Reviewerly/exHarmony huggingface dataset:
Smart Edge Detection Technique in X-ray Images for Improving PSNR using Prewitt Edge Detection Algorithm with Gaussian Filter in Comparison with Laplacian Algorithm

Aim: This study aims to propose smart edge detection techniques in x-ray images for improving PSNR using the Prewitt edge detection algorithm and comparing it with the laplacian algorithm. Materials and methods: For the design of edge detection technique to improve PSNR Prewitt edge detection algorithm is used along with a gaussian filter and it is compared with the laplacian algorithm. Prewitt edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group, the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc. com by keeping g-power at 80%, confidence interval at 95%, and the threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the Prewitt edge detection algorithm has a higher mean PSNR value of 39.19 db than the laplacian algorithm of 37.56 db. It is observed that the Prewitt edge detection algorithm is statistically significant p&lt;0.05 than the laplacian algorithm by performing an independent sample t-test. Conclusion: Prewitt edge detection has insignificantly greater PSNR when compared to the Laplacian algorithm.
Enhanced Local Knowledge with Proximity Values and Syntax-Clusters for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis (ALSA) aims to extract the polarity of different aspect terms in a sentence. Previous works leveraging traditional dependency syntax parsing trees (DSPT) to encode contextual syntactic information had obtained state-of-the-art results. However, these works may not be able to learn fine-grained syntactic knowledge efficiently, which makes them difficult to take advantage of local context. Furthermore, these works failed to exploit the dependency relation from DSPT sufficiently. To solve these problems, we propose a novel method to enhance local knowledge by using extensions of Local Context Network based on Proximity Values (LCPV) and Syntax-clusters Attention (SCA), named LCSA. LCPV first gets the induced trees from pre-trained models and generates the syntactic proximity values between context word and aspect to adaptively determine the extent of local context. Our improved SCA further extracts fine-grained knowledge, which not only focuses on the essential clusters for the target aspect term but also guides the model to learn essential words inside each cluster in DSPT. Extensive experimental results on multiple benchmark datasets demonstrate that LCSA is highly robust and achieves state-of-the-art performance for ALSA.
A DESCRIPTIVE STUDY ON STUDENTS’ SPEAKING ABILITY IN “DAILY CONVERSATION” CONTEXT AT HIGHER EDUCATION

Speaking is one of English skill that has been learnt from junior high school until higher education. It means that students in higher education should have good capability in doing speaking in all contexts, include in daily conversation context. For this reason, this study was designed to determine the extent to which students' ability in speaking especially in daily conversation context. This study took 22 students in Educational Faculty of Tompotika Luwuk University as the participants. Descriptive qualitative is a method that chosen in this study. The data have been collected from observation and documentation. Observation and documentation collected when the students doing conversation. The result of this study shows that the students’ speaking ability in daily conversation context can be categorized into “poor”. It was proven by the students who got only 18,18% in good category, 22,72% in average category, 40,92% in poor category, and 18,2% in very poor category. Finally, it can be conclude that the students’ speaking ability in daily conversation context at higher education of Educational Faculty of Tompotika Luwuk University categorized into poor.
Analisis Kesulitan Belajar Matematika Siswa Di Kelas VI SD Negeri 101944 Deli Muda

&#x0D; &#x0D; &#x0D; &#x0D; Decreased student learning outcomes indicate the occurrence of difficulties in learning. This research was conducted to determine students' learning difficulties in mathematics in grade VI, the level of students' learning difficulties in mathematics, and to find out the causes of students' learning difficulties in mathematics in class VI SD Negeri 101944 Deli Muda. The research design is descriptive analysis, where this research uses quantitative research methods. the level of learning difficulties of students, especially in learning mathematics in class VI amounted to 63.1% which were identified as having learning difficulties in class, or in other words there were 12 out of 19 students / I who experienced learning difficulties with the type of learning difficulties in mathematics (Discalculia) which included difficulties in understand the concept of arithmetic in the material for addition of integers, mixed arithmetic operations, and understand how to solve problems in the material for solving area and perimeter problems in plane and geometric shapes. There is 5.2% at the level of difficulty with the type of reading difficulty or (dyslexia) or in other words there is 1 student who experiences an error when reading the questions. The level of difficulty with the type of writing learning difficulty (Disgraphia) is 0%, because there are no students in grade VI who have difficulty writing. There are several factors that cause learning difficulties in mathematics to occur, namely internal factors that occur from within the student, which include student interest and motivation in learning, study habits, and the health condition of the student's body. While external factors that occur from outside the student's self, which include environmental conditions, the relationship between teacher and student, and media/learning aids.&#x0D; &#x0D; &#x0D; &#x0D;
AN OVERVIEW OF THE EXISTING TEXT-TO-SPEECH(TTS) ALGORITHMS BASED ON DEEP LEARNING

Text-to-speech technology has been around for a long time, but it is still considered challenging. The creation of human-like synthesized speech requires the solution of many tasks. As an example, the definition of intonation, the polyphony of systems, the combination of pairs of phonemes without audible flaws, and the definition of prosody. Various methods exist for synthesizing speech from the text, including parametric synthesis, compilation synthesis, domain-specific synthesis, and full speech synthesis by rules. With the advent of the necessary computing resources and the accumulation of sufficient data for training, machine learning methods have become widely used in speech synthesis. With significant improvements in text-to-speech algorithms, text-to-speech is now widely used in mobile devices, smart speakers, and voice assistants. In spite of this, the algorithms of this class, developed at the present time, are not always able to complete the assigned task correctly. In some cases, they are unable to correctly put stress on the right parts of the text or speak with the right intonation. In this study, the existing text-to-speech deep learning algorithms and models are reviewed and compared. The comparison is made by taking into account the positive and negative aspects of the algorithms, the complexity of implementing the algorithms, and the availability of the existing libraries for implementation. We consider the following algorithms as the main ones: WaveNet, DeepVoice 3, Tacatron 2, and Transformer.
Vehicle Steering System for People with Disability using Gesture Control

Gesture recognition and control is a relatively modern concept, humans through their body gestures generate physical control signals in order to interact and control technologies around them. It has found important application in the field of robotics, electrical appliances, computer gaming, fitness accessories and other sectors. This paper showcases a new mechanism which assists people with disabilities, specifically people who don’t have completely developed arms or have lost their arms in an accident during their lifetime. The mechanism employs accelerometer to recognize gesture and generate control signals accordingly. These signals then have been used to steer a vehicle. Accelerometer is installed on a module which is strapped on to the tip of disabled person’s amputated arm. This mechanism being electronic in nature is an upgrade from previously available mechanical driving assistance mechanisms.
Automatic Quality of Experience Measuring for Video Conference in Real-Time

In recent years, especially with COVID-19, video conference applications have become very important. Millions of peoples around the world have become to communicate with each other through using video conference applications. The most critical factor in the performance success of video conference applications is the user's perception of the quality of the experience. In this work, an Extreme Learning Machine (ELM) model was proposed for predicting video quality of experience. The proposed system extracts several features from videos that have a significant impact on the quality of the experience. The model performance was validated with unseen data. Spearman’s Rank Correlation Coefficient (SRCC), Root Mean Square Error (RMSE), Pearson’s Linear Correlation Coefficient (PLCC) metrics have been used to measure the accuracy of the model and correlation. The results demonstrate that the proposed model had better performance than models used by the previous researchers that were used for predicting video QoE in terms of precision, correlation, and running time. Index Terms— Extreme Learning Machine, Quality of Experience, Quality of Service, Video Streaming.
Persisting misogyny: a gendered analysis of online harassment of Indian content creators on Instagram and its impact on mental health

As the digital medium increasingly becomes an irreplaceable part of life for people, trolling is on the rise too. The anonymity provided by the digital platform helps trolls abuse and harass an online content creator. This study attempts to study the patterns of trolling faced by online content creators in India. The analysis of trolling comments reveals deep-rooted misogyny in trolls’ minds, with women being subjected to demeaning abuses that are sexual. The study also discusses what effect trolling has on content creators’ mental health and how they negotiate with it. With social networking use spreading through the daily lives of the bulk of the world’s population, it’s becoming more critical than ever to comprehend a user’s interaction with these new-age multimedia networks and recognize the degree and sort of effect these sites can have on a user’s behavior in both personal and professional settings. The answers of the participants offer insight into various viewpoints on social media use, with a focus on the Instagram website. According to the results of this report, New Zealand females have not substantially modified their social media behavior habits, but have refined their use in a way that now supports their personal and professional lives.
Analysis and comparison for prediction of Diabetic Pregnant women using Innovative Principal Component Analysis algorithm over Support Vector Machine Algorithm with Improved Accuracy

Aim: The study’s aim is to analyze and compare the accuracy, sensitivity, and precision of diabetic prediction among pregnant women using the innovative Principal Component Analysis algorithm and Support Vector Analysis. Materials and Methods: This study involves two groups: Principal Component Analysis (N=20) algorithm and Support Vector Machine (N=20) with a sample size of 40 for each group. The sample size calculation uses a pre-test power of 80%, a threshold of 0.05, and a confidence interval of 95%. Results: Performance of algorithms are measured using accuracy, sensitivity, and precision. Principal Component Analysis algorithm results in mean accuracy of 79.43% significantly different with P=0.488(p&gt;0.05), a sensitivity of 79.29% with P=0.096 (p&lt;0.05), and a precision of 83.57%. Support Vector Machine algorithm results in mean accuracy of 77.67%, a sensitivity of 76.67%, and a precision of 83.54%. Conclusion: Principal Component Analysis algorithm performed significantly better than the Support Vector Machine algorithm for Diabetic prediction.
Society and Genius in K. E. Tsiolkovsky`s Approach: Cultural and Philosophical Aspect

The study deals with the issue of the relationship between society and those individuals who are called geniuses from the point of view of the concept of the Russian thinker, autodidact, scientist and teacher K.E. Tsiolkovsky, who presented the main points on this topic in his works. The text consistently collects and systematically presents the specifics of the figure of genius itself and the qualities that it should possess, as well as a modern view of the acceptance of the genius of discoveries. As an example of a creative and brilliant personality in relation to other people and the view that we have today, the method of neurography is considered. At the final stage, the question is raised about the possibility of genius and the formation of a type of society that can be called perfect. The author draws parallels between the views of K.E. Tsiolkovsky and his Western contemporary J. Dewey on education and creative activity in it, in the context of educating a future genius. As a result, the author reviews the ranks of personalities proposed by K.E. Tsiolkovsky.
Las nuevas tecnologías y su implementación en el sistema educativo

Education is an essential part for social development, this is because through it there is training in which people learn values as well as obtain various skills through the knowledge acquired through education, with the passage of time education has had great changes this is due to the various tools and systems that have emerged to have a greater scope on the people, so that they can understand their environment more efficiently to develop their skills; The birth and implementation of technology has generated great changes within society has brought with it a great variety of tools as well as the implementation of technological systems that allow human activities to be developed more efficiently as well as in less time, reducing costs and labor so that since its arrival society has been evolving on a recurring basis. The objective of the article is to show that new technologies have had a great relevance in the educational system, which with its implementation has brought great benefits for teaching and learning that develops around education.
impact of 360° performance appraisals on employees’ performance: The case of the Lebanese sector

Today, it is critical for businesses to consider employee satisfaction at work and methods for increasing company performance. The primary issue of the research is whether performance assessments are performed and administered properly in Lebanon. If so, what performance criteria are utilized to assess employee’s performance. The purpose of this research is to examine the relationship between performance appraisals and job satisfaction in the banking sector. Additionally, this research analyses the problems associated with excessive employee turnover in the banking industry. Additionally, the research will assess the relevance and value of the human resources department in the banking sector with regards to employee competency retention. Additionally, the Human Resources Department is critical in implementing the banking sector's employee retention strategy.
The effect of online games on students’ mathematics learning outcomes at SMPN 1 Sintuk Toboh Gadang

The purpose of this study was to see the significant effect of online games on students' mathematics learning outcomes at SMP N 1 Sintuk Toboh Gadang. The approach used in this study is a quantitative approach with the type of correlational research, namely research conducted between two or more variables that aims to find out whether there is a relationship and if there is, how close the relationship is and whether or not the relationship is meaningful. The instrument used in this study was a questionnaire or questionnaire and learning outcomes test used to determine the effect of online games on students' mathematics learning outcomes. The analysis used in this study was simple linear regression analysis. The results showed that: 1. The mathematics learning outcomes of grade VIII SMPN 1 Sintuk Toboh Gadang students who liked to play online games varied widely 2. There was an influence between playing online games on the mathematics learning outcomes of eighth grade students of SMPN 1 Sintuk Toboh Gadang, which was indicated by F count = 47, 85 F table = 4.6 this can also be seen from the regression coefficient value of -0.882. The simple linear regression equation obtained is Y = 184.41-0.6X where Y is the result of learning mathematics and X is the online games variable. The conclusion is that there is a negative effect of online games on students' mathematics learning outcomes in class VIII SMPN 1 Sintuk Toboh Gadang, both up and down and all independent variables (X) have an effect on the dependent variable (Y) of 77.7%.
Factors Explaining Metaverse Users’ Charity Willingness

From the onset of the COVID-19 pandemic, the metaverse emerged as a new platform where people interact and communicate. Of many changes this technology introduced, people active in the virtual world do not have many opportunities to run into neighbors in need as it has before the pandemic. So this research set out to investigate what factors are associated with people’s willingness to give to charity amidst the pandemic. More importantly, the group difference between metaverse users and non-users was analyzed and compared. The result shows that personal attributes like extroversion, compassion, resilience, and even academic achievement were positively correlated with people’s willingness to give to charity. In the group comparison, metaverse users were more likely to give charity to people in need as opposed to non-users. The users were more likely to have received help from others, thus more willing to give back to those who needed help.
Implementation Of Mathematics E-Learning For Class IV Students Of MI Al-Azhar Menganti Gresik

Education is the process of changing attitudes and behavior of a person or group of people in an effort to mature human beings through teaching and training efforts. Learning mathematics at this time due to covid-19, learning is very inefficient and cannot meet face-to-face between teachers, students and friends. Therefore, because of the COVID-19 pandemic, students are asked to study at home using e-learning that utilizes social media. This study uses a descriptive qualitative approach with the research subject of the principal, the head of the curriculum, class teachers and fourth grade students. Data collection methods used are observation, interviews and documentation. The data analysis used is the Miles and Huberman model with data reduction techniques, data presentation and conclusion drawing. The results of the research regarding the implementation of e-learning mathematics for class IV MI-Azhar students are the implementation of learning that is carried out without direct face-to-face between teachers and students as well as accessing the internet network and using social media to exchange information and interactions. Among them is by using the WhatsApp application, Google Meet and YouTube.
Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease

Recently, transfer learning technique has proved to be powerful in enhancing development of deep learning methods for sickle cell disease (SCD) detection as a complement to the clinical method where a hemoglobin electrophoresis machine is used. This is evidenced by a number of models and algorithms with ≥90% prediction accuracy. From literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3 and ReNet. However, training and testing of these methods are limited on one model and separate dataset which may lead to biased results due to implementation in variation of these models which affects results produced. To this end, there exists a need to evaluate the SCD models using the same dataset. Thus, in this research study, we carried out a comparative investigation and evaluated predominate pre-trained models used to detect SCD using the same dataset to ascertain which one has the best accuracy. We used secondary dataset obtained from an online dataset. In our study, we have discovered that Inception V3 yielded the highest accuracy of 97.3% followed by VGG19 at 97.0%, VGG16 at 91%, ResNet50 at 82% and ReNet at 67%, and the CNN-scratch model achieved 81% accuracy. Results from our study will aid researchers and industry practitioners to make decision on the best deep learning model to use while detecting SCD.
Application-Aware Analysis of Network Neutrality: A Scalable Real-Time Method

Internet access subscribers expect a satisfying quality of experience for any accessed service, independently from time, place, and service- and content-type. Besides the everincreasing amount of Internet data, the spectrum of video service platforms offering sharing and streaming also got significantly more comprehensive. Internet access providers try to avoid the exhaustion of network bandwidth by investing in network capacity or setting up higher-level resource management within their infrastructure. The primary question in this domain is how resource management constrains the subscriber to access an arbitrary service and experience good service quality.This question directly relates to network neutrality fundamentals. This paper presents a real-time full-reference objective method to assess network neutrality. It contributes three novelties to support user-centric analysis of potential restraints affecting Internet access quality: i) the proposal supports application-specific measurements and involves real content and real traffic, ii) the measured traffic originates from the content provider’s cloud infrastructure, iii) reference is created in real time. Accordingly, the proposal introduces a novel measurement layout. The key component is the emulated client that provides the real-time reference by emulating the access properties of the real client and accessing the same content simultaneously. We demonstrate the method’s feasibility with an applicationaware proof-of-concept use case: video streaming from a public VoD provider. We have validated the method against the emulated network parameters using an extensive series of laboratory measurements.
Enhancing Visual Domain Randomization with Real Images for Sim-to-Real Transfer

In order to train reinforcement learning algorithms, a significant amount of experience is required, so it is common practice to train them in simulation, even when they are intended to be applied in the real world. To improve robustness, camerabased agents can be trained using visual domain randomization, which involves changing the visual characteristics of the simulator between training episodes in order to improve their resilience to visual changes in their environment. In this work, we propose a method, which includes realworld images alongside visual domain randomization in the reinforcement learning training procedure to further enhance the performance after sim-to-real transfer. We train variational autoencoders using both real and simulated frames, and the representations produced by the encoders are then used to train reinforcement learning agents. The proposed method is evaluated against a variety of baselines, including direct and indirect visual domain randomization, end-to-end reinforcement learning, and supervised and unsupervised state representation learning. By controlling a differential drive vehicle using only camera images, the method is tested in the Duckietown self-driving car environment. We demonstrate through our experimental results that our method improves learnt representation effectiveness and robustness by achieving the best performance of all tested methods.