Reviewerly/exHarmony huggingface dataset:
Effect of physical characteristics of strings on the instrumental sound of violin

The stringed instrument can produce different vibration characteristics depending on the materials and types of vibrating parts such as strings, bridge, and body. So, the timbre of the violin is considered to be affected by the physical characteristics of the strings and body. In recent years, experiments have been conducted to compare the timbre quality of several violins of different materials and shapes, and their evaluation has also been conducted. On the other hand, there have been few studies on strings for stringed instruments. There are many types of strings. The strings are the source of vibration, with different materials and physical properties, and these are thought to affect the timbre. In this study, violins made by different manufacturers, with strings of different materials, will be used to examine what trends exist in the frequency spectrum of instrumental sounds and how the physical characteristics influence the sound impression.
Based on Pre-trained Transfer Learning and Improved Decoder for Myanmar Speech Recognition

End-to-end (E2E) speech recognition based on deep neural networks has become the mainstream way to build high-performance speech recognition systems. Training end-to-end (E2E) models relies on large-scale “audio-text” pair datasets, which poses a challenge for studying speech recognition of non-generic languages under low-resource conditions. Unsupervised pre-training methods have achieved good performance in speech recognition for many low- resource languages. We build a Myanmar language speech recognition baseline system based on self-supervised speech pre-trained models under low-resource conditions. To further study the methods to improve the accuracy of Myanmar speech recognition, we propose two optimization methods: (1) Different from the current mainstream method of transfer learning in frontend modules, we use the HuBERT self-supervised speech representation model, which pre-trained on massive unlabeled common language speech data, as the encoder of the E2E Myanmar speech recognition system to extract high performance speech recognition features; (2) To better adapt to long sequence-dependent tasks during the decoding process, we proposed and implemented an improved decoder framework based on the S4 module. The experimental results show that the character error rate of the baseline Myanmar speech recognition system constructed in this paper is 12.6%. After introducing the two improvement methods proposed in this paper, its character error rate decreases to 8.2%.
Text Classification of Modern Mongolian Legal Documents Using BERT Models

This paper investigates the application of deep learning-based natural language processing techniques in modern Mongolian legal documents. In particular, we explore several methods that apply Bidirectional Encoder Representations from Transformers (BERT) models for classifying modern Mongolian legal documents. Based on our findings, we propose BERT-based models called LEGAL-BERT-Mongolian. We demonstrated two variants of LEGAL-BERT-Mongolian, i.e., uncased-LEGAL-BERT-Mongolian and cased-LEGAL-BERT-Mongolian, for classifying modern Mongolian legal documents. The uncased-LEGAL-BERT-Mongolian model achieved the best results, with a precision of 0.91, recall of 0.87, and F1 score of 0.89, whereas the cased-LEGAL-BERT-Mongolian model achieved a precision of 0.87, recall of 0.83, and F1 score of 0.85.
Hyperbolic Manifold Learning on Differential Expression Signatures

&lt;p&gt;Our paper contributes to understanding differential expression gene (DEG) signatures, a crucial element in the study of gene expression patterns. One of the key findings of our research, which has not been previously published, is the assertion that the DEG signature space significantly manifests hyperbolic properties. This discovery has far-reaching implications for both the fields of bioinformatics and machine learning, such as drug-target interaction prediction and visualizations for drug discovery.&lt;/p&gt;
Cyber Intrusion detection in Industry 4.0 Data using Machine Learning

&lt;p&gt;Industry 4.0, also known as the Fourth Industrial Revolution, is characterized by the incorporation of advanced manufacturing technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and automation. With the increasing adoption of Industry 4.0 technologies, it becomes crucial to implement effective security measures to safeguard these systems from cyber attacks. The development of intrusion detection systems (IDS) that can detect and respond to cyber threats in real-time is crucial for securing Industry 4.0 systems. This research topic seeks to investigate the various techniques and methodologies employed in developing IDS for Industry 4.0 systems, with a particular concentration on identifying the most effective solutions for protecting these systems from cyber attacks. In this study, we compared supervised and unsupervised intrusion detection algorithms. We utilized data collected from heterogeneous sources, including Telemetry datasets of IoT and The industrial Internet of things (IIoT) sensors, Operating systems (OS) datasets of Windows 7 and 10, as well as Ubuntu 14 and 18 TLS and Network traffic datasets simulated by the School of Engineering and Information Technology (SEIT), UNSW Canberra @ the Australian Defence Force Academy (ADFA). The preliminary results of IDS accuracy are extremely encouraging on the selected data for this study (Windows OS and Ubuntu OS), which motivates the continuance of this line of inquiry using a variety of other data sources to formulate a general recommendation of IDS for Industry 4.0.&lt;/p&gt;
Physical and Chemical Quality of Fresh Maggots Cultivated with Special Application of The Media Used

