Reviewerly/exHarmony huggingface dataset:
Geochemical investigation of gold and chalcophile minerals of Rawayau Area Katsina State, Nigeria

This present study investigated the Gold and Chalcophile mineralisation potentials of the Rawayau area in Northwestern Nigeria. The aim of the investigation is to delineate the likely Gold and Chalcophile mineralisation occurrences in the study area so as to aid further exploration studies that will identify Gold rich targets worth investing resources for detail exploration project before mining. To achieve this, 17 samples (7 rock samples and 10 sediments) were collected and subjected to laboratory analysis at the National Geologic Survey Agency (NGSA) in Kaduna. The geochemical study showed that the Au concentration ranged from below detection level (bdl) to 0.09ppm in the rock samples, while ranging from 0.013ppm to 0.137ppm in the sediments. Ag concentration ranged from 0.37ppm to 0.97ppm with in the rock samples, while ranging from 0.088ppm to 0.229ppm in the sediments, thus showing a higher concentration in both rock and sediments than Au. Significant positive relationship was observed between Ni, Cr, Cu, Mn, Fe, Zn and Co from the correlation analysis. It also revealed a subsurface increment of Gold and Chalcophile concentration of Nickel, Copper and Zinc with higher concentration of Nickel, Copper, Zinc, Manganese, Chromium and Lead in comparison to Gold and Silver. The study concludes that the Rawayau area holds much prospects for Gold and other minerals. It is suggested that further exploratory studies be carried out so as to pave way for the commencement of mining activities in the study area”.
Meta Auxiliary Learning for Low-resource Spoken Language Understanding

Spoken language understanding (SLU) treats automatic speech recognition (ASR) and natural language understanding (NLU) as a unified task and usually suffers from data scarcity. We exploit an ASR and NLU joint training method based on meta auxiliary learning to improve the performance of low-resource SLU task by only taking advantage of abundant manual transcriptions of speech data. One obvious advantage of such method is that it provides a flexible framework to implement a low-resource SLU training task without requiring access to any further semantic annotations. In particular, a NLU model is taken as label generation network to predict intent and slot tags from texts; a multi-task network trains ASR task and SLU task synchronously from speech; and the predictions of label generation network are delivered to the multi-task network as semantic targets. The efficiency of the proposed algorithm is demonstrated with experiments on the public CATSLU dataset, which produces more suitable ASR hypotheses for the downstream NLU task.
Information System Analysis with Delone &amp; Mclean Model Approach on Online Services of Perbanas Institute library

The purpose of this study was to determine the effect of system quality, information quality, service quality on satisfaction, and the effect of satisfaction on net benefits using the DeLone &amp; McLean model. The population in this study were Perbanas Institute students who had used the Online Library Service. The sampling technique was selected using purposive sampling and a sample of 100 people. Data retrieval using primary data with a questionnaire tool and the method used is SEM (Structural Equation Modeling) with the help of the SmartPLS application. The results showed that system quality, information quality and service quality had a positive and significant effect on user satisfaction, then user satisfaction had a positive and significant effect on net benefits. In the intervening, relationship system quality, information quality and service quality have a positive and significant effect on net benefits through user satisfaction.
Analisis Penggunaan Literasi Digital Sebagai Media Pembelajaran Guru di SD Negeri 2 Tambun

The type of research used is qualitative research which aims to describe the implementation of digital literacy as a learning medium by teachers. This research was conducted at the State Elementary School 2 Tambun with the research subjects namely the principal, teachers and students. Data collection techniques used are observation, interviews and documentation and research data analysis techniques using the pre-field stage, the field stage, and the data analysis stage obtained from the field. The results of the study show aspects of teacher training through the principal's instructions and also independently always follow training activities related to digital literacy and from the aspect of applying and implementing digital literacy teachers have implemented it in the learning process at school to students while from understanding they have understood and are able to use literacy. digital as a digital learning media.
Does Chinese Exchanges Affect Bitcoin Price?

