Authorship attribution involves determining the original author of an anonymous text from a pool of potential authors. The author attribution task has applications in several domains, such as plagiarism detection, digital text forensics, and information retrieval. While these applications extend beyond any single language, existing research has predominantly centered on English, posing challenges for application in languages such as Sinhala due to linguistic disparities and a lack of language processing tools. We present the first comprehensive study on cross-topic authorship attribution for Sinhala texts and propose a solution that can effectively perform the authorship attribution task even if the topics within the test and training samples differ. Our solution consists of three main parts: (i) extraction of topic-independent stylometric features, (ii) generation of a small candidate author set with the help of similarity search, and (iii) identification of the true author. Several experimental studies were carried out to demonstrate that the proposed solution can effectively handle real-world scenarios involving a large number of candidate authors and a limited number of text samples for each candidate author.

Mobile devices have become an integral part of our routine activities. Some of the activities involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services, etc.). These mobile electronic services (e-Services) typically require a method to securely identify and authenticate a claimed identity. Currently, e-Services typically use a knowledge-based authentication method by demonstrating the knowledge of a secret (e.g. password), but it is vulnerable to a number of security attacks, e.g. shoulder spoofing and brute force attacks. To thwart the attacks and to make the authentication method more secure, this paper describes our efforts in investigating the benefits of integrating touch dynamics biometrics, into a PIN-based authentication method. It reports the collection of a comprehensive reference dataset from 150 subjects, the extraction of feature data from the dataset, and the classifications and the use of the feature data to identify a user. Experimental results show that, even when the PIN is exposed, 9 out of 10 impersonation attempts can be successfully detected.