International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

Selecting a Right Interestingness Measure for Rare Association Rules

Abstract: In the literature, the properties of several interestingness measures have been analyzed and a framework has been proposed for selecting a right interestingness measure for extracting association rules. As rare association rules contain useful knowledge, researchers are making efforts to investigate efficient approaches to extract the same. In this paper, we make an effort to analyze the properties of interestingness measures for determining the interestingness of rare association rules. Based on the analysis, we suggest a set of properties a user should consider while selecting a measure to find the interestingness of rare associations. The experiments on real-world datasets show that the measures that satisfy the suggested properties can determine the interestingness of rare association rules.

Vessel Bend-Based Cup Segmentation in Retinal Images

Abstract: In this paper, we present a method for cup boundary detection from monocular colour fundus image to help quantify cup changes. The method is based on anatomical evidence such as vessel bends at cup boundary, considered relevant by glaucoma experts. Vessels are modeled and detected in a curvature space to better handle inter-image variations. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A reliable subset called r-bends is derived using a multi-stage strategy and a local splinetting is used to obtain the desired cup boundary. The method has been successfully tested on 133 images comprising 32 normal and 101 glaucomatous images against three glaucoma experts. The proposed method shows high sensitivity in cup to disk ratio-based glaucoma detection and local assessment of the detected cup boundary shows good consensus with the expert markings.

Plantation monitoring and yield estimation using autonomous quadcopter for precision agriculture

Abstract: Recently, quadcopters with their advance sensors and imaging capabilities have become an imperative part of the precision agriculture. In this work, we have described a framework which performs plantation monitoring and yield estimation using the supervised learning approach, while autonomously navigating through an inter-row path of the plantation. The proposed navigation framework assists the quadcopter to follow a sequence of collision-free GPS way points and has been integrated with ROS (Robot Operating System). The trajectory planning and control module of the navigation framework employ convex programming techniques to generate minimum time trajectory between way-points and produces appropriate control inputs for the quadcopter. A new ‘pomegranate dataset’ comprising of plantation surveillance video and annotated frames capturing the varied stages of pomegranate growth along with the navigation framework are being delivered as a part of this work.

Assistive technology for children with autism - lessons for interaction design

Abstract: This paper describes our experiences from developing assistive tools for children with autism. We describe two applications - AutVisComm, an assistive communication system developed on ubiquitous tablets, and Autinect, a set of activities to teach social skills to children with autism that use Microsoft Kinect™ as a controller. Both these systems were developed in close collaboration with teachers and parents of children with autism. We present here our approach to the design and evaluation of the system that incorporated the cognitive profiles of children with autism and the needs of their caregivers. We also present a summary of the key lessons learnt from these experiences.

Part-Of-Speech Tagging for Gujarati Using Conditional Random Fields

Abstract: This paper describes a machine learning algorithm for Gujarati Part of Speech Tagging. The machine learning part is performed using a CRF model. The features given to CRF are properly chosen keeping the linguistic aspect of Gujarati in mind. As Gujarati is currently a less privileged language in the sense of being resource poor, manually tagged data is only around 600 sentences. The tagset contains 26 different tags which is the standard Indian Language (IL) tagset. Both tagged (600 sentences) and untagged (5000 sentences) are used for learning. The algorithm has achieved an accuracy of 92% for Gujarati texts where the training corpus is of 10,000 words and the test corpus is of 5,000 words.