International Journal of Computer Networks and Communications Security

Volume 4, Issue 12, December 2015




Using Kinect System and OpenCV Library for Digits Recognition

Pages: 315-322 (8) | [Full Text] PDF (530 KB)
B Khelil, H Amiri
Electrical Engineering Department, National Engineering School of Tunis, SITI-LAB, Tunis, Tunisia

Abstract -
The recently developed depth sensors, e.g., the Kinect sensor, have provided new opportunities for human-computer interaction (HCI). Although great progress has been made by leveraging the Kinect sensor, e.g., in human body tracking, face recognition and human action recognition, robust hand gesture recognition remains an open problem. Compared to the entire human body, the hand is a smaller object with more complex articulations and more easily affected by segmentation errors. It is thus a very challenging problem to recognize hand gestures. This paper proposes hand static gesture recognition digits 0-5 successfully captured through Kinect, which acts as a sign language for conveying information, by using OpenCV library.
Index Terms - Hand Gesture Recognition, OpenCV, Depth image, Microsoft Kinect

Citation - B Khelil, H Amiri. "Using Kinect System and OpenCV Library for Digits Recognition." International Journal of Computer Science and Software Engineering 4, no. 12 (2015): 315-322.


Using Machine Learning Techniques to Predict Introductory Programming Performance

Pages: 323-328 (6) | [Full Text] PDF (262 KB)
S Bergin, A Mooney, J Ghent, K Quille
Department of Computer Science, Maynooth University, Maynooth, Co. Kildare, Ireland
Sytorus, The Capel Building, Dublin 7, Ireland

Abstract -
Learning to program is difficult and can result in high drop out and failure rates. Numerous research studies have attempted to determine the factors that influence programming success and to develop suitable prediction models. The models built tend to be statistical, with linear regression the most common technique used. Over a three year period a multi-institutional, multivariate study was performed to determine factors that influence programming success. In this paper an investigation of six machine learning algorithms for predicting programming success, using the pre-determined factors, is described. Naïve Bayes was found to have the highest prediction accuracy. However, no significant statistical differences were found between the accuracy of this algorithm and logistic regression, SMO (support vector machine), back propagation (artificial neural network) and C4.5 (decision tree). The paper concludes with a recent epilogue study that re-validates the factors and the performance of the naïve Bayes model.
Index Terms - Learning to Program, Programming Predictors, Machine Learning, Naive Bayes

Citation - S Bergin, A Mooney, J Ghent, K Quille. "Using Machine Learning Techniques to Predict Introductory Programming Performance." International Journal of Computer Science and Software Engineering 4, no. 12 (2015): 323-328.


Regression Testing of Virtual Prototypes Using Symbolic Execution

Pages: 329-334 (6) | [Full Text] PDF (302 KB)
B Lin, D Qian
Department of Computer Science, Portland State University, Portland, OR 97207, USA
Intel Corporation, Hillsboro, OR 97124, USA

Abstract -
Recently virtual platforms and virtual prototyping techniques have been widely applied for accelerating software development in electronics companies. It has been proved that these techniques can greatly shorten time-to-market and improve product quality. One challenge is how to test and validate a virtual prototype. In this paper, we present how to conduct regression testing of virtual prototypes in different versions using symbolic execution. Suppose we have old and new versions of a virtual prototype, we first apply symbolic execution to the new version and collect all path constraints. Then the collected path constraints are used for guiding the symbolic execution of the old version. For each path explored, we compare the device states between these two versions to check if they behave the same. We have applied this approach to a widely-used virtual prototype and detected numerous differences. The experimental results show that our approach is useful and efficient.
Index Terms - Regression Testing, Virtual Prototypes, Virtual Platform, Symbolic Execution

Citation - B Lin, D Qian. "Regression Testing of Virtual Prototypes Using Symbolic Execution." International Journal of Computer Science and Software Engineering 4, no. 12 (2015): 329-334.