Reviewerly/exHarmony huggingface dataset:
Iterative Voting under Uncertainty for Group Recommender Systems (Research Abstract)

Group Recommendation Systems (GRS's) assist groups when trying to reach a joint decision. I use probabilistic data and apply voting theory to GRS’s in order to minimize user interaction and output an approximate or definite “winner item
Implementing Machine Learning Algorithms to Predict Donor Status: Preliminary Work with Data from an Institution of Higher Learning

Identifying potential donors allows institutions of higher learning to conduct more effective fundraising campaigns. Machine learning classification algorithms can be useful in building models to predict donor status. However, when data contains imbalanced classes, like the data we used for this project, models tend to over-index the majority class, which was non-donors in this case. These results have significant implications for institutions in that they may not pursue entities that may, in fact, become donors. In order to improve the usefulness of our model, we used a resampling technique called random undersampling (RUS) to balance the data and also the area under the receiver operating characteristic curve (AUC-ROC) metric to evaluate the performance. Our final model improved its predictive power from 67% to 76%. Institutions of higher learning can use this machine learning model to more efficiently target the pool of potential donors, saving money and time. Future research will focus on improving the predictive accuracy of our model by exploring other data manipulation techniques that minimize the effect of imbalanced data, changing thresholds for classification algorithms, and using genetic programming and feature engineering.
Capturing EEG Spectral Microstates Through K Means Clustering

In this report, I propose an analytical approach based on k means clustering to identify functionally relevant spectral microstates in EEG data. As a proof of principle, the analysis is applied to an EEG data set in which participants performed a continuous arithmetic task. In order to assess the functional relevance of the identified ‘spectral microstates’, their occurrence are compared to inter-individual differences in arithmetic task performance.
Automated Build with Phing

If version control is one side of the coin, then automated build is the other. Version control allows multiple developers to work collaboratively on a single project. With many coders each deploying a project in their own space, automated build soon becomes essential. One developer may have her web-facing directory in /usr/local/apache/htdocs; another might use /home/bibble/public_html. Developers may use different database passwords, library directories, or mail mechanisms. A flexible codebase might easily accommodate all of these differences, but the effort of changing settings and manually copying directories around your file system to get things working would soon become tiresome—especially if you need to install code in progress several times a day (or several times an hour).
Perancangan dan Pembuatan Sistem Informasi Apotek di Puskesmas Banjarsengon

Banjarsengon public health center is one of the health agencies that deal with patient care and treatment. The pharmacy section of the Banjarsengon Health Center in carrying out registration, recording medical records, drug data collection and managing reports is still done manually. In addition, data collection errors are often encountered which can lead to inaccurate reporting data. The purpose of this research is to solve problems in drug data collection by designing a pharmacy information system at the Banjarsengon health center. The making of pharmacy information system application was using Microsoft Visual Studio 2019. System development was using waterfall method. Data collections consist of observation, interviews and documentation. The result of black box testing showed that this pharmacy information system can operate properly. The advantages are the pharmacy information system been integrated with the operation of pharmacists and management that can reduce the possibility of errors that affect the service to patients, improve the efficiency of the drug recording system quickly and accurately, facilitate pharmacists in recording, reduce and add drug stock automatically and also print reports in pdf form.
Analyze K-Value Selected Method of K-Means Clustering Algorithm to Clustering Province Based on Disease Case

Disease cases throughout Indonesia has increased as seen from the Indeks Pembangunan Masyarakat (IPKM). Globalization has the effect of increasing human mobility across provinces, thus accelerating the process of spreading epidemics that could pose a threat for Indonesia. The speed of action from government is needed to reducing the level if outbreaks of the disease. For this reason, accuracy from the government is needed to solving this problem. The data were taken from data disease cases in 2015 which consisted of 34 provinces in Indonesia based on the Central Statistics Agency in Indonesian. In K-Means clustering, determining of K-value is needed because it affects in convergence results. To solve this problem, this research analyzes three methods of K-Value, there are Silhouette, Elbow, and Gap Statistics Methods.The result of testing three methods of determining K-value obtained execution times on Silhouette 13.09s, Elbow 14.76s, and Gap Statistics 20.28s. So, choosing Silhouette method produces 2 optimal clusters, there are low cluster level (C1) and high cluster level (C2). The correlation matrix to understand the relationship between each disease is performed and a value of 0.88 is obtained there is the strong linear correlation between Pneumonia and Pulmonary TB. Then, modeling the relationship between these two variables by fitting linear equations. The results of C1 cluster based on disease cases were obtained 32 provinces and for C2 cluster were 2 provinces there areWest Java and East Java. Based on the results of the clustering can be input to the Indonesian government to tackle disease cases in all provinces in Indonesia
Apakah Metode Project Based Learning Dapat Meningkatkan Keterampilan Menulis Siswa di Masa Pandemi Covid-19?

