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Kerren, Andreas, Dr.-Ing.ORCID iD iconorcid.org/0000-0002-0519-2537
Publications (10 of 165) Show all publications
Kotlarek, J., Kwon, O.-H., Ma, K.-L., Eades, P., Kerren, A., Klein, K. & Schreiber, F. (2020). A Study of Mental Maps in Immersive Network Visualization. Paper presented at 13th IEEE Pacific Visualization Symposium (PacificVis '20), Tianjin, China, 2020.
Open this publication in new window or tab >>A Study of Mental Maps in Immersive Network Visualization
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2020 (English)Manuscript (preprint) (Other academic)
Abstract [en]

The visualization of a network influences the quality of the mental map that the viewer develops to understand the network. In this study, we investigate the effects of a 3D immersive visualization environment compared to a traditional 2D desktop environment on the comprehension of a networks structure. We compare the two visualization environments using three tasks—interpreting network structure, memorizing a set of nodes, and identifying the structural changes—commonly used for evaluating the quality of a mental map in network visualization. The results show that participants were able to interpret network structure more accurately when viewing the network in an immersive environment, particularly for larger networks. However, we found that 2D visualizations performed better than immersive visualization for tasks that required spatial memory.

Keywords
visualization, network visualization, graph drawing, immersive analytics, mental map, empirical studies
National Category
Computer Sciences Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-92788 (URN)
Conference
13th IEEE Pacific Visualization Symposium (PacificVis '20), Tianjin, China, 2020
Available from: 2020-03-09 Created: 2020-03-09 Last updated: 2020-03-23Bibliographically approved
Chatzimparmpas, A., Martins, R. M., Jusufi, I. & Kerren, A. (2020). A survey of surveys on the use of visualization for interpreting machine learning models. Information Visualization, 1-27
Open this publication in new window or tab >>A survey of surveys on the use of visualization for interpreting machine learning models
2020 (English)In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, p. 1-27Article in journal (Refereed) Epub ahead of print
Abstract [en]

Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a metaanalysis (i.e. a ‘‘survey of surveys’’) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.

Place, publisher, year, edition, pages
Sage Publications, 2020
Keywords
Survey of surveys, literature review, visualization, explainable machine learning, interpretable machine learning, taxonomy, meta-analysis
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-90815 (URN)10.1177/1473871620904671 (DOI)000523935300001 ()
Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-04-28
Kerren, A., Hurter, C. & Braz, J. (Eds.). (2020). Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, Valletta, Malta, February 27-29, 2020: Volume 3: IVAPP. Paper presented at International Conference on Information Visualization Theory and Applications (IVAPP '20), Valletta, Malta, February 27-29, 2020. SciTePress
Open this publication in new window or tab >>Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, Valletta, Malta, February 27-29, 2020: Volume 3: IVAPP
2020 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

This book contains the proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), in cooperation with the ACM Special Interest Group on Computer Human Interaction (SIGCHI), the French Association for Computer Graphics (AFIG), the EUROGRAPHICS Portuguese Chapter, the European Association for Computer Graphics (EUROGRAPHICS) and the Society for Imaging Science and Technology (IS&T).

The proceedings here published demonstrate new and innovative solutions and highlight technical problems in each field that are challenging and worthy of being disseminated to the interested research audiences.

VISIGRAPP 2020 was organized to promote a discussion forum about the conference’s research topics between researchers, developers, manufacturers and end-users, and to establish guidelines in the development of more advanced solutions.

We received a high number of paper submissions for this edition of VISIGRAPP, 455 in total, with contributions from 58 countries. This attests to the success and global dimension of VISIGRAPP. To evaluate each submission, we used a hierarchical process of double-blind evaluation where each paper was reviewed by two to six experts from the International Program Committee (IPC).

