Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF CLASSROOM MANAGEMENT USED BY TEACHER IN TEACHING ENGLISH

This research used a qualitative research design. The English teacher at SMAN 1 Enrekang's 11 IPA 1 grade served as the study's subject. In-depth interviews and observation were the research tools used to get the data. For both the interview and the field notes, the researcher utilized semi-structured questions. The five aspects of classroom management that were the focus of the study were discipline, rules and routines, relationships, engaged and motivated instruction, and physical design. The actual layout of the teacher's classroom had two separate seating configurations. The first arrangement was in tidy rows for when the teacher delivered lessons and made presentations. The second arrangement had separate tables set up for small group discussions. This demonstrated the teacher's flexibility in changing the seating position to accommodate various class activities. The teacher devised routines to encourage discipline and enthusiasm in the learning process, as well as a learning agreement with each student at the start of each lesson. The teacher used team-building exercises that required collaboration to reach a common objective as well as small group talks to encourage relationships among the students. In order to establish relationships with the pupils, the teacher also adopted a personal style, frequently encouraging them at the beginning of lessons and complimenting their efforts. The teacher used a variety of teaching tools, including a whiteboard for presentations and questioning and laptops and speakers for games, to keep the class interested and motivated.
TOPIC MODELING IN COVID-19 VACCINATION REFUSAL CASES USING LATENT DIRICHLET ALLOCATION AND LATENT SEMANTIC ANALYSIS

COVID -19 vaccination is a program provided by the Indonesian government to minimize the spread of the virus. The COVID-19 vaccination program in Indonesia goes hand in hand with issues that are circulating, causing controversy and rejection of vaccination on social media, especially Twitter. There are many factors that influence vaccine rejection on Twitter, to summarize frequently discussed topics and find out hidden topics, this study uses the Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) methods from 1797 Twitter scrapping data. Both models require a set of words that have been converted into a matrix, so before conducting LDA topic modeling, the dataset will undergo a bag of word (BOW) calculation. Meanwhile, in LSA topic modeling, the existing dataset will undergo word weighting of frequently occurring words using Term Frequency - Inverse Document Frequency (TF-IDF). This study was conducted to find and summarize hidden information in the form of frequently discussed topics, thus understanding public opinions related to the COVID -19 vaccination refusal case. LDA and LSA methods will display topics based on the probability and mathematical calculations of word occurrences in each topic in the document. The topics that appear will be further analyzed through coherence score by applying a limit of 20 topics to display the best value. Further modeling experiments are carried out to display topics through LDA and LSA models, this study takes 6 topics with the highest coherence values including the right of individuals to choose whether to be vaccinated or not (0.484607), the Ribka Tjiptaning controversy (0.473368), rejection of the COVID-19 vaccine by groups represented by public figures (0.463631), punishment for non-compliance in the form of fines (0.324924), and halal certification (0.312521).
ELEMENTARY MUSIC MAKING: HISTORY, CONTEMPORARY EXPERIENCE

The content of the article reveals the historical aspects and modern experience of elementary music making. Elementary music making, as a type of musical activity, pedagogical approach and methodology, is characterized by accessibility, comprehensively uses words, music, movement, playing an elementary musical instrument, theatricalization, develops students’ creative and communicative abilities, implements the principle of “learning in action”. Elementary music-making originates from pedagogical experiments and the “Orff-Sshulwerk” method of Karl Orff and Gunild Kettmann. Its original sources are the music of various peoples of the world. The method of elementary music making was modeled and tested by German teachers in the process of conducting music classes at the Güntherschule (school of music and choreography, Munich). Later, elementary music-making was developed in the work of K. Orff on the radio, more than ten programs on musical education aimed at educators, teachers and parents were created. These radio broadcasts and elementary music-making began to gain popularity throughout Germany. Pedagogical principles were embodied in the five-volume manual “Orff-Sshulwerk. Music for Kinder”. The Orff Institute was opened for teachers and educators to implement elementary music-making ideas. Elementary music making as a technique is based on the perception of music through hearing, voice, body, movement, elementary instrument, interaction, which help children learn the basics of music, dance, theater. Elementary music making consists of: rhythmizing and solmization of poems, language games and exercises, singing, vocal improvisation, playing elementary musical instruments, improvised movement, pantomime, dance, spontaneous theatricalization Today, elementary music making is part of the educational process of preschool, primary, secondary, after-school and correctional education institutions. Educators actively use elementary music-making to teach students music, develop musical and performing, creative abilities, as a therapeutic tool. Elementary music-making has found many supporters in modern music education of Ukraine: the public organization “Orff-Shulwerk Association of Ukraine” was created
Embodying Spatial Sound Synthesis with AI in Two Compositions for Instruments and 3-D Electronics

