arXiv subjects

Vikas Kumar

Publications and source records attributed to Vikas Kumar.

At least 37 records · Page 2Linked to original sources

arXiv2023

Deep dive into language traits of AI-generated Abstracts

Generative language models, such as ChatGPT, have garnered attention for their ability to generate human-like writing in various fields, including academic research. The rapid proliferation of generated texts has bolstered the need for automatic identification to uphold transparency and trust in the information. However, these generated texts closely resemble human writing and often have subtle differences in the grammatical structure, tones, and patterns, which makes systematic scrutinization challenging. In this work, we attempt to detect the Abstracts generated by ChatGPT, which are much shorter in length and bounded. We extract the texts semantic and lexical properties and observe that traditional machine learning models can confidently detect these Abstracts.

cs.CL↗

arXiv2023

Shell-model study of $β^+$/EC-decay half-lives for $Z = 21-30$ nuclei

In the present work, we have reported $β^+$/EC-decay half-lives for $Z = 21 -30$ nuclei using large-scale shell-model. %and N $\leq$ A recent study shows that some proton-rich nuclei in this region belong to the island of inversion. We have performed calculations for these nuclei using KB3G effective interaction, while for Ni, Cu, and Zn nuclei we have used JUN45 effective interaction in the $f_{5/2}pg_{9/2}$ model space. The calculated quenching factors for $fp$ and $f_{5/2}pg_{9/2}$ space using KB3G and JUN45 effective interactions are also reported. Shell-model results of $β$-decay half-lives, excitation energies, log$ft$ values, and branching fractions are discussed and compared with the available experimental data. We have obtained a reasonable agreement with the available data.

nucl-th↗

arXiv2023

Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization

Collaborative filtering (CF) has become a popular method for developing recommender systems (RSs) where ratings of a user for new items are predicted based on her past preferences and available preference information of other users. Despite the popularity of CF-based methods, their performance is often greatly limited by the sparsity of observed entries. In this study, we explore the data augmentation and refinement aspects of Maximum Margin Matrix Factorization (MMMF), a widely accepted CF technique for rating predictions, which has not been investigated before. We exploit the inherent characteristics of CF algorithms to assess the confidence level of individual ratings and propose a semi-supervised approach for rating augmentation based on self-training. We hypothesize that any CF algorithm's predictions with low confidence are due to some deficiency in the training data and hence, the performance of the algorithm can be improved by adopting a systematic data augmentation strategy. We iteratively use some of the ratings predicted with high confidence to augment the training data and remove low-confidence entries through a refinement process. By repeating this process, the system learns to improve prediction accuracy. Our method is experimentally evaluated on several state-of-the-art CF algorithms and leads to informative rating augmentation, improving the performance of the baseline approaches.

cs.IR↗

arXiv2023

Lattice attack on group ring NTRU: The case of the dihedral group

Group ring NTRU (GR-NTRU) provides a general structure to design different variants of NTRU-like schemes by employing different groups. Although, most of the schemes in literature are built over cyclic groups, nonabelian groups can also be used. Coppersmith and Shamir in 1997 have suggested that noncommutativity may result in better security against some lattice attacks for some groups. Lattice attacks on the public key of NTRU-like cryptosystems try to retrieve the private key by solving the shortest vector problem (SVP) or its approximation in a lattice of a certain dimension, assuming the knowledge of the public key only. This paper shows that dihedral groups do not guarantee better security against this class of attacks. We prove that retrieving the private key is possible by solving the SVP in two lattices with half the dimension of the original lattice generated for GR-NTRU based on dihedral groups. The possibility of such an attack was mentioned by Yasuda et al.(IACR/2015/1170). In contrast to their proposed approach, we explicitly provide the lattice reduction without any structure theorem from the representation theory for finite groups. Furthermore, we demonstrate the effectiveness of our technique with experimental results.

cs.CR↗

arXiv2023

Conformal Group Recommender System

Group recommender systems (GRS) are critical in discovering relevant items from a near-infinite inventory based on group preferences rather than individual preferences, like recommending a movie, restaurant, or tourist destination to a group of individuals. The traditional models of group recommendation are designed to act like a black box with a strict focus on improving recommendation accuracy, and most often, they place the onus on the users to interpret recommendations. In recent years, the focus of Recommender Systems (RS) research has shifted away from merely improving recommendation accuracy towards value additions such as confidence and explanation. In this work, we propose a conformal prediction framework that provides a measure of confidence with prediction in conjunction with a group recommender system to augment the system-generated plain recommendations. In the context of group recommender systems, we propose various nonconformity measures that play a vital role in the efficiency of the conformal framework. We also show that defined nonconformity satisfies the exchangeability property. Experimental results demonstrate the effectiveness of the proposed approach over several benchmark datasets. Furthermore, our proposed approach also satisfies validity and efficiency properties.

cs.IR↗

arXiv2023

A Bibliographic Study on Artificial Intelligence Research: Global Panorama and Indian Appearance

