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Publications (10 of 48) 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
Bae, J., Karlsson, A., Mellin, J., Ståhl, N. & Torra, V. (2019). Complex Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 157-169). Springer
Open this publication in new window or tab >>Complex Data Analysis
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2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 157-169Chapter in book (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL); Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-16811 (URN)10.1007/978-3-319-97556-6_9 (DOI)000464719500010 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-09-30Bibliographically approved
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G. & Boström, J. (2019). Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design. Journal of Chemical Information and Modeling, 59(7), 3166-3176
Open this publication in new window or tab >>Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design
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2019 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, no 7, p. 3166-3176Article in journal (Refereed) Published
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.

Keywords
algorithms, molecules
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17503 (URN)10.1021/acs.jcim.9b00325 (DOI)000477074900010 ()31273995 (PubMedID)2-s2.0-85070180995 (Scopus ID)
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-08-16Bibliographically approved
Holst, A., Bouguelia, M.-R., Görnerup, O., Pashami, S., Al-Shishtawy, A., Falkman, G., . . . Soliman, A. (2019). Eliciting structure in data. In: Christoph Trattner, Denis Parra, Nathalie Riche (Ed.), CEUR Workshop Proceedings: . Paper presented at 2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019. CEUR-WS, 2327
Open this publication in new window or tab >>Eliciting structure in data
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2019 (English)In: CEUR Workshop Proceedings / [ed] Christoph Trattner, Denis Parra, Nathalie Riche, CEUR-WS , 2019, Vol. 2327Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
CEUR-WS, 2019
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2327
Keywords
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16748 (URN)2-s2.0-85063227224 (Scopus ID)
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-09-30Bibliographically approved
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G. & Boström, J. (2019). Improving the use of deep convolutional neural networks for the prediction of molecular properties. In: Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González (Ed.), Practical Applications of Computational Biology and Bioinformatics, 12th International Conference: . Paper presented at PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics, Toledo, June 20-22, 2018 (pp. 71-79). Cham: Springer, 803
Open this publication in new window or tab >>Improving the use of deep convolutional neural networks for the prediction of molecular properties
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2019 (English)In: 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, p. 71-79Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 803
Keywords
drug discovery, graph convolutional neural network, molecular property prediction, bioinformatics, convolution, neural networks, open source software, open systems, cheminformatics, convolutional neural network, deep convolutional neural networks, graph structures, molecular charge, molecular properties, predictive power, deep neural networks
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16230 (URN)10.1007/978-3-319-98702-6_9 (DOI)000468071900009 ()2-s2.0-85052956812 (Scopus ID)978-3-319-98701-9 (ISBN)978-3-319-98702-6 (ISBN)
Conference
PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics, Toledo, June 20-22, 2018
Available from: 2018-09-25 Created: 2018-09-25 Last updated: 2019-06-07Bibliographically 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
Holst, A., Karlsson, A., Bae, J. & Bouguelia, M.-R. (2019). Interactive clustering for exploring multiple data streams at different time scales and granularity. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Interactive clustering for exploring multiple data streams at different time scales and granularity
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019
Keywords
Clustering, Interaction, Time scales, Time series, Fleet operations, Gaussian distribution, Time measurement, Application scenario, Different time scale, Gaussian Mixture Model, Multiple data streams, Multivariate time series, Time-scales, Data mining
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17512 (URN)10.1145/3304079.3310286 (DOI)2-s2.0-85069762696 (Scopus ID)978-1-4503-6296-2 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-26Bibliographically approved
Bae, J., Havsol, J., Karpefors, M., Karlsson, A. & Mathiason, G. (2019). Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types. In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018: . Paper presented at ISCBI 2018 : 2018 6th International Symposium on Computational and Business Intelligence. Basel, Switzerland August 22 - 29 2018 (pp. 89-93). IEEE Computer Society
Open this publication in new window or tab >>Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types
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2019 (English)In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, IEEE Computer Society, 2019, p. 89-93Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
IEEE Computer Society, 2019
Keywords
Bayesian non-parametrics, Bio-sensor, short text, topic modeling, wearable, Biosensors, Information analysis, Bayesian nonparametrics, Dirichlet process, Market analysis, Short texts, Text information, Wearable devices, Wearable sensors
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16746 (URN)10.1109/ISCBI.2018.00027 (DOI)000462379700017 ()2-s2.0-85063041846 (Scopus ID)978-1-5386-9450-3 (ISBN)978-1-5386-9451-0 (ISBN)
Conference
ISCBI 2018 : 2018 6th International Symposium on Computational and Business Intelligence. Basel, Switzerland August 22 - 29 2018
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-09-30Bibliographically approved
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
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2973-3112

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