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Bae, Juhee
Publications (10 of 18) Show all publications
Bae, J., Helldin, T., Riveiro, M., Nowaczyk, S., Bouguelia, M.-R. & Falkman, G. (2020). Interactive clustering: A comprehensive review. ACM Computing Surveys, 53(1), Article ID 1.
Open this publication in new window or tab >>Interactive clustering: A comprehensive review
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2020 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 53, no 1, article id 1Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2020
Keywords
Clustering, Evaluation, Feedback, Interaction, Interactive, User, Surveys, Computer science
National Category
Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18266 (URN)10.1145/3340960 (DOI)2-s2.0-85079573488 (Scopus ID)
Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-03-04Bibliographically approved
Olson, N. & Bae, J. (2019). Biosensors-Publication Trends and Knowledge Domain Visualization. Sensors, 19(11), Article ID 2615.
Open this publication in new window or tab >>Biosensors-Publication Trends and Knowledge Domain Visualization
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 11, article id 2615Article in journal (Refereed) Published
Abstract [en]

The number of scholarly publications on the topic of biosensors has increased rapidly; as a result, it is no longer easy to build an informed overview of the developments solely by manual means. Furthermore, with many new research results being continually published, it is useful to form an up-to-date understanding of the recent trends or emergent directions in the field. This paper utilizes bibliometric methods to provide an overview of the developments in the topic based on scholarly publications. The results indicate an increasing interest in the topic of biosensor(s) with newly emerging sub-topics. The US is identified as the country with highest total contribution to this area, but as a collective, EU countries top the list of total contributions. An examination of trends over the years indicates that in recent years, China-based authors have been more productive in this area. If research contribution per capita is considered, Singapore takes the top position, followed by Sweden, Switzerland and Denmark. While the number of publications on biosensors seems to have declined in recent years in the PubMed database, this is not the case in the Web of Science database. However, there remains an indication that the rate of growth in the more recent years is slowing. This paper also presents a comparison of the developments in publications on biosensors with the full set of publications in two of the main journals in the field. In more recent publications, synthetic biology, smartphone, fluorescent biosensor, and point-of-care testing are among the terms that have received more attention. The study also identifies the top authors and journals in the field, and concludes with a summary and suggestions for follow up research.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
bibliometrics, biosensors, emerging trends, scholarly publications
National Category
Information Studies Biomedical Laboratory Science/Technology
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17409 (URN)10.3390/s19112615 (DOI)000472133300193 ()31181820 (PubMedID)2-s2.0-85067796636 (Scopus ID)
Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2019-11-08Bibliographically approved
Bae, J., Karlsson, A., Mellin, J., Ståhl, N. & Torra, V. (2019). Complex Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 157-169). Springer
Open this publication in new window or tab >>Complex Data Analysis
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2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 157-169Chapter in book (Refereed)
Abstract [en]

Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL); Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-16811 (URN)10.1007/978-3-319-97556-6_9 (DOI)000464719500010 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-09-30Bibliographically approved
Holst, A., Bouguelia, M.-R., Görnerup, O., Pashami, S., Al-Shishtawy, A., Falkman, G., . . . Soliman, A. (2019). Eliciting structure in data. In: Christoph Trattner, Denis Parra, Nathalie Riche (Ed.), CEUR Workshop Proceedings: . Paper presented at 2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019. CEUR-WS, 2327
Open this publication in new window or tab >>Eliciting structure in data
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2019 (English)In: CEUR Workshop Proceedings / [ed] Christoph Trattner, Denis Parra, Nathalie Riche, CEUR-WS , 2019, Vol. 2327Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. 

