12th International Symposium on Intelligent Distributed Computing, Bilbao, Spanien

Abbrevation

IDC

Link

http://idc2018.jrlab.science/

City

Bilbao

Country

Spain

Deadline Paper

2018-04-09

Start Date

2018-10-15

End Date

2018-10-17

Abstract

12th INTERNATIONAL SYMPOSIUM ON INTELLIGENT DISTRIBUTED SYSTEMS The 12th International Symposium on Intelligent Distributed Computing (IDC 2018) will be held in October 2018 in Bilbao, Spain. The main goal of these series of symposiums is to gather researchers and practitioners to foster and ease rich discussions around the latest findings, research achievements and ideas in the area of Intelligent Distributed Computing. The IDC provides an open forum for enhancing the collaboration between researchers, lecturers, and students from Intelligent Computing and Distributing Computing communities. Intelligent Computing covers a hybrid palette of methods and techniques ranging from classical artificial intelligence, information sciences or computational intelligence to more recent trends such as swarm intelligence, bio–inspired computation, cloud computing or machine learning. Distributed Computing develops technologies and methods to build complex computational systems composed of collaborating software components spread over different computational elements. Recent trends on this field are Ephemeral Computing, Federated Learning or Swarm Robotics. Thus, the field of Intelligent Distributed Computing seeks for the design and implementation of new generation of intelligent distributed systems, adapting or hybridizing researches in both Intelligent Computing and Distributed Computing. IDC 2018 welcomes research works centered on all aspects of intelligent distributed computing, with an intention to balance between theoretical research ideas and their practicability as well as industrial applicability. To this end, scholars and practitioners from academia and industrial fields are invited to submit high–quality original contributions to IDC 2018. The structure of the symposium consists of regular sessions with technical contributions reviewed and selected by an international program committee, as well as of special sessions focused on multi–disciplinary and cutting–edge topics. IDC 2018 has a special interest in novel architectures, systems and methods that facilitate distributed / parallel / multi–agent biocomputing for solving complex computational and real–life problems. The scope of this edition spans further to embrace recent paradigms in Distributed Computation. Symposium topics include, but are not limited to: –Intelligent Distributed and High–Performance Architectures Hybrid distributed systems involving software agents and human actors Intelligent grid and cloud infrastructures Agent–based wireless sensor networks Distributed frameworks and middleware for the Internet of Things GPU, multicore, and many–core intelligent computing Intelligent high–performance architectures Context–aware intelligent computing Virtualization infrastructures for intelligent computing Bio–inspired and nature–inspired distributed computing –Organization and Management Autonomic and adaptive distributed computing Intelligent service composition and orchestration Self–organizing and adaptive distributed systems Emerging behaviors in complex distributed systems Intelligent integration of heterogeneous data and processes Methodologies for development of intelligent distributed systems and applications –Ehemeral and Unreliable computing Theory and applications of complex ephemeral or unreliable environments Design and deployment of ephemeral computing systems Application of Soft Computing methods on computational environments featuring ephemeral behavior (unreliability, dynamicity, and/or heterogeneity) Meta–heuristics for modeling and analyzing systems with ephemeral properties, such as social network dynamics, ephemeral clustering and pattern mining, ephemeral computational creativity or content generation. –Intelligent Distributed Knowledge Representation and Processing Information extraction and retrieval in distributed environments Knowledge integration and fusion from distributed sources Data mining and knowledge discovery in distributed environments Semantic and knowledge grids Ontologies and meta–data for describing heterogeneous resources and services Distributed fusion of sensor data streams Big Data Processing –Networked Intelligence E–service and web intelligence Intelligence in mobile, ubiquitous and wearable computing Intelligence in peer–to–peer systems Intelligence in distributed and networked multimedia systems Security, privacy, trust and reputation –Parallel metaheuristics for optimization Global single–population master–slave, panmictic population GAs Single–population fine–grained GAs Multiple–population, multiple–deme, distributed, island based, coarse grained, GAs Evolutionary simulated annealing, Distributed Tabu Search Parallel Variable Neighborhood Search Swarm intelligence methods based on distributed knowledge sharing: bat algorithm, firefly algorithm, imperialist competitive algorithm, particle swarm optimization, ant colony optimization, artificial bee colony, golden ball, coral reefs optimization, water cycle algorithm, cuckoo search, harmony search... Hybridization of Swarm Intelligence techniques, Memetic Computing, Adaptive Swarm Intelligence methods Distributed Evolutionary Techniques, Cellular Evolutionary Algorithm, Hyper Heuristics Applications of swarm intelligence techniques for distributed or cooperative environments –Distributed swarm robotics systems Recent advances on Soft Computing methods for Robotics Novel applications of Swarm Robotics, with a priority on real–world scenarios New synergies between Swarm Intelligence and Swarm Robotics Coordination and control of Swarm Robotic Systems Applications of Swarm Intelligence for collaborative positioning and route optimization in robotic swarms Distributed inference in Swarm Robotics Self–organization in robotics enabled by Swarm Intelligence Nature inspired methods for data science and machine learning Recent advances on nature inspired methods for data science Novel applications of bio–inspired methods to data mining, with priority on real–world scenarios –Nature–inspired methods for supervised and unsupervised data mining Hybridizing bio–inspired methods with machine learning and data mining techniques Nature–inspired methods for feature selection and/or instance generation/selection Implementation of bio–inspired methods using Big Data technologies Federated learning: theory and applications –Intelligent Distributed Applications Distributed problem solving and decision making Intelligent applications in e–business/e–commerce, e–learning, e–health, e–science, e–government, crisis management, smart grid Modeling, simulation and development of intelligent distributed systems Simulation of groups and crowds Intelligent data processing Intelligent robots