Mathematical and Engineering Methods in Computer Science: 10th International Doctoral Workshop, MEMICS 2015, Telc, Czech Republic, October 23-25, 2015, Revised Selected Papers

by Jan Kofron (Editor), Tomás Vojnar (Editor)

Overview

This volume contains the post-conference proceedings of the 10th Doctoral Workshop on Mathematical and Engineering Methods in Computer Science, MEMICS 2015, held in Telč, Czech Republic, in October 2015.

The 10 thoroughly revised full papers were carefully selected out of 25 submissions and are presented together with 3 invited papers. The topics covered include: security and safety, bioinformatics, recommender systems, high-performance and cloud computing, and non-traditional computational models (quantum computing, etc.).Ioinformatics, recommender systems, high-performance and cloud computing, non-traditional computational models (quantum etc.).

Product Details

ISBN-13:	9783319298160
Publisher:	Springer International Publishing
Publication date:	03/13/2016
Series:	Lecture Notes in Computer Science , #9548
Edition description:	1st ed. 2016
Pages:	157
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Programming Support for Future Parallel Architectures.- Flexible Interpolation for Efficient Model Checking.- Understanding Transparent and Complicated Users as Instances of Preference Learning for Recommender Systems.- Span-Program-Based Quantum Algorithms for Graph Bipartiteness and Connectivity.- Fitting Aggregation Operators.- Practical Exhaustive Generation of Small Multiway Cuts in Sparse Graphs.- Self-adaptive Architecture for Multi-sensor Embedded Vision System.-
Exceptional Configurations of Quantum Walks with Grover's Coin.- Performance Analysis of Distributed Stream Processing Applications Through Colored Petri Nets.- GPU-Accelerated Real-Time Mesh Simplification Using Parallel Half Edge Collapses.- Classifier Ensemble by Semi-supervised Learning: Local Aggregation Methodology.- The Challenge of Increasing Safe Response of Antivirus Software Users.- Weak Memory Models as LLVM-to-LLVM Transformations.