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Serguei A. Mokhov
Concordia University
Montreal, Canada
 

Serguei A. Mokhov obtained his PhD from Concordia University, Montreal, Quebec, Canada, where he completed his bachelor’s and master’s degrees in computer science and information systems security. Mokhov’s diverse research interests include intentional programming, distributed and autonomic computing, computer forensics, information systems security, artificial intelligence, software engineering, and computer graphics. His PhD dissertation was in the intentional cyberforensics area. Mokhov’s reviewing interests at Computing Reviews are just as diverse.

Professionally, he has worked in the Faculty of Engineering and Computer Science’s Academic and Information Technology Services (AITS) group as a systems administrator performing application development and Linux desktop support. He currently works on the Faculty’s network infrastructure, providing support, installation, security, and maintenance, as well as developing tools.

Mokhov’s current R&D projects focus on multiple aspects of the Forensic Lucid specification and evaluation language for digital investigations; open-source tools based on the modular audio recognition framework (MARF), such as MARFCAT and MARFPCAT for code and network pattern analysis and classification; and the general intentional programming system (GIPSY). Mokhov is also a part-time faculty member (since 2003), and has taught more than ten courses on various subjects in computer science and software engineering at the undergraduate and graduate levels.

He has served as a referee and reviewer for several academic journals and conferences, and has published a number of research works. He is also an assistant editor at Scholarpedia, a peer-reviewed, open-access encyclopedia.


     

Background subtraction based on phase feature and distance transform
Xue G., Sun J., Song L. Pattern Recognition Letters 33(12): 1601-1613, 2012.  Type: Article

Xue et al. present a new background subtraction algorithm for images with various degrees of complexity in this detailed paper. The high-level algorithm is presented in figure 7. At its core is a new phase-based model created for backg...

 

 Speeding up CRC32C computations with Intel CRC32 instruction
Gueron S. Information Processing Letters 112(5): 179-185, 2012.  Type: Article

Gueron introduces an algorithm to speed up the CRC32C implementation using the corresponding central processing unit (CPU) CRC32 instruction from Intel to compute cyclic redundancy checks of an arbitrary length buffer with inputs rangi...

 

Automated segmentation of macular layers in OCT images and quantitative evaluation of performances
Ghorbel I., Rossant F., Bloch I., Tick S., Paques M. Pattern Recognition 44(8): 1590-1603, 2011.  Type: Article

Ghorbel and others introduce “automated segmentation of macular layers in [optical coherence tomography, OCT] images and quantitative evaluation of performances,” a recent multi-layer segmentation work in OCT image ...

 

The anti-forensics challenge
Dahbur K., Mohammad B.  ISWSA 2011 (Proceedings of the 2011 International Conference on Intelligent Semantic Web-Services and Applications, Amman, Jordan, Apr 18-20, 2011) 1-7, 2011.  Type: Proceedings

Dahbur and Mohammad introduce this paper as a survey of tools and techniques in the emerging field of anti-forensics, together with their classification. The authors explicitly address only computer anti-forensics (CAF), deferring netw...

 

Stochastic-based pattern-recognition analysis
Canals V., Morro A., Rosselló J. Pattern Recognition Letters 31(15): 2353-2356, 2010.  Type: Article

The title of this paper is supposedly derived from the authors’ stochastic logic approach to a pattern recognition problem. However, the title is a bit misleading: stochastic pattern recognition has been around for quite a wh...

 
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