Student’s writing skill is an important aspect of linguistic ability, yet many students develop a poor interest in writing skill. The Classroom Action Research is based on the low ability of student’s writing skills. For that reason through the method of project based learning, it is expected that student’s writing skills should increase. It was conducted at SMP Negeri 4 Manggar, during the covid-19 pandemic. This research used three cycles which each cycle consisting of two meetings with four stages (design, implementation, observation, and reflection). The data obtained were formative test results, student activeness observation sheets, teaching and learning activities and questionnaires. The results showed that student learning rate of each cycle had increased. The percentage of classical completeness in first cycle was 53%, second cycle was 73%, and third cycle was 93%. The student activeness in the teaching and learning process in first cycle was 65%, second cycle was 83%, and third cycle was 92%. Overall it can be concluded that the Project Based Learning method can improve the writing skills ability of class VIII-A1 students of SMP Negeri 4 Manggar.
Tracking and Detection of Vehicles using Locality Sensitive Histogram (LSH) Feature Extraction

Detection and tracking has become a vital chore in most of the computer vision applications. It analyzes the behavior of the object and detects when it appears in other frames. In this paper, a locality sensitive histogram (LSH) algorithm along with SVM is used to detect and track the objects. Locality Sensitive Histogram is used for feature extraction and detection. It is computed at each pixel location, by adding a floating-point value to bin, which is its unique nature. The extracted features are subjected to Linear SVM classifier and then the object is tracked by eliminating false positives. This proposed method precisely tracks and detects the object well with different challenges. Experimental results demonstrate the performance of the proposed algorithm with an accuracy of 89% considering several challenging factors. Evaluation of various other algorithms using different performance parameters is also tabulated in the diagram and shows that the proposed method is topmost performer in tracking the objects. This method can be utilized to track different objects of different scale and track efficiently.
Bi-Crypto: An Efficient System with Enhanced Security

A convenient two factor (2f) authentication is used in smart card password verification. Thus, the two factors are “dynamic ID-based” or “anonymous”. To preserve user privacy, a tamper resistant security feature used in smart cards. Reverse engineering and power analysis techniques were used to reveal some sensitive information from the smart card memory. The smart card verification is securely implemented in memory than in the database that can be easily attacked by any person. A day to day application such as e-banking, e-health and e-governance maintains password tables on server. During login process user identity is transmitted as clear over public networks. Various non-tamper resistant schemes on OTP put forward but claim to be ambitious in design process. Truly a 2f scheme can make sure that the user whoever possess a valid OTP and password can be authorized by the server.
Simultaneous Monocular Visual Odometry and Depth Reconstruction with Scale Recovery

In this paper, we propose a deep neural networkthat can estimate camera poses and reconstruct thefull resolution depths of the environment simultaneously usingonly monocular consecutive images. In contrast to traditionalmonocular visual odometry methods, which cannot estimatescaled depths, we here demonstrate the recovery of the scaleinformation using a sparse depth image as a supervision signalin the training step. In addition, based on the scaled depth,the relative poses between consecutive images can be estimatedusing the proposed deep neural network. Another novelty liesin the deployment of view synthesis, which can synthesize anew image of the scene from a different view (camera pose)given an input image. The view synthesis is the core techniqueused for constructing a loss function for the proposed neuralnetwork, which requires the knowledge of the predicted depthsand relative poses, such that the proposed method couples thevisual odometry and depth prediction together. In this way,both the estimated poses and the predicted depths from theneural network are scaled using the sparse depth image as thesupervision signal during training. The experimental results onthe KITTI dataset show competitive performance of our methodto handle challenging environments.&lt;br&gt;
High Fidelity Elastic Green's Functions for Subduction Zone Models Consistent With the Global Standard Geodetic Reference System

