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E-grāmata: Intelligent Distributed Computing X: Proceedings of the 10th International Symposium on Intelligent Distributed Computing - IDC 2016, Paris, France, October 10-12 2016

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This book presents the combined peer-reviewed proceedings of the tenth International Symposium on Intelligent Distributed Computing (IDC"2016), which was held in Paris, France from October 10 th to 12 th , 2016. The 23 contributions address a range of topics related to theory and application of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.

Adaptive Scaling Up/Down for Elastic Clouds.-A Dynamic Model to enhance the Distributed Discovery of SWs in P2P Overlay Networks.- Simulation of Dynamic Systems Using BDI Agents: Initial Steps.-A Multi-Agent Middleware for the Deployment of Distributed Applications in Smart Environments.-A Guidance of Ambient Agents Adapted to Opportunistic Situations Extended Context Patterns - A Visual Language for Context-Aware Applications.-MDE4IoT: Supporting the Internet of Things with Model-Driven Engineering.-Detection of tra c anomalies in multi-service networks based on a fuzzy logical inference.-Recon guration of RBAC schemes by genetic algorithms.-String-based Malware Detection for Android Environments.- Optimal Con guration Model of a Fleet of Unmanned Vehicles for Interoperable Missions .-Spatial Tuples: Augmenting Physical Reality with Tuple Spaces.- Exploring unknown environments with multi-modal locomotion swarm.- Group Trust: Finding Trust-based Group Structures in Social Commu

nities .- Non-intrusive Monitoring of Attentional Behavior in Teams.- A Speculative Computation Approach for Con ict Styles Assessment with Incomplete Information.- Forming Classes in an e-Learning Social Network Scenario.-Scheduling Optimization in Grid with VO Stakeholders" Preferences.- On the Application of Bio-inspired Heuristics for Network Routing with Multiple QoS Constraints.-Dealing with the Best Attachment Problem via Heuristics.-Towards Collaborative Sensing using Dynamic Intelligent Virtual Sensors .-Intelligent Data Metrics for Urban Driving with Data Fusion and Distributed Machine Learning.-A Probabilistic Sample Matchmaking Strategy for Imbalanced Data Streams with Concept.-Author Index.
Adaptive Scaling Up/Down for Elastic Clouds.-A Dynamic Model to enhance
the Distributed Discovery of SWs in P2P Overlay Networks.- Simulation of
Dynamic Systems Using BDI Agents: Initial Steps.-A Multi-Agent Middleware for
the Deployment of Distributed Applications in Smart Environments.- 

A Guidance of Ambient Agents Adapted to Opportunistic Situations Extended
Context Patterns A Visual Language for Context-Aware Applications.-MDE4IoT:
Supporting the Internet of Things with Model-Driven Engineering.-Detection of
trac anomalies in multi-service networks based on a fuzzy logical
inference.-Reconguration of RBAC schemes by genetic algorithms.-String-based
Malware Detection for Android Environments.- Optimal Conguration Model of a
Fleet of Unmanned Vehicles for Interoperable Missions .-Spatial Tuples:
Augmenting Physical Reality with Tuple Spaces.- Exploring unknown
environments with multi-modal locomotion swarm.- Group Trust: Finding
Trust-based Group Structures in Social Communities .- Non-intrusive
Monitoring of Attentional Behavior in Teams.- A Speculative Computation
Approach for Conict Styles Assessment with Incomplete Information.- Forming
Classes in an e-Learning Social Network Scenario.-Scheduling Optimization in
Grid with VO Stakeholders Preferences.- On the Application of Bio-inspired
Heuristics for Network Routing with Multiple QoS Constraints.-Dealing with
the Best Attachment Problem via Heuristics.-Towards Collaborative Sensing
using Dynamic Intelligent Virtual Sensors .-Intelligent Data Metrics for
Urban Driving with Data Fusion and Distributed Machine Learning.-A
Probabilistic Sample Matchmaking Strategy for Imbalanced Data Streams with
Concept.-Author Index.