Ahmed Imran

2papers

2 Papers

CYJan 20, 2022
How Do Socio-Demographic Patterns Define Digital Privacy Divide?

Hamoud Alhazmi, Ahmed Imran, Mohammad Abu Alsheikh

Digital privacy has become an essential component of information and communications technology (ICT) systems. There are many existing methods for digital privacy protection, including network security, cryptography, and access control. However, there is still a gap in the digital privacy protection levels available for users. This paper studies the digital privacy divide (DPD) problem in ICT systems. First, we introduce an online DPD study for understanding the DPD problem by collecting responses from 776 ICT users using crowdsourcing task assignments. Second, we propose a factor analysis-based statistical method for generating the DPD index from a set of observable DPD question variables. In particular, the DPD index provides one scaled measure for the DPD gap by exploring the dimensionality of the eight questions in the DPD survey. Third, we introduce a DPD proportional odds model for analyzing the relationship between the DPD status and the socio-demographic patterns of the users. Our results show that the DPD survey meets the internal consistency reliability with rigorous statistical measures, e.g., Cronbach's $α=0.92$. Furthermore, the DPD index is shown to capture the underlying communality of all DPD variables. Finally, the DPD proportional odds model indicates a strong statistical correlation between the DPD status and the age groups of the ICT users. For example, we find that young users (15-32 years) are generally more concerned about their digital privacy than senior ones (33 years and over).

SDMar 27, 2014
Automatic Segmentation of Broadcast News Audio using Self Similarity Matrix

Sapna Soni, Ahmed Imran, Sunil Kumar Kopparapu

Generally audio news broadcast on radio is com- posed of music, commercials, news from correspondents and recorded statements in addition to the actual news read by the newsreader. When news transcripts are available, automatic segmentation of audio news broadcast to time align the audio with the text transcription to build frugal speech corpora is essential. We address the problem of identifying segmentation in the audio news broadcast corresponding to the news read by the newsreader so that they can be mapped to the text transcripts. The existing techniques produce sub-optimal solutions when used to extract newsreader read segments. In this paper, we propose a new technique which is able to identify the acoustic change points reliably using an acoustic Self Similarity Matrix (SSM). We describe the two pass technique in detail and verify its performance on real audio news broadcast of All India Radio for different languages.