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  • 1.
    Aoga, John O. R.
    et al.
    Ecole Doctorale Science Pour Ingenieur, Université d’Abomey-Calavi, Abomey-Calavi, Benin.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Skövde Artificial Intelligence Lab (SAIL).
    Veljanoska, Stefanija
    Université de Rennes 1, CNRS/CREM-UMR621, Rennes, France.
    Nijssen, Siegfried
    ICTEAM, Université catholique de Louvain, Belgium.
    Schaus, Pierre
    ICTEAM, Université catholique de Louvain, Belgium.
    Impact of Weather Factors on Migration Intention Using Machine Learning Algorithms2024In: Operations Research Forum, E-ISSN 2662-2556, Vol. 5, no 1, article id 8Article in journal (Refereed)
    Abstract [en]

    A growing attention in the empirical literature has been paid on the incidence of climate shocks and change on migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks toward an individual’s intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We performed several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they influence the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) the weather features improve the prediction performance, although socioeconomic characteristics have more influence on migration intentions, (ii) a country-specific model is necessary, and (iii) the international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

  • 2.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Aoga, John
    Université d'Abomey Calavi, Ecole Doctorale Science Pour Ingénieur, Benin.
    Forecasting migration intention using multivariate time series2020In: ICVISP 2020: Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing, New York: Association for Computing Machinery (ACM), 2020, p. 1-6, article id 3448883Conference paper (Refereed)
    Abstract [en]

    This paper aims to analyze international migrations in western African countries using irregular multivariate monthly time series containing a few values. Existing methods of filling in missing values have limitations because there are not enough values to infer them. In this study, we explore two approaches to solve this problem. One approach is to aggregate the values annually to eliminate missing values. The other is to use the Random Forest (RF) based approach to fill in the missing values. Then, we predict the international migration intentions using deep learning approaches and time series dataset. We demonstrate that a RF-based imputation outperforms a zero filling approach (used as the baseline) with Long Short-Term Memory (LSTM) method. Moreover, we show that analyzing the monthly subregion-based time series provides better insights than the yearly country-based time series. 

  • 3.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Visual Data Analysis2019In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 133-155Chapter in book (Refereed)
    Abstract [en]

    Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

  • 4.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Havsol, Jesper
    AstraZeneca, Gothenburg, Sweden.
    Karpefors, Martin
    AstraZeneca, Gothenburg, Sweden.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types2019In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, IEEE Computer Society, 2019, p. 89-93Conference 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. 

  • 5.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. University of Skövde .
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. University of Skövde.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. University of Skövde.
    Identifying Root Cause and Derived Effects in Causal Relationships2017In: Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I / [ed] Sakae Yamamoto, Springer, 2017, p. 22-34Conference paper (Refereed)
    Abstract [en]

    This paper focuses on identifying factors that influence the process of finding a root cause and a derived effect in causal node-link graphs with associated strength and significance depictions. We discuss in detail the factors that seem to be involved in identifying a global cause and effect based on the analysis of the results of an online user study with 44 participants, who used both sequential and non-sequential graph layouts. In summary, the results show that participants show geodesic-path tendencies when selecting causes and derived effects, and that context matters, i.e., participant’s own beliefs, experiences and knowledge might influence graph interpretation.

  • 6.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Understanding Indirect Causal Relationships in Node-Link Graphs2017In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 3, p. 411-421Article in journal (Refereed)
    Abstract [en]

    To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node-link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi-attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.

  • 7.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Jönköping University, Department of Computer Science and Informatics, School of Engineering, Jönköping, Sweden.
    Nowaczyk, Slawomir
    University of Halmstad, School of Information Technology, Halmstad, Sweden.
    Bouguelia, Mohamed-Rafik
    University of Halmstad, School of Information Technology, Halmstad, Sweden.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Interactive clustering: A comprehensive review2020In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 53, no 1, article id 1Article in journal (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.

