Reviewerly/exHarmony huggingface dataset:
Movie Review Application

The Movie review app is an app that allows users to search for movies by title, view detailed information about specific movies, and leave reviews for movies they have watched. The app's user interface was designed to be responsive and intuitive, with a clean and modern look. To build the app, the students used a variety of tools and technologies, including React components, the fetch function, and a movie database API. The students also implemented routing using the react-router-dom library and handled form submission and input validation. In addition to the core movie review functionality, the students also added several additional features, such as the ability to filter search results by release year and the option to sort reviews by date or rating. Overall, the movie review app was a successful student project that demonstrated the students' skills in web development and their ability to work with React..
Developing Professional Ethics for Teachers by Using Community Processes of Professional Learning Through Information Technology Systems for Students Practicing Professional Teaching Experience in Bachelor of Education Program in Early Childhood Education at Chiang Mai Rajabhat University, the Office of the Private Education Commission

The objectives of this research were: 1) to investigate the current conditions and problems related to a teacher’s professional code of ethics of teachers; 2) to analyze factors supporting the success in developing a teacher’s professional code of ethics; 3) to develop professional learning community processes in developing teacher’s professional code of ethics through information technology systems for pre-service teachers; and 4) to propose guidelines for developing a teacher professional code of ethics by using professional learning community processes through information technology systems for pre-service teachers in the early childhood education program at Chiang Mai Rajabhat University, the Office of the Private Education Commission. The instruments used in this research were an in-depth interview form, a meeting record form, and an evaluation form. Data were analyzed by content analysis, classifying them into issues, mean and standard deviation, and summarizing into categories. Results of the research found as follows: 1) Results of investigating the current conditions and problems related to a teacher’s professional code of ethics consisted of five aspects. 2) Results of analyzing factors supporting the success in developing teacher professional ethics consisted of six aspects. 3) Results of developing professional learning community processes in developing teacher professional code of ethics through information technology systems of pre-service teachers in the early childhood education program at Chiang Mai Rajabhat University, the Office of the Private Education Commission consisted of nine components. The benefit and feasibility were at the highest level. 4) Results of proposing the guidelines for developing the professional code of ethics by using professional learning community through information technology systems of pre-service teachers in the early childhood education program at Chiang Mai Rajabhat University, the Office of the Private Education Commission consisted of nine items.
An Overview of Various Techniques Involved in Detection of Anomalies from Surveillance Cameras

In recent years, the use of surveillance cameras is rapidly increasing in both public and private areas to enhance the security measures. Many companies are recruiting people to monitor the activities captured by surveillance cameras and due to human error they may failed to monitor the abnormal events. So, an automated system to detect the anomalous events acts as a significant approach in surveillance applications. Due to sparse occurrence of anomalous activities, the detection of anomalies is remaining as a challenging task. To overcome these drawbacks, many researchers have worked to develop an effective anomaly detection methods using different approaches. This study prioritized some existing approaches to detect anomalies takes place in surveillance videos. The existing researches utilized University of Central Florida (UCF) Crime video dataset to collect the data about the anomalous activities, UCF crime video dataset consist of 13 categories of anomalies which consist of 1900 surveillance videos. The key parameters such as accuracy, recall, F1 score and Area Under Curve (AUC) are evaluated to analyse the efficiency of the existing anomaly detection methods. This survey acts as a tool for future researchers to overcome the drawbacks in the existing methods and create a novel anomaly detection approach.
Communication Strategies in Synchronous and Asynchronous Media

This study compares the communication strategies employed in synchronous and asynchronous media during the learning of English. E-learning has been employed more frequently. This condition enables English subjects to be taught and learned in a setting without face-to-face interaction. E-learning is available in both synchronous and asynchronous modes. Based on the communication methods that arose in both modes, this study was done to present the emergence of communication strategies in both media. The present study involved 36 university students who were enrolled in a teaching methodology course and one lecturer. Specifically, the data were gathered through WhatsApp Group observation and Zoom interviews. The discussion of the study was presented using an explanatory format because it was a qualitative study. From the study, there were 3 types of communication strategies found in synchronous (Zoom), namely codeswitching (87%), time-gaining (13%), and foreignizing (0,3%). While in asynchronous mode using WhatsApp Group, codeswitching (12,5%) and literal translation (87,5%) were found. and synchronous media (Zoom). It was found that the students’ engagement in the WhatsApp group was higher (82%) than was found in Zoom (18%). However, the attitude and awareness of the students also played a significant role in the development of the communication strategies. This study contributes to the research on communication strategies found not only in oral form but also in written form and can be a consideration to promote the teaching of communication strategies.
IMPLEMENTASI PEMIKIRAN DIGITALISASI DAN FUTURISTIK DALAM PEMBELAJARAN MATEMATIKA DI SD