The IPC selected for oral presentation and for publication as full papers 16 papers from GRAPP, 8 from HUCAPP, 9 papers from IVAPP, and 46 papers from VISAPP, which led to a result for the full-paper acceptance ratio of 17% and a high-quality program. Apart from the above full papers, the conference program also features 118 short papers and 109 poster presentations. We hope that these conference proceedings, which are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index, SCOPUS, DBLP, Semantic Scholar, Google Scholar, EI and Microsoft Academic, will help the Computer Vision, Imaging, Visualization, Computer Graphics and Human-Computer Interaction communities to find interesting research work. Moreover, we are proud to inform that the program also includes four plenary keynote lectures, given by internationally distinguished researchers, namely Matthias Niessner (Technical University of Munich, Germany), Anthony Steed (University College London, United Kingdom), Alan Chalmers (University of Warwick, United Kingdom) and Helen Purchase (University of Glasgow, United Kingdom), thus contributing to increase the overall quality of the conference and to provide a deeper understanding of the conference’s interest fields.

Furthermore, a short list of the presented papers will be selected to be extended into a forthcoming book of VISIGRAPP Selected Papers to be published by Springer during 2020 in the CCIS series. Moreover, a short list of presented papers will be selected for publication of extended and revised versions in a special issue of the open access Information Journal (IVAPP), a special issue of the Pattern Recognition and Artificial Intelligence Journal (VISAPP) and a special issue of The Visual Computer journal (GRAPP and HUCAPP). All papers presented at this conference will be available at the SCITEPRESS Digital Library. Three awards are delivered at the closing session, to recognize the best conference paper, the best student paper and the best poster for each of the four conferences. There is also an award for best industrial paper to be delivered at the closing session for HUCAPP and VISAPP.

We would like to express our thanks, first of all, to the authors of the technical papers, whose work and dedication made it possible to put together a program that we believe to be very exciting and of high technical quality. Next, we would like to thank the Area Chairs, all the members of the program committee and auxiliary reviewers, who helped us with their expertise and time. We would also like to thank the invited speakers for their invaluable contribution and for sharing their vision in their talks. Special thanks should be addressed to the INSTICC Steering Committee whose invaluable work made this event possible.

We wish you all an exciting conference and an unforgettable stay in Valletta, Malta. We hope to meet you again for the next edition of VISIGRAPP, details of which are available at http://www. visigrapp.org.

Place, publisher, year, edition, pages
SciTePress, 2020. p. 333
Keywords
Information Visualization
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-92522 (URN)978-989-758-402-2 (ISBN)
Conference
International Conference on Information Visualization Theory and Applications (IVAPP '20), Valletta, Malta, February 27-29, 2020
Available from: 2020-03-03 Created: 2020-03-03 Last updated: 2020-05-27Bibliographically approved
Mporas, I., Simaki, V., Paradis, C., Kerren, A. & Paraskevas, M. (2020). Special Issue on Natural Language Processing for Social Media Analysis. International journal on artificial intelligence tools, 29(2), 1-2, Article ID 2002001.
Open this publication in new window or tab >>Special Issue on Natural Language Processing for Social Media Analysis
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2020 (English)In: International journal on artificial intelligence tools, ISSN 0218-2130, Vol. 29, no 2, p. 1-2, article id 2002001Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
World Scientific, 2020
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-94038 (URN)10.1142/S0218213020020017 (DOI)000524003300001 ()
Available from: 2020-04-28 Created: 2020-04-28 Last updated: 2020-06-03Bibliographically approved
Chatzimparmpas, A., Martins, R. M., Jusufi, I., Kucher, K., Rossi, F. & Kerren, A. (2020). The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations. Paper presented at 22nd EG/VGTC Conference on Visualization (EuroVis '20), STAR track, 25-29 May 2020, Norrköping, Sweden. Computer graphics forum (Print), 39(3), 713-756
Open this publication in new window or tab >>The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
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2020 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 39, no 3, p. 713-756Article in journal (Refereed) Published
Abstract [en]

Machine learning (ML) models are nowadays used in complex applications in various domains such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

Keywords
trustworthy machine learning, visualization, interpretable machine learning, explainable machine learning
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-94115 (URN)10.1111/cgf.14034 (DOI)
Conference
22nd EG/VGTC Conference on Visualization (EuroVis '20), STAR track, 25-29 May 2020, Norrköping, Sweden
Available from: 2020-05-01 Created: 2020-05-01 Last updated: 2020-05-27
Skeppstedt, M., Ahltorp, M., Kucher, K., Kerren, A., Rzepka, R. & Araki, K. (2020). Topic modelling applied to a second language: A language adaption and tool evaluation study. In: Selected Papers – CLARIN Annual Conference 2019: . Paper presented at CLARIN Annual Conference 2019, 30 September - 2 October 2019, Leipzig, Germany.
Open this publication in new window or tab >>Topic modelling applied to a second language: A language adaption and tool evaluation study
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2020 (English)In: Selected Papers – CLARIN Annual Conference 2019, 2020Conference paper, Published paper (Refereed)
Abstract [en]

The Topics2Themes tool, which enables text analysis on the output of topic modelling, was originally developed for the English language. In this study, we explored and evaluated adaptations required for applying the tool to Japanese texts. That is, we adapted Topics2Themes to a language that is very different from the one for which the tool was originally developed. To apply Topics2Themes to Japanese texts, in which white space is not used for indicating word boundaries, the texts had to be pre-tokenised and white space inserted to indicate a token segmentation. Topics2Themes was also extended by the addition of word translations and phonetic readings to support users who are second-language speakers of Japanese. To evaluate the adaptation to a second language, as well as the reading support, we applied the tool to a corpus consisting of short Japanese texts. Twelve different topics were automatically identified, and a total of 183 texts representative for the twelve topics were extracted. A learner of Japanese carried out a manual analysis of these representative texts, and identified 35 reoccurring, fine-grained themes.

Keywords
Topic Models, Visualization, Japanese, Text Mining, Visual Text Analysis
National Category
Language Technology (Computational Linguistics) Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-93038 (URN)
Conference
CLARIN Annual Conference 2019, 30 September - 2 October 2019, Leipzig, Germany
Projects
DISA-DH
Funder
Swedish Research Council, 2017-00626
Note

TO BE PUBLISHED!

Available from: 2020-03-20 Created: 2020-03-20 Last updated: 2020-05-27
Chatzimparmpas, A., Martins, R. M. & Kerren, A. (2020). t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections. IEEE Transactions on Visualization and Computer Graphics, 1-18
Open this publication in new window or tab >>t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
2020 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, p. 1-18Article in journal (Refereed) Epub ahead of print
Abstract [en]

t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this work, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool’s effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Interpretable t-SNE, dimensionality reduction, high-dimensional data, explainable machine learning, visualization
National Category
Computer Sciences Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-93240 (URN)10.1109/TVCG.2020.2986996 (DOI)
Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2020-05-07
Simaki, V., Paradis, C. & Kerren, A. (2019). A two-step procedure to identify stance constructions in discourse from political blogs. Corpora, 14(3), 379-405
Open this publication in new window or tab >>A two-step procedure to identify stance constructions in discourse from political blogs
2019 (English)In: Corpora, ISSN 1749-5032, E-ISSN 1755-1676, Vol. 14, no 3, p. 379-405Article in journal (Refereed) Published
Abstract [en]

The Brexit Blog Corpus (BBC) is a collection of texts extracted from political blogs, which, in a recent study, was annotated according to a cognitive-functional stance framework by two independent annotators (Annotator A and B) using semantic criteria (Simaki et al. 2017). The goal was to label the stance or stances taken based on the overall meaning of a set of utterances. The annotators were not instructed to identify the lexical forms that were used to express the stances. In this study, we make use of those stance labelled utterances as a springboard to approach stance-taking in text from the opposite point of view, namely from how stance is realised through language. Our aim is to provide a description of the specific lexical elements used to express six stance categories, i.e., CONTRARIETY, HYPOTHETICALITY,  NECESSITY, PREDICTION, SOURCE OF KNOWLEDGE, and UNCERTAINTY. To this end, we followed a two-step experimental procedure. First, we performed a quantitative analysis of the stance labelled utterances in order to identify the lexical realisations of each stance category. Second, we carried out a meta-annotation of the data. Annotator B was instructed to single out the actual lexical forms of the constructions that triggered his semantic stance category decisions. This meta-annotation procedure made it possible for us to sift out the most salient lexical realisations of the constructions of each of the six category types on the basis of the qualitative assessments made by Annotator B. We then compared the results of the quantitative and the qualitative approaches, and we present a list of shared stance expressions for each stance category type.

Place, publisher, year, edition, pages
Edinburgh University Press, 2019
Keywords
stance-taking, social media text analysis, stance construction, meta-annotation, corpus annotation, Brexit, Blogs
National Category
General Language Studies and Linguistics
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-79384 (URN)10.3366/cor.2019.0179 (DOI)000495441800005 ()
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-11-29Bibliographically approved
Chatzimparmpas, A., Bibi, S., Zozas, I. & Kerren, A. (2019). Analyzing the Evolution of JavaScript Applications. In: Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE: . Paper presented at 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019), May 4-5, 2019, Heraklion, Greece (pp. 359-366). SciTePress, 1
Open this publication in new window or tab >>Analyzing the Evolution of JavaScript Applications
2019 (English)In: Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, SciTePress, 2019, Vol. 1, p. 359-366Conference paper, Published paper (Refereed)
Abstract [en]

Software evolution analysis can shed light on various aspects of software development and maintenance. Up to date, there is little empirical evidence on the evolution of JavaScript (JS) applications in terms of maintainability and changeability, even though JavaScript is among the most popular scripting languages for front-end web applications. In this study, we investigate JS applications’ quality and changeability trends over time by examining the relevant Laws of Lehman. We analyzed over 7,500 releases of JS applications and reached some interesting conclusions. The results show that JS applications continuously change and grow, there are no clear signs of quality degradation while the complexity remains the same over time, despite the fact that the understandability of the code deteriorates.

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Software Evolution, Lehman’s Laws, JavaScript, Maintenance, Software Quality
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-80639 (URN)10.5220/0007727603590366 (DOI)2-s2.0-85067472731 (Scopus ID)978-989-758-375-9 (ISBN)
Conference
14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019), May 4-5, 2019, Heraklion, Greece
Available from: 2019-02-18 Created: 2019-02-18 Last updated: 2020-05-14Bibliographically approved
Skeppstedt, M., Ahltorp, M., Kerren, A., Rzepka, R. & Araki, K. (2019). Application of a topic model visualisation tool to a second language. In: CLARIN 2019 Book of absracts: . Paper presented at CLARIN Annual Conference 2019, 30 September - 2 October 2019, Leipzig, Germany. CLARIN, Common Language Resources and Technology Infrastructure
Open this publication in new window or tab >>Application of a topic model visualisation tool to a second language
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2019 (English)In: CLARIN 2019 Book of absracts, CLARIN, Common Language Resources and Technology Infrastructure , 2019Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

We explored adaptions required for applying a topic modelling tool to a language that is very different from the one for which the tool was originally developed. The tool, which enables text analysis on the output of topic modelling, was developed for English, and we here applied it on Japanese texts. As white space is not used for indicating word boundaries in Japanese, the texts had to be pre-tokenised and white space inserted to indicate a token segmentation, before the texts could be imported into the tool. The tool was also extended by the addition of word translations and phonetic readings to support users who are second-language speakers of Japanese.

Place, publisher, year, edition, pages
CLARIN, Common Language Resources and Technology Infrastructure, 2019
Keywords
Topic Models, Visualization, Japanese, Text Mining, Visual Text Analysis
National Category
Language Technology (Computational Linguistics) Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-87108 (URN)
Conference
CLARIN Annual Conference 2019, 30 September - 2 October 2019, Leipzig, Germany
Projects
DISA-DH
Funder
Swedish Research Council, 2017-00626
Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2019-11-12Bibliographically approved
Projects
Advances in the description and explanation of stance in discourse using visual and computational text analytics - StaViCTA [2012-05659_VR]; Linnaeus University; Publications
Simaki, V., Paradis, C. & Kerren, A. (2019). A two-step procedure to identify stance constructions in discourse from political blogs. Corpora, 14(3), 379-405Simaki, V., Panagiotis, S., Paradis, C. & Kerren, A. (2018). Detection of Stance-Related Characteristics in Social Media Text. In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence (SETN '18): . Paper presented at The 10th Hellenic Conference on Artificial Intelligence (SETN '18), 9-15 July 2018, Patras, Greece. ACM Publications, Article ID 38.
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0519-2537

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