Reviewerly/exHarmony huggingface dataset:
[Source Analysis and Ecological Risk Assessment of Heavy Metals in the Arable Soil at the Geological High Background, Based on the Township Scale].

Due to the extensive development of carbonate rocks in southwest China, heavy metals are naturally occurring elements that have high natural background levels in the environment. Therefore, it is important to conduct ecological risk assessments and identify potential sources of heavy metals in the geological high background area. Based on the township scale, a total of 307 surface soil samples were collected in Qinglong Town, Fengjie County, Chongqing. The concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn and pH were analyzed and determined. The spatial distribution and source of soil heavy metals were discussed using the geostatistical analysis and an absolute principal component score-multiple linear regression (APCS-MLR) model in the studied area. The results showed that the average values of seven heavy metals (As, Cd, Cr, Cu, Hg, Ni, and Zn) in the arable soil exceeded the background values of Chongqing, and the cumulative effect of Cd and As was obvious. The concentrations of Cd significantly exceededthe screening values in The Risk Control Standard for Soil Environmental Quality and Soil Pollution in Agricultural Land (GB 15618-2018), with the over-standard rates of 52.12%. The spatial characteristics of soil heavy metal contents exhibited a pattern of high in the south and low in the north. PCA and APCS-MLR modeling revealed that the contributions of natural sources to Cr, Cu, Ni, and Zn were 86.62%, 64.34%, 76.44%, and 85.46%, respectively. As, Pb, and Hg mainly derived from industrial activities, which accounted for 74.63%, 61.90%, and 73.49%, respectively, and Cd was affected by both natural sources and industrial activities (accounting for 47.74% and 39.56% of the total Cd content, respectively). The evaluation of the soil by the Nemerow comprehensive index (P) showed that Cd pollution was relatively serious, accounting for 27.04% of soil pollution. The potential ecological hazard index showed that Cd and Hg were the main ecological hazard elements, and the distribution range of RI was 51.77 to 2228, indicating mainly mild and moderate risks, and the moderate and above risk areas in the study area were mainly located around the southern industrial source area. Altogether, our results revealed that in the study area, the heavy metal pollution was mainly caused by industrial activities, and the heavy metal pollution caused by geological background was mainly light to moderate. In conclusion, the medium and above risk areas in the study area were mainly caused by mineral and industrial activities, whereas the heavy metal pollution caused by geological background was mainly light to moderate pollution.
TRACKING AND DETECTION OF MULTIPLE MOVING OBJECTS IN VIDEO

Object detection and tracking has recently become a major issues in the field of computer vision.It is frequently employed in various fields, including security, automated driving systems, and surveillance.This programme can identify and follow several objects in video clips recorded by a stationary camera.
STOCK PRICE PREDICTION USING RANDOM FOREST REGRESSION

Stock price forecasting is a prominent and fascinating research issue in the financial and academic industries for calculating the size of nations.There was nothing like that.A substantial set of criteria for determining and foreseeing the amount of a stock exchange share.Many natural technologies exist to assist us in trying the prediction process, such as expertise in fundamental, time, mathematical, and series analysis, but not one of these techniques have been established to people as a valid and precise tool in calculating the value of stock markets or share market scales.In this study, we attempted to do something innovative by applying a Machine Learning technique to foresee or sense the stock exchange sensex's conduct tracking.Machine Learning techniques such as linear regression, support vector regression, decision trees, and the Random Forrest Regressor are employed to forecast the value of stocks and characterise the behaviour of both buyers and sellers of assets.We projected the stock price using the day's ending value and the stock price.After evaluating the accuracy of each model and establishing which is the more effective algorithm to estimate stock price, an algorithm with exceptional precision is chosen.We cannot foretell share market conditions since it is a murky field, and the share marketplaces can never be predicted.This study, on the other hand, can complete the task fast and technically, and that is the major purpose of this essay.
MONITORING MONKEYPOX DISEASE USING AI AND ML

Monkeypox is a viral disease that can be transmitted to humans from infected animals such as rodents and monkeys.It is a zoonotic disease and can cause a range of symptoms, from mild to severe, including fever, rash, and even death in rare cases.Monitoring and controlling the spread of the disease is crucial for public health.Artificial intelligence (AI) and machine learning (ML) have shown promising results in disease surveillance, prediction, and outbreak detection.In this paper, we discuss the potential of AI and ML in monitoring monkeypox disease and propose a framework for using these technologies to track and predict the spread of the disease.
Feature extraction based ensemble stacking for combating cyber threat in phishing URLs

