Nature of Computation and Communication: International Conference, ICTCC 2014, Ho Chi Minh City, Vietnam, November 24-25, 2014, Revised Selected Papers - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engin 2015 edition

Marc Notes: Revised conference papers.; Constituting the thoroughly refereed post-conference proceedings of the International Conference on Nature of Computation and Communication, ICTCC 2014, held in November 2014 in Ho Chi Minh City, Vietnam, the 34 revised full papers presented were carefully reviewed and selected from over 100 submissions. The papers cover formal methods for self-adaptive systems and discuss natural approaches and techniques for computation and communication. Table of Contents: Modular Design and Verification of Distributed Adaptive Real-Time Systems.- Modeling Swarm Robotics with Know Lang.- Reasoning on Data Streams: an Approach to Adaptation in Pervasive Systems.- Autonomic Computing Software for Autonomous Space Vehicles.- Logic-Based Modeling of Information Transfer in Cyber-Physical Multi-Agent Systems.- Categorical Structures of Self-Adaptation in Collective Adaptive Systems.- Self-Adaptive Traits in Collective Adaptive Systems.- A Context-Aware Traffic Engineering Model for Software-Defined Networks.- Efficient k-Nearest Neighbor Search for Static Queries over High Speed Time-Series Streams Reconstructing low degree triangular parametric surfaces based on inverse Loop subdivision.- Maximizing the lifetime of wireless sensor networks with the base station location.- An Application of PCA on Uncertainty of Prediction.- Co-modeling: an agent-based approach to support the coupling of heterogeneous models.- Slowdown-guided Genetic Algorithm for Job Scheduling in Federated Environments.- Security Aspects of Cloud Based Mobile Health Care Application Enhancement of Innovation Co-creation Processes and Ecosystems Through Mobile Technologies.- Development the method for Optimizing Cost of Software Quality Assurance based on Regression-based Model.- Un-normalized and random walk hypergraph Laplacian un-supervised learning.