Reviewerly/exHarmony huggingface dataset:
Music Education Teaching Quality Evaluation System Based on Convolutional Neural Network

Authors:
raw_author_name: Meiqun Liao
raw_affiliation_string: College of Arts, Xiamen University, Xiamen 361000, P. R. China
raw_author_name: Fei Huang
raw_affiliation_string: College of Arts, Xiamen University, Xiamen 361000, P. R. China

Abstract: Music education, as a quality training education, plays an important role in promoting the all-round development of students. Music evaluation is often highly subjective. However, the objective quantification can be achieved to a certain extent through the construction of music education and teaching quality evaluation system. In this study, CNN algorithm is applied to the evaluation model of music education teaching. Compared with the traditional manual feature extraction method, CNN algorithm can automatically capture the target depth features. In the experiment, the effectiveness of convolutional neural network is evaluated by means of model comparison and analysis. Taking GTZAN database as the experimental training sample, 2000 Mel spectra, 1500 training sets and 500 test sets were obtained, and the relevant parameters were initialised. The research results show that the dense network model has the highest accuracy rate in the training set in the identification and analysis of the relevant features of Mel spectrum. The model’s value is about 0.917, which is 0.024, 0.041, 0.086 and 0.098 higher than Res Net model, Perception Net model, VGG Net model and Alex Net model, respectively. Meanwhile, its loss curve effect, [Formula: see text]-value and AUC value reached the optimal level in all models, with the highest [Formula: see text]-value and AUC value reaching 0.926 and 0.922, respectively. In addition, in the test set, the average accuracy of this model is also better than other models. The comprehensive analysis shows that the model performs well in the evaluation system, which plays an important role in the evaluation of music education and teaching quality.
Design and Development of e-MARZ: A Practical Guide to Implementing Cost-effective VR Experiences for Music Education

Authors:
raw_author_name: None Abdul Rahman Safian
raw_affiliation_string: Department of Music and Performance Arts, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia
raw_author_name: None Zaharul Lailiddin Saidon
raw_affiliation_string: Department of Music and Performance Arts, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia
raw_author_name: None Mohd Nizam Nasrifan
raw_affiliation_string: Department of Music and Performance Arts, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia
raw_author_name: None Herry Rizal Djahwasi
raw_affiliation_string: Department of Music and Performance Arts, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia

Abstract: The study introduces e-MARZ, a Virtual Reality (VR) software designed to tackle challenges faced by music educators in enhancing music appreciation lessons within the Malaysian lower secondary school music curriculum. The research aims to improve student engagement, provide immersive and interactive learning experiences, and develop a user-friendly VR application. A video production process was employed to create 360-degree music performance videos, which were integrated into the e-MARZ software via interactive modules. This article mainly discusses the development and evaluation aspects, utilising the modified ADDIE Instructional model, m-ADDIE, during the development phase. The e-MARZ prototype underwent evaluation using the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ) for quantitative data collection and analysis to assess usability, with a sample of five music education experts and 82 secondary school music students. Limitations of the study include the use of 360 media of live performances tailored for secondary school students' music appreciation and employing 3DOF (degrees of freedom) instead of the more advanced 6DOF, potentially impacting the generalisability of findings and the scope of achievable VR experiences. Despite these limitations, the high usability scores obtained during the evaluation, as measured by SUS and PSSUQ, indicate the software's effectiveness in addressing its intended challenges. The e-MARZ software offers an immersive, user-friendly VR experience that is cost-effective and compatible with various devices. This research provides insights into producing cost-effective, user-friendly VR applications using 360 media, promoting accessible VR application development in education. As a result, the study transforms students' engagement with music appreciation, fostering a more immersive, interactive, and enjoyable learning experience.
Exploring the Potential of Wireless Integrated Configuration Technology as a Learning Tool

Authors:
raw_author_name: None Nor Masharah Husain
raw_affiliation_string: Department of Computer Science and Digital Technology, Faculty of Computing and Meta-Technology, University Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia

Abstract: The objective of this study was to determine the feasibility of utilising wireless integrated configuration technology among students enrolled in networking courses. The proposed method entails using Raspberry Pi as the key hardware component to facilitate wireless configuration, hence facilitating seamless connectivity and communication across wireless devices. The networking solution facilitates the transport of data, exchange of resources, and cooperation in a wireless manner, eliminating the need for physical cables or wires. The research methodology for this study entails descriptive analysis in the form of a survey, which will have an impact on the quantitative findings. The results of the needs study found that over 90% of students agreed that the use of wireless configuration devices would improve understanding and interest them in doing network configuration. Based on the findings from interview sessions with experts, it has been demonstrated that the utilisation of wireless integrated configuration technology possesses the capacity to engender an enjoyable learning experience for students. This technology enables students to engage in configuration tasks wirelessly, utilising numerous devices concurrently. Additionally, experts believe that students can learn how to configure it technically as well as theoretically. The results of this study were successful in determining the level of potential for the development of wireless devices and their usage in networking courses.
Deep Learning Approach for Classification of Tweets in Detecting Cyber Truculent

Authors:
raw_author_name: Adeniji Oluwashola David
raw_affiliation_string: Department of Computer Science, University of Ibadan, Nigeria.

Abstract: Identification and Classification of truculent tweets is a serious problem. Studies have shown how truculent tweets on Twitter have caused anxiety and in extreme cases death of victims. Various researches have applied Machine Learning approaches while others Deep Learning in classifying toxic sentences. Impressive results have been obtained using Deep Learning approaches, the focus of the research is to develop an extended model for deep learning classification using cyber tweets. The Model developed in this research work used labelled dataset (twitter_parsed_dataset.csv), Maximum Entropy was used to extract keywords and entities. One-Dimensional Convolutional Neural Network (1d-CNN) was used to detect truculent in tweets. The experimental result from the developed model consider four preprocess datasets for classification, the Unigram, Bigram, Trigram and N-gram. The result that was obtained for Accuracy 96.1%, Precision 93.6%, Recall 73.7% and F1-Score 83.8% for different test during classification. The result obtained from the developed model for accuracy, 96.1% was compared with related work Banerjee’s accuracy of 93.97%. The developed model can be used for auto detection of truculent messages in cyberspace.
A Novel Approach to Text Summarization Using Machine Learning

Authors:
raw_author_name: Namrata Dhanda
raw_affiliation_string: Amity University Uttar Pradesh Lucknow Campus, India.
raw_author_name: Kapil Kumar Gupta
raw_affiliation_string: Amity University Uttar Pradesh Lucknow Campus, India.

Abstract: Text summarization is a key strategy in the domains of information retrieval and natural language processing (NLP). Its objective is to reduce a lengthy written document into a clearer, more succinct summary of the information it contains. When a text document is too lengthy or intricate to analyse completely, as in news stories, academic papers, or legal documents, this approach is extremely helpful. The major challenge of text summarising is to take the most important and relevant information from the original text and convey it in an understandable and concise way. In this study, extractive and abstractive summarising techniques are the two primary categories of text summary methods. The paper also presents several algorithms that have been proposed for text summarization, including TextRank, Seq2Seq, and BART. TextRank is a simple and fast algorithm that works well for short documents, Seq2Seq is a deep learning-based approach that generates high-quality summaries, and BART is a transformer-based algorithm that provides the best results on benchmark datasets. The obtained ROUGE Score after passing TextRank, BART, and Seq2SEq algorithm significant also.
Comprehensive Survey of Cutting-Edge Methods for Software Architecture Evaluation

Authors:
raw_author_name: Amna Sajid
raw_affiliation_string: a:1:{s:5:"en_US"; s:49:"National University of Computer Emerging Sciences"; }
raw_author_name: Muhammad Waqas Arshad
raw_affiliation_string: University of Bolonga, Italy

Abstract: The crucial responsibility of assessing software architecture is of utmost importance in ensuring that a software system conforms to superior qualities. It is a crucial tool for cutting expenses and labor during the course of the software development lifecycle. The main goal of software architecture evaluation is to provide reliable methods for determining and improving the quality characteristics that are innate in software. This study is deeply devoted to investigating the wide range of techniques used in software architecture assessment. One of the main focuses of these evaluation techniques is scenario-based assessments, which offer a comprehensive picture of the software's behavior under different circumstances. Within the scope of this study, a thorough analysis is carried out on eighteen different methods. Thirteen of these fall into the category of early evaluation techniques, which are positioned to detect and address architectural problems at the outset of development. The other five strategies are classified as late assessment techniques and focus on validating and optimizing the software architecture in the latter stages of the development process.
Recognition of Handwritten Mathematical Expressions Using Systems of Convolutional Neural Networks

