Reviewerly/exHarmony huggingface dataset:
Detection of Multi-Class Website URLs Using Machine Learning Algorithms

Online phishing is one of the Internet's most widespread crime schemes.A common counter measure includes testing URLs against blacklists of established phishing websites, which are typically collected on the basis of manual verification and are inefficient.As the Internet scale expands, automatic URL detection is increasingly necessary to provide timely security for end-users.In this paper, we propose an efficient and versatile malicious URL detection system with a rich collection of features representing the diverse characteristics of phishing websites and their hosting platforms, including features that are difficult to forge.Using the Random Forests algorithm, our program benefits from both high detection capacity and low error levels.Based on our experience, this is the first research to carry out these large-scale websites / URL scanning and classification experiments, taking advantage of the distributed viewing points for the feature set.The results of the experiment show that our program can be used by the blacklist provider to create automated blacklists.
Statistical Capacity, Human Rights and FDI in Sub-Saharan Africa Patterns of FDI Attraction in Sub-Saharan Africa

Foreign Direct Investment (FDI) is commonly perceived as one of the main drivers of technological progress and socio-economic development. At the same time, FDI is often regarded as an instrument of stabilising authoritarian regimes, which disenfranchise the rights of citizens to increase rents generated by foreign firms. Given that both views are accurate, the improvement of human rights and economic development could constitute two conflicting goals. This particularly applies to Sub-Saharan Africa, where a sizeable number of countries are mired in poverty and governed by authoritarian power structures. In evaluating the importance of these soft factors, we examine two important institutional factors of FDI attraction: We address the question of whether human rights violations deter FDI attraction and explore whether FDI depends on the amount of available socio-economic information about the country to be invested in. For the latter, we use a novel variable, namely the Statistical Capacity Figures of the World Bank, which depicts an indicator of effectiveness of the national statistical systems. In order to analyse the relationship between human rights and FDI, we run a regression model covering 41 Sub-Saharan countries covering the years from 2006 to 2015.
The Unethical Practice of Omitting Language in State Science Standards: Denying Students True Democratic Freedom

Climate change due to global warming is impacting the natural environment and humanity. Yet, legislators in states whose economies are in oil production, a major contributor of carbon dioxide emissions through the combustion of fossil fuels, continue to reject the evidence. Legislators go as far to say these findings are “hyperbole” and teaching such ideas would influence young students to a one sided argument. Legislators, through bills and resolutions rejecting such language in state science academic standards, deny students access to equitable science education opportunities. I submit this denial is unethical. It denies students opportunities to analyze and develop solutions to a problem that jeopardizes their future and generations to come. I use the frameworks of Karl Marx and Antonio Gramsci to identify the underlying causes of this problem. Understanding the underlying causes of the problem provides educators with a clearer vision of our responsibilities to act in providing equitable science education for our students.&#x0D;
Research Data Management - Current status and future challenges for German non-university research institutions

This report describes the results of a workshop on research data management (RDM) that took place in June 2019. More than 50 experts from 46 different non-university institutes covering all Leibniz Sections participated. The aim of the workshop was the intra- and transdisciplinary exchange among RDM experts of different institutions and sections within the Leibniz Association on current questions and challenges but also on experiences and activities with respect to RDM. The event was structured in inspiring talks, a World Café to discuss ideas and solutions related to RDM and an exchange of experts following their affiliation to the different Leibniz sections. The workshop revealed that most institutions, independent of scientific fields, face similar overarching problems with respect to RDM, e.g. missing incentives and no awareness of the benefits that would arise from a proper RDM and data sharing. The event also endorsed that the Research Data Working Group of the Leibniz Association (AK Forschungsdaten) is a place for the exchange of all topics around RDM and enables discussions on how to refine RDM at all institutions and in all scientific fields.
Construction of portrait system of listed companies based on big data

