Mathematical and Engineering Methods in Computer Science

10th International Doctoral Workshop, MEMICS 2015, Tel¿, Czech Republic, October 23-25, 2015, Revised Selected Papers
Herausgegeben:Kofron, Jan; Vojnar, Tomás

• Broschiertes Buch

Produktbeschreibung

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

Produktdetails

• Lecture Notes in Computer Science 9548
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-29816-0
• 1st ed. 2016
• Seitenzahl: 168
• Erscheinungstermin: 4. Februar 2016
• Englisch
• Abmessung: 235mm x 155mm x 10mm
• Gewicht: 266g
• ISBN-13: 9783319298160
• ISBN-10: 331929816X
• Artikelnr.: 44404534

Inhaltsangabe

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.