Reviewerly/exHarmony huggingface dataset:
Web-based text editing system for Nigerian major languages

&#x0D; &#x0D; &#x0D; This study developed a web-based text editor to eliminate the incompatibility of computer keyboard with the three major indigenous languages in Nigeria. It also aims to reduce the time taken to produce characters with diacritical marks. The editors produced valid Unicode characters and require pressing less buttons to generating all the symbols of the alphabets for the three major indigenous languages in Nigeria. Client-side technologies were used to develop these applications. Three web pages, designated for Yorùbá, Igbo and Hausa language were generated with HTML. CSS was used to define the look and feel of the HTML elements on each page. Regular Expressions implemented in JavaScript functions were used to convert selected ASCII characters into desired Unicode characters. The editors are available at http://www.gazaliwakil.com.ng. The editors work well on latest version of browsers like (Google Chrome, Mozilla Firefox, Opera, and Internet Explorer). They are very light, consume minimal server resources and can work offline. The system was launched Fifty-one (51) times to extract data comprising the Loading, Scripting, Rendering, Painting, System, and Idle time. The obtained result showed that on the average, it takes about 13.77ms to load the HTML DOM elements, 42.83ms to load the javaScript, 13.10ms and 1.73ms for rendering and painting the page by CSS. Additional time taken are 43.91ms and 3,045.10ms for the system and idle time respectively. A total time of 3,160.43ms (3.16s) is required when any of the editors is launched before the page can accept inputs from the users. It also takes the editors 2.66ms to add diacritical marks on a letter. This would, in effect, not reduce the typing speed of users.&#x0D; &#x0D; &#x0D;
An Association Rule General Analytic System (ARGAS) for Hypothesis Testing in Qualitative and Quantitative Research

This paper describes an Association Rule General Analytic System (ARGAS) as an alternative to the General Linear Model (GLM) for hypothesis testing. We illustrate how the ARGAS can be used to analyze both qualitative and quantitative research data. The advantages of the ARGAS approach derives from the fact that it is designed to analyze words or numbers that are converted into words. Unlike the GLM, it does not have any distributional assumptions. Association rule calculations are well-developed and there are a variety of computer software applications available that expedite the computations. The purpose of this study is to illustrate how the ARGAS can be applied and how to interpret the results.
Generación de un modelo básico de evaluación de calidad de Aula Virtual de apoyo a la educación presencial en las carreras de grado de la UCSE-DAR

Quality assessment in educational institutions is a very important issue. Even more when the quality of the complexity of teaching and learning processes is being considered. New information and communications technology are being used daily, and this usage has expanded to the classroom in a natural manner without taking into account the educational possibilities provided by this tools. The main goal of this project is to provide a set of indicators that allow to measure the quality in the use of ICT and the educational aims set out when using them. Base on the evaluation of virtual classrooms through a whole year, and the analysis of these indicators and their variations, we created a Virtual Classroom (VC) prototype that would fulfill pedagogical quality standards, thus resulting in a tested model of a virtual classroom supporting on-site teaching.
A Detailed Survey on Machine Intelligence Based Frameworks for Software Defect Prediction

Software defect prediction has an important role to play in improving the quality of programming and helps to reduce the time and cost of programming testing. AI focuses on the advancement of computer  programs that can be instructed to develop and change at a time when new information is presented. The capacity of a machine to improve its exposure depends on past results. Machine learning improves the productivity of human learning, finds new things or structures that are obscured to people, and discovers important data in the archive. For this reason, distinctive machine learning  procedures are used to remove unnecessary, incorrect information from the data set. Software defect prediction is seen as an exceptionally significant capability when a product project is arranged and a much larger effort is expected to address this intricate issue using product measurement and deformity dataset. Metrics are the link between the mathematical value and are subsequently applied to the product for anticipation of deformity. The essential objective of this study paper is to comprehend the  existing strategies for foreseeing programming deformity.
Semi-supervised Conditional Density Estimation for Imputation and Classification of Incomplete Instances

Incomplete instances with various missing attributes in many real-world scenes have brought challenges to the classification task. There are some missing values imputation methods to fill the missing values with substitute values before classification. However, the separation between imputation and classification may lead to inferior performance since label information are ignored during imputation. Moreover, these imputation methods tend to initialize these missing values with strong prior assumptions, while the unreliability of such initialization is rarely considered. To tackle these problems, a novel semi-supervised conditional normalizing flow (SSCFlow) is proposed in this paper. SSCFlow explicitly utilizes the observed labels to facilitate the imputation and classification simultaneously by employing a semi-supervised algorithm to estimate the conditional probability density of missing values. Moreover, SSCFlow takes the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate the true conditional probability density of missing values. Experiments have been conducted with real-world datasets to demonstrate the robustness and efficiency of the proposed algorithm.
Real-time Attack-Scheme Visualization for Complex Exploit Technique Comprehension

Recent exploit techniques are highly complex, and it is not easy for cybersecurity learners to understand the attack strategies quickly and clearly. For efficient and comprehensive learning, this paper proposes an attack-scheme visualization system that fulfills three requirements: attack progress visualization in real-time, memory and register-level description, and concise description of the attack schemes. This paper exemplifies two cases: stack buffer overflow and ROP attacks, and demonstrates how the system operates and how users can learn that existing defense technologies are effective or ineffective depending on the execution environments.
Analyze Ming and Qing Literature Under Big Data Technology

Big data has brought too much convenience to our work and life. Through the development of big data, people are full of curiosity about all new things, especially more and more people are obsessed with Ming and Qing literature. The main purpose of this article is to study the application of big data technology in Ming and Qing literature. In this paper, under the social background of the era of big data, further study how Ming and Qing literature developed and the relationship between the two, and then continue to promote social development and progress. The experimental results of this article show that the user scale of Ming and Qing literature under big data technology has increased by 19%, and the application of big data technology in Ming and Qing literature has become more and more extensive, and its positioning in the cultural market has also been significantly improved. This is also conducive to creating more valuable literary works.
AN ANALYSIS OF TEACHERS’ CODE SWITCHING AND CODE MIXING IN EFL CLASSROOM

Rizkihadi, Karin. 2021. 1616500019. “An Analysis of Teachers’ Code Switching and Code Mixing in EFL Classroom”. Research Project. Strata 1 Program English Department, Faculty of Teacher Training and Education. Pancasakti University Tegal. The first advisor Dr. Taufiqulloh, M.Hum, Second Advisor N. L. Molla, S.Pd, M.Hum.Keywords: Teachers’ Code Switching and Code Mixing, Descriptive Qualitative Research, The Types and The Function of Code Switching and Code Mixing.The aim of this study was to identify the types of Code Switching and Code Mixing performed by an English teacher and to examine the functions of Code Switching and Code Mixing used by the teacher.This was descriptive qualitative research, the research employing documentation to collect the data and the researcher used descriptive method to describe the types and the functions of teachers’ code switching and code mixing. The subject of this study was eight grade of SMPN Satu Atap-1 Palangka Raya through Rina Rinae’s youtube channel. In collecting data, the writer watches the video and identifies the data based on reading the transcript.The result of analysis shows that (1) The total data is 74 unit data analysis of Code Switching, it found that tag-switching 36%, inter-sentential switching 26%, and intra-sentential switching 38%. It showed that the most common types of code switching of teacher’s utterances were intra-sentential switching. Furthermore, 64 data analysis of Code Mixing performed insertion 94%, alternation 6%, and there is no congruent lexicalization in the teacher’s utterances. Based on the data, the teacher commonly used insertion to mix the code. (2) From 74 data of Code Switching, the researcher just found six functions in teacher’s utterance. There are no quotation in the teacher’s utterances, 23% addressee specification, 21% repetition, 26% interjection, 16% message qualification, 11% personalization and objectivization, and 3% facility expression Moreover, the researcher just found five functions from 64 data of Code Mixing. There are no quotation in the teacher’s utterances, 47% addressee specification, no repetition in the teacher’s utterance, 3% interjection, 20% message qualification, 22% personalization and objectivization, and 8% facility expression. Based on the data, addressee specification is commonly used in Code Switching and Code Mixing of the teacher’s utterances.
English Proficiency Adaptive Test Series

Tests have been an important tool that has beenused as a benchmark by the students and teachers to evaluate their learning. Computer technology has played an important role in advancing the education and training industry since its advent and therefore, has been transforming the traditionalapproaches to education. As E-Learning is becoming a major trend, there is not much emphasis on E-Testing i.e. electronic and automated assessment of examinations. Conventionally, English language training tests require a lot ofresourcesandmanpowerallocation and thus are faced with many difficulties such as high cost of examination and limitations such as standardization of the test. Generating and assessing a test is a task that requires high expertise. In this paper, we analyze existing computer-basedtest systems for English proficiency and refine and combine rules to lay down a roadmap to an optimized, automated system to test English language Proficiency. The resulting system not only ensures lower costs and resource management but also helps in assessing the ability of an examinee more efficiently.
“Harthulp”: A smart question-and-answer platform for cardiac patients

In common Dutch cardiac care, patients only have few follow-up meetings with their cardiologists after they have been treated at the hospital. Sometimes, they have to wait several months for their next visit. Therefore, patients often turn to online platforms where they can ask questions to other patients or healthcare professionals, namely health-based social networks. These platforms are managed by a small group of volunteers, cardiologists, who seek to inform patients in order to make efficient use of the limited time at the hospital. However, it is expected that the number of cardiac patients on these networks will increase due to overall growth of the number of Dutch cardiac patients and the recent trend of searching health information on the internet. Hence, it remains the question whether health-based social networks can scale up with this development. To investigate the sustainability of health-based social networks, user posts from the Dutch social platform Hart Volgers and American social platform DailyStrength were analysed. The results show that the number of patients on these platforms grows rapidly, and they reveal that users prefer to contact cardiologists instead of fellow patients. These findings suggest that there is an urgent need for new solutions that keep these platforms future-proof. To address this problem, Harthulp is proposed, a smart question-and-answer platform for cardiac patients to provide better and more efficient aftercare. Harthulp introduces a question wizard that enables patients to quickly find information on the platform, so that it is not always necessary to ask a question to a healthcare professional and to wait for a reply. As a core component of the question wizard, a novel search engine has been developed which employs a deep learning model that captures the semantics of words on health-based social networks. In this way, patients can search with short questions and retrieve relevant posts while they may not contain the exact same words. It has been demonstrated that the proposed search engine significantly outperforms traditional search engines when retrieving relevant question-and-answer posts from Hart Volgers. A web interface has been designed to show how all components can be embedded in a single user-centered design. This design has been evaluated together with an experienced cardiologist.
Stochastic Optimization Methods for Modern Machine Learning Problems

Author(s): Sun, Yuejiao | Advisor(s): Yin, Wotao | Abstract: Optimization has been the workhorse of solving machine learning problems. However, the efficiency of these methods remains far from satisfaction to meet the ever-growing demand that arises in modern applications. In this context, the present dissertation will focus on two fundamental classes of machine learning problems: 1) stochastic nested problems, where one subproblem builds upon the solution of others; and, 2) stochastic distributed problems, where the subproblems are coupled through sharing the common variables. One key difficulty of solving stochastic nested problems is that the hierarchically coupled structure makes the computation of (stochastic) gradients, the basic element in first-order optimization machinery, prohibitively expensive or even impossible.We will develop the first stochastic optimization method, which runs in a single-loop manner and achieves the same sample complexity as the stochastic gradient descent method for non-nested problems. One key difficulty of solving stochastic distributed problems is the resource intensity, especially when algorithms are running atresource-limited devices. In this context, we will introduce a class of communication-adaptive stochastic gradient descent (SGD) methods, which adaptively reuse the stale gradients, thus saving communication. We will show that the new algorithms have convergence rates comparable to original SGD and Adam algorithms, but enjoy impressive empirical performance in terms of total communication round reduction.
ANALISIS KEAMANAN SITEM INFORMASI AKADEMIK FAKULTAS ILMU KOMPUTER INSTITUT TEKNOLOGI & BISNIS LEMBAH DEMPO MENGGUNAKAN FRAMEWORK COBIT 5

Information system security is very important to maintain the validity and integrity of data and ensure the availability of services for its users. COBIT Framework - COBIT is an IT governance guide framework and or can also be referred to as a supporting toolset that can be used to bridge the gap between needs and how technically it is to fulfill these needs in an organization. COBIT provides a framework guide that can control all organizational activities in detail and clearly so that it can help facilitate decision making at the top level in the organization. The Institute of Technology & Business (ITBis) is one of the educational institutions that relies most of its teaching process on information systems, and information technology as a means and infrastructure to provide services to students, lecturers and all staff as well as assist the implementation of activities in all existing units.
Emotion based Music player

Visual sentiment analysis, which investigates humans' emotional responses to visual stimuli such as images and videos, has been a fascinating and challenging problem. It attempts to recognize the high-level content of visual data. The success of current models can be attributed to the development of robust computer vision algorithms. The majority of existing models attempt to solve the problem by recommending either robust features or more complex models. The main proposed inputs are visual features from the entire image or video. Local areas have received little attention, which we believe is important to the emotional response of humans to the entire image. Image recognition is used to find people in images and analyze their sentiments or emotions. The CNN algorithm is used in this project to accomplish this task. Given an image, it will search for faces, identify them, place a rectangle in their positions, and describe the emotion found with a percentage of emotions displayed. The emotion output will be in audio format.
Анализ цикла дисциплин «Компьютерные науки» в инженерном образовании

Computer disciplines in the existing engineering education that is not related to the training of specialists for the IT sphere are not sufficiently taught. The article substantiates that the volume of disciplines of the Computer Science group should be increased to at least 10 % of the curriculum and further strive for the level of 15–20 %, which is due to the volume of competencies required by the economy related to IT. It is methodologically justified that the structure of the modern information sphere allows us to distinguish 4 groups of competencies that have different meanings and require at least 4 disciplines, which have different goals, level of deepening, requirements for the result, teaching and control technology.
Elastosonography in the Characterization of Breast Masses in Black Women in Lomé (Togo)

Objective: study the contribution of elastosonography in the characterization of breast masses. Methods: a prospective and descriptive study took place over a period of 6 months from February to July 2020 in the department of radiology of the Sylvanus Olympio teaching hospital in Lome. Patients presenting a breast mass diagnosed by the conventional ultrasound were invited to take part in the study. Result: in total, fifty-four patients met our selection criteria, for an overall frequency of 2.9%. The average age of our patients was 24.2 years old with the 20-29 years age group being the most represented. The mean lesion size was 18.8mm with extremes of 5 and 35mm. The lesions classified as Birads 3 were the most prevalent (40.4%) and they all had an elasticity score of less than 3. 75% of the lesions classified as Birads 4 also had an elasticity score less than 3. All lesions classified as Birads 5 had an elasticity score greater than 4. We obtained a statistically significant relationship between the Birads classification and the elastographic score and also by differentiating the mammary masses into benign and malignant lesions by these two modalities. Conclusion: elastosonography is a diagnostic imaging method that allows rapid and easy evaluation of breast lesions and it provides a valuable addition to the ultrasound data in real-time. It provides complementary diagnostic information that could potentially assist in the characterization of breast lesions by B-mode ultrasound.
ElectroOculoGraphy (EOG) Eye-Tracking for Virtual Reality

The key factor for the success of virtual reality is immersion, which is the condition where the user loses awareness of being in an environment which is not real. Immersion is achieved by replicating human senses. In this context, introducing eye-tracking into VR can increase the immersion and open up a wide variety of applications. Among eye-tracking methods, ElectroOculoGraphy (EOG) has the big advantage of having low requirements in terms of processing power and energy. This allows an efficient integration also in less powerful VR headsets. EOG eye-tracking exploits a property of the eye which behaves as an electric dipole. When the eyes rotate, the dipole vector rotates accordingly. Signals that are a measure of the rotations can be obtained with the EOG technique by placing electrodes on the skin around the eyes. It is recognised that these electrodes can be efficiently integrated on a VR headset on the foam mask surface which is contacting the user’s face. With a minimum total of five electrodes it is possible record signals that are a measure of horizontal and vertical conjugate (parallel) eye rotations, which can be converted into coordinates of the user’s gaze point in the virtual reality environment. In order to receive EOG signals, a stable contact between electrodes and the user’s skin must be assured. Standard VR headset masks do not provide uniform contact for all users, due to high variability of the human face shape. For this reason, the foam mask piece was re-designed by creating multiple sizes that could better accommodate different face shapes. An anthropometric analysis was conducted on a database of 3D scans of European subjects, with the aim of identifying three clusters of face shapes. Such results were then used to create digital mannequins to be exploited in the design of three mask sizes, with the ultimate purpose of improving the contact with the face and the overall comfort. Electronics and materials were researched and a final prototype with the desired characteristics was built with the purpose of demonstrating the integration of EOG into a VR headset and conduct user-tests. Finally, testing was successful to prove that the with such sizing system there is at least one size out of three that ensures stable contact with EOG electrodes for each subject tested.
ENABLING CROWD SOURCING FOR TASK MANAGEMENT THROUGH BLOCKCHAIN

The paradigm of data vending is one of the most unique approaches that have been the result of increasing evolution and technology leading towards the information age that will live today. Data is highly essential commodity that is required to maintain the steady pace of improvement and technological advancements in this world. This data is crucial for implementations in a wide variety of machine learning implementations and other deep learning evaluations. There is a lack of trust between the various entities in a data vending approach leading to words an effective deterioration of this essential system. Therefore to improve this approach significantly the proposed methodology in this research article elaborate on a data vending approach that achieve effective security through RSA encryption and the distributed Blockchain framework. The proposed methodology has been subjected to rigorous experimentation for its performance evaluation which is achieved significant improvements over the conventional approaches.