The high price of protein feed for livestock makes maggots potential as an alternative protein feed. The first stage in this research was maggot maintenance and maggot harvesting on the 15th day of maintenance. Next, measurements of the physical and chemical quality of fresh maggot were carried out. Parameters for measuring the physical quality of fresh maggot consist of harvest weight, individual weight, individual length and color. The parameters for measuring the chemical quality of fresh maggot consist of water content, ash, crude fat, crude fiber and crude protein. Data analysis was carried out using the Independent Sample T-test. The results showed that the maggot that was cultivated with the special media used had an average harvest weight of 1.24 kg, while the maggot that was cultivated without the special media used had an average harvest weight of 0.72 kg. The maggot that was cultivated with the special treatment of the media used contained 31.5% protein, while the maggot that was cultivated without the special treatment of the media used contained 32.9% protein. The conclusion of this study is that maggot that is cultivated with the special treatment of the media used produces maggot with superior physical quality compared to maggot that is cultivated without the special treatment of the media used.
Construction of an Intelligent Evaluation Model of Yield Risk Based on Empirical Probability Distribution

In order to improve the accuracy of yield risk evaluation, an intelligent evaluation model of yield risk based on empirical probability distribution is constructed. The dimensionality reduction method of risk factor based on principal component analysis is adopted. After adjusting the multiple data dimensions of risk factors that affect the rate of return to a unified dimension, the cluster-based evaluation index screening method is used to build the evaluation index set that best reflects the risk of the rate of return; The index weight vector equation method based on entropy weight and information entropy is used to set the evaluation index weight. Through the comprehensive evaluation model based on the empirical probability distribution of risk indicators, the empirical probability distribution information of risk indicators at all levels is analyzed, and the risk level of yield is intelligently evaluated. The research structure shows that the model can effectively evaluate the level of return risk and provide an effective reference for preventing and controlling investment return risk.
Mukh-Oboyob: Stable Diffusion and BanglaBERT enhanced Bangla Text-to-Face Synthesis

Facial image generation from textual generation is one of the most complicated tasks within the broader topic of Text-to-Image (TTI) synthesis. It is relevant in several fields of scientific research, cartoon and animation development, online marketing, game development, etc. There have been extensive studies on Text-to-Face (TTF) synthesis in the English language. However, the amount of relevant existing work in Bangla is limited and not comprehensive. As the TTF field is not vastly prospected for Bangla language, the objective of this study sets forth to explore the possibilities in the field of Bangla Natural Language Processing and Computer Vision. In this paper, a novel system for generating highly detailed facial images from textual descriptions in the Bangla language is proposed. The proposed system named Mukh-Oboyob consists of two essential components: a pre-trained language model, BanglaBERT, and Stable Diffusion. BanglaBERT, a transformer-based pre-trained text encoder, is a language model used to transform Bangla sentences into vector representations. Stable Diffusion is used by Mukh-Oboyob to generate facial images utilizing the text embedding of the Bangla sentences. Moreover, the work uti-lizes CelebA Bangla, a modified version of the CelebA dataset consisting of face images, Bangla facial attributes, and Bangla text descriptions to develop and train the proposed system. This paper establishes a system for image synthesis with excellent performance and detailed image outcomes, as evidenced by a comprehensive analysis incorporating both qualitative and quantitative measures, leading to the system under consideration achieving an impressive FID score of 34.6828 and an LPIPS score of 0.4541.
Prediksi Pajak Pertambahan Nilai pada Penyediaan Jasa dengan Metode Fuzzy Time Series Model Chen

For companies, tax is a burden or fee that must be paid to the state as a taxpayer. The taxes that must be paid by the company can affect the profits earned. Therefore, efforts are needed to reduce or minimize the tax burden. Efforts to minimize the tax burden include tax planning. Tax planning that is often used by companies is tax planning on Value Added Tax (VAT), because all production activities are closely related to the VAT burden. Tax planning for VAT can be done by maximizing the amount of input VAT. To be able to identify the amount of input VAT in the next period, predictions can be made on the input VAT value. The uncertain VAT value and limited data collection make it possible to predict the VAT value using the fuzzy time series method. One model that can be used in fuzzy time series is the Chen model, because it has better accuracy values than the Song and Chissom models. Based on this research, it can be seen that the results of the prediction of the VAT value for the provision of services at PT Pertamina Hulu Rokan Zone 1, for the period July 2023 using the fuzzy time series Chen model method in second order obtained IDR 1,455,000,000 with a forecasting accuracy of 82.1%. In this way, PT PHR Zone 1 can maximize input VAT of IDR 1,455,000,000 so that the goal of minimizing the tax burden is achieved.
“You've Got a Friend in Me”: A Formal Understanding of the Critical Friend Agent

