Institution
International Institute of Information Technology, Hyderabad
Education•Hyderabad, India•
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).
Topics: Authentication, Internet security, Wireless sensor network, Machine translation, Deep learning
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The proposed Fourier-Bessel expansion-based discrete energy separation algorithm (FB-DESA) method does not introduce any amplitude or phase modulation in the separated monocomponent signal leading to accurate estimations of the AE and IF functions.
74 citations
••
07 Oct 2012TL;DR: This paper uses conditional visibility probability to quickly identify a subset of cameras in which a feature is visible and combines the conditional probability with a distance measure to prioritize points for fast guided search for the image localization problem.
Abstract: Large scale reconstructions of camera matrices and point clouds have been created using structure from motion from community photo collections. Such a dataset is rich in information; it represents a sampling of the geometry and appearance of the underlying space. In this paper, we encode the visibility information between and among points and cameras as visibility probabilities. The conditional visibility probability of a set of points on a point (or a set of cameras on a camera) can rank points (or cameras) based on their mutual dependence. We combine the conditional probability with a distance measure to prioritize points for fast guided search for the image localization problem. We define dual problem of feature triangulation as finding the 3D coordinates of a given image feature point. We use conditional visibility probability to quickly identify a subset of cameras in which a feature is visible.
73 citations
••
13 May 2019
TL;DR: This work systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes, and proposes novel methods leveraging knowledge-based generalizations for bias-free learning.
Abstract: With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to pro-actively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been traditionally studied for structured datasets, but we aim at bias mitigation from unstructured text data. In this paper, we make two important contributions. First, we systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes. Second, we propose novel methods leveraging knowledge-based generalizations for bias-free learning. Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance. We experiment with multiple knowledge generalization policies and analyze their effect on general performance and in mitigating bias. Our experiments with two real-world datasets, a Wikipedia Talk Pages dataset (WikiDetox) of size ~ 96k and a Twitter dataset of size ~ 24k, show that the use of knowledge-based generalizations results in better performance by forcing the classifier to learn from generalized content. Our methods utilize existing knowledge-bases and can easily be extended to other tasks.
73 citations
••
TL;DR: Modified and improved open loop system are more competent as an alternative compared to the conventional methods for automated blind and lighting control systems and predict daylight more extensively, according to analysis of various daylight prediction methods.
72 citations
••
06 Oct 2005
TL;DR: Novel measures (both collocation based and context based measures) to measure the relative compositionality of MWEs of V-N type are defined and it is shown that the correlation of these features with the human ranking is much superior to the correlated of the traditional features withThe human ranking.
Abstract: Measuring the relative compositionality of Multi-word Expressions (MWEs) is crucial to Natural Language Processing. Various collocation based measures have been proposed to compute the relative compositionality of MWEs. In this paper, we define novel measures (both collocation based and context based measures) to measure the relative compositionality of MWEs of V-N type. We show that the correlation of these features with the human ranking is much superior to the correlation of the traditional features with the human ranking. We then integrate the proposed features and the traditional features using a SVM based ranking function to rank the collocations of V-N type based on their relative compositionality. We then show that the correlation between the ranks computed by the SVM based ranking function and human ranking is significantly better than the correlation between ranking of individual features and human ranking.
71 citations
Authors
Showing all 2066 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ravi Shankar | 66 | 672 | 19326 |
Joakim Nivre | 61 | 295 | 17203 |
Aravind K. Joshi | 59 | 249 | 16417 |
Ashok Kumar Das | 56 | 278 | 9166 |
Malcolm F. White | 55 | 172 | 10762 |
B. Yegnanarayana | 54 | 340 | 12861 |
Ram Bilas Pachori | 48 | 182 | 8140 |
C. V. Jawahar | 45 | 479 | 9582 |
Saurabh Garg | 40 | 206 | 6738 |
Himanshu Thapliyal | 36 | 201 | 3992 |
Monika Sharma | 36 | 238 | 4412 |
Ponnurangam Kumaraguru | 33 | 269 | 6849 |
Abhijit Mitra | 33 | 240 | 7795 |
Ramanathan Sowdhamini | 33 | 256 | 4458 |
Helmut Schiessel | 32 | 117 | 3527 |