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[EN] The concept of privacy in online social networks (OSNs) is a challenge, especially for teenagers. Previous works deal with teaching about privacy using educational online content, and media literacy. However, these tools do not necessarily promote less risky behaviors, and do not allow the assessment of users' behavior after the learning period. Moreover, few research studies about the effects of social gamification have been performed for this population segment (i.e., teenagers). To address this problem in this article, we propose the use of gamification in an OSN called Pesedia to facilitate the teaching/learning process, and assess its effectiveness in promoting suitable privacy behaviors. We tested our proposal comparing teenagers' performance in two editions of a course about social networks, and privacy (with, and without gamification) for one month. We measured the impact of gamification in the participants' behaviors toward privacy concepts as a consequence of the privacy teaching/learning process, and the participants' engagement in the educational process. The results show that there are significant differences in participants' behavior regarding privacy, and engagement in the gamified social network. Moreover, there is also a significant difference in participants' engagement for the gamified male participants. The gamified social network proposed in this article may be relevant, and useful for educators who wish to develop, and enhance teenagers' privacy skills, or for a broader base of aspects related to the development of digital competences, and technology in education. ; This work was supported in part by the Spanish Government Project TIN2017-89156-R, and in part by the FPI under Grant BFS-2015-074498. (Corresponding author: Elena Del Vol.) ; Alemany-Bordera, J.; Del Val, E.; García-Fornes, A. (2020). Assessing the Effectiveness of a Gamified Social Network for Applying Privacy Concepts: An Empirical Study with Teens. IEEE Transactions on Learning Technologies. 13(4):777-789. ...

© 2021 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. ; [EN] Argument mining is defined as the task of automatically identifying and extracting argumentative components (e.g., premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, rephrase, no relation). One of the main issues when approaching this problem is the lack of data, and the size of the publicly available corpora. In this work, we use the recently annotated US2016 debate corpus. US2016 is the largest existing argument annotated corpus, which allows exploring the benefits of the most recent advances in natural language processing in a complex domain like argument (relation) mining. We present an exhaustive analysis of the behavior of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT, and ALBERT) when predicting argument relations. Finally, we evaluate the models in five different domains, with the objective of finding the less domain-dependent model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus, and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus. ; This work was supported in part by the Spanish Government project under Grant TIN2017-89,156-R, in part by the FPI under grant BES-2015-074,498, and in part by the Valencian Government project under Grant PROMETEO/2018/002. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPUs used for this research. ; Ruiz-Dolz, R.; Alemany-Bordera, J.; Heras, S.; García-Fornes, A. (2021). Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation. IEEE Intelligent Systems. 36(6):62-70. ...

[EN] Multi-agent system paradigm has been envisioned as an appropriate solution for challenges in the area of smart-environments. Specifically, MAS add new capabilities such as adaption, reorganization, learning, coordination, etc. These features allow to deal with open issues in the context of smart-homes such as multi-occupancy, activity tracking or profiling activities and behaviors from multiple residents. In this paper, we present Magentix2 as a suitable MAS platform for the development of dynamic smart environments. Specifically, the use of Magentix2 (http://gti-ia.upv.es/sma/tools/magentix2/index.php) facilitates the management of the multiple occupancy in smart living spaces. Normative virtual organizations provide the possibility of defining a set of norms and organizational roles that facilitate the regulation and control of the actions that can be carried out by internal and external agents depending on their profile. Moreover, Magentix2 provides a tracing service to keep track of activities carried out in the system. We illustrate the applicability and benefits of Magentix2 in a set of scenarios in the context of smart-homes. ; This work is supported by the Spanish government grants PROMETEOII/2013/019,TIN2014-55206-R, TIN2015-65515-C4-1-R, H2020-ICT-2015-688095. ; Valero Cubas, S.; Del Val Noguera, E.; Alemany-Bordera, J.; Botti, V. (2017). Enhancing Smart-Home Environments using Magentix2. Journal of Applied Logic. 24:32-44. https://doi.org/10.1016/j.jal.2016.11.022 ; S ; 32 ; 44 ; 24

[EN] In this paper, we present the application of a multi-agent platform Magentix2 for the development of MAS in smart-homes. Specificallly, the use of Magentix2 (http://gti-ia.upv.es/sma/tools/magentix2/index.php) platform facilitates the management of the multiple occupancy in smart living spaces. Virtual organizations provide the possibility of defining a set of norms and roles that facilitate the regulation and control of the actions that can be carried out by the internal and external agents depending on their profile. We illustrate the applicability of our proposal with a set of scenarios. © Springer International Publishing Switzerland 2015. ; This work is supported by the Spanish government grants CONSOLIDER INGENIO 2010 CSD2007-00022, MINECO/FEDER TIN2012-36586-C03-01, TIN2011-27652-C03-01, and SP2014800. ; Valero Cubas, S.; Del Val Noguera, E.; Alemany Bordera, J.; Botti, V. (2015). Using Magentix2 in Smart-Home Environments. En 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Springer Verlag. 27-37. https://doi.org/10.1007/978-3-319-19719-7_3 ; S ; 27 ; 37 ; Bajo J, Fraile JA, Pérez-Lancho B, Corchado JM (2010) The thomas architecture in home care scenarios: a case study. Expert Syst Appl 37(5):3986–3999 ; Cetina C, Giner P, Fons J, Pelechano V (2009) Autonomic computing through reuse of variability models at runtime: The case of smart homes. Computer 42(10):37–43 ; Cook DJ (2009) Multi-agent smart environments. J Ambient Intell Smart Environ 1(1):51–55 ; Crandall AS, Cook DJ (2010) Using a hidden markov model for resident identification. In: 6th international conference on intelligent environments, pp 74–79. IEEE ; Criado N, Argente E, Botti V (2013) THOMAS: an agent platform for supporting normative multi-agent systems. J Logic Comput 23(2):309–333 ; Davidoff S, Lee MK, Zimmerman J, Dey A (2006) Socially-aware requirements for a smart home. In: Proceedings of the international symposium on intelligent, environments, pp 41–44 ; Grupo de Tecnología ...