Authors:
raw_author_name: Alexander I. Iliev
raw_affiliation_string: Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Bulgaria

Abstract: Accurate recognition of handwritten mathematical expressions has proven difficult due to their two-dimensional structure. Various machine-learning techniques have previously been employed to transcribe handwritten math, including approaches based on convolutional neural networks (CNNs) and larger encoder/decoder-based models. In this work, we explore a CNN-based method for transcribing handwritten math expressions into the typesetting language known as LaTeX. This approach utilizes machine learning not only for classifying individual characters but also for extracting individual characters from handwritten inputs and determining what forms of two-dimensionality exist within the expression. This approach achieves significant reliability when recognizing common mathematical expressions.
An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels

Authors:
raw_author_name: Xueqing Tian
raw_affiliation_string: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
raw_author_name: Siyuan Wang
raw_affiliation_string: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
raw_author_name: Huaqiao Xing
raw_affiliation_string: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

Abstract: Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically determined manually or through a large number of experimental iterations, and even the weighting parameters change as the dataset and the noisy rate change. This can lead to suboptimal results and be time-consuming. Therefore, we propose an adaptively weighted method for the combined active passive loss (APL) in remote sensing image retrieval with noisy labels. First, two metrics are selected to measure the noisy samples: the ratio of the entropy to the standard deviation and the difference of the predicted probabilities. Then, an adaptive weighted learning network with a hidden layer is designed to dynamically learn the weighting parameters. The network takes the above two metrics as inputs and is trained concurrently with the feature extraction network in each batch, without significantly increasing the computational complexity. Extensive experiments demonstrate that our improved APL method outperforms the original manually weighted APL method and other state-of-the-art robust loss methods while saving the time on manual parameter selection.
A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications

Authors:
raw_author_name: Ziying Liang
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
raw_author_name: Ting Shu
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
raw_author_name: Zuohua Ding
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China

Abstract: The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search efficiency and a slow convergence rate. To address these issues, this paper presents an improved whale optimization algorithm with multiple strategies, called Dynamic Gain-Sharing Whale Optimization Algorithm (DGSWOA). Specifically, a Sine–Tent–Cosine map is first adopted to more effectively initialize the population, ensuring a more uniform distribution of individuals across the search space. Then, a gaining–sharing knowledge based algorithm is used to enhance global search capability and avoid falling into a local optimum. Finally, to increase the diversity of solutions, Dynamic Opposition-Based Learning is incorporated for population updating. The effectiveness of our approach is evaluated through comparative experiments on blackbox optimization benchmarking and two engineering application problems. The experimental results suggest that the proposed method is competitive in terms of solution quality and convergence speed in most cases.
Metaheuristic Optimization Algorithms: an overview

Authors:
raw_author_name: None Dr. Benaissa Brahim
raw_affiliation_string: Instiute of Toyota Japan
raw_author_name: Musaddiq Al Ali
raw_affiliation_string: Department of Advanced Science and Technology, Toyota Technological Institute, 2-12-1 Hisakata, Tenpaku-ku, Nagoya, Aichi 468-8511, Japan

Abstract: Metaheuristic optimization algorithms are known for their versatility and adaptability, making them effective tools for solving a wide range of complex optimization problems. They don't rely on specific problem types, gradients, and can explore globally while handling multi-objective optimization. They strike a balance between exploration and exploitation, contributing to advancements in optimization. However, it's important to note their limitations, including the lack of a guaranteed global optimum, varying convergence rates, and their somewhat opaque functioning. In contrast, metaphor-based optimization algorithms, while intuitively appealing, have faced controversy due to potential oversimplification and unrealistic expectations. Despite these considerations, metaheuristic algorithms continue to be widely used for tackling complex problems. This research paper aims to explore the fundamental components and concepts that underlie optimization algorithms, focusing on the use of search references and the delicate balance between exploration and exploitation. Visual representations of the search behavior of selected metaheuristic algorithms will also be provided.
Prescribed intelligent elliptical pursuing by UAVs: A reinforcement learning policy

Authors:
raw_author_name: Xingling Shao
raw_affiliation_string: Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
raw_author_name: Tianyun Ding
raw_affiliation_string: Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
raw_author_name: Jun Liu
raw_affiliation_string: Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China

