Special Issue on International Journal of Imaging and Robotics

This paper presents a brief summary of the post-proceedings of the International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2014) and the Workshop on Intelligent Systems for Context-based Information Fusion (ISCIF) held in Salamanca in June from 4th to 6th, 2014. This special issue presents a selection of the best papers selected from those that were accepted on the symposium focused on image processing and robotics

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

From the Queue to the Quality of Service Policy: A Middleware Implementation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02481-8_61Quality of service policies in communications is one of the current trends in distributed systems based on middleware technology. To implement the QoS policies it is necessary to define some common parameters. The aim of the QoS policies is to optimize the user defined QoS parameters. This article describes how to obtain the common QoS parameters using message queues for the communications and control components of communication. The paper introduces the Queue-based Quality of Service Cycle concept for each middleware component. The QoS parameters are obtained directly from the queue parameters, and Quality of Service Policies controls directly the message queues to obtain the user-defined parameters values.The middleware architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). From the Queue to the Quality of Service Policy: A Middleware Implementation. En Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. Springer Verlag (Germany). 432-437. doi:10.1007/978-3-642-02481-8_61S432437Aurrecoechea, C., Campbell, A.T., Hauw, L.: A Survey of QoS Architectures. Multimedia Systems Journal, Special Issue on QoS Architecture 6(3), 138–151 (1998)OMG. Data Distribution Service for Real-Time Systems, v1.1. Document formal/2005-12-04 (December 2005)Botts, M., Percivall, G., Reed, C., Davidson, J.: OGC®. Sensor Web Enablement: Overview And High Level Architecture, OpenGIS Consortium Inc (2006)Poza, J.L., Posadas, J.I., Simó, J.E.: QoS-based middleware architecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence, Salamanca (2008)Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multi-media and QoS: A Survey 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet, pp. 1–37, XP002219363 (August 1998)ITU-T Recommendation E.800 (0894). Terms and Definitions Related to Quality of Service and Network Performance Including Dependability (1994)Stuck, B.W., Arthurs, E.: A Computer & Communications Network Performance Analysis Primer. Prentice Hall, Englewood Cliffs (1984)Jain, R.: The art of Computer Systems Performance Analysis. John Wiley & Sons Inc., New york (1991)Coulouris, G., Dollimore, J., Kindberg, T.: Distributed Systems. Concepts and Design, 3rd edn. Addison Wesley, Madrid (2001)Jung, J.-l.: Quality of Service in Telecommunications Part II: Translation of QoS Pa-rameters into ATM Performance Parameters in B-ISDN. IEEE Comm. Mag., pp. 112–117 (August 1996)Wohlstadter, E., Tai, S., Mikalsen, T., Rouvellou, I., Devanbu, P.: GlueQoS: Middleware to Sweeten Quality-of-Service Policy Interactions. In: ICSE, 26th International Conference on Software Engineering (ICSE 2004) (2004

A survey on quality of service support on middelware-based distributed messaging systems used in multi agent systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-19934-9_10Messaging systems are widely used in distributed systems to hide the details of the communications mechanism to the multi agents systems. However, the Quality of Service is treated in different way depending on the messaging system used. This article presents a review and further analysis of the quality of service treatment in the mainly messaging systems used in distributed multi agent systems. The review covers the issues related to the purpose of the functions provided and the scope of the quality of service offered by every messaging system. We propose ontology for classifying and decide which parameters are relevant to the user. The results of the analysis and the ontology can be used to select the most suitable messaging system to distributed multi agent architecture and to establish the quality of service requirements in a distributed system.The study described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government and European FEDER found. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2011). A survey on quality of service support on middelware-based distributed messaging systems used in multi agent systems. En International Symposium on Distributed Computing and Artificial Intelligence. Springer. 77-84. https://doi.org/10.1007/978-3-642-19934-9_10S7784Gaddah, A., Kunz, T.: A survey of middleware paradigms for mobile computing. Technical Report SCE-03-16. Carleton University Systems and Computing Engineering (2003)Foundation for Intelligent Physical Agents, http://www.fipa.org/Java Message Service Specification, http://java.sun.com/products/jms/docs.htmlCommon Object Request Broker Architecture, http://www.corba.org/Data Distribution Service, http://portals.omg.org/dds/Java Agent DEvelopment Framework, http://jade.tilab.com/Agent Oriented Software Pty Ltd., JACK Intelligent Agents: User Guide (1999)Nwana, H., Ndumu, D., Lee, L., Collis, J.: ZEUS: A tool-kit for building distributed multi-agent systems. Applied Artifical Intelligence Journal 13(1), 129–186 (1999)Perdikeas, M.K., Chatzipapadopoulos, F.G., Venieris, I.S., Marino, G.: Mobile Agent Standards and Available Platforms. Computer Networks Journal, Special Issue on ’Mobile Agents in Intelligent Networks and Mobile Communication Systems’ 31(10) (1999)Perrone, P.J., Chaganti, K.: J2EE Developer’s Handbook. Sam’s Publishing, Indianapolis (2003)Apache ActiveMQ, http://activemq.apache.org/IBM WebSphere MQSeries, http://mqseries.net/Object Management Group, http://www.omg.org/RTI Data Distribution Service. RTI corp., http://www.rti.com/OpenSplice DDS. PrismTech Ltd., http://www.prismtech.comVogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet. IETF Internet Draft, 1–37 (1998)Foundation for Intelligent Physical Agents. FIPA Quality of Service Ontology Specification. Doc: SC00094A (2002)Sun Microsystems, Inc. Java(TM) Message Service Specification Final Release 1.1 (2002)Object Management Group (OMG). The Common Object Request Broker Architecture and Specification. CORBA 2.4.2 (2001

