his.sePublikasjoner
Endre søk
Begrens søket
1 - 48 of 48
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Antonucci, Alessandro
    et al.
    Dalle Molle Institute for Artificial Intelligence (IDSIA), Lugano, Switzerland.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Sundgren, David
    Stockholm University, Sweden.
    Decision Making with Hierarchical Credal Sets2014Inngår i: Information Processing and Management of Uncertainty in Knowledge-Based Systems: 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014, Proceedings, Part III / [ed] Anne Laurent, Oliver Strauss Oliver, Bernadette Bouchon-Meunier, Ronald R. Yager, Springer, 2014, s. 456-465Konferansepaper (Fagfellevurdert)
  • 2.
    Bae, Juhee
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Havsol, Jesper
    AstraZeneca, Gothenburg, Sweden.
    Karpefors, Martin
    AstraZeneca, Gothenburg, Sweden.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types2019Inngår i: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, IEEE Computer Society, 2019, s. 89-93Konferansepaper (Fagfellevurdert)
    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. 

  • 3.
    Bae, Juhee
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mellin, Jonas
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Högskolan i Skövde, Institutionen för informationsteknologi.
    Ståhl, Niclas
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Complex Data Analysis2019Inngår i: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 157-169Kapittel i bok, del av antologi (Fagfellevurdert)
    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.

  • 4.
    Boström, Henrik
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Brohede, Marcus
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information.
    van Laere, Joeri
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Niklasson, Lars
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Nilsson, Marie
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Persson, Anne
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Ziemke, Tom
    Högskolan i Skövde, Institutionen för kommunikation och information.
    On the Definition of Information Fusion as a Field of Research2007Rapport (Annet vitenskapelig)
    Abstract [en]

    A more precise definition of the field of information fusion can be of benefit to researchers within the field, who may use uch a definition when motivating their own work and evaluating the contribution of others. Moreover, it can enable researchers and practitioners outside the field to more easily relate their own work to the field and more easily understand the scope of the techniques and methods developed in the field. Previous definitions of information fusion are reviewed from that perspective, including definitions of data and sensor fusion, and their appropriateness as definitions for the entire research field are discussed. Based on strengths and weaknesses of existing definitions, a novel definition is proposed, which is argued to effectively fulfill the requirements that can be put on a definition of information fusion as a field of research.

  • 5.
    Boström, Henrik
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On Evidential Combination Rules for Ensemble Classifiers2008Inngår i: Proceedings of the 11th International Conference on Information Fusion, IEEE , 2008, s. 553-560Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Ensemble classifiers are known to generally perform better than each individual classifier of which they consist. One approach to classifier fusion is to apply Shafer’s theory of evidence. While most approaches have adopted Dempster’s rule of combination, a multitude of combination rules have been proposed. A number of combination rules as well as two voting rules are compared when used in conjunction with a specific kind of ensemble classifier, known as random forests, w.r.t. accuracy, area under ROC curve and Brier score on 27 datasets. The empirical evaluation shows that the choice of combination rule can have a significant impact on the performance for a single dataset, but in general the evidential combination rules do not perform better than the voting rules for this particular ensemble design. Furthermore, among the evidential rules, the associative ones appear to have better performance than the non-associative.

