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: Computer science & Authentication. 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).

Analysis and Simulation of Noise in Power Line Communication Systems

Abstract: Internet has entered in our lives in a big way, from sending mails, reading the news, to the booking of tickets on-line, it has a special role in every onepsilas life. There is a great need in the near future to take the Internet to the remote places in our country, where network infrastructure is not in place. Many efforts are made in this direction and one of the technologies enabling this is the power line communication (PLC). The idea is to put information signals on to the power line, which is existing in most rural places. Hence the additional network cabling cost is saved. This paper describes the PLC technology, physical layer protocols, MAC layer and security issues. Two major challenges faced by PLC are the presence of noise and the security issues since power line is a broadcast medium. This paper analyses and simulates the noise model in power line communication systems.

Seismic Site Characterization in Delhi Region using Multi channel Analysis of Shear wave Velocity (MASW) Testing

Abstract: After the Bhuj earthquake the national capital region of Delhi attracted major attention of several scientific studies in the recent times. Since Delhi falls in zone IV (IS 1893:2002) with high seismic activity, there is a great need for site characterization and seismic hazard mapping of the area. Multi Channel Analysis of Surface Wave (MASW) tests were done in Delhi at 118 sites in predefined grids of 2kmX3km each. Shear-wave velocity, VS, is an important parameter for evaluating dynamic behavior of soil. This test was carried out using 48 channel digital engineering seismograph with 4.5 Hz geophones. Data was analyzed using SeisImager/SW software and two dimensional shear wave velocity models at every 5m depth from ground surface was developed. Also, the average shear wave velocity up to 30m (VS30) is measured which is used for site characterization. Based on the Vs30 value, Delhi is divided into three zones i.e., zone A (VS30 >350m/s), zone B (VS30 = 250- 350m/s) and zone C (VS30< 250m/s).

Towards Accurate Handwritten Word Recognition for Hindi and Bangla

Abstract: Building accurate lexicon free handwritten text recognizers for Indic languages is a challenging task, mostly due to the inherent complexities in Indic scripts in addition to the cursive nature of handwriting. In this work, we demonstrate an end-to-end trainable CNN-RNN hybrid architecture which takes inspirations from recent advances of using residual blocks for training convolutional layers, along with the inclusion of spatial transformer layer to learn a model invariant to geometric distortions present in handwriting. In this work we focus building state of the art handwritten word recognizers for two popular Indic scripts – Devanagari and Bangla. To address the need of large scale training data for such low resources languages, we utilize synthetically rendered data for pre-training the network and later fine tune it on the real data. We outperform the previous lexicon based, state of the art methods on the test set of Devanagari and Bangla tracks of RoyDB by a significant margin.

Meta-analysis of drought-tolerant genotypes in Oryza sativa: A network-based approach.

Abstract: Background Drought is a severe environmental stress. It is estimated that about 50% of the world rice production is affected mainly by drought. Apart from conventional breeding strategies to develop drought-tolerant crops, innovative computational approaches may provide insights into the underlying molecular mechanisms of stress response and identify drought-responsive markers. Here we propose a network-based computational approach involving a meta-analytic study of seven drought-tolerant rice genotypes under drought stress. Results Co-expression networks enable large-scale analysis of gene-pair associations and tightly coupled clusters that may represent coordinated biological processes. Considering differentially expressed genes in the co-expressed modules and supplementing external information such as resistance/tolerance QTLs, transcription factors, network-based topological measures, we identify and prioritize drought-adaptive co-expressed gene modules and potential candidate genes. Using the candidate genes that are well-represented across the datasets as ‘seed’ genes, two drought-specific protein-protein interaction networks (PPINs) are constructed with up- and down-regulated genes. Cluster analysis of the up-regulated PPIN revealed ABA signalling pathway as a central process in drought response with a probable crosstalk with energy metabolic processes. Tightly coupled gene clusters representing up-regulation of core cellular respiratory processes and enhanced degradation of branched chain amino acids and cell wall metabolism are identified. Cluster analysis of down-regulated PPIN provides a snapshot of major processes associated with photosynthesis, growth, development and protein synthesis, most of which are shut down during drought. Differential regulation of phytohormones, e.g., jasmonic acid, cell wall metabolism, signalling and posttranslational modifications associated with biotic stress are elucidated. Functional characterization of topologically important, drought-responsive uncharacterized genes that may play a role in important processes such as ABA signalling, calcium signalling, photosynthesis and cell wall metabolism is discussed. Further transgenic studies on these genes may help in elucidating their biological role under stress conditions. Conclusion Currently, a large number of resources for rice functional genomics exist which are mostly underutilized by the scientific community. In this study, a computational approach integrating information from various resources such as gene co-expression networks, protein-protein interactions and pathway-level information is proposed to provide a systems-level view of complex drought-responsive processes across the drought-tolerant genotypes.

Confusion Prediction from Eye-Tracking Data: Experiments with Machine Learning

Abstract: Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority Over Sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) finding that SMOTE provides a higher performance increase. Overall, this research contains implications for researchers interested in inferring users' cognitive states from eye-tracking data.