Abstract Green's functions (GFs) for elastic deformation due to unit slip on the fault plane comprise an essential tool for estimating earthquake rupture and underground preparation processes. These estimation results are often applied to generate important information for public such as seismic and tsunami hazard assessments. So, it is important to minimize the distortion of the estimation results on the numerical models used for calculating GFs to guarantee assessment reliability. For this purpose, we here calculated GFs based on a numerical model that is of high delity to obtain realistic topography and subsurface structural models of the Earth. We targeted two well-known subduction zones in Japan, the Nankai Trough and the Japan Trench. For these subduction zones, databases for realistic topography and subsurface structural models of the Earth are available in the \Japan integrated velocity structure model version 1", which was proposed for earthquake hazard assessments conducted by the Japanese government.Furthermore, in order to eliminate inconsistencies in data processing of the calculated and observed response, we used the same coordinate systems for processing GFs as those adopted widely to process space geodetic observation data for surface displacements. The ellipsoidal shape of the Earth, which is often approximated with a projected plane or a spherical shape, was also incorporated by faithfully following the denitions of the coordinate systems in Geodetic Reference System 1980, which is the global standard for space geodesy. To calculate elastic GFs based on such high delity subduction zone databases with the ellipsoidal shape of the Earth, we introduced the nite element (FE) method. In the FE meshes, the resolution of the topography and subsurface structure is the same as that of the original databases. Recent development of the state-of-the-art computation techniques for the rapid calculation of crustal deformation using large-scale FE models allows for GF calculation based on such a high delity model. However, it is generally not easy to perform such calculations. Thus, we released a library for the GFs calculated in this study to the geoscience community on a web server, aiming to contribute more reliable seismic and tsunami hazard assessment.
note on generalisations of the concordance index for survival data

Concordance index (c-index) was adapted to survival data by Harrell (1982). In its basic form, the index depends on censoring, however the issue can be effectively dealt with. More importantly, Harrell's c-index cannot be used with time-varying effects and/or time-dependent covariates, and several generalisations were proposed. We look at some of them, explore their differences, point to a basic difference between these generalisations, and strongly favour one type of generalisation.
A Quality Analysis Of Low Back Pain Videos On Youtube

Abstract The authors have requested that this preprint be removed from Research Square.
Guinea

The fourth and last technical assistance (TA) mission for the benefit of Guinea, under the project on improving external sector statistics (ESS) in 17 Francophone countries of West and Central Africa, funded by the Japanese government and administered by the IMF, took place in Conakry during August 26–30, 2019. The mission was hosted by the Central Bank of the Republic of Guinea (BCRG), which is the institution responsible for compiling the ESS. The main points addressed by the mission were to support (i) the process of participating in the coordinated direct investment survey (CDIS), (ii) the detailed technical work for improving the current and financial accounts, and (iii) the implementation of recommendations from previous missions.
Design and Implementation of e-Shopping System Using QR Code and Smart Mobile Phone

This paper presents a system that is used in one of the most important fields in our daily life, shopping and entertainment. The system exploits a mobile application, Quick Response (QR) code technique and network system. The QR tag is attached to each item in the market, while the mobile application can be installed in the Smart Phone. The smart phone is used to scan the attached QR tag and send the result to a network system and the network system will process the received data. A brief information will be brought from the local server database. However, if the customer is interested in more details, the network system brings these details from the main server and applies them on the smart phone screen. When the customer decides to buy the item, s/he can add it to the shopping list, then the system updates the bill, sends it back to the customer and to the cashier at the same time. The items will be prepared at the checkout point after payment. This system was designed, implemented, and tested practically so that the performance of that system is achieved for two main points: quick attainment of information and network congestion avoidance.
Review of the PV Electricity Production Estimate under the Effect of Climatic Disturbances and Sunspots by Using Deep-Learning Tools

The mathematical models used in the estimation of GHI on the Earth's surface are inconvenient because they always assume that the sky clarity index is constant. Hence, these models are often confronted with long-period empirical ground measurements that may exceeds 11 years. The impact of cloud cover on an electric power generation site is a very critical parameter for installing a solar power plant and evaluating its productivity. The state of knowledge about the sun influence, the greenhouse effect on climate change, and cloud occurrence can’t be described in a mathematical or numerical model.Therefore, in this paper, we propose the use of Deep-Learning techniques to predict any site’s productivity by analyzing its potential insolation. We also suggest the analysis of the ground and satellite- based measurements collected over 30 years. We propose the estimation of future climate change affecting cloud cover.
Forest Fire Prediction using Machine Learning Models based on DC, Wind and RH

The paper points out forest fire prediction using machine learning models on the basis of viz. DC, Wind, RH out of the several machine learning classifier algorithms, It is relevant that random forest algorithm generates optimum accuracy(99.61%).
The Effect of Product Experience Through Virtual Reality on Product Evaluation

IntroductionNowadays, virtual reality (VR) is widely used, and people have become more accustomed to it.VR has been studied for a long time, and many test products have been developed.However, its commercialization has been limited due to the lack of content and high purchasing prices.Nevertheless, start-up andThe Effect of Product Experience Through Virtual Reality on Product Evaluation
A Dynamic &amp; Combined Framework for Predicting Phishing Attack