  • 6.
    Bouguelia, Mohamed-Rafik
    et al.
    Center for Applied Intelligent Systems Research, Halmstad University, Sweden.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Pashami, Sepideh
    Center for Applied Intelligent Systems Research, Halmstad University, Sweden.
    Nowaczyk, Sławomir
    Center for Applied Intelligent Systems Research, Halmstad University, Sweden.
    Holst, Anders
    Swedish Institute of Computer Science, Sweden.
    Mode tracking using multiple data streams2018Inngår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 43, s. 33-46Artikkel i tidsskrift (Fagfellevurdert)
  • 7.
    Brax, Christoffer
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Niklasson, Lars
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Evaluating Precise and Imprecise State-Based Anomaly Detectors for Maritime Surveillance2010Inngår i: Proceedings of the 13th International Conference on Information Fusion, IEEE conference proceedings, 2010, s. Article number 5711997-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We extend the State-Based Anomaly Detection approach by introducing precise and imprecise anomaly detectors using the Bayesian and credal combination operators, where evidences over time are combined into a joint evidence. We use imprecision in order to represent the sensitivity of the classification regarding an object being  normal or anomalous. We evaluate the detectors on a real-world maritime dataset containing recorded AIS data and show that the anomaly detectors outperform   previously proposed detectors based on Gaussian mixture models and kernel density estimators. We also show that our introduced anomaly detectors perform slightly better than the State-Based Anomaly Detection approach with a sliding window.

  • 8.
    Helldin, Tove
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Situation Awareness in Telecommunication Networks Using Topic Modeling2018Inngår i: 2018 21st International Conference on Information Fusion, FUSION 2018, IEEE, 2018, s. 549-556Konferansepaper (Fagfellevurdert)
    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.

  • 9.
    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
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Said, Alan
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Bae, Juhee
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Girdzijauskas, Šarunas
    RISE SICS, Sweden.
    Nowaczyk, Sławomir
    CAISR, Halmstad, Sweden.
    Soliman, Amira
    RISE SICS, Sweden.
    Eliciting structure in data2019Inngår i: CEUR Workshop Proceedings / [ed] Christoph Trattner, Denis Parra, Nathalie Riche, CEUR-WS , 2019, Vol. 2327Konferansepaper (Fagfellevurdert)
    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. 

  • 10.
    Holst, Anders
    et al.
    RISE SICS, Stockholm, Sweden.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Bae, Juhee
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    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 granularity2019Inngår i: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019Konferansepaper (Fagfellevurdert)
    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. 

  • 11.
    Huhnstock, Nikolas Alexander
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Riveiro, Maria
    Högskolan i Jönköping, JTH, Datateknik och informatik.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    An Infinite Replicated Softmax Model for Topic Modeling2019Inngår i: 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, s. 307-318Konferansepaper (Fagfellevurdert)
    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.

    Fulltekst tilgjengelig fra 2020-07-25 00:00
  • 12.
    Huhnstock, Nikolas Alexander
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On the behavior of the infinite restricted boltzmann machine for clustering2018Inngår i: 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, s. 461-470Konferansepaper (Fagfellevurdert)
    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.

  • 13.
    Johansson, Ronnie
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Boström, Henrik
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    A Study on Class-Specifically Discounted Belief for Ensemble Classifiers2008Inngår i: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), IEEE Press, 2008, s. 614-619Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Ensemble classifiers are known to generally perform better than their constituent classifiers. Whereas a lot of work has been focusing on the generation of classifiers for ensembles, much less attention has been given to the fusion of individual classifier outputs. One approach to fuse the outputs is to apply Shafer’s theory of evidence, which provides a flexible framework for expressing and fusing beliefs. However, representing and fusing beliefs is non-trivial since it can be performed in a multitude of ways within the evidential framework. In a previous article, we compared different evidential combination rules for ensemble fusion. The study involved a single belief representation which involved discounting (i.e., weighting) the classifier outputs with classifier reliability. The classifier reliability was interpreted as the classifier’s estimated accuracy, i.e., the percentage of correctly classified examples. However, classifiers may have different performance for different classes and in this work we assign the reliability of a classifier output depending on the classspecific reliability of the classifier. Using 27 UCI datasets, we compare the two different ways of expressing beliefs and some evidential combination rules. The result of the study indicates that there is indeed an advantage of utilizing class-specific reliability compared to accuracy in an evidential framework for combining classifiers in the ensemble design considered.

