Reviewerly/exHarmony huggingface dataset:
Structured Speech Acts of Pre-Service EFL Teachers in Indonesia: Interlanguage Pragmatics

The fascinations that the study of speech acts holds for language teachers, researchers, and practitioners from severe problems to which language use fail to maintain amid the speakers’ communication are imperative to be investigated. This study examines structured speech acts use in terms of its kinds, functions, and how it is performed by pre-service EFL teachers in Indonesia. The theory of Austin’s and Searle’s speech acts were employed as the main reference. This study was a descriptive qualitative method. Content analysis was employed to uncover the description of the research phenomena which in the forms of words, clauses, phrases, and sentences. There were three students in one of the private universities in Lombok, NTB, Indonesia were taken as the respondents. There were twenty-four fragments being analyzed. As the result, the locutionary acts was performed 224 times, then followed by illocutionary acts 223 times, and perlocutionary acts 215 times. Representative function of speech acts was performed 163 times, directive 28 times, expressive 26 times, commissive 6 times, and declarative 0 times. The declarative sentence was highly performed 131 times by the speakers designated the way the speech was acts performed by the speakers.
The Effect Of Know, Want, Learn (KWL) Strategy Towards Students’ Motivation And Writing Achievement

The aim of study was to find out the effect of KWL strategy towards students’ motivation and writing achievement, as well as its correlation between them.This study applied quantitatif method with experimental study type. The technique of collecting the data of the study was using Pre-test and Post-test: the test was return test and oral test. And the data analysis technique used median, mode and mean to find the coralation between students’ motivation and writing skills. based on the explanation above, the researcher concluded that know, want to learn there was any effect towards students motivation and writing achievement. Based on the data analysis, the researcher found that the result of test using ANCOVA were accepted because the result of test by used application of SPSS 24 was F-test= 14. 972 higher than F-table = 4. 09 and sig. 0,000 lower than score of alpha 0,05. This meant that the null hypothesis (Ho) which said, “KWL strategy has no positive influenced towards students’ motivation and writing achievement.” was rejected, while the alternative hypothesis (Ha) which said, “KWL strategy has positive influenced towards student Motivation and Writing Achievement” was accepted.
The Use of Cooperative Integrated Reading and Composition (CIRC) to Improve the Students’ Reading Comprehension

In general, most of the students of seventh grade have difficulties in comprehending texts. The objective of this research is to investigate whether the Cooperative Integrated Reading and Composition (CIRC), can improve the students’ reading comprehension at a junior high school in East Lombok Regency. The participants of this research were 25 students. This research was classroom action research which was done in two cycles. The data of this research were collected by conducting observation, test, and documentation, with the criteria of minimum mastery (KKM) of 75. There were two kinds of activities observed namely the teacher’s activity and the students’ activity. The students’ ability was measured by using comprehension test. The result showed that the students successfully met the criteria of minimum mastery. It was indicated from the improvement of the students’ mean score from 67 in Cycle 1 to 81 in Cycle 2. Moreover, the teaching activities improve from 74 in cycle 1 to 94 in cycle 2. Furthermore, the students’ activity also improves from 47 in cycle 1 to 84 in cycle 2. In conclusion, CIRC method was found beneficial to improve the students’ reading comprehension, and to enhance the teaching learning process of reading.
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CONCEPT FUNCTIONAL TRANSFORMATIONS FOR FORMATION OF SYNTACTIC DESCRIPTION DIAGONALS TRANSFORMANT

The article justifies the existence an imbalance in the provision of video information services using infocommunication networks. It is shown that such an imbalance is due to the destructive actions of the opposing side. Many these attacks relate to the disabling of energy and telecommunications infrastructure. This leads to a significant drop in the bandwidth of the infocommunication network. Accordingly, it is necessary to localize the imbalance between the information load infocommunication network and its bandwidth in the face of crisis factors. To do this, you must use an integrated approach. The article discusses in detail the direction creating technologies for additional reduction of bit load without losing the semantic integrity of video information resources. However, for such technologies in the process of reducing the information load of the network, there is a contradiction. On the one hand, a reduction in the information load of the network is achieved. But on the other hand, there are losses in the integrity video information. So you need to create a new class of encoding methods. Accordingly, to build compression coding technologies, it is necessary to develop an approach concept. A theoretical basis has been created for constructing the technology of encoding transformants in an uneven diagonal format, taking into account its combinatorial configuration. It is based on a system of transformations that is outlined as a two-layer compressive encoding transformer in an uneven-diagonal spectral space.
Pemodelan Laju Kematian Pasien Covid-19 di RSUD Abdul Wahab Sjahranie Samarinda menggunakan Model Regresi Weibull

