On the Move to Meaningful Internet Systems: OTM 2015 Workshops: Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, EI2N, FBM, INBAST, ISDE, META4eS, and MSC 2015, Rhodes, Greece, October 26-30, 2015. Proceedings

International audienceThis volume constitutes the refereed proceedings of the following 8 International Workshops: OTM Academy; OTM Industry Case Studies Program; Enterprise Integration, Interoperability, and Networking, EI2N; International Workshop on Fact Based Modeling 2015, FBM; Industrial and Business Applications of Semantic Web Technologies, INBAST; Information Systems, om Distributed Environment, ISDE; Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society, META4eS; and Mobile and Social Computing for collaborative interactions, MSC 2015. These workshops were held as associated events at OTM 2015, the federated conferences "On The Move Towards Meaningful Internet Systems and Ubiquitous Computing", in Rhodes, Greece, in October 2015.The 55 full papers presented together with 3 short papers and 2 popsters were carefully reviewed and selected from a total of 100 submissions. The workshops share the distributed aspects of modern computing systems, they experience the application pull created by the Internet and by the so-called Semantic Web, in particular developments of Big Data, increased importance of security issues, and the globalization of mobile-based technologies

Connectionist-Symbolic Machine Intelligence using Cellular Automata based Reservoir-Hyperdimensional Computing

We introduce a novel framework of reservoir computing, that is capable of both connectionist machine intelligence and symbolic computation. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space, and it is used as the reservoir. The proposed framework is capable of long short-term memory and it requires orders of magnitude less computation compared to Echo State Networks. We prove that cellular automaton reservoir holds a distributed representation of attribute statistics, which provides a more effective computation than local representation. It is possible to estimate the kernel for linear cellular automata via metric learning, that enables a much more efficient distance computation in support vector machine framework. Also, binary reservoir feature vectors can be combined using Boolean operations as in hyperdimensional computing, paving a direct way for concept building and symbolic processing.Comment: Corrected Typos. Responded some comments on section 8. Added appendix for details. Recurrent architecture emphasize