Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

Magnitude and Correlates of Elevated Blood Pressure among Adolescent School Students Aged 15-19 Years in a Block of Murshidabad, West Bengal, India

Background: The prevalence of adolescent hypertension is on the rise due to multiplicity of certain risk factors, like obesity, unhealthy dietary behaviour, physical inactivity, tobacco use, alcohol addiction, and academic stress. The present study aimed to estimate the prevalence of elevated blood pressure and hypertension among adolescent school children and identify the factors influencing it.Methods: The present observational, cross-sectional study was conducted in two higher secondary schools in a block of Murshidabad district, West Bengal, from February to April 2021. The subjects included 15 to 19-year-old school students. Multistage random sampling method was used for selecting a sample size of 183 adolescent school children. Data were obtained by interviewing the study participants, measurement of blood pressure and anthropometric measurements. Chi-squared test and binary logistic regression were used for bivariate and Multivariable data analysis, respectively, with P<0.05 as the level of significance.Results: The mean of Systolic Blood Pressure and Diastolic Blood Pressure were 115.02+10.853 and 71.52+8.484 mm of Hg, respectively. The overall prevalence of elevated blood pressure and adolescent hypertension was 21.3% (95% CI 15.4-27.2). The prevalence was significantly higher among those with paternal education of above middle school (AOR=1.803, P=0.011), high socioeconomic status (AOR=3.16, P=0.02), and high Body Mass Index for their age (AOR=11.474, P<0.0001). Smart phone use (P=0.03) and family history of hypertension (P=0.029) were also found to significantly influence elevated blood pressure among the subjects in bivariate analysis.Conclusions: Measurement of blood pressure, as a part of school health programme, should be given priority with emphasis on physical activity at school, health promotion to avoid unhealthy diet, and restricted smart phone use

Parametric Optimization of Re-refining of Waste Lubricating Oil Using Bio-flocculant via Taguchi Approach

Over the past few decades, recycling used lubricants have drawn much attention as a cleaner technique. The current study focuses on the fabrication and application of bio flocculant (sodium alginate) from brown algae (Sargassum Muticum) for the refining of waste lubricating oil. Further the work illustrates on the optimization of the four process parameters like refining time, refining temperature, solvent-to-waste oil ratio, and flocculant dosage at three different levels (low, intermediate and high) using Taguchi approach during the process of refining of waste lubricating oil by clean and environmental friendly extraction flocculation method. The optimized parameters for maximization of the yield (91.31 %) were observed at refining time of 60 minutes, refining temperature of 80 ?, a solvent-to-waste oil ratio of 3:1, and a flocculant dosage of 1 g/kg of solvent. A good fit of the model could be achieved with a R2 of 0.9938 and p value of 0.018. The re-refined lubricating oil had a flash point, pour point, kinematic viscosity@40 ? and 100 ? of 234 ?, -33 ?,155.21 cSt and 17.11 cSt which are comparable to the virgin lubricating oil and hence refined oil can remarkably be used for specific purpose in automotive engine after addition of requisite amount of additives

Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

Peanut (Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a∗, b∗, u∗, v∗, green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a∗, u∗, GA, GGA, and CSI were significantly (p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting