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  • 1.
    Helldin, Tove
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Steinhauer, H. Joe
    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.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Situation Awareness in Telecommunication Networks Using Topic Modeling2018In: 2018 21st International Conference on Information Fusion, FUSION 2018, IEEE, 2018, p. 549-556Conference paper (Refereed)
    Abstract [en]

    For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.

  • 2.
    Huhnstock, Nikolas Alexander
    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.
    Riveiro, Maria
    Högskolan i Jönköping, JTH, Datateknik och informatik.
    Steinhauer, H. Joe
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    An Infinite Replicated Softmax Model for Topic Modeling2019In: Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani, Springer, 2019, p. 307-318Conference paper (Refereed)
    Abstract [en]

    In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.

    The full text will be freely available from 2020-07-25 00:00
  • 3.
    Huhnstock, Nikolas Alexander
    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.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Steinhauer, H. Joe
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    On the behavior of the infinite restricted boltzmann machine for clustering2018In: SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing / [ed] Hisham M. Haddad, Roger L. Wainwright, Richard Chbeir, New York, NY, USA: Association for Computing Machinery (ACM), 2018, p. 461-470Conference paper (Refereed)
    Abstract [en]

    Clustering is a core problem within a wide range of research disciplines ranging from machine learning and data mining to classical statistics. A group of clustering approaches so-called nonparametric methods, aims to cluster a set of entities into a beforehand unspecified and unknown number of clusters, making potentially expensive pre-analysis of data obsolete. In this paper, the recently, by Cote and Larochelle introduced infinite Restricted Boltzmann Machine that has the ability to self-regulate its number of hidden parameters is adapted to the problem of clustering by the introduction of two basic cluster membership assumptions. A descriptive study of the influence of several regularization and sparsity settings on the clustering behavior is presented and results are discussed. The results show that sparsity is a key adaption when using the iRBM for clustering that improves both the clustering performances as well as the number of identified clusters.

  • 4.
    Karlsson, Alexander
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Hammarfelt, Björn
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Steinhauer, H. Joe
    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.
    Olson, Nasrine
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Nelhans, Gustaf
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Nolin, Jan
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach2015In: Scientometrics, ISSN 0138-9130, E-ISSN 1588-2861, Vol. 102, no 3, p. 2255-2274Article in journal (Refereed)
  • 5.
    Karlsson, Alexander
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Steinhauer, H. Joe
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Evaluation of Evidential Combination Operators2013Conference paper (Refereed)
    Abstract [en]

    We present an experiment for evaluating precise and imprecise evidential combination operators. The experiment design is based on the assumption that only limited statistical information is available in the form of multinomial observations. We evaluate three different evidential combination operators; one precise, the Bayesian combination operator, and two imprecise, the credal and Dempster’s combination operator, for combining independent pieces of evidence regarding some discrete state space of interest. The evaluation is performed by using a score function that takes imprecision into account. The results show that the precise framework seems to perform equally well as the imprecise frameworks.

  • 6.
    Olson, Nasrine
    et al.
    University of Borås.
    Steinhauer, H. Joe
    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.
    Nelhans, Gustaf
    University of Borås.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Nolin, Jan
    University of Borås.
    Little Scientist, Big Data Information fusion towards meeting the information needs of scholars2014In: Assessing Libraries and Library Users and Use, 2014Conference paper (Other academic)
  • 7.
    Steinhauer, H. Joe
    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.
    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.
    Topic Modeling for Situation Understanding in Telecommunication Networks2017In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, 2017, p. 73-78Conference paper (Refereed)
  • 8.
    Steinhauer, H. Joe
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics.
    Mathiason, Gunnar
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics.
    Spatio-Temporal Awareness for Wireless Telecommunication Networks2018In: Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge, 2018, p. 49-50Conference paper (Refereed)
  • 9.
    Steinhauer, H. Joe
    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.
    Mathiason, Gunnar
    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.
    Anomaly Detection in Telecommunication Networks using Topic Models2018Conference paper (Refereed)
  • 10.
    Steinhauer, H. Joe
    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.
    Mathiason, Gunnar
    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.
    Topic modeling for anomaly detection in telecommunication networks2019In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, p. 1-12Article in journal (Refereed)
    Abstract [en]

    To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.

  • 11.
    Steinhauer, H. Joe
    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.
    Information Fusion2019In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 61-78Chapter in book (Refereed)
    Abstract [en]

    The study of information fusion comprises methods and techniques to automatically or semi-automatically combine information stemming from homogeneous or heterogeneous sources into a representation that supports a human user’s situation awareness for the purposes of decision making. Information fusion is not an end in itself but studies, adapts, applies and combines methods, techniques and algorithms provided by many other research areas, such as artificial intelligence, data mining, machine learning and optimization, in order to customize solutions for specific tasks. There are many different models for information fusion that describe the overall process as tasks building upon each other on different levels of abstraction. Information fusion includes the analysis of information, the inference of new information and the evaluation of uncertainty within the information. Hence, uncertainty management plays a vital role within the information fusion process. Uncertainty can be expressed by probability theory or, in the form of non-specificity and discord, by, for example, evidence theory.

  • 12.
    Steinhauer, H. Joe
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Traceable Uncertainty for Threat Evaluation in Air to Ground Scenarios2013In: Twelfth Scandinavian Conference on Artificial Intelligence: SCAI 2013 / [ed] Manfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani, IOS Press, 2013, p. 255-264Conference paper (Refereed)
    Abstract [en]

    In this paper we apply our method for traceable uncertainty to the application scenario of threat evaluation. The paper shows how the uncertainty within a decision support process can be traced and used to include a human decision maker in the decision making process by pointing to situations within the process where unusually high uncertainty is encountered. The human decision maker can then contribute with context information or expert knowledge to resolve the situation.

  • 13.
    Steinhauer, H. Joe
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Andler, Sten F.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Traceable Uncertainty2013In: Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, 2013, p. 1582-1589Conference paper (Refereed)
  • 14.
    Steinhauer, H. Joe
    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.
    Mathiason, Gunnar
    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.
    Root-Cause Localization using Restricted Boltzmann Machines2016In: 2016 19th International Conference on Information Fusion Proceedings, IEEE Computer Society, 2016, p. 248-255Conference paper (Refereed)
  • 15.
    Steinhauer, H. Joe
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Marsland, Stephen
    School of Engineering and Advanced Technology, Massey University, New Zealand.
    Guesgen, Hans W.
    School of Engineering and Advanced Technology, Massey University, New Zealand.
    Context Awareness for a Smart Environment Utilizing Context Maps and Dempster-Shafer Theory2012In: Impact Analysis of Solutions for Chronic Disease Prevention and Management: 10th International Conference on Smart Homes and Health Telematics, ICOST 2012, Artiminio, Italy, June 12-15, 2012. Proceedings / [ed] Mark Donnelly, Cristiano Paggetti, Chris Nugent, Mounir Mokhtari, Springer Berlin/Heidelberg, 2012, p. 270-273Conference paper (Refereed)
    Abstract [en]

    In this paper we describe context awareness for a smart home using previously collected qualitative data. Based on this, context experts estimate to what extent a behavior is likely to occur in the given situation. The experts’ estimations are then combined using Dempster-Shafer Theory. The result can be used to (a) predict the most likely behavior and (b) to verify to what extent a behavior that has been detected is usual in the given situation.

  • 16.
    Steinhauer, H. Joe
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Mellin, Jonas
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Automatic Early Risk Detection of Possible Medical Conditions for Usage Within an AMI-System2015In: Ambient Intelligence - Software and Applications / [ed] Amr Mohamed, Paulo Novais, António Pereira, Gabriel Villarrubia González, Antonio Fernández-Caballero, Springer Berlin/Heidelberg, 2015, p. 13-21Conference paper (Refereed)
    Abstract [en]

    Using hyperglycemia as an example, we present how Bayesian networks can be utilized for automatic early detection of a person’s possible medical risks based on information provided by un obtrusive sensors in their living environments. The network’s outcome can be used as a basis on which an automated AMI-system decides whether to interact with the person, their caregiver, or any other appropriate party. The networks’ design is established through expert elicitation and validated using a half-automated validation process that allows the medical expert to specify validation rules. To interpret the networks’ results we use an output dictionary which is automatically generated for each individual network and translates the output probability into the different risk classes (e.g.,no risk, risk).

  • 17.
    Torra, Vicenç
    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.
    Steinhauer, H. Joe
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Berglund, Stefan
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Artificial Intelligence2019In: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 9-26Chapter in book (Refereed)
    Abstract [en]

    This chapter gives a brief introduction to what artificial intelligence is. We begin discussing some of the alternative definitions for artificial intelligence and introduce the four major areas of the field. Then, in subsequent sections we present these areas. They are problem solving and search, knowledge representation and knowledge-based systems, machine learning, and distributed artificial intelligence. The chapter follows with a discussion on some ethical dilemma we find in relation to artificial intelligence. A summary closes this chapter.

1 - 17 of 17
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