With the development of the national economy, China's listed companies have increased year by year. The company's annual report, brokerage research report and even the comments in the stock market will become an important basis for reflecting the operation of the listed company. This paper first summarizes current technology related to corporate portrait in the world, and on the basis of predecessors, innovatively proposes multi-dimensional corporate portrait analysis and builds a corporate image system based on big data platform. The portrait system includes three dimensions: financial aspect, research report aspect and public opinion aspect. The level of technology is combined with popular classification labels, word cloud maps and Bidirectional GRU algorithms, etc. This paper uses the listed company BOE to give an example.
An Image Based Activity Recognition

As the age profile of many societies continues to grow, supporting health, both mental and physical, is of growing vitalness if self-regulating living is to be maintained.Recently, the term of Internet of Things (IOT) has emerged, which highlights the interconnection of all physical and virtually accessible resources with the purpose of collecting and exchanging data.Activity recognition is one of the concepts in ambient assisted living which has been rigorously explored in recent years.The researchers have mainly focused to use sensors to check elderly people.In this paper, we discuss an optical sensor-based monitoring system, for elder people geriatric care and fall detection.In particular, we establish centralized model which works as a decision maker of active recognition.We analyze the photo frame to improve the scalability of our proposed geriatric care system and propose fall detection algorithm to identify the activity of the person.
Studies on the readability and on the detection rate in a Mach–Zehnder interferometer-based implementation for high-rate, long-distance QKD protocols

We study the way that chromatic dispersion affects the visibility and the synchronization on Quantum Key Distribution (QKD) protocols in a widely-used setup based on the use of two fiber-based Mach-Zehnder (MZ) interferometers at transmitter/receiver stations. We identify the necessary conditions for the path length difference between the two arms of the interferometers for achieving the desired visibility given the transmission distance -- where the form of the detector's window can be considered. We also associate the above limitations with the maximum detection rate that can be recorded in our setup, including the quantum non-linearity phenomenon, and to the maximum time window of the detector's gate. Exploiting our results we provide two methods, depending on the clock rate of the setup, to perform chromatic dispersion compensation techniques to the signal for keeping the correct order of the transmitted symbols. At the end, we apply our theoretical outcomes in a more realistic QKD deployment, considering the case of phase-encoding BB84 QKD protocol, which is widely used. Our proposed methods, depending on the transmission distance and on the photon emission rate at transmitter station, can be easily generalized to every fiber-optic QKD protocol, for which the discrimination of each symbol is crucial.
MDEAN: Multi-View Disparity Estimation with an Asymmetric Network

In recent years, disparity estimation of a scene based on deep learning methods has been extensively studied and significant progress has been made. In contrast, a traditional image disparity estimation method requires considerable resources and consumes much time in processes such as stereo matching and 3D reconstruction. At present, most deep learning based disparity estimation methods focus on estimating disparity based on monocular images. Motivated by the results of traditional methods that multi-view methods are more accurate than monocular methods, especially for scenes that are textureless and have thin structures, in this paper, we present MDEAN, a new deep convolutional neural network to estimate disparity using multi-view images with an asymmetric encoder–decoder network structure. First, our method takes an arbitrary number of multi-view images as input. Next, we use these images to produce a set of plane-sweep cost volumes, which are combined to compute a high quality disparity map using an end-to-end asymmetric network. The results show that our method performs better than state-of-the-art methods, in particular, for outdoor scenes with the sky, flat surfaces and buildings.
A Novel Approach for Facial Attendance:AttendXNet

Recent advancements in the area of facial recognition and verification introduced thepossibility of facial attendance for various use cases. In this paper we present a system namedas AttendXNet. Our method uses the ResNet and Multi-layer feed forward network to achievethe state of art results. Extensive analysis of various deep learning and machine learningtechniques is described. Face anti-spoofing is a major challenge in facial attendance.Extended-MobileNet is used to resolve the same issue. We also introduced the end to endpipeline to implement an attendance system for various use cases.
uravu: Making Bayesian modelling easy(er)

uravu offers an easy to use interface for data analysis using Bayesian modelling in the Python programming language, aiming to make Bayesian modelling as easy to use as the scipy.optimize.curve_fit()method.This software acts to lower the barrier of entry to the use of packages such as:
Facial Image Inpainting with Variational Autoencoder

This paper proposed a learning-based approach to reveal diversity possible appearances under the missing area of an occluded unseen image. In general, there are a lot of possible facial appearances for the missing area; for example, a male with a scarf, it is difficult to predict he has a beard in the covered area or not? In this paper, we propose a novel method for facial image inpainting, which generates the missing facial appearance by conditioning on the observable appearance. Given a trained standard Variational Autoencoder (VAE) for un-occluded face generation. To be specified, we search for the possible set of VAE coding vector for the current occluded input image, and the predicted coding should be robust to the missing area. The possible facial appearance set is then recovered through the decoder of VAE model. Experiments show that our method successfully predicts recovered results in large missing regions; these results are diverse, and all are reasonable to be consistent with the observable facial area, i.e., both the facial geometry and the personal characteristics are preserved.
Segmentation of Fuzzy Enhanced Mammogram Mass Images by using K-Mean Clustering and Region Growing

Providing intention to encourage radiologist's appraisal for distinguishing proof or order of mammogram images, different methods were suggested by specialists since past two decades.By means of this technical paper, we propose segmentation on advanced mammogram imaging with k-means clustering and locale developing systems tending to support specialists or radiologists to figure out cancerous areas with computer-aided techniques.The suggested task is further classified within two stages: Applied/implemented preprocessing, at primary stage.With the pre-processing stage, we carried a median filter to expel undesirable salt and pepper clamor.Further, we apply fuzzy intensification operator (INT) to upgrade the distinction of intake images.During subsequent stage, improved fuzzy imaging conduces as input for k-mean clustering.Secondly, the locale developing technique is employed with previously generated clustered imagery to partition mammogram into homogeneous areas indicated through force from pixels.With the end goal of the experiment, we utilized the smaller than normal MAIS dataset.The experiment's end result shows that proposed strategy accomplishes higher precision.
Second Language Acquisition of the Professional Japanese Tour Guides as the Core of Japanese Teaching Development for Tourism Students

In line with the significance of appropriate Japanese learning methods and materials for tourism students, this study investigates how Japanese tour guides in Indonesia learn Japanese. This study aimed to find out some factors which influenced the process of Japanese tour guides’ second language acquisition and the integration of those factors into Japanese teaching. This study was a qualitative study with ethnography method approach. Interviews and observation were employed in this study as the research instruments in which 12 Japanese tour guides became the research objects. The findings showed that two main factors influenced the process of Japanese tour guides’ second language acquisition, namely intrinsic factors and extrinsic factors. Intrinsic factors were the factors which emerged within the tour guides themselves that affected the success of the language acquisition such as age, motivations, personality, aptitudes and the attitudes towards the Japanese language. Meanwhile, the extrinsic factors encompassed learning styles and teaching method that determined the success of language acquisition. Furthermore, the integration of those factors into the Japanese teaching generated some focuses on the teaching process, which were speaking fluently and interacting easily. Speaking fluently put more emphasis on the fluency rather than grammar mastery, while interacting easily was related to the implementation of intercultural language teaching.
PENDUGAAN MODEL PERAMALAN HARGA EKSPOR KOPI INDONESIA

ABSTRACT&#x0D; This study aims to examine the best forecasting model for the export price of Indonesian coffee. The data used in this study are monthly data on coffee prices from January 2012 to September 2019. Three price forecasting models used are moving average, single exponential smoothing and trend analysis are applied to determine the best model based on the lowest MAPE, MAD, and MSE values. The results showed the best model for forecasting the export price of coffee is the moving average (MA1) model because it has the smallest MAPE, MAD and MSE values ​​compared to other models.&#x0D; Keywords: Price, Coffee, Forecasting, Export
The effect of raters fatigue on scoring EFL writing tasks

