Reviewerly/exHarmony huggingface dataset:
An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction

Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques.
Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets

Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.
Using transfer adaptation method for dynamic features expansion in multi-label deep neural network for recommender systems

In this paper, we propose to use a convertible deep neural network (DNN) model with a transfer adaptation mechanism to deal with varying input and output numbers of neurons. The flexible DNN model serves as a multi-label classifier for the recommender system as part of the retrieval systems’ push mechanism, which learns the combination of tabular features and proposes the number of discrete offers (targets). Our retrieval system uses the transfer adaptation, mechanism, when the number of features changes, it replaces the input layer of the neural network then freezes all gradients on the following layers, trains only replaced layer, and unfreezes the entire model. The experiments show that using the transfer adaptation technique impacts stable loss decreasing and learning speed during the training process.&#x0D; &#x0D;
Game Analysis of Dual-channel Supply Chain Based on Different Rights Structure

Based on members' different bargaining power in a dual channels system, this paper discusses the impact ofthree games on channel pricing, demand and members' profits by building Manufacturer Stackelberg, Retailer Stackelberg and Vertical Nash game model.The study finds that:(1) When the coefficient of cross-price elasticity of demand is 0, the effects of the three game strategies on the competitive equilibrium solution of the two-channel supply chain are undifferentiated. (2) When the coefficient of cross-price elasticity of demand is not 0, under the Stackelberg game dominated by the retailer (manufacturer), the traditional channel (direct channel) has the highest pricing, and the demand of the traditional channel (direct channel) is the smallest, while the demand of the traditional channel (demand of the direct channel) is the largest in the Stackelberg game dominated by the manufacturer (retailer); both manufacturers and retailers are willing to choose to give up their power and do the Stackelberg game. Both manufacturers and retailers are willing to give up power in favor of being a follower of the Stackelberg game, and the Nash game is always a strict downside for the game participants. Finally, the validity of the conclusions is further tested by case studies.
Virtual Reality Technology Transitions and Reshape the Aesthetic Education Paradigm

With the rapid development of technology, virtual reality technology has gradually penetrated into the field of education. The traditional aesthetic education model is limited by the lack of time and space and interaction. The virtual reality technology is based on its immersive and interactive characteristics. Provides new possibilities. Elapse the aesthetic education from three aspects: immersive experience, interactive teaching and time and space reshaping. Through the comparative analysis of the case, the actual application and innovation teaching model of virtual reality technology in aesthetic education is discussed, and the virtual reality technology and aesthetic education are better integrated to provide more effective and innovative ways to cultivate students' aesthetic ability.
Factors Influencing Investment Decisions among College Students: A Comparative Analysis Between India &amp; Nigeria

Investment is done for the purpose to gain profitable returns. Investing in a well-diversified portfolio offers a multitude of benefits, including capital appreciation to grow wealth over time. As a student, managing finance can be challenging, but exploring opportunities for passive income can make a significant difference in financial stability and independence. This study endeavors to comparatively analyze the factors influencing investment decisions among college students in India and Nigeria. Demographics play a pivotal role in shaping investment choices, and understanding the differences in the attitude of these countries is vital for effective financial education initiatives. The study reveals that there is a positive relationship between different factors that students consider when making these decisions. Factors such as financial security, return on investment (ROI), and risk minimization are found to have a significant impact on their choices.
Web-Based Car Rental Information System At Cv. Mandarental Cirebon

The increasingly rapid development of information technology and science currently demands the existence of information systems that can support existing science and technology—currently CV. Mandarental Cirebon is growing and wants to increase car bookings for all renters in this city. The rental system used is web-based, but the features on the website still need to be completed to make it easy for customers to rent a car at CV. Problems that occur include inaccurate data, slow data processing, and waste of time. So, the information system within the company is very important to help and facilitate the business process to improve service to consumers. With this information system, it is hoped that it will provide more accurate information so that problems that occur in optimizing service to customers can be resolved at CV. Mandarental Cirebon. The methodology used in building this website includes several systematic steps. Data collection is carried out by observing to collect information, images, and other materials needed for website design.
Integrate between information systems engineering and software engineering theories for successful quality engineering measurement of software: Valid instrument pre-results

An extensive number of instruments and systems assessment tools are weak and not good enough in the appraisal of systems’ quality engineer success measurement. Thus, the comprehension of systems’ success is very serious. One of the purposes of this research topic is to develop a successful, novel, validated instrument for measuring system quality success based on the integration between theories of information systems (Seddon and DeLone &amp; McLean) and software engineering theory (ISO 25010). To ensure the quality of the instrument before use, eight academic experts have validated. The reason for expanding the number of experts to eight is to accurately build and evaluate the instrument because this instrument is the first one erring. After expert validation done successfully, researchers started the process of instrument pre-test. Pre-test verification and validation results done by test the instrument of 74 users. The results of the statistical testing were perfect. The Composite reliability proposed value is 0.7, The average variance extracted value is 0.5. Cronbach’s alpha is higher than 0.7. The value of Spearman’s reliable rhea is &gt;0.6. Results approved that this instrument is strong and perfect to be used as a valid tool for system success measurement.
The relationship between perceived learning, academic performance and academic engagement in virtual education for university students

