Birla Institute of Technology and Science

About: Birla Institute of Technology and Science is a education organization based out in Pilāni, Rajasthan, India. It is known for research contribution in the topics: Computer science & Population. The organization has 8897 authors who have published 13947 publications receiving 170008 citations.

Measuring privacy leaks in Online Social Networks

Abstract: Online Social Networking has gained huge popularity amongst the masses. It is common for the users of Online Social Networks (OSNs) to share information with digital friends but in the bargain they loose privacy. Users are unaware of the privacy risks involved when they share their sensitive information in the network. The users should be aware of their privacy quotient and should know where they stand in the privacy measuring scale. In this paper we have described and calculated the Privacy Quotient i.e a privacy metric to measure the privacy of the user's profile using the naive approach. In the starting of the paper we have given the detailed analysis of the survey that we have carried out to know how well do people understand privacy in online social networks. At last we have proposed a model that will ensure privacy in the unstructured data. It will make use of the Item Response Theory model to measure the privacy leaks in the messages and text that is being posted by the users of the online social networking sites.

LocText: relation extraction of protein localizations to assist database curation.

Abstract: The subcellular localization of a protein is an important aspect of its function However, the experimental annotation of locations is not even complete for well-studied model organisms Text mining might aid database curators to add experimental annotations from the scientific literature Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence LocText was created as a new method to extract protein locations from abstracts and full texts LocText learned patterns from syntax parse trees and was trained and evaluated on a newly improved LocTextCorpus Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4) After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast (Saccharomyces cerevisiae), and thale cress (Arabidopsis thaliana) Examining 60 novel, text-mined annotations, we found that 65% (human), 85% (yeast), and 80% (cress) were correct Of all validated annotations, 40% were completely novel, ie did neither appear in the annotations nor the text descriptions of Swiss-Prot LocText provides a cost-effective, semi-automated workflow to assist database curators in identifying novel protein localization annotations The annotations suggested through text-mining would be verified by experts to guarantee high-quality standards of manually-curated databases such as Swiss-Prot