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  • 8.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Mellin, Jonas
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics.
    Ståhl, Niclas
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Complex Data Analysis2019In: 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.

  • 9.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Li, Yurong
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Ståhl, Niclas
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Kojola, Niklas
    Group function R&I, SSAB, Stockholm, Sweden.
    Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System2020In: Metallurgical and materials transactions. B, process metallurgy and materials processing science, ISSN 1073-5615, E-ISSN 1543-1916, Vol. 51, no 4, p. 1632-1645Article in journal (Refereed)
    Abstract [en]

    The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset. © 2020, The Author(s).

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  • 10.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Li, Yurong
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Kojola, Niklas
    Group R and I, SSAB, Stockholm, Sweden.
    Ståhl, Niclas
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Understanding Robust Target Prediction in Basic Oxygen Furnace2021In: IEIM 2021: The 2nd International Conference on Industrial Engineering and Industrial Management, New York, NY: Association for Computing Machinery (ACM), 2021, p. 56-62Conference paper (Refereed)
    Abstract [en]

    The problem of using machine learning (ML) to predict the process endpoint for a Basic Oxygen Furnace (BOF) process used for steelmaking has been largely studied. However, current research often lacks both the usage of a rich dataset and does not address revealing influential factors that explain the process. The process is complex and difficult to control and has a multi-objective target endpoint with a proper range of heat temperature combined with sufficiently low levels of carbon and phosphorus. Reaching this endpoint requires skilled process operators, who are manually controlling the heat throughout the process by using both implicit and explicit control variables in their decisions. Trained ML models can reach good BOF target prediction results, but it is still a challenge to extract the influential factors that are significant to the ML prediction accuracy. Thus, it becomes a challenge to explain and validate an ML prediction model that claims to capture the process well. This paper makes use of a complex and full production dataset to evaluate and compare different approaches for understanding how the data can determine the process target prediction. One approach is based on the collected process data and the other on the ML approach trained on that data to find the influential factors. These complementary approaches aim to explain the BOF process to reveal actionable information on how to improve process control.

  • 11.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Evaluating Multi-Attributes on Cause and Effect Relationship Visualization2017In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP / [ed] Alexandru Telea; Jose Braz; Lars Linsen, SciTePress, 2017, p. 64-74Conference paper (Refereed)
    Abstract [en]

    This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

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  • 12.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    On the Visualization of Discrete Non-additive Measures2018In: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Vicenç Torra; Radko Mesiar; Bernard De Baets, Springer, 2018, p. 200-210Conference paper (Refereed)
    Abstract [en]

    Non-additive measures generalize additive measures, and have been utilized in several applications. They are used to represent different types of uncertainty and also to represent importance in data aggregation. As non-additive measures are set functions, the number of values to be considered grows exponentially. This makes difficult their definition but also their interpretation and understanding. In order to support understability, this paper explores the topic of visualizing discrete non-additive measures using node-link diagram representations.

  • 13.
    Christensen, Johanne
    et al.
    North Carolina State University, United States.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Watson, Benjamin
    North Carolina State University, United States.
    Talamadupula, Kartik
    IBM Research, United States.
    Spjut, Josef
    NVIDIA, United States.
    Joines, Stacy
    IBM Watson, United States.
    UIBK: User interactions for building knowledge2019In: International Conference on Intelligent User Interfaces, Proceedings IUI, Association for Computing Machinery (ACM), 2019, p. 131-132Conference 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. 

  • 14.
    Helldin, Tove
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Alklind Taylor, Anna-Sofia
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Intelligent User Interfaces: Trends and application areas2019Report (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.

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  • 15.
    Holst, Anders
    et al.
    RISE SICS, Stockholm, Sweden.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Bouguelia, Mohamed-Rafik
    Department of Intelligent Systems and Digital Design, Halmstad University, Sweden.
    Interactive clustering for exploring multiple data streams at different time scales and granularity2019In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019, p. 1-7, article id 2Conference 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. 

