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Golub, K., Hagelbäck, J. & Ardö, A. (2019). Automatic classification Using DDC on the Swedish Union Catalogue. In: European DDC Users Group, EDUG, Annual Meeting 9-10 May 2019: National Library of Sweden, Stockholm, Sweden. Paper presented at European DDC Users Group, EDUG, Annual Meeting 9-10 May 2019: National Library of Sweden, Stockholm, Sweden.
Open this publication in new window or tab >>Automatic classification Using DDC on the Swedish Union Catalogue
2019 (English)In: European DDC Users Group, EDUG, Annual Meeting 9-10 May 2019: National Library of Sweden, Stockholm, Sweden, 2019Conference paper, Oral presentation only (Other academic)
National Category
Information Studies
Research subject
Humanities, Library and Information Science
Identifiers
urn:nbn:se:lnu:diva-84605 (URN)
Conference
European DDC Users Group, EDUG, Annual Meeting 9-10 May 2019: National Library of Sweden, Stockholm, Sweden
Available from: 2019-06-04 Created: 2019-06-04 Last updated: 2019-08-07Bibliographically approved
Hagelbäck, J., Lincke, A., Löwe, W. & Rall, E. (2019). On the Agreement of Commodity 3D Cameras. In: 23rd International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'19: July 29 - August 1, 2019, USA): . Paper presented at 3rd International Conference on Image Processing, Computer Vision, & Pattern Recognition. CSREA Press
Open this publication in new window or tab >>On the Agreement of Commodity 3D Cameras
2019 (English)In: 23rd International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'19: July 29 - August 1, 2019, USA), CSREA Press, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The advent of commodity 3D sensor technol- ogy has, amongst other things, enabled the efficient and effective assessment of human movements. Machine learning approaches do not rely manual definitions of gold standards for each new movement. However, to train models for the automated assessments of a new movement they still need a lot of data that map recorded movements to expert judg- ments. As camera technology changes, this training needs to be repeated if a new camera does not agree with the old one. The present paper presents an inexpensive method to check the agreement of cameras, which, in turn, would allow for a safe reuse of trained models regardless of the cameras. We apply the method to the Kinect, Astra Mini, and Real Sense cameras. The results show that these cameras do not agree and that the models cannot be reused without an unacceptable decay in accuracy. However, the suggested method works independent of movements and cameras and could potentially save effort when integrating new cameras in an existing assessment environment.

Place, publisher, year, edition, pages
CSREA Press, 2019
Keywords
3D camera agreement, human movement assessment
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-89180 (URN)
Conference
3rd International Conference on Image Processing, Computer Vision, & Pattern Recognition
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-18
Hagelbäck, J., Liapota, P., Lincke, A. & Löwe, W. (2019). Variants of Dynamic Time Warping and their Performance in Human Movement Assessment. In: 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, las Vegas, USA): . Paper presented at 21st International Conference on Artificial Intelligence, ICAI'19: July 29 - August 1, 2019, las Vegas, USA. CSREA Press
Open this publication in new window or tab >>Variants of Dynamic Time Warping and their Performance in Human Movement Assessment
2019 (English)In: 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, las Vegas, USA), CSREA Press, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The advent of commodity 3D sensor technology enabled, amongst other things, the efficient and effective assessment of human movements. Statistical and machine learning approaches map recorded movement instances to expert scores to train models for the automated assessment of new movements. However, there are many variations in selecting the approaches and setting the parameters for achieving good performance, i.e., high scoring accuracy and low response time. The present paper researches the design space and the impact of sequence alignment on accuracy and response time. More specifically, we introduce variants of Dynamic Time Warping (DTW) for aligning the phases of slow and fast movement instances and assess their effect on the scoring accuracy and response time. Results show that an automated stripping of leading and trailing frames not belonging to the movement (using one DTW variant) followed by an alignment of selected frames in the movements (based on another DTW variant) outperforms the original DTW and other suggested variants thereof. Since these results are independent of the selected learning approach and do not rely on the movement specifics, the results can help improving the performance of automated human movement assessment, in general.

