Reviewerly/exHarmony huggingface dataset:
The Curriculum of ELT Education in Indonesian and Vietnamese Universities: A Comparison Study

Both Indonesia and Vietnam provide English as foreign language instruction. In contrast to Indonesia, where English proficiency is low, Vietnam has a moderate level of English proficiency. Although Vietnam has a moderate level of English proficiency, its curriculum is nearly identical to that of Indonesia, making it suitable for comparative research. This research aimed to identify the significant differences between Indonesian and Vietnamese universities’ English language teacher education curricula. This study employed a qualitative methodology to compare the English as a foreign language teacher education curricula in Vietnam and Indonesia. In this study, descriptive analysis was utilized. The English education curricula documents obtained from the university’s website were used for data analysis. The research settings were Universitas Pasifik Morotai in Indonesia and the Giao Su-IESUS Evangelical School of Universal Studies in Vietnam. The finding showed that Vietnamese Universities stressed the dimension of content knowledge, communication competence, contextual knowledge, and professionalism practice theory. However, teaching competence, research knowledge, Community Practice Membership, and general knowledge are often overlooked. Compared to the EFL curriculum in Vietnam, the Indonesian curriculum, particularly at Universitas Paisfik Morotai, emphasized communication skills, content knowledge, research knowledge, and teaching competence/pedagogical aspects
Thematic Progression Analysis in Different Gender and Major by Students Of 12th Grade in Writing Review Text

The present research was conducted to analyze the thematic progression in students’ writing review text at grade 12 at SMA N 12 Medan. Therefore this research was led to analyze the kinds of thematic progression used by the students in writing review text and to find out how thematic progression was realized in students writing review text. Descriptive Qualitative was applying in this research. This research conducted for twelve grade of science and social class, male and females students’ consist of (5 males + 5 females students from Science class) and ( 5 males + 5 females students from social class). The result shows the researcher found that both in social and science class, all of the thematic progression are found in the passage which written by them. The first the researcher found the Reiteration Theme or Constant theme in almost every passage that written by the students and it is same if the researcher compared it by Male and Female class. It means that the students in social and science class even male and female trying to keep their passage is coherence each other. Then also find Zig Zag theme, almost every students already able to build an information but still has a cohesion with another sentence. The last the researcher also found the multiple rheme pattern. It shows that the students able to build a lot of information from the first rheme.
Energy Cost and Machine Learning Accuracy Impact of k-Anonymisation and Synthetic Data Techniques

To address increasing societal concerns regarding privacy and climate, the EU adopted the General Data Protection Regulation (GDPR) and committed to the Green Deal. Considerable research studied the energy efficiency of software and the accuracy of machine learning models trained on anonymised data sets. Recent work began exploring the impact of privacy-enhancing techniques (PET) on both the energy consumption and accuracy of the machine learning models, focusing on k-anonymity. As synthetic data is becoming an increasingly popular PET, this paper analyses the energy consumption and accuracy of two phases: a) applying privacy-enhancing techniques to the concerned data set, b) training the models on the concerned privacy-enhanced data set. We use two privacy-enhancing techniques: k-anonymisation (using generalisation and suppression) and synthetic data, and three machine-learning models. Each model is trained on each privacy-enhanced data set. Our results show that models trained on k-anonymised data consume less energy than models trained on the original data, with a similar performance regarding accuracy. Models trained on synthetic data have a similar energy consumption and a similar to lower accuracy compared to models trained on the original data.
PEM360: A dataset of 360° videos with continuous Physiological measurements, subjective Emotional ratings and Motion traces

From a user perspective, immersive content can elicit more intense emotions than flat-screen presentations. From a system perspective, efficient storage and distribution remain challenging, and must consider user attention. Understanding the connection between user attention, user emotions and immersive content is therefore key.
Personalized Visual Scanpath Prediction Using IOR-ROI Weighted Attention Network

Predicting visual scanpaths plays an important role in modeling overt human visual attention and search behavior. Due to the rapid development of deep learning, previous scanpath prediction models have made significant progress. However, these methods only focus on common visual saliency, while ignoring differences in observational traits between individuals. Therefore, we propose a more challenging task, which is to provide personalized scanpath prediction for different subjects. In response to the above tasks, we designed a personalized scanpath prediction model with two branches. Specifically, we use the visual hit branch to realize the interaction between multiple features, obtain high-dimensional features with rich subject information, and standardize the appearance location of fixations at the same time. Then, weighted attention in scanpath prediction branch fuses image embedding and subject embedding to obtain an ROI map more similar to the observed characteristics of the subject using high-dimensional features obtained from the visual hit branch.
Is this the real life? Investigating the credibility of synthesized faces and voices created by amateurs using artificial intelligence tools.

The widespread availability and accessibility of artificial intelligence (AI) tools have enabled experts to create content that fools many and needs deep scrutiny to be discernible from reality; nevertheless, it is unclear whether presented with the same tools amateurs can also create synthesized faces and voices with similar ease. The possibility of creating this kind of content can be life-changing for smaller movie makers.
Recognition of Camera Angle and Camera Level in Movies from Single Frames

The position and orientation of the camera in relation to the subject(s) in a movie scene, namely camera “level” and camera “angle”, are essential features in the film-making process due to their influence on the viewer’s perception of the scene. In this paper, we propose the use of Convolutional Neural Networks (CNNs) for the automatic recognition of camera angles (categorized into five classes: Overhead, High, Neutral, Low, and Dutch) and camera levels (categorized into Aerial, Eye, Shoulder, Hip, Knee, and Ground) in movie frames. Our approach demonstrates remarkable effectiveness even when frames do not prominently feature the human figure. The training, validation, and test datasets are composed of frames sampled from an unprecedented variety of movie shots, freely available images, and labeled frames from cinematographic websites, for a total of over 24,000 images. Classification results for both camera angle and level achieve a weighted average precision and recall above 95%. To foster further research in domains such as movie stylistic analysis, video recommendation, and media psychology, we provide the developed models, annotation tool, and frame data through our project page at https://cinescale.github.io/.
Website Pintar Untuk Diet Harian Dengan Menggunakan Metode Fuzzy Tsukamoto

Food is the main factor to support the body in carrying out various activities. Balance in consuming food plays a very important role for health, too much consumption of the same type of food without balancing it with other foods can lead to things that can trigger various diseases such as diabetes, high blood pressure, cholesterol, and so on. The purpose of this research is to calculate the level of daily calorie requirements for people who are on a daily diet. The method used is the fuzzy tsukamoto method in calculating daily calories where there is a division of time for meal portions according to daily calories, for example breakfast, lunch, dinner. Research results using a case study of age: 25 years old, weight: 170kg, height: 165cm, gender: female, activity: retirement. From the case study get the results of 1279 calories and the division of calories according to time, breakfast: 256 calories, snack: 160 calories, lunch: 384 calories, snack: 160 calories, dinner: 320 calories.
Constraints and Solutions to Problems in Online Learning at SD Kristen Satya Wacana, Salatiga

The Covid-19 pandemic has had a significant impact on the world of education. Therefore, the government, through the relevant ministries, makes crucial policies by imposing distance or online learning. In an effort to support this learning, the government has also implemented an internet quota program for students by deactivating various social media so that students focus on learning. However, it cannot be denied that there are obstacles in learning. The results of the questionnaire show that network constraints play a major role in learning. With intermittent sound, the network which often crashes is enough to disrupt the online learning process. The solution offered by students' parents is to change the provider or wait until the network is normal. The dominant solution in the results of this study is to carry out learning variations, namely face-to-face and online as well as recording material and adding learning applications so that if there is a disruption to the learning network, the learning continues because the material is already stored on the students' or parents' mobile phones. All obstacles can be overcome thanks to good cooperation between schools, especially support teachers, parents and students.
Sistem Pakar Diagnosis Penyakit Tanaman Padi Dengan Metode Teorema Bayes

One of the cultivated plants is rice, oryza sativa in Latin, although some varieties are wild rice. Rice cultivation began in Indonesia around 15000 BC, although it is thought to have originated in India or Indochina. Rice is the most important crop in agrarian countries like China, India, Bangladesh and Indonesia. The japonica and indica groups usually contain rice plants. According to archaeological evidence, japonica rice was first domesticated around 9,000 years ago in the Yangtze Valley in southern China. Meanwhile, around 5,000 years ago, indica rice was first grown in the Ganges River valley. Rice-producing grass plants fall into this category. For most Indonesians, rice is a staple food. At the time of growth, rice plants are susceptible to various diseases which cause farmers to fail to harvest. To find out the diseases that attack rice plants, an expert who can diagnose rice plant diseases is needed. An expert is someone who understands the problem of diagnosing a disease. However, the lack of agricultural officers/extensioners in the Seputih Banyak District area. The purpose of this research is to apply an expert system using the Bayes theorem method to overcome problems regarding rice plant diseases and the limitations of agricultural officers/extensioners. And can be accessed by farmers anywhere when they need an epert system to diagnose a disease that attack rice plants without having to need assistance from extension agricultural extension officers.
EXPERT SYSTEM WITH FUZZY MANDANI FOR PREDICTION OF DIGESTIVE DISEASES

Digestive diseases of the stomach often occur in everyday life. So that almost everyone experiences the disease. every day the health center is full of patients who experience digestive disorders in the stomach. Not only medical treatment but also consultation regarding solutions for digestive ailments, especially in the stomach. Every day the health center records and reports the number of visiting patients, but consulting in this service is considered ineffective, so it takes quite a long time. To facilitate and speed up services at the puskesmas, an expert system was created to assist the puskesmas and patients in conducting consultation services the puskesmas. An expert system is a system that seeks to produce a decision that is equal to that of an expert or expert using a computer system so that the decision results are accurate. The expert system is implemented using the fuzzy Mamdani method so that it can assist in determining a diagnosis with the maximum. The results of this study are 6 types of digestive diseases in the stomach, using the Mamdani fuzzy expert system. So that it produces an accurate diagnosis. In making the web program, it has been tested using Black Box testing.
DECISION MAKING MODEL FOR MEASURING THE FEASIBILITY OF SOCIAL ASSISTANCE FOR TPS3R AREA OF PRINGSEWU DISTRICT

Poverty is a major social welfare problem in Indonesia. Various efforts have been made by the government to overcome the main problems of social welfare in accordance with the Constitution of the Republic of Indonesia Number 11 of 2009 concerning social welfare, such as the provision of social assistance, social rehabilitation, social security, social empowerment, and social protection. In fact, various efforts made by the government in overcoming social welfare problems in Indonesia are considered not optimal. The lack of optimal distribution of government social assistance is affected by several factors, among others, the large number of data used as parameters and data collection and calculations that are still carried out simply and manually cause errors so that the results obtained are not on target. The study was conducted by examining the determining variables of eligibility for social assistance recipients using theSimple Additive Weighting method. To optimize the determination of the eligibility of beneficiaries in the TPS3R area of Pringsewu district, in this study the determining criteria will be applied to a system using the Decision Support System model. The decision-making system will be carried out by applying several variables as a basis for decision making including, Domicile, Family Economic Status, Employment, Income, Beneficiary Status Assistance, Number of Dependents and Home Conditions. By using the Decision Support system, poverty alleviation programs in the TPS3R area of Pringsewu district can be carried out quickly, effectively and efficiently and on target
How to Implement Discovery Learning in English Language Teaching at Indonesia Higher Education?

This study aims to investigate the implementation of discovery learning in teaching English. This study was qualitative research, and it applied a case study. The researcher conducted her study at Universitas Sulawesi Barat Majene Regency West Sulawesi. The data source was from the sample research, and purposive sampling was the selected technique in this study. The researcher is a primary instrument in this case study research. The findings release there were six steps of discovery learning implementation. The lecturers carried out the process of discovery learning, which consists of six steps. The frequency of implementation of discovery learning can be described into different categories, namely: High Implemented Discovery Learning (HIDL), Moderate Implemented Discovery Learning (MIDL), and Law Implemented Discovery Learning (LIDL). All lecturers are the High Implemented Discovery Learning is marked by the highest frequency of using discovery learning in teaching English education students. The stages of this implementation are stimulation, problem identification, data collection, data processing, verification, and generalization. The highest frequency of using marked the selection of each category discovery learning in teaching English.
Digital Literacy Learning Module Improves Knowledge of Healthy Food in Indonesian PAUD Students

Children are active in online activities, especially on social media. It leads to an urgent need to increase digital literacy early to minimize the risks of online harm. This study analyzes the effectiveness of digital literacy modules and learning media in the COVID-19 pandemic by using digital learning resources such as videos and electronic books. This type of research is quantitative. The method used is a descriptive method with a quantitative approach. The sample in this study was 219 people, namely early childhood 5-6 years, teachers, and parents. The data collection technique for this research used a pre-test and post-test and a questionnaire with a Likert scale. The data analysis technique used is paired t-test. The study results demonstrate the relevance and applicability of e-learning modules in content and structure for children, particularly in developing awareness about healthy eating. It was concluded that digital literacy learning modules and media were quite effective and made it easier for students to absorb learning material from teachers with diverse backgrounds and abilities.
Speaking Skills of EFL Student Teachers of UIN Raden Fatah Palembang: Problems and Strategies

The purposes of this study were to find out the problems faced by the student teachers of UIN Raden Fatah Palembang in speaking English and to find out the strategies used by the lecturer of UIN Raden Fatah Palembang to cope with the problems. A case study was used and purposeful sampling was utilized. The interview was used to find out the student teachers’ problems in speaking English. Interviews and documentation were used to find out the lecturer’s strategies to cope with the problems. The problems and the strategies were: 1) lack of vocabulary mastery, to cope with the problem, the lecturer kept the student teachers on speaking the target language; 2) lack of grammar mastery and the strategy was the lecturer provided oral feedback to the student teachers’ grammar used; 3) lack of pronunciation, in order to cope with the problem, the lecturer provided some speaking activities and oral feedback; 4) anxiety-provoking situation, to deal with the problem, the lecturer used small groups, promoted equal participation after giving preparation time, and gave positive feedback; 5) lack of self-confidence, to deal with the problems, the lecturer applied small groups discussion, gave them the equal opportunity to speak after giving preparation time, and enforced repetition; 6) the interference of the mother tongue, in order to cope with the problem, the lecturer applied some speaking activities and encouraged them to speak the target language. By knowing the EFL student teachers’ problems and the strategies to cope with them, the lecturer could prepare herself in case she encounters the aforementioned problems again and find out more appropriate strategies.
Internet of Things-driven Human Activity Recognition of Elderly and Disabled People Using Arithmetic Optimization Algorithm with LSTM Autoencoder

In recent times, mobile communications and Internet of Things (IoT) techniques have been technologically advanced to gather environmental and human data for many applications and intelligent services. Remote monitoring of disabled and older people living in smart homes is very difficult. Human activity recognition (HAR) is an active research area for classifying human movement and application in many regions like rehabilitation, healthcare systems, medical diagnosis, surveillance from smart homes, and elderly care. HAR data are gathered in wearable devices that contain many kinds of sensors or with the mobile sensor aid. Lately, deep learning (DL) algorithm has shown remarkable performance in classifying human activity on HAR information. This paper presents a new Arithmetic Optimization Algorithm with LSTM Autoencoder (AOA-LSTMAE) for HAR technique in the IoT environment. In the presented AOA-LSTMAE technique, the major intention is to recognize several types of human activities in the IoT environment. To accomplish this, the AOA-LSTMAE technique mainly derives the P-ResNet model for feature extraction. In addition, the AOA-LSTMAE technique utilizes the LSTMAE classification model for the recognition of different activities. For improving the recognition efficacy of the LSTMAE model, AOA is used as a hyperparameter optimization system. The simulation validation of the AOA-LSTMAE technique is tested on benchmark activity recognition data. The simulation results of the AOA-LSTMAE technique and compared methods stated the improvement of the proposed model with an accuracy of 99.12% over other recent algorithms.
Harmonicity Spectrum

Perceiving harmonic information (especially weak harmonic information) in time series has important scientific and engineering significance. Fourier spectrum and time-frequency spectrum are commonly used tools for perceiving harmonic information, but they are often ineffective in perceiving weak harmonic signals because they are based on energy or amplitude analysis. Based on the theory of Normal time-frequency transform (NTFT) and complex correlation coefficient, a new type of spectrum, the Harmonicity Spectrum (HS), is developed to perceive harmonic information in time series. HS is based on the degree of signal harmony rather than energy or amplitude analysis, and can therefore perceive very weak harmonic information in signals sensitively. Simulation examples show that HS can detect harmonic information that cannot be detected by Fourier spectrum or time-frequency spectrum. Acoustic data analysis shows that HS has better resolution than traditional LOFAR spectrum.
Fast Gao-Like Decoding of Horizontally Interleaved Linearized Reed–Solomon Codes

Both horizontal interleaving as well as the sum-rank metric are currently attractive topics in the field of code-based cryptography, as they could mitigate the problem of large key sizes. In contrast to vertical interleaving, where codewords are stacked vertically, each codeword of a horizontally $$s$$ -interleaved code is the horizontal concatenation of s codewords of $$s$$ component codes. In the case of horizontally interleaved linearized Reed–Solomon (HILRS) codes, these component codes are chosen to be linearized Reed–Solomon (LRS) codes. We provide a Gao-like decoder for HILRS codes that is inspired by the respective works for non-interleaved Reed–Solomon and Gabidulin codes. By applying techniques from the theory of minimal approximant bases, we achieve a complexity of $$\tilde{\mathcal {O}}({s^{2.373} n^{1.635}})$$ operations in $$\mathbb F_{q^m}$$ , where $$\tilde{\mathcal {O}}({\cdot })$$ neglects logarithmic factors, $$s$$ is the interleaving order and n denotes the length of the component codes. For reasonably small interleaving order $$s\ll n$$ , this is subquadratic in the component-code length n and improves over the only known syndrome-based decoder for HILRS codes with quadratic complexity. Moreover, it closes the performance gap to vertically interleaved LRS codes for which a decoder of complexity $$\tilde{\mathcal {O}}({s^{2.373} n^{1.635}})$$ is already known. We can decode beyond the unique-decoding radius and handle errors of sum-rank weight up to $$\frac{s}{s+ 1} (n - k)$$ for component-code dimension k. We also give an upper bound on the failure probability in the zero-derivation setting and validate its tightness via Monte Carlo simulations.