arXiv subjects

Satish Chand

Publications and source records attributed to Satish Chand.

6 recordsLinked to original sources

arXiv2024

From Pro, Anti to Informative and Hesitant: An Infoveillance study of COVID-19 vaccines and vaccination discourse on Twitter

COVID-19 pandemic has brought unprecedented challenges to the world, and vaccination has been a key strategy to combat the disease. Since Twitter is one of the most widely used public microblogging platforms, researchers have analysed COVID-19 vaccines and vaccination Twitter discourse to explore the conversational dynamics around the topic. While contributing to the crisis informatics literature, we curate a large-scale geotagged Twitter dataset, GeoCovaxTweets Extended, and explore the discourse through multiple spatiotemporal analyses. This dataset covers a longer time span of 38 months, from the announcement of the first vaccine to the availability of booster doses. Results show that 43.4% of the collected tweets, although containing phrases and keywords related to vaccines and vaccinations, were unrelated to the COVID-19 context. In total, 23.1% of the discussions on vaccines and vaccinations were classified as Pro, 16% as Hesitant, 11.4% as Anti, and 6.1% as Informative. The trend shifted towards Pro and Informative tweets globally as vaccination programs progressed, indicating a change in the public's perception of COVID-19 vaccines and vaccination. Furthermore, we explored the discourse based on account attributes, i.e., followers counts and tweet counts. Results show a significant pattern of discourse differences. Our findings highlight the potential of harnessing a large-scale geotagged Twitter dataset to understand global public health communication and to inform targeted interventions aimed at addressing vaccine hesitancy.

cs.SI↗

arXiv2023

GeoCovaxTweets: COVID-19 Vaccines and Vaccination-specific Global Geotagged Twitter Conversations

Social media platforms provide actionable information during crises and pandemic outbreaks. The COVID-19 pandemic has imposed a chronic public health crisis worldwide, with experts considering vaccines as the ultimate prevention to achieve herd immunity against the virus. A proportion of people may turn to social media platforms to oppose vaccines and vaccination, hindering government efforts to eradicate the virus. This paper presents the COVID-19 vaccines and vaccination-specific global geotagged tweets dataset, GeoCovaxTweets, that contains more than 1.8 million tweets, with location information and longer temporal coverage, originating from 233 countries and territories between January 2020 and November 2022. The paper discusses the dataset's curation method and how it can be re-created locally, and later explores the dataset through multiple tweets distributions and briefly discusses its potential use cases. We anticipate that the dataset will assist the researchers in the crisis computing domain to explore the conversational dynamics of COVID-19 vaccines and vaccination Twitter discourse through numerous spatial and temporal dimensions concerning trends, shifts in opinions, misinformation, and anti-vaccination campaigns.

cs.SI↗

arXiv2022

Pairing-Friendly Elliptic Curves: Revisited Taxonomy, Attacks and Security Concern

Major families of pairing-friendly elliptic curves, including BN, BLS12, BLS24, KSS16, and KSS18 have recently been vulnerable to number field sieve (NFS) attacks. Due to the recent attacks on discrete logs in F_(q^k ), selecting such curves became relevant again. This paper revisited the topic of selecting pairing-friendly curves at different security levels. First, we expanded the classification given by Freeman et al. [1] by identifying new families that were not previously mentioned, such as a complete family with variable differentiation and new sparse families of curves. We discussed individual curves and a comprehensive framework for constructing parametric families. We estimated the security and assessed families of the pairing-friendly curve to discover families of curves better than BN, KSS, and BLS in terms of the required key size. We also evaluated the complexity of the optimal ate pairing that has never been discussed before, except by Barbulescu et al. [2]. We demonstrated that the recent attack (TNFS) on pairing needs to increase the key size. We compared families of curves in the context of key size and selected a suitable alternative to an elliptic curve.

cs.CR↗

arXiv2021

An efficient mining scheme for high utility itemsets

Knowledge discovery in databases aims at finding useful information, which can be deployed for decision making. The problem of high utility itemset mining has specifically garnered huge research focus in the past decade, as it aims to find the patterns from the databases that conform to an objective utility function. Several algorithms exist in literature to mine the high utility items from the databases; however, most of them require large execution time and have high memory consumption. In this paper, we propose a new algorithm, R-Miner, based on a novel data structure, called the residue maps, that stores the utility information of an item directly and is used for the mining process. Several experiments are undertaken to assess the efficacy of the proposed algorithm against the benchmark algorithms. The experimental results indicate that the R-Miner algorithm outperforms the state-of-the-art mining algorithms.

cs.DB↗

arXiv2021

Recommending Researchers in Machine Learning based on Author-Topic Model

The aim of this paper is to uncover the researchers in machine learning using the author-topic model (ATM). We collect 16,855 scientific papers from six top journals in the field of machine learning published from 1997 to 2016 and analyze them using ATM. The dataset is broken down into 4 intervals to identify the top researchers and find similar researchers using their similarity score. The similarity score is calculated using Hellinger distance. The researchers are plotted using t-SNE, which reduces the dimensionality of the data while keeping the same distance between the points. The analysis of our study helps the upcoming researchers to find the top researchers in their area of interest.

cs.IR↗

arXiv2021

Pairing for Greenhorn: Survey and Future Perspective

Pairing is the most powerful tool in cryptography that maps two points on the elliptic curve to the group over the finite field. Mostly cryptographers consider pairing as a black box and use it for implementing pairing-based cryptographic protocols. This paper aims to give the overview of pairing as simple as possible for greenhorn and those who are working and wish to work in the pairing. The paper gives the concrete background of pairing and recommends an appropriate pairing among different choices for constructing pairing-based cryptographic protocols. We also analyze the bandwidth and computational efficiency of pairing and submitting those pairing suitable for implementing a cryptographic protocol for lightweight devices. Additionally, we discuss the extension of bilinear pairing to tri-linear and multilinear pairing and discuss a few assumptions to check their feasibility to implement multilinear pairing.

cs.CR↗