Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 73) Show all publications
Westphal, F., Lavesson, N. & Grahn, H. (2019). A case for guided machine learning. In: A. Holzinger, P. Kieseberg, A. M. Tjoa & E. Weippl (Ed.), Machine learning and knowledge extraction: Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings. Paper presented at International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) CD-MAKE 2019, Canterbury, UK, August 26–29, 2019 (pp. 353-361). Cham: Springer
Open this publication in new window or tab >>A case for guided machine learning
2019 (English)In: Machine learning and knowledge extraction: Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings / [ed] A. Holzinger, P. Kieseberg, A. M. Tjoa & E. Weippl, Cham: Springer, 2019, p. 353-361Conference paper, Published paper (Refereed)
Abstract [en]

Involving humans in the learning process of a machine learning algorithm can have many advantages ranging from establishing trust into a particular model to added personalization capabilities to reducing labeling efforts. While these approaches are commonly summarized under the term interactive machine learning (iML), no unambiguous definition of iML exists to clearly define this area of research. In this position paper, we discuss the shortcomings of current definitions of iML and propose and define the term guided machine learning (gML) as an alternative.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 11713
Keywords
Definition, Guided machine learning, Human-in-the-loop, Interactive machine learning, Data mining, Extraction, Learning algorithms, Current definition, Learning process, Personalizations, Position papers, Machine learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hj:diva-46552 (URN)10.1007/978-3-030-29726-8_22 (DOI)2-s2.0-85072874206 (Scopus ID)978-3-030-29725-1 (ISBN)978-3-030-29726-8 (ISBN)
Conference
International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) CD-MAKE 2019, Canterbury, UK, August 26–29, 2019
Funder
Knowledge Foundation, 20140032
Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2019-10-14Bibliographically approved
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2019). An Expertise Recommender System based on Data from an Institutional Repository (DiVA). In: Leslie Chan & Pierre Mounier (Ed.), Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers (pp. 135-149). OpenEdition Press
Open this publication in new window or tab >>An Expertise Recommender System based on Data from an Institutional Repository (DiVA)
Show others...
2019 (English)In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan & Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

Place, publisher, year, edition, pages
OpenEdition Press, 2019
Keywords
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hj:diva-45613 (URN)979-1-0365-3801-8 (ISBN)979-1-0365-3802-5 (ISBN)
Available from: 2019-06-18 Created: 2019-08-19 Last updated: 2019-08-19Bibliographically approved
Green, D. & Lavesson, N. (2019). Chaos theory and artificial intelligence may provide insights on disability outcomes. Developmental Medicine & Child Neurology, 61(10), 1120-1120
Open this publication in new window or tab >>Chaos theory and artificial intelligence may provide insights on disability outcomes
2019 (English)In: Developmental Medicine & Child Neurology, ISSN 0012-1622, E-ISSN 1469-8749, Vol. 61, no 10, p. 1120-1120Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
John Wiley & Sons, 2019
National Category
Computer and Information Sciences Social Sciences Interdisciplinary
Identifiers
urn:nbn:se:hj:diva-46217 (URN)10.1111/dmcn.14328 (DOI)000485038400001 ()31476084 (PubMedID)2-s2.0-85071753346 (Scopus ID)
Available from: 2019-09-17 Created: 2019-09-17 Last updated: 2020-01-15Bibliographically approved
Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H. & Lavesson, N. (2019). Higher order mining for monitoring district heating substations. In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019: . Paper presented at 6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Washington, United States, 5 - 8 October, 2019 (pp. 382-391). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8964173.
Open this publication in new window or tab >>Higher order mining for monitoring district heating substations
Show others...
2019 (English)In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 382-391, article id 8964173Conference paper, Published paper (Refereed)
Abstract [en]

