Assess and enhancing attention in learning activities

The rapid progress of technologies has enabled the development of innovative environment in learning activities when the student used computer devices with access to Internet. The goal of this paper is to propose an ambient intelligent (AmI) system, directed at the teacher that indicates the level of attention of the students in the class when it requires the use of the computer connected to the Internet. This AmI system captures, measures, and supervises the interaction of each student with the computer (or laptop) and indicates the level of attention of students in the activities proposed by the teacher. When the teacher has big class, he/she can visualize in real time the level of engagement of the students in the proposed activities and act accordingly when necessary. Measurements of attention level are obtained by a proposed model, and user for training a decision support system that in a real scenario makes recommendations for the teachers so as to prevent undesirable behaviour and change the learning styles.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Analysis learning styles though attentiveness

Attention is one of the most widely misused and overgeneralized constructs found in the educational, learning, instructional, and psychological sciences. It would be convenient for teachers if they could grasp the attentiveness states of learners in their classes precisely so that they could try to improve the way to deliver the course material in a manner that could attract more learners. When students are doing learning activities using the news technologies is very hard for the teacher detected if each student her/his level of attentiveness. Furthermore, different student learn in different ways, each one preferring a different learning style. This paper presents an experience using different learning styles with a system that monitoring attention, with the aim of providing a nonintrusive and non-invasive way, reliable and easy tool that can be used freely in schools, without changing or interfering with the established working routines. Specifically, we look at desk students in learning activities, in which the student spends long time interacting with the computer.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

