on Ontology Design and Patterns (WOP) between 2017 and 2020. This yearly event, which attracts a large number of researchers and professionals in the field of ontology engineering and ontology design patterns, covers issues related to quality aspects of ontology engineering and ODPs for data and knowledge representation, and is usually co-located with the International Semantic Web Conference (ISWC), apart from WOP 2020, which was held virtually due to the COVID-19 pandemic. Topics covered by the papers collected here focus on recent advances in ontology design and patterns, and range from a method to instantiate content patterns, through a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations and applications.

    The book provides an overview of important advances in ontology engineering and ontology design patterns, and will be of interest to all those working in the field.

  • 34.
    Blomqvist, Eva
    et al.
    Linköping University, Sweden.
    Hahmann, Torsten
    University of Maine, USA.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Hitzler, Pascal
    Kansas State University, USA.
    Hoekstra, Rinke
    Elsevier B.V., Netherlands.
    Mutharaju, Raghava
    IIIT-Delhi, India.
    Poveda-Villalón, María
    Universidad Politécnica de Madrid, Spain.
    Shimizu, Cogan
    Kansas State University, USA .
    Skjæveland, Martin G.
    University of Oslo, Norway.
    Solanki, Monika
    University of Oxford, UK.
    Svátek, Vojtěch
    Prague University of Economics and Business, Czech Republic.
    Zhou, Lu
    Kansas State University, USA.
    Preface2021In: Advances in Pattern-Based Ontology Engineering / [ed] E. Blomqvist, T. Hahmann, K. Hammar, P. Hitzler, R. Hoekstra, R. Mutharaju, M. Poveda-Villalón, C. Shimizu, M. G. Skjæveland, M. Solanki, V. Svátek, & L. Zhou, Amsterdam: IOS Press, 2021, , p. 395p. i-viiiChapter in book (Refereed)
  • 35.
    Blomqvist, Eva
    et al.
    Linköping University, Linköping, Sweden.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Presutti, Valentina
    ISTC-CNR, Rome, Italy.
    Engineering Ontologies with Patterns - The eXtreme Design Methodology2016In: Ontology Engineering with Ontology Design Patterns / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 23-50Chapter in book (Refereed)
    Abstract [en]

    When using Ontology Design Patterns (ODPs) for modelling new parts of an ontology, i.e., new ontology modules, or even an entire ontology from scratch, ODPs can be used both as inspiration for different modelling solutions, as well as concrete templates or even “building blocks” reused directly in the new solution. This chapter discusses how ODPs, and in particular Content ODPs, can be used in ontology engineering. In particular, a specific ontology engineering methodology is presented, which was originally developed for supporting ODP use. However, this methodology, the eXtreme Design (XD), also has some characteristics that set it apart from most other ontology engineering methodologies, and which may be interesting to consider regardless of how much emphasis is put on the ODP usage. Towards the end of the chapter some XD use cases are also reported and discussed, as well as lessons learned from applying XD. The chapter is concluded through a summary and discussion about future work.

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  • 36.
    Blomqvist, Eva
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Levashova, Tatiana
    Öhgren, Annika
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Smirnov, Alexander
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Configuration of Dynamic SME Supply Chains Based on Ontologies2005In: Holonic and Multi-Agent Systems for Manufacturing: Second International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2005, Copenhagen, Denmark, August 22-24, 2005. Proceedings / [ed] V. Marik, R. W. Brennan & M. Pechoucek, Springer Berlin/Heidelberg, 2005, p. 246-256Conference paper (Refereed)
    Abstract [en]

    Due to the increasing implementation of agile and networked manufacturing, supply chain has entered a new phase, virtual supply chain. The phase is characterized by the integration of activities, operations, and functions carried out at different and geographically distributed supply chain stages. The paper proposes an approach to the configuration of a network of small and medium-sized enterprises (SMEs) being integrated into a supply chain. The SME supply chain configuration is based on a shared domain ontology for supply chain management, offering the configuration task as a function of supply chain management. Principles of the development of the shared ontology and possible ways of matching between enterprise and domain ontologies are considered.

  • 37.
    Boeva, Veselka
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Angelova, Milena
    Technical University Sofia, BUL.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Rosander, Oliver
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Tsiporkova, Elena
    Collective Center for the Belgian Technological Industry, BEL.
    Evolutionary clustering techniques for expertise mining scenarios2018In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2 / [ed] van den Herik J.,Rocha A.P., SciTePress, 2018, p. 523-530Conference paper (Refereed)
    Abstract [en]

    The problem addressed in this article concerns the development of evolutionary clustering techniques that can be applied to adapt the existing clustering solution to a clustering of newly collected data elements. We are interested in clustering approaches that are specially suited for adapting clustering solutions in the expertise retrieval domain. This interest is inspired by practical applications such as expertise retrieval systems where the information available in the system database is periodically updated by extracting new data. The experts available in the system database are usually partitioned into a number of disjoint subject categories. It is becoming impractical to re-cluster this large volume of available information. Therefore, the objective is to update the existing expert partitioning by the clustering produced on the newly extracted experts. Three different evolutionary clustering techniques are considered to be suitable for this scenario. The proposed techniques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

  • 38.
    Bollmann, Marcel
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Aralikatte, Rahul
    Murrieta Bello, Héctor
    Hershcovich, Daniel
    de Lhoneux, Miryam
    Søgaard, Anders
    Moses and the Character-Based Random Babbling Baseline: CoAStaL at AmericasNLP 2021 Shared Task2021In: Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas, Association for Computational Linguistics, 2021, p. 248-254Conference paper (Refereed)
    Abstract [en]

    We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios. Unsuccessfully. In the end, we submitted two runs: (i) a standard phrase-based model, and (ii) a random babbling baseline using character trigrams. We found that it was surprisingly hard to beat (i), in spite of this model being, in theory, a bad fit for polysynthetic languages; and more interestingly, that (ii) was better than several of the submitted systems, highlighting how difficult low-resource machine translation for polysynthetic languages is.

  • 39.
    Boström, Henrik
    et al.
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Mondrian conformal predictive distributions2021In: Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR , 2021, Vol. 152, p. 24-38Conference paper (Refereed)
    Abstract [en]

    The distributions output by a standard (non-normalized) conformal predictive system all have the same shape but differ in location, while a normalized conformal predictive system outputs distributions that differ also in shape, through rescaling. An approach to further increasing the flexibility of the framework is proposed, called Mondrian conformal predictive distributions, which are (standard or normalized) conformal predictive distributions formed from multiple Mondrian categories. The effectiveness of the approach is demonstrated with an application to regression forests. By forming categories through binning of the predictions, it is shown that for this model class, the use of Mondrian conformal predictive distributions significantly outperforms the use of both standard and normalized conformal predictive distributions with respect to the continuous- ranked probability score. It is further shown that the use of Mondrian conformal predictive distributions results in as tight prediction intervals as produced by normalized conformal regressors, while improving upon the point predictions of the underlying regression forest.

  • 40.
    Boström, Henrik
    et al.
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Vesterberg, Anders
    Scania CV AB, Sweden.
    Predicting with Confidence from Survival Data2019In: Conformal and Probabilistic Prediction and Applications / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni Smirnov, 2019, p. 123-141Conference paper (Refereed)
    Abstract [en]

    Survival modeling concerns predicting whether or not an event will occur before or on a given point in time. In a recent study, the conformal prediction framework was applied to this task, and so-called conformal random survival forest was proposed. It was empirically shown that the error level of this model indeed is very close to the provided confidence level, and also that the error for predicting each outcome, i.e., event or no-event, can be controlled separately by employing a Mondrian approach. The addressed task concerned making predictions for time points as provided by the underlying distribution. However, if one instead is interested in making predictions with respect to some specific time point, the guarantee of the conformal prediction framework no longer holds, as one is effectively considering a sample from another distribution than from which the calibration instances have been drawn. In this study, we propose a modification of the approach for specific time points, which transforms the problem into a binary classification task, thereby allowing the error level to be controlled. The latter is demonstrated by an empirical investigation using both a collection of publicly available datasets and two in-house datasets from a truck manufacturing company.

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  • 41.
    Boström, Henrik
    et al.
    Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Borås, Sweden.
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Accelerating difficulty estimation for conformal regression forests2017In: Annals of Mathematics and Artificial Intelligence, ISSN 1012-2443, E-ISSN 1573-7470, Vol. 81, no 1-2, p. 125-144Article in journal (Refereed)
    Abstract [en]

    The conformal prediction framework allows for specifying the probability of making incorrect predictions by a user-provided confidence level. In addition to a learning algorithm, the framework requires a real-valued function, called nonconformity measure, to be specified. The nonconformity measure does not affect the error rate, but the resulting efficiency, i.e., the size of output prediction regions, may vary substantially. A recent large-scale empirical evaluation of conformal regression approaches showed that using random forests as the learning algorithm together with a nonconformity measure based on out-of-bag errors normalized using a nearest-neighbor-based difficulty estimate, resulted in state-of-the-art performance with respect to efficiency. However, the nearest-neighbor procedure incurs a significant computational cost. In this study, a more straightforward nonconformity measure is investigated, where the difficulty estimate employed for normalization is based on the variance of the predictions made by the trees in a forest. A large-scale empirical evaluation is presented, showing that both the nearest-neighbor-based and the variance-based measures significantly outperform a standard (non-normalized) nonconformity measure, while no significant difference in efficiency between the two normalized approaches is observed. The evaluation moreover shows that the computational cost of the variance-based measure is several orders of magnitude lower than when employing the nearest-neighbor-based nonconformity measure. The use of out-of-bag instances for calibration does, however, result in nonconformity scores that are distributed differently from those obtained from test instances, questioning the validity of the approach. An adjustment of the variance-based measure is presented, which is shown to be valid and also to have a significant positive effect on the efficiency. For conformal regression forests, the variance-based nonconformity measure is hence a computationally efficient and theoretically well-founded alternative to the nearest-neighbor procedure. 

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  • 42.
    Botteghi, N.
    et al.
    University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Enschede, Netherlands.
    Alaa, K.
    IAV GmbH (Volkswagen Group), Intelligent Driving Functions RD Center, Berlin, Germany.
    Poel, M.
    University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Enschede, Netherlands.
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Brune, C.
    University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Enschede, Netherlands.
    Mersha, A.
    Research Group of Mechatronics, Saxion University of Applied Sciences, Enschede, Netherlands.
    Stramigioli, S.
    University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Enschede, Netherlands.
    Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces2021In: IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 190-197Conference paper (Refereed)
    Abstract [en]

    Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the context of robotics, in which the cost of real-world data is usually extremely high, Reinforcement Learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework combining the learning of a low-dimensional state representation, from high-dimensional observations coming from the robot's raw sensory readings, with the learning of the optimal policy, given the learned state representation. We evaluate our framework in the context of mobile robot navigation in the case of continuous state and action spaces. Moreover, we study the problem of transferring what learned in the simulated virtual environment to the real robot without further retraining using real-world data in the presence of visual and depth distractors, such as lighting changes and moving obstacles. A video of our experiments can be found at: https://youtu.be/rUdGPKr2Wuo.

  • 43.
    Botteghi, N.
    et al.
    Robotics and Mechatronics, Faculty of Electrical Engineering Mathematics and Computer Science, University of Twente, Netherlands.
    Kamilaris, A.
    Research Centre on Interactive Media Smart Systems and Emerging Technologies, Nicosia, Cyprus.
    Sinai, L.
    Robotics and Mechatronics, Faculty of Electrical Engineering Mathematics and Computer Science, University of Twente, Netherlands.
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Multi-Agent Path Planning of Robotic Swarms in Agricultural Fields2020In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences / [ed] N. Paparoditis, C. Mallet, F. Lafarge, S. Hinz, R. Feitosa, M. Weinmann, B. Jutzi, Copernicus GmbH , 2020, Vol. 5, no 1, p. 361-368Conference paper (Refereed)
    Abstract [en]

    Collaborative swarms of robots/UAVs constitute a promising solution for precision agriculture and for automatizing agricultural processes. Since agricultural fields have complex topologies and different constraints, the problem of optimized path routing of these swarms is important to be tackled. Hence, this paper deals with the problem of optimizing path routing for a swarm of ground robots and UAVs in different popular topologies of agricultural fields. Four algorithms (Nearest Neighbour based on K-means clustering, Christofides, Ant Colony Optimisation and Bellman-Held-Karp) are applied on various farm types commonly found around Europe. The results indicate that the problem of path planning and the corresponding algorithm to use, are sensitive to the field topology and to the number of agents in the swarm.

  • 44.
    Botteghi, N.
    et al.
    Robotics and Mechatronics University of Twente, Enschede, Netherlands.
    Obbink, R.
    Robotics and Mechatronics University of Twente, Enschede, Netherlands.
    Geijs, D.
    Robotics and Mechatronics University of Twente, Enschede, Netherlands.
    Poel, M.
    Datamanagement and Biometrics, University of Twente, Enschede, Netherlands.
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Brune, C.
    Applied Analysis University of Twente, Enschede, Netherlands.
    Mersha, A.
    Research Group of Mechatronics, Saxion University of Applied Sciences, Enschede, Netherlands.
    Stramigioli, S.
    Robotics and Mechatronics University of Twente, Enschede, Netherlands.
    Low dimensional state representation learning with reward-shaped priors2020In: Proceedings - International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 3736-3743Conference paper (Refereed)
    Abstract [en]

    Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal policy directly from the sensory inputs, i.e the observations, often requires processing and storage of a huge amount of data. In the context of robotics, the cost of data from real robotics hardware is usually very high, thus solutions that achieve high sample-efficiency are needed. We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space. This mapping is learned with unsupervised learning using loss functions shaped to incorporate prior knowledge of the environment and the task. Using the samples from the state space, the optimal policy is quickly and efficiently learned. We test the method on several mobile robot navigation tasks in a simulation environment and also on a real robot. A video of our experiments can be found at: https://youtu.be/dgWxmfSv95U.

  • 45.
    Botteghi, N.
    et al.
    Robotics and Mechatronics, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Netherlands.
    Sirmacek, Beril
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Schulte, R.
    Robotics and Mechatronics, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Netherlands.
    Poel, M.
    Datamanagement and Biometrics, Faculty of Electric Engineering, Mathematics and Computer Science, University of Twente, Netherlands.
    Brune, C.
    Applied Mathematics, Faculty of Electric Engineering, Mathematics and Computer Science, University of Twente, Netherlands.
    Reinforcement learning helps slam: Learning to build maps2020In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives / [ed] N. Paparoditis, C. Mallet, F. Lafarge, S. Zlatanova, S. Dragicevic, G. Sithole, G. Agugiaro, J. J. Arsanjani, P. Boguslawski, M. Breunig, M. A. Brovelli, S. Christophe, A. Coltekin, M. R. Delavar, M. Al Doori, E. Guilbert, C. C. Fonte, J. Haworth, U. Isikdag, I. Ivanova, Z. Kang, K. Khoshelham, M. Koeva, M. Kokla, Y. Liu, M. Madden, M. A. Mostafavi, G. Navratil, D. R. Paudyal, C. Pettit, A. Spanò, E. Stefanakis, W. Tu, G. Vacca, L. Díaz-Vilariño, S. Wise, H. Wu, and X. G. Zhou, International Society for Photogrammetry and Remote Sensing , 2020, Vol. 43, no B4, p. 329-336Conference paper (Refereed)
    Abstract [en]

    In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments.

  • 46.
    Buendia, Ruben
    et al.
    Department of Information Technology, University of Borås, Borås, Sweden.
    Kogej, Thierry
    Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Engkvist, Ola
    Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Carlsson, Lars
    Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Toccaceli, Paolo
    Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom.
    Ahlberg, Ernst
    Data Science and AI, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors2019In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, no 3, p. 1230-1237Article in journal (Refereed)
    Abstract [en]

    Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn - ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.

  • 47.
    Coppens, Sam
    et al.
    Ghent University.
    Hammar, KarlJönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).Knuth, MagnusUniversity of Potsdam.Neumann, MarcoKONA LLC, New York, USA.Ritze, DominiqueUniversity of Mannheim.Sack, HaraldUniversity of Potsdam.Vander Sande, MielGhent University.
    WaSABi 2013: Semantic Web Enterprise Adoption and Best Practice: Proceedings of the Workshop on Semantic Web Enterprise Adoption and Best Practice Co-located with 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, October 22, 20132013Conference proceedings (editor) (Refereed)
  • 48.
    Dhanabalachandran, Kaviya
    et al.
    Institute of Artificial Intelligence, Bremen University, Germany.
    Hassouna, Vanessa
    Institute of Artificial Intelligence Bremen University, Germany.
    Hedblom, Maria M.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Kümpel, Michaela
    Institute of Artificial Intelligence Bremen University, Germany.
    Leusmann, Nils
    Institute of Artificial Intelligence Bremen University, Germany.
    Beetz, Michael
    Institute of Artificial Intelligence Bremen University, Germany.
    Cutting Events: Towards Autonomous Plan Adaption by Robotic Agents through Image-Schematic Event Segmentation2021In: Proceedings of the 11th on Knowledge Capture Conference, ACM Digital Library, 2021, p. 25-32Conference paper (Refereed)
    Abstract [en]

    Autonomous robots struggle with plan adaption in uncertain and changing environments. Although modern robots can make popcorn and pancakes, they are incapable of performing such tasks in unknown settings and unable to adapt action plans if ingredients or tools are missing. Humans are continuously aware of their surroundings. For robotic agents, real-time state updating is time-consuming and other methods for failure handling are required. Taking inspiration from human cognition, we propose a plan adaption method based on event segmentation of the image-schematic states of subtasks within action descriptors. For this, we reuse action plans of the robotic architecture CRAM and ontologically model the involved objects and image-schematic states of the action descriptor cutting. Our evaluation uses a robot simulation of the task of cutting bread and demonstrates that the system can reason about possible solutions to unexpected failures regarding tool use.

  • 49.
    Dhanabalachandran, Kaviya
    et al.
    Institute of Artificial Intelligence, University of Bremen, Germany.
    Hedblom, Maria M.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Beetz, Michael
    Institute of Artificial Intelligence, University of Bremen, Germany.
    A balancing act: Ordering algorithm and image-schematic action descriptors for stacking objects by household robots2022In: Proceedings of the Joint Ontology Workshops 2022, Episode VIII: The Svear Sommar of Ontology / [ed] T. P. Sales, M. Hedblom & H. Tan, CEUR-WS , 2022Conference paper (Refereed)
    Abstract [en]

    Optimising object order in stacking problems remains a hard problem for cognitive robotics research. In this paper, we continue our work on using the spatiotemporal relationships called image schemas to represent affordance spaces founded on object properties. Based on object properties, we introduce a stacking-order algorithm and describe the action descriptors using an image-schematic event segmentation format by describing a small subset using the Image Schema Logic ISL𝐹𝑂𝐿.

  • 50.
    Dhanabalachandran, Kaviya
    et al.
    Institute of Artificial Intelligence, University of Bremen, Germany.
    Hedblom, Maria M.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Beetz, Michael
    Institute of Artificial Intelligence, University of Bremen, Germany.
    Getting on top of things: Towards intelligent robotic object stacking through image-schematic reasoning2022In: Proceedings of the Sixth Image Schema Day 2022: Jönköping, Sweden, March 24-25th, 2022 / [ed] M. M. Hedblom & O. Kutz, CEUR-WS , 2022Conference paper (Refereed)
    Abstract [en]

    In this extended abstract, we present initial work on intelligent object stacking by household robots using a symbolic approach grounded in image schema research. Image schemas represent spatiotemporal relationships that capture objects’ affordances and dispositions. Therefore, they offer the first step to ground semantic information in symbolic descriptions. We hypothesise that for a robot to successfully stack objects of different dispositions, these relationships can be used to more intelligently identify both task constraints and relevant event segments. 

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