The digital era is the division of the economy caused by technological developments that refer to opportunities that impact education. This influence is called digitization in the learning process. The method used is a literature study by reviewing ten articles in national journals and then analyzing them according to the indicators of this study, namely digitization and futuristics. The results showed that (1) Learning is not limited by time and space; (2) Ease of obtaining information not only by teachers and students but also everyonel (3) Education requires learning to follow the development of innovative and creative technology to produce curriculum updates and other learning tools. One example of technological growth in line with futuristic thinking that can apply to mathematics learning in elementary schools is the application of GeoGebra, which can assist students in visualizing the mathematical learning process and tools for teachers in providing geometry to students.
PENERAPAN MEDIA MONTASE UNTUK MENINGKATKAN KETERAMPILAN BERBICARA SISWA KELAS V SD NEGERI 07/III SUNGAI ABU KABUPATEN KERINCI

This study discusses the application of montage media to improve the speaking skills of fifth grade students at SD Negeri 07/III Sungai Abu, Kerinci Regency. The cycle process which includes four stages, namely planning, implementation, observation and reflection becomes part of the implementation of this research. Based on observation, interview and documentation techniques, the data obtained is then processed through data reduction, data display, and drawing conclusions. The results of the study found that the application of montage media to improve students' speaking skills was carried out in the following steps: (1) the teacher used a storytelling strategy (2) the teacher prepared tools and drawing materials from various references according to the sub-theme of learning material in the form of newspapers, magazines, Or pictures from the internet. (3) the teacher divides students classically from groups consisting of 4-5 students per group, in pairs then individually. (4) the teacher gives rewards such as a shining star symbol. Whereas the results of the learning process on speaking skills are shown from the results in each cycle that has been carried out, the achievement is with an average pre-action of 44.42%, the first cycle has increased to an average of 54.80% while the second cycle has increased significantly to 80.57%.
LANGUAGE ERRORS IN THE PRACTICE OF SPEAKING INDONESIAN LANGUAGE FOR INTERNATIONAL SCHOOL STUDENTS

This journal aims to find out the language errors in the practice of speaking Indonesian language for the International School students in Chandra Kumala School. It is known that the low students' learning motivation in Indonesian language in a school with an international curriculum. This case study research was carried out in the Covid-19 pandemic situation, and the data collection techniques used an in-depth-interview, observation, and note taking. The results show that (1) language errors in speaking can be classified into 4 types, pronunciation errors, intonation errors, sentence errors, fluency and pauses; (2) the language errors were caused by 5 factors, i.e nervosity, mastery of topics, lack of vocabularies, diction, and grammatical mastery. The students who are mostly Indonesian citizens are expected to know and fluent to speak Indonesian language. But it does not happen in the International School. The impact of this phenomenon is that students do not have a psychological attachment to the Indonesian language. If this is allowed, students will grow up to become Indonesian citizens who are ignorant of their own language.
MYTH AS THE BASIS OF MUSICAL MEANING-MODELLING IN THE WORKS OF A. BAKSHI

The modern cultural situation is characterized by the increasing role of mythology and mythmaking. The relevance of the study of myth and the mechanisms of mythologization is associated with its special role in music and theater, with the desire to know the deep, essential connections and relationships, the common in the special. The purpose of the work is to comprehend the mechanism of creative approach to the myth. In the XXth – early XXIst centuries, artistic creativity is not only inspired by images, myth plots, but creates its own myth based on archetypes in the system of mythological modelling. The interaction of music and myth at the point of the ritual opens up new possibilities of meaning and expression. The use of myth in its cognitive aspect gives music a stable structure, fixing semantic points in the form of a plot, a story. Myth acquires the energy of a living voice in sonic mysteries. In the work of the composer A. Bakshi, the musical formulas that contribute to the expression of mythological content are actualized. The article identifies three approaches to the use of mythopoetic structures: myth as a plot basis of musical image, work with sound equivalents of archetypal components of myth, and creation of mythologizing sound structures. In the first case, myth serves as the narrative source of one of the sections of the mystery "Polyphony of the World", 2001. Mythological thinking becomes a way and method of working with musical matter (performance "Hotel Room in the City of NN", 1994); finally, myth is created by sound expression ("Sidur-Mystery", 1992). Aesthetically, myth is interpreted as an artistic and semantic matrix, leading to the comprehension of the sonic material.
Software that target metadata determine if science is at sea or in a fishbowl

Scientists are challenged to share information given the lack of adequate software: Software must enable authors to execute and share investigations with regard to the provenance of physical material. In this work, I outline eight challenges that the scientific community faces to produce software that target the needs of authors to share provenance.
FORMATION OF MUSICAL THINKING IN THE INTERPRETATION ACTIVITIES OF THE PERFORMER

The article deals with the processes of musical thinking in the context of the performer’s interpretative activity. It is the performer’s interpretation that is the intermediary between the author’s recording and the listener. Each interpretation is based on an idea with its semantic, intellectual and emotional nuances. Since the performer’s values, views, motor associations can change throughout the creative work, some levels of interpretation remain unchanged, for example, the parameters of the composer’s competence zone. The types of musical thinking and operational mechanisms that determine the performer’s interpretative activity are responsible for the diversity. The article analyzes the relationship between emotional-figurative and intellectual components of musical thinking. It is emphasized that the art of interpretation is the consequence of the performer’s creative activity. The sign of the development of creative thinking is the ability to carry out the plan of future interpretation at all stages of learning. In creating one’s own interpretation of the content of a musical work, a significant part is determined by the performer’s imagination, perception and personal attitude to the artistic text. In working on the character of a piece, often not clearly defined by the author’s intention, appropriate performance tasks are drawn up, which determine the choice of artistic colors and playing techniques. The process of creating a performer’s interpretation and the desire to reveal the musical image of the work as fully as possible involves a comprehensive study of the composer’s work, identifying traditions and such stages in the process of reading the author’s text as analysis, differentiation and synthesis.
Accurate Throughput Prediction for Improving QoE in Mobile Adaptive Streaming

Video streaming is the most important mobile application today. To improve users' quality of experience (QoE), the client player runs adaptive bitrate (ABR) algorithms that dynamically select the bitrate for video chunks based on throughput or delivery time predictions. This paper aims to design an accurate predictor for mobile adaptive streaming by investigating all its components, including input features, output target, and mapping function. We construct the first theoretical framework that reveals potential factors affecting chunk throughput and delivery time. To verify this framework, we provide formulation analysis and measurement observations based on 2500+ video sessions collected in real-world mobile networks. We find that previous works have failed to achieve accurate prediction due to overlooking the impact of the transport mechanism and application behavior on throughput. Furthermore, we show that throughput is a better target for data-driven predictors than delivery time, due to the long-tailed distribution of delivery time. Based on the above, we propose Lumos, a decision-tree-based throughput predictor that can be integrated into various ABR algorithms. Extensive experiments in real-world mobile Internet show that Lumos achieves high prediction accuracy and improves the QoE of MPC by 6.3%, and MPC+Lumos outperforms Pensieve by 19.2%.
Siamese Network Representation for Active Learning

Active learning is a crucial part of machine learning aiming to reduce the amount of labeled data by selecting the most informative data to be annotated. Most of the previous proposed active learning methods are based on aleatoric or epistemic uncertainties obtained by learning models while ignoring relationships within the data itself. We propose an efficient similarity-based active learning method using siamese convolutional neural networks. Pairs of image data are sent into the siamese network and similarity between them is computed on their output features. We evaluate our method on image classification, and validate the method on CIFAR10/100 and Caltech101 dataset. Our method outperforms at most 3.17% accuracy than Bayesian-based method and 6.31% than random sample. In addition, we propose a hierarchical clustering method for pool-based sampling strategies, which will boost the representation stage of our method. We also conduct an ablation study to fully explore the efficiency of our method.
AI-based personalized Human Activity Recognition in walking and trekking sports: A case study

Human Activity Recognition (HAR) is a topic of interest in several areas, for example, health and sports. There are several ways to perform HAR tasks, which must be understood before creating the desired tools. In this article, we carry out a theoretical study to understand the HAR problem and its main techniques using AI. In addition, we present a case study in which we developed a prototype HAR system for walking and trekking. In this study, we evaluated new hardware for HAR with IMUs with 9 degrees of freedom. This system, composed of four sensors called SPUs was attached to the users lower limbs. A device called WPU collects information from these sensors. With the fusion of the collected data, AI techniques were applied with CNN models reaching an accuracy of 98% to classify the human activities in this context, thus creating new perspectives for using AI to HAR in sports.
Statistically bias-minimized peculiar velocity catalogs from Gibbs point processes and Bayesian inference

The peculiar velocities of galaxies can serve as excellent cosmological probes provided that the biases inherent to their measurements are contained prior to the start of any study. This paper proposes a new algorithm based on an object point process model whose probability density is built to statistically reduce the effects of Malmquist biases and uncertainties due to lognormal errors in radial peculiar velocity catalogs. More precisely, a simulated annealing algorithm allows for the probability density describing the point process model to be maximized. The resulting configurations are bias-minimized catalogs. We conducted tests on synthetic catalogs mimicking the second and third distance modulus catalogs of the Cosmicflows project from which peculiar velocity catalogs are derived. By reducing the local peculiar velocity variance in catalogs by an order of magnitude, the algorithm permits the recovery of the expected one, while preserving the small-scale velocity correlation. It also allows for the expected clustering to be retrieved. The algorithm was then applied to the observational catalogs. The large-scale structure reconstructed with the Wiener-filter technique applied to the bias-minimized observational catalogs matches that of the local cosmic web well, as supported by redshift surveys of local galaxies. These new bias-minimized versions of peculiar velocity catalogs can be used as a starting point for several studies, from plausible estimations of the most probable value for the Hubble constant, H 0 , to the production of simulations constrained to reproduce the local Universe.
PVP: Personalized Video Prior for Editable Dynamic Portraits using StyleGAN

Abstract Portrait synthesis creates realistic digital avatars which enable users to interact with others in a compelling way. Recent advances in StyleGAN and its extensions have shown promising results in synthesizing photorealistic and accurate reconstruction of human faces. However, previous methods often focus on frontal face synthesis and most methods are not able to handle large head rotations due to the training data distribution of StyleGAN. In this work, our goal is to take as input a monocular video of a face, and create an editable dynamic portrait able to handle extreme head poses. The user can create novel viewpoints, edit the appearance, and animate the face. Our method utilizes pivotal tuning inversion (PTI) to learn a personalized video prior from a monocular video sequence. Then we can input pose and expression coefficients to MLPs and manipulate the latent vectors to synthesize different viewpoints and expressions of the subject. We also propose novel loss functions to further disentangle pose and expression in the latent space. Our algorithm shows much better performance over previous approaches on monocular video datasets, and it is also capable of running in real‐time at 54 FPS on an RTX 3080.
RECENT TRENDS, OPPORTUNITIES AND CHALLENGES OF PERVASIVE LEARNING

Pervasive learning is a learner-centred approach to learning that is designed to be accessible, engaging, and effective. It transcends time and space, allowing learners to access information and resources whenever they want, wherever they are. Pervasive learning has been an alternative to formal institutional education. However, there is a lack of research on pervasive learning in English language education. The article aims to fill this gap by conducting a systematic literature review of research articles published between 2020 and 2023 on pervasive learning in English language education. The article identifies the trends, opportunities, and challenges of pervasive learning in this context, and provides some future directions for research and practice. The article concludes that pervasive learning has the potential to revolutionize English language education, but it also faces some challenges and limitations that need to be addressed. The article contributes to the existing knowledge on pervasive learning and offers some insights and recommendations for teachers, students, administrators, and policymakers in English language education.
Sentiment Analysis and Topic Modeling of Social Media Data to Understand the Response of Indonesian Public regarding the Enforcement of the Lockdown Policy during Covid-19-Pandemic

Abstract The COVID-19 pandemic has caused significant disruption in many areas worldwide, and communication via social media has extensively increased as a result of social distance and lockdown. This study analyses social media data to investigate the reaction of the Indonesian public regarding the government's policy of lockdown implementation measures in response to the Covid-19 outbreak. The sentiment analysis and topic modeling were performed based on the corpus collected from twitter database using keyword “PPKM” as the term used for public activity restriction during the year 2020 in Indonesia. The sentiment trend analysis revealed a prominent dominance of negative tweets throughout the entire period under examination. Extensive increase in the volume of tweets after the 23rd week of lockdown indicates a prevailing a massive dissatisfaction and frustration among the Indonesian public due to lockdown. Several intriguing insights have been revealed by the analysis of hashtags and most popular terms. Topic modeling carry out in this study successfully facilitated the identification of alterations in the topics that were being discussed by the Indonesian citizen concerning the enforcement of the lockdown.