The present study identifies and assesses the bibliographic trend in Artificial Intelligence (AI) research for the years 2015-2020 using the science mapping method of bibliometric study. The required data has been collected from the Scopus database. To make the collected data analysis-ready, essential data transformation was performed manually and with the help of a tool viz. OpenRefine. For determining the trend and performing the mapping techniques, top five open access and commercial journals of AI have been chosen based on their citescore driven ranking. The work includes 6880 articles published in the specified period for analysis. The trend is based on Country-wise publications, year-wise publications, topical terms in AI, top-cited articles, prominent authors, major institutions, involvement of industries in AI and Indian appearance. The results show that compared to open access journals; commercial journals have a higher citescore and number of articles published over the years. Additionally, IEEE is the prominent publisher which publishes 84% of the top-cited publications. Further, China and the United States are the major contributors to literature in the AI domain. The study reveals that neural networks and deep learning are the major topics included in top AI research publications. Recently, not only public institutions but also private bodies are investing their resources in AI research. The study also investigates the relative position of Indian researchers in terms of AI research. Present work helps in understanding the initial development, current stand and future direction of AI.

cs.DL↗

arXiv2022

Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data

Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedback of other similar customers. Data Sparsity is one of the major drawbacks with collaborative filtering technique arising due to the less number of transactions and feedback data. In order to reduce the sparsity problem, techniques called transfer learning/cross-domain recommendation has emerged. In transfer learning methods, the data from other dense domain(s) (source) is considered in order to predict the missing ratings in the sparse domain (target). In this paper, we come up with a novel transfer learning approach for cross-domain recommendation, wherein the cluster-level rating pattern(codebook) of the source domain is obtained via a co-clustering technique. Thereafter we apply the Maximum Margin Matrix factorization (MMMF) technique on the codebook in order to learn the user and item latent features of codebook. Prediction of the target rating matrix is achieved by introducing these latent features in a novel way into the optimisation function. In the experiments we demonstrate that our model improves the prediction accuracy of the target matrix on benchmark datasets.

cs.IR↗

arXiv2022

Inductive Conformal Recommender System

Traditional recommendation algorithms develop techniques that can help people to choose desirable items. However, in many real-world applications, along with a set of recommendations, it is also essential to quantify each recommendation's (un)certainty. The conformal recommender system uses the experience of a user to output a set of recommendations, each associated with a precise confidence value. Given a significance level $\varepsilon$, it provides a bound $\varepsilon$ on the probability of making a wrong recommendation. The conformal framework uses a key concept called \emph{nonconformity measure} that measures the strangeness of an item concerning other items. One of the significant design challenges of any conformal recommendation framework is integrating nonconformity measures with the recommendation algorithm. This paper introduces an inductive variant of a conformal recommender system. We propose and analyze different nonconformity measures in the inductive setting. We also provide theoretical proofs on the error-bound and the time complexity. Extensive empirical analysis on ten benchmark datasets demonstrates that the inductive variant substantially improves the performance in computation time while preserving the accuracy.

cs.LG↗

arXiv2021

Shell-model study for GT-strengths corresponding to $β$ decay of $^{60}$Ge and $^{62}$Ge

In the present work, we have reported a comprehensive shell-model study of GT-strengths for recently available experimental data for $^{60}$Ga and $^{62}$Ga from RIKEN Nishina Center [Phys. Rev. C 103, 014324 (2021)] populated by $β$ decay of the $^{60}$Ge and $^{62}$Ge, respectively. We have performed shell-model calculations in two different model spaces, the first set of calculations in the $fp$ model space using KB3G and GXPF1A interactions, while the second set in $f_{5/2}pg_{9/2}$ model space using JUN45 and jj44b effective interactions. Our shell-model results in $fp$ model space are in a reasonable agreement with the available experimental data.

nucl-th↗

arXiv2021

Reconfigurable Intelligent Surface-assisted Edge Computing to Minimize Delay in Task Offloading

The advantage of computational resources in edge computing near the data source has kindled growing interest in delay-sensitive Internet of Things (IoT) applications. However, the benefit of the edge server is limited by the uploading and downloading links between end-users and edge servers when these end-users seek computational resources from edge servers. The scenario becomes more severe when the user-end's devices are in the shaded region resulting in low uplink/downlink quality. In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted edge computing system, where the benefits of RIS are exploited to improve the uploading transmission rate. We further aim to minimize the delay of worst-case in the network when the end-users either compute task data in their local CPU or offload task data to the edge server. Next, we optimize the uploading bandwidth allocation for every end-user's task data to minimize the maximum delay in the network. The above optimization problem is formulated as quadratically constrained quadratic programming. Afterward, we solve this problem by semidefinite relaxation. Finally, the simulation results demonstrate that the proposed strategy is scalable under various network settings.