This study aimed to determine whether the three dimensions of academic engagement (cognitive, emotional and behavioral) were positively associated with perceived learning and academic performance. The participants were 301 university students from Lima. Structural equation models were used to test the proposed theoretical relationship between the variables. The results indicated that the model showed satisfactory fit indices (CFI = 0.956, TLI = 0.949, RMSEA = 0.043, SRMR = 0.062). Perceived learning was found to be predicted by cognitive engagement (β = 0.447, p &lt; 0.01) and emotional engagement (β = 0.230, p &lt; 0.05). However, there was no statistically significant relationship between behavioral engagement and perceived learning (β = 0.035, p = 0.840). On the other hand, academic performance was predicted by behavioral engagement (β = 0.393, p &lt; 0.05) but not by cognitive (β = -0.164, p = 0.301) or emotional (β = 0.001, p = 0.991) engagement. The study highlights the importance of fostering academic engagement in university students to enhance both their academic performance and perceived learning.
Pemanfaatan Media Audio Visual pada Kegiatan Bercerita dalam Meningkatkan Perkembangan Bahasa Anak di TK Khalifah 23 Palembang

Language is an important component in early childhood development. Thus, a stimulus is needed to increase this development with various activities, namely storytelling activities using audio-visual media. This research aims to be one way to improve children's abilities in language development through storytelling activities through audio-visual media. This research will be carried out in 2 cycles, using classroom action research methods, improvement plans, objects, and reflection. The place where this research was carried out was TK Khalifah 23 Palembang. Based on the results of research activities that have been carried out, the results obtained are cycle I children's language abilities from a total of 15 children, 8 children (53%) have developed very well, 4 children (27%) have developed as expected. 3 children (20%) have started to develop. In the improvement cycle II, the results obtained were that 13 children (87%) had received very good grades, 2 children (13%) had obtained good grades and were developing according to expectations. So we can conclude that storytelling activities using audio-visual media carried out at TK Khalifah 23 Palembang have been able to improve children's language skills. What can be seen from each cycle is that there is a very significant increase.
Music Education Teaching Quality Evaluation System Based on Convolutional Neural Network

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.
Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform

The integration of neuromorphic computing into the Dynex platform signifies a transformative step in computational technology, particularly in the realms of machine learning and optimization. This advanced platform leverages the unique attributes of neuromorphic dynamics, utilizing neuromorphic annealing - a technique divergent from conventional computing methods - to adeptly address intricate problems in discrete optimization, sampling, and machine learning. Our research concentrates on enhancing the training process of Restricted Boltzmann Machines (RBMs), a category of generative models traditionally challenged by the intricacy of computing their gradient. Our proposed methodology, termed “quantum mode training”, blends standard gradient updates with an off-gradient direction derived from RBM ground state samples. This approach significantly improves the training efficacy of RBMs, outperforming traditional gradient methods in terms of speed, stability, and minimized converged relative entropy (KL divergence). This study not only highlights the capabilities of the Dynex platform in progressing unsupervised learning techniques but also contributes substantially to the broader comprehension and utilization of neuromorphic computing in complex computational tasks.
Design and Development of e-MARZ: A Practical Guide to Implementing Cost-effective VR Experiences for Music Education

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

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.
Assessing The General Sentiment of Malaysian Manufacturers in Adopting Blockchain Technology to Improve Supply Chain Processes

The popularity of blockchain technology has seen a dramatic increase throughout the year of 2021 and 2022, with numerous use cases in various global industries. This includes the use of cryptocurrencies, non-fungible tokens, decentralized autonomous organizations, and others. In Malaysia, there is also an apparent increase in general awareness towards the technology, with Bank Negara Malaysia and Securities Commission actively participating in the Committee for Blockchain and Distributed Ledger Technology. This study specifically examined at the Malaysian manufacturing industry, attempting to assess their general sentiments towards blockchain technology and how it could help to improve various supply chain processes. The quantitative study received 114 valid responses from management level employees with Malaysian manufacturers, enabling the researchers to assess the task technology fit, perceived ease of use (PEOU), perceived usefulness (PUSE), and technology readiness index (TRI). The analysis indicated that there is indeed a high task technology fit in the Malaysian manufacturing sector. In addition, this industry showed a positive sentiment towards blockchain technology, with high scores in PEOU, PUSE, and TRI. The high TRI numbers also had a positive significant relationship on the decision to adopt this technology within the manufacturing companies to improve various supply chain processes.
Deep Learning Approach for Classification of Tweets in Detecting Cyber Truculent

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

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.