Extending MAM5 Meta-Model and JaCalIVE Framework to Integrate Smart Devices from Real Environments

[EN] This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system.This work is partially supported by the TIN2009-13839-C03-01, TIN2011-27652-C03-01, 547CSD2007-00022, COST Action IC0801, FP7-294931 and the FPI grant AP2013-01276 548 awarded to Jaime-Andres Rincon.Rincón Arango, JA.; Poza Luján, JL.; Julian Inglada, VJ.; Posadas Yagüe, JL.; Carrascosa Casamayor, C. (2016). Extending MAM5 Meta-Model and JaCalIVE Framework to Integrate Smart Devices from Real Environments. PLoS ONE. 11(2):1-27. https://doi.org/10.1371/journal.pone.0149665S127112Luck, M., & Aylett, R. (2000). Applying artificial intelligence to virtual reality: Intelligent virtual environments. Applied Artificial Intelligence, 14(1), 3-32. doi:10.1080/088395100117142Barella A, Ricci A, Boissier O, Carrascosa C. MAM5: Multi-Agent Model For Intelligent Virtual Environments. In: 10th European Workshop on Multi-Agent Systems (EUMAS 2012); 2012. p. 16–30.Omicini, A., Ricci, A., & Viroli, M. (2008). Artifacts in the A&A meta-model for multi-agent systems. Autonomous Agents and Multi-Agent Systems, 17(3), 432-456. doi:10.1007/s10458-008-9053-xYu Ch, Nagpal R. Distributed Consensus and Self-Adapting Modular Robots. In: IROS-2008 workshop on Self-Reconfigurable Robots and Applications; 2008. Available from: http://www.isi.edu/robots/iros08wksp/Papers/iros08-wksp-paper.pdfLidoris G, Buss M. A Multi-Agent System Architecture for Modular Robotic Mobility Aids. In: European Robotics Symposium 2006; 2006. p. 15–26. Available from: http://link.springer.com/chapter/10.1007/11681120_2Yu, C.-H., & Nagpal, R. (2010). A Self-adaptive Framework for Modular Robots in a Dynamic Environment: Theory and Applications. The International Journal of Robotics Research, 30(8), 1015-1036. doi:10.1177/0278364910384753Barbero A, González-Rodríguez MS, de Lara J, Alfonseca M. Multi-Agent Simulation of an Educational Collaborative Web System. In: European Simulation and Modelling Conference; 2007. Available from: http://sistemas-humano-computacionais.wikidot.com/local--files/capitulo:colaboracao-auxiliada-por-computador/%5BBarbero%202007%5D%20Multi-Agent%20Simulation%20of%20an%20Educational%20Collaborative%20Web%20System.pdfRanathunga S, Cranefield S, Purvis MK. Interfacing a cognitive agent platform with a virtual world: a case study using Second Life. In: AAMAS; 2011. p. 1181–1182. Available from: http://www.aamas-conference.org/Proceedings/aamas2011/papers/B20.pdfAndreoli R, De Chiara R, Erra U, Scarano V. Interactive 3d environments by using videogame engines. In: Information Visualisation, 2005. Proceedings. Ninth International Conference on. IEEE; 2005. p. 515–520. Available from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1509124Dignum, F. (2011). Agents for games and simulations. Autonomous Agents and Multi-Agent Systems, 24(2), 217-220. doi:10.1007/s10458-011-9169-2dos Santos C, Osorio F. AdapTIVE: an intelligent virtual environment and its application in e-commerce. In: Computer Software and Applications Conference, 2004. COMPSAC 2004. Proceedings of the 28th Annual International; 2004. p. 468–473 vol.1.Kazemi, A., Fazel Zarandi, M. H., & Moattar Husseini, S. M. (2008). A multi-agent system to solve the production–distribution planning problem for a supply chain: a genetic algorithm approach. The International Journal of Advanced Manufacturing Technology, 44(1-2), 180-193. doi:10.1007/s00170-008-1826-5Dimuro GP, Costa ACdR, Gonçalves LV, Hubner A. Interval-valued Hidden Markov Models for recognizing personality traits in social exchanges in open multiagent systems. Repositório Institucional da Universidade Federal do Rio Grande. 