cs.IT↗

arXiv2021

A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data

Recommender systems(RS), especially collaborative filtering(CF) based RS, has been playing an important role in many e-commerce applications. As the information being searched over the internet is rapidly increasing, users often face the difficulty of finding items of his/her own interest and RS often provides help in such tasks. Recent studies show that, as the item space increases, and the number of items rated by the users become very less, issues like sparsity arise. To mitigate the sparsity problem, transfer learning techniques are being used wherein the data from dense domain(source) is considered in order to predict the missing entries in the sparse domain(target). In this paper, we propose a transfer learning approach for cross-domain recommendation when both domains have no overlap of users and items. In our approach the transferring of knowledge from source to target domain is done in a novel way. We make use of co-clustering technique to obtain the codebook (cluster-level rating pattern) of source domain. By making use of hinge loss function we transfer the learnt codebook of the source domain to target. The use of hinge loss as a loss function is novel and has not been tried before in transfer learning. We demonstrate that our technique improves the approximation of the target matrix on benchmark datasets.

cs.IR↗

arXiv2021

The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G

Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed.

cs.NI↗

arXiv2021

Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management

Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscript we illustrate a Machine Learning (ML) framework for addressing the problem of predicting Type 2 Diabetes Mellitus (T2DM) together with a solution for risk stratification, intervention and management. These ML models align with how physicians think about disease management and mitigation, which comprises these four steps: Identify, Stratify, Engage, Measure.

cs.LG↗

arXiv2021

OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas

Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary. A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout. However, the space-varying distortions in panoramic images are not compatible with the translational equivariance property of standard convolutions, thus degrading performance. Instead, we propose to use spherical convolutions. The resulting network, which we call OmniLayout performs convolutions directly on the sphere surface, sampling according to inverse equirectangular projection and hence invariant to equirectangular distortions. Using a new evaluation metric, we show that our network reduces the error in the heavily distorted regions (near the poles) by approx 25 % when compared to standard convolutional networks. Experimental results show that OmniLayout outperforms the state-of-the-art by approx 4% on two different benchmark datasets (PanoContext and Stanford 2D-3D). Code is available at https://github.com/rshivansh/OmniLayout.

cs.CV↗

arXiv2021

Pattern Formation Study of an Eco-epidemiological Model with Cannibalism and Disease in Predator Population

Pattern formation analysis of eco-epidemiological models with cannibalism and disease has been less explored in the literature. Therefore, motivated by this, we have proposed a diffusive eco-epidemiological model and performed pattern formation analysis in the model system. Sufficient conditions for local asymptotic stability and global asymptotic stability for the constant positive steady state are obtained by linearization and Lyapunov function technique. A priori estimate for the positive steady state is obtained for the nonexistence of the nonconstant positive solution using Cauchy and Poincaré inequality. The existence of the nonconstant positive steady states is studied using Leray-Schauder degree theory. The importance of the diffusive coefficients which are responsible for the appearance of stationary patterns is observed. Pattern formation is done using numerical simulation. Further, the effect of the cannibalism and disease are observed on the dynamics of the proposed model system. The movements of prey and susceptible predator plays a significant role in pattern formation. These movements cause stationary and non-stationary patterns. It is observed that an increment in the movement of the susceptible predator as well as cannibalistic attack rate converts non-Turing patterns to Turing patterns. Lyapunov spectrum is calculated for quantification of stable and unstable dynamics. Non-Turing patterns obtained with parameter set having unstable limit cycle are more interesting and realistic than stationary patterns. Stationary and non-stationary non-Turing patterns are obtained.

math.AP↗

arXiv2021

Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition

Facial expression recognition from videos in the wild is a challenging task due to the lack of abundant labelled training data. Large DNN (deep neural network) architectures and ensemble methods have resulted in better performance, but soon reach saturation at some point due to data inadequacy. In this paper, we use a self-training method that utilizes a combination of a labelled dataset and an unlabelled dataset (Body Language Dataset - BoLD). Experimental analysis shows that training a noisy student network iteratively helps in achieving significantly better results. Additionally, our model isolates different regions of the face and processes them independently using a multi-level attention mechanism which further boosts the performance. Our results show that the proposed method achieves state-of-the-art performance on benchmark datasets CK+ and AFEW 8.0 when compared to other single models.

cs.CV↗

arXiv2021

Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare

Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems. While the need for accountability of AI models is near ubiquitous, healthcare in particular is a challenging field where accountability of such systems takes upon additional importance, as decisions in healthcare can have life altering consequences. In this paper we present preliminary results on fairness in the context of classification parity in healthcare. We also present some exploratory methods to improve fairness and choosing appropriate classification algorithms in the context of healthcare.

cs.LG↗

arXiv2021

Emergency Department Optimization and Load Prediction in Hospitals

Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs). ED resources, including nurse staffing, are strained by such increases in patient volume. Accurate forecasting of incoming patient volume in emergency departments (ED) is crucial for efficient utilization and allocation of ED resources. Working with a suburban ED in the Pacific Northwest, we developed a tool powered by machine learning models, to forecast ED arrivals and ED patient volume to assist end-users, such as ED nurses, in resource allocation. In this paper, we discuss the results from our predictive models, the challenges, and the learnings from users' experiences with the tool in active clinical deployment in a real world setting.

cs.LG↗