Reviewerly/exHarmony huggingface dataset:
Identification of geochemical anomalies associated with Sn-W mineralization in the Dong Van region, North-Eastern Vietnam, using statistical methods

Sn-W multimetal mineralization in the Dong Van region, North-Eastern Vietnam was studied using statistical and multivariate approaches based on 890 samples of stream bottom sediments collected for assaying for 27 elements. The findings of frequency analysis demonstrated that Pb, As, Bi, Li, Sn, W, Ta, Ce, Ag, Sb, and Be have close ties with multimetal ores, implying that these elements can be used as prospecting indicators for multimetal mineralization. In addition, correlation matrix and dendrogram studies were also applied to subdivide the elements in the stream bottom sediment samples assays into two groups: associated with multimetal mineralization (Be-Sn-W-Bi, and, to a lesser extent, Li-Pb sub-groups) and not associated with the mineralization: (As-Cd-Sc-Cr-Ce-La, Co-Ni-V, and Ga-Ge-Ba sub-groups). Sn and W were found to be the best indicator elements for the mineralization, according to the findings of geochemical modeling and location of their anomalies in the region. Furthermore, extensive Sn and W anomalies were identified in the Dong Van region (using threshold values (mean ± 3 STD), providing the most important indications for multimetal mineralization prospecting in the region. The studies also suggest genetic ties between the region’s multimetal mineralization and the northwest-southeast fault system and concealed granitoid blocks. Finally, the performed statistical analyses (with the use of threshold values) of stream bottom sediments assays allowed revealing indicator elements and their geochemical anomalies and using them as an effective tool in further prospecting and exploration for multimetal mineralization in the region.
Multi-Faceted Decision Making Using Multiple Reinforcement Learning to Reducing Wasteful Actions

Reinforcement learning can lead to autonomous behavior depending on the environment. However, in complex and high-dimensional environments, such as real environments, a large number of trials are required for learning. In this paper, we propose a solution for the learning problem using local learning to select an action based on the surrounding environmental information. Simulation experiments were conducted using maze problems, pitfall problems, and environments with random agents. The actions that did not contribute to task accomplishment were compared between the proposed method and ordinary reinforcement learning method.
Credit Card Anti-Fraud Prediction Based on Ensemble earning

In recent years, due to the development of Internet technology in the financial industry, cardless and cashless payments have become increasingly popular. At present, people only need to bind their cards to their cell phones to scan the code for payment. In the meanwhile, as the use of credit cards is vigorously promoted nationwide, younger generations relied more on the credit card debt for consumption. However, although these developments have brought a lot of convenience to people's travel and their life, the virtual nature of the Internet and the "Enjoy First, Pay Later" feature of credit cards have led to a significant increase in potential credit risk in the entire transaction, triggering more and more new credit card fraud incidents. While the overall financial environment in China is relatively healthy, the fact that fraudulent behaviors such as credit card overdrafts, counterfeit cards, and credit card frauds are all personal conduct, making it difficult to detect and prevent these individuals from acting illegally in a timely manner. Although fraud represents a small percentage of the overall transaction size, its bad debt losses to merchants and banks can be significant. In order to make a timely prediction of fraud, there are already many single-model machine learning methods such as decision trees and logistic regression. However, the generalization ability of these models is not good enough in the face of complex user behavior features. In addition, since the probability of fraudulent behavior is very small, resulting in few samples that can be trained, even if a model has a high accuracy rate, there is no guarantee that it can accurately predict fraudulent behavior. Therefore, this paper proposes the Adaboost method based on ensemble learning and uses SMOTE oversampling based on a few classes of fraudulent samples of user features to solve the above existing problems.
Current Situation Analysis and Development Path of Sports Ground Operation under National Fitness Service System

Using the method of literature, field investigation, etc., to effectively interpret the operation of sports facilities under the national fitness service system, based on the current situation of sports site construction and management, combined with the connotation and extension of the national fitness service system, to build an ideal national fitness service system in southern Jiangsu. The results show that the sports field construction and management system reform in the south of Jiangsu province has made qualitative progress, the management mode is gradually diversified and the management situation has been improved to a certain extent. The number of sports venues and facilities per capita exceeded the national planning standards; The type of site facilities is single, and the matching of supply and demand needs to be improved; The level of sports management organization is not perfect, and the two-way system urgently needs to be run in. Suggestions are put forward: standardize the elaboration of standards, urban and rural same frequency high quality fitness; Perfect policy incentive mechanism, improve management professional level; Promoting regional exchanges and cooperation and establishing mechanisms for joint contribution and sharing; Multi-element digital development, enabling a new form of national fitness.
Cyber attack awareness and prevention in network security

&lt;span&gt;This article aims to provide an overview of cyber attack awareness and prevention in network security. This article discussed the different types of cyber attacks, current trends of cyber attacks, how to prevent cyber attacks and uum students' awareness of cyber attacks. First, we will go over the different types of cyber attack, current trend, impact of cyber attack and the prevention. The approach entailed comparing and observing the outcomes of 13 different papers. The survey's findings would demonstrate the results obtained after analyzing the data collection which are the questionnaire filled out by respondents after watching the cyber attack awareness video to improve awareness of students through the cyber attack. Depending on the outcome of this survey, we will have a better understanding of current students' knowledge and awareness of cyber attacks, allowing us to improve students' understanding of cyber threats and the necessity of cyber security.&lt;/span&gt;
Serious Game model for dyslexic children

Dyslexia is a hidden neurological disorder in which children suffering with weak phonological awareness and that affects his/her poor academic records. A dyslexic child is quit intellectual and interestingly good in different fields of life but weak in language-based learning. They feel difficult to identify similar face letters which led them to misspell and wrong pronunciation. Serious gaming and cognitive learning through assistive applications emerged in the 21st century. Numerous serious gaming applications are developed to combat the issue for effective cognitive learning for dyslexic children and the degree of success is vary due to the lack of standard postulates to design and develop a novel system for such young hearts with pure entertainment that cure their cognitive weakness. It‟s quite difficult to claim that a serious game meets the standard learning pedagogy for a special group of users. This research paper proposed a serious gaming model for dyslexic children to pursue the issue of standard rules for serious gaming applications for developers to achieve effective learning outcomes.
Do You Hear My Point?

The field of experimental electronic music for space, or spatial music, has many barriers that prohibit and inhibit individuals from engaging with it. Not only can one's physical ability restrict their engagement with these works, but so too can their economic capacity as well as the compositional and loudspeaker systems that appear inherent to the field itself. While these may be viewed as separate issues, they are very much interconnected. The problems within this field stem from institutionalized practice, which has elevated particular features of musical works above accessibility, resulting in a field that requires foundational change in order to involve people who are currently excluded. This chapter will review the barriers to engagement with the field of spatial music and look at the author's attempts at breaking them down. Alongside creating their own artworks, the author has established artistic frameworks intended to be used by artists when creating works in order to remove or dismantle these obstacles. The author's arguments for foundational change of the field rely heavily on d/Deaf and hard-of-hearing discourse and the way that discourse intersects with the perceived salient features of the field.
Efficient Quantum State Tracking in Noisy Environments

Abstract Quantum state tomography, which aims to find the best description of a quantum state---the density matrix, is an essential building block in quantum computation and communication. Standard techniques for state tomography are incapable of tracking changing states and often perform poorly in the presence of environmental noise. Although there are different approaches to solve these problems theoretically, experimental demonstrations have so far been sparse. Our approach, matrix-exponentiated gradient tomography, is an online tomography method that allows for state tracking, updates the estimated density matrix dynamically from the very first measurements, is computationally efficient, and converges to a good estimate quickly even with noisy data. The algorithm is controlled via a single parameter, its learning rate, which determines the performance and can be tailored in simulations to the individual experiment. We present an experimental implementation of matrix-exponentiated gradient tomography on a qutrit system encoded in the transverse spatial mode of photons. We investigate the performance of our method on stationary and evolving states, as well as significant environmental noise, and find fidelities of around 95% in all cases.
Automatic Detection of White Blood Cancer and Lung Cancer Using Machine Learning

Abstract: When abnormal white blood cells multiply cancerously, leukaemia results, harming the bone marrow and blood. The body's immune system is disturbed by the body's excessive generation of immature white blood cells, or lymphoblast, and its capacity to create red blood cells and platelets is decreased in the bone marrow as a result. Early diagnosis of these cells is crucial for preventing health issues in patients. In the past, the procedure was manually completed by an expert in a sizable amount of time. On a short dataset, the proposed model uses an improved Dense Convolutional neural network framework to predict the kind of cancer in a generic manner. which is able to locate both lung cancer and leukemia. Tumors can spread to other organs and grow quickly in lung cancer. An aberrant cell proliferation that can harm other normal tissue cells marks the beginning of cancer. Using tiny images to identify the white blood cancer, the imaging technology known as computerized tomography (CT) is frequently used to diagnose lung cancer.
Web Personalized Recommendation Model Using Temporal Fuzzy Association Rule Mining

Abstract: A web access log file contains timely sequenced log entries which include essential fields to indicate user activities. Analysis of these patterns provides valuable information for web designer to quickly respond to their individual needs. Many industries are struggling to retain regular interested customers for the improvement of customer relationship. Retrieval of relevant information automatically from these log files for interested group of users is a difficult process, since acquiring interested user profiles which evolves continuously with respect to time are not so easy. The paper presents a novel Web Personalized Recommendation Model (WPRM) using temporal fuzzy association rule mining technique. Temporal Fuzzy Association Rule Mining (FTARM) technique is proposed and applied on a focused set of interested users to provide intelligent recommendations. The proposed model results in less execution time and reduced memory utilization with high accuracy.
Design and Implementation of Store Management System based on IDEA

This With the continuous development of computer network and information industry, the use of manual data processing has been unable to meet the current needs, and more and more businesses have to change their business model and use computers to manage manpower and materials. The store management system is designed to improve the operation rate of individual businesses, so as to facilitate the store management personnel to manage the purchase and sale of goods. In this paper, the author studies on store the overall management system adopts B/S structure, using IntelliJ IDEA as a development environment of the whole code, front-end using easyUI framework for the preparation of the JSP page, the back-end using SSM framework, using MySQL database for data persistence, through these finished goods management module technology, purchasing and supply management module, order management module, user management module development, and through the function test and analysis, system can stable operation, to achieve the desired effect. The realization of the system can improve the management efficiency of individual businesses on materials, procurement, sales and personnel, making the operation of staff more convenient and avoiding mistakes in the operation process.
Lecturers’ Basic Psychological Needs Satisfaction and Its Relationship with Students’ Disposition towards Studying during Online Teaching in the COVID-19 Pandemic

When examining learning at university, especially in a pandemic context, it is important to analyze the actions taken by lecturers to promote students’ disposition towards studying. The objectives of this research project were to analyze the activities lecturers implement in class to promote their students’ disposition towards studying; examine lecturers’ basic psychological needs satisfaction levels; and determine the relationship between feedback, beliefs, and affect for self-regulation and lecturers’ basic psychological needs satisfaction. A quantitative methodological approach, with a descriptive, correlational, and cross-sectional design was used, with a sample of 97 lecturers from Chilean universities. Data collection took place through a battery of instruments: the promotion of disposition towards studying survey; the feedback scale for self-regulation; the beliefs and affect in self-regulation survey; the satisfaction of basic psychological needs survey; and a sociodemographic questionnaire. The results showed that the promotion of disposition towards studying was a key factor to face social distancing and the loss of self-regulation for learning because of the COVID-19 pandemic. In addition, a positive correlation was found between beliefs and affect in relation to self-regulation and satisfaction levels of basic psychological needs in the participating lecturers.
What is Proprioception?

This paper aims to explore the particularity of proprioception. Because of its internality, inseparable descriptiveness and ‘need no proof’ characteristics, it is different from perceptual knowledge, and because of its non-intentional, it is different from practical knowledge. Besides, as the intermediary of mind and body, it provides an important foundation for mind and external perception and intentional action.
Impact of Number of Features Selected and Size of Training Data on the Accuracy of Machine Learning Based Cloud Security Algorithms – An Empirical Analysis

Background: Machine learning (ML) techniques have proven to be very effective in providing security in a cloud environment considering the continuous evolving nature of threats. Some of the factors that influence the accuracies of ML models include the specific ML algorithm used, sample size, the number of features selected and portion of dataset used for training. Many studies have conducted empirical analyses of the effects of one or more combination of these factors on predicted accuracies of ML models. However, the effect of the portion of the entire dataset that is used for training the ML model as well as the number of features extracted from the dataset in predicting the accuracy of an ML model is yet to be investigated.AimThis study uses Ordinary Least Square (OLS) regression to investigate if the number of features selected and the size of training data are useful in predicting the accuracies obtained in ML based approaches to cloud security.Method: For this research, wehave two independent variables (number of features selected and the size of training data) and one dependent variable (accuracy). We initially selected 16 (sixteen) studies conducted within the last 5 (five) years for our study. We extracted the number offeatures used, the size of the training data and the accuracies obtained from these studies. After identifying and discarding outliers from the extracted values, we were left with 12 (twelve) studies. We conducted our analysis on these 12 studies. Results: The result of our analysis shows that there exist a weak positive and negative relationships among the dependent and independent variables.Although, our analysis shows weak positive and negative relationships among the variables, our model is useful in predicting the accuracies of ML models given the number of features selected and the size of the training data
Contemporary Technological Changes Impact on the Talent Training Objectives of Higher Vocational Education

This study discusses the impact of contemporary technological changes on the cultivation objectives of higher vocational education.Today, the wave of the new science and technology revolution is impacting all fields of human society. Each occupation shows corresponding technical requirements and is subject to changes in science and technology, people's habits and other factors, resulting in a distinctive era. The technological revolution and occupational changes also require higher vocational education to make corresponding improvements and changes. The new round of scientific and technological revolution puts higher and more complex requirements on the structure of human knowledge and ability. Composite talents with multiple skills are needed. The boundaries of academic talents, engineering talents, technical talents and skillful talents will be blurred more and more, and having system thinking and innovation ability will become the basic requirements for all kinds of talents. The talent training objectives of higher vocational education should strengthen the cultivation of systematic thinking and innovation ability, and cultivate composite talents who master multiple skills. Further adhere to the doctrine of all-round development of human beings, to ensure that the development of various aspects of human physical and intellectual, ability and aspirations in coordination.
All Paths Lead to Rome

All roads lead to Rome is the core idea of the puzzle game Roma. It is played on an $n \times n$ grid consisting of quadratic cells. Those cells are grouped into boxes of at most four neighboring cells and are either filled, or to be filled, with arrows pointing in cardinal directions. The goal of the game is to fill the empty cells with arrows such that each box contains at most one arrow of each direction and regardless where we start, if we follow the arrows in the cells, we will always end up in the special Roma-cell. In this work, we study the computational complexity of the puzzle game Roma and show that completing a Roma board according to the rules is an \NP-complete task, counting the number of valid completions is #Ptime-complete, and determining the number of preset arrows needed to make the instance \emph{uniquely} solvable is $\Sigma_2^P$-complete. We further show that the problem of completing a given Roma instance on an $n\times n$ board cannot be solved in time $\mathcal{O}\left(2^{{o}(n)}\right)$ under ETH and give a matching dynamic programming algorithm based on the idea of Catalan structures.
Effect of Blended Teaching Mode in Colleges and Universities Based on Automation Technology on College Students’ English Performance

In today’s society, the rapid development of information technology has had a profound impact on human work and life as well as on education. In recent years, the rapid development of the Internet and information technology has laid a solid foundation for the digital transmission of information, which has changed the mode of information transmission and improved the efficiency of information dissemination. As an important part of network information dissemination, blended teaching is rapidly developing from an on-demand learning platform to an interactive learning platform and has now become a blended teaching platform in China. The main problem of the current blended teaching model is that the effect of improving performance is not good. In order to solve this problem, this paper introduces automation technology, which can significantly improve the extraction accuracy of information extraction methods in specific fields by using rich domain knowledge and features and complete the extraction tasks at multiple levels. The automation method based on automation technology has great advantages in this regard and effectively improves the English performance. Two groups of voluntary college students were randomly selected to conduct a one-semester blended teaching model designed in this paper to investigate the ’students’ English scores. The average grades of students were 95.2, 96.7, 96.8, and 97.2, respectively, which is a big improvement over the existing system.
Multiple Inference: A Python package for comparing multiple parameters

Scientists often want to compare many parameters.For example, scientists often run randomized control trials to study the effects of many treatments, use observational data to compare many geographic regions, and study how public policy will impact many subgroups of people.multiple-inference implements many of the latest econometric and statistical tools for making such comparisons, including inference after ranking, simultaneous confidence sets, and Bayesian estimators.It uses a statsmodels-like API and provides template notebooks for ease of use.In just a few clicks, researchers can upload a .csvfile of conventional estimates (e.g., ordinary least squares or instrumental variables estimates) to a Jupyter binder and click "run" to apply a suite of multiple inference tools to their data.