Abstract The situated spatial presence of musical instruments has been well studied in the fields of acoustics and music perception research, but so far it has not been the focus of human–AI interaction. We respond critically to this trend by seeking to reembody interactive electronics using data derived from natural acoustic phenomena. Two musical works, composed for human soloist and computer-generated live electronics, are intended to situate the listener in an immersive sonic environment in which real and virtual sources blend seamlessly. To do so, we experimented with two contrasting reproduction setups: a surrounding Ambisonic loudspeaker dome and a compact spherical loudspeaker array for radiation synthesis. A large database of measured radiation patterns of orchestral instruments served as a training set for machine learning models to control spatially rich 3-D patterns for electronic sounds. These are exploited during performance in response to live sounds captured with a spherical microphone array and used to train computer models of improvisation and to trigger corpus-based spatial synthesis. We show how AI techniques are useful to utilize complex, multidimensional, spatial data in the context of computer-assisted composition and human–computer interactive improvisation.
Classification of Receipt of BPNT Social Assistance Using the C4.5 Algorithm (Case of the Grujugan Kidul Village Office)

BPNT social assistance is a government program that is unsustainable to help the lives of people who are selected selectively and periodically, social assistance is intended for some people in villages who are considered financially unable, so they need help from the government. BPNT social assistance, commonly known as social assistance, is assistance charged by the Regional Revenue and Expenditure Budget. The Municipal Government of Bondowoso provides various kinds of assistance programs, one of which is the BPNT Social Assistance, the distribution of which is carried out by the Grujugan Kidul Village Office. In the selection process to determine the beneficiaries of BPNT Social Assistance at the Grujugan Kidul Village Office, they still have not fully used information technology to support employee performance. So there are obstacles and it takes a long time. Therefore, we need a system that can help employees more easily determine beneficiaries. The application of Data Mining is a series of processes to find added value semi-manually in the form of unknown knowledge from a data set. In this study, the parameters are income, employment, and DTKS status. By implementing the C4.5 Data Mining Algorithm, it is hoped that it will make it easier and faster for employees to determine recipients of BPNT Social Assistance at the Grujugan Kidul Village Office. Paper, we describe the formatting guidelines for Proceedings. Maximum 200 words.
OCCNET: Improving Imbalanced Multi-Centred Ovarian Cancer Subtype Classification in Whole Slide Images

Ovarian carcinoma is known for its diverse subtypes with unique morphologies and clinical characteristics, causing considerable diagnostic complexities. While deep learning has emerged as a promising avenue for subtype identification, the intrinsic imbalance in different datasets from multiple medical institutions is still a challenging issue. In this paper, we introduce a novel Ensemble Attention Mechanism (EAM) specifically tailored to enhance subtype identification within imbalanced, multicentered ovarian cancer Whole Slide Images (WSIs), namely OCCNet. The OCCNet framework incorporate ensemble learning principles and attention mechanisms to adaptively balance the feature representation and effectively integrate every representation which mitigates inherent imbalances of class distribution. Then, one publicly available extensive Multi-centered Dataset from the ongoing Kaggle Competition is used to evaluate OCCNet's performance. Results show that the proposed method achieved a 93.67% F1-Score and a 93% Balanced Accuracy, revealing its feasibility and superiority.
A Multimodal Sentiment Analysis Method Based on Interactive Attention

Sentiment analysis, a crucial aspect of human-computer interactions and healthcare, faces challenges in multi-modal data representation and fusion. Traditional methods often fail to adequately capture the complexities of integrating diverse modalities. Addressing this, we introduce the Cross Attention Fusion (CAF) algorithm, a novel approach for effective multi-modal sentiment analysis. The CAF algorithm uniquely employs a dual attention mechanism—global and local—to enhance the representation and integration of features across modalities. Our extensive experiments, conducted on the CH-SIMS dataset, demonstrate that the CAF algorithm significantly outperforms existing unimodal and multimodal methods in accuracy. These results highlight the CAF algorithm’s superiority in capturing nuanced sentiment expressions, marking a notable advancement in the field of sentiment analysis.
THE QUALITY OF EDUCATIONAL SERVICES ASSESSING ALGORITHM FROM A COMPETITIVE STANDPOINT

