Making Use of Empty Intersections to Improve the Performance of CbO-Type Algorithms

This paper describes how improvements in the performance of Close-by-One type algorithms can be achieved by making use of empty intersections in the computation of formal concepts. During the computation, if the intersection between the current concept extent and the next attribute-extent is empty, this fact can be simply inherited by subsequent children of the current concept. Thus subsequent intersections with the same attribute-extent can be skipped. Because these intersections require the testing of each object in the current extent, significant time savings can be made by avoiding them. The paper also shows how further time savings can be made by forgoing the traditional canonicity test for new extents, if the intersection is empty. Finally, the paper describes how, because of typical optimizations made in the implementation of CbO-type algorithms, even more time can be saved by amalgamating inherited attributes with inherited empty intersections into a single, simple test

Faecal miRNA profiles associated with age, sex, BMI, and lifestyle habits in healthy individuals

For their stability and detectability faecal microRNAs represent promising molecules with potential clinical interest as non-invasive diagnostic and prognostic biomarkers. However, there is no evidence on how stool miRNA profiles change according to an individual's age, sex, and body mass index (BMI) or how lifestyle habits influence the expression levels of these molecules. We explored the relationship between the stool miRNA levels and common traits (sex, age, BMI, and menopausal status) or lifestyle habits (physical activity, smoking status, coffee, and alcohol consumption) as derived by a self-reported questionnaire, using small RNA-sequencing data of samples from 335 healthy subjects. We detected 151 differentially expressed miRNAs associated with one variable and 52 associated with at least two. Differences in miR-638 levels were associated with age, sex, BMI, and smoking status. The highest number of differentially expressed miRNAs was associated with BMI (n = 92) and smoking status (n = 84), with several miRNAs shared between them. Functional enrichment analyses revealed the involvement of the miRNA target genes in pathways coherent with the analysed variables. Our findings suggest that miRNA profiles in stool may reflect common traits and lifestyle habits and should be considered in relation to disease and association studies based on faecal miRNA expressio

