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Publications (10 of 17) Show all publications
Huhnstock, N. A., Karlsson, A., Riveiro, M. & Steinhauer, H. J. (2019). An Infinite Replicated Softmax Model for Topic Modeling. In: Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani (Ed.), Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings. Paper presented at 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019 (pp. 307-318). Springer
Open this publication in new window or tab >>An Infinite Replicated Softmax Model for Topic Modeling
2019 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11676
Keywords
Restricted Boltzmann machine, Unsupervised learning, Topic modeling, Adaptive Neural Network
National Category
Computer Sciences Language Technology (Computational Linguistics)
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17664 (URN)10.1007/978-3-030-26773-5_27 (DOI)978-3-030-26772-8 (ISBN)978-3-030-26773-5 (ISBN)
Conference
16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019
Available from: 2019-09-09 Created: 2019-09-10 Last updated: 2019-09-11Bibliographically approved
Torra, V., Karlsson, A., Steinhauer, H. J. & Berglund, S. (2019). Artificial Intelligence. In: Alan Said, Vicenç Torra (Ed.), Data Science in Practice: (pp. 9-26). Springer
Open this publication in new window or tab >>Artificial Intelligence
2019 (English)In: 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.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Philosophy Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Consciousness and Cognitive Neuroscience
Identifiers
urn:nbn:se:his:diva-16784 (URN)10.1007/978-3-319-97556-6_2 (DOI)000464719500003 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-17 Created: 2019-04-17 Last updated: 2019-09-30Bibliographically approved
Steinhauer, H. J. & Karlsson, A. (2019). Information Fusion. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 61-78). Springer
Open this publication in new window or tab >>Information Fusion
2019 (English)In: 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.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Information Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16781 (URN)10.1007/978-3-319-97556-6_4 (DOI)000464719500005 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-16 Created: 2019-04-16 Last updated: 2019-09-30Bibliographically approved
Steinhauer, H. J., Helldin, T., Mathiason, G. & Karlsson, A. (2019). Topic modeling for anomaly detection in telecommunication networks. Journal of Ambient Intelligence and Humanized Computing, 1-12
Open this publication in new window or tab >>Topic modeling for anomaly detection in telecommunication networks
2019 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, p. 1-12Article in journal (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Telecommunication anomaly detection, Topic modeling, Decision-making
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17527 (URN)10.1007/s12652-019-01372-5 (DOI)
Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-19Bibliographically approved
Steinhauer, H. J., Helldin, T., Mathiason, G. & Karlsson, A. (2018). Anomaly Detection in Telecommunication Networks using Topic Models. In: : . Paper presented at Modeling Decisions for Artificial Intelligence, 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018.
Open this publication in new window or tab >>Anomaly Detection in Telecommunication Networks using Topic Models
2018 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16491 (URN)
Conference
Modeling Decisions for Artificial Intelligence, 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018
Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2019-06-13Bibliographically approved
Huhnstock, N. A., Karlsson, A., Riveiro, M. & Steinhauer, H. J. (2018). On the behavior of the infinite restricted boltzmann machine for clustering. In: Hisham M. Haddad, Roger L. Wainwright, Richard Chbeir (Ed.), SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing: . Paper presented at SAC 18 The 33rd Annual ACM Symposium on Applied Computing, Pau, France, April 9-13, 2018 (pp. 461-470). New York, NY, USA: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>On the behavior of the infinite restricted boltzmann machine for clustering
2018 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2018
Keywords
clustering, unsupervised, machine learning, restricted boltzmann machine
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16505 (URN)10.1145/3167132.3167183 (DOI)000455180700067 ()2-s2.0-85050522612 (Scopus ID)978-1-4503-5191-1 (ISBN)
Conference
SAC 18 The 33rd Annual ACM Symposium on Applied Computing, Pau, France, April 9-13, 2018
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2019-02-14Bibliographically approved
Helldin, T., Steinhauer, H. J., Karlsson, A. & Mathiason, G. (2018). Situation Awareness in Telecommunication Networks Using Topic Modeling. In: 2018 21st International Conference on Information Fusion, FUSION 2018: . Paper presented at FUSION 2018 21st International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom (pp. 549-556). IEEE
Open this publication in new window or tab >>Situation Awareness in Telecommunication Networks Using Topic Modeling
2018 (English)In: 2018 21st International Conference on Information Fusion, FUSION 2018, IEEE, 2018, p. 549-556Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Data handling, Data visualization, Information fusion, Design suggestions, Exploratory data analysis, Human operator, Information fusion techniques, Situation awareness, Telecommunication operators, Topic Modeling, Wireless telecommunications, Data mining
National Category
Computer and Information Sciences Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16297 (URN)10.23919/ICIF.2018.8455529 (DOI)2-s2.0-85054066646 (Scopus ID)978-0-9964527-6-2 (ISBN)978-0-9964527-7-9 (ISBN)978-1-5386-4330-3 (ISBN)
Conference
FUSION 2018 21st International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom
Available from: 2018-10-15 Created: 2018-10-15 Last updated: 2019-02-08Bibliographically approved
Steinhauer, H. J., Helldin, T. & Mathiason, G. (2018). Spatio-Temporal Awareness for Wireless Telecommunication Networks. In: Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge. Paper presented at International Journal Conference for Artificial Intelligence (IJCAI), IJCAI-ECAI-2018 Workshop on Learning and Reasoning, July 13 - 14, 2018 Stockholm (Sweden) (pp. 49-50).
Open this publication in new window or tab >>Spatio-Temporal Awareness for Wireless Telecommunication Networks
2018 (English)In: 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, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16490 (URN)
Conference
International Journal Conference for Artificial Intelligence (IJCAI), IJCAI-ECAI-2018 Workshop on Learning and Reasoning, July 13 - 14, 2018 Stockholm (Sweden)
Note