Place, publisher, year, edition, pages
CSREA Press, 2019
Keywords
Dynamic Time Warping variants, human movement assessment
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-89181 (URN)
Conference
21st International Conference on Artificial Intelligence, ICAI'19: July 29 - August 1, 2019, las Vegas, USA
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-18
Golub, K., Hagelbäck, J. & Ardö, A. (2018). Automatic classification using DDC on the Swedish Union Catalogue. In: Philipp Mayr, Douglas Tudhope, Joseph Busch, Koraljka Golub, Marjorie Hlava & Marcia Zeng (Ed.), Proceedings of the 18th European Networked Knowledge Organization Systems (NKOS 2018) Workshop, Porto, Portugal, September 13, 2018: . Paper presented at 18th European Networked Knowledge Organization Systems Workshop (NKOS 2018), Porto, Portugal, September 13, 2018 (pp. 4-16). CEUR-WS.org
Open this publication in new window or tab >>Automatic classification using DDC on the Swedish Union Catalogue
2018 (English)In: Proceedings of the 18th European Networked Knowledge Organization Systems (NKOS 2018) Workshop, Porto, Portugal, September 13, 2018 / [ed] Philipp Mayr, Douglas Tudhope, Joseph Busch, Koraljka Golub, Marjorie Hlava & Marcia Zeng, CEUR-WS.org , 2018, p. 4-16Conference paper, Published paper (Refereed)
Abstract [en]

With more and more digital collections of various information re- sources becoming available, also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization systems. While the ultimate purpose is to understand the value of automatically produced Dewey Decimal Classification (DDC) classes for Swedish digital collections, the paper aims to evaluate the performance of two machine learning algorithms for Swe- dish catalogue records from the Swedish union catalogue (LIBRIS). The algo- rithms are tested on the top three hierarchical levels of the DDC. Based on a data set of 143,838 records, evaluation shows that Support Vector Machine with linear kernel outperforms Multinomial Naïve Bayes algorithm. Also, using keywords or combining titles and keywords gives better results than using only titles as input. The class imbalance where many DDC classes only have few records greatly affects classification performance: 81.37% accuracy on the training set is achieved when at least 1,000 records per class are available, and 66.13% when few records on which to train are available. Proposed future research involves an exploration of the intellectual effort put into creating the DDC to further improve the algorithm performance as commonly applied in string matching, and to test the best approach on new digital collections that do not have DDC assigned.

Place, publisher, year, edition, pages
CEUR-WS.org, 2018
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2200
Keywords
LIBRIS, Dewey Decimal Classification, automatic classification, machine learning, Support Vector Machine, Multinomial Naïve Bayes, subject access
National Category
Information Studies
Research subject
Humanities, Library and Information Science
Identifiers
urn:nbn:se:lnu:diva-78378 (URN)2-s2.0-85053933816 (Scopus ID)
Conference
18th European Networked Knowledge Organization Systems Workshop (NKOS 2018), Porto, Portugal, September 13, 2018
Available from: 2018-10-19 Created: 2018-10-19 Last updated: 2019-05-24Bibliographically approved
Qureshi, S., Hagelbäck, J., Iqbal, S. M., Javaid, H. & Lindley, C. (2018). Evaluation of Classifiers for Emotion Detection while Performing Physical and Visual Tasks: Tower of Hanoi and IAPS. In: Kohei Arai, Supriya Kapoor, Rahul Bhatia (Ed.), Intelligent Systems and Applications. IntelliSys 2018: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1. Paper presented at Intelligent Systems Conference (IntelliSys), 6-7 September, 2018, London (pp. 347-363). Springer
Open this publication in new window or tab >>Evaluation of Classifiers for Emotion Detection while Performing Physical and Visual Tasks: Tower of Hanoi and IAPS
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2018 (English)In: Intelligent Systems and Applications. IntelliSys 2018: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 / [ed] Kohei Arai, Supriya Kapoor, Rahul Bhatia, Springer, 2018, p. 347-363Conference paper, Published paper (Refereed)
Abstract [en]

With the advancement in robot technology, smart human-robot interaction is of increasing importance for allowing the more excellent use of robots integrated into human environments and activities. If a robot can identify emotions and intentions of a human interacting with it, interactions with humans can potentially become more natural and effective. However, mechanisms of perception and empathy used by humans to achieve this understanding may not be suitable or adequate for use within robots. Electroencephalography (EEG) can be used for recording signals revealing emotions and motivations from a human brain. This study aimed to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. For experimental purposes, we used visual (IAPS) and physical (Tower of Hanoi) tasks to record human emotional states in the form of EEG data. The obtained EEG data processed, formatted and evaluated using various machine learning techniques to find out which method can most accurately classify EEG data according to associated affective/emotional states. The experiment confirms the choice of a method for improving the accuracy of results. According to the results, Support Vector Machine was the first, and Regression Tree was the second best method for classifying EEG data associated with specific affective/emotional states with accuracies up to 70.00% and 60.00%, respectively. In both tasks, SVM was better in performance than RT. 

