Blockwise Repeated Burst Error Correcting Linear Codes

This paper presents a lower and an upper bound on the number of parity check digits required for a linear code that corrects a single sub-block containing errors which are in the form of 2-repeated bursts of length b or less. An illustration of such kind of codes has been provided. Further, the codes that correct m-repeated bursts of length b or less have also been studied

On the stability of a multiplicative type sum form functional equation

In this paper we intend to discuss the stability of a sum form functional equation\begin{align*}\sum\limits\limits^n_{i=1}\sum\limits\limits^m_{j=1}f\left(p_iq_j\right)=\sum\limits\limits^n_{i=1}k\left(p_i\right)\sum\limits\limits^m_{j=1}q^{\beta }_j\end{align*}where $f, k$ are real valued mappings each having the domain $I$; $(p_1,\ldots,p_n)\in \Gamma_n$, $(q_1,\ldots,q_m)\in\Gamma_m$; $n\ge 3$, $m\ge 3$ are fixed integers and $\beta$ is a fixed positive real power different from 1 satisfying the conventions $0^{\beta }:= 0$ and $1^\beta:=1$

MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings

Bodily behavioral language is an important social cue, and its automated analysis helps in enhancing the understanding of artificial intelligence systems. Furthermore, behavioral language cues are essential for active engagement in social agent-based user interactions. Despite the progress made in computer vision for tasks like head and body pose estimation, there is still a need to explore the detection of finer behaviors such as gesturing, grooming, or fumbling. This paper proposes a multiview attention fusion method named MAGIC-TBR that combines features extracted from videos and their corresponding Discrete Cosine Transform coefficients via a transformer-based approach. The experiments are conducted on the BBSI dataset and the results demonstrate the effectiveness of the proposed feature fusion with multiview attention. The code is available at: https://github.com/surbhimadan92/MAGIC-TBRComment: 4 pages, 2 Tables and 3 Figure

Assessment of surface water from physicochemical parameters: A detailed study of a selected portion of the Jaipur district

Unwanted changes in the physical, chemical, and biological features of air, water, and soil pose a serious hazard to people all over the world. Water is highly polluted with various dangerous chemicals as a result of the rising human population, industry, fertilizer use, and man-made activity. Weathering of rocks and leaching of soils, mining processes, mixing of different domestic contaminants (detergents), and other factors contaminate natural water. Because of the usage of contaminated drinking water, various water-borne diseases affect human health; therefore it is vital to monitor the quality of drinking water at regular intervals. This study comprises a water assessment using Physico-chemical parameters. This research data has been collected from Jamwa-Ramgarh, Virat Nagar, Bassi, and Amber tehsil in the Jaipur district during the study period (2019-2022). This paper has included appropriate methodologies for the determination of Physico-chemical parameters. Temperature, acidity, hardness, pH, sulfate, chloride, DO, BOD, COD, alkalinity, and other physicochemical parameters are calculated because they all are necessary to analyze water quality. the determination of the concentration of certain heavy metals(Fe, Zn, Cd, Cu), is also included in this paper because these heavy metals are dangerous to water species and also produce poison inside the water.&nbsp

Head matters : explainable human-centered trait prediction from head motion dynamics

We demonstrate the utility of elementary head-motion units termed kinemes for behavioral analytics to predict personality and interview traits. Transforming head-motion patterns into a sequence of kinemes facilitates discovery of latent temporal signatures characterizing the targeted traits, thereby enabling both efficient and explainable trait prediction. Utilizing Kinemes and Facial Action Coding System (FACS) features to predict (a) OCEAN personality traits on the First Impressions Candidate Screening videos, and (b) Interview traits on the MIT dataset, we note that: (1) A Long-Short Term Memory (LSTM) network trained with kineme sequences performs better than or similar to a Convolutional Neural Network (CNN) trained with facial images; (2) Accurate predictions and explanations are achieved on combining FACS action units (AUs) with kinemes, and (3) Prediction performance is affected by the time-length over which head and facial movements are observed

Multi-block Two Repeated Fixed Burst Error Correcting Linear Codes

During the digital transmission of information, errors are bound to occur. The errors may be random or burst errors. In this paper, we have obtained necessary and sufficient conditions for the existence of linear codes over  that are capable of correcting -repeated burst errors of length(fixed) and -repeated burst errors of length (fixed) within sub-blocks of unequal lengths. We have also constructed a  parity-check matrix for such a code.

SARS-CoV-2 Reinfection Rate and Estimated Effectiveness of the Inactivated Whole Virion Vaccine BBV152 Against Reinfection Among Health Care Workers in New Delhi, India

A surge of COVID-19 occurred from March to June 2021, in New Delhi, India, linked to the B.1.617.2 (Delta) variant of SARS-CoV-2. COVID-19 vaccines were rolled out for health care workers (HCWs) starting in January 2021. To assess the incidence density of reinfection among a cohort of HCWs and estimate the effectiveness of the inactivated whole virion vaccine BBV152 against reinfection. This was a retrospective cohort study among HCWs working at a tertiary care center in New Delhi, India. Vaccination with 0, 1, or 2 doses of BBV152. The HCWs were categorized as fully vaccinated (with 2 doses and ≥15 days after the second dose), partially vaccinated (with 1 dose or 2 doses with <15 days after the second dose), or unvaccinated. The incidence density of COVID-19 reinfection per 100 person-years was computed, and events from March 3, 2020, to June 18, 2021, were included for analysis. Unadjusted and adjusted hazard ratios (HRs) were estimated using a Cox proportional hazards model. Estimated vaccine effectiveness (1 - adjusted HR) was reported. Among 15 244 HCWs who participated in the study, 4978 (32.7%) were diagnosed with COVID-19. The mean (SD) age was 36.6 (10.3) years, and 55.0% were male. The reinfection incidence density was 7.26 (95% CI: 6.09-8.66) per 100 person-years (124 HCWs [2.5%], total person follow-up period of 1696 person-years as time at risk). Fully vaccinated HCWs had lower risk of reinfection (HR, 0.14 [95% CI, 0.08-0.23]), symptomatic reinfection (HR, 0.13 [95% CI, 0.07-0.24]), and asymptomatic reinfection (HR, 0.16 [95% CI, 0.05-0.53]) compared with unvaccinated HCWs. Accordingly, among the 3 vaccine categories, reinfection was observed in 60 of 472 (12.7%) of unvaccinated (incidence density, 18.05 per 100 person-years; 95% CI, 14.02-23.25), 39 of 356 (11.0%) of partially vaccinated (incidence density 15.62 per 100 person-years; 95% CI, 11.42-21.38), and 17 of 1089 (1.6%) fully vaccinated (incidence density 2.18 per 100 person-years; 95% CI, 1.35-3.51) HCWs. The estimated effectiveness of BBV152 against reinfection was 86% (95% CI, 77%-92%); symptomatic reinfection, 87% (95% CI, 76%-93%); and asymptomatic reinfection, 84% (95% CI, 47%-95%) among fully vaccinated HCWs. Partial vaccination was not associated with reduced risk of reinfection. These findings suggest that BBV152 was associated with protection against both symptomatic and asymptomatic reinfection in HCWs after a complete vaccination schedule, when the predominant circulating variant was B.1.617.2