Reviewerly/exHarmony huggingface dataset:
Determination of the erosion level of a porphyry copper deposit using soil geochemistry

As exploration is time-consuming, costly, and risky, determination of the erosion surface of a metalliferous deposit before geophysical surveying and exploration drilling might be very helpful. Geochemical haloes can be used to determine whether the erosion surface is supra-ore or sub-ore and thus reduce the risk of exploration operations. The aim of this investigation is to determine the erosion surface of the North ROK porphyry deposit (NRPD) in northwestern British Columbia in Canada using linear productivity (LP), which is the content of an element defining the halo multiplied by the width of the halo. A total of 2045 soil samples from the B horizon were analysed using ICP-MS for 36 elements, including Cu, Mo, Pb, Zn, Au, As, Ag, Ni, Co, Fe, and Mn. The data-set was snalysed to obtain the statistical parameters and the elements Cu, Mo, Pb, and Zn were chosen to calculate the linear productivity and the total linear productivity. These four elements were modelled using probability plots to identify and separate subpopulations in terms of anomalous haloes and background, including the threshold values of each subpopulation. The results of the probability plot modelling and thresholds values were then used to map the distribution of each element in a GIS to calculate the linear productivity. The total linear productivity indicated that the erosion surface is supra-ore. Finally, a 3D orebody model of the Cu, Mo, Pb, and Zn distributions was constructed using borehole data and used to validate the results.
The Use of YouTube as a Supplementary Resource in Students' Speaking Skills

This study aimed to investigate the use of YouTube videos as a supplementary resource for EFL students. It uses a descriptive qualitative research method. The sample in this study was ten students in senior high school who had experienced learning EFL through YouTube as a supplementary resource for speaking fluency. The data collection instrument is a google form questionnaire with open-ended and close-ended questions. According to the findings, using YouTube in EFL classes positively supports speaking fluency for EFL learners, particularly in shaping and organizing ideas, enhancing motivation, developing and supporting peer collaboration, improving vocabulary, and reducing students' shyness. The current study's findings have clear implications for the teacher and students on how the students utilize YouTube as a supplementary resource in increasing their speaking fluency; the resource should focus more on developing student speaking fluency.
Zoom vs. Microsoft Teams: Students’ Preference

Learning is a process from knowing nothing to knowing more and it can be done by ourselves or teachers. However, normally learning with a teacher is done face-to-face, but due to the pandemic, much of the learning was changed to online. Online learning is done virtually, usually using media as a learning platform; the media used for online learning is Zoom and Microsoft Teams. This article aims to discover Student's Preference Between Zoom and Microsoft Teams as The Media for Online Learning and their reasons for choosing the media. In addition, this research was conducted using a quantitative method; the researchers distributed a Google form to students, which contained several questions they had to answer related to the Zoom platform and Microsoft Teams as a medium for online learning. They also had to tell the reasons they chose the media. The result of this research is divided into two categories: completeness of features and convenience of use. Zoom and Microsoft Teams have some of the same features and also have their advantages and disadvantage. Most students choose Microsoft Teams because of the complete features, but they mostly choose Zoom for convenience. From the results obtained, it is hoped that in the future, people who read this paper will find it easier to choose what platforms they will use for online learning and choose the right media. It is hoped that it can increase the enthusiasm of students to learn.
ESL Teachers’ Views on the Implementation of CEFR in Reading Skill

One of the most essential elements of learning English is the ability to read fluently. Mastering reading skills among ESL (English as a Second Language) learners is important in this global environment as it helps to widen their vocabulary acquisition and gradually helps them to enhance their other language skills. Although there is a large volume of published studies describing the implementation of CEFR (Common European Framework of Reference) in the English education system, these studies have not dealt with the implementation of CEFR specifically in reading skill. Thus, this study aims to find out more about ESL teachers’ views on the implementation of CEFR in reading skill. Besides that, the findings of the study will be able to identify the current arising issues or challenges in the teaching of reading skill. Data for this study was gathered through an online survey which was participated by 42 primary ESL teachers from Negeri Sembilan. The findings revealed that primary ESL teachers have mixed views on the implementation of CEFR in reading skill in terms of applicability. Although, the findings suggest that CEFR improves ESL learners’ reading skills, the results revealed that there is a need to adapt and modify CEFR related reading texts to fit in the Malaysian context with a local touch. Thus, the findings of this study provide useful information for other researchers who are interested in analysing the need of adapting the CEFR textbooks and reading texts to be in line with the Malaysian context.
Mathematics Behind the Heisenberg Uncertainty Principle

Most physics books do not reveal clearly how the Heisenberg uncertainty principle was derived. This uncertainty comes from the consequence of the wave-particle duality of matter giving statement that position and momentum cannot be measured in the same time. This article tries to reveal mathematics background behind the expression the Heisenberg uncertainty using supported mathematics background such as Fourier transform, Fourier transform integral, the probability of Gaussian distribution and it ends up with the expression of wave function which describe the localized particle giving relation Heisenberg uncertainty principle.
Indonesian Language Performance of Mentally Retarded Children: Reference for Writing Literacy Text Needs

This research examines the Indonesian language performance of mentally retarded children by identifying the vocabulary and sentence patterns. These two elements have implications for effective reading speed, which is needed in implementing the school literacy program. Identifying the vocabulary and sentence patterns of mentally retarded children is important to show the causes and overcome the low effective reading speed problems. Based on the data and discussion, the vocabulary is dominated by concrete nouns – monomorphemics, and the dominant sentence pattern is a monoclause – active. Meanwhile, the limitations of abstract thinking trigger the dominance of vocabulary characteristics and sentence patterns. Hence they fail to apply grammatical aspects in forming polymorphemic words and sentences with multiclauses. Text interventions are needed following their Indonesian language performance and repertoire to support their involvement in the school literacy program.
Client Side Cryptography Based Security for Cloud Computing System

The Cloud computing system has a wide range of because of its flexibility and scalability method authentication mechanism and TKSE algorithm.The system also allows the owner of the data to set predefined keywords for their files that are uploaded into the cloud.However, data breaches and unauthorized access to cloud data are major concerns.Our system enables clients to access their data using a two method authentication and the user can find this data using a keyword search algorithm.
Image Forgery Detection based on Fusion of Lightweight Deep Learning Models

The popularity of capturing images has increased in recent years, as images contain a wealth of information that is essential to our daily lives.Although various tools are available to improve image quality, they are often used to falsify images, leading to the spread of misinformation.This has resulted in a significant increase in image forgeries, which is now a major concern.To address this, a decision fusion method is proposed in this project, which uses lightweight deep learning-based models for detecting image forgery.The proposed approach involves two phases that utilize pretrained and fine-tuned models, including SqueezeNet, MobileNetV2, and ShuffleNet, to extract features from images and detect image forgery.In the first phase, lightweight models are used to extract features from images without regularization, while in the second phase, fine-tuned models with fusion and regularization are employed to detect image forgery.
Accurate 5 Accounting Application Competency Improvement for High School Students

SMA Swasta Panglima Polem is one of the high schools located in Labuhan Batu districts. This school has been accredited A and has 2 (two) majors, namely Natural Sciences (IA) and Social Sciences (IS). The problem that occurs is that there is a gap between accounting subjects and their application in the world of work today. This is not in line with the development of the accounting sector which has utilized Information and Communication Technology (ICT). Students need the ability and knowledge in using accounting applications. In addition, students also need to be instilled with a thorough attitude and the ability to solve problems so that every job can be completed properly and correctly. Students' understanding of accounting is only obtained through theory and practice from books, so students do not have an idea of ​​how the flow of the accounting system runs in a company. Accurate 5 training is given to students for 3 days starting with the opening of the training, introduction of applications, conducting financial transactions, viewing and analyzing financial reports, to conducting post-tests to students. The training went good and showed good results. This is shown by the enthusiasm of the students, the appreciation of the principal, and the results of the training which show an increase in students' understanding of accounting applications, especially Accurate 5.
Pemetaan Groundwater Recharge Potential (GRP) Dengan Pendekatan SIG (Sistem Informasi Geografis), Studi Kasus Daerah Aliran Sungai Cibeusi, Kawasan Jatinangor, Kabupaten Sumedang

Jatinangor District is located on the west side of Sumedang Regency which is directly adjacent to Bandung Regency. In accordance with Regional Regulation No. 4 of 2018 concerning the Sumedang Regency Spatial Planning, Jatinangor District is a priority area for higher education areas and urban settlements. Urbanization and population growth that occur can affect the frequency of groundwater use which continues to increase [1]. The groundwater table in the Jatinangor area and its surroundings has decreased due to the conversion of the recharge zone function which has an impact on reducing the area of the recharge zone [2]. The purpose of this research activity is to determine the potential of the recharge zone in the Cibeusi Watershed in Jatinangor District, Sumedang Regency. Determining the groundwater recharge potential (GRP) in research activities carried out using the GIS approach and the AHP method. AHP is part of the Multi-Criteria Decision Making (MCDM) method which was developed into an efficient and reliable method for making decisions in GIS analysis [3], [4]. The results showed that the weighted values of the five parameters used resulted in five different class categories. low category with a percentage of 5.5%, moderate category with a percentage of 54.5%, good category with a percentage of 34.5% and excellent category with a percentage of 4.6%. The best groundwater catchment area potential of 4.6% of the total Cibeusi watershed area is in the Cileunyi Wetan area with an infiltration zone elevation ranging from 837.5-887.5m asl
Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation

Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022. Keywords: Artificial Intelligence, Artificial Neural Network (ANN) K-Fold Cross Validation, COVID-19 Cases, Data Mining, Prediction.
Image Hashing Robust Against Cropping and Rotation

Image recognition is an important mechanism used in various scenarios. In the context of multimedia forensics, its most significant task is to automatically detect already known child and adolescent pornography in a large set of images. When fighting disinformation, it is used to identify images taken out of context or image montages. For this purpose, numerous methods based on robust hashing and feature extraction are already known, and recently also supported by machine learning. However, in general, these methods are either only partially robust to changes such as rotation and pruning, or they require a large amount of data and computation. We present a method based on a simple block hash that is efficient to compute and memory efficient. To be robust against cropping and rotation, we combine the method with image segmentation and a method to normalize the rotation of the objects. Our evaluation shows that the method produces results comparable to much more complex approaches, but requires fewer resources.
Detection Prevention and Proactive Prevention of Phishing Website

Phishing is frequently a routine attack on people when fake websites are used to trick individuals into divulging all of their personal information. Phishing records process tool URLs are used to steal personal data, including user names, passwords, and online banking activity. Attackers that utilize phishing techniques use websites with rectangular diplomas as a visual and semantic spoof of the real websites. Phishing strategies have advanced swiftly as the age has progressed; however, this may be avoided by using anti-phishing tools to spot phishing. A potent tool frequently utilized in the direction of phishing assaults is the machine planning to apprehend. The suggested system has also investigated the capabilities and methods of machine learning for detection.
Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19

Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public’s response is through Twitter’s social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation-based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately. Keywords: Aspect-based sentiment, Deep learning, Emotion detection, Machine learning, Pearson correlation, Public opinion.
Detecting Novelties with Empty Classes

For open world applications, deep neural networks (DNNs) need to be aware of previously unseen data and adaptable to evolving environments. Furthermore, it is desirable to detect and learn novel classes which are not included in the DNNs underlying set of semantic classes in an unsupervised fashion. The method proposed in this article builds upon anomaly detection to retrieve out-of-distribution (OoD) data as candidates for new classes. We thereafter extend the DNN by $k$ empty classes and fine-tune it on the OoD data samples. To this end, we introduce two loss functions, which 1) entice the DNN to assign OoD samples to the empty classes and 2) to minimize the inner-class feature distances between them. Thus, instead of ground truth which contains labels for the different novel classes, the DNN obtains a single OoD label together with a distance matrix, which is computed in advance. We perform several experiments for image classification and semantic segmentation, which demonstrate that a DNN can extend its own semantic space by multiple classes without having access to ground truth.
Learning Structured Output Representations from Attributes using Deep Conditional Generative Models

Structured output representation is a generative task explored in computer vision that often times requires the mapping of low dimensional features to high dimensional structured outputs. Losses in complex spatial information in deterministic approaches such as Convolutional Neural Networks (CNN) lead to uncertainties and ambiguous structures within a single output representation. A probabilistic approach through deep Conditional Generative Models (CGM) is presented by Sohn et al. in which a particular model known as the Conditional Variational Auto-encoder (CVAE) is introduced and explored. While the original paper focuses on the task of image segmentation, this paper adopts the CVAE framework for the task of controlled output representation through attributes. This approach allows us to learn a disentangled multimodal prior distribution, resulting in more controlled and robust approach to sample generation. In this work we recreate the CVAE architecture and train it on images conditioned on various attributes obtained from two image datasets; the Large-scale CelebFaces Attributes (CelebA) dataset and the Caltech-UCSD Birds (CUB-200-2011) dataset. We attempt to generate new faces with distinct attributes such as hair color and glasses, as well as different bird species samples with various attributes. We further introduce strategies for improving generalized sample generation by applying a weighted term to the variational lower bound.
TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis

In this paper, we present TMR, a simple yet effective approach for text to 3D human motion retrieval. While previous work has only treated retrieval as a proxy evaluation metric, we tackle it as a standalone task. Our method extends the state-of-the-art text-to-motion synthesis model TEMOS, and incorporates a contrastive loss to better structure the cross-modal latent space. We show that maintaining the motion generation loss, along with the contrastive training, is crucial to obtain good performance. We introduce a benchmark for evaluation and provide an in-depth analysis by reporting results on several protocols. Our extensive experiments on the KIT-ML and HumanML3D datasets show that TMR outperforms the prior work by a significant margin, for example reducing the median rank from 54 to 19. Finally, we showcase the potential of our approach on moment retrieval. Our code and models are publicly available at https://mathis.petrovich.fr/tmr.
A novel algorithm can generate data to train machine learning models in conditions of extreme scarcity of real world data

Training machine learning models requires large datasets. However, collecting, curating, and operating large and complex sets of real world data poses problems of costs, ethical and legal issues, and data availability. Here we propose a novel algorithm to generate large artificial datasets to train machine learning models in conditions of extreme scarcity of real world data. The algorithm is based on a genetic algorithm, which mutates randomly generated datasets subsequently used for training a neural network. After training, the performance of the neural network on a batch of real world data is considered a surrogate for the fitness of the generated dataset used for its training. As selection pressure is applied to the population of generated datasets, unfit individuals are discarded, and the fitness of the fittest individuals increases through generations. The performance of the data generation algorithm was measured on the Iris dataset and on the Breast Cancer Wisconsin diagnostic dataset. In conditions of real world data abundance, mean accuracy of machine learning models trained on generated data was comparable to mean accuracy of models trained on real world data (0.956 in both cases on the Iris dataset, p = 0.6996, and 0.9377 versus 0.9472 on the Breast Cancer dataset, p = 0.1189). In conditions of simulated extreme scarcity of real world data, mean accuracy of machine learning models trained on generated data was significantly higher than mean accuracy of comparable models trained on scarce real world data (0.9533 versus 0.9067 on the Iris dataset, p < 0.0001, and 0.8692 versus 0.7701 on the Breast Cancer dataset, p = 0.0091). In conclusion, this novel algorithm can generate large artificial datasets to train machine learning models, in conditions of extreme scarcity of real world data, or when cost or data sensitivity prevent the collection of large real world datasets.
Computing Expected Motif Counts for Exchangeable Graph Generative Models

Estimating the expected value of a graph statistic is an important inference task for using and learning graph models. This note presents a scalable estimation procedure for expected motif counts, a widely used type of graph statistic. The procedure applies for generative mixture models of the type used in neural and Bayesian approaches to graph data.