  • 14.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Dependable and generic high-level information fusion: methods and algorithms for uncertainty management2007Rapport (Annet vitenskapelig)
    Abstract [en]

    The main goal of information fusion can be seen as exploiting diversities in information to improve decision making. The research field of information fusion can be divided into two parts: low-level information fusion and high-level information fusion. Most of the research so far, has concerned the lower levels, e.g., signal processing and multisensor data fusion, while high-level information fusion, e.g., clustering of entities, has been relatively uncharted. High-level information fusion aims at providing decision support (human or automatic) concerning situations. A crucial issue for decision making based on such support is trust, defined as “accepted dependence”, where dependence or dependability is an overall term for other concepts, e.g., reliability. Dependability requirements in high-level information fusion refer to properties of belief measures and hypotheses regarding situations. Even though meeting such requirements is considered to be a precondition for trust in fusion-based decision-making; research in high-level information fusion that addresses this issue is scarce. Since most of the research in high-level information fusion relate to defense applications, another important issue is to generalize existing terminology, methods, and algorithms, in order to allow for researchers in other domains to more easily adopt such results. In this report, it is argued that more research is needed for these issues and a set of research questions for future research is presented.

  • 15.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Evaluating Credal Set Theory as a Belief Framework in High-Level Information Fusion for Automated Decision-Making2008Rapport (Annet vitenskapelig)
    Abstract [en]

    The goal of high-level information fusion is to provide effective decision-support regarding situations, e.g., relations between events. One of the main ways that has been proposed in order to achieve this is to reduce uncertainty regarding the situation by utilizing multiple sources of information. There exist two types of uncertainty: aleatory and epistemic. Aleatory uncertainty, also known as uncertainty due to chance, cannot be reduced regardless of the amount of information. Epistemic uncertainty, on the other hand, also known as uncertainty due to lack of information, can be reduced if more information becomes available. Since the goal of high-level information fusion states that we want to reduce uncertainty by utilizing information, we conclude that the type of uncertainty referred to is epistemic in nature. Uncertainty in high-level information fusion is most often expressed via a belief framework. The most common such framework in high-level information fusion is  precise Bayesian theory. In this thesis proposal we argue that precise Bayesian theory cannot adequately represent epistemic uncertainty and that there exists another befief framework referred to as credal set theory that possesses this ability. This can actually be demonstrated by such simple as tossing a coin. In precise Bayesian theory, assuming no prior information about the coin, the same probability of "Head" can be adopted as the belief before any information is available, as a prior, as well as later when a large amount of information is available, as a posterior. By utilizing credal set theory, where a credal set is defined as a closed convex set of probability measures, this case amounts to representing the prior of "Head" as a probability interval, and a posterior with a smaller interval. The idea is that when a large amount of information is available, the interval converges into a point, i.e., the length of the interval, or degree of imprecision, reflects the degree of epistemic uncertainty. In precise Bayesian theory, a common automated decision-making strategy is to decide for the action that maximizes the expected utility with respect to a utility function and a probability measure. Since the probability measure cannot adequately reflect the amount of information of which it is based on, this is an approach that does not take epistemic uncertainty into consideration, i.e., it is possible to decide for an action based on a high degree of epistemic uncertainty, and not even be aware of it. By utilizing credal set theory, epistemic uncertainty is reflected by imprecision in both probabilities and expected utilities. The main problem addressed in this thesis proposal is to decide if better automated decisions can be made by utilizing credal set theory as a belief framework in high-level information fusion, in comparison to precise Bayesian theory. The research question addressed is whether it is possible to characterize, in terms of degree of epistemic uncertainty, when and why one framework is better suited than the other for this purpose.