  • 16.
    Holst, Anders
    et al.
    RISE SICS, Sweden.
    Bouguelia, Mohamed-Rafik
    CAISR, Halmstad, Sweden.
    Görnerup, Olof
    RISE SICS, Sweden.
    Pashami, Sepideh
    CAISR, Halmstad, Sweden.
    Al-Shishtawy, Ahmad
    RISE SICS, Sweden.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Girdzijauskas, Šarunas
    RISE SICS, Sweden.
    Nowaczyk, Sławomir
    CAISR, Halmstad, Sweden.
    Soliman, Amira
    RISE SICS, Sweden.
    Eliciting structure in data2019In: CEUR Workshop Proceedings / [ed] Christoph Trattner, Denis Parra, Nathalie Riche, CEUR-WS , 2019, Vol. 2327Conference 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. 

  • 17.
    Holst, Anders
    et al.
    RISE SICS, Stockholm, Sweden.
    Pashami, Sepideh
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Incremental causal discovery and visualization2019In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019, p. 1-6Conference 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. 

  • 18.
    Karlsson, Alexander
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Duarte, Denio
    Campus Chapecó, Federal University of Fronteira sul, Chapecó, Brazil.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Evaluation of the dirichlet process multinomial mixture model for short-text topic modeling2018In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, USA: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 79-83, article id 8638311Conference paper (Refereed)
    Abstract [en]

    Fast-moving trends, both in society and in highly competitive business areas, call for effective methods for automatic analysis. The availability of fast-moving sources in the form of short texts, such as social media and blogs, allows aggregation from a vast number of text sources, for an up to date view of trends and business insights. Topic modeling is established as an approach for analysis of large amounts of texts, but the scarcity of statistical information in short texts is considered to be a major problem for obtaining reliable topics from traditional models such as LDA. A range of different specialized topic models have been proposed, but a majority of these approaches rely on rather strong parametric assumptions, such as setting a fixed number of topics. In contrast, recent advances in the field of Bayesian non-parametrics suggest the Dirichlet process as a method that, given certain hyper-parameters, can self-adapt to the number of topics of the data at hand. We perform an empirical evaluation of the Dirichlet process multinomial (unigram) mixture model against several parametric topic models, initialized with different number of topics. The resulting models are evaluated, using both direct and indirect measures that have been found to correlate well with human topic rankings. We show that the Dirichlet Process Multinomial Mixture model is a viable option for short text topic modeling since it on average performs better, or nearly as good, compared to the parametric alternatives, while reducing parameter setting requirements and thereby eliminates the need of expensive preprocessing. 

  • 19.
    Kim, Johoo
    et al.
    Purdue University, West Lafayette, IN, United States.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Hastak, Markand
    Purdue University, West Lafayette, IN, United States.
    Emergency information diffusion on online social media during storm Cindy in U.S.2018In: International Journal of Information Management, ISSN 0268-4012, E-ISSN 1873-4707, Vol. 40, p. 153-165Article in journal (Refereed)
    Abstract [en]

    Social media plays a critical role in propagating emergency information during disasters. Governmental agencies have opened social media accounts for emergency communication channels. To understand the underlying mechanism of user behaviors and engagement, this study employs social network analysis to investigate information network and diffusion across news, weather agencies, governmental agencies, organizations and the public during the 2017 Storm Cindy in the U.S. This study identified certain types of Twitter users (news and weather agencies) were dominant as information sources and information diffusers (the public and organizations). However, the information flow in the network was controlled by numerous types of users including news, agency, weather agencies and the public. The results highlighted the importance of understanding the unique characteristics of social media and networks for better emergency communication system. © 2018 Elsevier Ltd

  • 20.
    Olson, Nasrine
    et al.
    Swedish School of Library and Information Science (SSLIS), University of Borås, Borås, Sweden.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Biosensors-Publication Trends and Knowledge Domain Visualization2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 11, article id 2615Article in journal (Refereed)
    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.

