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Himanshu Gupta

Publications and source records attributed to Himanshu Gupta.

69 records · Page 4Linked to original sources
arXiv2022

DeepMTL Pro: Deep Learning Based MultipleTransmitter Localization and Power Estimation

In this paper, we address the problem of Multiple Transmitter Localization (MTL). MTL is to determine the locations of potential multiple transmitters in a field, based on readings from a distributed set of sensors. In contrast to the widely studied single transmitter localization problem, the MTL problem has only been studied recently in a few works. MTL is of great significance in many applications wherein intruders may be present. E.g., in shared spectrum systems, detection of unauthorized transmitters and estimating their power are imperative to efficient utilization of the shared spectrum. In this paper, we present DeepMTL, a novel deep-learning approach to address the MTL problem. In particular, we frame MTL as a sequence of two steps, each of which is a computer vision problem: image-to-image translation and object detection. The first step of mage-to-image translation essentially maps an input image representing sensor readings to an image representing the distribution of transmitter locations, and the second object detection step derives precise locations of transmitters from the image of transmitter distributions. For the first step, we design our learning model Sen2Peak, while for the second step, we customize a state-of-the-art object detection model YOLO-cust. Using DeepMTL as a building block, we also develop techniques to estimate transmit power of the localized transmitters. We demonstrate the effectiveness of our approach via extensive large-scale simulations and show that our approach outperforms the previous approaches significantly (by 50% or more) in performance metrics including localization error, miss rate, and false alarm rate. Our method also incurs a very small latency. We evaluate our techniques over a small-scale area with real testbed data and the testbed results align with the simulation results.

cs.NI↗
arXiv2022

The least Euclidean distortion constant of a distance-regular graph

In 2008, Vallentin made a conjecture involving the least distortion of an embedding of a distance-regular graph into Euclidean space. Vallentin's conjecture implies that for a least distortion Euclidean embedding of a distance-regular graph of diameter $d$, the most contracted pairs of vertices are those at distance $d$. In this paper, we confirm Vallentin's conjecture for several families of distance-regular graphs. We also provide counterexamples to this conjecture, where the largest contraction occurs between pairs of vertices at distance $d{-}1$. We suggest three alternative conjectures and prove them for several families of distance-regular graphs.

math.CO↗
arXiv2022

Autonomous Heavy-Duty Mobile Machinery: A Multidisciplinary Collaborative Challenge

Heavy-duty mobile machines (HDMMs) are a wide range of machinery used in diverse and critical application areas which are currently facing several issues like skilled labor shortage, poor safety records, and harsh work environments. Consequently, efforts are underway to increase automation in HDMMs for increased productivity and safety, eventually transitioning to operator-less autonomous HDMMs to address skilled labor shortages. However, HDMM are complex machines requiring continuous physical and cognitive inputs from human-operators. Thus, developing autonomous HDMM is a huge challenge, with current research and developments being performed in several independent research domains. Through this study, we use the bounded rationality concept to propose multidisciplinary collaborations for new autonomous HDMMs and apply the transaction cost economics framework to suggest future implications in the HDMM industry. Furthermore, we introduce a conceptual understanding of collaborations in the autonomous HDMM as a unified approach, while highlighting the practical implications and challenges of the complex nature of such multidisciplinary collaborations. The collaborative challenges and potentials are mapped out between the following topics: mechanical systems, AI methods, software systems, sensors, connectivity, simulations and process optimization, business cases, organization theories, and finally, regulatory frameworks.

cs.HC↗
arXiv2021

Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell

A code smell is a surface indicator of an inherent problem in the system, most often due to deviation from standard coding practices on the developers part during the development phase. Studies observe that code smells made the code more susceptible to call for modifications and corrections than code that did not contain code smells. Restructuring the code at the early stage of development saves the exponentially increasing amount of effort it would require to address the issues stemming from the presence of these code smells. Instead of using traditional features to detect code smells, we use user comments to manually construct features to predict code smells. We use three Extreme learning machine kernels over 629 packages to identify eight code smells by leveraging feature engineering aspects and using sampling techniques. Our findings indicate that the radial basis functional kernel performs best out of the three kernel methods with a mean accuracy of 98.52.

cs.SE↗
arXiv2021

Prediction of Students performance with Artificial Neural Network using Demographic Traits

Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability. Due to the increase in the rate of poor graduates, it is necessary to design a system that helps to reduce this menace as well as reduce the incidence of students having to repeat due to poor performance or having to drop out of school altogether in the middle of the pursuit of their career. It is therefore necessary to study each one as well as their advantages and disadvantages, so as to determine which is more efficient in and in what case one should be preferred over the other. The study aims to develop a system to predict student performance with Artificial Neutral Network using the student demographic traits so as to assist the university in selecting candidates (students) with a high prediction of success for admission using previous academic records of students granted admissions which will eventually lead to quality graduates of the institution. The model was developed based on certain selected variables as the input. It achieved an accuracy of over 92.3 percent, showing Artificial Neural Network potential effectiveness as a predictive tool and a selection criterion for candidates seeking admission to a university.

