You are cordially invited to attend this week's CSE 699/ICS 599/COE 599 following Graduate Seminars:
Location: 22 - 130
Date: Wednesday Nov. 13, 2013
First Seminar:
Duration: 2:30 - 3:10 pm
Speaker: Dr. Musab AlTurki, ICS Department
Title: Rewriting Semantics and Formal Analysis of the COMPASS Modeling Language (CML)
Abstract
The COMPASS Modeling language (or CML) is a language that is being developed by a consortium of researchers and companies to model Systems of Systems (SoS). It is an integrated language with a variety of components, including: rich state, object orientation, pointers, concurrency, communication, discrete time, and process and channel mobility. A kernel of CML has both an axiomatic semantics in Hoare & He's unifying theories of programming (UTP), and a structural operational semantics specified by a set of inference rules. This talk introduces CML with examples, and explores aspects of its axiomatic semantics in UTP and its operational semantics as inference rules. We then describe a translation from structural operational semantics to an executable rewriting semantics based on rewriting logic, on which formal analysis in Maude can be applied. The talk concludes with a discussion of future work. This work was conducted as part of the Post-Doctoral Summer Research program (Summer 2013) funded by the British Council through KFUPM.
Biography
Dr. Musab A. AlTurki is an assistant professor in the Information and Computer Science department at King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. He received his PhD degree in computer science from the University of Illinois at Urbana-Champaign in 2011. His research interests are in applications of formal methods to program analysis and verification, specification and design of distributed and concurrent programming languages and systems, and to analysis of security properties of systems. He is a recipient of the prestigious King Abdullah Scholar Award for scientific excellence in 2008.
Second Seminar:
Duration 3:10 - 3:50 pm
Speaker: Mr. Amer Binsaadoon, ICS Department
Title: Statistical analysis of mammographic features and its classification using support vector machine
Abstract
With the fast growing of technologies in the different scientific areas, a lot of medical centers around the world pretend that it is a big cause of cancer. One of the most common cancers is the breast cancer. We will present a paper that aims at designing a support vector machine (SVM)-based classifier for breast cancer detection with higher degree of accuracy. It introduces a best possible training scheme of the features extracted from the mammogram, by first selecting the kernel function and then choosing a suitable training-test partition. Prior to classification, detailed statistical analysis viz., test of significance, density estimation have been performed for identifying discriminating power of the features in between malignant and benign classes. A comparative study has been performed in respect to diagnostic measures viz., confusion matrix, sensitivity and specificity. Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification. Furthermore, the overall classification accuracy obtained by using the proposed classification strategy is 99.385% for dataset-I and 93.726% for dataset-II, respectively.
Biography
Mr. Amer Binsaadoon is currently a masters student at King Fahd University of Petroleum and Minerals at Information and Computer Science Department. Mr. Binsaadoon graduated and got his B.S. degree from Cairo University Faculty of Computers and Information from Information Technology Department at May 2009.
Mr. Binsaadoon research interest lies in the field of speech processing especially for the Arabic language. Also, He is working now on his thesis in improving the Arabic Handwritten Text recognition and trying to discover new features helping toward accuracy enhancement.
