Abstract

This paper describes the Neural Machine Translation systems used by IIIT Hyderabad (CVIT-MT) for the translation tasks part of WAT-2019. We participated in tasks pertaining to Indian languages and submitted results for English-Hindi, Hindi-English, English-Tamil and Tamil-English language pairs. We employ Transformer architecture experimenting with multilingual models and methods for lowresource languages.

Abstract

This paper proposes the use of Shewhart test to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggybacking and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The time-series data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime. Published in: 2019 IEE

Abstract

In this paper, we study scalable parallel algorithms for estimating the farness-centrality value of the nodes in a given undirected and connected graph. Our algorithms consider approaches that are more suitable for sparse graphs. To this end, we propose four optimization techniques based on removing redundant nodes, removing identical nodes, removing chain nodes, and making use of decomposition based on the biconnected components of the input graph. We test our techniques on a collection of real-world graphs for the time taken and the average error percentage. We further analyze the applicability of our techniques on various classes of real-world graphs. We suggest why certain techniques work better on certain classes of graphs.

Abstract

Smart grid (SG) technology is regarded as the next generation of the power grid, which makes use of the two-way flows of electricity, as well as information, Pathways to a Smarter Power System. https://doi.org/10.1016/B978-0-08-102592-5.00013-2 © 2019 Elsevier Ltd. All rights reserved. 371 in order to build a broadly distributed automated energy delivery network. The Smart grid is a critical infrastructure that plays a critical role in the daily lives of people. Both the Smart Grid and its data must be protected from cyberattacks at all times [1, 2]. In SG, four components are present, namely sensing, control, communication, and actuation systems. The most important component of SG is the smart meter (SM), which consists of sensing and communication modules. In addition, there are service systems from the service providers (SPs), which consist of several modules for control, communication, and actuation. SMs are broadly used in homes for monitoring energy consumption in real time. Moreover, power pricing information to consumers is also provided by the SMs [3–6]. A detailed survey on SG including its applications can be found in [7–14]. In the following subsections, we now discuss the smart grid framework developed by the National Institute of Standards and Technology (NIST) and its taxonomy, based on domains and targeted research areas.

Abstract

It has long been understood that there is an interplay between affect and cognition((Kort & Reilly, 2003), but this interaction, based on the recent chess studies((Guntz, Crowley, Vaufreydaz, Balzarini, & Dessus, 2018), is much more intertwined than what the established theories postulate. To understand the underlying mechanisms in greater detail we propose an integrated model using Chrest and Nengo. We analyze the results based on simulations with data from previous empirical studies. Keywords: Affect, Cognition , Chess, Nengo , Chrest

Abstract

A method is proposed for time delay estimation (TDE) from mixed source (speaker) signals collected at two spatially separated microphones. The key idea in this proposal is that the crosscorrelation between corresponding segments of the mixed source signals is computed using the outputs of single frequency filtering (SFF) obtained at several frequencies, rather than using the collected waveforms directly. The advantage of the SFF output is that it will have high signal-to-noise ratio regions in both time and frequency domains. Also it gives multiple evidences, one from each of the SFF outputs. These multiple evidences are combined to obtain robustness in the TDE. The estimated time delays can be used to determine the number of speakers present in the mixed signals. The TDE is shown to be robust against different types and levels of degradations. The results are shown for actual mixed signals collected at two spatially separated microphones in a live laboratory environment, where the mixed signals contain speech from several spatially distributed speakers.

Abstract

Ragas are a fundamental concept to the Indian Classical music which provides immense scope for artists to improvise aesthetically. Classification of ragas have been studied since the inception but even the best models in practice have ambiguities in the classification of certain ragas and this has troubled many musicians and musicologists. We have constructed a new model to classify ragas which adheres to all the existing conventions and resolves these ambiguities.

Abstract

In this paper, we describe the simulation of Ising minimization on a classical machine by executing variational quantum algorithms on our density-matrix simulator. We outline the Ising formulation of the Graph Partitioning problem and the Hamiltonian Cycle problem, and solve the Max-Cut variant of graph partitioning for a weighted square graph Sq2 using the Quantum Approximate Optimization Algorithm. We finally study the effect of errors present in Noisy Intermediate-Scale Quantum processors on the obtained solutions. This paper illustrates the approach to approximately solving hard combinatorial optimization problems using a hybrid quantum-classical scheme and describes the issues in hardware implementation of such schemes. The simulations of NISQ noise models will be useful in understanding the performance and capabilities of such approaches. Index Terms—Non-deterministic Polynomial, Ising Model, Combinatorial Optimization, Variational Quantum Algorithms

Abstract

—Real time air pollution monitoring is vital in densely populated areas to assess the micro level air pollution. Monitoring the air quality can be useful for providing accurate information to the public and to help policy decision makers to take actions that can improve the quality of air. Monitoring of the pollutants using Internet of Things (IoT) is a cost effective solution. This has the capability to provide high spatial and temporal air pollutants levels depending on the density of the IoT network. In this paper, we present an end to end prototype solution for monitoring of air pollutants. low cost LoraWAN CO2, CO and PM2.5 sensors are used in this prototype application. We also implemented a real time monitoring system using web dashboards with the capability to perform real time analysis using open source software elasticsearch and kibana. A prototype web dashboard showing the real time values of the sensor values along with temperature and humidity values is also developed. Index Terms—Air pollutants, Real time, Visualization, Analysis

Abstract

: In this research paper, the problem of indexing physical locations across a planet (like time indexing based on dates/ months/years) is addressed and a solution is provided. Different countries around the world devised various geographical location specification codes (along with state/ province names, district names, city/ town/ village names etc.). But, to this day, there is no addressing scheme to specify various types of geographical locations such as hospitals, hotels etc. around the world. Apart from coarse grain localization of spatial locations using longitude, latitude (on the globe), and fine grain specification of spatial positions is an open research problem. In this research paper, we provide an interesting solution to this problem and its applications. This research paper is an effort in that direction to provide global position addressing. Various interesting applications of such local/global position indexing are provided. It is hoped that such indexing when linked with other addresses such as IPv6 addresses will be very useful.