With the fast improvement of web applications, and with the solace gave by these web applications, web clients’ use this advantages, as it were, that they make practically the entirety of their everyday exercises, for example, newspaper reading, shopping, bill payment, ticket booking and entertainment with the assistance of the web. This wonder powers the clients of the web to get associated with the web for a drawn-out time and consequently it builds the odds of the clients to get trapped in the snare of phishing – an assault made by programmers to take delicate data by enticing the clients with rewarding offers at first and afterward diverting them to a fake website(which the client may not presume) where they can mislead the client by requesting that they present their credentials(usually clients present their credentials without realizing that these are phony offers made with a sole aim of taking delicate data ). Notwithstanding the caution and mindfulness given by the web community right now, and more phishing craftsman prevail in their attack. Likewise, these phishing craftsmen create novel attacks, for example, tab grabbing, site mirroring and so forth that draws in increasingly more web clients to be trapped in the snare of phishing. Anyway, numerous tools and methodologies have been created to forestall phishing and to caution clients orally and outwardly. Still, the achievement paces of the phishing attack stay high and furthermore the methodologies identified with phishing detection endures high false negative and false positive proportion. In this proposed system numerous mechanisms used to prevent phishing have been analyzed and a proficient system has been proposed to forestall phishing.
The Petrogenetic Emplacement of Imori Complex, a Mesozoic Alkaline Granite North Central Nigeria: As a Case Study for Emplacement of Some Isolated Minor Intrusions within the Nigerian Ring-Complexes and Elsewhere

As a Case Study for Emplacement of Some Isolated Minor Intrusions within the Nigerian Ring-Complexes and Elsewhere IntroductionThe ring-complexes of Nigeria and Niger Republics constitute a mega-province, which is one of the best studied examples of mid-plate magmatism in the world.The Imori Complex is part of the cycles of intrusive rocks of the Mesozoic alkaline complexes in the Nigerian province.It is one of the ring-complexes that form the western edge of the Jos Plateau (Bowden and Kinnaird, 1984).The structural aspects of the Imori Complex differ from those of a typical ring-complex as described from other ring-complexes in the province (Jacobson et al., 1958, MacLeod et al., 1971, Bowden and Turner 1974and Ike, 1983).The Imori Complex lacks ring-dyke, cauldron subsidence structures and it is a minor intrusion.The Complex lies south of the Kudaru Complex (Fig. 1) covering a surface area of about 30km2and it is oval in shape.The Complex has been incised by streams and consequently development of shallow gorges on the southern part.The Complex consists of five rock units namely; the basement rock, rhyolite, biotite granite, arfvedsonite granite and a mionrdoleritic dyke (Fig. 2).
Gesture controlled Mouse Pointer for Rehabilitation tools to aid Paralyzed patients

The aim of the paper deals with controlling the mouse pointer and carrying out the functions of a trackpad with the help of hand gestures.Moving our hand would enable to change tabs, double click an item, drag objects and carry out other simple tasks.Most of the prevailing work uses, an external camera for better image capture and detection which increases the battery power and additional hardware cost.The Deep Neural Network based methodology used in this paper would greatly help partially paralyzed or unwell people due to its increased sensitivity and precision in capturing the gestures even in subdued Lighting.
Taylor expansion based stereo image reversible data hiding

Abstract The reversible data hiding allows original image to be completely recovered from the stego image when the secret data has been extracted, it is has drawn a lot of attentions from researchers. In this paper, a novel Taylor Expansion (TE) based stereo image reversible data hiding method is presented. Since the prediction accuracy is essential to the data hiding performance, a novel TE based predictor using correlations of two views of the stereo image is proposed. TE can fully exploit strong relationships between matched pixels in the stereo image so that the accuracy of the prediction can be improved. Then, histogram shifting is utilized to embed data to decrease distortion of stereo images, and multi-level hiding can increase embedding capacity. Experimental results show that the proposed method is superior to some existing data hiding methods considering embedding capacity and the quality of the stego stereo images.
Mask Region Based Convolution Neural Network (R-CNN) based Smart System for Anomaly Detection in Pedestrian Walkways

Recently, anomaly detection becomes a fascinating research application which usually raises an alarm in scenarios where the event varies from the actual event. Anomaly detection can be treated as a coarse-level video understanding problem that determines the existence of anomalies from habitual events. This paper introduces a new anomaly detection model by the use of Mask region based convolution neural network (R-CNN). The application of mask in the detection process helps to precisely identify the presence of anomalies in the scene. The effectiveness of the Mask R-CNN based anomaly detection model is validated against UCSD anomaly detection dataset. An extensive quantitative and experimental outcome evidently shows the superior nature of the presented model over the compared methods in a significant manner.
Web Locators

Now that you have your framework installed, and the first test case is successfully running, let’s proceed to the next logical step. In this chapter, you learn how to locate the elements of a web page to interact with them. You also learn different locator strategies and how WebdriverIO uniquely uses them. The chapter discusses the following.