China was one of the first nations to implement cryptocurrencies back in 2013.The highest trade volumes have previously been reported on the Chinese exchanges in cryptocurrencies.However, after four years, the China government shut down cryptocurrency exchanges and initial coins offering websites.The problem arises as to whether the Chinese exchange affects the price of Bitcoin in case of prohibition.Therefore, the aim of this research is to analyse the causal link between the Chinese exchange and the Bitcoin prices.The causality between the Bitcoin price and Chinese stock market are examined through the vector autoregressive model (VAR).There is short run causality from Shanghai Composite Index and Shenzhen Composite Index to Bitcoin price.This study found that Bitcoin could be a potential hedge against the Shenzhen Composite Index while the Shanghai Composite Index is merely a factor that affects Bitcoin price in the short run.In contrast to existing studies, this study considers the causality between the Bitcoin price and the Chinese exchanges.
Coverage Properties of a Neural Network Estimator of Finite Population Total in High-Dimensional Space

The problem in nonparametric estimation of finite population total particularly when dealing with high-dimensional datasets is addressed in this paper. The coverage properties of a robust finite population total estimator based on a feedforward backpropagation neural network developed with the help of a superpopulation model are computed, and a comparison with existing model-based estimators that can handle high-dimensional datasets is conducted to evaluate the estimator’s performance using simulated datasets. The results presented in this paper show good performance in terms of bias, MSE, and mean absolute error for the feedforward backpropagation neural network estimator as compared to other identified existing estimators of finite population total in high-dimensional datasets. In this regard, the paper recommends the use of the proposed estimator in estimating population parameters such as population total in the presence of high-dimensional datasets.
Node Similarity-based Search Method for Medical Insurance Heterogeneous Information Network

Among the current medical insurance fraud detection methods, fraudulent nodes that can be detected mainly depend on empirical rules to determine medical insurance fraud patterns or a large amount of fraud data for machine learning. However, since the increasing concealment of fraud, rules and models designed based on empirical rules cannot cope with the rapidly changing fraud patterns. At the same time, the labels of fraudulent samples in medical insurance data are small in magnitude and unevenly distributed, making it challenging to support active mining. In addition, if the classical meta-path or meta-structure is used to model the medical insurance heterogeneous information network, patient information will be lost when connecting multi-order paths such as patient-physician-disease and bring ambiguous information that does not match the actual medical insurance data. In order to solve the above problems, this paper proposes a node similarity-based search method for medical insurance heterogeneous information network. On the one hand, the method uses GraphSAGE to learn the global low-dimensional representation of patient nodes and recalls nodes relevant to the query node as search candidate sets. On the other hand, by defining weighted meta-path and weighted meta-structure, the method solves the problem of ambiguity in the representation of heterogeneous information network. Based on weighted meta-path and weighted meta-structure, new algorithms W-Pathsim and W-Strucsim are proposed to calculate the similarity of nodes in heterogeneous information network. Finally, our method uses a multi-layer perceptron to return the nodes list that is highly similar to the query node in the candidate set to assist in medical insurance review. Experiments show that our method is better than the compared baseline methods.
Sveatoslav and Vsevolod Moscalenco, names that will shine forever on the vault of the scientific universe

The twin brothers, academicians Sveatoslav and Vsevolod Moscalenco, have made a remarkable contribution to contemporary physical science. In the 1960s, Vsevolod Moscalenco laid the foundations of the scientific school of theoretical physics and mathematics, which he later strengthened and made it known in the scientific world; while Sveatoslav Moscalenco predicted, for the first time, the phenomenon of condensation Bose-Einstein, thus opening a new direction in the solid body physics, which developed and branched out with the contribution of researchers from various countries. They have consistently advocated the cause of science, becoming moral landmarks for the scientific community. The author emphasizes the main scientific achievements, captures the moral portrait of the Moscalenco brothers, whom he met and with whom he worked for over six decades.
3d Image Processing Robot

Abstract: For an 3D IMAGE PROCESSING robot to truly succeed, there must be a variety of ways for robot to interpret its surrounding environment, both quickly and accurately. This report focuses on investigating improvements for a sub- problem of environmental perception, namely 3D image processing, referring to localizing and classifying objects of interests in a specified environment. The objects of interests in a process security scan of baggage or to analyse scans of materials to understand their structure. Recently python3 &amp; machine learning programming approaches have made significant progress in the field of 3d object detection. Thus, several techniques have been developed for the real time tasks such as the autonomous robot. However, as images lack some information about depth which is essential for environmental perception for autonomous robot, they struggle a lot to preserve distance between object and robot. So, in this project in order to avoid such delays we have used high quality camera OF 1080p. This Report investigates the performance of both the minicomputers and as well as the cameras being mounted on the robot for 3d analysis which uses the state-of-the-3d image processing models. The end-to-end models of python3 and OpenCV are ultimately performed and experimented various times to perform 3D Detections. The models were trained and evaluated on the recently released data set and the most promising model was able to detect the 3d image of the object.
Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations

With the prevailing use of smartphones and location-based services, vast amounts of trajectory data are collected and used for many applications. When trajectory data are sent to a third-party research institute for analytical applications, the privacy of users would be severely disclosed. For example, the relationship between users will be revealed from the correlation between trajectories. In this paper, we propose a method for releasing trajectory datasets without revealing correlation between trajectories, called RDPT. In RDPT, we first quantify the trajectory correlation and convert the problem of protecting trajectory correlations into reducing the trajectory similarities of users and preserving the utility of the perturbed trajectories. Based on the insight, we model a multi-objective optimization problem and solve the problem with the particle swarm optimization algorithm modified to satisfy differential privacy. Then we generate synthetic trajectories and the correlations between trajectories are reduced. We conduct extensive experiments on three real trajectory datasets. The experimental results show that RDPT achieves almost equivalent data utility to and better privacy than the existing methods.
E-voting System Using Block-Chain

Abstract: This paper presents the E-voting system using block chain, which is a multiplatform interface designed to cast votes which promises secure and transparent elections. The block chain provides a doubly linked hash structure of every vote cast, hence any tempering to it results in a new different structure so we can say that the links are immutable. Every vote cast is counted as a transaction with a unique hash address. A collection of rules known as smart contract is stored on block chain to speedthe transaction. At the end, the total number of transactions are counted which turns out to be the actual results of the elections. This unique combination allows secure and user-friendly experience to the users at the same time.
Card Fraud Detection using Approach of Machine Learning

Abstract: The usage of internet banking and credit cards is growing at an exponential rate. As more people use credit cards, online banking, and debit cards, the probability of becoming a victim of fraud of various kinds also increases. In recent times, there have been a number of instances in which users of credit card companies have, as a result of a lack of understanding, given their card information, personal information, and one-time password to an unidentified fraudulent caller. As a direct consequence of this, fraudulent activity will occur on the account. Fraud is a problem for the same reason that it is tough to track down a con artist who used a phone identity sim or made the call that utilized an internet provider: it is difficult to find them. Therefore, in order to detect fraudulent activity, this research makes use of supervised methodologies and algorithms, and the results are quite accurate. Customers lose trust in an organization when it engages in activities that are fraudulent or illegal, which in turn has a huge negative impact on the organization. Additionally, it has an effect on the total income and turnover of the company. The isolation forest technique is used in this study to classify data sets acquired from professional survey firms in order to detect fraud activities.
Action-points in human driving and in SUMO

When following a vehicle, drivers change their acceleration at so called action-points (AP), and keep it constant in between them. By investigating a large data-set of car-following data, the state- and time-distributions of the APs is analyzed. In the state-space spanned by speed-difference and distance to the lead vehicle, this distribution of APs is mostly proportional to the distribution of all data-points, with small deviations from this. Therefore, the APs are not concentrated around certain thresholds as is claimed by psycho-physical car-following models.Instead, small distances indicate a slightly higher probability of finding an AP than is the case for large distances. A SUMO simulation with SUMO's implementation of the Wiedemann model confirms this view: the AP's of the Wiedemann model follow a completely different distribution than the empirical ones.
Bilangan Terhubung Titik Pelangi Kuat Graf Octa-Chain (OCm)

An Octa-Chain graph (OCm) is a graph formed by modifying the cycle graph C8 by adding an edge connecting the midpoints in C8. The minimum number of colors used to color the vertices in a graph so that every two vertices have a rainbow path is called the rainbow vertex-connected number denoted by rvc (G). While the minimum number of colors used to color the vertices in a graph so that every two vertices are always connected by a rainbow path is called a strong rainbow vertex connected number and is denoted by srvc (G). This study aims to determine the rainbow vertex-connected number (rvc) and the strong rainbow-vertex-connected number (srvc) in the Octa-Chain graph (OCm). The results obtained from this research are the rainbow vertex-connected number rvc (OCm)=2m and the strong rainbow-vertex-connected number srvc (OCm)=2m.
Penerapan Regresi Least Absolute Shrinkage And Selection Operator (LASSO) Untuk Mengidentifikasi Variabel Yang Berpengaruh Terhadap Kejadian Stunting di Indonesia