2008;.Woźniak, M., Graña, M., & Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, 16, 3-17. doi:10.1016/j.inffus.2013.04.006Jia L, Zhenjiang M. Entertainment Oriented Intelligent Virtual Environment with Agent and Neural Networks. In: IEEE International Workshop on Haptic, Audio and Visual Environments and Games, 2007. HAVE 2007; 2007. p. 90–95.Corchado, E., Woźniak, M., Abraham, A., de Carvalho, A. C. P. L. F., & Snášel, V. (2014). Recent trends in intelligent data analysis. Neurocomputing, 126, 1-2. doi:10.1016/j.neucom.2013.07.001Ricci A, Viroli M, Omicini A. Give agents their artifacts: the A&A approach for engineering working environments in MAS. In: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems; 2007. p. 150. Available from: http://dl.acm.org/citation.cfm?id=1329308Barella, A., Valero, S., & Carrascosa, C. (2009). JGOMAS: New Approach to AI Teaching. IEEE Transactions on Education, 52(2), 228-235. doi:10.1109/te.2008.925764Behrens, T. M., Hindriks, K. V., & Dix, J. (2010). Towards an environment interface standard for agent platforms. Annals of Mathematics and Artificial Intelligence, 61(4), 261-295. doi:10.1007/s10472-010-9215-9Ricci A, Viroli M, Omicini A. A general purpose programming model & technology for developing working environments in MAS. In: 5th International Workshop Programming Multi-Agent Systems(PROMAS 2007); 2007. p. 54–69. Available from: http://lia.deis.unibo.it/~ao/pubs/pdf/2007/promas.pdfChee-Yee Chong, & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247-1256. doi:10.1109/jproc.2003.814918Kushner D. The making of arduino. IEEE Spectrum. 2011;26.Schmidt, A., & van Laerhoven, K. (2001). How to build smart appliances? IEEE Personal Communications, 8(4), 66-71. doi:10.1109/98.944006Salzmann C, Gillet D. Smart device paradigm standardization for online labs. In: 4th IEEE Global Engineering Education Conference (EDUCON); 2013.Gonzalez-Jorge, H., Riveiro, B., Vazquez-Fernandez, E., Martínez-Sánchez, J., & Arias, P. (2013). Metrological evaluation of Microsoft Kinect and Asus Xtion sensors. Measurement, 46(6), 1800-1806. doi:10.1016/j.measurement.2013.01.011Cook, D. J., & Das, S. K. (2007). How smart are our environments? An updated look at the state of the art. Pervasive and Mobile Computing, 3(2), 53-73. doi:10.1016/j.pmcj.2006.12.001Compton, M., Barnaghi, P., Bermudez, L., García-Castro, R., Corcho, O., Cox, S., … Taylor, K. (2012). The SSN ontology of the W3C semantic sensor network incubator group. Journal of Web Semantics, 17, 25-32. doi:10.1016/j.websem.2012.05.003Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors, 15(8), 18080-18101. doi:10.3390/s150818080Castrillón-Santan, M., Lorenzo-Navarro, J., & Hernández-Sosa, D. (2014). Conteo de personas con un sensor RGBD comercial. Revista Iberoamericana de Automática e Informática Industrial RIAI, 11(3), 348-357. doi:10.1016/j.riai.2014.05.006Rincon JA, Julian V, Carrascosa C. An Emotional-based Hybrid Application for Human-Agent Societies. In: 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. vol. 368; 2015. p. 203–214.Rincon JA, Julian V, Carrascosa C. Applying a Social Emotional Model in Human-Agent Societies. In: Workshop WIHAS’15. Intelligent Human-Agent Societies‥ vol. 524 of CCIS; 2015. p. 377–388.Leccese, F., Cagnetti, M., & Trinca, D. (2014). A Smart City Application: A Fully Controlled Street Lighting Isle Based on Raspberry-Pi Card, a ZigBee Sensor Network and WiMAX. Sensors, 14(12), 24408-24424. doi:10.3390/s141224408Mateevitsi V, Haggadone B, Leigh J, Kunzer B, Kenyon RV. Sensing the environment through SpiderSense. In: Proceedings of the 4th Augmented Human International Conference. ACM; 2013. p. 51–57.Kavitha R, Thiyagarajan N. Distributed Intelligent Street Lamp Monitoring and Control System Based on Zigbee. International Journal of Science and Research (IJSR) PP; p. 2319–7064.Pan, M.-S., Yeh, L.-W., Chen, Y.-A., Lin, Y.-H., & Tseng, Y.-C. (2008). A WSN-Based Intelligent Light Control System Considering User Activities and Profiles. IEEE Sensors Journal, 8(10), 1710-1721. doi:10.1109/jsen.2008.2004294Villarrubia, G., De Paz, J., Bajo, J., & Corchado, J. (2014). Ambient Agents: Embedded Agents for Remote Control and Monitoring Using the PANGEA Platform. Sensors, 14(8), 13955-13979. doi:10.3390/s14081395

