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Video retrieval

Name: 
Klaus Schöffmann
Start date: 
08.05.2016
End date: 
14.05.2016
Home department: 
Alpen-Adria-Universität Klagenfurt

Particle Physics Model for Content-based 3D Exploration

Authors: 
Miroslav Macík, Jakub Lokoč, Přemysl Čech and Tomáš Skopal
Indexed by WOS: 
Not indexed by WOS
Indexed by SCOPUS: 
Not indexed by SCOPUS
Type: 
Proceedings
State: 
Accepted
Language: 
EN
Conference/Journal name: 
14th International Workshop on Content-Based Multimedia Indexing (CBMI)
Publisher: 
IEEE
Year: 
2016
Pages: 
1-6
Location: 
Bucharest, Romania

k-NN Classification of Malware in HTTPS Traffic Using the Metric Space Approach

Authors: 
Jakub Lokoč, Jan Kohout, Přemysl Čech, Tomáš Skopal, Tomáš Pevný
Indexed by WOS: 
Not indexed by WOS
Indexed by SCOPUS: 
Not indexed by SCOPUS
Type: 
Proceedings
State: 
Published
Language: 
EN
Conference/Journal name: 
11th Pacific Asia Workshop, PAISI 2016
Publisher: 
Springer
Year: 
2016
Volume: 
LNCS 9650
Pages: 
131-145
Location: 
Auckland, New Zealand
ISBN: 
978-3-319-31862-2
ISSN: 
0302-9743

Jedna noc s informatikou a matematikou

Date: 
26.01.2016

Přednášky:

  • Jakub Lokoč: Vyhledávání ve videu

Large RNA secondary structure conservation annotation using secondary structure-based MSA

Authors: 
Jan Pešek, David Hoksza
Indexed by WOS: 
Not indexed by WOS
Indexed by SCOPUS: 
Not indexed by SCOPUS
Type: 
Journal
State: 
Accepted
Language: 
EN
Conference/Journal name: 
International Journal of Bioscience, Biochemistry and Bioinformatics
Publisher: 
International Academy Publishing
Year: 
2016
Volume: 
6
Issue: 
1
Pages: 
18-25
ISSN: 
2010-3638

RNA secondary structure visualization using tree edit distance

Authors: 
Richard Eliáš, David Hoksza
Indexed by WOS: 
Not indexed by WOS
Indexed by SCOPUS: 
Indexed by SCOPUS
Type: 
Journal
State: 
Accepted
Language: 
EN
Conference/Journal name: 
International Journal of Bioscience, Biochemistry and Bioinformatics
Publisher: 
International Academy Publishing
Year: 
2016
Volume: 
6
Issue: 
1
Pages: 
9-17
ISSN: 
2010-3638

Multi-sketch Semantic Video Browser

Authors: 
David Kuboň, Adam Blažek, Jakub Lokoč, Tomáš Skopal
Indexed by WOS: 
Not indexed by WOS
Indexed by SCOPUS: 
Indexed by SCOPUS
Type: 
Proceedings
State: 
Published
Language: 
EN
Conference/Journal name: 
MMM 2016
Publisher: 
Springer
Year: 
2016
Volume: 
LNCS 9517
Pages: 
406-411
Location: 
Miami, USA, January 4-6
ISBN: 
978-3-319-27673-1
ISSN: 
0302-9743

Comparison of metric space browsing strategies for efficient image exploration

Authors: 
Přemysl Čech and Tomáš Grošup
Indexed by WOS: 
Not indexed by WOS
Indexed by SCOPUS: 
Indexed by SCOPUS
Type: 
Proceedings
State: 
Published
Language: 
EN
Conference/Journal name: 
Content-Based Multimedia Indexing (CBMI)
Publisher: 
IEEE
Year: 
2015
Pages: 
6
Location: 
Prague

Web Image Extractor

Type: 
Multimedia
One line description: 
Image feature signatures extractor demo implemented in web browser
Annotation: 

A demo which presents a feature extraction method that captures color and texture information from an image and produces adaptive signatures for similarity search models, where distances like SQFD or EMD can be used. The method and its parallel implementation for GPUs is presented in our publication (listed below). We are currently transforming the code, it can be used as OpenCV module. 

The demo is also a proof of concept that goes against the current trends in web applications. We propose to offload computations from the servers (or cloud) to end users by performing computationally demanding tasks in the browser. In this case, we claim that in a web application that collects the images from the users, the feature extraction process can be performed by the browser while the image is being uploaded.

Developers: 
martin.krulis

P2RANK

Type: 
Bioinformatics & Cheminformatics
One line description: 
Ligand-binding site prediction
Annotation: 
 
P2RANK is a novel machine learning-based method for prediction of ligand binding sites from protein structure. P2RANK uses Random Forests classifier to infer ligandability of local chemical neighborhoods near the protein surface which are represented by specific near-surface points and described by aggregating physico-chemical features projected on those points from neighboring protein atoms. The points with high predicted ligandability are clustered and ranked to obtain the resulting list of binding site predictions. P2RANK is freely available at http://siret.ms.mff.cuni.cz/p2rank.
 
 
Developers: 
david.hoksza
radoslav.krivak

Introduction

 
P2RANK is a novel machine learning-based method for prediction of ligand binding sites from protein structure.
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