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  • 21.
    Said, Alan
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Parra, Denis
    Pontifical Catholic University of Chile, Santiago, Chile.
    Pashami, Sepideh
    Halmstad University, Sweden.
    IDM-WSDM 2019: Workshop on interactive data mining2019In: 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 (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).

  • 22.
    Ståhl, Niclas
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Utilising Data from Multiple Production Lines for Predictive Deep Learning Models2022In: Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference / [ed] Kenji Matsui; Sigeru Omatu; Tan Yigitcanlar; Sara Rodríguez González, Cham: Springer, 2022, p. 67-76Conference paper (Refereed)
    Abstract [en]

    A Basic Oxygen Furnace (BOF) for steel making is a complex industrial process that is difficult to monitor due to the harsh environment, so the collected production data is very limited given the process complexity. Also, such production data has a low degree of variability. An accurate machine learning (ML) model for predicting production outcome requires both large and varied data, so utilising data from multiple BOFs will allow for more capable ML models, since both the amount and variability of data increases. Data collection setups for different BOFs are different, such that data sets are not compatible to directly join for ML training. Our approach is to let a neural network benefit from these collection differences in a joint training model. We present a neural network-based approach that simultaneously and jointly co-trains on several data sets. Our novelty is that the first network layer finds an internal representation of each individual BOF, while the other layers use this representation to concurrently learn a common BOF model. Our evaluation shows that the prediction accuracy of the common model increases compared to separate models trained on individual furnaces’ data sets. It is clear that multiple data sets can be utilised this way to increase model accuracy for better production prediction performance. For the industry, this means that the amount of available data for model training increases and thereby more capable ML models can be trained when having access to multiple data sets describing the same or similar manufacturing processes. 

  • 23.
    Ventocilla, Elio
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Said, Alan
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    A Billiard Metaphor for Exploring Complex Graphs2017In: Second Workshop on Supporting Complex Search Tasks / [ed] Marijn Koolen; Jaap Kamps; Toine Bogers; Nick Belkin; Diane Kelly; Emine Yilmaz, CEUR-WS , 2017, Vol. 1798, p. 37-40Conference paper (Refereed)
    Abstract [en]

    Exploring and revealing relations between the elements is a fre-quent task in exploratory analysis and search. Examples includethat of correlations of attributes in complex data sets, or facetedsearch. Common visual representations for such relations are di-rected graphs or correlation matrices. These types of visual encod-ings are often - if not always - fully constructed before being shownto the user. This can be thought of as a top-down approach, whereusers are presented with a full picture for them to interpret andunderstand. Such a way of presenting data could lead to a visualoverload, specially when it results in complex graphs with highdegrees of nodes and edges. We propose a bottom-up alternativecalled Billiard where few elements are presented at rst and fromwhich a user can interactively construct the rest based on whats/he nds of interest. The concept is based on a billiard metaphorwhere a cue ball (node) has an eect on other elements (associatednodes) when stroke against them.

  • 24.
    Ventocilla, Elio
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Boeva, Veselka
    Blekinge Institute of Technology, Department of Computer Science and Engineering.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Lavesson, Niklas
    Jönköping University, School of Engineering.
    Towards a Taxonomy for Interpretable and Interactive Machine Learning2018In: / [ed] David W. Aha, Trevor Darrell, Patrick Doherty, Daniele Magazzeni, 2018, p. 151-157Conference paper (Refereed)
    Abstract [en]

    We propose a taxonomy for classifying and describing papers which contribute to making Machine Learning (ML) techniques interactive and interpretable for users. The taxonomy is composed of six elements – Dataset, Optimizer, Model, Predictions, Evaluator and Goodness – where each can bemade available for user interpretation and interaction. We give definitions to the terms interpretable and interactive in the context of useroriented Machine Learning, describe the role of each of the elements in the taxonomy, and describe papers as seen through the lens of the proposed taxonomy.

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