Title: An Integrated, Robust Approach to Lane Marking Detection and Lane Tracking (Şerit Tespiti ve Şerit İzleme'ye Entegre, Sağlam Bir Yaklaşım )
Time: 11:00
Date: 21.11.2019
Location: UHeM Toplantı Salonu
Short Bio: Cem Mutlu is a Computational Science and engineering master's degree student. He graduated from the ITU, with bachelors degree in Electronics Engineering. He is currently a software developer at Apsiyon R&D center.
Abstract: Lane Detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this presentation we show a method for lane detection using steerable Filters. Steerable filters provide robustness to lighting changes and shadows and perform well in picking out both circular reflector road markings as well as painted line road markings. The filter results are then processed to eliminate outliers based on the expected road geometry and used to update a road and vehicular model along with data taken internallyfom the vehicle. Results are shown for a 9000-fame image sequence that include varying lane markings, lighting conditions, showing, and occlusion by other vehicles
Title: Machine Learning Aided Android Malware Classification
Time: 11:00
Date: 05.12.2019
Location: UHeM Toplantı Salonu
Short Bio: Büşra Çayören is a Cybersecurity Engineering and Cryptography master degree student. She graduated from the DEU with bachelors degree in Computer Engineering. She is currently a information Security engineer at Vakıfbank.
Abstract: The widespread adoption of Android devices and their capability to access significant private and confidential information have resulted in these devices being targeted by malware developers. Existing Android malware analysis techniques can be broadly categorized into static and dynamic analysis. In the paper, writers present two machine learning aided approaches for static analysis of Android malware. The first approach is based on permissions and the other is based on source code analysis utilizing a bag-of-words representation model. Their permission-based model is computationally inexpensive, and is implemented as the feature of OWASP Seraphimdroid Android app that can be obtained from Google Play Store. Their evaluations of both approaches indicate an F-score of 95.1% and F-measure of 89% for the source code-based classification and permission-based classification models, respectively.