Reviewerly/exHarmony huggingface dataset:
Shoe Review Sentiment Analysis Using Machine Learning and Deep Learning with Word2vec

In this modern era, internet use is continually increasing and will run with an increasing amount of existing data, such as text data. Characteristics of unstructured text are a challenge in text processing feature extraction and encourage sentiment analysis research to be carried out. The availability of a lot of text data on the internet is a challenge in sentiment analysis because it requires a complex approach. This study uses the baseline Deep Learning (DL) method, namely Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) with word2vec. It uses Machine Learning (ML), namely Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naïve Bayes (GNB) with the proposed method, word2vec, and the dataset used is the shoe review dataset which consists of 389,877 reviews. From the discussion above, to carry out sentiment analysis, a more suitable method is to use the CNN baseline method with word embedding word2vec to get an accuracy value of 91.53%. The novelty of this study is the increase in the classification accuracy value from previous studies.
ANALISIS DETEKSI FRAUD LAPORAN KEUANGAN DENGAN METODE BENEISH M-SCORE (STUDI KASUS PT BANK TABUNGAN NEGARA PERSERO TBK)

The financial statements present important disclosures in maintaining the quality of the capital market also explain the current state of the company and its going concern in the future. However, many financial reports are disclosed as misstatements due to manipulation, falsification, and fraud by changing the value and financial information. Self-detection of financial statement fraud is an important matter that must be prioritized. This study uses the Beneish M-Score model to detect potential manipulation of annual financial reports with case study in PT Bank Tabungan Negara (BTn) Persero Tbk for the 2017-2019 book year.&#x0D; Based on the results of data processing, the M-Score value of the company’s financial statement in 2017, 2018, and 2019 respectively was -2,16; -2,20; and -1,87. Those value were greater than the reference value was -2,22. So it can be concluded that based on this model there was potential for manipulation in PT BTN Persero Tbk’s financial statements. The Beneish M-Score model is used to detect potential financial statement fraud or income manipulation in the company’s financial statements. Moreover this model is very useful for shareholders, investors, and creditors to examine the fairness of company’s financial statement. &#x0D; Keywords : Beneish M-Score, Fraud, Financial Statement, Bank BTN Persero Tbk
Teachers’ Knowledge and Practice of Using ICT Tools in High School Science Teaching and Learning

Information and communication technology (ICT) tools are the integral part of innovative science teaching. Effective uses of ICT tools play a vital role in teaching and learning in this era. This research was done to analyze the level of expertise and practice of using ICT tools among teachers in classroom teaching. The study is based on quantitative descriptive design. Four municipalities and 16 schools were selected randomly. Fifty science teachers were selected on the basis of census sampling from Siraha district. The collected data were evaluated by utilizing statistical tools which indicates that most science teachers have inadequate access to ICT tools and have moderate knowledge and practice of handling them. All teachers used mobile phone and internet for personal purpose but ICT tools practices were poor in an integrated science classroom. The traditional lecture-based pattern of teaching, lack of ICT-specific knowledge and practice, content-based knowledge of teachers, aged teachers, inadequate ICT infrastructure and lack of ICT-friendly environment are the major barriers to the transfer of ICT teaching-learning process. The study suggested that there is the need for conducting of ICT based educational training, and preparing the teaching guidelines for regular use of ICT tools.
Development of a technological map of a mathematics lesson on the Federal State Educational Standard

The article systematizes the material on the construction of the technological map of the lesson. An example of a step-by-step development of a math lesson for the 5th grade on the topic &amp;quot;Signs of divisibility by 3 and 9&amp;quot; and the author's version of filling out the technological map of the lesson is presented.
IMPROVING LEARNING MOTIVATION BY APPLYING USER-CENTRED DESIGN AND AUGMENTED REALITY ON 3D INTERACTIVE APPLICATION

Conventional methods in teaching are still prevalent in the educational field to present knowledge to students. This method is still relevant today; however, it needs technology to enhance the experience and support an intelligent education system. Witama Primary School is one of the schools that applies modern technologies in teaching processes to improve learning motivation, especially in natural science. Students need an interactive instrument or tool that attracts attention and boosts their learning motivation while studying solar systems using a smartphone. Therefore, to present a virtual solar system experience, this study aims to create an interactive 3D solar system using Augmented Reality (AR) on iOS mobile-based by implementing a user-centered design (UCD) method to understand requirements and expectations. UCD consists of four main stages, specifying the context of use, user and organizational requirements, product design solution, and evaluation design before developing the system to ensure the finery development process as the goal is to improve the student's focus and learning motivation. According to the User Experience Questionnaire results to thirty respondents, the obtained aspect of attractiveness is 2.222, the perspicuity aspect is 2.129, the efficiency aspect is 1.902, the dependability aspect is 1.689, the stimulation aspect is 2.222, and the novelty aspect is 1.727. Overall results showed that the interactive mobile app is in the good to excellent range. Moreover, the students and teachers find the product exciting and feel motivated to use the mobile app further
Application of information modeling technology in geotechnics