The article is devoted to the problem of studying approaches to assessing the quality of educational services and the components of the competitiveness of an educational institution in the context of changing societal requirements for the educational process. Based on the theoretical analysis of the categories “quality of educational services” and “competitiveness of a university,” a comprehensive conceptual approach to assessing the quality of educational services from the perspective of assessing the competitiveness of a university is proposed, which distinguishes the micro-level, meso-level and macro-level. For each of them, goals, objectives, principles and criteria for managing the development of the quality of educational services are proposed and formulated. The results of a study conducted among students regarding attitudes towards the quality of educational services provided can be used in the development of programs to improve the quality of educational services as part of the university’s competitiveness strategy. It was revealed that the majority of students are generally satisfied with both the process of receiving educational services and the result of developing professional competencies, which meets the requirements of the competency-based approach. The developed sequence of the process of assessing the quality of educational services, consisting of defining goals, principles, types, assessment indicators, conducting an assessment and analyzing the results obtained, is the basis of a generalized algorithm for assessing the quality of a university's educational services.
End-to-End Quiz-Style Question Generation for Educational Purposes

Question Generation (QG) is a branch of Natural Language Processing (NLP). It aims to generate natural language questions based on given contents. By combining NLU and NLG, this paper aims to develop a fully functional question generation system which can be used in real-world applications for educational purposes. Crafting exam-style questions is an essential component of education, it serves diverse objectives where both the students and educators can use it to facilitate the education process. A pipelined system is introduced with two modules, an Answer Extraction module, and a Question Generation module. The Answer Extraction module is an encoder-decoder model that extracts question-worthy key-phrases from contexts. The Question Generation module consists of a set of specialized models finetuned on different types of questions that are generated in an end-to-end manner. The types of questions that can be generated by the proposed system are: WH, MCQ, complete, and true/false. The proposed comprehensive evaluation demonstrates the effectiveness of the system in generating educationally valuable question-answer pairs using only context paragraphs as input. This highlights the practical applicability of question generation techniques.
Video Compression with Arbitrary Rescaling Network

We propose a practical downsampling compression scheme, as shown in Fig 1, to use a neural network as a pre-processing module for traditional codecs to improve compression performance. Specifically, we propose a neural network-based pre-processing method to improve video compression performance by downsampling high-resolution videos. Our method, called the rate-guided arbitrary rescaling network (RARN), uses a pre-trained entropy module [1] to estimate bitrate and guide the sampling process. We also use a transformer-based virtual codec (TVC) to simulate the performance of traditional codecs, using a swing-transformer-based invertible neural network to learn distortion from standard codecs, and the cyclic shift attention [2] in the network can approximate the prediction modes of HEVC. Our method, which is compatible with standard codecs (HEVC and VVC), performs well in various sampling ratios according to experimental results.
Scalability and Performance Evaluation of NewSQL and Relational Databases: A Comparative Benchmark Study

The purpose of this article is to test and compare the scalability and performance of NewSQL databases (CockroachDB and SingleStoreDB) versus standard relational databases (MySQL and PostgreSQL). The dataset chosen for this benchmark was an TPC-H generated database, making it a suitable dataset for testing database scalability and efficiency in handling complex queries and big amounts of data.To execute the benchmarking, we will construct assessment criteria based on 3 main categories: Process, Performance and Strategy. In this way, it will be possible to evaluate the behavior of each solution, in different contexts and how they use the respective functionalities to obtain the best result. A Benchmarking tool is essential for obtaining sustainable, reliable, and community-accepted indicators, as it uses standard metrics to assess and classify behavior and performance. Based on these paradigms and the mechanism used for data generation, the benchmark tool chosen for this paper is the TPC-H Benchmark, which explores the data organization and complexity.The results of this benchmarking will give significant insights into the strengths and drawbacks of NewSQL and RDBMS databases, as well as whether the database is better suited for certain use cases. Furthermore, using a dataset like TPC-H will deliver more accurate and useful results, allowing developers and database administrators to make more educated database selection selections. Another point to check is the behavior of technologies in the face of a large volume of data, which is something natural and increasingly common nowadays.
Gated Multi Encoders and Multitask Objectives for Dialectal Speech Recognition in Indian Languages

