Reviewerly/exHarmony huggingface dataset:
Automatic summarization of YouTube video transcription text using term frequency-inverse document frequency

Automatic summarization &lt;span&gt;is a technique for quickly introducing key information by abbreviating large sections of material. Summarization may apply to text and video with a different method to display the abstract of the subject. Natural language processing is employed in automated text summarization in this research, which applies to YouTube videos by transcribing and applying the summary stages in this study. Based on the number of words and sentences in the text, the method term frequency-inverse document frequency (TF-IDF) was used to extract the important keywords for the summary. Some videos are long and boring or take more time to display the information that sometimes finds in a few minutes. Therefore, the essence of the proposed system is to find the way to summarize the long video and introduce the important information to the user as a text with few numbers of lines to benefit the students or the researchers that have no time to spend with long videos for extract the useful data. The results have been evaluated using Rouge method on the convolutional neural network (CNN)-dailymail-master data set.&lt;/span&gt;
Fraud Detection Using Random Forest Classifier, Logistic Regression, and Gradient Boosting Classifier Algorithms on Credit Cards

The following credit card records were used in this study of 284.807 transactions made by credit card holders in Europe for two days from the Kaggle dataset. This is a very poor data set, having 492 transactions, an imbalance of only 0.172% of the 284.807 transactions. The purpose of this study is to obtain the best model and then simulate it by electronically detecting unauthorized financial transactions in bank payment systems. The dataset for this study is unbalanced class data with 99.80% for the major class and 0.2% for the minor class. This type of class-imbalanced data problem is solved by applying method a combination of minority oversampling techniques using Synthetic Minority Oversampling Technique (SMOTE). To determine the most appropriate and accurate classification in solving class balance problems, comparisons were made with the Random Forest Classifier (RFC), Logistic Regression (LGR), and Gradient Boosting Classifier (GBC) algorithms. The test results in this study are the Random Forest Classifier (RFC) algorithm is better than other algorithms because it has the highest accuracy the percentage of data-train is 100% and data-test is 99.99% and the evaluation of the AUC score as a result of algorithm testing is 0.9999.
A Study On English Teachers’ Perceptions Towards The Use Of Augmented Reality (AR) In Secondary School ESL Classroom

: In meeting the needs of the new generation of learners, the idea of implementing the use of new technology such as Augmented Reality (AR) in English as a Second Language (ESL) classrooms can be a step toward in refining the teaching and learning atmosphere. Nevertheless, the use of this technology relies heavily on the teacher’s level of acceptance and skills for it to be successful in classrooms. TESL Programme is one of the popular programmes in higher education institutions. In line with the advancement of technology in education and current transformation happening at schools, it is essential for the TESL curriculum at higher education institutions be revised according to the current training needs of the in-service teachers. Thus, this study intended to determine the English language teachers’ perceptions towards the use of AR by applying the Technology Acceptance Model (TAM) as the base of the research model. A survey was conducted on 180 English teachers from 20 schools in the district of Petaling Perdana. The items in the questionnaire elicited information on the levels of AR acceptance of the respondents based on the four variables of TAM, that are perceived usefulness (PU), perceived ease of use (PEoU), attitude towards use (ATT) and behavioural intention (BI) to use AR in ESL classroom. Findings indicate that the levels of AR acceptance of the English teachers are relatively high, as the respondents recorded a high level of PU, ATT and BI towards the use of AR while PEoU was at moderate level. The results from multiple regression analysis indicate that PU and ATT of the English teachers directly influence their BI to use AR in ESL classroom. The findings suggest that English teachers are acceptive and ready for the implementation of AR technology. These finding provide the much needed information for the TESL curriculum review in ensuring the higher education institutions provide programmes fulfil the demand of the technology transformation taking place at schools. Several implications derived from the salient findings were presented for curriculum developers and TESL instructors to consider in revising the curriculum and programme delivery strategies.
VALIDITY OF INFOGRAPHIC LEARNING MEDIA THEME OF GREEN GROWTH IN TRAINING CRITICAL THINKING SKILLS

