Miracle-dz/UniFAR-FTUs huggingface dataset:
Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States

Spatially explicit livestock disease models require demographic data for individual farms or premises. In the U.S., demographic data are only available aggregated at county or coarser scales, so disease models must rely on assumptions about how individual premises are distributed within counties. Here, we addressed the importance of realistic assumptions for this purpose. We compared modeling of foot and mouth disease (FMD) outbreaks using simple randomization of locations to premises configurations predicted by the Farm Location and Agricultural Production Simulator (FLAPS), which infers location based on features such as topography, land-cover, climate, and roads. We focused on three premises-level Susceptible-Exposed-Infectious-Removed models available from the literature, all using the same kernel approach but with different parameterizations and functional forms. By computing the basic reproductive number of the infection (R0) for both FLAPS and randomized configurations, we investigated how spatial locations and clustering of premises affects outbreak predictions. Further, we performed stochastic simulations to evaluate if identified differences were consistent for later stages of an outbreak. Using Ripley’s K to quantify clustering, we found that FLAPS configurations were substantially more clustered at the scales relevant for the implemented models, leading to a higher frequency of nearby premises compared to randomized configurations. As a result, R0 was typically higher in FLAPS configurations, and the simulation study corroborated the pattern for later stages of outbreaks. Further, both R0 and simulations exhibited substantial spatial heterogeneity in terms of differences between configurations. Thus, using realistic assumptions when de-aggregating locations based on available data can have a pronounced effect on epidemiological predictions, affecting if, where, and to what extent FMD may invade the population. We conclude that methods such as FLAPS should be preferred over randomization approaches.
Evaluating Binary n-gram Analysis For Authorship Attribution

Authorship attribution techniques focus on characters and words. However the inclusion of words with meaning may complicate authorship attribution. Using only function words provides good authorship attribution with semantic or character n-gram analyses but it is not yet known whether it improves binary n-gram analyses. The literature mostly reports on authorship attribution at word or character level. Binary n-grams interpret text as binary. Previous work with binary n-grams assessed authorship attribution of full texts only. This paper evaluates binary n-gram authorship attribution over text stripped of content words as well as over a range of cross-domain scenarios. This paper reports a sequence of experiments. First the binary n-gram analysis method is directly compared with character n-grams for authorship attribution. Then it is evaluated over three forms of input text, full text, stop words and function words only, and content words only. Subsequently, it was tested over cross-domain and cross-genre texts, as well as multiple-author texts.
Information on ability and provision of incentives: regression discontinuity estimates on the effects of assigning college students to remedial courses

How do students react to e ort-enhancing incentives and information on their academic ability? I present Sharp Regression Discontinuity estimates on the impact that assigning college students' to remedial education has on their subsequent achievements, exploiting a new data set on European Bachelor students. Results indicate that students do not get discouraged when placed in remedial courses. However, the assignment to remediation does not trigger any positive and signi cant e ect on persistence in college, credits accumulation, and the probability to pass the college-level exam of the remedial subject. I also exclude the presence of heterogeneous e ects. These ndings crucially contribute to the growing literature on measures to enhance college completion and performance. JEL codes: I21, I23, J24.
Leadermember exchange, organizational citizenship behaviours and performance of Ghanaian Technical University Lecturers

This study investigates the mediating role of organizational citizenship behaviours (OCBs) on the leader-member exchange (LMX) and employee performance relation and the degree to which work experience moderates the relation between leader-member exchange and OCBs. Lecturers from six technical universities in Ghana, making up three hundred and thirty-six lecturers, were selected using convenience sampling. The participants completed self-administered surveys. OCBs fully mediated the association between LMX and employee performance. Furthermore, the findings indicate that the interplay between LMX and work experience on OCBs is compensatory in nature such that as work experience increases, the positive association between LMX and OCBs decrease. Managers of higher education institutions should create enabling work environments that encourage high-quality LMX and citizenship behaviours. Moreover, as work experience tends to attenuate the positive influence of LMX on OCBs, managers in higher education should focus their attention on employees with low rather than high work experience. This research adds to the employee performance literature through examining a novel link among leader-member exchange, organizational citizenship behaviours and performance. Key words: Leader-member exchange, organizational citizenship behaviours, Ghana, Technical University.
Vision-Based Localization and Scanning of 1D UPC and EAN Barcodes with Relaxed Pitch, Roll, and Yaw Camera Alignment Constraints