Study on Gibbs Optimization-Based Resource Scheduling Algorithm in Data Aggregation Networks

In data aggregation networks (WSNs, ad hoc, mesh, etc.), it is key to schedule the network resources, such as channels and TDMA time slots, to minimize the communication conflict and optimize the network data-gathering performance. In this paper, the resources scheduling problem is formulated as a vertex coloring problem in graph theory. Then, a multi-channel TDMA scheduling algorithm based on the Gibbs optimization is proposed. By defining the Gibbs energy expression according to the objective function of the problem, the joint probability of channel and time slot can be computed for the optimized selection of channels and time slots. This algorithm is low-complexity and its convergence performance can be proven. Experiments with different network parameters demonstrate that the proposed algorithm can reduce the communication conflict, improve the network throughput, and effectively reduce the network transmission delay and scheduling length for the data aggregation networks

Protein Sequence Comparison Based on Physicochemical Properties and the Position-Feature Energy Matrix

© 2017 The Author(s). We develop a novel position-feature-based model for protein sequences by employing physicochemical properties of 20 amino acids and the measure of graph energy. The method puts the emphasis on sequence order information and describes local dynamic distributions of sequences, from which one can get a characteristic B-vector. Afterwards, we apply the relative entropy to the sequences representing B-vectors to measure their similarity/dissimilarity. The numerical results obtained in this study show that the proposed methods leads to meaningful results compared with competitors such as Clustal W

Study on Gibbs Optimization-Based Resource Scheduling Algorithm in Data Aggregation Networks

In data aggregation networks (WSNs, ad hoc, mesh, etc.), it is key to schedule the network resources, such as channels and TDMA time slots, to minimize the communication conflict and optimize the network data-gathering performance. In this paper, the resources scheduling problem is formulated as a vertex coloring problem in graph theory. Then, a multi-channel TDMA scheduling algorithm based on the Gibbs optimization is proposed. By defining the Gibbs energy expression according to the objective function of the problem, the joint probability of channel and time slot can be computed for the optimized selection of channels and time slots. This algorithm is low-complexity and its convergence performance can be proven. Experiments with different network parameters demonstrate that the proposed algorithm can reduce the communication conflict, improve the network throughput, and effectively reduce the network transmission delay and scheduling length for the data aggregation networks

Glutamylation of an HIV-1 protein inhibits the immune response by hijacking STING

Summary: Cyclic GMP-AMP synthase (cGAS) recognizes Y-form cDNA of human immunodeficiency virus type 1 (HIV-1) and initiates antiviral immune response through cGAS-stimulator of interferon genes (STING)-TBK1-IRF3-type I interferon (IFN-I) signalingcascade. Here, we report that the HIV-1 p6 protein suppresses HIV-1-stimulated expression of IFN-I and promotes immune evasion. Mechanistically, the glutamylated p6 at residue Glu6 inhibits the interaction between STING and tripartite motif protein 32 (TRIM32) or autocrine motility factor receptor (AMFR). This subsequently suppresses the K27- and K63-linked polyubiquitination of STING at K337, therefore inhibiting STING activation, whereas mutation of the Glu6 residue partially reverses the inhibitory effect. However, CoCl2, an agonist of cytosolic carboxypeptidases (CCPs), counteracts the glutamylation of p6 at the Glu6 residue and inhibits HIV-1 immune evasion. These findings reveal a mechanism through which an HIV-1 protein mediates immune evasion and provides a therapeutic drug candidate to treat HIV-1 infection