Infographic learning media is one of the intermediaries in the learning process made with graphic design, namely canvas. This media infographic is used to give Canva learning materials to students to achieve a learning goal. This study aims to develop learning media infographics and determine the level of validity. The media used is research and development or Research and Development adapted from the Borg and Gall development model, consisting of three stages, namely Research and Information Collecting, Planning, and Preliminary Form of Product. The research instruments used in this study were interview sheets and validation sheets. This study indicates that the level of validity of the infographic learning media on the theme of the green growth in training critical thinking skills of seventh-grade students obtained an average value of 78.94% with a valid category so that infographic learning media can be used in the learning process.
English Translation Template Retrieval Based on Semantic Distance Ontology Knowledge Recognition Algorithm

With the rapid development of the Internet, data information has begun to spread rapidly on the Internet, and the types and fields involved are becoming more and more diverse. With the deepening of internationalization, English information has also become a common entry for tourism. Relatively speaking, the translation of English documents is also on the agenda. The intricate literature information has put forward a more specialized and vertical demand for retrieval. Traditional retrieval systems usually use string matching algorithms to search, but this technology cannot perform semantic expansion of query conditions. In the case of “multiple words with one meaning” and “polysemy of one word,” the retrieval accuracy of traditional retrieval systems decreases. In order to solve the previously mentioned problems, this study proposes semantic retrieval based on domain ontology technology. The purpose of semantic retrieval is to semantically expand the user’s query conditions on the basis of accurately understanding the retrieval conditions. At the same time, with the help of the constructed domain ontology, it provides more accurate and comprehensive retrieval services for document retrieval. The experimental results of this study show that, for the clustering of the entire dataset, the number of ontology concepts in each article is 61% of that, before merging, which effectively reduces the complexity of the semantic network model. Therefore, the validity of the ontology concept merging algorithm proposed in this study is verified.
Pulling, Pressing, and Sensing with In-Flat: Transparent Touch Overlay for Smartphones

Smartphones’ screens are touch-sensitive and offer rich visual output capabilities, but do not allow users to control a wide range of pressure input nor provide rich pressure sensations. We propose In-Flat, an input/output pressure-sensitive overlay for smartphones. In-Flat consists of a transparent inflatable skin-like silicon layer that can be placed on the top or the back of a smartphone. As an output device, In-Flat offers tangible affordances and dynamic pressure feedback coupled with visual display. As an input device, In-Flat enables users to continuously perform a wide range of input gestures, notably press and pinch-and-pull gestures. Thus, In-Flat can be used to finely manipulate visual objects in mobile interaction or mediate interpersonal touch communications. In contrast to previous studies that mostly focus on press, we investigated the performance of pinch-and-pull and compared it with press. Our experiment (N=12) showed that participants could perform pinch-and-pull (83.8%) as well as press (84.7%), but felt having more control when performing pinch-and-pull. We explored the use of In-Flat to enable multimodal interaction that couples visual display and touch input/output. Participants appreciated this coupling as well as the touch sensation supplied by the In-Flat device.
Deep Neural Patchworks: Coping with Large Segmentation Tasks

Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in particular on a common GPU. Especially in biomedical imaging, where 3D images are common, the problems are apparent. A typical approach to solve this limitation is to break the task into smaller subtasks by dividing images into smaller image patches. Another approach, if applicable, is to look at the 2D image sections separately, and to solve the problem in 2D. Often, the loss of global context makes such approaches less effective; important global information might not be present in the current image patch, or the selected 2D image section. Here, we propose Deep Neural Patchworks (DNP), a segmentation framework that is based on hierarchical and nested stacking of patch-based networks that solves the dilemma between global context and memory limitations.
Internet Entrepreneurship Information Resource Sharing System Based on Improved Deep Learning Algorithm

In order to promote the circulation of Internet entrepreneurial information, based on information theory and general security theory, this paper explains how to construct the attack channel and defense channel of the k-anonymity model, so as to provide a reliable network foundation for the transmission of related information. In order to fully solve the problems existing in multiuser requests, this paper proposes two modules, single-request sharing mode and multirequest sharing mode, to process query requests in the request queue to maximize the reuse of request results. Moreover, the model constructed in this paper needs to be processed through deep learning first and then shared through the intelligent model. Therefore, the data learning and data sharing of the model are mainly analyzed during model validation. The experimental simulation results show that the intelligent sharing model of Internet entrepreneurial information resources based on deep learning proposed in this paper has good data learning and data transmission effects.
An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data

Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-informative features to enhance the performance of data mining techniques. It is also considered as one of the key success factors for classification problems in high-dimensional datasets. This paper proposes an efficient wrapper feature selection method based on Grey Wolf Optimizer (GWO). GWO is a recent metaheuristic algorithm that has been widely employed to solve diverse optimization problems. However, GWO mainly follows the search directions toward the leading wolves, making it prone to fall into local optima, especially when dealing with high-dimensional problems, which is the case when dealing with many biological datasets. An enhanced variation of GWO called EGWO, which adapts two enhancements, is introduced to overcome this specific shortcoming. In the first place, the transition parameter concept is incorporated to move GWO from the exploration phase to the exploitation phase. Several adaptive non-linear decreasing formulas are introduced to control the transition parameters. In the second place, a random-based search strategy is exploited to empower diversity during the search process. Two binarization schemes using S-shaped and V-shaped transfer functions are incorporated to map the continuous search space into a binary one for FS. The efficiency of the proposed EGWO is validated on ten high-dimensional low-samples biological data. Our experiments show the promising performance of EGWO compared to the original GWO approach and other state-of-the-art techniques in terms of dimensionality reduction and the enhancement of classification performance.
Examining the Relationship Between Job Insecurity, Organizational Commitment and Turnover Intention in the COVID-19 Process

With the onset of the COVID-19 pandemic, many sectors have begun to be affected.The tourism sector is one such sector and has been heavily affected by restrictions on international movement of.Affected by this situation, hotels have decided to close their doors or have adopted a limited-service approach.For this reason, tourism employees have become unable to see their way in an uncertain process and have encountered different practices such as hourly paid work, being sent on unpaid leave, and being given a short-term employment allowance.The reflections of this negative impact on the employees are a matter of curiosity.In this context, this study aims to reveal the relationship between job insecurity, organizational commitment, and turnover intention in five-star hotels during the COVID-19.To enable this, data were collected using a questionnaire.The study results support the conceptual model that includes the variables of job insecurity, organizational commitment, turnover intention and organizational response to COVID-19.Organizational Response to COVID-19 was found to have a negative and significant effect on job insecurity and also to have a positive and significant effect on organizational commitment.Job insecurity has a negative and significant effect on organizational commitment.Organizational commitment has a negative and significant effect on turnover intention and job insecurity was found to have a positive and significant effect on turnover intention.
Being Diverse is Not Enough: Rethinking Diversity Evaluation to Meet Challenges of News Recommender Systems

Modern societies face many challenges, one of them is the rise of affective polarization over the last 4 decades. In an attempt to understand its reasons, many researchers have questioned the role of Social Media in general, and Recommender Systems (RS) in particular, on the emergence of these extreme behaviors. Diversity in News Recommender Systems (NRS) was quickly perceived as a major issue for the preservation of a healthy democratic debate. However, after more than 15 years of research in Artificial Intelligence on the subject, the understanding of the real impact of diversity in recommendations remains limited. Through a case analysis on the well-known MIND dataset, we propose a critique of the diversity-aware recommendation and evaluation approaches, and provide some take-home messages related to the need of adapted datasets, diversity metrics and analytical methodologies.
Binaural Headphone Monitoring to Enhance Musicians’ Immersion in Performance

Musicians face challenges when using stereo headphones to perform with one another, due to a lack of audio intelligibility and the loss of their usual benchmarks. Also, high levels of click tracks in headphone mixes hinder performance subtleties and harm performers’ aural health. This chapter discusses the approaches and outcomes of eight case studies in professional situations that aimed at comparing the experiences of orchestra conductors and instrumentalists while monitoring their performances through binaural versus stereo headphones. These studies assessed three solutions combining augmented and mixed reality technologies that include binaural with head tracking to conduct a large film-scoring orchestra and jazz symphonic with a click track; binaural without head tracking to improvise in trio or on previously recorded takes in the studio; and active binaural headphones to record diverse genres on a click track or soundtrack. Findings concur to show that better audio intelligibility and recreated natural-sounding acoustics through binaural rendering enhance performers’ listening comfort, perception of a realistic auditory image, and musical expression and creativity by increasing their feeling of immersion. Findings also demonstrate that the reduction of source masking effects in binaural versus stereo headphone mixes enables performers to monitor less click track, and therefore protect their creative experience and aural health.
Research on the Innovation of Music Teaching in Universities Based on Artificial Intelligence Technology

At present, music education in colleges is in a period of rapid development in China. At the same time, music education in universities is facing innovation and reform of teaching modes. How to improve music education in colleges and universities has become an important issue for music teachers in colleges. Music teaching and management activities for students in general universities can enrich students’ talents and expand their knowledge. It can also help them develop a positive emotional psychology and develop positive and healthy character characteristics, both of which are vital for college students’ healthy development. Innovation and modification in music teaching and management activities for students is the only way to increase the quality and effectiveness of music teaching for nonarts students and to promote the overall quality of students. Based on this, this paper proposes an innovative research method of college music teaching based on artificial intelligence technology. The method introduces a fuzzy evaluation algorithm to establish a two-level teaching evaluation index system and calculates the weights of each index based on fuzzy mathematical theory. In data processing, the SVM algorithm in the field of data mining is used to classify all collected teaching evaluation data in advance through supervised learning, which significantly improves the efficiency of data processing. The experimental results show that the model in this paper can well assess the quality of music teaching in colleges and universities and play a role in promoting the progress of music teaching in colleges and universities.
Feature Engineering for Interpretable Machine Learning for Quality Assurance in Radiation Oncology

Chart checking is a time intensive process with high cognitive workload for physicists. Previous studies have partially automated and standardized chart checking, but limited studies implement data-driven approaches to reduce cognitive workload for quality assurance processes. This study aims to evaluate feature selection methods to improve the interpretability and transparency of machine learning models in predicting the degree of difficulty for a pretreatment physics chart check. We compare chi-square, mutual information, feature importance thresholding, and greedy feature selection for four different classifiers. Random forest has the highest performance with SMOTE oversampling using mutual information for feature selection (accuracy 84.0%, AUC 87.0%, precision 80.0%, recall 80.0%). This study demonstrates that feature selection methods can improve model interpretability and transparency.
Improved CenterNet for Accurate and Fast Fitting Object Detection

Accurate and fast detection of typical fittings is the prerequisite of condition monitoring and fault diagnosis. At present, most successful fitting detectors are anchor-based, which are challenging to meet the requirements of edge deployment. In this paper, we propose a novel anchor-free method called HRM-CenterNet. Firstly, the lightweight MobileNetV3 is introduced into CenterNet to extract multi-scale features of different layers. In addition, the lightweight receptive field enhancement module is proposed for the deep layer features to further enhance the characterization power of global features and generate more accurate heatmaps. Finally, the high-resolution feature fusion network with iterative aggregation is designed to reduce the loss of spatial semantic information in subsampling and further improve the accuracy of small and occlusion objects. Experiments are carried out on the TFITS and PASCAL VOC datasets. The results show that the size of the network is more than 60% lower than that of CenterNet. Compared with other detectors, our method achieves comparable accuracy with all accurate models at a much faster speed and meets the performance requirements of real-time detection.
Challenges and Opportunities of Big data and IoT in the Electronic Banking Industry: A Systematic Literature Review

Abstract The banking industry is one of the most complex and sensitive industries that undergoes significant changes daily. Most financial institutions and banks strive to diversify and innovate payment processes, making them more challenging and secure so that they can improve their digital skills .Understanding customer behavior has become a key factor in market success, which is why the Internet of Things (IoT) can be the best way to solve the problem of collecting and sharing data over the Internet, among different objects. Based on these concepts, the purpose of this paper is to show the current perspective of the e-banking industry with big data and the IoT, as well as the impact of big data on various financial and banking sectors, especially its impact on financial markets and banking, financial institutions, Internet credit services companies, risk analysis and so on. Since big data in the field of electronic banking and finance is a completely new concept, future research opportunities will be mentioned at the end of this study.
A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers

The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Despite a lot of empirical studies and recent progress in theoretical understanding of VQE's optimization landscape, the convergence for optimizing VQE is far less understood. We provide the first rigorous analysis of the convergence of VQEs in the over-parameterization regime. By connecting the training dynamics with the Riemannian Gradient Flow on the unit-sphere, we establish a threshold on the sufficient number of parameters for efficient convergence, which depends polynomially on the system dimension and the spectral ratio, a property of the problem Hamiltonian, and could be resilient to gradient noise to some extent. We further illustrate that this overparameterization threshold could be vastly reduced for specific VQE instances by establishing an ansatz-dependent threshold paralleling our main result. We showcase that our ansatz-dependent threshold could serve as a proxy of the trainability of different VQE ansatzes without performing empirical experiments, which hence leads to a principled way of evaluating ansatz design. Finally, we conclude with a comprehensive empirical study that supports our theoretical findings.