Given the importance of testing, in general, and scoring writing tasks in particular, the negative effect of fatigue on human raters is important to investigate. This study aimed to (1) explore the relationship between fatigue and scoring composition tasks written by upper-intermediate EFL learners; and (2) to investigate the discrepancy of the frequency of comments among EFL raters while scoring composition tasks. Four raters were selected, and each given 28 composition tasks to score and comment on. The data were analyzed through SPSS software by running ANOVA, Pearson correlation coefficient, and post-hoc tests. Results suggested that the scores assigned to the first 16 tasks were significantly lower than those assigned to the last 12 tasks and that the last four tasks were scored highest. Based on the results obtained from the questionnaire, the observed diversity is argued to be rooted in raters’ fatigue and result in test bias. Furthermore, findings indicated that the frequency of comments given by the raters on the first 12 essays was significantly higher than those on the last 16 essays (the highest and the lowest frequency of comments were observed in the first four, and the last four scored essays, respectively).
Distance and Speed Estimation of Moving Object using Video Processing

This paper aims to describe the comprehensive approach in order to localize target of any moving object or vehicles in video under various environmental conditions in each application basis. The fundamental step for automated video analysis, which is based on object detected in many vision application. Using Centroid value of an moving vehicle, distance travelled by the vehicle is calculate this approach is implemented in OPENCV(Python) by using video processing functions. The accuracy of the system proposed in the speed measurement is nearly 90 percent comparable to the actual speed of the moving vehicles, as the error rate is very low.
Computer aided diagnosis for breast cancer based on the gabor filter technique

The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.
Gender classification using custom convolutional neural networks architecture

Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed Convolutional Neural Network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-of-the-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively.
The Extent to which Autistic Student’s Special Education Centers Use Computer Software

The study aimed to identify the extent to which autism student's special education centers use computer software. The study applied descriptive approach, and the researcher has developed a questionnaire consisted of three dimensions and (27) statements, while the sample group covered (106) autism educators chosen randomly from Amman City in the country of Jordan. The results showed the easiness of accessing the services of the centers, and the quick responses in the center were at medium level, while confidentiality and credibility in obtaining information in the center were at high level. Also, there were no statistical significance difference in the extent to which autism student’s special education centers use computer software according to gender, qualification, and experience, while the results also show that there are no statistical significants in the level of access to services in the centers.
Effects of Health-Related Knowledge and Aerobic Exercise on Lower Secondary School Students' Obesity Awareness and Physical Activity Levels

The objective of this study was to examine the effects of health-related knowledge and aerobic exercise on lower secondary school students' obesity awareness and physical activity levels. A total of 50 obese and 20 normal-weight female students, ranging in age from 12-14 (mean 12.10±1.7), participated in the study. Obesity awareness was determined using the Obesity Awareness Scale, which also incorporates awareness of nutrition and physical activity. Physical activity levels were determined using a pedometer. The participants were divided into one obese experimental group and 2 control groups, obese and normal weight. The obese experimental group received instruction in health and nutrition and followed an aerobic exercise program, while the control groups received no such instruction and did not participate in an exercise program. The health education program was conducted once a week, while the aerobic exercise program was performed three times a week, with each session lasting 45-60 minutes. At the conclusion of the study, the obesity awareness, nutrition awareness, and physical activity awareness scores of the students in the obese experimental group increased significantly compared to those of the obese control and normal control groups. Similarly, the physical activity levels of the students in the obese control group increased with respect to those of the obese and normal-weight control groups. These findings suggest that health-related knowledge and aerobic exercise were effective in increasing obesity awareness and the physical activity levels of lower secondary school students.
Traffic Control System and Technologies: A Survey

The rapid increase in vehicles and large time delays between traffic lights have been the major reason behind the cost of congestion.To overcome this problem, several techniques and methods have been introduced throughout the years which are trying to make traffic control systems dynamic.The conventional traffic control systems work on fixed timings allocated to each side of the junction which cannot be altered, In smart traffic control systems, these timings are controlled according to traffic density.The purpose of this study is to investigate whether these methods are feasible and if we can find any correlations between them.Methods discussed in this paper include the use of IR sensors, RFID tags, Thermal cameras and Reinforcement learning.