  • 16.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Evaluating Credal Set Theory as a Belief Framework in High-Level Information Fusion for Automated Decision-Making2010Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    High-level information fusion is a research field in which methods for achieving an overall understanding of the current situation in an environment of interest are studied. The ultimate goal of these methods is to provide effective decision-support for human or automated decision-making. One of the main proposed ways of achieving this is to reduce the uncertainty, coupled with the decision, by utilizing multiple sources of information. Handling uncertainty in high-level information fusion is performed through a belief framework, and one of the most commonly used such frameworks is based on Bayesian theory. However, Bayesian theory has often been criticized for utilizing a representation of belief and evidence that does not sufficiently express some types of uncertainty. For this reason, a generalization of Bayesian theory has been proposed, denoted as credal set theory, which allows one to represent belief and evidence imprecisely. In this thesis, we explore whether credal set theory yields measurable advantages, compared to Bayesian theory, when used as a belief framework in high-level information fusion for automated decision-making, i.e., when decisions are made by some pre-determined algorithm. We characterize the Bayesian and credal operators for belief updating and evidence combination and perform three experiments where the Bayesian and credal frameworks are evaluated with respect to automated decision-making. The decision performance of the frameworks are measured by enforcing a single decision, and allowing a set of decisions, based on the frameworks’ belief and evidence structures. We construct anomaly detectors based on the frameworks and evaluate these detectors with respect to maritime surveillance. The main conclusion of the thesis is that although the credal framework uses considerably more expressive structures to represent belief and evidence, compared to the Bayesian framework, the performance of the credal framework can be significantly worse, on average, than that of the Bayesian framework, irrespective of the amount of imprecision.

  • 17.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Dahlbom, Anders
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Zhong, Hui
    Advanced Technology and Research, Volvo Group Trucks Technology, Gothenburg, Sweden.
    Evidential Combination Operators for Entrapment Prediction in Advanced Driver Assistance Systems2014Inngår i: Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings, 2014, s. 194-203Konferansepaper (Fagfellevurdert)
  • 18.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Duarte, Denio
    Campus Chapecó, Federal University of Fronteira sul, Chapecó, Brazil.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Bae, Juhee
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Evaluation of the dirichlet process multinomial mixture model for short-text topic modeling2018Inngår i: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, USA: Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 79-83, artikkel-id 8638311Konferansepaper (Fagfellevurdert)
    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.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Hammarfelt, Björn
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    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 approach2015Inngår i: Scientometrics, ISSN 0138-9130, E-ISSN 1588-2861, Vol. 102, nr 3, s. 2255-2274Artikkel i tidsskrift (Fagfellevurdert)
  • 20.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    An Empirical Comparison of Bayesian and Credal Combination Operators2010Inngår i: FUSION 2010: 13th international Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK, IEEE conference proceedings, 2010, s. Article number 5711907-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We are interested in whether or not representing and maintaining imprecision is beneficial when combining evidences from multiple sources. We perform two experiments that contain different levels of risk and where we measure the performance of the Bayesian and credal combination operators by using a simple score function that measures the informativeness of a reported decision set. We show that the Bayesian combination operator performed on centroids of operand credal sets outperforms the credal combination operator when no risk is involved in the decision problem. We also show that if a risk component is present in the decision problem, a simple cautious decision policy for the Bayesian combination operator can be constructed that outperforms the corresponding credal decision policy.

  • 21.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Andler, Sten F
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    An Empirical Comparison of Bayesian and Credal Networks for Dependable High-Level Information Fusion2008Inngår i: Proceedings of the 11th International Conference on Information Fusion, IEEE Press, 2008, s. 1359-1366Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Bayesian networks are often proposed as a method for high-level information fusion. However, a Bayesian network relies on strong assumptions about the underlying probabilities. In many cases it is not realistic to require such precise probability assessments. We show that there exists a significant set of problems where credal networks outperform Bayesian networks, thus enabling more dependable decision making for this type of problems. A credal network is a graphical probabilistic method that utilizes sets of probability distributions, e.g., interval probabilities, for representation of belief. Such a representation allows one to properly express epistemic uncertainty, i.e., uncertainty that can be reduced if more information becomes available. Since reducing uncertainty has been proposed as one of the main goals of information fusion, the ability to represent epistemic uncertainty becomes an important aspect in all fusion applications.

  • 22.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    An Empirical Comparison of Bayesian and Credal Set Theory for Discrete State Estimation2010Inngår i: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods: 13th International Conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010. Proceedings, Part I / [ed] Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann, Springer Berlin/Heidelberg, 2010, s. 80-89Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We are interested in whether or not there exist any advantages of utilizing credal set theory for the discrete state estimation problem. We present an experiment where we compare in total six different methods, three based on Bayesian theory and three on credal set theory. The results show that Bayesian updating performed on centroids of operand credal sets significantly outperforms the other methods. We analyze the result based on degree of imprecision, position of extreme points, and second-order distributions.

  • 23.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Swedish Defence Research Agency (FOI), Stockholm.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Characterization and Empirical Evaluation of Bayesian and Credal Combination Operators2011Inngår i: Journal of Advances in Information Fusion, ISSN 1557-6418, Vol. 6, nr 2, s. 150-166Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We address the problem of combining independent evidences from multiple sources by utilizing the Bayesian and credal combination operators. We present measures for degree of conflict and imprecision, which we use in order to characterize the behavior of the operators through a number of examples. We introduce discounting operators that can be used whenever information about the reliability of sources is available. The credal discounting operator discounts a credal set with respect to an interval of reliability weights, hence, we allow for expressing reliability of sources imprecisely. We prove that the credal discounting operator can be computed by using the extreme points of its operands. We also perform two experiments containing different levels of risk where we compare the performance of the Bayesian and credal combination operators by using a simple score function that measures the informativeness of a reported decision set. We show that the Bayesian combination operator performed on centroids of operand credal sets outperforms the credal combination operator when no risk is involved in the decision problem. We also show that if a risk component is present in the decision problem, a simple cautious decision policy for the Bayesian combination operator can be constructed that outperforms the corresponding credal decision policy.

  • 24.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Imprecise Probability as an Approach to Improved Dependability in High-Level Information Fusion2008Inngår i: Interval / Probabilistic Uncertainty and Non-Classical Logics / [ed] Van-Nam Huynh, Yoshiteru Nakamori, Hiroakira Ono, Jonathan Lawry, Vladik Kreinovich, Hung T. Nguyen, Springer Berlin/Heidelberg, 2008, s. 70-84Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    The main goal of information fusion can be seen as improving human or automatic decision-making by exploiting diversities in information from multiple sources. High-level information fusion aims specifically at decision support regarding situations, often expressed as “achieving situation awareness”. A crucial issue for decision making based on such support is trust that can be defined as “accepted dependence”, where dependence or dependability is an overall term for many other concepts, e.g., reliability. This position paper reports on ongoing and planned research concerning imprecise probability as an approach to improved dependability in high-level information fusion. We elaborate on high-level information fusion from a generic perspective and a partial mapping from a taxonomy of dependability to high-level information fusion is presented. Three application domains: defense, manufacturing, and precision agriculture, where experiments are planned to be implemented are depicted. We conclude that high-level information fusion as an application-oriented research area, where precise probability (Bayesian theory) is commonly adopted, provides an excellent evaluation ground for imprecise probability.

  • 25.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On the Behavior of the Robust Bayesian Combination Operator and the Significance of Discounting2009Inngår i: ISIPTA ’09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications / [ed] Thomas Augustin, Frank P. A. Coolen, Serafin Moral, Matthias C. M. Troffaes, Society for Imprecise Probability , 2009, s. 259-268Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We study the combination problem for credal sets via the robust Bayesian combination operator. We extend Walley's notion of degree of imprecision and introduce a measure for degree of conflict between two credal sets. Several examples are presented in order to explore the behavior of the robust Bayesian combination operator in terms of imprecision and conflict. We further propose a discounting operator that suppresses a source given an interval of reliability weights, and highlight the importance of using such weights whenever additional information about the reliability of a source is available.

  • 26.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Evaluation of Evidential Combination Operators2013Konferansepaper (Fagfellevurdert)
    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.

  • 27.
    Karlsson, Alexander
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Sundgren, David
    Department of Computer and Systems Sciences, DSV, Stockholm University, DSV, Forum 100, Kista, Sweden.
    Second-Order Credal Combination of Evidence2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We utilize second-order probability distributions for modeling second-order information over imprecise evidence in the form of credal sets. We generalize the Dirichlet distribution to a shifted version, denoted the S-Dirichlet, which allows one to restrict the support of the distribution by lower bounds. Based on the S-Dirichlet distribution, we present a simple combination schema denoted as second-order credal combination (SOCC), which takes second-order probability into account. The combination schema is based on a set of particles, sampled from the operands, and a set of weights that are obtained through the S-Dirichlet distribution. We show by examples that the second-order probability distribution over the imprecise joint evidence can be remarkably concentrated and hence that the credal combination operator can significantly overestimate the imprecision.

  • 28.
    Olson, Nasrine
    et al.
    University of Borås.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Nelhans, Gustaf
    University of Borås.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Nolin, Jan
    University of Borås.
    Little Scientist, Big Data Information fusion towards meeting the information needs of scholars2014Inngår i: Assessing Libraries and Library Users and Use, 2014Konferansepaper (Annet vitenskapelig)
  • 29.
    Rana, Rakesh
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    A framework for identifying and evaluating technologies of interest for effective business strategy: Using text analytics to augment technology forecasting2017Inngår i: 5th International Symposium on Computational and Business Intelligence (ISCBI 2017), IEEE, 2017, s. 110-115, artikkel-id 8053555Konferansepaper (Fagfellevurdert)
  • 30.
    Riveiro, Maria
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Johansson, Ronnie
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Modeling and analysis of energy data: state-of-the-art and practical results from an application scenario2011Rapport (Annet vitenskapelig)
    Abstract [en]

    This paper presents a comprehensive summary of the state-of-the-art of energy efficiency research. The literature review carried out focuses on the application of data mining and data analysis techniques to energy consumption data, as well as  descriptions  of  tools, applications and research prototypes to manage the consumption of energy. Moreover, preliminary results of the application of a clustering technique to energy consumption data illustrate the review.

  • 31.
    Siegmund, Florian
    et al.
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Ng, Amos H. C.
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization of Stochastic Systems2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In Multi-objective Optimization many solutions have to be evaluated in order to provide the decision maker with a diverse Pareto-front. In Simulation-based Optimization the number of optimization function evaluations is very limited. If preference information is available however, the available function evaluations can be used more effectively by guiding the optimization towards interesting, preferred regions. One such algorithm for guided search is the R-NSGA-II algorithm. It takes reference points provided by the decision maker and guides the optimization towards areas of the Pareto-front close to the reference points.In Simulation-based Optimization the modeled systems are often stochastic and a reliable quality assessment of system configurations by resampling requires many simulation runs. Therefore optimization practitioners make use of dynamic resampling algorithms that distribute the available function evaluations intelligently on the solutions to be evaluated. Criteria for sampling allocation can be a.o. objective value variability, closeness to the Pareto-front indicated by elapsed time, or the dominance relations between different solutions based on distances between objective vectors and their variability.In our work we combine R-NSGA-II with several resampling algorithms based on the above mentioned criteria. Due to the preference information R-NSGA-II has fitness information based on distance to reference points at its disposal. We propose a resampling strategy that allocates more samples to solutions close to a reference point.Previously, we proposed extensions of R-NSGA-II that adapt algorithm parameters like population size, population diversity, or the strength of the Pareto-dominance relation continuously to optimization problem characteristics. We show how resampling algorithms can be integrated with those extensions.The applicability of the proposed algorithms is shown in a case study of an industrial production line for car manufacturing.