Place, publisher, year, edition, pages
CEUR-WS, 2019
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2327
Keywords
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16748 (URN)2-s2.0-85063227224 (Scopus ID)
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-09-30Bibliographically approved
Said, A., Bae, J., Parra, D. & Pashami, S. (2019). IDM-WSDM 2019: Workshop on interactive data mining. In: WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining: . Paper presented at 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 11 February 2019 through 15 February 2019 (pp. 846-847). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>IDM-WSDM 2019: Workshop on interactive data mining
2019 (English)In: WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery (ACM), 2019, p. 846-847Conference paper, Published paper (Refereed)
Abstract [en]

The first Workshop on Interactive Data Mining is held in Melbourne, Australia, on February 15, 2019 and is co-located with 12th ACM International Conference on Web Search and Data Mining (WSDM 2019). The goal of this workshop is to share and discuss research and projects that focus on interaction with and interactivity of data mining systems. The program includes invited speaker, presentation of research papers, and a discussion session. © 2019 held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Data mining, Human-in-the-loop, Interactive classification and clustering, Interactive dashboards, Visual modeling, Information retrieval, Websites, Data mining system, Interactive classification, Interactive data mining, Melbourne, Australia, Research papers, Visual model
National Category
Other Computer and Information Science Information Systems Interaction Technologies
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16671 (URN)10.1145/3289600.3291376 (DOI)000482120400120 ()2-s2.0-85061736320 (Scopus ID)978-1-4503-5940-5 (ISBN)
Conference
12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 11 February 2019 through 15 February 2019
Note

Conference code: 144821; Export Date: 1 March 2019; Conference Paper

Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2019-09-12Bibliographically approved
Holst, A., Pashami, S. & Bae, J. (2019). Incremental causal discovery and visualization. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019 (pp. 1-6). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Incremental causal discovery and visualization
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Causal Discovery, Correlation, Incremental Visualization, Correlation methods, Data mining, Visualization, Causal graph, Causal relations, Discovery algorithm, Incremental discoveries, Novel visualizations, Data visualization
National Category
Probability Theory and Statistics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17509 (URN)10.1145/3304079.3310287 (DOI)2-s2.0-85069768142 (Scopus ID)978-1-4503-6296-2 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2019-11-08Bibliographically approved
Helldin, T., Bae, J. & Alklind Taylor, A.-S. (2019). Intelligent User Interfaces: Trends and application areas. Skövde: University of Skövde
Open this publication in new window or tab >>Intelligent User Interfaces: Trends and application areas
2019 (English)Report (Other academic)
Abstract [en]

This report outlines trends and application areas within the research field of intelligent user interfaces(IUIs) from 2010-2018. The purpose of the report is to give an overview of the IUI research area andpoint out particular subfields that have been given attention in the recent years, indicating possible trendsfor future research. Our report indicates that the field of IUIs is very broad, resulting in rather diverseresearch trends within the area. However, general trends could be identified, such as an increasing interest inbetter human-machine decision-making, where strategies for explaining the automatic reasoning are beinginvestigated together with ways of improving the trustworthiness of the systems and their possible adaptationsto individuals’ needs. The report also outlines research on multimodal interactions, adaptivity and humanrobotcollaboration, addressing challenges such as increased human workload, unobtrusiveness, privacy andmultiparty communication.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2019. p. 16
Series
SUSI, ISSN 1653-2325
Keywords
Intelligent user interfaces, AI, HCI
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-18303 (URN)
Available from: 2020-03-12 Created: 2020-03-12 Last updated: 2020-03-20
Holst, A., Karlsson, A., Bae, J. & Bouguelia, M.-R. (2019). Interactive clustering for exploring multiple data streams at different time scales and granularity. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Interactive clustering for exploring multiple data streams at different time scales and granularity
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019Conference paper, Published paper (Refereed)
Abstract [en]