State-of-the-art intelligent and interactive agents, such as Alexa or Siri, often present overly conforming behaviour during interactions with humans. This can result in a misalignment between end-user expectations and agent behaviour. To overcome this barrier in human-AI interactions, we introduce the Critical Friend (CF), a conceptual idea that guides critical behaviour in human-human interactions. We present our results as a formal understanding that can be described through description logic and utilised for reasoning capabilities, enabling implementations of the CF as an intelligent interactive agent.
Artificial Intelligences (AI) In Education

In this study, the researcher mainly focuses on how artificial intelligence (AI) has fused in the education system, How the learners and educators are using it in appropriate way. The main objective of this study is to understand the level of awareness and impact of AI in the education field. The statistical tools used for this study is Factorial ANOVA. In this study, the researcher highlights on various factors (knowledge level and year of experience) helps in determining level of awareness. Similarly, (implication of AI and Usage of AI) helps in determining level of impact. This study is based on both primary and secondary sources of data. Primary data is acquired through conducting interviews to the users of AI, and by circulating questionnaires to AI users. Whereas, secondary data is compiled with the help of many research journals, articles, and books. sample size of 25 is considered for measuring the replies from the selected respondents Some of the important findings of this paper by applying statistical tools can be concluded as the overall level of awareness is fairly higher and good, which shows learners has a good awareness level about AI in the education field along with that it is analyzed that the execution of AI technology in the education field will positively impact the overall effect of AI technology.
Efficiency and Effectiveness of Electronic Portfolio on BS Information Technology Practicum Students

This research, conducted at West Visayas State University-Pototan Campus, evaluates the effectiveness of Electronic Portfolios as a reflection tool for Practicum Students in the BS Information Technology program aligned with the university's mission of fostering lifelong learners and community transformation. The study ran from February 6 to March 31, 2023, engaging 40 On-the-Job Trainees. Data collection included qualitative interviews, quantitative Likert scale responses, and pre-test and post-test questionnaires. Results reveal E-Portfolios' high efficiency and effectiveness without significant differences based on gender or practicum grades. A strong correlation exists between efficiency and effectiveness. Portfolios positively impact student performance, fostering creativity, improving language and typing skills, and facilitating self-reflection. This research underscores E-Portfolios' potential as valuable tools in outcome-based education, supporting student learning and achievements while aligning with the university's mission of producing empowered change agents.
Deep Learning with Pytorch: Siamese Network

"DEEP LEARNING WITH PYTORCH: SIAMESE NETWORK" is a work that addresses person re-identification (re-ID), a difficult computer vision challenge that entails identifying the same person from several camera angles. Because SNNs may learn similarity instead of straight classification, they are becoming a preferred method for this kind of assignment. Using this method, a ranking loss function is optimized by two concurrent CNNs that learn an embedding, or reduced dimensional representation, of the input images. An overview of the procedures involved in person re-identification using SNNs is given in the study, including training, testing, deployment, network architecture, and data preparation. It makes use of the Triplet Ranking Loss function, a popular loss function for SNNs.For similarity-based learning tasks including face recognition, image matching, and document similarity, Siamese Neural Networks are one kind of neural network design that is utilized. The paper offers a thorough tutorial on training a Siamese neural network for a goal based on similarity, namely using the Siamese Neural Network (SNN) to re-identify images taken by different cameras.
The Students' Linguistic Intelligence Profile on Speaking Skill at SMA Negeri 21 Makassar

This research aims to reveal the students' linguistic intelligence profile on speaking skills. This research employed a qualitative research design, with the use of qualitative methods explored the relation between the students' linguistic intelligence to their skills or abilities in speaking. This study involved the students of class XI grade SMA Negeri 21 Makassar, consisted 50 students, in which the research participants were divided into two classes, XI.4 and XI.5, with 25 students per class. In the process of collecting the data, several instruments were utilized such as observation, students' English intelligence assessment that contained students' responses to linguistic intelligence, and interviews. This research analyzed the data through three phases, including data reduction, data display, and verification, in order to investigate the impact of students speaking skills as measured by their intelligence in linguistics, which were then classified into four categories such as low, average, good, and excellent. The result of this study revealed that the majority of students in both classes XI.4 and XI.5 mostly came into the good category regarding their linguistic capabilities, with 10 and 11 students, respectively. These findings indicated that a significant number of the students were dominating in the aspect of could speak the target language with correct intonation and pronunciation but still required corrections and guidance since the students were not too fluent in performing speech. Moreover, two students in grade XI.5 showed a high standard of fluency, vocabulary, and a good understanding of grammar and idioms, placing them in the "excellent" category. Both of these students demonstrated their linguistic knowledge can adapt well to various communication situations, and be aware of relevant communication norms and ethics.
Search-Based Optimisation of LLM Learning Shots for Story Point Estimation

