Reviewerly/exHarmony huggingface dataset:
‘I Can Teach English More Confidently’: How Madrassas’ and Pesantrens’ English Teachers Perceived ELTT Program

The study sought to investigate the perception of English teachers in madrassas and pesantrens toward the English Language Teacher Training (ELTT) program they underwent for five months. A convergent mixed-method research design was used, comprising a set of questionnaires administered to thirty-two teachers across three cities, namely Banjarmasin, Metro, and Palembang. Subsequently, interviews were conducted with twelve teachers who participated in the study. Interactive data analysis was employed to analyze the results obtained from the questionnaires and interviews. The study found that numerous teachers reported receiving significant benefits in their teaching and learning skills after participating in the ELTT program, including a boost in self-esteem when delivering English language instruction and enhanced English teaching abilities. However, participants also highlighted internet connectivity and time management as major hurdles that required attention. Hence, it is recommended that the Ministry of Religious Affairs continues to implement the program since it provides teachers with opportunities to enhance their English teaching skills. Consequently, this will enable students to receive greater exposure to the English language, facilitating their learning process.
Control of an Autonomous Unmanned Aerial Vehicle Using Reinforcement Learning

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatable avoid trees and fly through the forest.
Estimation of Parameters of Non-Gaussian Asymmetric Processes with a Moving Average Using the Polynomial Maximization Method PMM

In this paper consider is the application of the Polynomial Maximization Method PMM to find estimates of the parameters of non-Gaussian Moving Average model. This approach is adaptive and is based on the analysis of higher-order statistics. The case of asymmetry of distributions of Moving Average of the stochastic processes is also considered. It is shown that the asymptotic variance of estimates of the Polynomial Maximization Method (2nd order) have such analytical expressions, whose allow to finding estimates and analyzing their uncertainties. Above approach can be significantly less than the variance of the classic estimates based on minimizing the Conditional Sum of Squares or Maximum Likelihood (in the Gaussian case). The increase of accuracy depends on the values of the coefficient’s asymmetry and the kurtosis of residuals. The results of statistical modeling by the Monte Carlo Method confirm the effectiveness of the proposed approach.
Biometric Authentication based on Liveness Detection Using Face Landmarks and Deep Learning Model

This paper describes the approach to active liveness detection of the face using facial features and movements. The project aims to create a better method for detecting liveness in real-time on an application programming interface (API) server. The project is built using Python programming with the computer vision libraries OpenCV, dlib and MediaPipe and the deep learning library Tensorflow. There are five modules in active liveness detection progress related to different parts or movements on the face: headshakes, nodding, eye blinks, smiles, and mouths. The functionality of modules runs through face landmarking through dlib and MediaPipe and detection of face features through Tensorflow Convolutional Neural Network (CNN) trained in two different approaches: smile detection and eye-blink detection. The result of implementing face landmarking shows an accurate result through the pre-trained model of MediaPipe and the pre-trained parameter of the dlib 68 landmarking model. And more than 90% classification model accuracy in precision, recall, and f1-score for both trained CNNs in detecting smiles and eyes blinking through the Scikit-Learn classification report. In addition, the prototype API is also implemented using the Python RESTful API library, FastAPI, to test the detection functionality in the prototype Android application. The prototype result is outstanding, as the model excellently requests and retrieves from the API server. The possible research path gives the success of real-time detection on API servers for easy implementation of liveness detection on low-spec client devices.
TPPSO: A Novel Two-Phase Particle Swarm Optimization

Particle swarm optimization (PSO) is a stout and rapid searching algorithm that has been used in various applications. Nevertheless, its major drawback is the stagnation problem that arises in the later phases of the search process. To solve this problem, a proper balance between investigation and manipulation throughout the search process should be maintained. This article proposes a new PSO variant named two-phases PSO (TPPSO). The concept of TPPSO is to split the search process into two phases. The first phase performs the original PSO operations with linearly decreasing inertia weight, and its objective is to focus on exploration. The second phase focuses on exploitation by generating two random positions in each iteration that are close to the global best position. The two generated positions are compared with the global best position sequentially. If a generated position performs better than the global best position, then it replaces the global best position. To prove the effectiveness of the proposed algorithm, sixteen popular unimodal, multimodal, shifted, and rotated benchmarking functions have been used to compare its performance with other existing well-known PSO variants and non-PSO algorithms. Simulation results show that TPPSO outperforms the other modified and hybrid PSO variants regarding solution quality, convergence speed, and robustness. The convergence speed of TPPSO is extremely fast, making it a suitable optimizer for real-world optimization problems.
Sustainable Tourism Development in Jordan: The Impact of COVID-19 on Tourism Revenues and its Role in Unemployment Rates

Objective: This study aimed to examine out the effect of COVID-19 on Jordanian tourism revenue and its role in unemployment. Methods: A questionnaire was distributed to a random sample of 300 workers in the tourism sectors such as restaurants; hotels; travel agencies; and handicraft shops, based in the cities of Amman, Jerash, Aqaba, and Petra. The data was analyzed using SPSS, which includes distribution of frequency, t-test, and analysis of factors. Results: The results of the study showed that Covid-19 has contributed negatively impact in Jordanian tourism sector, increasing unemployment levels sharply and decreasing the sector`s revenues. The government`s measures to fight the pandemic were found to have a negative impact on the vitality of the tourism sector, especially the consequences of the various stages of lockdown that worsen the tourism sector`s ability to recover. Conclusions: The study recommended the necessity of cooperation between the public and private sectors to find innovative and creative actions to stimulate the return of tourism revenues and employment in this sector.
Education And Training On Searching For Teaching Materials From The Internet For Teachers In Pangkep District

This Community Service Program (PkM) is entitled Education and Training on Searching Teaching Materials from the Internet for Teachers in Pangkep Regency. In the midst of globalization, teachers should not be like frogs in a shell. Teachers must have broad and globalized insights as an effort to improve education. Teachers should not only transfer information/learning materials that are "stale" (not contemporary) to students. The material taught by teachers does not solely come from textbooks, but there is information as learning material that can be obtained through other sources. One source that can be used as a medium to browse the latest information is the internet. The internet is currently not a rare item. Almost every time we use the internet in every activity to find various references and information needed. Currently in Pangkep district, teachers certainly have a variety of learning media and teaching materials used in the teaching and learning process, teachers access a variety of information for their teaching materials. It's just that to be able to access it, you need skills in the form of ways or techniques to make the internet a medium for searching learning resources. In fact, most teachers in Pangkep District do not know and master the techniques of operating and using the internet. Given the importance of this, it is necessary to hold training on the use of the internet as a medium for searching learning resources for teachers in Pangkep District.
Frame-Padded Multiscale Transformer for Monocular 3D Human Pose Estimation

Monocular 3D human pose estimation is an ill-posed problem in computer vision due to its depth ambiguity. Most existing works supplement the depth information by extracting temporal pose features from video frames, and they have made notable progress. However, these approaches divide a long sequence of video frames into multiple short sequences for separate processing, which leads to the loss of complementary information between sequences. Furthermore, the short-term temporal correlation among frames in a sequence is often not fully exploited. To model temporal dependencies efficiently, we propose the frame-padded multiscale transformer approach, which includes a frame-padded video sequence preprocessing step and a multiscale temporal transformer backbone. Our approach addresses the omission of the temporal features of edge frames in existing approaches by padding video frames in the shallow layer. In addition, we extract the temporal information of 3D human poses using a multiscale transformer to enhance the short-term correlation of human pose skeleton keypoints. Extensive experiments validate the effectiveness of our approach on two popular datasets: Human3.6 M and MPI-INF-3DHP. The results show that our approach achieves state-of-the-art performance. The code and model are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ygx7/FMFormer</uri> .
Dominant SIngle-Modal SUpplementary Fusion (SIMSUF) For Multimodal Sentiment Analysis

Multimodal sentiment analysis remains a big challenge due to the lack of effective fusion solutions. An effective fusion is expected to obtain the correct semantic representation for all modalities, and simultaneously thoroughly explore the contribution of each modality. In this paper, we propose a dominant SIngle-Modal SUpplementary Fusion (SIMSUF) approach to perform effective multimodal fusion for sentiment analysis. The SIMSUF is composed of three major components, a dominant modality supplementary module, a modality enhancement module, and a multimodal fusion module. The dominant modality supplementary module realizes dominant modality determination by estimating mutual dependence between every two modalities, and then the dominant modality is adopted to supplement other modalities for representative feature learning. To further explore the modality contribution, we propose a two-branch modality enhancement module, where one branch learns common representation distribution for multiple modalities, and simultaneously a specific modality enhancement branch is presented to perform semantic difference enhancement and distribution difference enhancement for each modality. Finally, a dominant modality leading fusion module is designed to fuse multimodal representations of two branches for sentiment analysis. Extensive experiments are evaluated on the CMU-MOSEI and CMU-MOSI datasets. Experiment results certify that our approach is superior to the state-of-the-art approaches. The source code of this work is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HumanCenteredUndestanding/SIMSUF</uri> .
Comparative analysis of the approximation of derivatives of Caputo and Riemann-Liouville fractional orders in the bases of biorthogonal polynomials

On the basis of the model problem, a comparative analysis of the use of the spectral method in the basis of orthogonal polynomials for calculating the derivatives of fractional orders of Caputo and Riemann-Liouville was carried out. The rate of convergence of the approximation was analyzed
Optimised NLP Model for MCQ Generation through Advanced Batching and Tokenization

Abstract: This research presents a pioneering methodology forenhancing Natural Language Processing (NLP) models through optimized Word Sense Disambiguation (WSD) and Multiple- Choice Question (MCQ) generation. By employing innovative strategies in batching and tokenization, this study revolutionizes the efficiency and accuracy of NLP tasks. This approach entails meticulous optimization of tokenization processes and concurrent batch operations, resulting in substantial computational efficien- cies without compromising the precision of WSD and MCQ generation. The proposed framework sets a new standard in NLP, offering robust enhancements in computational efficacy andlanguage comprehension tasks
Flood Inundation Modeling for the Lower Bengkulu Sub-Watershed of Bengkulu Province Using the Hec-Ras 5.0.7 Program Based on Ras Mapper and Arc-Gis 10.8

The Down Stream Air Bengkulu Watershed is part of the Air Bengkulu Watershed in Bengkulu Province. Flooding in this area frequently leads to major subdistrict, economy, and environmental impacts. The aim of the research is to develop a flood forecasting model that is capable of mapping potential flood areas in order to support flood control efforts. The method used is a hybrid model between a hydrological model, a hydraulics model and a Geographic Information System (GIS). The hydrological model is input to HEC-RAS software, It’s developed based on Snyder's Synthetic Unit Hydrograph (SUH). The hydraulics model was prepared using the software of HEC-RAS version 5.0.7 and the it’s output was used as material for preparing potential flood inundation maps. The potential flood inundation map was prepared by overlay method with the help of the Arc-GIS version 10.8 application. The research results show that the peak flood time is 7.43 hours with peak discharge for a 100 year return period of 1,542 m3/second. Flood inundation occurred in nine sub-Districts including Talang Empat District (552,819 ha), Karang Tinggi (391,648 ha), Selebar (24,118 ha), Singaranpati (95,806 ha), Ratu Samban (6,838 ha), Ratu Agung (212,173 ha), Sungaiserut (541,659 ha), Muara Bangkahulu (395,495 ha), and Pondok Kubang (197,167 ha). The conclusion of the research is that the model developed is able to answer peak times, area and water level of inundation as well as potential locations that will be flooded.
Implementation Of Augmented Reality Technology For Object Length Measurement Applications

During the transitional time between Covid-19 and endemic, all Indonesian schools, and SMKN 47 in particular, had restricted educational opportunities. Due to challenges encountered by the instructor and 36 students majoring in accounting in grade 12, they were unable to carry out teaching and learning activities at school; art lesson formats with three-dimensional learning modules must be visualized in 3D. However, with hybrid teaching and learning utilizing presentations, displaying the learned 3D forms is less successful. In order to visualize three dimensions in fine arts classes using augmented reality technology, we require a new learning medium. The application of the Markerless Based Tracking approach in this work enables the presentation of a tracked 3D model in the surrounding environment in real-time by merging the actual and virtual worlds as if their boundaries did not exist. The augmented reality system scans flat surfaces utilizing points, as opposed to markers or other auxiliary media. Points used as pedestals or containers to elevate three-dimensional items. There are three sculptures and three traditional dwellings on show. Online examination outcomes average resulted a 78.2% that the AR Fine Arts application by instructors and students are therefore consistent and well accepted.
ESTIMATION OF A BI-RESPONSE TRUNCATED SPLINE NONPARAMETRIC REGRESSION MODEL ON LIFE EXPECTANCY AND PREVALENCE OF UNDERWEIGHT CHILDREN IN INDONESIA

Researchers use the nonparametric regression method because it provides excellent flexibility in the modeling process. Nonparametric regression procedures can be used if the relationship pattern between the predictor and response variables is unknown. The truncated spline method is one of the most frequently used nonparametric regression methods. A truncated spline is a polynomial slice with continuous segmented properties, and the resulting curve is relatively smooth. The advantage of truncated splines is that they can be used on data that experience behavior changes at specific intervals. The nonparametric spline truncated bi-response regression approach is used when one or more predictor variables affect the two response variables with the assumption that there is a correlation between the response variables. This study aimed to obtain the best spline truncated bi-response nonparametric regression model on life expectancy data and the prevalence of underweight children in Indonesia in 2021. The data used comes from the Central Bureau of Statistics and the Indonesian Ministry of Health. The optimal knot point selection method uses the Generalized Cross Validation (GCV) method. The results showed that the best model formed was obtained using three-knot points based on a minimum GCV value of 22.77 and a coefficient of determination of 99.58%.
ALGEBRAIC CRYPTANALYSIS ON NTRU-HPS AND NTRU-HRSS