We approach the problem of identifying and interpreting clusters over different time scales and granularity in multivariate time series data. We extract statistical features over a sliding window of each time series, and then use a Gaussian mixture model to identify clusters which are then projected back on the data streams. The human analyst can then further analyze this projection and adjust the size of the sliding window and the number of clusters in order to capture the different types of clusters over different time scales. We demonstrate the effectiveness of our approach in two different application scenarios: (1) fleet management and (2) district heating, wherein each scenario, several different types of meaningful clusters can be identified when varying over these dimensions. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Clustering, Interaction, Time scales, Time series, Fleet operations, Gaussian distribution, Time measurement, Application scenario, Different time scale, Gaussian Mixture Model, Multiple data streams, Multivariate time series, Time-scales, Data mining
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17512 (URN)10.1145/3304079.3310286 (DOI)2-s2.0-85069762696 (Scopus ID)978-1-4503-6296-2 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-11-08Bibliographically approved
Bae, J., Havsol, J., Karpefors, M., Karlsson, A. & Mathiason, G. (2019). Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types. In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018: . Paper presented at ISCBI 2018 : 2018 6th International Symposium on Computational and Business Intelligence. Basel, Switzerland August 22 - 29 2018 (pp. 89-93). IEEE Computer Society
Open this publication in new window or tab >>Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types
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2019 (English)In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, IEEE Computer Society, 2019, p. 89-93Conference paper, Published paper (Refereed)
Abstract [en]

The technology and techniques for bio-sensors are rapidly evolving. Accordingly, there is significant business interest to identify upcoming technologies and new targets for the near future. Text information from internet reflects much of the recent information and public interests that help to understand the trend of a certain field. Thus, we utilize Dirichlet process topic modeling on different media sources containing short text (e.g., blogs, news) which is able to self-adapt the learned topic space to the data. We share the observations from the domain experts on the results derived from topic modeling on wearable biosensors from multiple media sources over more than eight years. We analyze the topics on wearable devices, forecast and market analysis, and bio-sensing techniques found from our method. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2019
Keywords
Bayesian non-parametrics, Bio-sensor, short text, topic modeling, wearable, Biosensors, Information analysis, Bayesian nonparametrics, Dirichlet process, Market analysis, Short texts, Text information, Wearable devices, Wearable sensors
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16746 (URN)10.1109/ISCBI.2018.00027 (DOI)000462379700017 ()2-s2.0-85063041846 (Scopus ID)978-1-5386-9450-3 (ISBN)978-1-5386-9451-0 (ISBN)
Conference
ISCBI 2018 : 2018 6th International Symposium on Computational and Business Intelligence. Basel, Switzerland August 22 - 29 2018
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-09-30Bibliographically approved
Christensen, J., Bae, J., Watson, B., Talamadupula, K., Spjut, J. & Joines, S. (2019). UIBK: User interactions for building knowledge. In: International Conference on Intelligent User Interfaces, Proceedings IUI: . Paper presented at 24th International Conference on Intelligent User Interfaces, IUI 2019; Marina del Ray; United States; 16 March 2019 through 20 March 2019 (pp. 131-132). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>UIBK: User interactions for building knowledge
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2019 (English)In: International Conference on Intelligent User Interfaces, Proceedings IUI, Association for Computing Machinery (ACM), 2019, p. 131-132Conference paper, Published paper (Refereed)
Abstract [en]

This half-day workshop seeks to bring together practitioners and academics interested in the challenges of structuring interactions for subject matter experts (SMEs) who are providing knowledge and/or feedback to an AI system, but are not well-versed in the underlying algorithms. Since the information provided by SMEs directly effects the efficacy of the final system, collecting the correct data is a problem that navigates issues ranging from curating data that may be tainted to structuring data collection tasks in such a way as to mitigate user boredom. The goal of this workshop is to discuss methods and new paradigms for productively interacting with users while collecting knowledge. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Model building, User experience, User interfaces, Data acquisition, Model buildings, AI systems, Data collection, Half-day workshop, Subject matter experts, User interaction
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17884 (URN)10.1145/3308557.3313122 (DOI)2-s2.0-85074451214 (Scopus ID)978-1-4503-6673-1 (ISBN)
Conference
24th International Conference on Intelligent User Interfaces, IUI 2019; Marina del Ray; United States; 16 March 2019 through 20 March 2019
Available from: 2019-11-14 Created: 2019-11-14 Last updated: 2020-01-29Bibliographically approved
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