cs.CY↗
arXiv2021

An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models

Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. This metric is an indication of a deeper problem lies within the code and is associated with an issue which is prominent to experienced software developers with acceptable coding practices. Recent studies have often observed that codes having code smells are often prone to a higher probability of change in the software development cycle. In this paper, we developed code smell prediction models with the help of features extracted from source code to predict eight types of code smell. Our work also presents the application of data sampling techniques to handle class imbalance problem and feature selection techniques to find relevant feature sets. Previous studies had made use of techniques such as Naive - Bayes and Random forest but had not explored deep learning methods to predict code smell. A total of 576 distinct Deep Learning models were trained using the features and datasets mentioned above. The study concluded that the deep learning models which used data from Synthetic Minority Oversampling Technique gave better results in terms of accuracy, AUC with the accuracy of some models improving from 88.47 to 96.84.

cs.SE↗
arXiv2020

DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis

Memes have become an ubiquitous social media entity and the processing and analysis of suchmultimodal data is currently an active area of research. This paper presents our work on theMemotion Analysis shared task of SemEval 2020, which involves the sentiment and humoranalysis of memes. We propose a system which uses different bimodal fusion techniques toleverage the inter-modal dependency for sentiment and humor classification tasks. Out of all ourexperiments, the best system improved the baseline with macro F1 scores of 0.357 on SentimentClassification (Task A), 0.510 on Humor Classification (Task B) and 0.312 on Scales of SemanticClasses (Task C).

cs.CV↗
arXiv2019

CDAG: A Serialized blockDAG for Permissioned Blockchain

Blockchain is maintained as a global log between a network of nodes and uses cryptographic distributed protocols to synchronize the updates. As adopted by Bitcoin and Ethereum these update operations to the ledger are serialized, and executed in batches. To safeguard the system against the generation of conflicting sets of updates and maintain the consistency of the ledger, the frequency of the updates is controlled, which severely affects the performance of the system. This paper presents Converging Directed Acyclic Graph (CDAG), as a substitute for the chain and DAG structures used in other blockchain protocols. CDAG allows multiple parallel updates to the ledger and converges them at the next step providing finality to the blocks. It partitions the updates into non-intersecting buckets of transactions to prevent the generation of conflicting blocks and divide the time into slots to provide enough time for them to propagate in the network. Multiple simultaneous updates improve the throughput of CDAG, and the converging step helps to finalize them faster, even in the presence of conflicts. Moreover, CDAG provides a total order among the blocks of the ledger to support smart contracts, unlike some of the other blockDAG protocols. We evaluate the performance of CDAG on Google Cloud Platform using Google Kubernetes Engine, simulating a real-time network. Experimental results show that CDAG achieves a throughput of more than 2000 transactions per second and confirms them well in under 2 minutes. Also, the protocol scales well in comparison to other permissioned protocols, and the capacity of the network only limits the performance.

cs.CR↗
arXiv2018

Efficiently Processing Workflow Provenance Queries on SPARK

In this paper, we investigate how we can leverage Spark platform for efficiently processing provenance queries on large volumes of workflow provenance data. We focus on processing provenance queries at attribute-value level which is the finest granularity available. We propose a novel weakly connected component based framework which is carefully engineered to quickly determine a minimal volume of data containing the entire lineage of the queried attribute-value. This minimal volume of data is then processed to figure out the provenance of the queried attribute-value. The proposed framework computes weakly connected components on the workflow provenance graph and further partitions the large components as a collection of weakly connected sets. The framework exploits the workflow dependency graph to effectively partition the large components into a collection of weakly connected sets. We study the effectiveness of the proposed framework through experiments on a provenance trace obtained from a real-life unstructured text curation workflow. On provenance graphs containing upto 500M nodes and edges, we show that the proposed framework answers provenance queries in real-time and easily outperforms the naive approaches.

cs.DC↗
arXiv2018

Optimal Spectrum Management in Two-User Interference Channels

In this work, we address the problem of optimal spectrum management in continuous frequency domain in multiuser interference channels. The objective is to maximize the weighted sum of user capacities. Our main results are as follows: (i) For frequency-selective channels, we prove that in an optimal solution, each user uses maximum power; this result also generalizes to the cases where the objective is to maximize the weighted product (i.e., proportional fairness) of user capacities. (ii) For the special case of two users in flat channels, we solve the problem optimally.

cs.IT↗
arXiv2017

Effect of Composition Gradient on Magnetothermal Instability Modified by Shear and Rotation