An Abstract Framework for Non-Cooperative Multi-Agent Planning

[EN] In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents¿ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.This work was partially funded by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by Universitat Politecnica de Valencia (UPV) PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana Fondo Social Europeo.Jordán, J.; Bajo, J.; Botti, V.; Julian Inglada, VJ. (2019). An Abstract Framework for Non-Cooperative Multi-Agent Planning. Applied Sciences. 9(23):1-18. https://doi.org/10.3390/app9235180S118923De Weerdt, M., & Clement, B. (2009). Introduction to planning in multiagent systems. Multiagent and Grid Systems, 5(4), 345-355. doi:10.3233/mgs-2009-0133Dunne, P. E., Kraus, S., Manisterski, E., & Wooldridge, M. (2010). Solving coalitional resource games. Artificial Intelligence, 174(1), 20-50. doi:10.1016/j.artint.2009.09.005Torreño, A., Onaindia, E., Komenda, A., & Štolba, M. (2018). Cooperative Multi-Agent Planning. ACM Computing Surveys, 50(6), 1-32. doi:10.1145/3128584Fikes, R. E., & Nilsson, N. J. (1971). Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3-4), 189-208. doi:10.1016/0004-3702(71)90010-5Hoffmann, J., & Nebel, B. (2001). The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research, 14, 253-302. doi:10.1613/jair.855Dukeman, A., & Adams, J. A. (2017). Hybrid mission planning with coalition formation. Autonomous Agents and Multi-Agent Systems, 31(6), 1424-1466. doi:10.1007/s10458-017-9367-7Hadad, M., Kraus, S., Ben-Arroyo Hartman, I., & Rosenfeld, A. (2013). Group planning with time constraints. Annals of Mathematics and Artificial Intelligence, 69(3), 243-291. doi:10.1007/s10472-013-9363-9Guo, Y., Pan, Q., Sun, Q., Zhao, C., Wang, D., & Feng, M. (2019). Cooperative Game-based Multi-Agent Path Planning with Obstacle Avoidance*. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). doi:10.1109/isie.2019.8781205v. Neumann, J. (1928). Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, 100(1), 295-320. doi:10.1007/bf01448847Mookherjee, D., & Sopher, B. (1994). Learning Behavior in an Experimental Matching Pennies Game. Games and Economic Behavior, 7(1), 62-91. doi:10.1006/game.1994.1037Ochs, J. (1995). Games with Unique, Mixed Strategy Equilibria: An Experimental Study. Games and Economic Behavior, 10(1), 202-217. doi:10.1006/game.1995.1030Applegate, C., Elsaesser, C., & Sanborn, J. (1990). An architecture for adversarial planning. IEEE Transactions on Systems, Man, and Cybernetics, 20(1), 186-194. doi:10.1109/21.47820Sailer, F., Buro, M., & Lanctot, M. (2007). Adversarial Planning Through Strategy Simulation. 2007 IEEE Symposium on Computational Intelligence and Games. doi:10.1109/cig.2007.368082Willmott, S., Richardson, J., Bundy, A., & Levine, J. (2001). Applying adversarial planning techniques to Go. Theoretical Computer Science, 252(1-2), 45-82. doi:10.1016/s0304-3975(00)00076-1Nau, D. S., Au, T. C., Ilghami, O., Kuter, U., Murdock, J. W., Wu, D., & Yaman, F. (2003). SHOP2: An HTN Planning System. Journal of Artificial Intelligence Research, 20, 379-404. doi:10.1613/jair.1141Knuth, D. E., & Moore, R. W. (1975). An analysis of alpha-beta pruning. Artificial Intelligence, 6(4), 293-326. doi:10.1016/0004-3702(75)90019-3Vickrey, W. (1961). COUNTERSPECULATION, AUCTIONS, AND COMPETITIVE SEALED TENDERS. The Journal of Finance, 16(1), 8-37. doi:10.1111/j.1540-6261.1961.tb02789.xClarke, E. H. (1971). Multipart pricing of public goods. Public Choice, 11(1), 17-33. doi:10.1007/bf01726210Groves, T. (1973). Incentives in Teams. Econometrica, 41(4), 617. doi:10.2307/1914085Savaux, J., Vion, J., Piechowiak, S., Mandiau, R., Matsui, T., Hirayama, K., … Silaghi, M. (2016). DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents. 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). doi:10.1109/wi.2016.0057Buzing, P., Mors, A. ter, Valk, J., & Witteveen, C. (2006). Coordinating Self-interested Planning Agents. Autonomous Agents and Multi-Agent Systems, 12(2), 199-218. doi:10.1007/s10458-005-6104-4Ter Mors, A., & Witteveen, C. (s. f.). Coordinating Non Cooperative Planning Agents: Complexity Results. IEEE/WIC/ACM International Conference on Intelligent Agent Technology. doi:10.1109/iat.2005.60Hrnčíř, J., Rovatsos, M., & Jakob, M. (2015). Ridesharing on Timetabled Transport Services: A Multiagent Planning Approach. Journal of Intelligent Transportation Systems, 19(1), 89-105. doi:10.1080/15472450.2014.941759Galuszka, A., & Swierniak, A. (2009). Planning in Multi-agent Environment Using Strips Representation and Non-cooperative Equilibrium Strategy. Journal of Intelligent and Robotic Systems, 58(3-4), 239-251. doi:10.1007/s10846-009-9364-4Rosenthal, R. W. (1973). A class of games possessing pure-strategy Nash equilibria. International Journal of Game Theory, 2(1), 65-67. doi:10.1007/bf01737559Jordán, J., Torreño, A., de Weerdt, M., & Onaindia, E. (2017). A better-response strategy for self-interested planning agents. Applied Intelligence, 48(4), 1020-1040. doi:10.1007/s10489-017-1046-5Veloso, M., Muñoz-Avila, H., & Bergmann, R. (1996). Case-based planning: selected methods and systems. AI Communications, 9(3), 128-137. doi:10.3233/aic-1996-9305VOORNEVELD, M., BORM, P., VAN MEGEN, F., TIJS, S., & FACCHINI, G. (1999). CONGESTION GAMES AND POTENTIALS RECONSIDERED. International Game Theory Review, 01(03n04), 283-299. doi:10.1142/s0219198999000219Han-Lim Choi, Brunet, L., & How, J. P. (2009). Consensus-Based Decentralized Auctions for Robust Task Allocation. IEEE Transactions on Robotics, 25(4), 912-926. doi:10.1109/tro.2009.2022423Monderer, D., & Shapley, L. S. (1996). Potential Games. Games and Economic Behavior, 14(1), 124-143. doi:10.1006/game.1996.0044Friedman, J. W., & Mezzetti, C. (2001). Learning in Games by Random Sampling. Journal of Economic Theory, 98(1), 55-84. doi:10.1006/jeth.2000.2694Aamodt, A., & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1), 39-59. doi:10.3233/aic-1994-7104Bertsekas, D. P. (1988). The auction algorithm: A distributed relaxation method for the assignment problem. Annals of Operations Research, 14(1), 105-123. doi:10.1007/bf02186476Bertsekas, D. P., & Castanon, D. A. (1989). The auction algorithm for the transportation problem. Annals of Operations Research, 20(1), 67-96. doi:10.1007/bf0221692

AIDeM: Agent-Based Intrusion Detection Mechanism

The availability of services can be comprimised if a service request sent to the web services server hides some form of attack within its contents. This article presents AIDeM (An Agent-Based Intrusion Detection Mechanism), an adaptive solution for dealing with DoS attacks in Web service environments. The solution proposes a two phased mechanism in which each phase incorporates a special type of CBR-BDI agent that functions as a classifier. In the first phase, a case-based reasoning (CBR) engine utilizes a Naïves Bayes strategy to carry out an initial filter, and in the second phase, a CBR engine incorporates a neural network to complete the classification mechanism. AIDeM has been applied within the FUSION@ architecture to improve its current security mechanism. A prototype of the architecture was developed and applied to a case study. The results obtained are presented in this study.The availability of services can be comprimised if a service request sent to the web services server hides some form of attack within its contents. This article presents AIDeM (An Agent-Based Intrusion Detection Mechanism), an adaptive solution for dealing with DoS attacks in Web service environments. The solution proposes a two phased mechanism in which each phase incorporates a special type of CBR-BDI agent that functions as a classifier. In the first phase, a case-based reasoning (CBR) engine utilizes a Naïves Bayes strategy to carry out an initial filter, and in the second phase, a CBR engine incorporates a neural network to complete the classification mechanism. AIDeM has been applied within the FUSION@ architecture to improve its current security mechanism. A prototype of the architecture was developed and applied to a case study. The results obtained are presented in this study

La celebridad deportiva como intermediario e instrumento cultural de derechos fundamentales e igualdad étnica-racial. Un estudio de la campaña corporativa “common thread”, publicada por la marca deportiva Nike en instagram

Corporate brands play a social role through the discourses they broadcast on their social media. The sports celebrity acts as a cultural intermediary and communicating instrument in the value system that the brand brings together in defence of social, cultural and ethnic diversity (Turner, 2004; Erdogan, 1999; Marshall, 1997). From this perspective, the aim of this proposal is to detect the socio-cultural elements that articulate the discourse constructed by the Nike sports brand in defence of racial and ethnic equality in sport. Methodologically, the content analysis approach is adopted (Krippendorff, 2013). The data consists of 3 short films that make up a campaign created by the sports brand Nike called "Common Thread". The criterion for their selection was the discursive projection that the brand emits in favour of socio-cultural equality on Instagram. In order to define the model of analysis, the classical studies of Greimas (1976) and McCracken (1989) is taken as a starting point. The results show that the brand manifests itself on social media as a social agent that promotes equality and racial-ethnic rights for the black sports community through sport and culture

Multi‐agent technologies in economics

This paper summarizes some of the trends in the use of multi‐agent technologies in economics. Multiple agent systems, fuzzy sets and neural networks are critical tools used to investigate the emerging economics research agenda related to data mining, dynamic markets stock selection and bank stress testing. This paper reviews the contributions of four such examples.Ministerio de Economía y Competitividad TIN2015‐65515‐C4‐3‐

Monitoring level attention approach in learning activities

In this article we focus on a new field of application of ICT techniques and technologies in learning activities. With these activities with computer platforms, attention allows us to break down the problem of understanding a speculative scenario into a series of computationally less demanding and localized lack of attention. The system considers the students’ attention level while performing a task in learning activities. The goal is to propose an architecture that measures the level of attentiveness in real scenario, and detect patterns of behavior in different attention levels among different students. Measurements of attention level are obtained by a proposed model, and user for training a decision support system that in a real scenario makes recommendations for the teachers so as to prevent undesirable behavior.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