Linear regression analysis is an analytical method that can be used to analyze data and draw meaningful conclusions about the dependence of one variable on another variable. In linear regression analysis there are several assumptions that must be met, namely normal distribution, there is no correlation between errors. There are several obstacles that cause the assumption to be unfulfilled, for example the occurrence of correlations between independent variables (multicollinearity). The analysis in this study uses the Least Absolute Shrinkage And Selection Operator (LASSO) regression method with the Least Angle Regression (LAR) algorithm because the stunting data in Indonesia has multicollinearity problems among the independent variables used. LASSO which can solve the case of multicollinearity in the regression at the same time it is possible to reduce the regression coefficient from the highly correlated independent variable to exactly zero. The LASSO coefficient obtained uses quadratic so that the LAR algorithm is used which is more efficient in LASSO computing. Based on the analysis that has been carried out, it is concluded that the variables of exclusive breastfeeding (X1), protein consumption (X2), DPT-HB exercise (X5), maternal height (X8) and diarrhea (X9) had an effect on stunting in Indonesia in 2018.
Explaining Contextualized Word Embeddings in Biomedical Research – A Qualitative Investigation

Contextualized word embeddings proved to be highly successful quantitative representations of words that allow to efficiently solve various tasks such as clinical entity normalization in unstructured texts. In this paper, we investigate how the Saussurean sign theory can be used as a qualitative explainable AI method for word embeddings. Our assumption is that the main goal of XAI is to produce confidence and/or trust, which can be gained through quantitative as well as quantitative approaches. One important result is related to the fact that the differential structure of language as explained by Saussure corresponds to the possibility of adding and subtracting word embeddings. On the other hand, these mathematical structures provide insights into the inner workings of natural language.
A Study on Smart Voice Assistants

Smart voice assistants are agents or software agents that have the ability to analyse human speech and respond appropriately with a combined voice. In today's world, there are a plethora of smart assistants available, like Google's Assistant, Amazon's Alexa, and a slew of others. They're all highly popular smart voice assistants that come pre-trained or, as we like to call it, inbuilt in our Android phones and also embedded in home speakers, with a plethora of apps. Its operation is pretty simple: users just ask their smart assistants a query, and the assistants begin processing in accordance with the owner's wishes give him with the greatest possible answer to his inquiries. This research paper discusses the progress of fundamental functionality as well as some of the features of smart voice assistants that are now in use. It also talked about the privacy and security concerns that might arise with voice assistants. In addition, a brief research was conducted at the end of this article to determine the customer's degree of satisfaction with this technology. It has also been observed that youngsters who do not know how to write asking inquiries to smart assistants may influence their minds and aid them in resolving their problems, so strengthening their notion. As a result, the right interaction between the user (human) and the computer has been built in terms of voice driven interfaces, which is directly based on natural language processing technologies in this work. Keywords: Speech Recognition, Voice Assistants, Artificial Intelligence Software Agents, Human Computer Interaction, Advance in Security Purpose
Automated Statistics Extraction of Public Security Events Reported Through Microtexts on Social Networks

Lately, Rio de Janeiro State has been characterized by the occurrence of successive public security events (shootings, assaults, robberies, etc.), causing great insecurity, affecting the daily lives of the population, and worrying public security agencies in the fight against crime. Although the indicators of public security events recently decreased, there is still a feeling of insecurity, while the population uses social networks to notify illegal acts that occurred in their vicinity. Although this collaboration is limited to the crimes that occurred, many published messages are difficult to interpret. Knowledge Discovery is a process of extracting data in an implicit, previously unknown, and useful way that can be applied for different purposes. In this context, Natural Language Processing is a powerful tool that allows the extraction of information from these unstructured data. This work proposes a methodology for automatic knowledge extraction, in the form of statistics related to public security events posted on social networks, particularly the ones occurred in Rio de Janeiro. The main contribution of this work is the proposal of a methodology for the construction of an Information System that allows the collection of statistics of notified public security events. In addition to this methodology, which can also be used in the construction of other Information Systems, this work contributes with a public security event recognition model that has a performance of 95%, and an available dataset that can be used to support other researches, such as: the identification of new behavior patterns, the discovery of hidden knowledge, among other fronts.
A New Method for Recognizing the Input Congestion in Data Envelopment Analysis

One of the most attractive issues in Data Envelopment Analysis (DEA) is the evaluation of congestion of Decision Making Units (DMUs), which occurs if the decreases in certain inputs lead to increases in certain outputs without worsening any other input or output and equivalently, if the increases in certain inputs lead to decreases in certain outputs without improving any other input or output.Most of the existing approaches for detecting the congestion of DMUs in the literature on DEA employ the traditional definition of congestion and consider the situation in which the inputs and outputs can only change proportionally.This study proposes a method for recognizing the congestion of each input of a selected unit under evaluation for the case when the input and output of the respective unit can change disproportionally.The potential application of the proposed method is illustrated based on a dataset related to 16 institutes of basic research of the Chinese Academy of Sciences (CAS) for the year 2010, which was also reported in the literature.