In this work, several methods have been proposed towards improving the performance of dialectal automatic speech recognition (ASR). A novel encoder architecture has been introduced that is suited for multi-dialect ASR training. Further, we propose Multi-Task Self-Supervised learning (SSL) fine-tuning using CTC and dialect identification. Additionally, the use of different language models (LM) to improve the performance of dialectal ASR has been investigated. Around 800 hours of Bengali and Bhojpuri data, released as a part of the MADASR ASRU challenge have been used to train these models. The work shows that the proposed multi-encoder ASR observes a relative reduction of 7.5% and 9% in WER in Bhojpuri and Bengali, respectively. Additionally, we also observe a 1-2% WER reduction in fine-tuning SSL, further improving performance in these languages. Moreover, we observe advantages in using dialect-specific LM decoding based on predicted dialect.
Summarize While Translating: Universal Model With Parallel Decoding for Summarization and Translation

Recently, multi-decoder and universal models have attracted increased interest in speech and language processing as they allow learning common representations across tasks. These models learn a common representation by sharing a part of or all network parameters. Moreover, such a universal model can handle tasks unseen during training (zero-shot tasks). However, these models do not fully exploit inter-dependencies between tasks during decoding since they usually perform decoding for each task independently. In this paper, we propose to address this issue by extending the universal model to perform multi-task parallel decoding with a cross-attention module between decoders to capture task inter-dependencies explicitly. We also introduce a novel multi-stream beam search algorithm to allow such parallel decoding. We test our proposed model on multi-lingual (English and Portuguese) text/speech translation and summarization, confirming its potential, especially in zero-shot tasks.
Reproducing Whisper-Style Training Using An Open-Source Toolkit And Publicly Available Data

Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech data. It generalizes well to various speech recognition and translation benchmarks even in a zero-shot setup. However, the full pipeline for developing such models (from data collection to training) is not publicly accessible, which makes it difficult for researchers to further improve its performance and address training-related issues such as efficiency, robustness, fairness, and bias. This work presents an Open Whisper-style Speech Model (OWSM), which reproduces Whisperstyle training using an open-source toolkit and publicly available data. OWSM even supports more translation directions and can be more efficient to train. We will publicly release all scripts used for data preparation, training, inference, and scoring as well as pretrained models and training logs to promote open science. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/espnet/espnet
Espnet-Summ: Introducing a Novel Large Dataset, Toolkit, and a Cross-Corpora Evaluation of Speech Summarization Systems

Speech summarization has garnered significant interest and progressed rapidly over the past few years. In particular, end-to-end models have recently emerged as a competitive alternative to cascade systems for abstractive video summarization. This paper aims to establish progress in this rapidly evolving research field, by introducing ESPNet-SUMM, a new open-source toolkit that facilitates a comprehensive comparison of end-to-end and cascade speech summarization models on 4 different speech summarization tasks spanning diverse applications. Experiments demonstrate that end-to-end models perform better for larger corpora with shorter inputs. This work also introduces Interview, the largest public open-domain multiparty interview corpus with $4400 \mathrm{~h}$ of conversations between radio hosts and guests. Finally, this work explores the use of multiple datasets to improve end-to-end summarization, and experiments demonstrate the benefit of multi-style training over fine-tuning. 1
Joint Prediction and Denoising for Large-Scale Multilingual Self-Supervised Learning

Multilingual self-supervised learning (SSL) has often lagged behind state-of-the-art (SOTA) methods due to the expenses and complexity required to handle many languages. This further harms the reproducibility of SSL, which is already limited to few research groups due to its resource usage. We show that more powerful techniques can actually lead to more efficient pre-training, opening SSL to more research groups. We propose WavLabLM, which extends WavLM’s joint prediction and denoising to 40k hours of data across 136 languages. To build WavLabLM, we devise a novel multi-stage pre-training method, designed to address the language imbalance of multilingual data. WavLabLM achieves comparable performance to XLS-R on ML-SUPERB with less than $10 \%$ of the training data, making SSL realizable with academic compute. We show that further efficiency can be achieved with a vanilla HuBERT Base model, which can maintain $94 \%$ of XLS-R’s performance with only $3 \%$ of the data, 4 GPUs, and limited trials. We open-source all code and models in ESPnet.
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections Through Federated Learning

Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech. As language evolves and new terms come into use, these models can become outdated and stale. In the context of models trained on the server but deployed on edge devices, errors may result from the mismatch between server training data and actual on-device usage. In this work, we seek to continually learn from on-device user corrections through Federated Learning (FL) to address this issue. We explore techniques to target fresh terms that the model has not previously encountered, learn long-tail words, and mitigate catastrophic forgetting. In experimental evaluations, we find that the proposed techniques improve model recognition of fresh terms, while preserving quality on the overall language distribution.