One of the ways Large Language Models (LLMs) are used to perform machine learning tasks is to provide them with a few examples before asking them to produce a prediction. This is a meta-learning process known as few-shot learning. In this paper, we use available Search-Based methods to optimise the number and combination of examples that can improve an LLM’s estimation performance, when it is used to estimate story points for new agile tasks. Our preliminary results show that our SBSE technique improves the estimation performance of the LLM by 59.34% on average (in terms of mean absolute error of the estimation) over three datasets against a zero-shot setting.
The Effectiveness of Learning Media and Discourse Markers Mastery on Students’ Speaking Skill

Teachers must consider the most efficient use of speaking learning media in order to produce students with enhanced speaking skills through mastery of various discourse markers. This study examines the effectiveness of two speaking learning media, Scrabble Word (SW) and flashcards, among students with varying levels of discourse marker mastery. This quasi-experimental study employed a 2x2 factorial design. During the learning period, 100 students were divided into two groups and given SW or flashcards as instructional media. sampling technique using random sampling. Data was collected using tests of speaking ability and familiarity with various discourse markers. The results demonstrate that the speaking skills of students with varying degrees of DM mastery who use SW learning media are superior to those of students who use flashcard learning media. Students with a high level of discourse mastery and access to SW learning media demonstrated an improvement in their speaking abilities. This is evidenced by the values of Fo = 7.911 and Sig. 0.005&lt;0.05; consequently, SW can be an effective and enjoyable tool for teaching speaking.
Penerapan Media Pembelajaran Android Pada Mata Kuliah Elemen Mesin: Analisis Kebutuhan dan Kesiapan

Learning media is important in supporting an effective and interactive learning process. In the current digital era, the use of Android-based technology as a learning tool has become a major concern in improving the quality of education. This article discusses the application of Android-based learning media in the Mechanical Elements course. This research investigates the readiness and need for using Android-based Aqidah learning media. This study used a survey method with semester II student subjects from the Automotive Technology Vocational Education Study Program at Lancang Kuning University. Data was collected by filling out a questionnaire, and the results were analyzed descriptively. The research results show several important things. First, all students (100%) feel interested in Android-based learning media. Second, after understanding the use of Android learning media, around 87.6% of students acknowledged the need for this media in learning. Third, applying Aydin and Tasci's ELR model shows that readiness to use Android learning media is included in the Ready category. However, several factors need to be improved slightly. The average ELR value is 3.73, exceeding the threshold of 3.4, indicating readiness. However, it should be noted that the Self-Improvement aspect has a low ELR value, namely 3.24, indicating the need for improvement. Based on these findings,
Airport Runway Defect Detection Device: A Project-Based Learning Media

In the Aviation industry, runways play a significant role in flight safety. Damage to the runway can cause aircraft accidents that can harm several parties. The use of labor in inspection activities tends to frequent human error. Therefore, the author makes a neural network technology tool to detect runway damage at the airport. This research aims to design a runway defect detection device used as a project-based learning media for Transportation Cadets, especially Palembang Aviation Polytechnic Cadets, so they can absorb learning material quickly and find a new learning atmosphere. This research uses the research and development method by analyzing the needs qualitatively. The results of this study indicate that the design scenario and how this tool works can help in learning, especially project-based learning. Hence, the design of this tool can be utilized as a reference in the development and use of the tool.
Journalists' Objectivity via Social Media: Jordan

Journalists play an essential role in society through their daily news practice. The news produced must be correct. Fair and balanced reporting makes an informed public. These elements are part of objectivity in the news. However, social media have challenged many aspects of journalism. With the emergence of social media platforms, journalists have become more prone to deviating from journalistic objectivity. Instantaneity has challenged many journalists to practice their journalistic work objectively via social media. This study aims to identify the challenges Jordanian journalists face in implementing objectivity on social media platforms. In-depth interviews were conducted with 11 journalists working in various Jordanian news organizations. Thematic analysis was used to reach the results. This study expands current knowledge on journalistic practice in Jordan, particularly on social media and objectivity in news reporting. Findings from this study suggest two main challenges faced by Jordanian journalists in their social media use. Firstly, journalists are challenged by the lack of clear guidelines and policies to separate official and personal views. Secondly, complex features embedded within social media settings along with the behavior of its users further add to the challenges faced by journalists in Jordan as they employ objectivity in news reporting. KEYWORDS Jordanian journalists, journalistic objectivity, journalists’ challenges, news organizations, news platforms, social-media practices