Model regresi Weibull adalah pengembangan dari distribusi Weibull, yakni distribusi Weibull yang dipengaruhi langsung oleh kovariat. Model-model regresi Weibull yang dibahas pada penelitian ini adalah model regresi survival Weibull dan regresi hazard Weibull. Model regresi Weibull pada penelitian ini diaplikasikan pada data waktu pasien penderita penyakit COVID-19 di RSUD Abdul Wahab Sjahranie Samarinda tahun 2021. Event penelitian ini adalah kematian pasien COVID-19. Tujuan penelitian ini adalah mengetahui model regresi survival Weibull dan regresi hazard Weibull pada data waktu rawat inap pasien penderita penyakit COVID-19, mengetahui faktor-faktor yang berpengaruh terhadap peluang pasien tidak meninggal (survive) dan laju kematian pasien penderita penyakit COVID-19, serta mengetahui interpretasi model regresi survival Weibull dan regresi hazard Weibull. Metode penaksiran parameter adalah Maximum Likelihood Estimation (MLE). Pengujian hipotesis parameter terdiri dari pengujian hipotesis parameter secara serentak dan secara parsial. Kesimpulan penelitian adalah penaksir Maximum Likelihood (ML) diperoleh menggunakan metode Iteratif Newton-Raphson. Berdasarkan pengujian hipotesis, faktor-faktor yang berpengaruh terhadap peluang tidak meninggal (survive) dan laju kematian pasien penyakit COVID-19 di RSUD Abdul Wahab Sjahranie Samarinda adalah saturasi oksigen.Kata Kunci: Iteratif Newton-Raphson, Regresi Hazard Weibull, Regresi Survival Weibull, MLE, Penyakit COVID-19. The Weibull regression model is the development of the Weibull distribution, namely the Weibull distribution which is affected directly by the covariates. The Weibull regression models discussed in this study are the Weibull survival regression model and the Weibull hazard regression model. The Weibull regression model in this study was applied to data of COVID-19 patients hospitalization time at the Abdul Wahab Sjahranie Hospital in Samarinda 2021. The event of this study was the death of the COVID-19 patients. The purpose of this study was to determine the Weibull survival regression model and Weibull hazard regression to data of COVID-19 patients hospitalization time, to know the factors that influence the chance of patients survive and the mortality rate of COVID-19 patients, and to interpret of the Weibull survival regression and Weibull hazard regression model. The parameter estimation method was Maximum Likelihood Estimation (MLE). Hypothesis parameter testing consists of parameter testing simultaneously and partially. Conclusion of this study that the Maximum Likelihood (ML) estimator was obtained using the Newton-Raphson iterative method. Based on hypothesis testing, the factors affecting the chance of survive and the mortality rate of COVID-19 patients at the Abdul Wahab Sjahranie Hospital Samarinda is oxygen saturation.Keywords: Newton-Raphson Iterative, Hazard Weibull Regression, Survival Weibull Regression, MLE, COVID-19.
Mask Use Detection in Public Places Using the Convolutional Neural Network Algorithm

With the corona virus which has become a world pandemic. Currently doing activities in public places, the use of masks is very necessary, the reason this mask needs to be considered because masks play an important role in preventing the virus from entering the body. Coupled with the continued increase in the spread of the coronavirus, of course, masks are very important to use. Various technologies are designed to break the chain of the spread of Covid-19 which has spread to various countries including Indonesia. Based on the problems described, this study aims to detect objects in images that use masks and do not use masks. This research consists of three stages: data collection, training, and testing of a model. The model here is helpful for mask detection to detect and classify faces with masks and without masks. Next, the model will be tested for its accuracy. The accuracy obtained was 99% tested using a webcam in real time. The algorithm used is the Convolutional Neural Network (CNN) with preprocessing techniques.
Shooting Posture Correction System Based on Deep Learning

In view of the lack of effective guidance when people practice shooting posture at home, I designed this model.By importing the video captured by the camera into the model, the model will track the athletes' shoulder, elbow, wrist and other key points in the video, and then calculate the bending Angle of the elbow, and finally judge whether the athletes' shooting posture is standard.The experiment proves that this model has reached the expected goal.
A New Face Recognition System

Face identification from picture datasets is the objective of a recent research study. Data from Alan Grant, Claire Dearing, Elliot Sattler, Ian Malcolm, John Hammond and Owen Grady are now being used in the current study. Deep learning were utilized to create facial recognition. According to the results of the simulation, face recognition takes less time when using compressed photos than it did with the prior model. In addition, the suggested task consumes a less amount of storage space. The suggested work’s accuracy is determined to be superior to that of the usual technique. As a result, the suggested study has developed a more efficient method for recognizing several faces.
A Study of Hypervisor Based Virtualization and Related Major Security Issues in Cloud Computing Architecture

Abstract: With the use of cloud computing, a comprehensive web platform made up of many different services may be created and used as needed. Through the internet, cloud computing offers its services in an affordable, dependable, and productive manner. By offering services on a rental basis, cloud computing lowers the expenditure required to buy gear, software, and software licenses. It offers backups to maintain multiple copies of the data and lowers the cost of licensing. With the use of cloud computing, a comprehensive web platform made up of many different services may be created and used as needed. One of today's most fascinating technologies is cloud computing, which is scalable, versatile, and can lower the cost and complexity of applications. These advantages turned cloud computing from a far-fetched notion into one of the most rapidly expanding fields of technology today. Actually, virtualization technology is based on an older technique called virtualization, which contains security flaws that need to be fixed before they influence cloud computing. Additionally, limited security features of virtualization technology are needed to protect a wide-area system like the cloud. Therefore, modifications to the conventional virtualization design are needed to create an effective security solution. In order to safeguard the cloud environment, this article suggests novel security architecture in a virtualization technology based on hypervisors.
Project Success and Transformational Leadership: Evaluating the Mediating Impact of Subordinates' Innovative Work Behaviour and Knowledge Sharing Behaviour

For the past twenty years, researchers have concentrated on project success. Concern over the elements that affect project success is on the rise. The study's specific goal was to ascertain how innovative work behaviours and knowledge-sharing behaviours of subordinates played a mediating role in the relationship between transformational leadership and project success. A post-positivist strategy is used in this cross-sectional analysis of theoretical models. Between January and June 2022, information was gathered through purposeful sampling from 60 small and medium-sized firms. The firms that were contacted range widely in size, industry, and speciality. This study distributed 550 questionnaires comprised of 4 scales to 120 organizations; 375 were returned, out of which 350 were valid. The findings demonstrate that transformative leadership is significantly associated with project success. In addition, knowledge sharing and innovative subordinate work behaviour considerably mediate this relationship. This study stressed that transformational leaders may encourage their people by maintaining team communication and cohesion regardless of the circumstances. In addition, education and training in soft skills enhance subordinates' knowledge sharing and innovative work behaviour, and their combination with transformational leadership is a more effective predictor of project success. Future research can use longitudinal analysis by examining other potential variables in different economies and communities. Through the lens of social exchange theory, the findings of this study make a novel addition by elucidating the limited understanding of the effect of transformational leadership on project success as mediated by knowledge sharing and innovative work behaviour.
FORECASTING THE NUMBER OF FOREIGN TOURISM IN BALI USING THE HYBRID HOLT-WINTERS-ARTIFICIAL NEURAL NETWORK METHOD

Bali was one of the destinations frequently visited by tourists because it had natural beauty, especially in the tourism sector. The number of foreign tourists coming to Bali until 2019 had increased, but there had been a very significant decrease in 2020. Forecasting the number of tourists coming to Bali in the future was needed to provide input or recommendations to the government and business people in anticipating decisions taken in the process of developing the tourism sector in Bali. One of the forecasting methods that can be used was the Holt-Winters method. The Holt-Winters method was part of Exponential Smoothing which is based on smoothing stationary, trend and seasonal elements. However, the Holt-Winters method can only capture linear patterns, so a method was needed that can capture non-linear patterns. The Artificial Neural Network method was proposed to overcome the shortcomings of the Holt-Winters Method. This research was focused on the number of foreign tourists visiting Bali using the Hybrid Holt Winters-Artificial Neural Network method. The results showed that the data on the number of foreign tourists fluctuated every month. The best method for predicting the number of foreign tourists was the Hybrid Holt-Winters (α = 0.987, β = 0.000001, and γ = 1)-Artificial Neural Network (12-15-1) because it has the best accuracy as indicated by the MAD value of 0.036684, MSE 0.01098698 and MAPE 6.30417%.
A lightweight unsupervised approach for adverse health detection and digital biomarker discovery in people living with dementia

Abstract Background Sensor‐based remote health monitoring of persons living with dementia (PLwD) can be used to gain insights into their health and monitor the progression of their condition, with minimal intrusion. This helps minimize preventable hospital admissions, while allowing researchers to improve their understanding of dementia. Existing approaches for detecting activity and behavioural anomalies in PLwD are challenged by noise in data, lack of annotated datasets, multivariate data, scalability, data drift and explainability. Method We propose and evaluate a solution based on the Matrix Profile, an exact, ultra‐fast distance‐based anomaly detection algorithm, specifically the Contextual Matrix Profile (CMP), to detect anomalies that may indicate unusual activity and onset of UTI. Daily household movement data collected via passive infrared (PIR) sensors are used to generate CMPs from location‐wise sensor counts, duration and change in hourly movement patterns. We create CMP‐based multivariate anomaly detection models to generate a single daily normalized anomaly score for each patient. We discover digital biomarkers of anomalies and evaluate our method vs. three state‐of‐the‐art algorithms. Result CMP‐based models yield up to 85% recall with only a 5% alert rate, when evaluated on a subset of 9363 days from 15 participant households with 41 clinically validated incidences of urinary tract infections (UTI) and hospitalization, collected by the UK Dementia Research Institute between August 2019 and July 2021. Our multidimensional CMP model offers the best balance of recall vs. anomalies raised, with excellent generalisation. We discover that bathroom early AM activity (midnight to 6 am) is the prime cross‐patient digital biomarker of anomalies. This validates findings in literature that unusual bathroom activity is a clinically significant feature in UTI for dementia. We also demonstrate a cross‐patient view of anomaly patterns. Conclusion We address the need for anomaly detection and scoring using multivariate time series sensor data in remote health monitoring. The CMP allows configurability, ability to denoise and detect patterns, and explainability to clinical practitioners. With higher sensitivity, fewer alerts and better overall performance than state‐of‐the‐art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique for dementia and beyond.
MR image harmonization based on cross‐center style transfer using deep generative adversarial network

Abstract Background Due to heterogeneities in protocols such as magnetic field strength, coil channels, and scanner manufacturers, data shift problems occur. This causes a serious performance drop in predictions tested to other hospitals. We proposed a multi center‐cross transfer network aimed to adapt institutions with minimal loss of predictions for Alzheimer disease. Method Three datasets collected from hospitals with different manufacturers and protocols were included in this study (Table1). We used a deep generative adversarial network (GAN) consisting of 4 models: The generator learns to generate images by inputting a source image and a style code. The style encoder and mapping network extracts the style code of images and transforms into style spaces of multiple domains. The discriminator distinguishes between real and fake images from multiple domains. The integrated model was adjusted while training based on four losses: adversarial loss, cycle‐consistency loss, style‐reconstruction loss, and style‐diversification loss. To resolve the data imbalance problem, 1200 slices were randomly extracted per institution. For validation, we evaluated how well the proposed deep learning model transforms images while maintaining image quality and variation. Next, classification was performed using a convolutional neural network model (Resnet 50) to check how successfully transfers styles of an institution to another one. Result We quantitatively evaluated transfering styles between the desired source and reference institution (Figures 1 and 2). Through interpolation and consistency loss coefficient differences, the source to reference transformation process can be checked continuously (Figure3). The proposed model performed well with SSIM of 0.897, LPIPS of 0.862, which are metrics for assessing quality and variation of image transfering model. To predict harmonization ability, we compared classification performance between each pair of datasets before and after style transfering, respectively. The accuracies for each pair shows dramatic decrese in performance of discriminating different centers: ADNI‐SMC (91.20 to 64.35), ADNI‐GMC (96.52 to 66.66) and GMC‐SMC (87.84 to 57.89). Conclusion We proposed a multi center‐cross transfer network that freely converts protocol at any desired institution. This work will be able to predict Alzheimer’s biomarkers robustly by harmonizing all data from multiple hospitals, while preserving disease properties encoded in the dataset.
Project Based Learning Model Digital Media Assistance in Learning Persuasive Speech Text in SMP/MTs

The purpose of this research is to explain the design of project-based learning model with the help of digital media in teaching persuasive speech texts in SMP/MTs.The method used is descriptive qualitative method.This study uses a qualitative approach with the ADDIE model development method.Data collection was in the form of teacher and student activities in every aspect of learning model design, speech text assessment instrument, viewing instrument sheets and presentation of speech text products.The form of data in this study is in the form of pretest and posttest scores for speech text assessment sheets.From the results of the research and discussion, it can be concluded that the ability to write speech texts in the pretest and posttest obtained an average score of 68,70 in pretest and 86,24 in the posttest, including in the good category.The result of the analysis of the N-gain score are 0,5635 in the range 0.3 ≤ g ≤ 0.7 in the medium category.From user/educator data on the implementation of the digital media-assisted learning model design used with interpretation criteria an average of 89% is included in the very feasible category.That is, the design of a project-based learning model assisted by digital media is able to improve students' abilities in making speech texts.Which illustrates an increase in effectiveness and value scores after using the project-based learning model.The design of the Digital Media Assisted Project-Based Learning Model in Persuasive Speech Text Learning is a combination of Project Based Learning and Digital media, so the design of this model is very much needed by teachers in persuasive Speech text learning.From the implementation of the use of the digital media-assisted Project based Learning model, it can be concluded that the developed digital media-assisted project-based learning model in learning persuasive speech texts in SMP/MTs is very feasible for teachers and students to use in learning.