Reviewerly/exHarmony huggingface dataset:
Application of remote sensing techniques in lithology identification in Almeria

Remote sensing is emerging as an important method of information extraction in the field of lithology identification as a technology that can provide a cost-effective way of detecting and monitoring the physical characteristics of an area. This paper uses remote sensing techniques for lithology identification in Almeria, discusses the flaws and applicability of remote sensing techniques, and proposes a combined remote sensing approach. The lithological results identified by remote sensing indicate that northern Almeria is dominated by metamorphic and carbonate rock and the southern part is predominantly mafic rocks and felsi rocks. The identification results in Almeria from remote sensing imagery synthesised using Landsat-8 multiple band sets differ from actual lithology distribution. It shows the limitations of remote sensing techniques for lithology identification. Based on the limitations of remote sensing techniques demonstrated in the case study, this paper discusses how remote sensing improves the identification and analysis of lithology in other research cases by means of technical improvements combined with other techniques.
A Review of Text Sentiment Analysis Methods and Applications

This paper reviews the application of natural language processing in sentiment analysis. Sentiment analysis is an important task aimed at automatically identifying and inferring sentiment tendencies and sentiment intensity in texts. This paper first introduces the application areas of sentiment analysis, including practical applications of text sentiment analysis. Then, text pre-processing techniques such as word separation, deactivation removal and punctuation processing are discussed. Then, feature extraction and representation methods are explored, including bag-of-words model, TF-IDF, word embedding and Word2Vec, attention mechanism and Transformer. In addition, methods for sentiment analysis, such as sentiment dictionaries and rule-based methods, traditional machine learning methods, and deep learning-based methods, are presented. Finally, the application areas of sentiment analysis are discussed and conclusions are given. The review in this paper will help readers understand the current status and development trend of natural language processing applications in sentiment analysis, as well as the advantages and disadvantages of different methods in sentiment analysis.
Geometry Optimization: A Comparison of Different Open-Source Geometry Optimizers

Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open- source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and energy calcula- tions. The findings demonstrate how the choice of the coordinate system influences the optimization process to reach an equilibrium structure. With fewer steps, internal co- ordinates outperform Cartesian coordinates while the choice of the initial Hessian and Hessian update method in quasi-Newton approaches made by different optimization al- gorithms also contributes to the rate of convergence. Furthermore, an available open- source machine learning method based on Gaussian Process Regression (GPR) was evaluated for energy minimizations over surrogate potential energy surfaces with both Cartesian and internal coordinates, with internal coordinates outperforming Cartesian. Overall, geomeTRIC, ASE, and DL-FIND with their default optimization method as well as with GPR-based model using Hartree–Fock theory with the 6-31G** basis set, needed a comparable number of geometry optimization steps to the approach of Baker using a unit matrix as the initial Hessian to reach the optimized geometry. Due to its performance and the fact that DL-FIND is feature rich we use it as the default optimizer for QUICK.
Representation of Fisha's Female Image in the Novel Tears of God by Aguk Irawan M.N

This study aims to: to obtain an objective picture of the representation of the female image of Fisha in the Novel Air Mata Allah by Aguk Irwan M.N. The approach used in this study is a qualitative research approach. The qualitative research approach is a research procedure that produces descriptive data in terms of physical images and psychic images. Analysis and discussion of the image of Fisha Agustina in the Novel Tears of God, tells about the journey and struggle of Fisha's character in facing various complex life problems.
Deep Multi-Source Supervised Domain Adaptation with Class Imbalance

Abstract Deep multi-source domain adaptation is based on deep neural networks and exploits knowledge from multiple source domains to improve predictions on a target domain.In this paper, we are especially interested in investigating domain adaptation in a supervised context, with limited data and class imbalance. We propose a new multi-source supervised domain adaptation approach that is able to transfer both shared knowledge across all source domains and source domain specific knowledge toward a target domain. Transfer contribution weights are computed during training based on domain divergence. They are used to balance each source domain impact on learning during the model training phase, limiting negative transfer as much as possible. We conduct extensive experiments to show that our approach competes and even outperforms other state-of-the-art domain adaptation approaches on both image benchmark datasets and real-world tabular medical data. We perform statistical analysis to better evaluate our experimental results, and conduct an ablation study to evaluate the usefulness of each component of the method.
THE CONCEPTUAL MENTAL MODEL OF EXPLANATION IN AN ARTIFICIAL INTELLIGENCE SYSTEM