We model the intracluster medium as a weakly collisional plasma that is a binary mixture of the hydrogen and the helium ions, along with free electrons. When, owing to the helium sedimentation, the gradient of the mean molecular weight (or equivalently, composition or helium ions' concentration) of the plasma is not negligible, it can have appreciable influence on the stability criteria of the thermal convective instabilities, e.g., the heat-flux-buoyancy instability and the magnetothermal instability (MTI). These instabilities are consequences of the anisotropic heat conduction occurring preferentially along the magnetic field lines. In this paper, without ignoring the magnetic tension, we first present the mathematical criterion for the onset of composition gradient modified MTI. Subsequently, we relax the commonly adopted equilibrium state in which the plasma is at rest, and assume that the plasma is in a sheared state which may be due to differential rotation. We discuss how the concentration gradient affects the coupling between the Kelvin--Helmholtz instability and the MTI in rendering the plasma unstable or stable. We derive exact stability criterion by working with the sharp boundary case in which the physical variables---temperature, mean molecular weight, density, and magnetic field---change discontinuously from one constant value to another on crossing the boundary. Finally, we perform the linear stability analysis for the case of the differentially rotating plasma that is thermally and compositionally stratified as well. By assuming axisymmetric perturbations, we find the corresponding dispersion relation and the explicit mathematical expression determining the onset of the modified MTI.

astro-ph.GA↗
arXiv2017

On the Helium fingers in the intracluster medium

In this paper we investigate the convection phenomenon in the intracluster medium (the weakly-collisional magnetized inhomogeneous plasma permeating galaxy clusters) where the concentration gradient of the Helium ions is not ignorable. To this end, we build upon the general machinery employed to study the salt finger instability found in the oceans. The salt finger instability is a form of double diffusive convection where the diffusions of two physical quantities---heat and salt concentrations---occur with different diffusion rates. The analogous instability in the intracluster medium may result owing to the magnetic field mediated anisotropic diffusions of the heat and the Helium ions (in the sea of the Hydrogen ions and the free electrons). These two diffusions have inherently different diffusion rates. Hence the convection caused by the onset of this instability is an example of double diffusive convection in the astrophysical settings. A consequence of this instability is the formation of the vertical filamentary structures having more concentration of the Helium ions with respect to the immediate neighbourhoods of the filaments. We term these structures as Helium fingers in analogy with the salt fingers found in the case of the salt finger instability. Here we show that the width of a Helium finger scales as one-fourth power of the radius of the inner region of the intracluster medium in the supercritical regime. We also determine the explicit mathematical expression of the criterion for the onset of the heat-flux-driven buoyancy instability modified by the presence of inhomogeneously distributed Helium ions.

astro-ph.GA↗
arXiv2016

Revisiting Evidence of Chaos in X-ray Light Curves: The Case of GRS 1915+105

Nonlinear time series analysis has been widely used to search for signatures of low-dimensional chaos in light curves emanating from astrophysical bodies. A particularly popular example is the microquasar GRS 1915+105, whose irregular but systematic X-ray variability has been well studied using data acquired by the Rossi X-ray Timing Explorer. With a view to building simpler models of X-ray variability, attempts have been made to classify the light curves of GRS 1915+105 as chaotic or stochastic. Contrary to some of the earlier suggestions, after careful analysis, we find no evidence for chaos or determinism in any of the GRS 1915+105 classes. The dearth of long and stationary data sets representing all the different variability classes of GRS 1915+105 makes it a poor candidate for analysis using nonlinear time series techniques. We conclude that either very exhaustive data analysis with sufficiently long and stationary light curves should be performed, keeping all the pitfalls of nonlinear time series analysis in mind, or alternative schemes of classifying the light curves should be adopted. The generic limitations of the techniques that we point out in the context of GRS 1915+105 affect all similar investigations of light curves from other astrophysical sources.

astro-ph.HE↗
arXiv2016

Stability Analysis of Convection in the Intracluster Medium

We use the machinery usually employed for studying the onset of Rayleigh--Bénard convection in hydro- and magnetohydro-dynamic settings to address the onset of convection induced by the magnetothermal instability and the heat-flux-buoyancy-driven-instability in the weakly-collisional magnetized plasma permeating the intracluster medium. Since most of the related numerical simulations consider the plasma being bounded between two `plates' on which boundary conditions are specified, our strategy provides a framework that could enable a more direct connection between analytical and numerical studies. We derive the conditions for the onset of these instabilities considering the effects of induced magnetic tension resulting from a finite plasma beta. We provide expressions for the Rayleigh number in terms of the wave vector associated with a given mode, which allow us to characterize the modes that are first to become unstable. For both the heat-flux-buoyancy-driven-instability and the magnetothermal instability, oscillatory marginal stable states are possible.

physics.flu-dyn↗