Reviewerly/exHarmony huggingface dataset:
HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection

The same multi-word expressions may have different meanings in different sentences.They can be mainly divided into two categories, which are literal meaning and idiomatic meaning.Non-contextual-based methods perform poorly on this problem, and we need contextual embedding to understand the idiomatic meaning of multi-word expressions correctly.We use a pre-trained language model, which can provide a context-aware sentence embedding, to detect whether multi-word expression in the sentence is idiomatic usage.
Rancang Bangun Sistem Penyusunan Portofolio dan CV Berbasis Visual Builder

The problem of students who will and have graduated from college is in terms of job search, among others, their lack of understanding in making a CV with a good format so as to make opportunities accepted in a job a little. Design this system based on visual builder that is able to process data and facilitate storage automatically. This sample is obtained from observation data with the target of Unwaha students, this method is used because it has ease in finding an existing fact. The analytical technique used in this research is qualitative descriptive which is done by analyzing or describing the process and the results of the design of the developed system. Design this system based on visual builder that is able to process data and facilitate storage automatically. For that, there is a system that can facilitate the creation of a CV and Portfolio so that students no longer need to be dizzy in loading and publishing their CV.
Pengaruh Tekanan Klien Dan Rotasi Audit Terhadap Independensi Auditor: Pendekatan Eksperimen

The auditor is an external party who will check and ensure that the client's financial statements are free from material misstatement so that the quality of the audit provided must be good. Therefore it is important for an auditor to have an independent attitude. Independence is a basic attitude that must be possessed by an auditor because the auditor is required to be impartial to one party. But in reality, to maintain independence is not easy. One of the factors that affect the independence of an auditor is client pressure. In previous studies, it was found that client pressure was able to affect auditor independence because the pressure was related to audit fees. With the pressure there is a possibility of decreasing the level of auditor independence. In order to maintain independence, an effort is needed, one of which is auditor rotation. Auditor rotation is auditor rotation that must be carried out by the company, with the aim of producing good audit quality and maintaining auditor independence. Auditor rotation is also carried out to prevent a close relationship between the auditor and his client. Based on these circumstances, this study was conducted to examine the effect of audit rotation and client pressure on independence. Data collection was carried out through the 2 X 2 experimental method between subjects. The participants in this research experiment were students, lecturers/teachers, auditors and other occupations. The experiment was carried out using the help of information technology. The experimental task given is an adjustment audit. The results showed that client pressure had an effect on auditor independence and auditor rotation was able to increase auditor independence. However, the results of this study have not been able to prove interaction hypothesis between client pressure and auditor rotation on auditor independence.
Research on innovation of university leadership network management based on the concept of knowledge management

In the era of knowledge economy and network, colleges and universities are increasingly becoming a collection of real world and virtual time and space. More and more college students obtain information, learning, and educational resources through the Internet. Under the influence of the Internet world, colleges and universities are using the current situation of network management. The traditional management concept is difficult to play a management role, and management is becoming more and more difficult. It is urgent to establish a new management model that adapts to the requirements of the times, to utilize the value of human resources by expanding personnel allocation, to support the high-quality development of teaching work, and to help colleges and universities. achieve sustainable development. Starting from this demand, innovating the existing management model based on the knowledge management concept has become an effective strategy for integrating human resources and knowledge resources in colleges and universities in the new era and improving the level of comprehensive management. The core of knowledge management is the innovation of network management, which aims to provide certain scientific thinking for university management and promote the sustainable development of universities.
Mining Techniques for Streaming Data

The huge explosion in using real time technology leads to infinite flow of data which known as data streams. The characteristics of streaming data require different techniques for processing due its volume, velocity and volatility, beside issues related to the limited storage capabilities. Hence, this research highlights the significant aspects to consider when building a framework for mining data streams. It reviews the methods for data stream summarizing and creating synopsis, and the approaches of processing these data synopses. The goal is to present a model for mining the streaming data which describes the main phases of data stream manipulation.
AN ANALYSIS OF STUDENTS’ ERROR IN TRANSLATING INDONESIAN SENTENCES INTO ENGLISH

This research was an error analysis of the first semester students of Sumbawa University of Technology. The aims of the research were to find out what types of error that the students committed and what error that they committed the most. The research was a descriptive study, where the population of the research was all first semester students, while the sample was the students of Electrical Engineering Class which consists of 34 students. The technique of data collection was done through giving the students translation tests. The data was analyzed by identifying, classifying, and describing the errors. Based on the result of analysis, it was found that (1) there were four types of error that students committed, they were omission, addition, misordering and misformation error: (2) type of error that students committed the most was omission, and the least type of error that students committed was miordering. More research is needed to clarify why the students committed certain errors such as omission addition etc. in detail. Further research is also recommended for investigating the factors affecting students of committing the errors.
PENERAPAN KINEMASTER SEBAGAI MEDIA PEMBELAJARAN BAHASA INDONESIA KELAS IV DIMASA COVID-19

The Kinemaster application is an application that people use to edit various animated photos or videos. This application aims to make it easier for teachers to create a variety of learning media. Almost everyone uses applications for work that function as the use of this sophisticated technology. The relationship of learning Indonesian is very closely related. The relationship is to teach learning materials, teachers need technology media to help them explain the material. This application is the best in its use. Doing the practice of making media using the kinemaster application must look at the situation, not all learning media are made through the application, it can also be manually depending on our views and see the situation and condition of students in learning. A wise teacher must be able to use technology wisely too, where the teacher is able to use technology well.
TRANSVERSAL COMPETENCES AS A RESULT OF STUDENT’S MODERN WORLD VIEW FORMATION

In this article we are talking about competence - which is an integrated result of the interaction of several components: motivational - expressed in interest in activities and the presence of personal meanings for solving problems; target - associated with the ability to set personal goals, the ability to design actions and deeds; orientational - provides for accounting for external and internal levels of activity;functional - knowledge, skills, ways of working, information literacy are used to form their own models of actions and decision-making; control - the presence of accurate meters of activity and its results; evaluative - introspection and adequate assessment.
Change detection of pulmonary embolism using isomeric cluster and computer vision

&lt;p&gt;Visual change detection functions in X-ray analytics and computer vision attempt to divide X-ray images toward front and backside areas. There are various difficulties in change detection such as weather changes and shadows; real-time processing; intermittent object motion; lighting variation; and diverse object forms. Traditionally, this issue has been addressed via backdrop modeling methods and the creation of custom features. We present a new feature descriptor called pulmonary embolism detection using isomeric cluster (PEDIC), uses the concept of isomerism. The isomeric and cluster isomerism characteristics of the PEDIC are distinguish it from other graphs. At isomeric thetical orientations, the cluster pattern corresponds to consecutive differences in pixel intensity between the two images. Also, the clusters are oppositely orientated, and both clusters conform to a specified isomeric feature. The local area's lines and corner point information are identified and recorded using the PEDIC in several different directions. We introduced multiresolution PEDIC, which incorporates the multiresolution Gaussian filter to achieve increased resilience in the system. We expanded our research to include rotation-invariant characteristics. We also proposed inter-PEDIC and intra-PEDIC to identify motion changes in X-ray sequences, which allowed them to extract spatiotemporal characteristics.&lt;/p&gt;
Introduction to the Fracture Parameters and their Measurements Using Digital Image Correlation

In this study an attempt has been made to calculate various fracture parameters from experimental results for different size beams under three-point bending.Analyzing the full field of displacement, strain, crack propagation and formation of Fracture Process Zone (FPZ) in notched plain concrete beams using Digital Image correlation.In addition, analysis of fracture parameters obtained from the Rilem Method of Hillerborge, Jenq and Shah, and Bazant Size Effect Model has been carried out and further validated with existing literature and it has been found in good agreement.
PENGEMBANGAN MATERI AJAR BILINGUAL PADA MATERI PERSEGI, PERSEGI PANJANG, DAN SEGITIGA BERBASIS REALISTIC MATHEMATIC EDUCATION (RME) DI KELAS III SEKOLAH DASAR

This research is a development research (R&amp;D) which aims to develop bilingual teaching materials on square, rectangular, and triangle-based material based on RME (Realistic Mathematical Education) in grade III Elementary. This study uses the Plomp model with the research subjects of third grade students at SD Negeri 011 Kembang Harum. The development of teaching materials is carried out in 3 phases; preliminary research, development or prototype, and assessment. Data collection techniques are interviews and questionnaires. The validation of teaching materials was carried out by 3 experts; material experts, educational technology experts, and language experts. The validation results from the three experts or validators show that the bilingual teaching material on the RME-based square, rectangular, and triangular materials obtained an average percentage of 90.55% with the "very valid" category. Practicality tests were conducted on teachers and students. The results from students obtained an average percentage of 96% in the "very practical" category and from teachers obtained an average percentage of 92% in the "very practical" category. Based on the results of the validation and practicality obtained, it can be concluded that the bilingual teaching materials on the square, rectangular, and triangular materials developed meet the valid and practical criteria.
Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models

Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive psychology, and treatment follow-up. The Hopkins Verbal Learning Test (HVLT) is a general verbal knowledge and memory assessment administered repeatedly as part of a neurophysiological experiment to examine an individual's performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via the hybrid Hamiltonian Monte Carlo (HMC) under the Bayesian Regression Models using 'Stan' (BRMS) package of R. Comparisons of the fitted models were done using leave-one-out information criteria (LOO-CV), Widely applicable information criterion (WAIC), and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests [from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study dataset] using the hybrid Hamiltonian-Markov Chain Monte Carlo approach.
Convolution neural networks for hand gesture recognation

Hand gestures (not static or fixed positions) are movements of fingers and the arm to communicate messages. Hand gesture recognition is the process of identifying meaningful expressions involving the human hand. Pictorial representation of gestures will enable to understand human computer interaction (HCI). This paper describes a system using convolution neural network (CNN) for recognizing the 26 letters of the English alphabet signaled with hand gestures. A Python program was developed to recognize the gestures made in front of a web camera. The hand gestures obtained are categorized using CNN with a trained model. The model was constructed using 1,100 gestures images. The recognition rate was obtained with 91% of accuracy. The proposed method was found to be highly efficient in distinguishing and classifying gestures.
A Novel Approach to Generate Hourly Photovoltaic Power Scenarios

Photovoltaic power is playing an ever-increasing role in the energy mix of countries worldwide. It is a stochastic energy source, and simulation models are needed to establish reliable risk management. This paper presents a novel approach for simulating hourly solar irradiation and—as a consequence—photovoltaic power based on easily accessible data such as wind, temperature, and cloudiness. Solar simulations are generated via a multiplication factor that scales the maximum possible solar irradiation. Photovoltaic simulations are then derived using formulas that approximate the physical interdependencies. The resulting simulations are unbiased on an annual level and reasonably reflect historic irradiation movements. Interpreting our approach as a descriptive model, we find that error values vary over the year and with granularity. Errors are highest when considering hourly values in wintertime, especially in the morning or late afternoon.
Towards An Exploration of Developing Issues and the Relationship Between Information Technology Governance and Multi-stakeholder Security Governance Scaling for Cyber Security Decision-Makers within the UK Small and Medium-Sized Enterprises Aviation

The trajectory of our present efforts is to feature the developing issues and explore the relationship between Information Technology Governance (ITG) and Multi- stakeholder Security Governance Scaling (MSGS) for decision-makers within the UK’s SME Aviation, which should benefit academia and practitioners. By conducting an extensive literature review, covering expert and scholarly writing, lean implementation thinking, SMEs cyber policymaking industrial and grand synergy, a profound knowledge level and concise synthesis can be presented, which informs the exploratory secondary qualitative research. In creating the framework, a process theory approach is pursued by transcribing primary data into secondary data to develop models and identify themes with a purposive survey sample from SMEs decision-makers. This submission sets the stage afor our discourses and outlines our research agenda Keywords: Cyber Security, Cyber Policy, Information Technology Governance, Culture, Agile, Multi-stakeholder Security Governance Scaling, Information Technology, Scaling Techniques, SME Aviation, Security
English Semantic Analysis Algorithm and Application Based on Improved Attention Mechanism Model

This work provides an enhanced attention model by addressing the drawbacks of standard English semantic analysis methods. This work provides the semantic component analysis and intelligent algorithm structure in order to investigate the intelligent algorithm of sentence component-focused English semantic analysis. In addition, the whole process of intelligently analyzing English semantics is investigated. In the process of English semantic analysis, semantic ambiguity, poor semantic analysis accuracy, and incorrect quantifiers are continually optimized and solved based on semantic analysis. It improves the accuracy of English semantic analysis. In the long sentence semantic analysis test, improving the performance of attention mechanism semantic analysis model is also ideal. It is proved that the performance of the proposed algorithm model is obviously improved compared with the traditional model in order to continuously promote the accuracy and quality of English language semantic analysis.
Sign Language Detection System

Sign language is a system for communication using visual gestures and signs for deaf and dumb people. This language is especially used by people who can’t speak or hear. Sign language is oldest and natural form of language for communication, since most of the people are not aware of sign language, hence it is tedious to understand, for solving this problem we have built a real time method using convolution neural networks (CNN) for hand-based gestures to detect sign language. In our CNN model, first the hand is passed through a filter and then the filter is applied. Finally, the hand is passed through a classifier which predicts the class of the hand gestures. Our model provides 98% accuracy for the alphabet A-Z letters. On the other hand, the most common hurdle deaf and dumb come across is communication with the normal people and with the fact i.e., each and every normal person doesn't know the sign language. Another main feature of the project is to create a communication system for the deaf people. The functionality of these part of the system is to translate audio message to the corresponding sign language. This part of the system takes the audio message as input, and converts the audio recorded message into the respective sign image and videos and displays the relevant American sign language or GIFs which we have already defined. With the aid of this part of the system, communication between normal and deaf people gets feasible. Overall, the main idea of the project is to make a system which can help deaf and dumb to interact with normal people.
Expert System Based on Multi-Stage Approach Combining Feature Selection with Machine Learning Techniques for Diagnosis of Thyroid Disease

The thyroid gland produces thyroid hormones levothyroxine (abbreviated T4) and triiodothyronine (abbreviated T3).These hormones play an important role in protein synthesis, body temperature regulation, and total energy generation and regulation.Many disorders affect the thyroid gland, some of which are very frequent, such as hypothyroidism and hyperthyroidism.Thyroid disorders (TD) impact 42 million individuals in India, with hypothyroidism being the most common, affecting one in every ten adults.According to a study report published in the journal Lancet in February 2022 1 type 1 diabetes among people under the age of 25 accounted for at least 73.7% of the overall 16,300 diabetes fatalities in this age group in 2019.This is even though this illness is largely treatable.To reduce such TD, early detection of the disease is essential.A fast, accurate, and interpretable machine learning model is a research subject.Fewer features reduce the computational effort and improve interpretation.A 3-Stage hybrid feature selection approach and several classification models are evaluated on the TD dataset obtained from the kaggle.comwebsite with 29 features and one outcome variable.Stage-1 uses a Genetic Algorithm and Logistic Regression Architecture for Feature Selection and selects 13 features well correlated with the class but not among themselves.Stage-2 utilizes the same Genetic Algorithm and Logistic Regression Architecture for Feature Selection to select 11 features.In Stage-3, Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Extra Trees (ET), Random Forest (RF), and Gradient Boosting (GDB) are used with the 11 features to identify patients with or without TD.Data splitting, several metrics, and statistical tests are used, along with 10-fold cross-validation, to do a comparative analysis.LR, NB, SVM, ET, RF, and GDB demonstrate improvement across performance measures by reducing the number of features to 11.When compared to prior research, many performance metrics such as accuracy, sensitivity, specificity, f-measure, AUC values, and kappa statistics showed superior outcomes with fewer features.Finally, with 100% classification results, the proposed ensemble model demonstrated its worth.The output findings were compared to those of previous research on the same dataset, and the proposed model was determined to be the most successful across all performance dimensions. 1.https://www.downtoearth.org.
TRAINING SPECIFIC COMPETENCIES IN MATHEMATICS THROUGH WATERFALL TYPE PROBLEMS

The article addresses the issue of training specific mathematics competencies in middle school and high school by solving waterfall problems. The results of the survey on the application of cascade problems to the study of mathematics are analyzed in the article. The advantages and disadvantages of using cascade problems in the educational process are presented. It is concluded that cascade problems are effective both in the teaching-learning of mathematics and in the evaluation of school results.
The Role of a New Generation of Textbooks in the Introduction of Competence Approach to the Education System (On the example of disciplines “Strength of materials” and “Technical Mechanics”)

The article discusses the creation of a new generation of textbooks aimed at developing a symbiotic relationship between higher education, science and industry, as well as ensuring the unity of “Knowledge-skill-qualification-competency” in individual activities based on relevance. Also, in the didactic combination of theory and practice, a model of personal and professional qualities of the participants involved in the education of a harmoniously developed generation is recommended.