We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Clustering Analysis, Data Mining, District Heating Substations, Fault Detection, Higher Order Mining, Minimum Spanning Tree, Outlier Detection, Advanced Analytics, Anomaly detection, Clustering algorithms, Data visualization, District heating, Fault tree analysis, Fiber optics, Trees (mathematics), Consensus clustering, Data analysis techniques, Heating substations, Higher-order, Minimum spanning trees, Sequential-pattern mining, Visualization technique, Cluster analysis
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hj:diva-47935 (URN)10.1109/DSAA.2019.00053 (DOI)2-s2.0-85079289447 (Scopus ID)9781728144931 (ISBN)
Conference
6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Washington, United States, 5 - 8 October, 2019
Note

Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KK, 201400

This work is part of the research project “Scalable resource-efficient systems for big data analytics“ funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Available from: 2020-03-05 Created: 2020-03-05 Last updated: 2020-03-05Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2019). How to Measure Energy Consumption in Machine Learning Algorithms. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018 (pp. 243-255).
Open this publication in new window or tab >>How to Measure Energy Consumption in Machine Learning Algorithms
Show others...
2019 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2019, p. 243-255Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11329
Keywords
Computer architecture, Energy efficiency, Green computing, Machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-45614 (URN)10.1007/978-3-030-13453-2_20 (DOI)9783030134525 (ISBN)
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018
Funder
Knowledge Foundation, 20140032
Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-08-19Bibliographically approved
Westphal, F., Lavesson, N. & Grahn, H. (2019). Learning character recognition with graph-based privileged information. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR: . Paper presented at 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019, Sydney, Australia, 20 - 25 September 2019 (pp. 1163-1168). IEEE
Open this publication in new window or tab >>Learning character recognition with graph-based privileged information
2019 (English)In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, IEEE, 2019, p. 1163-1168Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a pre-training method for neural network-based character recognizers to reduce the required amount of training data, and thus the human labeling effort. The proposed method transfers knowledge about the similarities between graph representations of characters to the recognizer by training to predict the graph edit distance. We show that convolutional neural networks trained with this method outperform traditional supervised learning if only ten or less labeled images per class are available. Furthermore, we show that our approach performs up to 33% better than a graph edit distance based recognition approach, even if only one labeled image per class is available. 

Place, publisher, year, edition, pages
IEEE, 2019
Series
Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, ISSN 1520-5363, E-ISSN 2379-2140
Keywords
Character recognition, Convolutional neural networks, Graph matching, Learning using privileged information, Convolution, Graphic methods, Graph edit distance, Graph matchings, Graph representation, Labeled images, Method transfers, Pre-training, Training data
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hj:diva-47958 (URN)10.1109/ICDAR.2019.00188 (DOI)2-s2.0-85079896010 (Scopus ID)9781728128610 (ISBN)
Conference
15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019, Sydney, Australia, 20 - 25 September 2019
Funder
Knowledge Foundation, 20140032
Available from: 2020-03-11 Created: 2020-03-11 Last updated: 2020-03-11Bibliographically approved
Abghari, S., Boeva, V., Lavesson, N., Grahn, H., Ickin, S. & Gustafsson, J. (2018). A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences. In: The 17th IEEE International Conference on Machine Learning and Applications Special Session on Machine Learning Algorithms, Systems and Applications: . Paper presented at IEEE International Conference on Machine Learning and Applications, ICMLA, Orlando. IEEE
Open this publication in new window or tab >>A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences
Show others...
2018 (English)In: The 17th IEEE International Conference on Machine Learning and Applications Special Session on Machine Learning Algorithms, Systems and Applications, IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Clustering, Minimum spanning tree, Outlier detection, Sequential pattern mining
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-42988 (URN)
Conference
IEEE International Conference on Machine Learning and Applications, ICMLA, Orlando
Funder
Knowledge Foundation, 20140032
Available from: 2018-10-09 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2018). An Expertise Recommender SystemBased on Data from an Institutional Repository (DiVA). In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing: . Paper presented at 22nd edition of the International Conference on ELectronic PUBlishing - Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure, Toronto.
Open this publication in new window or tab >>An Expertise Recommender SystemBased on Data from an Institutional Repository (DiVA)
Show others...
2018 (English)In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors inacademy.

Keywords
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hj:diva-42989 (URN)0.4000/proceedings.elpub.2018.17 (DOI)
Conference
22nd edition of the International Conference on ELectronic PUBlishing - Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure, Toronto
Note

open access

Available from: 2019-02-15 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved
Westphal, F., Lavesson, N. & Grahn, H. (2018). Document Image Binarization Using Recurrent Neural Networks. In: Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018: . Paper presented at 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), vienna (pp. 263-268).
Open this publication in new window or tab >>Document Image Binarization Using Recurrent Neural Networks
2018 (English)In: Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018, 2018, p. 263-268Conference paper, Published paper (Refereed)
Abstract [en]

In the context of document image analysis, image binarization is an important preprocessing step for other document analysis algorithms, but also relevant on its own by improving the readability of images of historical documents. While historical document image binarization is challenging due to common image degradations, such as bleedthrough, faded ink or stains, achieving good binarization performance in a timely manner is a worthwhile goal to facilitate efficient information extraction from historical documents. In this paper, we propose a recurrent neural network based algorithm using Grid Long Short-Term Memory cells for image binarization, as well as a pseudo F-Measure based weighted loss function. We evaluate the binarization and execution performance of our algorithm for different choices of footprint size, scale factor and loss function. Our experiments show a significant trade-off between binarization time and quality for different footprint sizes. However, we see no statistically significant difference when using different scale factors and only limited differences for different loss functions. Lastly, we compare the binarization performance of our approach with the best performing algorithm in the 2016 handwritten document image binarization contest and show that both algorithms perform equally well.

Keywords
image binarization, recurrent neural networks, Grid LSTM, historical documents, Text analysis, Labeling, Recurrent neural networks, Heuristic algorithms, Training, Degradation, Ink
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hj:diva-42991 (URN)10.1109/DAS.2018.71 (DOI)978-1-5386-3346-5 (ISBN)
Conference
2018 13th IAPR International Workshop on Document Analysis Systems (DAS), vienna
Funder
Knowledge Foundation, 20140032
Available from: 2018-07-06 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved
Westphal, F., Grahn, H. & Lavesson, N. (2018). Efficient document image binarization using heterogeneous computing and parameter tuning. International Journal on Document Analysis and Recognition, 21(1-2), 41-58
Open this publication in new window or tab >>Efficient document image binarization using heterogeneous computing and parameter tuning
2018 (English)In: International Journal on Document Analysis and Recognition, ISSN 1433-2833, E-ISSN 1433-2825, Vol. 21, no 1-2, p. 41-58Article in journal (Refereed) Published
Abstract [en]

In the context of historical document analysis, image binarization is a first important step, which separates foreground from background, despite common image degradations, such as faded ink, stains, or bleed-through. Fast binarization has great significance when analyzing vast archives of document images, since even small inefficiencies can quickly accumulate to years of wasted execution time. Therefore, efficient binarization is especially relevant to companies and government institutions, who want to analyze their large collections of document images. The main challenge with this is to speed up the execution performance without affecting the binarization performance. We modify a state-of-the-art binarization algorithm and achieve on average a 3.5 times faster execution performance by correctly mapping this algorithm to a heterogeneous platform, consisting of a CPU and a GPU. Our proposed parameter tuning algorithm additionally improves the execution time for parameter tuning by a factor of 1.7, compared to previous parameter tuning algorithms. We see that for the chosen algorithm, machine learning-based parameter tuning improves the execution performance more than heterogeneous computing, when comparing absolute execution times. © 2018 The Author(s)

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Automatic parameter tuning, Heterogeneous computing, Historical documents, Image binarization, Bins, History, Image analysis, Learning systems, Document image binarization, Government institutions, Heterogeneous platforms, Parameter tuning algorithm, Parameter estimation
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-42993 (URN)10.1007/s10032-017-0293-7 (DOI)000433193500003 ()2-s2.0-85041228615 (Scopus ID)
Available from: 2019-02-15 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0535-1761

Search in DiVA

Show all publications