Abstract: This paper proposes a prescribed intelligent elliptical pursuing algorithm driven by deep reinforcement learning (DRL) for Unmanned Aerial Vehicles (UAVs), aiming to achieve an optimal performance without violating appointed-time constraints. Specifically, an unknown system dynamics estimator (USDE) with merely one filtering coefficient is employed to counteract uncertainties effectively. Subsequently, an elliptical enclosing controller is devised for UAVs to achieve asymptotic convergence towards the target ellipse orbit. Particularly, a proximal policy optimization (PPO)-based optimal learning term is contrived to achieve control optimality related to consumption and efficiency, where a refined reward function is established to characterize the appointed-time constraints. The salient merit is that an efficient learning paradigm is formulated to fulfill a near optimal appointed-time enclosing free from prescribed performance control. Finally, a plenty of simulations substantiate the feasibility of developed algorithm.
Analysis of Patient Health Using Arduino and Monitoring System

Authors:
raw_author_name: Abu Shufian
raw_affiliation_string: Department of Electrical and Electronic Engineering, American International University Bangladesh (AIUB), 408/1 Kuratoli, Dhaka 1229, Bangladesh.

Abstract: The proposed project is a health monitoring system using Arduino that measures key health parameters such as heart rate, blood oxygen level, and body temperature. The system consists of sensors such as DHT11 for temperature, MAX30100 for heart rate and blood oxygen level, and an Arduino Nano for processing the data. The measured results are displayed on an LCD screen and a buzzer sounds when the results are ready. The system has been designed with simplicity and ease-of-use in mind, making it accessible for personal use. While the system has limitations such as sensor accuracy and lack of IoT functionality, it still has potential for improving individual health monitoring and wellness. In the future, potential areas for development include the inclusion of more sensors, IoT functionality, and machine learning algorithms for personalized insights and recommendations. Overall, the proposed health monitoring system is a promising step towards empowering individuals to take a more active role in monitoring their health and wellness.
Water Meter Reading Application System Development using Image Processing A Case Study from Sindangsari Village Water Services

Authors:
raw_author_name: Umar Ali Ahmad
raw_author_name: Reza Rendian Septiawan
raw_author_name: Ratna Astuti Nugraheni
raw_author_name: Angga Rusdinar
raw_author_name: Ashri Dinimaharawati

Abstract: PAMDES is a drinking water company managed by local villagers. The water meter data are read and recorded manually without any technology, which is ineffective and inefficient. Digital image processing can be implemented to read and record the water meter data automatically. When implemented in the water meter, it can help PAMDES officers to read the data without the internet, without changing the conventional water meter device, and the water meter data can be read and recorded effectively and efficiently. This research used the agile method, one of the methods used in the Software Development Life Cycle (SDLC). The method is done repetitively within a short period of time. The output of this research is an application with a digital image processing model that can read water meter data up to 82% in normal conditions and still can be improved. This research aims to make the water meter data reading and recording more effective and efficient and to contribute to the transformation of Sindangsari village into a digitalized village.
Sustainable Development for Smart Healthcare using Privacy-preserving Blockchain-based FL Framework

Authors:
raw_author_name: D. Karthika Renuka
raw_affiliation_string: Department of IT, PSG College of Technology, Coimbatore, Tamilnadu, India
raw_author_name: R. Anusuya
raw_affiliation_string: Department of EEE, PSG College of Technology Coimbatore, Tamilnadu, India
raw_author_name: L. Ashok Kumar
raw_affiliation_string: Department of EEE, PSG College of Technology Coimbatore, Tamilnadu, India

Abstract: Artificial Intelligence (AI) methods need to learn from an adequately large dataset to achieve clinical-grade accuracy and validation, which is vital in the healthcare field. However, sensitive medical data is usually fragmented, and not shared due to security and patient privacy policies. In this context, our work aims at classifying abdominal and chest radiographs by applying Federated Learning (FL) without exchanging patient data. FL framework has been implemented on distributed data across multiple clients. In the framework, a multilayer perceptron is used as a deep learning model for the classification task. FL is a novel approach in which machine learning models are built with the collaboration of multiple clients controlled by a central server or service provider. FL model ensures data privacy and security by retaining the training data decentralized. FL model provides security and privacy for patients by training individual models in distributed clients and sharing merely the model weights.