An energy-aware algorithm for electric vehicle infrastructures in smart cities

[EN] The deployment of a charging infrastructure to cover the increasing demand of electric vehicles (EVs) has become a crucial problem in smart cities. Additionally, the penetration of the EV will increase once the users can have enough charging stations. In this work, we tackle the problem of locating a set of charging stations in a smart city considering heterogeneous data sources such as open data city portals, geo-located social network data, and energy transformer substations. We use a multi-objective genetic algorithm to optimize the charging station locations by maximizing the utility and minimizing the cost. Our proposal is validated through a case study and several experimental results.This work was partially supported by MINECO/FEDER, Spain RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by UPV, Spain PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana -Fondo Social Europeo, Spain.Palanca Cámara, J.; Jordán, J.; Bajo, J.; Botti Navarro, VJ. (2020). An energy-aware algorithm for electric vehicle infrastructures in smart cities. Future Generation Computer Systems. 108:454-466. https://doi.org/10.1016/j.future.2020.03.001S454466108Gan, L., Topcu, U., & Low, S. H. (2013). Optimal decentralized protocol for electric vehicle charging. IEEE Transactions on Power Systems, 28(2), 940-951. doi:10.1109/tpwrs.2012.2210288Ma, T., & Mohammed, O. A. (2014). Optimal Charging of Plug-in Electric Vehicles for a Car-Park Infrastructure. IEEE Transactions on Industry Applications, 50(4), 2323-2330. doi:10.1109/tia.2013.2296620Needell, Z. A., McNerney, J., Chang, M. T., & Trancik, J. E. (2016). Potential for widespread electrification of personal vehicle travel in the United States. Nature Energy, 1(9). doi:10.1038/nenergy.2016.112Franke, T., & Krems, J. F. (2013). Understanding charging behaviour of electric vehicle users. Transportation Research Part F: Traffic Psychology and Behaviour, 21, 75-89. doi:10.1016/j.trf.2013.09.002Shukla, A., Pekny, J., & Venkatasubramanian, V. (2011). An optimization framework for cost effective design of refueling station infrastructure for alternative fuel vehicles. Computers & Chemical Engineering, 35(8), 1431-1438. doi:10.1016/j.compchemeng.2011.03.018Nie, Y. (Marco), & Ghamami, M. (2013). A corridor-centric approach to planning electric vehicle charging infrastructure. Transportation Research Part B: Methodological, 57, 172-190. doi:10.1016/j.trb.2013.08.010Tu, W., Li, Q., Fang, Z., Shaw, S., Zhou, B., & Chang, X. (2016). Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach. Transportation Research Part C: Emerging Technologies, 65, 172-189. doi:10.1016/j.trc.2015.10.004Dong, J., Liu, C., & Lin, Z. (2014). Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data. Transportation Research Part C: Emerging Technologies, 38, 44-55. doi:10.1016/j.trc.2013.11.001He, J., Yang, H., Tang, T.-Q., & Huang, H.-J. (2018). An optimal charging station location model with the consideration of electric vehicle’s driving range. Transportation Research Part C: Emerging Technologies, 86, 641-654. doi:10.1016/j.trc.2017.11.026Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., & Newth, D. (2015). Understanding Human Mobility from Twitter. PLOS ONE, 10(7), e0131469. doi:10.1371/journal.pone.0131469Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287. doi:10.1016/s0045-7825(01)00323-

Modelling an intelligent interaction system for increasing the level of attention

Learning activities using technologies is one of the common education methods. Its advantages allow that students can learn with concepts more practical’s. However in this environment not all the students can be attentive. In this research an Ambient Intelligent System has been designed using biometrics behaviors for detecting learner inattentiveness. The learning attentiveness of a student can be determined precisely and the teacher has access to these results and might improve news strategies.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Supervising and improving attentiveness in human computer interaction

The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students’ level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio