Reviewerly/exHarmony huggingface dataset:
A comparative Study of the Evolution Different Mobile Generations for Wireless Communication

The mobile communication system has revolutionized human life and made the world a small village through communications and data transmission in the form of voice calls, messages or videos with very high download speeds. This paper discusses the different communication technologies for mobile phone generations, starting from the first generation of primitive to the second and third generation, in terms of mobile voice technologies, then the fourth generation as signs of rapid data download speed started, as the speed of connection to the fourth generation networks is ten times higher than the generation connection The third, which led to the improvement of data traffic in the communications network, the high mobility recipe for people who make calls, in addition to improving global system management and improving voice quality, then the great revolution in the fifth generation that is characterized by covering all geographical areas with an increase in the high-frequency spectrum, which is characterized at high frequencies starting from 6 GHz to 100 GHz, in addition to comparing all these generations through illustrative tables.
MEASUREMENT OF HAEMOCYTES OF GASTROPODS THROUGH AN AUTOMATED IMAGE SCREENING APPROACH BY USING IMAGE PROCESSING TOOL

The present study was screened to determine the number of total haemocytes and measurement (mean radius) of haemocytes, cytoplasm, and nuclei in an image of gastropod haemocytes (Leishman stained) by using Cell Profiler (CP) version 2.1.0; an image analysis tool. The results of primary (nucleus), secondary (haemocyte) and tertiary (cytoplasm) object data, shown that marking of nuclei, haemocytes and cytoplasm by outline and total accepted objects were obtained 23 nos. in the studied image. The area shape of objects especially haemocytes, cytoplasm, and nuclei (arbitrary unit) in an image were also obtained. In conclusion, this study helps to easily screen the total nos. of haemocytes and area (mean radius) measurement of haemocytes, cytoplasm, and nuclei in the image.&#x0D;
Why &amp; How to Make a Personal Website (for Free)

When authors make their papers open access (OA), they increase their impact and contribute to social justice. 1 An impediment are
TRAINING AND DEVELOPMENT OF DIGITAL SKILLS IN GENERAL EDUCATION

Based on the analysis of the curricula in computer science subjects in the countries with traditions in the field and the experience of the Republic of Moldova, it is argued the need to further promote the curricular reform in general education. It is proposed to re-conceptualize the current and summative assessment methods in computer science disciplines by basing them on the evaluation of digital products developed by students: documents and electronic presentations, Web documents, computer programs, simulation models. It is recommended that the assessment itself be conducted in a digital environment, giving students the opportunity to demonstrate their knowledge and skills directly on the computer.
Developing Inquiry Basic Learning Models To Improve Skills Writing For Class Iv Students At Sd Negeri 2 Bungatan

This study aims to investigate the use of the inquiry-based learning model in writing activities. This is because the inquiry-based learning model is often used for learning science and mathematics, but in the application of the Indonesian language is rarely used. Many efforts can be done to improve Indonesian language learning, especially in writing skills. One of them is through the inquiry-based learning model. To present it attractively, preparation is required, starting from selecting materials, preparing a place, preparing a model, and presenting writing activities. The method used in this research is a mixed method which is combining of quantitative and qualitative methods. The research subjects were fourth-grade elementary school students consisting of 36 students in the experimental class and 36 students in the control class. Quantitative methods are applied to analyze data type intervals for student learning outcomes, while qualitative methods are applied to analyze ordinal data types for writing skills. Model validation is done by calculating the average assessment of experts and teachers, while the effectiveness of learning done by utilizing the results of individual student-written tests. The inquiry-based learning model is proven to be valid, practical, and effective according to the requirements of the learning steps. The results showed that there were significant differences between the two classes that applied the inquiry-based learning model and the conventional learning model. The results showed that the percentage of speaking skills for the control class was 82% the frequency of writing activities, the quality of students' writing was 84%, 84% writing creativity, and essay writing skills was 86%. While the experimental class was 90% the frequency of writing activities, the quality of the students' writing was 92%, the writing creativity was 92%, the writing skills were 94%. This shows that the application of the inquiry learning model can improve students' writing skills
" Estimation of Reference Evapotranspiration Using Support Vector Machines: a Case Study of Adana, Turkey. "

Evapotranspiration is an important parameter in hydrological and meteorological studies. Evapotranspiration forecasting plays an important role in irrigation management and hydraulic designs, especially during dry periods. In this study, average temperature (T), relative humidity (RH), wind speed (U), solar radiation (SR) parameters were used to estimate the daily evapotranspiration amount. Daily evapotranspiration estimation (ET0) was made according to the Penman-Monteith method recommended by FAO (Food and Agriculture Organization) as a standard method. The Penman-Monteith method was considered as the reference equation. Support Vector Machines (SVM) methods with four different input combinations were used to estimate the daily evapotranspiration of Adana province. SVM models were compared with each other and the reference equations’ results. According to the results obtained from SVM models, SVM3 model giave slightly better results according to the higher determination coefficient and lowest error data.
Analysis and Design of “Universitas Dinamika” Web Page Design in Creating Customer Experience and Buying Interest of New Students from Sidoarjo

The community's need for higher education in Indonesia continues to grow, seen from the increasing number of higher education institutions every year. Dinamika University as one of 328 Private Universities (PU) in East Java has implemented marketing tools using a website ( www.dinamika.ac.id ). However, the availability of the website has not been proven to be effective in attracting new students to register at Dinamika University. From 5134 new students, 3785 students (74%) received admissions information through offline media, 1256 students (24%) through online media, and 93 students (2%) through other media. This study aims to determine the effect of the design of visual and verbal web page elements on the buying interest of new students through customer experience. This study combines the PLS-SEM as an analytical method and the Double Diamond method in designing web page recommendations using Warp PLS 7.0 and Adobe XD. 100 experimental respondents were obtained from the purposive sampling method. In the process of designing web page recommendations, the crazy 8 design method will be used to sketch design, supervoting to select design recommendations, and usability testing to test prototypes. The results of this study prove that there is a significant influence between visual web page design on customer experience, verbal web page design on customer experience, verbal web page design on buying interest, customer experience on buying interest, visual web page design on buying interest through customer experience. However, there is also an insignificant effect between visual web page design on buying interest, verbal web page design on buying interest through customer experience. The results of testing the prototype obtained an average score of "Very Good" in its usefulness value.
Applying Text Rank to Build an Automatic Text Summarization Web Application

Abstract: Automatic Text Summarization is one of the most trending research areas in the field of Natural Language Processing. The main aim of text summarization is to reduce the size of a text without losing any important information. Various techniques can be used for automatic summarization of text. In this paper we are going to focus on the automatic summarization of text using graph-based methods. In particular, we are going to discuss the implementation of a general-purpose web application which performs automatic summarization on the text entered using the Text Rank Algorithm. Summarization of text using graph-based approaches involves pre-processing and cleansing of text, tokenizing the sentences present in the text, representing the tokenized text in the form of numerical vectors, creating a similarity matrix which shows the semantic similarity between different sentences present in the text, representing the similarity matrix as a graph, scoring and ranking the sentences and extracting the summary. Keywords: Text Summarization, Unsupervised Learning, Text Rank, Page Rank, Web Application, Graph Based Summarization, Extractive Summarization
An Artificial Neural Network based approach along with Recursive Elimination Feature Selection Combined Model to detect Breast Cancer

Cancer is one of the deadliest diseases and of all the cancers breast cancer is the prime reason of cancer death among women as compared to men, today 1 in 8 women are suffering from breast cancer according to the American Cancer Society (Information collected from cancer.orgwebsite https://bit.ly/3uc13B3).As of now breast cancer accounts for 41 % of cancer deaths in women and this number is likely to increase by 2030, according to the world health organization (Link: https://bit.ly/3ioT8eh) in 2020 there were 2.3 million women diagnosed with breast cancer and the death toll was 685 ,000.Some of the factors that are contributing to this disease are usage of alcohol, exposure to cigarette smoke, no or minimal breastfeeding, lack of physical activity, family history of breast cancer, obesity, gene change and exposure to radiation.Through this research paper we are trying to investigate breast cancer triggering factors and applying data mining models to work on early detection based on patients' medical history and predicting where this disease will reoccur or not with accurate results.Our data mining model uses Recursive feature Elimination method with cross validation, Feature Selection and stacked with Artificial Neural Network to detect breast cancer.Furthermore, we have also compared the Artificial neural network classifier with other Machine learning algorithms to find the accuracy.To add some statistics into our model we have used concepts like Specificity, Sensitivity, ROC-AOC Score, Kappa-Cohen score, Wilcoxon Signed Rank test and other statistical parameters to check and compare the Artificial Neural Network model with other Machine Learning Classifier Algorithms.In our model we have represented other Machine learning classifier algorithms as Logistic Regression, Decision tree, Random Forest classifier, K-Nearest Neighbor, Support Vector Machine, Gradient Boosting and Naive-Bayes.In this Paper we have Proposed a stacked model which can outperform other Machine Learning Classifier Algorithm by calculating various statistical parameters and by conducting non-parametric test to prove our hypothesis.In this research Paper the authors observed 98 percent accuracy by using Artificial Neural Network Based Approach along with Recursive Elimination Feature Selection combined model with Hyperparameter Tuning so that we observed sensitivity being 100 percent and specificity 99 percent also the ROC-AUC Score is 100 percent and the kappa score is 99 percent.
INFLUENCE OF E-LEARNING SERVICE QUALITY ON E-LEARNING STUDENT LOYALTY THROUGH E-LEARNING STUDENT SATISFACTION AT UNIVERSITY IN EAST JAVA

More than one year has passed since the Covid-19 pandemic in Indonesia, which has impacted the distance learning process. E-learning is one of the facilities provided by the University that helps the course of online lectures. This study aims to determine the effect of e-learning service quality on e-learning student loyalty through e-learning student satisfaction. The method used is a quantitative method with data collection, distributing questionnaires to active users of e-learning at universities in East Java. The data were analyzed using WarpPLS 7.0. The results in this study indicate that e-learning service quality affects e-learning student satisfaction and e-learning student loyalty. Likewise, e-learning student satisfaction affects e-learning student loyalty.
A Quantifiable Analysis of Ambivalence in Tweets

Abstract: To detect emotions of the writer in any textual content is a requirement that has been persistent in the industry. Much of the research work has been done on this topic but only a handful of them explore the quantified association of the given text with the purpose of recognizing mixed emotions and emotional ambivalence, our work tends to do so. Emotional Ambivalence is a state in which person feels a blend of both positive and negative emotions. Therefore, we needed a suitable and brief emotion model capableof being bipolar to begin our work with, so we chose Paul Ekman’s Emotion model. This model focuses only on the six primary emotions which are: anger, fear, joy, sadness, surprise and disgust. In our work we used a Lexicon based approach for emotion recognition.The objective was achieved with the help of newly created lexicon for bi-grams that contained annotations according to their PMI scores. We also created a subset of Twitter Emotional Corpus (TEC) by passing it to our annotators, to test our approach. The automatic process through which the original corpus was created did have some wrong annotations; thereforea manual cross validation of this corpus was required. So, we chose three annotators for this purpose and fetched a uniform agreed corpus on whichwe worked further. The output of our approach detects a combination of the emotions felt associated with their respective values. Keywords: Emotion Analysis, Affect Computing, Sentiment Analysis
Fingerprint,Face and Voice Recognition Based Attendance Monitoring System

Abstract: In This project aims to record the attendance without manual intervention. In the area of technology that changes and modifies daily, use biometrics is the most popular and trending technology. Taking attendance manually for a class of almost 60-80 students can be a time-consuming task if thought of it in a long run Each person has a unique biometric feature such as fingerprint, face structure, voice detection etc. Keywords— Boimetrics, Fringerprint, Face Structure,Voice Detection.
Heavy-loaded Vehicles Detection Model Testing using Synthetic Dataset

Currently, many roads in Indonesia are damaged. This is due to the presence of large vehicles and large loads that often pass. The more omissions are carried out, the more damaged and severe the road is. The central government and local governments often carry out road repairs, but this problem is often a problem. Damaged roads are indeed many factors, one of which is the road load. The road load is caused by the number of vehicles that carry more than the specified capacity. There are many methods used to monitor roads for road damage. The weighing post is a means used by the government in conducting surveillance. This research is not a proposal to monitor the road, but this is only to create a model for the purpose of detecting heavily or lightly loaded vehicles. This research is to classify using Convolutional Neural Network (CNN) with pre-trained Resnet50. The model generated from the Convolutional Neural Network training process reaches above 90%. Generate Image deep learning algorithms such as the Generative Adversarial Network currently generate a lot of synthetic images. The testing dataset that will be used is generated from style transfer. The model is tested using a testing dataset from the generated style transfer. Style transfer is a method of generating images by combining image content with image styles. The model is pretty good at around 92% for training and 88% for testing, can it detect image style transfer? The Convolutional Neural Network model is said to be good if it is able to recognize the image correctly, considering that the accuracy of the model is very good. One of the reasons why the training model is good but still makes errors during testing, then the image dataset is overfitting
Truly Scalable and Reusable SOA Services

Abstract: Web Services are materialized as a major technology carried out for automated interaction between distributed and miscellaneous applications. It is defined as a software service that provides business solutions consumed by different service requester. It can be accessed by a standard web protocol. Service composition is the mechanism used for selecting, reusing and combining existing web services to build new web services. With immense increase in web services, quality assessment plays an essential role in the selection approach. QoS is defined as the ability to guarantee the requirements like latency, reliability, bandwidth, etc. in order to satisfy a service level agreement between an application provider and end-user. QoS based dynamic service composition leads to the upward growth of an organization that implements multiple services to provide its business solution. The proposed research work elaborates the research work on different methodologies of QoS prediction with its implications to provide a proper road map for future research on efficient service composition. Keywords: SOA, service reuse, scalable, web services, QoS.
Financial Sector Development and Economic Growth Nexus: Empirical Evidence from Sierra Leone

The study examines the nexus between financial sector development and economic growth in Sierra Leone over the period 1980-2018. The stationarity test result shows that the order of integration of variables included in the model was a mixture of I (0) and I (1). Therefore, the study employs the bounds testing approach to Cointegration and error correction models developed within the Autoregressive Distributed Lag (ARDL) framework to explore the long-and short-run effects of financial sector development on economic growth. The study uses two different measures of financial development including, credit to the private sector and broad money. The study also investigates whether there are other determinants of economic growth. Evidence of short-run dynamics was found amongst the various financial development indicators and economic growth. The results showed that credit to the private sector and broad money influenced economic growth in the short-run. Again, inflation and government consumption expenditure were found to impede economic growth in the short-run. Contrarily, Gross capital formation and FDI were found to stimulate economic growth in the short-run. The study recommends that policy makers should take caution in the choice of financial development indicator as a policy instrument for the attainment of growth and development. Again on the basis of empirical evidence, policies to improve the accessibility of affordable credit by the private sector, including small and medium scale enterprises should be enforced
FORMATION OF COGNITIVE ABILITIES AND SKILLS IN STUDENTS

This article discusses the research, cognitive abilities and skills of younger students, the phased formation of initial research knowledge and skills in the educational and research activities of elementary school. From the moment the children enter the first grade, the teacher faces the problem of organizing the solution of common educational and research problems with absolutely different levels of development of the research and cognitive experience of schoolchildren. The activation of the personal position of a younger student in the educational process on the basis of independently acquired knowledge is new and has a personal meaning for a particular student. The theoretical analysis of the content of the concept of "research ability" in primary school children, we believe, should be considered as a separate type of intellectual activity, which is formed as a result of the functioning of the cognitive activity of younger students and search activity, which includes work of a research nature. For their formation, it is necessary to use traditional technologies in primary school lessons in combination with informational (modern) ones, paying the most attention to the latter, since they have an advantage at the present stage of education.
“Real Time Twitter Sentimental Analysis”

Abstract: This Project represents the work related to Real-Time Twitter Sentimental Analysis. In this paper, we present a framework for Real-time opinion investigation of Twitter information. The proposed framework depends on highlight extraction from tweets, utilizing both morphological elements and semantic data. For the feeling examination task, we embrace a managed learning approach, where we train different classifiers in light of the removed elements. At last, we present the plan and execution of an ongoing framework engineering in Storm, which contains the component extraction and order errands, and scales well concerning input information size and information appearance rate. Through a trial assessment, we exhibit the benefits of the proposed framework, both regarding grouping exactness as well as adaptability and execution. Discovery of sadness through messages sent by a client via web-based entertainment can be a perplexing errand because of the ubiquity and patterns in them. Lately, messages and online entertainment has turned out to be an extremely close portrayal of an individual's life and his psychological state. This is an enormous reserve of information about an individual's way of behaving and can be utilized for location of different psychological sicknesses (discouragement for our situation) utilizing Natural Language Processing and Deep Learning. Keywords: Machine Learning, Python, Web Development, HTML, CSS, JavaScript, Django, Jupyter Note Book.
Design and Simulation of Synthetic Palm Vein Image Generation

The unavailability of large-scale palm vein databases due to their intrusiveness have posed challenges in exploring this technology for large-scale applications. Hence, this research modelled and generated synthetic palm vein images from only a couple of initial samples using statistical features. Variations were introduced to the three optimized statistical features (S3; mean vectors, covariance matrices and correlation coefficient, S2; mean vectors and covariance matrices, S1; mean vectors, NS; acquired images) which were used to generate synthetic palm vein images employing Self Organizing Map (SOM) as classifier and were evaluated based on Equal Error Rate (EER), Average Recognition Accuracy (ARA) and Average Recognition Time (ART). The results obtained from the experiment showed that EERs were 0.22, 0.51, 0.58 and 4.36 for S3, S2, S1 and NS respectively. S3 had superior ARA (99.83%) compared with S2 (99.77 %), S1 (99.70 %) and NS (98.33 %). The ARTs obtained were 84.97s, 75.55s, 84.04s and 681.74s for S1, S2, S3 and NS respectively with S2 (75.55s) having significantly least value. This is on the ground that the more the optimized statistical features, the better the recognition accuracy. The research outcome justifies the extraction of mean vectors, covariance matrices and correlation coefficient with GASOM.