NTRU is a lattice-based public-key cryptosystem designed by Hoffstein, Pipher, and Silverman in 1996. NTRU published on Algorithmic Number Theory Symposium (ANTS) in 1998. The ANTS’98 NTRU became the IEEE standard for public key cryptographic techniques based on hard problems over lattices in 2008. NTRU was later redeveloped by NTRU Inc. in 2018 and became one of the finalists in round 3 of the PQC (Post-Quantum Cryptography) standardization process organized by NIST in 2020. There are two types of NTRU algorithms proposed by NTRU Inc., which are classified based on parameter determination, NTRU-HPS (Hoffstein, Pipher, Silverman) and NTRU-HRSS (Hulsing, Rijnveld, Schanck, Schwabe). Algebraic cryptanalysis on ANTS’98 NTRU had previously been carried out in 2009 and 2012. In this paper, algebraic cryptanalysis is performed on NTRU-HPS with q=2048, n=509 (ntruhps2048509) and NTRU-HRSS with n=701 (ntruhrss701). This research aims to evaluate the resistance of NTRU-HPS and NTRU-HRSS algorithms against algebraic cryptanalysis by reconstructing the private key value. As a result, NTRU-HPS and NTRU-HRSS resistance to algebraic cryptanalysis.
​​ACTIVITIES DESIGN STRATEGIES OF BLENDED ENGLISH GRAMMAR TEACHING IN HIGHER VOCATIONAL COLLEGES​

The outbreak of COVID-19 has made online teaching an important part of daily teaching. In the post-epidemic period, blended teaching will inevitably become a new teaching form in teaching activities, bringing a new classroom revolution and learning revolution to English grammar teaching in higher vocational colleges. Therefore, based on the characteristics analysis of EFL learners’ learning behavior, colleges and universities should transform the teaching methods with the combination of information technology and depth fusion of education teaching. It is important to make full use of the Massive Open Online Course (MOOC), Small Private Online Course (SPOC) and flip classroom teaching methods by the cohesion of course activities to improve the teaching paradigm online. Besides, with the help of intelligent platform to make out the learning behavior analysis data is also available which is benefits to content visualization, network class differentiation, entity classroom interaction and intelligent teaching management, realizing the "internet + intelligent" education, to build more high-quality courses. This paper is to adopt two classes students by different teaching methods, which are blended teaching method and traditional teaching method to explore the students’ learning behavior influential factors and activity design for blended teaching mode framework. Based on SPOC blended teaching course into the actual grammar teaching, this study discusses the English grammar blended teaching strategy for EFL learners. The aim is to improve the teaching efficiency of teachers and students' learning ability, and provide theoretical guidance for the design of the blended teaching mode of English grammar in higher vocational colleges.
ZERO-INFLATED NEGATIVE BINOMIAL MODELING IN INFANT DEATH CASE DUE TO PNEUMONIA IN EAST JAVA PROVINCE

Pneumonia is an acute infectious disease of the respiratory tract and an infection caused by a virus, bacteria or fungus that attacks the lung tissue. Several cases of pneumonia have resulted in deaths that occurred in toddlers aged 12-59 months. Based on official health in profile data, East Java's health in 2021 has a zero number of deaths under five aged 12-59 months due to pneumonia. Modeling data with many response variables is zero and there is overdispersion can be done using Zero Inflated Negative Binomial (ZINB) regression. This study aims to model the number of infant deaths aged 12-59 months due to pneumonia in East Java Province based on seven factors that are considered to influence the number of deaths in infants due to pneumonia. From this model, it can be seen that the factors that significantly influence the death of infants aged 12-59 months due to pneumonia in East Java Province using Zero Inflated Negative Binomial (ZINB) regression. The results of testing the parameters of the ZINB regression model show that the predictor variables that have a partial significant effect on the negative binomial model in East Java are the percentage of infants who received complete basic immunization, the percentage of coverage of under-five health services, the percentage of under-five children with malnutrition, the percentage of LBW (low birth weight babies). Selection of the best model is obtained by using the Bayesian Information Criterion (BIC) of 101,587.