http://www.iiia.csic.es/LR2018/talks_proceedings

Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2019-02-14Bibliographically approved
Steinhauer, H. J., Helldin, T., Karlsson, A. & Mathiason, G. (2017). Topic Modeling for Situation Understanding in Telecommunication Networks. In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC): . Paper presented at 27th International Telecommunication Networks and Applications Conference (ITNAC), 22-24 November 2017, Melbourne, Australia (pp. 73-78). IEEE
Open this publication in new window or tab >>Topic Modeling for Situation Understanding in Telecommunication Networks
2017 (English)In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, 2017, p. 73-78Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
Series
International Telecommunication Networks and Applications Conference (ITNAC), E-ISSN 2474-154X
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14591 (URN)10.1109/ATNAC.2017.8215362 (DOI)000427574400013 ()2-s2.0-85046642703 (Scopus ID)978-1-5090-6796-1 (ISBN)978-1-5090-6795-4 (ISBN)978-1-5090-6797-8 (ISBN)
Conference
27th International Telecommunication Networks and Applications Conference (ITNAC), 22-24 November 2017, Melbourne, Australia
Available from: 2017-12-18 Created: 2017-12-18 Last updated: 2019-03-05Bibliographically approved
Steinhauer, H. J., Karlsson, A., Mathiason, G. & Helldin, T. (2016). Root-Cause Localization using Restricted Boltzmann Machines. In: 2016 19th International Conference on Information Fusion Proceedings: . Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany - July 5-8, 2016 (pp. 248-255). IEEE Computer Society
Open this publication in new window or tab >>Root-Cause Localization using Restricted Boltzmann Machines
2016 (English)In: 2016 19th International Conference on Information Fusion Proceedings, IEEE Computer Society, 2016, p. 248-255Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2016
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12884 (URN)000391273400034 ()2-s2.0-84992092150 (Scopus ID)9780996452748 (ISBN)978-1-5090-2012-6 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany - July 5-8, 2016
Available from: 2016-09-07 Created: 2016-09-07 Last updated: 2018-04-12Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2949-4123

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