Place, publisher, year, edition, pages
Springer, 2018
Series
Advances in Intelligent Systems and Computing,, ISSN 2194-5357, E-ISSN 2194-5365 ; 868
Keywords
K-Nearest Neighbor (KNN), Regression Tree (RT), Bayesian Network (BNT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Tower of Hanoi (ToH), Cognitive Psychology, Human Computer Interaction (HCI), Electroencephalography (EEG)
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-78544 (URN)10.1007/978-3-030-01054-6_25 (DOI)2-s2.0-85057084220 (Scopus ID)978-3-030-01053-9 (ISBN)978-3-030-01054-6 (ISBN)
Conference
Intelligent Systems Conference (IntelliSys), 6-7 September, 2018, London
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2019-08-29Bibliographically approved
Jerčić, P., Hagelbäck, J. & Lindley, C. (2018). Physiological Affect and Performance in a Collaborative Serious Game Between Humans and an Autonomous Robot. In: Clua E., Roque L., Lugmayr A., Tuomi P. (Ed.), Entertainment Computing – ICEC 2018: 17th IFIP TC 14 International Conference, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 17–20, 2018. Paper presented at 17th IFIP TC 14 International Conference, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 17–20, 2018 (pp. 127-138). Springer
Open this publication in new window or tab >>Physiological Affect and Performance in a Collaborative Serious Game Between Humans and an Autonomous Robot
2018 (English)In: Entertainment Computing – ICEC 2018: 17th IFIP TC 14 International Conference, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 17–20, 2018 / [ed] Clua E., Roque L., Lugmayr A., Tuomi P., Springer, 2018, p. 127-138Conference paper, Published paper (Refereed)
Abstract [en]

This paper sets out to examine how elicited physiological affect influences the performance of human participants collaborating with the robot partners on a shared serious game task; furthermore, to investigate physiological affect underlying such human-robot proximate collaboration. The participants collaboratively played a turn-taking version of a serious game Tower of Hanoi, where physiological affect was investigated in a valence-arousal space. The arousal was inferred from the galvanic skin response data, while the valence was inferred from the electrocardiography data. It was found that the robot collaborators elicited a higher physiological affect in regard to both arousal and valence, in contrast to their human collaborator counterparts. Furthermore, a comparable performance between all collaborators was found on the serious game task.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11112
Keywords
Autonomous robots, Serious games, Collaborative play, Robot-assisted play, Emotions, Physiology Affect
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-78542 (URN)10.1007/978-3-319-99426-0_11 (DOI)2-s2.0-85053772889 (Scopus ID)978-3-319-99426-0 (ISBN)978-3-319-99425-3 (ISBN)
Conference
17th IFIP TC 14 International Conference, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 17–20, 2018
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2019-08-29Bibliographically approved
Jercic, P., Wen, W., Hagelbäck, J. & Sundstedt, V. (2018). The Effect of Emotions and Social Behavior on Performance in a Collaborative Serious Game Between Humans and Autonomous Robots. Paper presented at 9th International Conference on Social Robotics (ICSR), Tsukuba, JAPAN, Nov 22-24, 2017. International Journal of Social Robotics, 10(1), 115-129
Open this publication in new window or tab >>The Effect of Emotions and Social Behavior on Performance in a Collaborative Serious Game Between Humans and Autonomous Robots
2018 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 10, no 1, p. 115-129Article in journal (Refereed) Published
Abstract [en]

The aim of this paper is to investigate performance in a collaborative human–robot interaction on a shared serious game task. Furthermore, the effect of elicited emotions and perceived social behavior categories on players’ performance will be investigated. The participants collaboratively played a turn-taking version of the Tower of Hanoi serious game, together with the human and robot collaborators. The elicited emotions were analyzed in regards to the arousal and valence variables, computed from the Geneva Emotion Wheel questionnaire. Moreover, the perceived social behavior categories were obtained from analyzing and grouping replies to the Interactive Experiences and Trust and Respect questionnaires. It was found that the results did not show a statistically significant difference in participants’ performance between the human or robot collaborators. Moreover, all of the collaborators elicited similar emotions, where the human collaborator was perceived as more credible and socially present than the robot one. It is suggested that using robot collaborators might be as efficient as using human ones, in the context of serious game collaborative tasks.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Autonomous robots Serious games Collaborative play Social interaction Robot-assisted play Emotions
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-68500 (URN)10.1007/s12369-017-0437-4 (DOI)000423152900008 ()2-s2.0-85040810298 (Scopus ID)
Conference
9th International Conference on Social Robotics (ICSR), Tsukuba, JAPAN, Nov 22-24, 2017
Available from: 2017-10-30 Created: 2017-10-30 Last updated: 2019-08-29Bibliographically approved
Hagelbäck, J. (2016). Hybrid pathfinding in StarCraft. IEEE Transactions on Computational Intelligence and AI in Games, 8(4), 319-324, Article ID 7063238.
Open this publication in new window or tab >>Hybrid pathfinding in StarCraft
2016 (English)In: IEEE Transactions on Computational Intelligence and AI in Games, ISSN 1943-068X, E-ISSN 1943-0698, Vol. 8, no 4, p. 319-324, article id 7063238Article in journal (Refereed) Published
Abstract [en]

Micro-management is a very important aspect of RTS games. It involves moving single units or groups of units effectively on the battle field, targeting the most threatening enemy units and use the unit's special abilities when they are the most harmful for the enemy or the most beneficial for the player. Designing good micro-management is a challenging task for AI bot developers. In this paper we address the micro-management sub-task of positioning units effectively in combat situations. Two different approaches are evaluated, one based on potential fields and the other based on flocking algorithms. The results show that both the potential fields version and the flocking version clearly increases the win percentage of the bot, but the difference in wins between the two is minimal. The results also show that the more flexible potential fields technique requires much more hardware resources than the more simple flocking technique.

National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-58295 (URN)10.1109/TCIAIG.2015.2414447 (DOI)000391470800002 ()2-s2.0-85027459451 (Scopus ID)
Available from: 2016-11-25 Created: 2016-11-25 Last updated: 2018-01-13Bibliographically approved
Hagelbäck, J., Hilborn, O., Jercic, P., Johansson, S. J., Lindley, C. A., Svensson, J. & Wen, W. (2014). Psychophysiological Interaction and Empathic Cognition for Human-robot Cooperative Work (PsyIntEC). In: Florian Röhrbein, Germano Veiga, Ciro Natale (Ed.), Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe: Technology Transfer Experiments from the ECHORD Project (pp. 283-299). Springer, 94
Open this publication in new window or tab >>Psychophysiological Interaction and Empathic Cognition for Human-robot Cooperative Work (PsyIntEC)
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2014 (English)In: Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe: Technology Transfer Experiments from the ECHORD Project / [ed] Florian Röhrbein, Germano Veiga, Ciro Natale, Springer, 2014, Vol. 94, p. 283-299Chapter in book (Refereed)
Abstract [en]

The aim of the PsyIntEC project is to explore affective and cognitive modeling of humans in human-robot interaction (HRI) as a basis for behavioral adaptation. To achieve this we have explored human affective perception of relevant modalities in human-human and human-robot interaction on a collaborative problem-solving task using psychophysiological measurements. The experiments conducted have given us valuable insight into the communicational and affective queues interplaying in such interactions from the human perspective. The results indicate that there is an increase in both positive and negative emotions when interacting with robots compared to interacting with another human or solving the task alone, but detailed analysis on shorter time segments is required for the results from all sensors to be conclusive and significant.

Place, publisher, year, edition, pages
Springer, 2014
Series
Springer Tracts in Advanced Robotics, ISSN 1610-7438 ; 94
Keywords
human-robot interaction, psychophysiology, affective modeling, robotics
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-42381 (URN)10.1007/978-3-319-03838-4_14 (DOI)978-3-319-03837-7 (ISBN)978-3-319-03838-4 (ISBN)
Available from: 2015-04-15 Created: 2015-04-15 Last updated: 2018-01-11Bibliographically approved
Togelius, J., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., Yannakakis, G. N. & Grappiolo, C. (2013). Controllable procedural map generation via multiobjective evolution. Genetic Programming and Evolvable Machines, 14(2), 245-277
Open this publication in new window or tab >>Controllable procedural map generation via multiobjective evolution
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2013 (English)In: Genetic Programming and Evolvable Machines, ISSN 1389-2576, E-ISSN 1573-7632, Vol. 14, no 2, p. 245-277Article in journal (Refereed) Published
Abstract [en]

This paper shows how multiobjective evolutionary algorithms can be used to procedurally generate complete and playable maps for real-time strategy (RTS) games. We devise heuristic objective functions that measure properties of maps that impact important aspects of gameplay experience. To show the generality of our approach, we design two different evolvable map representations, one for an imaginary generic strategy game based on heightmaps, and one for the classic RTS game StarCraft. The effect of combining tuples or triples of the objective functions are investigated in systematic experiments, in particular which of the objectives are partially conflicting. A selection of generated maps are visually evaluated by a population of skilled StarCraft players, confirming that most of our objectives correspond to perceived gameplay qualities. Our method could be used to completely automate in-game controlled map generation, enabling player-adaptive games, or as a design support tool for human designers.

Keywords
Real-time strategy games, RTS, Procedural content generation, Evolutionary computation, Multiobjective optimisation, StarCraft
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-42377 (URN)10.1007/s10710-012-9174-5 (DOI)
Available from: 2015-04-15 Created: 2015-04-15 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8591-1035

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