Geotechnics is an essential component of construction and engineering that involves studying how rock and soil interact with structures.Borehole drilling and sample collections are traditional methods used to collect subsurface data, but they are time-consuming, expensive, and only cover a small area.Information modeling technology can address these challenges by building intricate digital models of subsurface conditions that are more accurately and effectively used to design and analyze structures such as buildings, bridges, tunnels, and dams.This article discusses the main stages of creating an information model of a geotechnical object, including preparatory work, preliminary tests and work volume determination, field tests, and laboratory tests.The resulting digital engineering-geological model should include the geometric dimensions of the investigated soil mass and the physical and mechanical characteristics of the soils in the IFC data format.
A stratigraphic classification estimation method by Artificial Neural Networks for Geotechnical Information Database

The purpose of this study is to confirm the applicability of Artificial Neural Networks for stratigraphic classification estimation.The study proposes a machine learning procedure in consideration with ununiformity of spatial density of investigation locations, their depth and different appearance frequency of soils.The proposed method has been applied to Geotechnical Information Database in Fukuoka plain, Japan.The results have shown the proposed method is extremely effective for stratigraphic classification estimation by Artificial Neural Networks.
Metaverse to foster learning in higher education

&lt;p align="justify"&gt;This research refers to the new information and communication technologies that have emerged in recent years and that are being used in education as a pedagogical resource. Thus, Metaverse have been applied in universities obtaining great benefits such as improving interaction and communication between students and teachers through personalized avatars regardless of the physical limitations of the real world, developing skills and abilities that encourage learning and immersion, leaving aside the traditionalist learning. The use of Metaverse in education aims to make the student the protagonist of his or her own learning and, in addition, seeks to improve collaborative learning to encourage active participation among students through the interaction of avatars within the Metaverse. As a practical alternative and an emerging trend, the tool “AltspaceVR” is proposed for higher education. The sample of this study consisted of 159 graduates between the ages of 18 and 35 who were pursuing their doctoral, bachelor’s, master’s or other studies, and 7 experts working as teachers in Spanish universities, including a doctor in educational methodologies who has been named “Professor of the Year” on several occasions. Among the main conclusions of the study we have obtained that students consider that the implementation of Metaverse in higher education will promote learning and immersion, allowing the development of pedagogical activities in a more innovative and interactive way thanks to its graphical interface. On the other hand, from the results of the survey we conclude that the teacher lacks knowledge to make adequate use of Metaverse as a pedagogical resource, using only two-dimensional and mainly verbal platforms that only generate a traditionalist and non-interactive learning.&lt;/p&gt;
Convolutional Neural Network Based Data Security in Image Steganography

Steganography has made significant progress in recent years but struggles with various obstacles and hurdles.Image steganography refers to the technique of maintaining privacy under a cover picture.This data might take the shape of words, pictures, or videos.This research offers a compact, simple, and fully convolutional design to incorporate a hidden picture within a cover picture and to recover the contained hidden picture from the input image.The paper bases its proposal on an in-depth learning approach and picture-based general steganography techniques.The proposed method uses Convolutional Neural Network (CNN) based steganography to hide the secret information and steganalysis to recover the secret information.Also, it focuses on performance metrics like Peak Signal Noise Ratio (PSNR), Universal Image Quality Index (UQI) and Spatial Correlation Co-efficient (SCC).The outcomes of the experiments have shown that the presented scheme has superior outcomes in terms of concealing capability, confidentiality and resilience and interpretability compared to previous deep-learning picture steganographic techniques.
Modeling of sub-ballast and geogrid interaction

The article presents the modeling results of the soil and geosynthetics interaction.To achieve this goal, a mathematical model was developed and FEM calculations were performed using the Plaxis program.The results of the calculations showed the coincidence of the soil behavior in the course of physical and mathematical modeling.The results of calculations using the Plaxis software package confirmed the influence of the angle of internal friction and the angle of dilatancy on the increase in the pulling force due to the attachment of a larger volume of soil.The influence of the geogrid ribs on the stress-strain state of the stabilized soil massif was estimated.Reducing the number of ribs leads to the concentration of shear zones along the web of the geogrid in close proximity to it, which reduces the pull-out resistance.
Displacement field measurement of stacked geotextile tubes by digital image correlation

Displacements often need to be measured to evaluate the stability of earth dams.Various instruments for measuring the displacement exist, but most contacting position sensors can measure the displacement only at a certain position.Digital image correlation is a promising technique for measuring a displacement field through a camera without any contact with the measurement points.In this study, variations in the displacement fields of stacked geotextile tubes with the water level are evaluated using digital image correlation.Experimental results show that the vertical and horizontal displacements of the bottom layers are larger than the top layers with an increased water level.The top layer mainly moves backward and then moves upward and forward.In contrast, the bottom layer consistently moves upward and forward after an initial downward and backward movement.Thus, the digital image correlation method suggested in this study may be effectively used to evaluate the behavior of a temporary dam structured with geotextile tubes.
The Impact of Effective Performance Appraisal on Employee Productivity

Performance appraisal is a process in which employees are evaluated based on their work performance and productivity. An effective performance appraisal system can have a significant impact on employee productivity, job satisfaction, and overall organizational performance. Research studies have shown that effective performance appraisal systems lead to increased employee motivation, job satisfaction, and engagement. Employees who receive regular feedback and are aware of their strengths and weaknesses tend to be more productive and focused on achieving their goals. Furthermore, an effective performance appraisal system provides a platform for employees to communicate their concerns and aspirations to their supervisors. This communication helps to build a better relationship between employees and their managers, which in turn promotes a positive work culture. Moreover, an effective performance appraisal system helps organizations to identify the training needs of their employees. This identification leads to a more targeted and effective training program that enhances employee skills, knowledge, and productivity. In conclusion, an effective performance appraisal system is a critical tool for enhancing employee productivity. It provides employees with regular feedback, promotes better communication between employees and managers, and identifies training needs. Organizations that implement an effective performance appraisal system tend to have more engaged and productive employees, leading to increased organizational performance.
Causes of Students’ Error in Writing Recount Text at 9th Grade SMPN 5 Kec. Kapur IX

This study aims to find out the types of errors and the causes of students’ error in writing recount texts. The researchers found some student errors in the recount text at 9th grade SMP N 5 Kec.Kapur IX related to errors’ made by students. The first is that some students find it difficult to deliver idea in writing recount text. The second some students lack vocabulary so that students have difficulty in writing recount text. The last some students grammar have difficulties writing recount text. This type of research is descriptive qualitative. The instruments of this research are documentation and interview. The informant of this research is all students of class IX SMP N 5 Kec.Kapur IX which consists of 2 classes. The sample from this study was 31 students who were taken through purposive sampling. This research was conducted in February 2022. To find the type of error in writing the recount text, the researcher checked the students’ recount text answer sheets, while the researcher interviewed the students and recorded it through the researchers phone. The results of this study indicate that there is one error in writing recount text that is the use of the simple past tense. While the cause of student error based on the results of interview are carelessness, first language interference and translation. In conclusion, this study was conducted to find out the types of errors and the causes of students’ errors in writing recount texts at 9th grade SMP N 5 Kec.Kapur IX.
An Adaptive Ensemble Learning-Based Smart Face Recognition and Verification Framework with Improved Heuristic Approach

Face recognition is an uncontrollable surveillance system but faces complex issues regarding minimal resolution, expression modification, and motion blur.Nowadays, researchers find more interest in face recognition analysis on pattern recognition and computer vision models.Several advancements in deep learning approaches for face recognition displayed a better efficacy rate in standard datasets.However, real-time face recognition approaches are applied and offer a poor efficacy rate.This modification leads to more changes in the testing and training set.Among these changes, misalignment of the face over testing and training effectively degrades the face recognition rate.Hence, resolving the abovementioned complications in the classical face recognition and verification technique is essential.Thus, a brand new face recognition and verification approach is developed based on optimal features and other approaches.Initially, images associated with face recognition are attained from the standard dataset and offered to pre-processing phase using median filtering.Then, the pre-processed images are fed to the feature extraction phase, the spatial features are acquired by Local Binary Pattern (LBP), and the spectral features are attained by 3 Discrete Wavelet Transform (3-DWT) levels.Later, the attained features are concatenated and utilized to obtain the optimal features by the Adaptive Crossover and Stallions Percentage-based Wild Horse Optimization (ACSP-WHO).Further, the attained optimal features are fed as the input to Ensemble Learning Network (ELNet)-based face recognition and verification phase, where the ensemble technique having Support Vector Machine (SVM), Deep Neural Networks (DNN), and Adaboost are utilized, and also the ACSP-WHO tunes their parameters to attain the practical face recognized and verified outcome in the developed model.Finally, the suggested face recognition and verification framework secured an effective efficacy rate than the traditional approaches in different experimental observations.
Facial Expression Recognition for Low Resolution Images using Local and Global Features with SVM Classifier

Human Machine interaction similar to human-human interaction is possible if machines can recognize expressions on human faces during communication. Facial expression recognition is easy for humans but a difficult task for machines. Facial expression recognition (FER) by machines is expected inthe next generation of computers.Image acquisition, preprocessing, feature extraction and classification are the steps in FER.The accuracy of recognition depends on all these steps.Happy, sad, angry, fearful, surprised, disgusted and neutral are the seven expressions considered for classification.Local Binary Pattern (LBP), Histogram of Orientations (HOG), Shift Invariant Feature Transform (SIFT) and Optical Flow (OPF) are the four different feature extraction methods that are used.This paper compares the different feature extraction methods, and multiclass SVM is used as a classifier.The experiment is performed on three different datasets.The novelty of the work is the low-resolution dataset and combination of local and global features.Local feature extractors LBP and HOG are more efficient than global feature extractors SIFT and OPF.Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to reduce the dimensionality and improve the speed of execution.The confusion matrix is used to compute the accuracy of recognition, specificity and sensitivity.Recognition accuracy is suitable for images captured in controlled laboratory scenarios, but still, more work is required for wild images on both feature extraction and classifier.
Handwritten Character Recognition of Kannada Scripts using Novel Feature Extraction Techniques and BMCNN Classifier

Handwritten Character Recognition (HCR) is one of the most popular research in recent years.Many HCR systems were developed based on various languages.However, only a few works are based on handwritten Kannada characters.Recognising handwritten Kannada characters is challenging because of the curvy and symmetric nature of the Kannada characters.Although various works were conducted for Kannada HCR, some issues must be solved.Hence, this work proposed BMCNN-based Kannada HCR.In the preprocessing phase, 3M filtering and the CLAHE techniques perform noise reduction and contrast enhancement.Then, the image is resized, angle rotated and mirror-inverted to obtain better accuracy of the input image.Then, the zonal, pattern and gradient features are extracted from the preprocessed image.Next, the significant features are selected by ISSA and then given to the BMCNN classifier to recognise the input Kannada character.To prove the efficiency of the proposed framework, the experimental analysis is conducted in terms of various measures and compared with state-ofart techniques.The results showed that the proposed recognition technique performs better than the existing techniques.
Applicability Evaluation of Full-Reference Image Quality Assessment Methods for Computed Tomography Images

Abstract Image quality assessments (IQA) are an important task for providing appropriate medical care. Full-reference IQA (FR-IQA) methods, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), are often used to evaluate imaging conditions, reconstruction conditions, and image processing algorithms, including noise reduction and super-resolution technology. However, these IQA methods may be inapplicable for medical images because they were designed for natural images. Therefore, this study aimed to investigate the correlation between objective assessment by some FR-IQA methods and human subjective assessment for computed tomography (CT) images. For evaluation, 210 distorted images were created from six original images using two types of degradation: noise and blur. We employed nine widely used FR-IQA methods for natural images: PSNR, SSIM, feature similarity (FSIM), information fidelity criterion (IFC), visual information fidelity (VIF), noise quality measure (NQM), visual signal-to-noise ratio (VSNR), multi-scale SSIM (MSSSIM), and information content-weighted SSIM (IWSSIM). Six observers performed subjective assessments using the double stimulus continuous quality scale (DSCQS) method. The performance of IQA methods was quantified using Pearson’s linear correlation coefficient (PLCC), Spearman rank order correlation coefficient (SROCC), and root-mean-square error (RMSE). Nine FR-IQA methods developed for natural images were all strongly correlated with the subjective assessment (PLCC and SROCC &gt; 0.8), indicating that these methods can apply to CT images. Particularly, VIF had the best values for all three items, PLCC, SROCC, and RMSE. These results suggest that VIF provides the most accurate alternative measure to subjective assessments for CT images.