Reviewerly/exHarmony huggingface dataset:
The Effect of Reading Aloud Strategy for Developing Reading Habits

The purpose of this study is to determine whether or not the reading aloud technique aided eighth-grade junior high school students in developing reading habits. This is pre-experimental quantitative research. The instruments utilized in this study will be a test and a questionnaire. This study's population included 56 students from two classes, with 29 serving as the sample. The data was collected in the classroom using a reading-aloud activity. Descriptive and inferential statistics were utilized to analyze the data from the reading aloud application, as well as questionnaire analysis to assess the students' reading habits. The data demonstrated that majority of the students in the classroom were acquiring reading habits as a result of Reading Aloud. Strategy was applied and it proved that the Reading Aloud Strategy was effective to develop the students’ reading habits. Based on the findings, it is therefore recommended among others that students should start to apply this strategy in case to develop their reading habits which are useful for their future and it was recommended that teachers should help students pay more attention to their reading attitude. It can be done by providing a variety of books, encouraging independent reading, or organizing class activities. And the teacher encourages their students to read extensively.
Investigating Students’ Perception Using Audio-Visual Material in Listening Class

Listening comprehension is one of the most difficult English language skills for learners. Since listening is an important part of communication, as foreign learners we should acquire listening skills. However, learning listening is lack motivation and most students find difficulties in learning listening. To support learners in learning listening is needed an appropriate medium that facilitates learners in improving their listening comprehension skills. This study aimed to investigate students' perceptions using audio-visual material as a media in listening class. A survey study using a quantitative approach was designed to collect the data. The researcher used a survey questionnaire with 20 Likert Scale questions and distributed them through Google Forms. The sample of this study is 9th-grade students from SMP Shalahuddin in total 109 students from classes A, B, C, and D. After collected the data, the researcher analyzes the data using SPSS. The result shows that students had positive experiences in using audio-visual material in listening class. Most of the students agree that audio visual is interesting, motivating, and entertaining them. Audio-visual also can help students to improve their listening comprehension. However, they find difficulties when the speaker speaks too fast, the speaker explains unfamiliar material or uses unfamiliar accents, and pronunciation.
Implementation of Reading-Log to Increase Students’ Interest on Literacy at Islamic Boarding School

This study aims to investigate the implementation of reading log to students’ reading. This study focuses on reading log and students’ interest in literacy. Reading log, it is hoped that students will be able to become a tool to control reading as well as a tool to measure students' interest in literacy. To obtain the research objectives, the researcher implemented quantitative research with one shot case study deign. The research was conducted Pondok Pesantren Zubdatul Asrar Parepare. The instruments in this study are reading log and questionnaires about students’ interest in literacy. Based on the analysis, by implementing the reading log, the results are as follow: 1) students can know exactly what they read, 2) reading became enjoyment and meaningful, 3) students are able to think critically, 4) the students are in time to complete their task in reading log.
Using “Engage, Study, Activate" (ESA) Method in Improving Students’ Vocabulary

AbstractThe researcher discovered that the second-grade students at SMPN 1 Watopute struggled with vocabulary mastering. The teacher reported that several words were not understood by the students during teaching-learning exercises. They were also unable to communicate in English, both orally and in writing, due to a lack of vocabulary competence. As a result, the researcher advised working with the instructor to fix the problem using the Engage, Study, Activate (ESA) strategy. The research topic was "How can the ESA (Engage, Stimulate, Action) strategy increase students' vocabulary acquisition in the second grade of SMPN2Ranteangin?" This was a classroom action research that included the following steps: basic research, planning, action, observing, analyzing, and reflecting in each cycle. The goal of this study is to help students enhance their vocabulary mastery. This study was divided into two rounds. The first cycle was a failure since only 60.8 percent of the students achieved the required score (75). After the second round, 86.9 percent of the students were able to pass the minimal score, indicating that the research was effective. Furthermore, the students demonstrated behavioral gains. They were more engaged in learning and ready to inquire and answer to the teacher. Consequently, the researcher decided to end the research.Keywords: ESA method; improving; mastery; vocabulary.
Analysis of the Students' Persuasive Paragraph Writing Ability at University

