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Halas, R., Mesiar, R., Pocs, J. & Torra, V. (2019). A note on some algebraic properties of discrete Sugeno integrals. Fuzzy sets and systems (Print), 355, 110-120
Open this publication in new window or tab >>A note on some algebraic properties of discrete Sugeno integrals
2019 (English)In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 355, p. 110-120Article in journal (Refereed) Published
Abstract [en]

Based on the link between Sugeno integrals and fuzzy measures, we discuss several algebraic properties of discrete Sugeno integrals. We recall that the composition of Sugeno integrals is again a Sugeno integral, and that each Sugeno integral can be obtained as a composition of binary Sugeno integrals. In particular, we discuss the associativity, dominance, commuting and bisymmetry of Sugeno integrals.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Associativity, L-measure, Sugeno integral
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16471 (URN)10.1016/j.fss.2018.01.009 (DOI)000450287700009 ()2-s2.0-85041636724 (Scopus ID)
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-02-14Bibliographically 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: 2019-08-06Bibliographically 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: 2019-06-10Bibliographically approved
Torra, V., Navarro-Arribas, G. & Stokes, K. (2019). Data privacy. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 121-132). Springer
Open this publication in new window or tab >>Data privacy
2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 121-132Chapter in book (Refereed)
Abstract [en]

In this chapter we present an overview of the topic data privacy. We review privacy models and measures of disclosure risk. These models and measures provide computational definitions of what privacy means, and of how to evaluate the privacy level of a data set. Then, we give a summary of data protection mechanisms. We provide a classification of these methods according to three dimensions: whose privacy is being sought, the computations to be done, and the number of data sources. Finally, we describe masking methods. Such methods are the data protection mechanisms used for databases when the data use is undefined and the protected database is required to be useful for several data uses. We also provide a definition of information loss (or data utility) for this type of data protection mechanism. The chapter finishes with a summary.

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 Other Computer and Information Science Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Physics and Mathematics
Identifiers
urn:nbn:se:his:diva-16766 (URN)10.1007/978-3-319-97556-6_7 (DOI)000464719500008 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-11 Created: 2019-04-11 Last updated: 2019-08-19Bibliographically approved
Said, A. & Torra, V. (2019). Data Science: An Introduction. In: Alan Said, Vicenç Torra (Ed.), Data Science in Practice: (pp. 1-6). Springer
Open this publication in new window or tab >>Data Science: An Introduction
2019 (English)In: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 1-6Chapter in book (Refereed)
Abstract [en]

This chapter gives a general introduction to data science as a concept and to the topics covered in this book. First, we present a rough definition of data science, and point out how it relates to the areas of statistics, machine learning and big data technologies. Then, we review some of the most relevant tools that can be used in data science ranging from optimization to software. We also discuss the relevance of building models from data. The chapter ends with a detailed review of the structure of the book.

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 Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16778 (URN)10.1007/978-3-319-97556-6_1 (DOI)000464719500002 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-06-10Bibliographically approved
Senavirathne, N. & Torra, V. (2019). Integrally private model selection for decision trees. Computers & security (Print), 83, 167-181
Open this publication in new window or tab >>Integrally private model selection for decision trees
2019 (English)In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 83, p. 167-181Article in journal (Refereed) Published
Abstract [en]

Privacy attacks targeting machine learning models are evolving. One of the primary goals of such attacks is to infer information about the training data used to construct the models. “Integral Privacy” focuses on machine learning and statistical models which explain how we can utilize intruder's uncertainty to provide a privacy guarantee against model comparison attacks. Through experimental results, we show how the distribution of models can be used to achieve integral privacy. Here, we observe two categories of machine learning models based on their frequency of occurrence in the model space. Then we explain the privacy implications of selecting each of them based on a new attack model and empirical results. Also, we provide recommendations for private model selection based on the accuracy and stability of the models along with the diversity of training data that can be used to generate the models. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Data privacy, Integral privacy, Machine learning model space, Privacy models, Privacy preserving machine learning, Decision trees, Attack model, Machine learning models, Model comparison, Model Selection, Privacy Attacks, Privacy preserving, Training data, Machine learning
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16682 (URN)10.1016/j.cose.2019.01.006 (DOI)000465367100013 ()2-s2.0-85062062700 (Scopus ID)
Available from: 2019-03-08 Created: 2019-03-08 Last updated: 2019-07-10Bibliographically approved
Torra, V. & Senavirathne, N. (2019). Maximal c consensus meets. Information Fusion, 51, 58-66
Open this publication in new window or tab >>Maximal c consensus meets
2019 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 51, p. 58-66Article in journal (Refereed) Published
Abstract [en]

Given a set S of subsets of a reference set X, we define the problem of finding c subsets of X that maximize the size of the intersection among the included subsets. Maximizing the size of the intersection means that they are subsets of the sets in S and they are as large as possible. We can understand the result of this problem as c consensus sets of S, or c consensus representatives of S. From the perspective of lattice theory, each representative will be a meet of some sets in S. In this paper we define formally this problem, and present heuristic algorithms to solve it. We also discuss the relationship with other established problems in the literature.

