Reviewerly/exHarmony huggingface dataset:
Evaluation Model of Teachers' Teaching Ability Based on Improved Random Forest with Grey Relation Projection

The development and improvement of teachers' teaching ability is the guarantee to continuously improve the overall teaching ability and school running level of universities. Therefore, it is of great significance to scientifically evaluate teachers' teaching ability. Aiming at the problems of traditional algorithms, such as weak generalization performance and difficult determination of parameters and model structure, a random forest algorithm is introduced into the field of teaching ability prediction. At an equal time, the ordinary grey correlation algorithm is accelerated through the usage of the projection principle, and instructor instructing capacity assessment mannequin based totally on grey projection expanded random woodland algorithm is proposed. The grey correlation degree judgment matrix is used to represent the correlation between historical samples and influencing factors, and the weight of influencing factors is established by the direct weight method to weight the judgment matrix. The random woodland algorithm is used to set up the prediction model, the grey projection is used to display screen the pattern set education model, and ultimately, the characteristic vector is entered to whole the prediction. The experimental results show that the new method has high prediction accuracy, robustness, and effectiveness. It not only enriches the evaluation methods of teachers' teaching ability but also provides a quantitative evaluation model reference for teachers' teaching ability evaluation.
A Cognitive Agent Model of Burnout for Front-line Healthcare Professionals in Times of COVID-19 Pandemic

The global pandemic (Covid-2019) has severely affected all aspects of our life and even changed the way we live and work.As the pandemic outspread, healthcare professionals need urgent interventions to control the harmful consequences.Encountering such a crisis makes them more prone to negative psychological ramifications in their work environment, making them unable to provide the proper support.Burnout is the most negative feeling increased among healthcare professionals while compacting the virus.One essential move to remedy the impacts of burnout is understanding its determinants and their causal relationships.This paper addresses the design of a computational (cognitive) agent model of burnout for healthcare professionals using a temporal-causal network model.Several determinants of burnout with their causal relationships were identified from the literature and formalised to construct the proposed cognitive agent model.In addition, different simulation experiments were implemented to obtain a clear insight into the causal relationships among burnout determinants and those experiments are exhibited similar behaviours to exiting literature.Furthermore, the developed model was evaluated using two different methods: mathematical analysis to prove its implementation was achieved right and automated logical verification to check several properties as shown in the literature to confirm that the suitable model was built.The obtained cognitive agent model could be helpful to develop a covid-19-aware analytics software agent that can monitor healthcare professionals' mental health.
Adaptive Learning System of Ontology using Semantic Web to Mining Data from Distributed Heterogeneous Environment

Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments achieving both semantic web and SNA tools. Its contribution clearly appears on the community productions and skills developments. Python 3.9.0 platform was used for data pre-processing, feature extraction and clustering via Naïve Bayesian and support vector machine. Two case studies were conducted to test the accuracy rate of the proposed system. The accuracy rate for the case studies was (90.771%) and (90.1149 %) respectively, which is considered an affective precision when it is compared with the related scenario with the same data set.
Facial Expression Recognition System

Emotion recognition is a prominent tough problem in machine vision systems. The significant way humans show emotions is through facial expressions. In this paper we used a 2D image processing method to recognize the facial expression by extracting of features. The proposed algorithm passes through few preprocessing steps initially. And then the preprocessed image is partitioned into two main parts Eyes and Mouth. To identify the emotions Bezier curves are drawn for main parts. The experimental result shows that the proposed technique is 80% to 85% accurate.
XR4ISL: Enabling Psychology Experiments in Extended Reality for Studying the Phenomenon of Implicit Social Learning

We present XR4ISL, an XR system designed to support psychology experiments examining Implicit Social Learning, a fundamental phenomenon that guides human behavior, cognition, and emotion. We discuss XR4ISL with reference to MR4ISL, a previous system designed for Mixed Reality only, and reflect on differences between Mixed and Virtual Reality for psychology experiments.
New Blockchain Protocol for Partial Confidentiality and Transparency (PPCT)

Running behind new technologies is increasingly becoming a non-circumventable requirement for organisms' survival.This is not only a strategy to gain a competitive advantage in the market but it is a determinant key for their continuity and persistence.The Blockchain is at the heart of this technological revolution for which transparency, accessibility to the public and the sense of sharing are fundamental properties of its design.Despite its importance, leveraging this technology in an ethical and secure manner by ensuring confidentiality and privacy is a top concern.Through this work, we try to design a new approach to validate transactions within the Blockchain.Entitled "Protocol for Partial Confidentiality & Transparency PPCT", this new protocol makes possible to seek a compromise between the two requirements: Confidentiality & Transparency.It allows introducing a new notion of confidentiality that we have named partial confidentiality.Subsequently, it applies it on the transactions exchanged while ensuring the process of their validations by the different nodes of the Blockchain.In addition, and through the use of hashing and digital signature functions, this protocol also ensures integrity and authentication within its validation process.To present this work, we will first discuss the state of the art on the different current privacy approaches and our motivation behind this work.Then we will explain more about the different stages of this process, its benefits and areas for improvement.
A Unification of Weighted and Unweighted Particle Filters

Particle filters (PFs), which are successful methods for approximating the solution of the filtering problem, can be divided into two types: weighted and unweighted PFs. It is well known that weighted PFs suffer from the weight degeneracy and curse of dimensionality. To sidestep these issues, unweighted PFs have been gaining attention, though they have their own challenges. The existing literature on these types of PFs is based on distinct approaches. In order to establish a connection, we put forward a framework that unifies weighted and unweighted PFs in the continuous-time filtering problem. We show that the stochastic dynamics of a particle system described by a pair process, representing particles and their importance weights, should satisfy two necessary conditions in order for its distribution to match the solution of the Kushner--Stratonovich equation. In particular, we demonstrate that the bootstrap particle filter (BPF), which relies on importance sampling, and the feedback particle filter (FPF), which is an unweighted PF based on optimal control, arise as special cases from a broad class and that there is a smooth transition between the two. The freedom in designing the PF dynamics opens up potential ways to address the existing issues in the aforementioned algorithms, namely weight degeneracy in the BPF and gain estimation in the FPF.
Analisis Tingkat Kesiapan Pengguna Sistem Informasi Koreksi Essay Otomatis Berbasis Web Menggunakan Model Technology Readiness Index (TRI)

During the pandemic, the learning model directly impacts both students and teachers, especially in evaluating and correcting assignments to measure student learning. Evaluation and correction of the learning process at both the high school and high school and university levels are generally carried out multiple-choice examinations during this pandemic. Many tools can be used to carry out multiple-choice assessments. The essay assessment is usually done manually and takes a long time because the lecturer has to make corrections one by one. On the other hand, information retrieval is a concept that can be used to build an automatic essay correction information system so that the correction of essay questions can be done quickly and efficiently, as in multiple-choice works. The automatic essay correction information system is a relatively new technology, especially for its plural use in this pandemic. A new technology that will be applied in a plural manner requires a measurement. One of the measurements that can be used is the Technology Readiness Index (TRI). TRI is used because it can measure the readiness and tendency of users to implement an information system. The case study in this research was conducted at the Faculty of Informatics (FIF) Telkom Institute of Technology Purwokerto (ITTP). The TRI measurement of the automatic essay correction information system carried out at FIF produces a TRI of 3.27. This value is between 2.90 and 3.51 (2.90 ≤ TRI ≤ 3.51). Consisted of an optimism value of 1.01, and innovativeness value of 0.52, a discomfort value of 0.55, and an insecurity value of 1.19.
Rational Modular Encoding in the DCR Setting: Non-interactive Range Proofs and Paillier-Based Naor-Yung in the Standard Model

Range proofs allow a sender to convince a verifier that committed integers belong to an interval without revealing anything else. So far, all known non-interactive range proofs in the standard model rely on groups endowed with a bilinear map. Moreover, they either require the group order to be larger than the range of any proven statement or they suffer from a wasteful rate. Recently (Eurocrypt’21), Couteau et al. introduced a new approach to efficiently prove range membership by encoding integers as a modular ratio between small integers. We show that their technique can be transposed in the standard model under the Composite Residuosity ( $$\mathsf {DCR}$$ ) assumption. Interestingly, with this modification, the size of ranges is not a priori restricted by the common reference string. It also gives a constant ratio between the size of ranges and proofs. Moreover, we show that their technique of encoding messages as bounded rationals provides a secure standard model instantiation of the Naor-Yung CCA2 encryption paradigm under the $$\mathsf {DCR}$$ assumption.
Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa

&lt;span&gt;Sentiment analysis was a system for recognizing and extracting opinions in documents. There were two weaknesses in sentiment analysis. The first weakness was preprocessing in sentiment analysis can’t recognize slang words so that important words that should have been recognized became unrecognizable. The Second was the feature extraction process in sentiment analysis only recognized words based on the form of the word but can’t recognize the similar word. In this paper, we proposed spelling checker and wordnet to fix these weaknesses. We also used k-nearest neighbor (KNN), Naïve Bayes, and decision tree as methods for check classify the text. The purpose of this research was first to know the effects of used Wordnet and spelling checkers in sentiment analysis and second was to improve the data processing process in the existing sentiment analysis. The dataset that we used in the research was a list of tweets in Bahasa. The results showed wordnet and spelling checker make a decrease in the valued of false positives, false negatives, and true negatives in the calculation of the confusion matrix. It can increase the accuracy of the K-NN from 43% to 72%, Naïve Bayes from 41% to 71% and decision tree from 47% to 75%.&lt;/span&gt;
AN ANALYSIS OF STUDENTS’ HANDWRITING THROUGH GRAPHOLOGY TO MOTIVATE STUDENTS BASED ON ITS LEARNING STYLE

The objectives of this study were to find out students' learning styles, character or personality, and learning motivation in English by using graphology as a method of analysis. This research used a qualitative approach. The method used in this research was content analysis. This research was conducted in SMK PGRI 3 Kota Serang with a total of the participant is 10 students. The data was obtained by collecting data of students’ handwriting and through the interview of confirming the accuracy of the analysis. The results of this research were: the accuracy of students' characters and personalities is 82,6% and the accuracy of students' motivation is 77,5%. As a final result, the average students have standard to high motivation in learning English.
Automated Blood Analysers and Their Testing Principles: A Comparative Study

Computerized hematology analyzers are the absolute most significant instruments in the present clinical laboratory. Hematology analyzers are the principal supplies in clinical labs. A very good quality, high-volume analyzers convey dependable red platelet checks, platelet tallies, and 5-section differentials of white platelets, recognizing lymphocytes, monocytes, neutrophils, eosinophils, and basophils. CELL-DYN SapphireTM is a very good quality hematology analyzer made by Abbott Technologies. The UniCel® DxH 800 Analyzer is a quantitative, automated hematology analyzer developed for use in clinical laboratories for in vitro diagnostic screening of patient populations. XE-5000 is an automated hematology analyzer used in clinical laboratories for in vitro diagnostic. XE-5000 can examine and can give output for 67 parameters. The XN-2000 platform combines two investigative components that can be customized to suit a specific clinical application. Without the need for large track-based structures, costly stains, or reflexive testing, the ADVIA® 2120i Hematology Machine will fully simplify your hematology lab. Photometry is the method used in hemoglobin measurement. The traditional method for counting cells is electrical impulse, otherwise called as Coulter Principle. It is used in most hematology analyzers. In optical light scatter measurement, through a diluted blood spectrum a beam of laser light is passed and that is projected into the flow cell by hydrodynamic focusing. This review aimed to overview the working principle of various Hematological analyzers in common use.
Influence of Social Media in the Dissemination of Employment Opportunities in Sultanate of Oman

Social media presently plays a vital role in searching for jobs and the recruitment of employees in all industries across the world. It is used both by job seekers and employers prominently in generating employment opportunities. This research work is carried out to study the influence of social media in the dissemination of employment opportunities with the objectives of (1) to study the different social media used by job seekers in searching for jobs, (2) to find the most preferred social media in searching for jobs and (3) to explore the effectiveness of social media by the job seekers in searching for jobs. The results revealed that more job seekers use online job sites to find jobs and they perceive that WhatsApp is the most preferred social media in finding jobs, followed by Google+, Instagram and Twitter. It is also found that information quality, the scope of networking and privacy &amp; safety are the factors affecting the effectiveness of social media in finding job opportunities.
Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms

One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.
Suicide Rate Predictions In Pakistan By using Neural Networks

Suicide is the understudied subject in Pakistan that is a cause of death all over the world. Seventy-fivepercent of suicide occurs in LMIC.In Pakistan information about suicide is limited. The study is about tofind the number of suicide from major cities of Pakistan and then predict the number of suicides by usingNeural Networks Algorithm. About 24639 cases were found in our research from 2001-18 in majorcities of Pakistan. Hanging and poisoning were the most common methods of suicide. The peak age ofsuicide committers was 20-35 included males and females. The lowest number of suicide was inBahawalpur (130 from 2001 to 2018) and the Highest was in Lahore (5925 from 2001 to 2018).
ELECTRONIC VOTING AS AN INSTRUMENT FOR FREE, FAIR AND CREDIBLE ELECTIONS IN NIGERIAN POLITICAL SYSTEM: ISSUES AND CHALLENGES

Electoral management is one of the recurrent discussions in emerging democracies across the globe. Issues of electoral fraud are most times predicated on the nature of electoral management since most elections in the affected countries are done manually. Improving on the electoral management has therefore become a focus of concern that the Nigerian state also aligned itself with in order to better its course of the democratic enterprise. Automation of electoral processes from the manual practice is primarily to address noticeable negativities associated with the manual system. This paper is an explanatory one that seeks to analyze the incentives, setbacks and challenges of the electronic voting system. The paper utilized secondary sources through careful analysis of literature on the subject matter in addition to personal observations and discussions among intellectual colleagues. The incentives of E-voting include but are not limited to reduction of electoral fraud, delays, manipulations of results and other irregularities including cost. The paper also maintains that the e-voting system has its own drawbacks in the forms of IT logistics, infrastructure, lack of trust, issues of transparency among other challenges. It concludes that there should be a pilot test at the micro level for an experiment before adopting on a large-scale national election, networking of government agencies to be fully IT compliant, inter alia.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0988/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
The Design And Performance Of Musical Instruments

By challenging the students to design and build a musical instrument, we teach the fundamentals of engineering design and acoustics, as well as introduce statics, dynamics, fluid mechanics, and vibrations to liberal arts students and first-year engineers.Using the instrument as a nonthreatening medium, students get a chance to do hands-on problem solving from the beginning of their college career.One of the unique aspects of this course is that it is co-taught by the music department and the mechanical engineering department.Students also learn the fundamentals of composition and actually perform an original composition on their instruments in an end-ofsemester concert.Students found this course very rewarding and a number of them have continued refining their instruments after the end of the course.In the following paper, we present a general overview of the class as well as the student response with examples of student work and thoughts.
Security and privacy issues in federated healthcare – An overview

Abstract Securing medical records is a significant task in Healthcare communication. The major setback during the transfer of medical data in the electronic medium is the inherent difficulty in preserving data confidentiality and patients’ privacy. The innovation in technology and improvisation in the medical field has given numerous advancements in transferring the medical data with foolproof security. In today’s healthcare industry, federated network operation is gaining significance to deal with distributed network resources due to the efficient handling of privacy issues. The design of a federated security system for healthcare services is one of the intense research topics. This article highlights the importance of federated learning in healthcare. Also, the article discusses the privacy and security issues in communicating the e-health data.
Low illumination Image Enhancement based on Improved Retinex Algorithm

&lt;p&gt;Aiming at the problems of insufficient illumination and low contrast of low illumination image, an improved Retinex low illumination image enhancement algorithm is proposed. Firstly, the brightness component V of the original image is extracted in HSV color space, and its enhancement by Single-Scale Retinex (SSR) is used to obtain the reflection component. For the edge problem caused by the estimation of illumination component, the Gaussian weighted bilateral filter is used as the filter function to maintain the edge information. Then, the saturation component S is adaptively stretched to improve the color saturation. However, different low illumination images have different contrast, and some images have insufficient contrast enhancement, so a global adaptive algorithm is introduced to modify the contrast and obtain the final image. According to the logarithmic characteristics of human vision, it can adaptively enhance the contrast of different images without over enhancement. Experimental results show that the proposed algorithm can effectively improve the visual quality of the image, the contrast is improved significantly and image edge details are protected, and objective evaluations such as average gradient, information entropy and peak signal-to-noise ratio have been improved.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;