Two algorithms are presented for vision-based localization of 1D UPC and EAN barcodes with relaxed pitch, roll, and yaw camera alignment constraints. The first algorithm localizes barcodes in images by computing dominant orientations of gradients (DOGs) of image segments and grouping smaller segments with similar DOGs into larger connected components. Connected components that pass given morphological criteria are marked as potential barcodes. The second algorithm localizes barcodes by growing edge alignment trees (EATs) on binary images with detected edges. EATs of certain sizes mark regions as potential barcodes. The algorithms are implemented in a distributed, cloud-based system. The system’s front end is a smartphone application that runs on Android smartphones with Android 4.2 or higher. The system’s back end is deployed on a five node Linux cluster where images are processed. Both algorithms were evaluated on a corpus of 7,545 images extracted from 506 videos of bags, bottles, boxes, and cans in a supermarket. All videos were recorded with an Android 4.2 Google Galaxy Nexus smartphone. The DOG algorithm was experimentally found to outperform the EAT algorithm and was subsequently coupled to our in-place scanner for 1D UPC and EAN barcodes. The scanner receives from the DOG algorithm the rectangular planar dimensions of a connected component and the component’s dominant gradient orientation angle referred to as the skew angle. The scanner draws several scanlines at that skew angle within the component to recognize the barcode in place without any rotations. The scanner coupled to the localizer was tested on the same corpus of 7,545 images. Laboratory experiments indicate that the system can localize and scan barcodes of any orientation in the yaw plane, of up to 73.28 degrees in the pitch plane, and of up to 55.5 degrees in the roll plane. The videos have been made public for all interested research communities to replicate our findings or to use them in their own research. The front end Android application is available for free download at Google Play under the title of NutriGlass.
Adapting New Data In Intrusion Detection Systems

Most of the introduced anomaly intrusion detection system (IDS) methods focus on achieving better detection rates and lower false alarm rates. However, when it comes to real-time applications many additional issues come into the picture. One of them is the training datasets that are continuously becoming outdated. It is vital to use an up-to-date dataset while training the system. But the trained system will become insufficient if network behaviors change. As well known, frequent alteration is in the nature of computer networks. On the other hand it is costly to continually collect and label datasets while frequently training the system from scratch and discarding old knowledge is a waste. To overcome this problem, we propose the use of transfer learning which benefits from the previous gained knowledge. The carried out experiments stated that transfer learning helps to utilize previously obtained knowledge, improves the detection rate and reduces the need to recollect the whole dataset.
Study On Insulin Levels and Insulin Resistance in Obese: Findings from a Tertiary Care Hospital, Andhra Pradesh

INTRODUCTION: Overweight and obesity are major risk factors for a number of chronic diseases, including diabetes. The possible mechanism by which obesity increases insulinresistance is large fat stores leading to increased turnover of freefatty acids and triglycerides in the skeletal muscle thereby increasinginsulin resistance. MATERIAL & METHODS: A hospital based observational study was carried out where 125participants were selected by convenient sampling technique in the study duration of 6 months from March to August 2019. Obese individuals with BMI >25 kg/m 2 and previously undiagnosed cases of Type 2 diabetes mellitus were included. Fasting Insulin levels were assessed using ELISA method. Homeostatic model assessment for insulin resistance (HOMA-IR) index was calculated by using the formula: fasting insulin concentration (microunits per millilitre) X fasting glucose concentration (millimolar)/22.5RESULTS: More than half the proportion of the cases (n=65, 52%) belonged to 30-40 years age group. Gender distribution showed a slight female preponderance with 50.4% (n=63) being females and 49.6% males. With regards to severity of obesity, one third of them were severely obese. Mean Homeostatic model assessment for insulin resistance (HOMA-IR) index was found to be 2.96. More than one third (36%, n=45) individuals in the present study had Insulin resistance. CONCLUSIONS: Obesity and Insulin resistance showed a definite significant association with degree of obesity playing an important role. Metabolic characteristics such as fasting plasma glucose, 2 hour glucose levels, fasting insulin levels and triglycerides were significantly more among Insulin resistance group.
Investigating Multifractality of Solar Irradiance Data through Wavelet Based Multifractal Spectral Analysis

It has been already revealed that the daily Solar Irradiance Data during the time period from October, 1984 to October, 2003 obtained by Earth Radiation Budget Satellite (ERBS) exhibits an Anti-persistent trend having multi-periodic phenomena. The solar irradiance time series data being a complex non linear signal in this paper we have tried to detect the irregularity and multifractality in the signal using continuous wavelet transform modulus maxima (WTMM) algorithm. Singularity spectrum of the signal has been obtained to measure the degree of multifractality of the Solar Irradiance signal. The qualitative measure of the degree of multifractality of the Solar Irradiance signal will help us to decide the nature of the signal processing tools that can be used to extract the features of the signal in our future work. This may also give an input to the research work of researchers on the solar physics and geophysics.
B3O: Bayes Empirical Bayes by Bayesian Optimization

There is rapidly growing interest in using Bayesian optimization to tune learning parameters and hyperparameters for approximate inference algorithms that take a long time to run. For example, Spearmint is a popular software package for selecting the optimal number of layers and learning rate in neural networks. But given that there is uncertainty about which hyperparameters give the best predictive performance, and given that fitting a model for each choice of hyperparameters is costly, it is arguably wasteful to “throw away” all but the best result, as per Bayesian optimization. In this paper, I consider an alternative approach that uses all the samples from the Bayesian optimization procedure to average over the uncertainty in model hyperparameters. The resulting approach, Bayes Empirical Bayes by Bayesian Optimization (B3O), predicts held-out data better that Bayesian optimization in two experiments on logistic regression and latent Dirichlet allocation.
Greater Control and Transparency in Personal Data Processing

Although the European General Data Protection Regulation affords data subjects more control over how their personal data is stored and processed, there is a need for technical solutions to support these legal rights. In this position paper we assess the level of control, transparency and compliance offered by three different approaches (i.e., defacto standard, SPECIAL, Solid). We propose a layered decentralised architecture based on combining SPECIAL and Solid. Finally, we introduce our usage control framework, which we use to compare and contrast the level of control and compliance offered by the four different approaches.
A Generalization of VNL-Rings and PP -Rings

Let R be a ring. An element a of R is called a left PP -element if Ra is projective. The ring R is said to be a left almost PP -ring provided that for any element a of R, either a or 1 − a is left PP . We develop, in this paper, left almost PP -rings as a generalization of von Neumann local (VNL) rings and left PP -rings. Some properties of left almost PP -rings are studied and some examples are also constructed.
Performance Evaluation of Neural Classifiers Through Confusion Matrices To Diagnose Skin Conditions

In this paper we have aimed to diagnose skin conditions using Artificial Intelligence (AI) based classifier algorithms and do the performance analyses of those presented algorithms through confusion matrices. These algorithms are being used in a large array of different areas including medicine, and display very distinct characteristics in the sense that they are grouped under different categories such as supervised, unsupervised, statistical, or optimization. The objective of this study is to diagnose skin conditions using seven different well-known and popular as well as emerging Artificial Intelligence based algorithms and to help general practitioners and/or dermatologists develop a careful and supportive approach that leads to a probable diagnosis of skin conditions or diseases. These algorithms we chose as neural classifiers include BackPropagation (BP), Random Forest (RF), Support Vector Machines (SVMs), Linear Vector Quantization (LVQ), Self-Organizing Maps (SOMs), Naïve Bayes, and finally Bayesian Networks. All of these algorithms have been tested and their results of diagnosing skin conditions/diseases by using data set from Dermatology Database have been compared.
A NOTE ON THE ESTIMATION OF DEGREE OF DIFFERENCING IN LONG MEMORY TIME SERIES ANALYSIS

In this paper we investigate the properties of the estimator of degree of differencing the fractional d in long memory time series analysis via consistent spectral density estimation. It is shown that the proposed estimator is more eficient than some of the others in practice. I. Tntrredoctiors. The interest in fractional differencing and its applications to long memory time series modelling have become important to theoretical and applied time series analysis after the pioneering work by [ 5 ] and 161. Since then a large number of authors have reported some interesting and mathematically elegant results on the modelling of long memory processes. This class of models is motivated by (i) unbounded spectral densities at the frequency o = 0, (ii) hyperbolic decay of the autocorrelation function (acf), and (iii) the Hurst effect. When modelling long memory time series, it is convenient to use the following two filters in tandem: (a) transform the original long memory {X,) to a short memory ( q ) via (1.1) = (1 B ) ~ X,, d E(-%, $), where B is the backshift operator such that B ~ X , = X,-, , j 2 0; (b) transform the short memory (TI to white noise { Z , ] via where @(B)= 1-@,B. @,BP and O(B)=l -O,B. O,B4 are stationary autogressive (AR) and invertible moving average (MA) operators, respectively. The resulting process is generated by 224 M. S . P e i r i s and J. R. Court where V = f -B, and is said to constitute a family of fractionally integrated ARMA(p, g ) or ARIMA(p, d, q) processes, d E(-i, 4). This family has a wide variety of applications in almost every area in scientific endeavour (e.g., stream flow, rainfall, temperature, economic). The problem of estimation of parameters has received a considerable attention in the last decade, and among others [4] suggests to estimate d (first) independent of the other parameters using the periodogram of the data near frequency zero. Once the d is confidently estimated from the observed series, the unique whitening filter for a short memory {I. ;) may be obtained following [I]. Several other methods have also been proposed to estimate the degree of differencing (fractional), d, in the literature. However, the aim of this note is to improve the efficiency of estimation of d over [4] in the mean square sense when the estimate is obtained through a lag-window estimator of the spectral density of {X,}. With that view in mind, in the next section we review some of the available methods of estimation of d. 2. Some available methods of estimating d. In this section, we review some existing methods of estimating d. a. Janacek [7] proposes an estimator dm based on the log spectrum. That is
Volcanic SiO2-cristobalite: A natural product of chemical vapor deposition

Abstract Cristobalite is a low-pressure, high-temperature SiO2 polymorph that occurs as a metastable phase in many geologic settings, including as crystals deposited from vapor within the pores of volcanic rocks. Such vapor-phase cristobalite (VPC) has been inferred to result from silica redistribution by acidic volcanic gases but a precise mechanism for its formation has not been established. We address this by investigating the composition and structure of VPC deposited on plagioclase substrates within a rhyolite lava flow, at the micrometer to nanometer scale. The VPC contains impurities of the form [AlO4/Na+]0—coupled substitution of Al3+ charge-balanced by interstitial Na+—which are typical of cristobalite. However, new electron probe microanalysis (EPMA) element maps show individual crystals to have impurity concentrations that systematically decline from crystal cores-to-rims, and atom probe tomography reveals localized segregation of impurities to dislocations. Impurity concentrations are inversely correlated with degrees of crystallinity [observed by electron backscatter diffraction (EBSD), hyperspectral cathodoluminescence, laser Raman, and transmission electron microscopy (TEM)], such that crystal cores are poorly crystalline and rims are highly ordered tetragonal α-cristobalite. The VPC-plagioclase interfaces show evidence that dissolution-reprecipitation reactions between acidic gases and plagioclase crystals yield precursory amorphous SiO2 coatings that are suitable substrates for initial deposition of impure cristobalite. Successive layers of cubic β-cristobalite are deposited with impurity concentrations that decline as Al-bearing gases rapidly become unstable in the vapor cooling within pores. Final cooling to ambient temperature causes a displacive transformation from β→α cristobalite, but with locally expanded unit cells where impurities are abundant. We interpret this mechanism of VPC deposition to be a natural proxy for dopant-modulated Chemical Vapor Deposition, where halogen-rich acidic gases uptake silica, react with plagioclase surfaces to form suitable substrates and then deposit SiO2 as impure cristobalite. Our results have implications for volcanic hazards, as it has been established that the toxicity of crystalline silica is positively correlated with its purity. Furthermore, we note that VPC commonly goes unreported, but has been observed in silicic lavas of virtually all compositions and eruptive settings. We therefore suggest that despite being metastable at Earth’s surface, cristobalite may be the most widely occurring SiO2 polymorph in extrusive volcanic rocks and a useful indicator of gas-solid reaction having occurred in cooling magma bodies.
Comprehensive Infrared Image Edge Detection Algorithm