Automatic Detection of Sensitive Information in Educative Social Networks

[EN] Detecting sensitive information with privacy in mind is a relevant issue on Social Networks. It is often difficult for users to manage the privacy associated with their posts on social networks taking into account their possible consequences. The main objective of this work is to provide users information about the sensitivity of the information they will share when they decide to publish a message in online media. For this purpose, an assistant agent to detect sensitive information based on different types of categories detected in the message (i.e., location, personal data, health, personal attacks, emotions, etc.) is proposed. Entity recognition libraries, ontologies, dictionaries, and sentiment analysis will be used to detect the different categories. This agent is integrated into the social network Pesedia, aimed for children and teenagers, and through a soft-paternalism mechanism provides information to users about the sensitivity of certain content and help them in making decisions about its publication. The agent decision process will be evaluated with a dataset elaborated from messages of the social network Twitter.This work is supported by the Spanish Government project TIN2017-89156-R.Botti-Cebriá, V.; Del Val Noguera, E.; García-Fornes, A. (2020). Automatic Detection of Sensitive Information in Educative Social Networks. Springer. 184-194. https://doi.org/10.1007/978-3-030-57805-3_18S184194Official legal text. https://gdpr-info.eu/Aghasian, E., Garg, S., Gao, L., Yu, S., Montgomery, J.: Scoring users’ privacy disclosure across multiple online social networks. IEEE Access 5, 13118–13130 (2017)Aguado, G., Julian, V., Garcia-Fornes, A.: Towards aiding decision-making in social networks by using sentiment and stress combined analysis. Information 9(5), 107 (2018)Alemany, J., del Val, E., Alberola, J., García-Fornes, A.: Enhancing the privacy risk awareness of teenagers in online social networks through soft-paternalism mechanisms. Int. J. Hum Comput Stud. 129, 27–40 (2019)Alemany, J., del Val, E., Alberola, J., García-Fornes, A.: Estimation of privacy risk through centrality metrics. Future Generation Computer Systems (2018)Alemany, J., Del Val, E., Alberola, J.M., Garćia-Fornes, A.: Metrics for privacy assessment when sharing information in online social networks. IEEE Access 7, 143631143645 (2019)Byrt, T., Bishop, J., Carlin, J.B.: Bias, prevalence and kappa. J. Clin. Epidemiol. 46, 423–429 (1993). https://doi.org/10.1016/0895-4356(93)90018-VByrt, T., Bishop, J., Carlin, J.B.: Bias, prevalence and kappa. J. Clin. Epidemiol. 46(5), 423–429 (1993)Caliskan Islam, A., Walsh, J., Greenstadt, R.: Privacy detective: Detecting private information and collective privacy behavior in a large social network. In: Proceedings of the 13th Workshop on Privacy in the Electronic Society. ACM (2014)Dhir, A., Kaur, P., Chen, S., Lonka, K.: Understanding online regret experience in Facebook use-effects of brand participation, accessibility & problematic use. Comput. Hum. Behav. 59, 420–430 (2016)Falotico, R., Quatto, P.: Fleiss’ kappa statistic without paradoxes. Qual. Quant. 49(2), 463–470 (2015)Mao, H., Shuai, X., Kapadia, A.: Loose tweets: an analysis of privacy leaks on Twitter. In: Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society, pp. 1–12. ACM (2011)Observatorio Español de las Drogas y Adicciones: ESTADÍSTICAS 2018 Alcohol, tabaco y drogas ilegales en España. Ministerio de Sanidad, Servicios Sociales e Igualdad (2018)Mothersbaugh, D.L., Foxx, W.K., Beatty, S.E., Wang, S.: Disclosure antecedents in an online service context: the role of sensitivity of information. J. Serv. Res. 15(1), 76–98 (2012)Amigas, P.: Privacy and data protection. Accessed 14 Jun 2019Pensa, R.G., Di Blasi, G.: A privacy self-assessment framework for online social networks. Expert Syst. Appl. 86, 18–31 (2017)Requena, F., Ayuso, L.: Individualism or complementarity? The effect of digital personal networks on face-to-face personal networks. Inf. Commun. Soc. 22(14), 2097–2111 (2019)Rumbold, J.M., Pierscionek, B.K.: What are data? A categorization of the data sensitivity spectrum. Big Data Res. 12, 49–59 (2018)Sánchez, D., Viejo, A.: Privacy risk assessment of textual publications in social networks. In: ICAART (1), pp. 236–241 (2015)Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)Talukder, N., Ouzzani, M., Elmagarmid, A.K., Elmeleegy, H., Yakout, M.: Privometer: privacy protection in social networks. In: 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010). IEEE (2010)Wang, Q., Xue, H., Li, F., Lee, D., Luo, B.: # donttweetthis: scoring private information in social networks. Proc. Priv. Enhancing Technol. 2019(4), 72–92 (2019)Wang, Y., Leon, P.G., Acquisti, A., Cranor, L.F., Forget, A., Sadeh, N.: A field trial of privacy nudges for Facebook. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 2367–2376. ACM (2014)Wang, Y., Leon, P.G., Chen, X., Komanduri, S.: From Facebook regrets to Facebook privacy nudges. Ohio St. LJ 74, 1307 (2013

Equitable Conceptual Clustering using OWA operator

The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, June 3rd - 6th, 2018, Melbourne, Australia.International audienceWe propose an equitable conceptual clustering approach based on multi-agent optimization. In the context of conceptual clustering, each cluster is represented by an agent having its own satisfaction and the problem consists in finding the best cumulative satisfaction while emphasizing a fair compromise between all individual agents. The fairness goal is achieved using an equitable formulation of the Ordered Weighted Averages (OWA) operator. Experiments performed on UCI datasets and on instances coming from real application ERP show that our approach efficiently finds clusterings of consistently high quality