Cyber threat is a process of exploiting sensitive information by losing the golden triad (Confidentiality, Integrity, and Availability). The threats may include Trojans, Phishing, and DDOS attacks. Phishing works as a social engineering technique where in the malicious attacker attempts in such a way that the appearance to the user would seem legitimate thereby causing the user to fall in a trap which may lead to the compromising of the user's sensitive information by means of illegitimate URLs or links etc. According to previous records approximately 18% of businesses lost their financial information due to phishing attack. Phishing can exploit data such as credentials, personal data, medical, etc. In this work we proposed a feature extraction-based stacking model where the URL (uniform resource locators) is identified as a legitimate or illegitimate URL. In the process of feature extraction, the features are considered based on hostname _length, path_length, fd _length, tld_length, count of -, count of @, count of? count of %, count of., count of =, count of http, count of https, count of www, count of digits, count of letters, dir., Ip (v6 or v4). We trained the model with different machine learning algorithms for chosen the best models for stacking the models. We considered the best four machine learning algorithms to be stacked and a meta-layer for the final prediction. We compare the metrics such as accuracy, recall, precision, and F1-score with other state of art models.
An AI Based Electric Field Detection using UAV

The Idea of detecting the Electric Field which is sustained in the space universe will get detected in this system. The implementation of Unmanned Aerial Vehicle with autonomous control can specifically determine the system of its intensity, levels of deficiency, benefits and circuitry difficulties in the field when synchronizing with Artificial Intelligence. The traditional aircraft systems have a manual pilot operator to maintain security and safety, the charge existed in the medium can proportionally detect the quantum of charge, intensity, waveform of representation. This proposed framework consists of a decentralized control in architecture which simultaneously controls the monitors and the swarms of resource-constraint in UAV for their real-time requirements. The outcomes of UAVs can be mitigated significantly in space medium and their efficiency can be increased in realizing their diverse range of missions.
Research on the Database Construction of Talents for Professional Chinese in the Energy Industry

At present, the supply of professional Chinese talents in the energy industry is inadequate to meet the demands of the national energy strategy and energy cooperation under the background of “Belt and Road Initiative”. The training units lack a comprehensive and systematic understanding of the type and quantity of required talents, preventing them from conducting systematic research on talent training systems for professional Chinese. Therefore, this paper proposes using the theory of complex dynamic theory and big data method to extract the characteristics and relevant parameters of talents for professional Chinese required by energy cooperation in “Belt and Road Initiative”, to build a database for the cultivation of talents for professional Chinese in the energy industry, and to provide a reference for the cultivation of talents for professional Chinese in other domestic colleges.
Attention Cloud Map: Visualizing Attention Migration of Students’ Debugging Process in Semantic Code Space

Researchers explored the cognitive process of students by analyzing their eye-tracking data in some specified computer programming tasks. Although there are some quantified analysis and data visualization methods proposed in this area, none of them is designed to visualize the attention migration in debugging task. In this paper, we designed a novel kind of chart, i.e. Attention Cloud Map (ACM), for this purpose. We model the semantic code space into a polar coordinate system, and map the code lines and functions of the test program are mapped into points and sectors. We put the bug line in the center point, and mark the relevant code areas in the code space. By plotting the heat map of the amount of eye fixation attention in given time slices, we can obtain a serial of attention distributions in the code space. Experiment results show that, such attention cloud map can enable us to observe the debugging strategy of individual student, and abstract the average progress of one student group, which can be provide some references for the evaluation of students’ debugging performances.
Implementation of Secure ID Encryption with Outsourced Revocation Technique In Cloud Environment

Public key encryption can be replaced with Identity-Based Encryption (IBE), because of this handling public keys and certificates is easier at Public Key Infrastructure (PKI) simpler.Nevertheless, the overhead calculation at the Private Key Generator (PKG) One important efficiency factor when a person revokes their account limitations of IBE.In a typical PKI environment, effective revocation has been extensively investigated.Nevertheless, IBE aims to reduce the burdensome maintenance of certificates.We provide a revocable IBE scheme for the server-assisted environment after first including outsourced computing into IBE to address the critical issue of identity revocation.According to our design, a Key Update Cloud Service Provider will handle the majority of the duties connected to key generation that are involved with key issuance and key update operations, leaving PKG and users to handle a limited number of simple tasks locally.In order to do this, we develop a special collusion-resistant mechanism that deploys a hybrid private key for every user and uses an AND gate to link and tie the identity component and the time component.Moreover, we suggest alternative architecture that, according to the freshly developed Refereed Delegation of Computation paradigm, is probably safe.Lastly, we offer a wide variety of experimental findings that highlight the potency of our suggested structure.
A Pilot Study to Locate Historic Scientific Data in a University Archive

Historic data in analog (or print) format is a valuable resource that is utilized by scientists in many fields. This type of data may be found in various locations on university campuses including offices, labs, storage facilities, and archives. This study investigates whether biological data held in one institutional university archives could be identified, described, and thus made potentially useful for contemporary life scientists. Scientific data was located and approximately half of it was deemed to be of some value to current researchers and about 20% included enough information for the study to be repeated. Locating individual data sets in the collections at the University Archives at the University of Minnesota proved challenging. This preliminary work points to possible ways to move forward to make raw data in university archives collections more discoverable and likely to be reused. It raises questions that can help inform future work in this area.
Mapping of Flood Inundation and Eco-hydraulic Analyses to Minimize Food Discharge in Tributaries

Eco-hydraulic analyses begin with the arrangement of tributaries. This research aimed to minimize the discharge of flood run-off downstream and map the flood inundation by spatial analysis uses DEMNAS data and mapping of flood inundation areas using ArcGIS. Analysis of return period flood points using HEC-RAS version 5.0.7. The data needed is the cross section of the river, the distance between the sections, the Manning's roughness number, the return period flood discharge and the slope of the river. The integration between topographic maps, watersheds and flood water levels can display areas that are potentially affected by inundation floods, so that the flood inundation limits and flood inundation areas can be calculated. This research examined proper eco-hydraulics design so that it could reduce discharge, identify locations prone to flooding, and describe the magnitude of the flood impact quantitatively. The results eco-hydraulic method obtained the design border width of 100 m, the condition before the existing river border arrangement was carried out, the inundation height was 0.30 – 1.13 m and after the river border arrangement the discharge could be reduced to 113.09 – 209 m3/s and the inundation height is 0 – 0.31 m. Based on the research results, it is known that border arrangement can provide benefits for flood control measures.
運動の継続意欲向上を促すリズム補正システム

In this study, we propose a rhythm correction system for promoting motivation towards exercise. Recently, music-based exercise has been attracting attention as an approach for preventive care. However, some elderly people may experience a large discrepancy between the rhythm of the music and their own movements, which prevents them from enjoying exercise and reduces their motivation towards exercise. As a promising approach to solve such issue, we developed a system that aims at correcting discrepancy between musical rhythm and movements according to the characteristics of each user. An experimental evaluation was carried out and results hinted at the potential of the system for enhancing users’ sensation of getting into the rhythm and improving their motivation towards exercise.
Behavior of a third order nonlinear generalized rational recursive sequence

In this paper, we investigate the global stability, boundedness of solutions of the recursive sequence(1), n = 0, 1, ... where a i and b i ∈ (0, ∞) , i = 0, 1, 2 with the initial conditions J -2 , J -1 , and J 0 ∈ (0, ∞).
SELF ASSESSMENT WEB-BASED APPLICATION

Self-Assessment Web-Based App is an online test simulator to perform online self-assessment and conduct the test effectively and without wasting time checking test papers manually.The main purpose of the self-assessment application is to identify students effectively and efficiently using a high-performance machine that not only saves a lot of time, but also provides fast and accurate results.For students, paper, pen, etc. without using additional items, they can provide information wherever they want according to their convenience and time on the internet.The online self-assessment app provides students with a quick and easy way to take the exam.It also provides 100% accuracy and safety results immediately after self-testing.Users can review their results after completing the self-assessment.This helps students conduct remote self-assessment and can provide users with security and convenience, among other benefits.Self-assessment is an essential part of personal and professional development, and web-based applications make it easy for people to assess their own abilities and talents.This work focuses on exploring the development of online self-assessments using PHP as the programming language and HTML, CSS and JavaScript as the user interface.This article discusses the process used to create the practice, including the reasons for the research, research and participants, data collection methods and procedures, methods and methods for data analysis, and ethical issues.Research has shown that online self-assessment tools are useful resources for individuals to assess their skills and abilities and provide an easy-to-use, user-friendly interface.
Development of empathy and ethical values through role-playing games as innovation for education in values

For some authors, the development of empathy is a central aspect of the development of ethical values. Role-playing games can be the ideal vehicle to transfer learning from fiction to reality. The present study analyzes in an exploratory and descriptive way the scores obtained in the Test of Cognitive and Affective Empathy (TECA) of a total sample of 208 participants. Although no major differences were found between those who had previously participated in role-playing games and those who had not, there was a tendency to obtain higher mean scores than those obtained by the TECA through its scales.
Three continents, three countries

The epidemiological situation on a global scale caused by the COVID-19 pandemic has led to the general confinement of the population and the need to close the physical spaces of schools of all levels of education. To overcome this, it was necessary to adopt emergency measures, including Emergency Remote Education (ERE), using technologies to interact synchronously with students. Besides, it was also required to reconfigure educational practices and develop skills in digital literacy to enable the continuity of educational experiences, respecting isolation but maintaining the interaction of teachers and students. This situation has not met borders, so we consider it essential to collect information about the degree of student satisfaction with the (ERE) caused by the COVID-19 pandemic in three countries (Portugal, Brazil and Turkey), located on three continents (Europe, America and Asia). Data were collected in 2019/2020 and 2020/2021 by the researchers who are conducting the study from different countries. The authors use a survey created on Google Forms to collect data, assuring the confidentiality of the data collected. We obtained 566 valid responses in the three countries: Portugal 140, Turkey 359 and Brazil 67. From this collection and subsequent statistical analysis, we can show, from the results obtained, that the models chosen in the three countries did not please in a unanimous way all the students involved. There are significant differences in access, knowledge of the platforms used and even in the methodologies used by different teachers concerning countries. On the contrary, about the possibility of moving to a permanent online model, there is unanimity, verifying from the opinions that face-to-face education is not replaceable by the model that has been used over the last two years.
Technology Innovation in STEM Education

The study examines research articles on STEM education and its technology innovation that have been indexed by the Scopus, Web of Science (WoS) databases, Springer, and Google, and aims to provide an in-depth analysis of the high-impact empirical studies in STEM education. The authors' conclusions are consistent with past studies on the topic, highlighting the increasing need for technology-based occupations as robots replace humans in industry and society. The study highlights several issues in the field of STEM education, including the gender divide and the effects of STEM education on people of all ethnicities. The authors provide an in-depth investigation of high-impact empirical studies in STEM education to understand the progress of research paradigms in the subject. The study offers an up-to-date view of the highly cited empirical research articles in STEM education and technology, serving as a solid foundation for future studies. It is recommended that future research continues to emphasize creativity, particularly in the field of science, in both research and education. The authors stress the importance of addressing the issues of the gender divide and the effects of STEM education on people of all ethnicities in future research.
Private capital flows and productivity in Sub-Saharan Africa: does the capital allocation puzzle matter?

The puzzling relationship between foreign private capital flows and productivity remains unresolved. We attempt to resolve the puzzle by extending the literature to investigate the effects of the flows across heterogeneous recipient sectors, using a unique hand-collected database of 18 African countries for 2006–2015. We uncover a new interesting finding that the effects of the flows on productivity growth depend on recipient sectors. The results suggest that the negative relationship between foreign private capital flows and economic growth documented in the existing literature, particularly in developing countries, is related to the fact that most of the flows went into sectors, such as extractives and infrastructure, which have less potential for productivity growth. Overall, our findings suggest that the capital allocation puzzle is a sectoral capital allocation puzzle.
AUTOMATED HTML CODE GENERATION FROM HAND DRAWN IMAGES USING MACHINE LEARNING METHODS

This paper introduces a novel method for deep learning-based HTML code generation from hand-drawn pictures.The suggested technique uses a convolutional neural network (CNN) to figure out how to transfer hand-drawn diagrams to the corresponding HTML code.The CNN is trained using a sizable dataset of HTML code and annotated hand-drawn illustrations.The trained model is fed a hand-drawn drawing during testing, and the model outputs the equivalent HTML code.The results show that the suggested method is effective at producing precise HTML code when applied to a series of hand-drawn designs.The suggested method could greatly cut down on the time and work needed for web development, especially for designers that like to sketch their concepts instead of using computer-aided design tools.
Designing a Set of Complexity Measures for Meta-Learning Based Instance Selection

A pre-processing method for data mining and machine learning is instance selection.The fundamental issue is that earlier methods still have trouble creating quality samples for classifier training.In this work, the system updates the specification of a few measures that were created for instance selection based on meta-learning.Moreover, the system Three sets of measurements, 59 databases, 16 instance selection techniques, two classifiers, and eight regression learners acting as meta-learners were all employed in an experimental research to compare the two.The findings imply that our metrics outperform those often employed by academics who used a meta-learning-based viewpoint perspective to approach instance selection.Compared to other papers used as benchmarks, this one offers learners more pertinent material.Based on the data that c-measures offer, the algorithm split them into three groups: overlap in property values, class separation, Manifold density, topology, and geometry.New criteria for dealing with meta-learning in IS are established by the approach employed in this work to construct the meta-data together with performance assessment.The system suggests an alternative strategy to the conventional one, in which the top candidate technique is chosen using a trained meta-classifier.Regression meta-learners that had been trained to forecast each potential method's performance were employed in this instance.Such an approach offers additional flexibility for handling variable weight allocations to the objectives in multi-objective problems, such as IS.