Gulshan Kumar

D. A. Sasi Kiran, Kritika Anand, Chaitanya Kharyal et al.

This paper describes a framework for the object-goal navigation task, which requires a robot to find and move to the closest instance of a target object class from a random starting position. The framework uses a history of robot trajectories to learn a Spatial Relational Graph (SRG) and Graph Convolutional Network (GCN)-based embeddings for the likelihood of proximity of different semantically-labeled regions and the occurrence of different object classes in these regions. To locate a target object instance during evaluation, the robot uses Bayesian inference and the SRG to estimate the visible regions, and uses the learned GCN embeddings to rank visible regions and select the region to explore next.

Parul Khurana, Geetha Ganesan, Gulshan Kumar et al.

Bibliometrics is useful to analyze the research impact for measuring the research quality. Different bibliographic databases like Scopus, Web of Science, Google Scholar etc. are accessed for evaluating the trend of publications and citations from time to time. Some of these databases are free and some are subscription based. Its always debatable that which bibliographic database is better and in what terms. To provide an optimal solution to availability of multiple bibliographic databases, we have implemented a single authentic database named as ``conflate'' which can be used for fetching publication and citation trend of an author. To further strengthen the generated database and to provide the transparent system to the stakeholders, a consensus mechanism ``proof of reference (PoR)'' is proposed. Due to three consent based checks implemented in PoR, we feel that it could be considered as a authentic and honest citation data source for the calculation of unified informetrics for an author.

Parul Khurana, Geetha Ganesan, Gulshan Kumar et al.

Numerous indexing databases keep track of the number of publications, citations, etc. in order to maintain the progress of science and individual. However, the choice of journals and articles varies among these indexing databases, hence the number of citations and h-index varies. There is no common platform exists that can provide a single count for the number of publications, citations, h-index, etc. To overcome this limitation, we have proposed a weighted unified informetrics, named "conflate". The proposed system takes into account the input from multiple indexing databases and generates a single output. Here, we have used the data from Scopus and WoS to generate a conflate dataset. Further, a comparative analysis of conflate has been performed with Scopus and WoS at three levels: author, organization, and journal. Finally, a mapping is proposed between research publications and distributed ledger technology in order to provide a transparent and distributed view to its stakeholders.

Nemath Ahmed, Aashit Singh, Srivyshnav KS et al.

Sleep state classification is vital in managing and understanding sleep patterns and is generally the first step in identifying acute or chronic sleep disorders. However, it is essential to do this without affecting the natural environment or conditions of the subject during their sleep. Techniques such as Polysomnography(PSG) are obtrusive and are not convenient for regular sleep monitoring. Fortunately, The rise of novel technologies and advanced computing has given a recent resurgence to monitoring sleep techniques. One such contactless and unobtrusive monitoring technique is Ballistocradiography(BCG), in which vitals are monitored by measuring the body's reaction to the cardiac ejection of blood. In this study, we propose a Multi-Head 1D-Convolution based Deep Neural Network to classify sleep-wake state and predict sleep-wake time accurately using the signals coming from a BCG sensor. Our method achieves a sleep-wake classification score of 95.5%, which is on par with researches based on the PSG system. We further conducted two independent studies in a controlled and uncontrolled environment to test the sleep-wake prediction accuracy. We achieve a score of 94.16% in a controlled environment on 115 subjects and 94.90% in an uncontrolled environment on 350 subjects. The high accuracy and contactless nature of the proposed system make it a convenient method for long term monitoring of sleep states.