  • 32.
    Steinhauer, H. Joe
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Helldin, Tove
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Topic Modeling for Situation Understanding in Telecommunication Networks2017Inngår i: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, 2017, s. 73-78Konferansepaper (Fagfellevurdert)
  • 33.
    Steinhauer, H. Joe
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Helldin, Tove
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Anomaly Detection in Telecommunication Networks using Topic Models2018Konferansepaper (Fagfellevurdert)
  • 34.
    Steinhauer, H. Joe
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Helldin, Tove
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Topic modeling for anomaly detection in telecommunication networks2019Inngår i: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, s. 1-12Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 35.
    Steinhauer, H. Joe
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Information Fusion2019Inngår i: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 61-78Kapittel i bok, del av antologi (Fagfellevurdert)
    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.

  • 36.
    Steinhauer, H. Joe
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Traceable Uncertainty for Threat Evaluation in Air to Ground Scenarios2013Inngår i: Twelfth Scandinavian Conference on Artificial Intelligence: SCAI 2013 / [ed] Manfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani, IOS Press, 2013, s. 255-264Konferansepaper (Fagfellevurdert)
    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.

  • 37.
    Steinhauer, H. Joe
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Traceable Uncertainty2013Inngår i: Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, 2013, s. 1582-1589Konferansepaper (Fagfellevurdert)
  • 38.
    Steinhauer, H. Joe
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Helldin, Tove
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Root-Cause Localization using Restricted Boltzmann Machines2016Inngår i: 2016 19th International Conference on Information Fusion Proceedings, IEEE Computer Society, 2016, s. 248-255Konferansepaper (Fagfellevurdert)
  • 39.
    Ståhl, Niclas
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Boström, Jonas
    Department of Medicinal Chemistry, CVMD iMED, AstraZeneca, Mölndal, Sweden.
    Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data2018Inngår i: Journal of Integrative Bioinformatics, E-ISSN 1613-4516, Vol. 16, nr 1Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as molecular charge and molecular weight) and local (such as atom hybridization and bond orders) information. In this paper, we show that this method significantly outperforms another recently proposed method based on deep convolutional neural networks on several datasets that are studied. Several best practices for training deep convolutional neural networks on chemical datasets are also highlighted within the article, such as how to select the information to be included in the model, how to prevent overfitting and how unbalanced classes in the data can be handled.

  • 40.
    Ståhl, Niclas
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Boström, Jonas
    Medicinal Chemistry, Early Cardiovascular, Renal and Metabolism, R&D BioPharmaceuticals , AstraZeneca , Mölndal , Sweden.
    Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design2019Inngår i: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, nr 7, s. 3166-3176Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

  • 41.
    Ståhl, Niclas
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Boström, Jonas
    Department of Medicinal Chemistry, CVMD iMED, AstraZeneca, Sweden.
    Improving the use of deep convolutional neural networks for the prediction of molecular properties2019Inngår i: Practical Applications of Computational Biology and Bioinformatics, 12th International Conference / [ed] Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González, Cham: Springer, 2019, Vol. 803, s. 71-79Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the method on the Side Effect Resource (SIDER) v4.1 dataset and show that it significantly outperforms another recently proposed method based on deep convolutional neural networks. We also reflect on how different types of information and input data affect the predictive power of our model. This reflection highlights several open problems that should be solved to further improve the use of deep learning within cheminformatics.

  • 42.
    Ståhl, Niclas
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    A self-organizing ensemble of deep neural networks for the classification of data from complex processes2018Inngår i: INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: APPLICATIONS, IPMU 2018, PT III / [ed] Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Perfilieva, I., Bouchon-Meunier, B., Yager, R.R., 2018, Vol. 855, s. 248-259Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.