The subject of research is the process of formation of explanations in artificial intelligence systems. To solve the problem of the opacity of decision-making in artificial intelligence systems, users should receive an explanation of the decisions made. The explanation allows you to trust these solutions and ensure their use in practice. The purpose of the work is to develop a conceptual mental model of explanation to determine the basic dependencies that determine the relationship between input data, as well as actions to obtain a result in an intelligent system, and its final solution. To achieve the goal, the following tasks are solved: structuring approaches to building mental models of explanations; construction of a conceptual mental model of explanation based on a unified representation of the user's knowledge. Conclusions. The structuring of approaches to the construction of mental models of explanations in intelligent systems has been carried out. Mental models are designed to reflect the user's perception of an explanation. Causal, statistical, semantic, and conceptual approaches to the construction of mental models of explanation are distinguished. It is shown that the conceptual model sets generalized schemes and principles regarding the process of functioning of the intellectual system. Its further detailing is carried out on the basis of a causal approach in the case of constructing an explanation for processes, a statistical approach when constructing an explanation about the result of the system's work, as well as a semantic approach when harmonizing the explanation with the user's basic knowledge. A three-level conceptual mental model of the explanation is proposed, containing levels of concepts regarding the basic principles of the functioning of the artificial intelligence system, an explanation that details this concept in an acceptable and understandable way for the user, as well as basic knowledge about the subject area, which is the basis for the formation of the explanation. In a practical aspect, the proposed model creates conditions for building and organizing a set of agreed explanations that describe the process and result of the intelligent system, considering the possibility of their perception by the user.
USING LONG SHORT-TERM MEMORY NETWORKS FOR NATURAL LANGUAGE PROCESSING

The problem of emotion classification is a complex and non-trivial task of language interpretation due to the natural language structure and its dynamic nature. The significance of the study is in covering the important issue of automatic processing of client feedbacks, collecting opinions and trend-catching. In this work, a number of existing solutions for emotion classification problem were considered, having their shortcomings and advantages illustrated. The evaluation of performance of the considered models was conducted on emotion classification on four emotion classes, namely Happy, Sad, Angry and Others. The model for emotion classification in three-sentence conversations was proposed in this work. The model is based on smileys and word embeddings with domain specificity in state of art conversations on the Internet. The importance of taking into account the information extracted from smileys as an additional data source of emotional coloring is investigated. The model performance is evaluated and compared with language processing model BERT (Bidirectional Encoder Representations from Transformers). The proposed model achieved better performance at classifying emotions comparing to BERT (having F1 score as 78 versus 75). It should be noted, that further study should be performed to enhance the processing by the model of mixed reviews represented by emotion class Others. However, modern performance of models for language representation and understanding did not achieve the human performance. There is a variety of factors to consider when choosing the word embeddings and training methods to design the model architecture.
The fair principles: Trusting in fair data repositories

The fair principles: Trusting in fair data repositories Andy Götz, ESRF data manager and PaNOSC coordinator, discusses the impact of applying the FAIR principles to research data. In the previous article in this series on FAIR data, we explained how the scientific world is undergoing a major change with the widespread adoption of the so-called FAIR principles for research data. FAIR stands for Findable, Accessible, Interoperable and Reusable and was first published in a paper in Nature in 2016.(1) The FAIR principles were proposed to ensure research data are made available to the scientific community so that they can be found, downloaded, understood, and reused. The goal is to make data used in scientific publications available to the community so they can verify the results, reproduce them, and eventually derive new results from them. Applying the FAIR principles systematically for research data will address the reproducibility - also known as the replicability - crisis (2) in science and make scientific data available for verifying results and used beyond their original purpose.
Analysis of Learning Design Using The Canva Application in Information and Communication Technology (ICT)Training

Canva is one of the programs in designing online learning that provides various tools such as presentations, infographic videos. The use of platforms for learning design is an innovation that must be developed by teachers. With the learning design, learning will be active and more interesting. The research used the Research and Development (R&amp;D) method with the ADDIE model. 5 stages in the ADDIE model, namely the stages of analysis (Analysis), Design (Design), Development (Development), Implementation (Implementation), and Evaluation (Evaluation). The results of the analysis show that the learning design using the Canva application in ICT training for madrasa teachers in Central Maluku district stated that 20% often used the Canva application in designing lessons and 60% had used the Canva platform or application in designing learning, 20% stated that they had never used Canva in designing learning and using the Canva-based learning animation video application.
Analisis Kecakapan Abad 21: Collaboration and Communication Skills Siswa Melalui Penerapan Discovery Learning

This study aims to determine the 21st century skills in the form of culinary skills and communication skills of class X students of SMK N 6 Semarang through the application of discovery learning models. This research is a quantitative descriptive research with a one shot case study research design. The population of this research is class X Culinary SMK N 6 Semarang. Sampling used a purposive sampling technique, namely class X Culinary 1 SMK N 6 Semarang. The skills assessment instrument used in this study was an observation sheet consisting of an observation sheet for collaboration skills and an observation sheet for communication skills. Based on the data analysis that has been carried out, the results show that the skill skills of students in class X culinary 1 at SMK N 6 Semarang are achieved in the very high category, so that it can be interpreted that the application of discovery learning is capable of reaching the age of the 21st century, namely skills skills and communication skills.
EFEKTIFITAS METODE MULTISENSORI DALAM MENINGKATKAN KETERAMPILAN MEMBACA SISWA DISLEKSIA DI SEKOLAH DASAR

Reading skill is an important ability for elementary school students to help understand the subject matter, communicate skills and solve problems that involve understanding, analyzing, and introducing concepts. The aim of the research is to provide an understanding for teachers in diagnosing dyslexic students, namely students who have difficulty reading so that their reading ability can be maximized through multisensory methods. The research was conducted on second-grade students at Margaluyu Public Elementary School, Serang City, involving four dyslexic students and one teacher. The research method uses descriptive qualitative data analysis techniques using the Huberman and Milles model through the stages of data collection, data reduction, data presentation, and conclusion or verification at the end. Data collection instruments used interview sheets, field notes, literature review, and analysis of treatment results documents. Based on the results of the study it was found that the multisensory method has effectively improved dyslexic reading skills in a systematic, direct, and explicit instructional manner by utilizing sensory functions related to visual, auditory, kinesthetic, and tactile. The increase in ability is evidenced in the results of the screening value which increased by 60% after the multisensory method was implemented. The multi-sensory method is very helpful in developing reading skills for dyslexic students, especially for students who have difficulty pronouncing certain letters
Effect of Young children's playfulness on leadership: Mediating effect of children resilience

The purpose of this study was to find out whether resilience has a mediating effect on the influence of children's playfulness on leadership. The subjects of the study are 304 young children enrolled in early childhood education facilities located in G Metropolitan City and J Province. Scale) adapted, modified, and supplemented by Kim Young-hee (1995), the early childhood leadership scale developed by Choi In-soo (2008), and the resilience scale developed by Lee Yeon-sil (2011) were used, and the SPSS 23.0 program was used. and analyzed the results. As a result, first, young children's playfulness, leadership, and resilience were higher than average, ranging from 3.42 to 3.65 on a 5-point scale. In order of social spontaneity, leadership was ranked in the order of initiative, sociability, immersion, and sensitivity, and resilience was ranked high in the order of competence elasticity, emotional elasticity, and relational elasticity. Second, it was found that there was a correlation between children's playfulness and leadership, playfulness and resilience, and leadership and resilience. Third, as a result of examining whether resilience has a mediating effect on the influence of children's playfulness on leadership, it was found that playfulness affects resilience and playfulness affects leadership. It was found that there is a mediating effect. The results of this study suggest that in order to develop leadership, a competency demanded by the future society, the play-centered curriculum emphasized in the field of early childhood education must be faithfully practiced. It suggests that it is necessary to recognize the importance of activities that promote resilience and to provide various experiences for this purpose.
Offensive Language Detection Using Soft Voting Ensemble Model

Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic moderation, such as the usage of machine learning, can help detect and filter this particular language for someone who needs it. This study focuses on improving the performance of the soft voting classifier to detect offensive language by experimenting with the combinations of the soft voting estimators. The model was applied to a Twitter dataset that was augmented using several augmentation techniques. The features were extracted using Term Frequency-Inverse Document Frequency, sentiment analysis, and GloVe embedding. In this study, there were two types of soft voting models: machine learning-based, with the estimators of Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and AdaBoost as the best combination, and deep learning-based, with the best estimator combination of Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The results of this study show that the soft voting classifier was better in performance compared to classic machine learning and deep learning models on both original and augmented datasets.
Pre-training Two BERT-Like Models for Moroccan Dialect: MorRoBERTa and MorrBERT

This research article presents a comprehensive study on the pre-training of two language models, MorRoBERTa and MorrBERT, for the Moroccan Dialect, using the Masked Language Modeling (MLM) pre-training approach. The study details the various data collection and pre-processing steps involved in building a large corpus of over six million sentences and 71 billion tokens, sourced from social media platforms such as Facebook, Twitter, and YouTube. The pre-training process was carried out using the HuggingFace Transformers API, and the paper elaborates on the configurations and training methodologies of the models. The study concludes by demonstrating the high accuracy rates achieved by both MorRoBERTa and MorrBERT in multiple downstream tasks, indicating their potential effectiveness in natural language processing applications specific to the Moroccan Dialect.
Integration of the Hybrid Decision Support System and Machine Learning Algorithm to Determine Government Assistance Recipients: A Case Study in the Indonesian Funding Program

The Indonesian government provides incentives to facilitate community development through various funding programs to improve the economy and restore the national economy. However, there were many obstacles in determining the proper target beneficiaries. This study aims to assist decision-makers in determining targeted and accountable beneficiary candidates. In this study, a hybrid Analytical Hierarchy Process (AHP) method with Simple Additive Weighting (SAW) was used and integrated with machine learning modeling using Logistic Regression (LR). The AHP approach is used to determine the weight of each criterion, and the SAW method is used to sort out each available alternative with the help of an expert team's assessment. Instead, the LR method is used for the predictive analysis and classification of the resulting data.
SMART CONTRACT IMPLEMENTATION USING BLOCKCHAIN IOV FOR VEHICLE ACCIDENT INVESTIGATION

Recent advancements in digital accident forensics, a conceptual evidence management paradigm developed using smart contracts and interplanetary file system in iov. This paper comprehensively summarizes the Smart contract implementation blockchain framework for vehicle accident investigation in IoV. We investigate comparing some review papers to find the classification of the smart contract. Using blockchain, evidence management provides an immutable and auditable method for investigating and resolving accident cases. Precisely we first investigate the security and privacy threats; therefore, Smart contracts provide effective access control for proof data and reports. On both the public and private Ethereum blockchains, the cost of setting up and executing transactions using smart contracts is assessed. However, we utilized the Inter Planetary File System most efficiently while minimizing memory and execution costs. Finally, we draw open research directions for building future digital-proof management.
SIMULATION-BASED NETWORK FAULT INJECTION IN THE CLOUDSIM PLUS CLOUD SIMULATION ENVIRONMENT

One effective method for assessing the dependability of computer systems is fault injection. This deliberate technique introduces faults into a system to assess its resilience and ability to handle abnormal conditions. Therefore, this study investigates and simulates the different network problems in the CloudSim Plus environment. CloudSim Plus is a simulation framework that enables the modeling and simulation of cloud computing environments, allowing researchers and practitioners to evaluate the performance and behavior of cloud-based systems and algorithms. Network fault detection and its management are essential duties in cloud systems. Moreover, the feasibility of manual monitoring and involvement has decreased as these infrastructures expand and change. This paper briefly introduces network problems and fault injection outcomes in CloudSim Plus nodes.