Edge detection is one of the most powerful image analysis tools for enhancing and detecting edges. Indeed, identifying and localizing edges are a low level task in a variety of applications such as 3-D reconstruction, shape recognition, image compression, enhancement, and restoration. This paper introduces a new algorithm for detecting edges based on color space models. In this RGB image is taken as an input image and transforming the RGB image to color models such as YUV, YCbCr and XYZ. The edges have been detected for each component in color models separately and compared with the original image of that particular model. In order to measure the quality assessment between images, SSIM (Structural Similarity Index Method) and VIF (Visual Information Fidelity) has been calculated. The results have shown that XYZ color model is having high SSIM value and VIF value. In the previous papers, edge detection based on RGB color model has low SSIM and VIF values. So by converting the images into different color models shows a significant improvement in detection of edges.
DNS Advanced Attacks and Analysis

Nowadays DNS is used to load balance, failover, and geographically redirect connections. DNS has become so pervasive it is hard to identify a modern TCP/IP connection that does not use DNS in some way. Unfortunately, due to the reliability built into the fundamental RFC-based design of DNS, most IT professionals don't spend much time worrying about it. If DNS is maliciously attacked — altering the addresses it gives out or taken offline the damage will be enormous. Whether conducted for political motives, financial gain, or just the notoriety of the attacker, the damage from a DNS attack can be devastating for the target. In this research we will review different DNS advanced attacks and analyze them. We will survey some of the most DNS vulnerabilities and ways of DNS attacks protection.
A Comparison Between Measured Maxillopharyngeal Angle On Lateral Cervical Radiograph with Modified Mallampati Classification In Predicting Difficult Larnygoscopy: A Blinded Interventional Study

Background: Existing techniques of predicting difficult laryngoscopy are inadequate requiring evaluation of Maxillopharyngeal Angle (MP-A) on lateral cervical radiograph described. Objectives: This study aimed to compare MP-A test with Modified Mallapati Test (MMT) in predicting their diagnostic values and Area Under Curve of Receiver Operating Characteristic Curve (AUCROCC) of both test. Methods: This is a double blinded interventional study of 93 patients. Each patient’s MMT score was assessed during preoperative assessment and subsequent MP-A test done by obtaining lateral cervical radiograph with the head in neutral position. Laryngeal view was assessed using Cormack-Lehane grade after induction of anesthesia, was used as reference standard to determine the diagnostic values of MMT and MP-A respectively. Results: The MP-A compared to MMT in predicting difficult larngoscopy had higher sensitivity (77.78 vs 44.44) specificity (88.10 vs 67.86) and accuracy (87.10 vs 65.59) with higher Odd Ratio(26.12 vs 1.68). The AUCROCC was significantly higher in MP-A test 0.83(95%CI: 0.67, 0.99) (P = 0.001) vs MMT 0.56(95%CI: 0.36, 0.76) (P = 0.546) with LR+ of 6.53 vs 1.38. Conclusion: The Maxillopharyngeal Angle test was superior in predicting difficult laryngoscopy as compared to Modified Mallampati Test.