Indian Institute of Technology Ropar

About: Indian Institute of Technology Ropar is a education organization based out in Ropar, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1014 authors who have published 2878 publications receiving 35715 citations.

Prediction and Localization of Student Engagement in the Wild

Abstract: Digital revolution has transformed the traditional teaching procedures, students are going online to access study materials. It is realised that analysis of student engagement in an e-learning environment would facilitate effective task accomplishment and learning. Well known social cues of engagement/disengagement can be inferred from facial expressions, body movements and gaze patterns. In this paper, student's response to various stimuli (educational videos) are recorded and cues are extracted to estimate variations in engagement level. We study the association of a subject's behavioral cues with his/her engagement level, as annotated by labelers. We have localized engaging/non-engaging parts in the stimuli videos using a deep multiple instance learning based framework, which can give useful insight into designing Massive Open Online Courses (MOOCs) video material. Recognizing the lack of any publicly available dataset in the domain of user engagement, a new ‘in the wild’ dataset is curated. The dataset: Engagement in the Wild contains 264 videos captured from 91 subjects, which is approximately 16.5 hours of recording. Detailed baseline results using different classifiers ranging from traditional machine learning to deep learning based approaches are evaluated on the database. Subject independent analysis is performed and the task of engagement prediction is modeled as a weakly supervised learning problem. The dataset is manually annotated by different labelers and the correlation studies between annotated and predicted labels of videos by different classifiers are reported. This dataset creation is an effort to facilitate research in various e-learning environments such as intelligent tutoring systems, MOOCs, and others.

Analytical Time-Domain Models for Performance Optimization of Multilayer GNR Interconnects

Abstract: In this paper, we are proposing novel analytical time domain models for side contact and top contact multilayer graphene nanoribbon (MLGNR) interconnects Our proposed models give physical insights about the transient behavior of these MLGNRs The proposed models have been verified with existing data as well as exhaustive simulation and exhibit excellent accuracy Based on our analysis, we identify limiting factors that need to be considered for the design of optimum top contact MLGNRs that exceed the performance of copper and match that of side contact MLGNR interconnects Finally, we compare the performance of our optimum top contact MLGNRs with optical interconnects to predict the future roadmap for next generation interconnect technology

Chimera patterns induced by distance-dependent power-law coupling in ecological networks.

Abstract: This paper reports the occurrence of several chimera patterns and the associated transitions among them in a network of coupled oscillators, which are connected by a long-range interaction that obeys a distance-dependent power law. This type of interaction is common in physics and biology and constitutes a general form of coupling scheme, where by tuning the power-law exponent of the long-range interaction the coupling topology can be varied from local via nonlocal to global coupling. To explore the effect of the power-law coupling on collective dynamics, we consider a network consisting of a realistic ecological model of oscillating populations, namely the Rosenzweig-MacArthur model, and show that the variation of the power-law exponent mediates transitions between spatial synchrony and various chimera patterns. We map the possible spatiotemporal states and their scenarios that arise due to the interplay between the coupling strength and the power-law exponent.

Phase diagram of a continuum traffic flow model with a static bottleneck

Abstract: The phase transitions are investigated in a continuum speed-gradient model with a static bottleneck under open boundary conditions. The bottleneck situation has been studied using two different approaches—explicit and implicit. The phase diagrams showing different traffic states are presented. The effect of strength of bottleneck has been analyzed, and it is found that the strength parameter has no qualitative effect in the explicit case while has considerable effect in the implicit case. Furthermore, the results of both the approaches are compared, and the consistency between them is discussed.

A section approach to a traffic flow model on networks

Abstract: The development of real time traffic flow models for urban road networks is of paramount importance for the purposes of optimizing and control of traffic flow. Motivated by the modeling of road networks in last decade, this paper proposes a different and simplified approach, known as section approach to model road networks in the framework of macroscopic traffic flow models. For evaluation of the traffic states on a single road, an anisotropic continuum GK-model developed by [Gupta and Katiyar, J. Phys. A38, 4069 (2005)] is used as a single-section model. This model is applied to a two-section single lane road with points of entry and exits. In place of modeling the effect of off- and on-ramps in the continuity equation, a set of special boundary condition is taken into account to treat the points of entry and exit. A four-section road network comprised of two one-lane roads is also modeled using this methodology. The performances of the section approaches are investigated and obtained results are demonstrated over simulated data for different boundary conditions.