This research deals with analysis of the students' persuasive paragraph writing ability at the third semester of Palopo cokroaminoto university. The population of this research is the third semester students of Primary School Teacher Education Study Program of Palopo Cokroaminoto University in academic year 2022/2023. The total number of population was 200 students. The researcher applied the purposive sampling technique and there are 35 students as sample. Writing test is the instrument of this research. Based on the findings and discussion in the previous chapter, the researcher concludes analysis of the students' persuasive paragraph writing ability at the third semester of Palopo cokroaminoto university was good classification by the mean score in value range 75-87. It can be supported by the students’ content mean score was 83.66. The students’ organization mean score was 81.83. The students’ vocabulary mean score was 81.83. The students’ language use mean score was 81.06. The students’ mechanics mean score was 80.00. Therefore, analysis of the students' persuasive paragraph writing ability at the third semester of Palopo cokroaminoto university was good classification
Sentiment Analysis on Social Media

In this advanced growing world, we can notice an enormous boost of user-generated data over various social media platforms and sentiment analysis. It has come across as dominant automation to get understanding from social media. Sentiment analysis is an analysis technique that studies people’s feelings and opinions and it has grown significantly over the years.The sentimental analysis serves as an offshoot of machine learning(ML), DataMining(DM), NLP, and computational linguistics, which also have components from sociology and psychology. Social media users are growing so fast that in 2019 there were up to 2.77 billion SM (social media) users all worldwide and in 2022 the number increased to 4.59 billion. In relation to this increase, the kind of information shared and uploaded in the structure of photos, audio, video, and text is massive
Movie recommendation system via fuzzy decision making based dual deep neural networks

The internet and social networks produce an increasing amount of data. There is a serious necessity for a recommendation system because exploring through the huge collection is time-consuming and difficult. In this study, a multi-modal classifier is introduced which makes use of the output from dual deep neural networks: GRU for text analysis and Faster R-CNN for image analysis. These two networks reduce overall complexity with minimal computational time while retaining accuracy. More precisely, the GRU network is utilized to process movie reviews and the Faster RCNN is used to recognize each frames of the movie trailers. Gated Recurrent Unit (GRU) is a well-known variety of RNN that computes sequential data across recurrent structures. Faster RCNN is an enhanced version of Fast RCNN, it combines with the rectangular region proposals and with the features is extract by the ResNet-101. Initially, the trailer of the movie is manually splitted into frames and these frames are pre-processed using fuzzy elliptical filter for image analysis and the movie reviews are also tokenized for text analysis. The pre-processed text is taken as an input for GRU to classify offensive and non-offensive movies and the pre-processed images are taken as an input for Faster R-CNN to classify violence and non- violence movies based on the extracted features from the movie trailer. Afterwards, the four classified outputs are given as input for fuzzy decision-making unit for recommending best movies based on the Mamdani fuzzy inference system with gauss membership functions. The performance of the dual deep neural networks was evaluated using the specific parameters like specificity, precision, recall, accuracy and F1 score measures. The proposed GRU yields accuracy range of 97.73% for reviews and FRCNN yields the accuracy range of 98.42% for movie trailer.
Shell and tube heat exchanger design for production graphene oxide nanoparticles from agricultural waste

Heat exchangers are used to transferring energy (heat), using fluids or gases, both hot and cold, from one area to another due to temperature differences. Almost all agricultural wastes serve as carbon products which are essential to produce graphene oxide, it is very possible to get graphene oxide by synthesizing it from agricultural waste which is expected to reduce the amount of waste on the earth. After carrying out the synthesis, it is necessary to test the characterization of the resulting product. The characterization stage requires a sintering process (20oC to 98oC) until the product is completely solidified. This research aims to design a heat exchanger that is highly effective in assisting the sintering process. For the design to be well-directed, several things must be done, such as calculating the main components (shell and tube) and designing a heat exchanger. Based on the TEMA standard and the use of simple calculations that refer to the attached calculation formula using Ms. Excel, hot and cold fluids used in this heat exchanger design are ethylene glycol and water. The length of the shell and tube as a whole is 5 m, the inner and outer shell diameters are 254 mm and 279.4 mm; the inner and outer tube diameters are respectively 22.09 mm and 25.40 mm. The calculation results show an excellent design effectiveness value of 86.14%. Continuous innovation in the design and materials of the heat exchanger can increase the effectiveness value to close to 100% so that it can help the graphene oxide synthesis process to make it cheaper and more efficient.
AUTOMATIC CAPTION FEATURES ON GOOGLE MEET AS A PRONUNCIATION ASSESSMENT TOOL