Place, publisher, year, edition, pages
NETHERLANDS: Elsevier, 2019
Keywords
clustering, consensus clustering, heuristic algorithms, Maximal c consensus meets, Cluster analysis, Clustering algorithms, Lattice theory, Set theory, Reference set
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16463 (URN)10.1016/j.inffus.2018.09.011 (DOI)000469155600006 ()2-s2.0-85056612105 (Scopus ID)
Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-07-10Bibliographically approved
Torra, V. & Narukawa, Y. (2019). On network analysis using non-additive integrals: extending the game-theoretic network centrality. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 23(7), 2321-2329
Open this publication in new window or tab >>On network analysis using non-additive integrals: extending the game-theoretic network centrality
2019 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 23, no 7, p. 2321-2329Article in journal (Refereed) Published
Abstract [en]

There are large amounts of information that can be represented in terms of graphs. This includes social networks and internet. We can represent people and their interactions by means of graphs. Similarly, we can represent web pages (and sites) as well as links between pages by means of graphs. In order to study the properties of graphs, several indices have been defined. They include degree centrality, betweenness, and closeness. In this paper, we propose the use of Choquet and Sugeno integrals with respect to non-additive measures for network analysis. This is a natural extension of the use of game theory for network analysis. Recall that monotonic games in game theory are non-additive measures. We discuss the expected force, a centrality measure, in the light of non-additive integral network analysis. We also show that some results by Godo et al. can be used to compute network indices when the information associated with a graph is qualitative.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Non-additive measures and integrals, Graphs, Aggregation, Network analysis
National Category
Computer Sciences
Research subject
Infection Biology
Identifiers
urn:nbn:se:his:diva-16741 (URN)10.1007/s00500-018-03710-9 (DOI)000461580400016 ()2-s2.0-85059054946 (Scopus ID)
Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-04-09Bibliographically approved
Salas, J. & Torra, V. (2018). A General Algorithm for k-anonymity on Dynamic Databases. In: Joaquin Garcia-Alfaro, Jordi Herrera-Joancomartí, Giovanni Livraga, Ruben Rios (Ed.), Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings. Paper presented at 2nd International Workshop on Cryptocurrencies and Blockchain Technology (CBT) / 13th International Workshop on Data Privacy Management (DPM), September 6-7, 2018, 2018, Barcelona, Spain (pp. 407-414). Cham: Springer, 11025
Open this publication in new window or tab >>A General Algorithm for k-anonymity on Dynamic Databases
2018 (English)In: Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings / [ed] Joaquin Garcia-Alfaro, Jordi Herrera-Joancomartí, Giovanni Livraga, Ruben Rios, Cham: Springer, 2018, Vol. 11025, p. 407-414Conference paper, Published paper (Refereed)
Abstract [en]

In this work we present an algorithm for k-anonymization of datasets that are changing over time. It is intended for preventing identity disclosure in dynamic datasets via microaggregation. It supports adding, deleting and updating records in a database, while keeping k-anonymity on each release. We carry out experiments on database anonymization. We expected that the additional constraints for k-anonymization of dynamic databases would entail a larger information loss, however it stays close to MDAV's information loss for static databases. Finally, we carry out a proof of concept experiment with directed degree sequence anonymization, in which the removal or addition of records, implies the modification of other records.

Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11025
Keywords
Big data privacy, k-anonymity, Graph anonymization, Geo-spatial data anonymization, Microaggregation, Dynamic data privacy
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17535 (URN)10.1007/978-3-030-00305-0_28 (DOI)000477970100028 ()978-3-030-00304-3 (ISBN)978-3-030-00305-0 (ISBN)
Conference
2nd International Workshop on Cryptocurrencies and Blockchain Technology (CBT) / 13th International Workshop on Data Privacy Management (DPM), September 6-7, 2018, 2018, Barcelona, Spain
Note

Also part of the Security and Cryptology book sub series (LNSC, volume 11025)

Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-08-16Bibliographically approved
Koloseni, D., Helldin, T. & Torra, V. (2018). Absolute and relative preferences in AHP-like matrices. In: Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre (Ed.), Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018). Paper presented at Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), Belfast, United Kingdom, August 21-24, 2018 (pp. 260-267). SINGAPORE: World Scientific Publishing Co. Pte. Ltd., 11
Open this publication in new window or tab >>Absolute and relative preferences in AHP-like matrices
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018) / [ed] Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre, SINGAPORE: World Scientific Publishing Co. Pte. Ltd. , 2018, Vol. 11, p. 260-267Conference paper, Published paper (Refereed)
Abstract [en]

The Analytical Hierarchy Process (AHP) has been extensively used to interview experts in order to find the weights of the criteria. We call AHP-like matrices relative preferences of weights. In this paper we propose another type of matrix that we call a absolute preference matrix. They are also used to find weights, and we propose that they can be applied to find the weights of weighted means and also of the Choquet integral.

Place, publisher, year, edition, pages
SINGAPORE: World Scientific Publishing Co. Pte. Ltd., 2018
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, ISSN 1793-7868 ; 11
National Category
Computer Sciences
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16409 (URN)10.1142/9789813273238_0035 (DOI)000468160600035 ()978-981-3273-22-1 (ISBN)978-981-3273-24-5 (ISBN)
Conference
Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), Belfast, United Kingdom, August 21-24, 2018
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-06-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0368-8037

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