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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)
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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:bth-18095 (URN)979-1-0365-3801-8 (ISBN)979-1-0365-3802-5 (ISBN)
Note

open access

Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-06-19Bibliographically 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). , 11329
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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, Vol. 11329, 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:bth-17209 (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: 2018-11-01 Created: 2018-11-01 Last updated: 2019-04-18Bibliographically approved
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2018). An Expertise Recommender System Based 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 System Based on Data from an Institutional Repository (DiVA)
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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:bth-16660 (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: 2018-06-29 Created: 2018-06-29 Last updated: 2019-06-18Bibliographically 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). IEEE
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, IEEE, 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.

Place, publisher, year, edition, pages
IEEE, 2018
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:bth-16749 (URN)10.1109/DAS.2018.71 (DOI)000467070300045 ()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: 2018-07-06 Last updated: 2019-06-28Bibliographically 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 Verlag, 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:bth-15891 (URN)10.1007/s10032-017-0293-7 (DOI)000433193500003 ()2-s2.0-85041228615 (Scopus ID)
Available from: 2018-02-15 Created: 2018-02-15 Last updated: 2018-08-27Bibliographically approved
Boeva, V., Angelova, M., Lavesson, N., Rosander, O. & Tsiporkova, E. (2018). Evolutionary clustering techniques for expertise mining scenarios. In: van den Herik J.,Rocha A.P. (Ed.), ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2: . Paper presented at 10th International Conference on Agents and Artificial Intelligence, ICAART, Funchal, Madeira (pp. 523-530). SciTePress, 2
Open this publication in new window or tab >>Evolutionary clustering techniques for expertise mining scenarios
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2018 (English)In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2 / [ed] van den Herik J.,Rocha A.P., SciTePress , 2018, Vol. 2, p. 523-530Conference paper, Published paper (Refereed)
Abstract [en]

The problem addressed in this article concerns the development of evolutionary clustering techniques that can be applied to adapt the existing clustering solution to a clustering of newly collected data elements. We are interested in clustering approaches that are specially suited for adapting clustering solutions in the expertise retrieval domain. This interest is inspired by practical applications such as expertise retrieval systems where the information available in the system database is periodically updated by extracting new data. The experts available in the system database are usually partitioned into a number of disjoint subject categories. It is becoming impractical to re-cluster this large volume of available information. Therefore, the objective is to update the existing expert partitioning by the clustering produced on the newly extracted experts. Three different evolutionary clustering techniques are considered to be suitable for this scenario. The proposed techniques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Place, publisher, year, edition, pages
SciTePress, 2018
Keywords
Data Mining, Expert Finding, Health Science, Knowledge Management, Natural Language Processing, Artificial intelligence, Cluster analysis, Natural language processing systems, Search engines, Clustering approach, Clustering solutions, Data elements, Evolutionary clustering, Retrieval systems, System database
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16224 (URN)2-s2.0-85046663632 (Scopus ID)9789897582752 (ISBN)
Conference
10th International Conference on Agents and Artificial Intelligence, ICAART, Funchal, Madeira
Available from: 2018-05-24 Created: 2018-05-24 Last updated: 2018-05-24Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2018). Hoeffding Trees with nmin adaptation. In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018): . Paper presented at 5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin (pp. 70-79). IEEE
Open this publication in new window or tab >>Hoeffding Trees with nmin adaptation
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2018 (English)In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE, 2018, p. 70-79Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient.In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin pa- rameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Proceedings of the International Conference on Data Science and Advanced Analytics, ISSN 2472-1573
Keywords
data stream mining; green artificial intelligence; energy efficiency; hoeffding trees; energy aware machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17207 (URN)10.1109/DSAA.2018.00017 (DOI)000459238600008 ()978-1-5386-5090-5 (ISBN)
Conference
5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin
Funder
Knowledge Foundation, 20140032
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-04-05Bibliographically approved
Westphal, F., Grahn, H. & Lavesson, N. (2018). User Feedback and Uncertainty in User Guided Binarization. In: Tong, H; Li, Z; Zhu, F; Yu, J (Ed.), International Conference on Data Mining Workshops: . Paper presented at 18th IEEE International Conference on Data Mining Workshops, ICDMW, Singapore; Singapore; 17 November 2018 through 20 November (pp. 403-410). IEEE Computer Society, Article ID 8637367.
Open this publication in new window or tab >>User Feedback and Uncertainty in User Guided Binarization
2018 (English)In: International Conference on Data Mining Workshops / [ed] Tong, H; Li, Z; Zhu, F; Yu, J, IEEE Computer Society, 2018, p. 403-410, article id 8637367Conference paper, Published paper (Refereed)
Abstract [en]

In a child’s development, the child’s inherent ability to construct knowledge from new information is as important as explicit instructional guidance. Similarly, mechanisms to produce suitable learning representations, which can be trans- ferred and allow integration of new information are important for artificial learning systems. However, equally important are modes of instructional guidance, which allow the system to learn efficiently. Thus, the challenge for efficient learning is to identify suitable guidance strategies together with suitable learning mechanisms.

In this paper, we propose guided machine learning as source for suitable guidance strategies, we distinguish be- tween sample selection based and privileged information based strategies and evaluate three sample selection based strategies on a simple transfer learning task. The evaluated strategies are random sample selection, i.e., supervised learning, user based sample selection based on readability, and user based sample selection based on readability and uncertainty. We show that sampling based on readability and uncertainty tends to produce better learning results than the other two strategies. Furthermore, we evaluate the use of the learner’s uncertainty for self directed learning and find that effects similar to the Dunning-Kruger effect prevent this use case. The learning task in this study is document image binarization, i.e., the separation of text foreground from page background and the source domain of the transfer are texts written on paper in Latin characters, while the target domain are texts written on palm leaves in Balinese script.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
guided machine learning, interactive machine learning, image binarization, historical documents
National Category
Computer Vision and Robotics (Autonomous Systems) Human Computer Interaction
Identifiers
urn:nbn:se:bth-17742 (URN)10.1109/ICDMW.2018.00066 (DOI)000465766800058 ()978-1-5386-9288-2 (ISBN)
Conference
18th IEEE International Conference on Data Mining Workshops, ICDMW, Singapore; Singapore; 17 November 2018 through 20 November
Funder
Knowledge Foundation, 20140032
Note

 "© 20XX IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Available from: 2019-03-27 Created: 2019-03-27 Last updated: 2019-07-01Bibliographically approved
García Martín, E., Lavesson, N. & Grahn, H. (2017). Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm. In: Rokia Missaoui, Talel Abdessalem, Matthieu Latapy (Ed.), Trends in Social Network Analysis: Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment (pp. 229-252). Cham, Switzerland: Springer
Open this publication in new window or tab >>Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm
2017 (English)In: Trends in Social Network Analysis: Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment / [ed] Rokia Missaoui, Talel Abdessalem, Matthieu Latapy, Cham, Switzerland: Springer, 2017, p. 229-252Chapter in book (Refereed)
Abstract [en]

Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of the Very Fast Decision Tree (VFDT) algorithm. These results are compared with a theoretical analysis on the algorithm, indicating that energy consumption is affected by the parameters design and that it can be reduced significantly while maintaining accuracy.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2017
Series
Lectures Notes in Social Networks, ISSN 2190-5428
Keywords
Energy efficiency, Green computing, Very Fast Decision Tree, Big Data
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15489 (URN)10.1007/978-3-319-53420-6_10 (DOI)978-3-319-53419-0 (ISBN)978-3-319-53420-6 (ISBN)
Funder
Knowledge Foundation, 20140032
Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2018-02-02Bibliographically approved
García Martín, E., Lavesson, N. & Grahn, H. (2017). Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree. In: Au M., Castiglione A., Choo KK., Palmieri F., Li KC. (Ed.), GPC 2017: Green, Pervasive, and Cloud Computing: . Paper presented at International Conference on Green, Pervasive and Cloud Computing (GPC), Cetara, Amalfi Coast, Italy (pp. 267-281). Cham, Switzerland: Springer, 10232
Open this publication in new window or tab >>Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree
2017 (English)In: GPC 2017: Green, Pervasive, and Cloud Computing / [ed] Au M., Castiglione A., Choo KK., Palmieri F., Li KC., Cham, Switzerland: Springer, 2017, Vol. 10232, p. 267-281Conference paper, Published paper (Refereed)
Abstract [en]

Large-scale data centers account for a significant share of the energy consumption in many countries. Machine learning technology requires intensive workloads and thus drives requirements for lots of power and cooling capacity in data centers. It is time to explore green machine learning. The aim of this paper is to profile a machine learning algorithm with respect to its energy consumption and to determine the causes behind this consumption. The first scalable machine learning algorithm able to handle large volumes of streaming data is the Very Fast Decision Tree (VFDT), which outputs competitive results in comparison to algorithms that analyze data from static datasets. Our objectives are to: (i) establish a methodology that profiles the energy consumption of decision trees at the function level, (ii) apply this methodology in an experiment to obtain the energy consumption of the VFDT, (iii) conduct a fine-grained analysis of the functions that consume most of the energy, providing an understanding of that consumption, (iv) analyze how different parameter settings can significantly reduce the energy consumption. The results show that by addressing the most energy intensive part of the VFDT, the energy consumption can be reduced up to a 74.3%.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743
Keywords
Machine learning, Big data, Very Fast Decision Tree, Green machine learning, Data mining, Data stream mining
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15490 (URN)10.1007/978-3-319-57186-7_21 (DOI)000426275000021 ()978-3-319-57185-0 (ISBN)978-3-319-57186-7 (ISBN)
Conference
International Conference on Green, Pervasive and Cloud Computing (GPC), Cetara, Amalfi Coast, Italy
Funder
Knowledge Foundation, 20140032
Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2018-03-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0535-1761

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