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. ; [EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures. ; This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498. ; Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723 ; S ; 143631 ; 143645 ; 7

[EN] Privacy Risk in Online Social Networks (OSNs) is one of the main concerns that has increased in the last few years. Even though social network applications provide mechanisms to control risk, teenagers are not often aware of the privacy risks of disclosing information in online social networks. The privacy decision-making process is complex and users often do not have full knowledge and enough time to evaluate all potential scenarios. They do not consider the audience that will have access to disclosed information or the risk if the information continues to spread and reaches an unexpected audience. To deal with these issues, we propose two soft-paternalism mechanisms that provide information to the user about the privacy risk of publishing information on a social network. That privacy risk is based on a complex privacy metric. To evaluate the mechanisms, we performed an experiment with 42 teenagers. The proposed mechanisms were included in a social network called Pesedia. The results show that there are significant differences in teenagers¿ behaviors towards better privacy practices when the mechanisms are included in the network. ; This work is partially supported by the Spanish Government project TIN2017-89156-R and the FPI grant BES-2015-074498. ; Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Enhancing the privacy risk awareness of teenagers in online social networks through soft-paternalism mechanisms. International Journal of Human-Computer Studies. 129:27-40. https://doi.org/10.1016/j.ijhcs.2019.03.008 ; S ; 27 ; 40 ; 129

143631 143645 7 ; S ; (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. [EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures. This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498. Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723

27 40 129 ; S ; [EN] Privacy Risk in Online Social Networks (OSNs) is one of the main concerns that has increased in the last few years. Even though social network applications provide mechanisms to control risk, teenagers are not often aware of the privacy risks of disclosing information in online social networks. The privacy decision-making process is complex and users often do not have full knowledge and enough time to evaluate all potential scenarios. They do not consider the audience that will have access to disclosed information or the risk if the information continues to spread and reaches an unexpected audience. To deal with these issues, we propose two soft-paternalism mechanisms that provide information to the user about the privacy risk of publishing information on a social network. That privacy risk is based on a complex privacy metric. To evaluate the mechanisms, we performed an experiment with 42 teenagers. The proposed mechanisms were included in a social network called Pesedia. The results show that there are significant differences in teenagers¿ behaviors towards better privacy practices when the mechanisms are included in the network. This work is partially supported by the Spanish Government project TIN2017-89156-R and the FPI grant BES-2015-074498. Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Enhancing the privacy risk awareness of teenagers in online social networks through soft-paternalism mechanisms. International Journal of Human-Computer Studies. 129:27-40. https://doi.org/10.1016/j.ijhcs.2019.03.008

[EN] Users are not often aware of privacy risks and disclose information in online social networks. They do not consider the audience that will have access to it or the risk that the information continues to spread and may reach an unexpected audience. Moreover, not all users have the same perception of risk. To overcome these issues, we propose a Privacy Risk Score (PRS) that: (1) estimates the reachability of an user¿s sharing action based on the distance between the user and the potential audience; (2) is described in levels to adjust to the risk perception of individuals; (3) does not require the explicit interaction of individuals since it considers information flows; and (4) can be approximated by centrality metrics for scenarios where there is no access to data about information flows. In this case, if there is access to the network structure, the results show that global metrics such as closeness have a high degree of correlation with PRS. Otherwise, local and social centrality metrics based on ego-networks provide a suitable approximation to PRS. The results in real social networks confirm that local and social centrality metrics based on degree perform well in estimating the privacy risk of users. ; This work is partially supported by the Spanish Government project TIN2014-55206-R and FPI grant BES-2015-074498. ; Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2018). Estimation of privacy risk through centrality metrics. Future Generation Computer Systems. 82:63-76. https://doi.org/10.1016/j.future.2017.12.030 ; S ; 63 ; 76 ; 82

[EN] The automatic recipe recommendation which take into account the dietary restrictions of users (such as allergies or intolerances) is a complex and open problem. Some of the limitations of the problem is the lack of food databases correctly labeled with its potential allergens and non-unification of this information by companies in the food sector. In the absence of an appropriate solution, people affected by food restrictions cannot use recommender systems, because this recommend them inappropriate recipes. In order to resolve this situation, in this article we propose a solution based on a collaborative multi-agent system, using negotiation and machine learning techniques, is able to detect and label potential allergens in recipes. The proposed system is being employed in receteame.com, a recipe recommendation system which includes persuasive technologies, which are interactive technologies aimed at changing users' attitudes or behaviors through persuasion and social influence, and social information to improve the recommendations ; This work was supported by the projects TIN2015-65515-C4-1-R and TIN2014-55206-R of the Spanish government and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politecnica de Valencia. ; Alemany-Bordera, J.; Heras Barberá, SM.; Palanca Cámara, J.; Julian Inglada, VJ. (2016). Bargaining agents based system for automatic classification of potential allergens in recipes. ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal. 5(2):43-51. https://doi.org/10.14201/ADCAIJ2016524351 ; S ; 43 ; 51 ; 5 ; 2