Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Predicting student learning from conversational cues(Springer Verlag service@springer.de, 2014) Adamson, D.; Bharadwaj, A.; Singh, A.; Ashe, C.; Yaron, D.; Rosé, C.In the work here presented, we apply textual and sequential methods to assess the outcomes of an unconstrained multiparty dialogue. In the context of chat transcripts from a collaborative learning scenario, we demonstrate that while low-level textual features can indeed predict student success, models derived from sequential discourse act labels are also predictive, both on their own and as a supplement to textual feature sets. Further, we find that evidence from the initial stages of a collaborative activity is just as effective as using the whole. © 2014 Springer International Publishing Switzerland.Item Climatic effects on sugarcane productivity in India: A stochastic production function application(Inderscience Publishers, 2015) Singh, A.; Sharma, P.; Ambrammal, S.K.The present study estimates the influence of climatic and non-climatic factors on mean yield and yield variability of sugarcane crop in different weather seasons (e.g., rainy, winter and summer) in India. Sugarcane mean-yield for fourteen major sugarcane growing states from different agro-ecological zones are delimitated in panel data during 1971-2009. Regression coefficient for mean yield and yield variability production function (i.e. risk increasing or decreasing inputs) has been estimated through log-linear regression model with the help of Just and Pope (stochastic) production function specification. Empirical results based on feasible generalise least square (FGLS) estimations shows a significant effect of rainfall, maximum and minimum temperatures on sugarcane mean yield and yield variability. Whereas, average maximum temperature in summer and average minimum temperature in rainy season have a negative and statistically significant impact on sugarcane mean yield. Sugarcane mean yield positively gets affected with average maximum temperature during rainy and winter season. © © 2015 Inderscience Enterprises Ltd.Item Investigating the "wisdom of crowds" at scale(Association for Computing Machinery, Inc acmhelp@acm.org, 2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.Item Associative study of Absorbing Aerosol Index (AAI) and precipitation in India during monsoon season (2005 to 2014)(SPIE spie@spie.org, 2016) Dubey, S.; Mehta, M.; Singh, A.Based on their interaction with solar radiations, aerosols may be categorized as absorbing or scattering in nature. The absorbing aerosols are coarser and influence precipitation mainly due to microphysical effect (participating in the formation of Cloud Condensation Nuclei) and radiative forcing (by absorbing electromagnetic radiations). The prominent absorbing aerosols found in India are Black Carbon, soil dust, sand and mineral dust. Their size, distribution, and characteristics vary spatially and temporally. This paper aims at showing the spatio-temporal variation of Absorbing Aerosol Index (AAI) and precipitation over the four most polluted zones of Indian sub-continent (Indo-Gangetic plains 1, Indo-Gangetic plains 2, Central and Southern India) for monsoon season (June, July, August, September) during the last decade (2005 to 2014). Zonal averages AAI have been found to be exhibiting an increasing trend, hence region-wise correlations have been computed between AAI and precipitation during monsoon. Daily Absorption Aerosol Index (AAI) obtained from Aura OMI Aerosol Global Gridded Data Product-OMAEROe (V003) and monthly precipitation from TRMM 3B42-V7 gridded data have been used. © 2016 SPIE.Item A comparison of linear discriminant analysis and ridge classifier on Twitter data(Institute of Electrical and Electronics Engineers Inc., 2017) Singh, A.; Prakash, B.S.; Chandrasekaran, K.This document is about the accuracy analysis of two of the most prominent classifiers present in today's academic arena. Classifiers are being used extensively in machine learning applications today and need to present a high rate of success to be considered useful. Tikhonov regularization incorporated within the Ridge Classifier is the basis for its classification. It utilises the LevenbergMarquardt algorithm for non-linear least-squares problems to classify objects. Linear Discriminant Analysis, on the other hand, utilises aspects of ANOVA[2,3] and regression analysis. LDA works by getting explicit information from the user. It needs the definition of the variables - both dependent and independent. It doesn't use any implicit assumptions in its modelling. There is no interconnection between the two variables initially. Using these two classifiers we compare their effectiveness at mapping a set of data scraped in real-time from Twitter to its corresponding generalised hashtag, and suggest why the differences, if any, arise. © 2016 IEEE.Item Risk aware portfolio construction using deep deterministic policy gradients(Institute of Electrical and Electronics Engineers Inc., 2018) Hegde, S.; Kumar, V.; Singh, A.Allocation of liquid capital to the financial instruments in a portfolio is typically done using a two-step process. In the first step, predictive techniques are used to determine the future risk and rewards for the instrument. In the subsequent step, a quadratic optimization problem is solved to obtain the allocation that maximizes a relevant measure of the portfolio performance computed using a combination of the risks and the rewards. Deep Reinforcement Learning (DRL) eliminates the need for a two step process to find the allocation across the instruments that will optimize a measure of portfolio performance obtained from the market. DRL based portfolio construction autonomously adjusts to a change in the environment unlike traditional machine learning algorithms used in prediction. The existing DRL methods suffer from the challenges of stability, and do not lend themselves well to the portfolio construction problem that has a continuous action space. Proposed in 2015, Deep Deterministic Policy Gradients (DDPG) is a type of actorcritic DRL algorithm that provides support for continuous action space which is encountered in portfolio construction. This paper evaluates the use of DDPG to solve the problem of risk aware portfolio construction. Simulations are done on a portfolio of twenty stocks and the use of both Rate of Return and Sortino ratio as a measure of portfolio performance are evaluated. Results are presented that demonstrate the effectiveness of DDPG for risk aware portfolio construction. The simulation results presented in this paper show that having a risk-aware measure of portfolio performance such as Sortino ratio give a portfolio with superior return and lower variance. © 2018 IEEE.Item When and where?: Behavior dominant location forecasting with micro-blog streams(IEEE Computer Society, 2018) Gautam, B.; Annappa, B.; Singh, A.; Agrawal, A.The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach. © 2018 IEEE.Item Naked-eye detection of inorganic fluoride and acetate ion in an aqueous medium using organic receptor: Real life application(American Institute of Physics Inc. subs@aip.org, 2019) Singh, A.; Darshak, R.; TrivediA new colorimetric chemosensors R was designed and synthesized for the recognition of biological important anions. The binding mode of R was analyzed by colorimetric, and UV-visible. Receptors R1 showed color changes from pale yellow to orange and pale yellow to wine red in the presence of fluoride and acetate ions in DMSO. In addition, receptor R showed high selectivity towards sodium salts of fluoride and acetate ion in an aqueous medium. Moreover, the designed receptor Ralso revealed highly promising results for the quantitative detection of fluoride in real samples like sea water, toothpaste, and mouthwash. © 2019 American Institute of Physics Inc.. All rights reserved.Item Ethereum Blockchain Enabled Secure and Transparent E-Voting(Springer Science and Business Media Deutschland GmbH, 2021) Rao, V.; Singh, A.; Rudra, B.The blockchain’s revolutionary concept is the underlying technology behind the popular examples such as Bitcoin and it now relies on the Web and online services. Nowadays, blockchain is famous for its use in cryptocurrencies, but many fintech activities and routine processes that were done offline can be done using blockchain. Smart contracts are abstract pieces of codes that need to be inserted into the network and enforced as planned in every phase of upgrading blockchains. With the population growing so fast across the globe, e-voting is an emerging online service-related issue. The smart contracts of blockchain enable to have a easy, safe, cheap, secure and transparent e-voting due to which blockchain is one of the top solutions for e-voting. Even in the many blockchains available in the world, Ethereum is one of the most consistent available blockchain and has widespread use because of which it is suitable for e-voting. An e-voting system must ensure that it is secure, as it should not allow duplicated votes and it should be able to protect attendants’ privacy being fully transparent too. In this paper, Ethereum wallets and Solidity language for smart contracts were used to make a sample small scale e-voting application. The blockchain was tested on local blockchain using ganache and ropsten test network. The Ethereum blockchain keeps the records of ballots and votes after an election is held. Users can use Ethereum wallets to directly submit theirs vote and those votes are handled with the consensus of each Ethereum node. © 2021, Springer Nature Switzerland AG.Item Multi-device Login Monitoring for Google Meet Using Path Compressed Double-Trie and User Location(Springer Science and Business Media Deutschland GmbH, 2021) Patil, A.; Singh, A.; Chauhan, N.Google Meet, much like other online video-conferencing platforms, has seen a surge in popularity for its reliability and convenience, which also makes it necessary to be analysed for vulnerabilities owing to its large user-base. This paper focuses on an observational study on Google Meet to find certain shortcomings, specifically the freedom users have to login through multiple devices and how it can aid attackers in certain scenarios. A simulated back-end architecture is developed to propose a proof of concept on tackling the explored issues using a path compressed double-trie structure and the location of user from his public IP address. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.