An Efficient Automated Attendance Entering System by Eliminating Counterfeit Signatures using Kolmogorov Smirnov Test

Maintaining the attendance database of thousands of students has become a tedious task in the universities in Sri Lanka This paper comprises of 3 phases signature extraction signature recognition and signature verification to automate the process We applied necessary image processing techniques and extracted useful features from each signature Support Vector Machine SVM multiclass Support Vector Machine and Kolmogorov Smirnov test is used to signature classification recognition and verification respectively The described method in this report represents an effective and accurate approach to automatic signature recognition and verification It is capable of matching classifying and verifying the test signatures with the database of 83 33 100 and 100 accuracy respectivel

An accurate fingerprint reference point determination method based on curvature estimation of separated ridges

This paper presents an effective method for the detection of a fingerprint’s reference point by analyzing fingerprint ridges’ curvatures. The proposed approach is a multi-stage system. The first step extracts the fingerprint ridges from an image and transforms them into chains of discrete points. In the second step, the obtained chains of points are processed by a dedicated algorithm to detect corners and other points of highest curvature on their planar surface. In a series of experiments we demonstrate that the proposed method based on this algorithm allows effective determination of fingerprint reference points. Furthermore, the proposed method is relatively simple and achieves better results when compared with the approaches known from the literature. The reference point detection experiments were conducted using publicly available fingerprint databases FVC2000, FVC2002, FVC2004 and NIST

Exploring the influence of facial verification software on student academic performance in online learning environments

In spite of the advances in technology in the e-learning field during the last decades, there is still a gap of software and tools that actually improve the assessment of this kind of education by preventing students from cheating when they perform their activities online. Currently, most learning management systems do not offer enough tools or characteristics to check that students are who they assure when they carry out their exercises or online tests. Facial verification software can be considered an interesting tool to answer this need. This facial software helps to verify the identity of the students when they perform their activities, with the intention of confirming whether they are who they claim to be. However, its use could modify the academic results of the students due to psychological factors (e.g. they could feel spied, ashamed or too controlled). The aim of this article is to investigate whether the utilization of facial verification software can modify the academic performance of students in their online activities. In this work, the grades of 70 master students were analyzed and the conclusions pointed out that the academic performance obtained by the students is similar for both groups: those who have used facial authentication and those who did not use it

Five groups of red giants with distinct chemical composition in the globular cluster NGC 2808

The chemical composition of multiple populations in the massive globular cluster (GC) NGC~2808 is addressed with the homogeneous abundance re-analysis of 140 red giant branch (RGB) stars. UVES spectra for 31 stars and GIRAFFE spectra for the other giants were analysed with the same procedures used for about 2500 giants in 23 GCs in our FLAMES survey, deriving abundances of Fe, O, Na, Mg, Si, Ca, Ti, Sc, Cr, Mn, and Ni. Iron, elements from alpha-capture, and in the Fe-group do not show intrinsic scatter. On our UVES scale the metallicity of NGC~2808 is [Fe/H]=-1.129+/-0.005+/-0.034$ (+/-statistical +/-systematic error) with sigma=0.030 (31 stars). Main features related to proton-capture elements are retrieved, but the improved statistics and the smaller associated internal errors allow to uncover five distinct groups of stars along the Na-O anticorrelation. We observe large depletions in Mg, anticorrelated with enhancements of Na and also Si, suggestive of unusually high temperatures for proton-captures. About 14% of our sample is formed by giants with solar or subsolar [Mg/Fe] ratios. Using the [Na/Mg] ratios we confirm the presence of five populations with different chemical composition, that we called P1, P2, I1, I2, and E in order of decreasing Mg and increasing Na abundances. Statistical tests show that the mean ratios in any pair of groups cannot be extracted from the same parent distribution. The overlap with the five populations recently detected from UV photometry is good but not perfect, confirming that more distinct components probably exist in this complex GC.Comment: 16 pages, 9 tables, 16 figures; accepted for publication on the Astrophysical Journa

Geochemical wolframite fingerprinting - the likelihood ratio approach for laser ablation ICP-MS data

Wolframite has been specified as a ‘conflict mineral’ by a U.S. Government Act, which obliges companies that use these minerals to report their origin. Minerals originating from conflict regions in the Democratic Republic of the Congo shall be excluded from the market as their illegal mining, trading, and taxation are supposed to fuel ongoing violent conflicts. The German Federal Institute for Geosciences and Natural Resources (BGR) developed a geochemical fingerprinting method for wolframite based on laser ablation inductively coupled plasma-mass spectrometry. Concentrations of 46 elements in about 5300 wolframite grains from 64 mines were determined. The issue of verifying the declared origins of the wolframite samples may be framed as a forensic problem by considering two contrasting hypotheses: the examined sample and a sample collected from the declared mine originate from the same mine (H 1 ), and the two samples come from different mines (H 2 ). The solution is found using the likelihood ratio (LR) theory. On account of the multidimensionality, the lack of normal distribution of data within each sample, and the huge within-sample dispersion in relation to the dispersion between samples, the classic LR models had to be modified. Robust principal component analysis and linear discriminant analysis were used to characterize samples. The similarity of two samples was expressed by Kolmogorov-Smirnov distances, which were interpreted in view of H 1 and H 2 hypotheses within the LR framework. The performance of the models, controlled by the levels of incorrect responses and the empirical cross entropy, demonstrated that the proposed LR models are successful in verifying the authenticity of the wolframite samples

Sorrentina peninsula: Geographical distribution of the indoor radon concentrations in dwellings—gini index application

The radon isotope (222Rn, half-life 3.8 days) is a radioactive byproduct of the238U decay chain. Because radon is the second biggest cause of lung cancer after smoking, dense maps of indoor radon concentration are required to implement effective locally based risk reduction strategies. In this regard, we present an innovative method for the construction of interpolated maps (kriging) based on the Gini index computation to characterize the distribution of Rn concentration. The Gini coefficient variogram has been shown to be an effective predictor of radon concentration inhomogeneity. It allows for a better constraint of the critical distance below which the radon geological source can be considered uniform, at least for the investigated length scales of variability; it also better distinguishes fluctuations due to environmental predisposing factors from those due to random spatially uncorrelated noise. This method has been shown to be effective in finding larger-scale geographical connections that can subsequently be connected to geological characteristics. It was tested using real dataset derived from indoor radon measurements conducted in the Sorrentina Peninsula in Campania, Italy. The measurement was carried out in different residences using passive detectors (CR-39) for two consecutive semesters, beginning in September– November 2019 and ending in September–November 2020, to estimate the yearly mean radon concentration. The measurements and analysis were conducted in accordance with the quality control plan. Radon concentrations ranged from 25 to 722 Bq/m3 before being normalized to ground level, and from 23 to 933 Bq/m3 after being normalized, with a geometric mean of 120 Bq/m3 and a geometric standard deviation of 1.35 before data normalization, and 139 Bq/m3 and a geometric standard deviation of 1.36 after data normalization. Approximately 13% of the tests conducted exceeded the 300 Bq/m3 reference level set by Italian Legislative Decree 101/2020. The data show that the municipalities under investigation had no influence on indoor radon levels. The geology of the monitored location is interesting, and because soil is the primary source of Rn, risk assessment and mitigation for radon exposure cannot be undertaken without first analyzing the local geology. This research examines the spatial link among radon readings using the mapping based on the Gini method (kriging)