ABSTRACTThis study aims to find out the use Google Meet Automatic Caption feature to assist teachers of non-native English to assess their students' English pronunciation. We used a mixed-method with the explanatory-sequential approach following it. This research study was done at Tidar University with 12 participants, further reduced to 4 in the participant selection step. As the data were both quantitative and qualitative, we used the Word Error Rate (WER) formula quantitatively and Qualitative Content Analysis qualitatively. The findings show that Artificial Intelligence (AI) has a very sensitive system in transforming sounds into written forms. It also has an auto-correction system that sometimes can substitute a word with a meaningless one if a speaker pronounces the word unclearly or to the nearest word if a speaker mispronounces it. Even though it does not accurately process the punctuation and there is no sufficient correction on grammar, we believe the AI can help teachers in a pronunciation assessment.ABSTRAKPenelitian ini bertujuan untuk mengetahui penggunaan fitur Teks Otomatis pada Google Meet untuk membantu para guru Bahasa Inggris bukan penutur asli saat menilai pengucapan bahasa Inggris siswa mereka. Kami menggunakan metode penelitian campuran dengan pendekatan eksplanatori-sekuensial. Penelitian ini dilakukan di Universitas Tidar dengan jumlah peserta sebanyak 12 orang yang dikurangi menjadi 4 orang pada tahap seleksi peserta. Data penelitian bersifat kuantitatif dan kualitatif. Untuk menganalisa data secara kuantitatif digunakan rumus Word Error Rate (WER). Sedangkan, secara kualitatif mengunakan Analisis Isi Kualitatif. Hasil penelitian menunjukkan bahwa AI (Artificial Intelligence/ Kecerdasan buatan) memiliki sistem yang sangat sensitif dalam mengubah suara menjadi bentuk tulisan. Fitur ini juga memiliki sistem koreksi otomatis yang terkadang dapat menggantikan kata yang tidak berarti jika pembicara mengucapkan kata dengan tidak jelas atau diubah ke kata terdekat jika pembicara salah mengucapkan kata tersebut. Meskipun tidak memproses tanda baca secara akurat dan tidak ada koreksi yang memadai pada tata bahasa, kami yakin AI dapat membantu para guru dalam penilaian pengucapan. How to Cite: Ramadhika, B., Yosintha, R., Yunianti, S. S. (2022). Automatic Caption Features on Google Meet as a Pronunciation Assessment Tool. IJEE (Indonesian Journal of English Education), 9(2), 396-410. doi:10.15408/ijee.v9i2.22482
COST of Graph Processing Using Actors

Graph processing is an increasingly important domain of computer science, with applications in data and network analysis, among others. Target graphs in these applications are often large, leading to the creation of "big data" systems designed to provide the scalability needed to analyze these graphs using parallel processing. However, researchers have shown that while these systems often provide scalability, they also often introduce overheads that exceed the benefits they provide, sometimes resulting in lower absolute performance than even simple serial implementations. This report studies the viability and performance of the actor model to implement scalable concurrent programs to perform common graph computations. We show that relatively simple actor-based implementations outperform both dedicated graph processing systems and the benchmark serial implementations.
An ensemble-based framework for mispronunciation detection of Arabic phonemes

Determination of mispronunciations and ensuring feedback to users are maintained by computer-assisted language learning (CALL) systems. In this work, we introduce an ensemble model that defines the mispronunciation of Arabic phonemes and assists learning of Arabic, effectively. To the best of our knowledge, this is the very first attempt to determine the mispronunciations of Arabic phonemes employing ensemble learning techniques and conventional machine learning models, comprehensively. In order to observe the effect of feature extraction techniques, mel-frequency cepstrum coefficients (MFCC), and Mel spectrogram are blended with each learning algorithm. To show the success of proposed model, 29 letters in the Arabic phonemes, 8 of which are hafiz, are voiced by a total of 11 different person. The amount of data set has been enhanced employing the methods of adding noise, time shifting, time stretching, pitch shifting. Extensive experiment results demonstrate that the utilization of voting classifier as an ensemble algorithm with Mel spectrogram feature extraction technique exhibits remarkable classification result with 95.9% of accuracy.
Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers

Vision Transformer (ViT) suffers from data scarcity in semi-supervised learning (SSL). To alleviate this issue, inspired by masked autoencoder (MAE), which is a data-efficient self-supervised learner, we propose Semi-MAE, a pure ViT-based SSL framework consisting of a parallel MAE branch to assist the visual representation learning and make the pseudo labels more accurate. The MAE branch is designed as an asymmetric architecture consisting of a lightweight decoder and a shared-weights encoder. We feed the weakly-augmented unlabeled data with a high masking ratio to the MAE branch and reconstruct the missing pixels. Semi-MAE achieves 75.9% top-1 accuracy on ImageNet with 10% labels, surpassing prior state-of-the-art in semi-supervised image classification. In addition, extensive experiments demonstrate that Semi-MAE can be readily used for other ViT models and masked image modeling methods.
A Quantum Approach for Stochastic Constrained Binary Optimization

Analytical and practical evidence indicates the advantage of quantum computing solutions over classical alternatives. Quantum-based heuristics relying on the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA) have been shown numerically to generate high-quality solutions to hard combinatorial problems, yet incorporating constraints to such problems has been elusive. To this end, this work puts forth a quantum heuristic to cope with stochastic binary quadratically constrained quadratic programs (QCQP). Identifying the strength of quantum circuits to efficiently generate samples from probability distributions that are otherwise hard to sample from, the variational quantum circuit is trained to generate binary-valued vectors to approximately solve the aforesaid stochastic program. The method builds upon dual decomposition and entails solving a sequence of judiciously modified standard VQE tasks. Tests on several synthetic problem instances using a quantum simulator corroborate the near-optimality and feasibility of the method, and its potential to generate feasible solutions for the deterministic QCQP too.
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification

Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Some QNNs with specific encoding methods can be efficiently simulated by classical surrogates, while others with quantum memory may perform better than classical classifiers. Here we systematically investigate the problem-dependent power of quantum neural classifiers (QCs) on multi-class classification tasks. Through the analysis of expected risk, a measure that weighs the training loss and the generalization error of a classifier jointly, we identify two key findings: first, the training loss dominates the power rather than the generalization ability; second, QCs undergo a U-shaped risk curve, in contrast to the double-descent risk curve of deep neural classifiers. We also reveal the intrinsic connection between optimal QCs and the Helstrom bound and the equiangular tight frame. Using these findings, we propose a method that uses loss dynamics to probe whether a QC may be more effective than a classical classifier on a particular learning task. Numerical results demonstrate the effectiveness of our approach to explain the superiority of QCs over multilayer Perceptron on parity datasets and their limitations over convolutional neural networks on image datasets. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhood-based (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction.
Quantum Representations of Sound: from mechanical waves to quantum circuits

By the time of writing, quantum audio still is a very young area of study, even within the quantum signal processing community. This chapter introduces the state of the art in quantum audio and discusses methods for the quantum representation of audio signals. Currently, no quantum representation strategy claims to be the best one for audio applications. Each one presents advantages and disadvantages. It can be argued that future quantum audio representation schemes will make use of multiple strategies aimed at specific applications. NOTE: This is an unedited abridged version of the pre-submission draft of a chapter, with the same title, published in the book Quantum Computer Music: Foundations, Methods and Advanced Concepts, by E. R. Miranda (pp. 223 - 274). Please refer to the version in this book for application examples and a discussion on sound synthesis methods based on quantum audio representation and their potential for developing new types of musical instruments. https://link.springer.com/book/10.1007/978-3-031-13909-3
Grid-Based Decimation for Wavelet Transforms with Stably Invertible Implementation

The constant center frequency to bandwidth ratio (Q-factor) of wavelet transforms provides a very natural representation for audio data. However, invertible wavelet transforms have either required non-uniform decimation -- leading to irregular data structures that are cumbersome to work with -- or require excessively high oversampling with unacceptable computational overhead. Here, we present a novel decimation strategy for wavelet transforms that leads to stable representations with oversampling rates close to one and uniform decimation. Specifically, we show that finite implementations of the resulting representation are energy-preserving in the sense of frame theory. The obtained wavelet coefficients can be stored in a timefrequency matrix with a natural interpretation of columns as time frames and rows as frequency channels. This matrix structure immediately grants access to a large number of algorithms that are successfully used in time-frequency audio processing, but could not previously be used jointly with wavelet transforms. We demonstrate the application of our method in processing based on nonnegative matrix factorization, in onset detection, and in phaseless reconstruction.