Quality of service and quality of control based protocol to distribute agents

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-14883-5_10This paper describes an agent s movement protocol. Additionally, a distributed architecture to implement such protocol is presented. The architecture allows the agents to move in accordance with their requirements. The protocol is based on division and fusion of the agents in their basic components called Logical Sensors. The movement of the agents is based on the quality of services (QoS) and quality of control (QoC) parameters that the system can provides. The protocol is used to know the impact that the movement of the agents may have on the system and obtain the equilibrium points where the impact is minimal.The architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2010). Quality of service and quality of control based protocol to distribute agents. En Distributed Computing and Artificial Intelligence: 7th International Symposium. Springer. 73-80. doi:10.1007/978-3-642-14883-5_10S7380Posadas, J.L., Poza, J.L., Simó, J.E., Benet, G., Blanes, F.: Agent Based Distributed Architecture for Mobile Robot Control. In: Engineering Applications of Artificial Intelligence, vol. 21(6), pp. 805–823. Pergamon Press Ltd., Oxford (2008)Object Management Group (OMG): Data Distribution Service for Real-Time Systems, v1.1. Document formal / 2005-12-04 (2005)Odum, E.P.: Fundamentals of Ecology, 3rd edn. W.B. Saunders Company, Philadelphia (1971)Aurrecoechea, C., Campbell, A.T., Hauw, L.: A Survey of QoS Architectures. ACM/Springer Verlag Multimedia Systems Journal, Special Issue on QoS Architecture 6(3), 138–151 (1998)Pardo-Castellote, G.O.: Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, vol. 19-22, pp. 200–206 (2003)International Telecommunication Union (ITU). Terms and Definitions Related to Quality of Service and Network Performance Including Dependability. ITU-T Recommendation E.800 (0894) (1994)Foundation for Intelligent Physical Agents. FIPA Quality of Service Ontology Specification, Experimental Doc: XC00094 (2002)Dorf, R.C., Bishop, R.H.: Modern Control Systems, 11th edn. Prentice Hall, Englewood Cliffs (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: Middleware with QoS Support to Control Intelligent Systems. In: 2nd International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP, pp. 211–216 (2008)Bellifemine, F., Poggi, A., Rimassa, G.: Jade: A FIPA-compliant agent framework. In: Proceedings of PAAM 1999, pp. 97–108 (1999)Poza, J.L., Posadas, J.L., Simó, J.E.: From the Queue to the Quality of Service Policy: A Middleware Implementation. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. Part II. LNCS, vol. 5518, pp. 432–437. Springer, Heidelberg (2009)Foundation for Intelligent Physical Agents. FIPA Agent Management Specification, Doc: FIPA00023 (2000)Jeong, B., Cho, H., Kulvatunyou, B., Jones, A.: A Multi-Criteria Web Services Composition Problem. In: Proceedings of the IEEE International Conference on Information Reuse and Integration, 2007 (IRI 2007), pp. 379–384. IEEE, Los Alamitos (2007)Poza, J.L., Posadas, J.L., Simó, J.E., Benet, G.: Distributed Agent Specification for an Intelligent control Architecture. In: 6th International Workshop on Practical Applications of Agents and Multiagent Systems. IWPAAMS (2007) ISBN 978-84-611-8858-

Multi-agent simulations for emergency situations in an airport scenario

This paper presents a multi-agent framework using Net- Logo to simulate humanand collective behaviors during emergency evacuations. Emergency situationappears when an unexpected event occurs. In indoor emergency situation, evacuation plans defined by facility manager explain procedure and safety ways tofollow in an emergency situation. A critical and public scenario is an airportwhere there is an everyday transit of thousands of people. In this scenario theimportance is related with incidents statistics regarding overcrowding andcrushing in public buildings. Simulation has the objective of evaluating buildinglayouts considering several possible configurations. Agents could be based onreactive behavior like avoid danger or follow other agent, or in deliberative behaviorbased on BDI model. This tool provides decision support in a real emergencyscenario like an airport, analyzing alternative solutions to the evacuationprocess.Publicad

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

Social Intelligence Design in Ambient Intelligence

This Special Issue of AI and Society contains a selection of papers presented at the 6th Social Intelligence Design Workshop held at ITC-irst, Povo (Trento, Italy) in July 2007. Being the 6th in a series means that there now is a well-established and also a growing research area. The interest in this research area is growing because, among other things, current computing technology allows other than the traditional efficiency-oriented applications associated with computer science and interface technology. For example, in Ambient Intelligence (AmI) applications we look at sensor-equipped environments and devices (robots, smart furniture, virtual humans and pets) that support their human inhabitants during their everyday activities. These everyday activities also include computer-mediated communication, collaboration and community activities