  • 43.
    Ståhl, Niclas
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Using recurrent neural networks with attention for detecting problematic slab shapes in steel rolling2019Inngår i: Applied Mathematical Modelling, ISSN 0307-904X, E-ISSN 1872-8480, Vol. 70, s. 365-377Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The competitiveness in the manufacturing industry raises demands for using recent data analysis algorithms for manufacturing process development. Data-driven analysis enables extraction of novel knowledge from already existing sensors and data, which is necessary for advanced manufacturing process refinement involving aged machinery. Improved data analysis enables factories to stay competitive against newer factories, but without any hefty investment. In large manufacturing operations, the dependencies between data are highly complex and therefore very difficult to analyse manually. This paper applies a deep learning approach, using a recurrent neural network with long short term memory cells together with an attention mechanism to model the dependencies between the measured product shape, as measured before the most critical manufacturing operation, and the final product quality. Our approach predicts the ratio of flawed products already before the critical operation with an AUC-ROC score of 0.85, i.e., we can detect more than 80 % of all flawed products while having less than 25 % false positive predictions (false alarms). In contrast to previous deep learning approaches, our method shows how the recurrent neural network reasons about the input shape, using the attention mechanism to point out which parts of the product shape that have the highest influence on the predictions. Such information is crucial for both process developers, in order to understand and improve the process, and for process operators who can use the information to learn how to better trust the predictions and control the process.

  • 44.
    Sundgren, David
    et al.
    Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On Dependence in Second-Order Probability2012Inngår i: Scalable Uncertainty Management: 6th International Conference, SUM 2012, Marburg, Germany, September 17-19, 2012. Proceedings / [ed] Eyke Hüllermeier, Sebastian Link, Thomas Fober & Bernhard Seeger, Springer Berlin/Heidelberg, 2012, s. 379-391Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a notion, relative independence, that models independence in relation to a predicate. The intuition is to capture the notion of a minimum of dependencies among variables with respect to the predicate. We prove that relative independence coincides with conditional independence only in a trivial case. For use in second-order probability, we let the predicate express first-order probability, i.e. that the probability variables must sum to one in order to restrict dependency to the necessary relation between probabilities of exhaustive and mutually exclusive events. We then show examples of Dirichlet distributions that do and do not have the property of relative independence. These distributions are compared with respect to the impact of further dependencies, apart from those imposed by the predicate.

  • 45.
    Sundgren, David
    et al.
    Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Uncertainty levels of second-order probability2013Inngår i: POLIBITS Research Journal on Computer Science and Computer Engineering With Applications, ISSN 1870-9044, nr 48, s. 5-11Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Since second-order probability distributions assign probabilities to probabilities there is uncertainty on two levels. Although different types of uncertainty have been distinguished before and corresponding measures suggested, the distinction made here between first- and second-order levels of uncertainty has not been considered before. In this paper previously existing measures are considered from the perspective of first- and second-order uncertainty and new measures are introduced. We conclude that the concepts of uncertainty and informativeness needs to be qualified if used in a second-order probability context and suggest that from a certain point of view information can not be minimized, just shifted from one level to another.

  • 46.
    Synnergren, Jane Marie
    et al.
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Ameén, Caroline
    Takara Bio Europe, Gothenburg, Sweden.
    Åkesson, Karolina
    Takara Bio Europe, Gothenburg, Sweden.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Andersson, Christian X.
    Takara Bio Europe, Gothenburg, Sweden.
    Sartipy, Peter
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Transcriptional profiling of human embryonic stem cells during mesodermal- and cardiac differentiation2016Konferansepaper (Fagfellevurdert)
  • 47.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Berglund, Stefan
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Artificial Intelligence2019Inngår i: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 9-26Kapittel i bok, del av antologi (Fagfellevurdert)
    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.

  • 48.
    Ulfenborg, Benjamin
    et al.
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Améen, Caroline
    Takara Bio Europe AB, Gothenburg, Sweden.
    Åkesson, Karolina
    Takara Bio Europe AB, Gothenburg, Sweden.
    Andersson, Christian X.
    Takara Bio Europe AB, Gothenburg, Sweden.
    Sartipy, Peter
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. Cardiovascular and Metabolic Disease Global Medicines Development Unit, AstraZeneca, Mölndal, Sweden.
    Synnergren, Jane
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells2017Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, nr 6, artikkel-id e0179613Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic-and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

1 - 48 of 48
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf