Reviewerly/exHarmony huggingface dataset:
ENGLISH LANGUAGE ACQUISITION THROUGH CHILDREN'S LITERATURE

Introduction: This research is focused on the methodological strategy "storytelling" with a view to contributions to the acquisition of the English language and the development of children's vocabulary.General objective: To investigate whether children's literature, through storytelling, works as a facilitator in the teaching of the English language.In order to respond to this objective, storytelling workshops in English were held in April 2016.Specific objectives: Select children's literature books that present significant content for learning English.Apply, in the classroom, the methodological strategy of storytelling in English.To analyze whether there was development of children's vocabulary through storytelling workshops based on children's literary works.Methodological aspects: The public responding to this research are students from a private bilingual school, which uses various methods to introduce the English language in a playful and natural way, including children's literature.The survey is divided into two phases, the first of which sought to raise the profile of the respondent public: age, sex, grade, etc.The second phase is to apply reading workshops in the classroom, using storytelling, with the aim of analyzing the development of children in relation to the acquisition of vocabulary in the English language, through storytelling in the target language.
Deep Features Selections with Binary Marine Predators Algorithm for Effective Classification of Image Datasets

The paper proposes a method for improving the accuracy of image classification by combining CNNs and the Binary Marine Predators Algorithm (BMPA). The CNNs used in the study, ResNet 50 and AlexNet, were trained on ImageNet and used to extract features from the images in the dataset. Features are taken from layers (avg_pool) in ResNet 50 and (drop7) in AlexNet. These features were then fed into the BMPA algorithm, which selected the most relevant features and removed irrelevant ones to improve the classification process. The proposed method is said to be efficient, capable of achieving higher classification accuracy, and able to select the best features. The authors believe that this approach could be applied to a variety of other image classification tasks. It is important to note that the effectiveness of this method should be evaluated on a range of datasets and compared to other state-of-the-art methods.
Identifying Ship Types Using Deep Learning Technique

Every country depends on trade, which is currently carried out by ships in roughly 80% of cases. Nearly 11 billion tonnes of cargo are moved annually by ship. According to the present global population, shipping moves the equivalent of 1.5 tonnes per person. The ability of shipping to provide affordable and effective long-distance transportation places it at the centre of the global economy. This present study to improves the architecture based on YOLOV5(You Only Look Once Version 5) is proposed for ship detection. The experimental findings, which are based on a dataset with five different ship classes types, demonstrate that the deep learning model with 50 training iterations performs best, with an accuracy of 95.6%
Application of Content-Base Recommendation Algorithms on Mobile Travel Applications

Tourism is a major contributor to economic, social, and cultural development for countries and territories. It allows for the understanding of different cultures and customs, and the digital era, with its abundance of mobile technology, allows travellers to experience touristic activities while staying connected to the digital world. Mobile technologies can enhance the traveller’s experience and provide accurate recommendations. We propose a novel mobile recommendation system, which includes its architecture, components, and relevant features. The results showed that the K-means clustering model used is suitable, as evaluated by the Silhouette score, Calinski-Harabasz, and Davies-Bouldin indexes.
Racial Slur detection using Natural Language Processes

Any statement or phrase that disparages a group of people because of their gender identity, religion, ethnicity, national origin, race, sexual orientation, or is referred to as a racial slur. Hate, mistrust, and intolerance are often propagated via hate speech. It may also be used to harm or frighten others. People may feel alienated, anxious, and exposed as a result. Hate crimes may also result from it. Furthermore, hate speech may undermine intergroup trust. It is crucial to identify hate speech since doing so may assist to stop these negative impacts. Social media has recently developed into a haven for hate speech. Social media hate speech may be detrimental to society. Free speech, racial slur, and laws not for it have all been striving issues. Any speech, behaviour, writing, or display that encourages violence or other unpleasant deeds against a cohort of people or an organisation based on that person's subscription in the group, or that defames or affects that person or personal based on that membership, is considered racial slur under the laws of most countries. The laws may designate a category based on particular identities. Many countries do not consider racial slur to be a legal word. Additionally, the constitution permits the majority of what is seen as “hate speech” in many countries, including the US. In different countries, a sufferer of racial slurs may seek justice through civil lawsuit, convict lawsuit, or both. As communications are delivered and received virtually instantly, social media networks (SMNs) are the quickest form of communication. The emergence of social media platforms has irreparably altered how people interact in our society, and one result of these changes is an increase in unacceptable behaviours like the use of nasty and angry language online. It might be challenging to express harsh or disrespectful ideas to someone right in the face. People believe it is safe to abuse people or publish harmful content online. Hate speech may be harmful to an individual or a community. For improper material on the Web to be changed or blocked, it must be detected. Massive amounts of text data need human processing and classification, which takes time and effort. But since there is so much data released each day, automated approaches are crucial for recognising this kind of material.
Vision and Multi-modal Transformers

Transformers that rely on the self-attention mechanism to capture global dependencies have dominated in natural language modelling and their use in other domains, e.g. speech processing, has shown great potential. The impressive results obtained on these domains leads computer vision researchers to apply transformers to visual data. However, the application of an architecture designed for sequential data is not straightforward for data represented as 2-D matrices. This chapter presents how Transformers were introduced in the domain of vision processing, challenging the historical Convolutional Neural Networks based approaches. After a brief reminder about historical methods in computer vision, namely convolution and self-attention, the chapter focuses on the modifications introduced in the Transformers architecture to deal with the peculiarities of visual data, using two different strategies. In a last part, recent work applying Transformer architecture in a multimodal context is also presented.
Employee Attrition Factors Based on Data Analytics

One of the pivotal factors for any business to grow in the era of Industry 4.0 is its Workforce. An employee being the key player in the organisation holds lot of substance and responsibility and when the employee leaves chair it creates a lot of disturbance in the business practices. Thus, it becomes truly essential to study the various factors of Employee Attrition and make predictions through Data Analysis. The deep understanding of these factors will eventually help the industry experts in controlling Attrition and increasing Retention. Moreover, it will unlock many new fields for researchers to explore. Employee Attrition can be resolved easily if detected on time and prediction can be made through machine learning algorithms, as in previous research work primary focus was on recovery but in this research article primary focus is given on detection. The major objective of this study is to analyse the performance of the various Machine Learning Models and find out the best model for Prediction of Attrition. In this study, 10 Machine Learning Models were implemented and compared their performance in context to 8 performance parameters and observed that AdaBoost Classifier, RandomForest Classifier, HistGradientBoosting Classifier, ExtraTrees Classifier where random state=12 is a seed value resulting higher value than other classifiers.
A Method to Estimate the Power Generation of PV Power Stations Along the Highway Based on Digital Maps

Solar energy, as major clean energy source, has been rapid development in recent years. Before the construction of PV power station, it is necessary to carry out reasonable planning, in order to make rational use of solar energy resources wisely. The power consumption along the highway is very stable and showing an increasing trend. Highway slopes are usually unused public area with high accessibility and few restrictions, which are regarded as potential installation sites. In order to estimate the power generation capacity of PV power stations at different locations along the highway, a method based on digital maps is proposed for calculating actual available power generation of PV power station along the highway. The method is applied to a case study of a highway in Linyi, Shandong Province, to determine the appropriate installation location of PV power stations.
Adoption of Blockchain Technology in Academic Certificate-Verification Systems

Receiving an academic certificate is a testimony that the individual receiving the certificate has achieved a number of specified learning outcomes. Still, due to the improvement in technology and the proliferation of the Internet, questionable businesses started offering fraudulent certificates and certain organizations started creating their own accreditation organizations that make it difficult to distinguish between fake and real academic certificates. In response to these problems, certificate verification policies and -processes are changing as electronic verification of certificates gradually gain popularity and are deployed in some institutions. Blockchain technology has emerged as a means of offering tamper proof digital certificates. This study examines the prevalent system of certificate verification and presents a framework for the implementation of blockchain technology in the context of Nigeria.
User friendly virtual reality software development and testing

Virtual reality (VR) has grown in popularity in industry and science. While the development of virtual reality applications is progressing at a fast pace, even today it is a challenge to precisely define the aspects that make virtual reality software of high quality, user-friendly and usable. The biggest issue is that traditional software testing standards can only be partly followed. Using virtual reality for scientific purposes can provide researchers with new possibilities and change human-computer interaction. In this paper, we conducted a user study and identified a number of aspects that are the basic factors in determining the usability of virtual reality software. The results indicate that excessive head and arm movements have an impact on the VR experience.
PENGELOLAAN SURAT DINAS DAN ARSIP BAGI KEPALA SEKOLAH DAN TENAGA ADMINISTRASI SEKOLAH DASAR DI KECAMATAN JATINEGARA KOTA ADMINISTRASI JAKARTA TIMUR

Elementary school administration is an activity to support the success of thequality of education in elementary schools. The scope of school administration activitiesincludes the management of official letters and school archives. Management of officialletters and archives in primary schools is carried out by the principal, assisted by a schooloperator. Their ability to manage official documents and school archives is still lacking.Efforts to increase the capacity of school principals and elementary school administrativepersonnel can be provided with training on managing official letters and school archives.The object (target audience) of the training activities is 150 people consisting of 2elementary school supervisors, 58 school principals, 83 school administration staff and 7SDN teachers in Jatinegara sub-district. The partners involved are the Regional EducationOffice of East Jakarta Administrative City. The training has been carried out for a day onThursday, October 8, 2020 through virtual zoom cloud metting. The training materialconsists of: school leadership and administration, management of official letters andmanagement of school records. The three materials were delivered by 3 lecturers of the UNJFIP Education Management study program with the lecture, question and answer andassignment method. The results of the training were very satisfying to the participants andthe organizing committee, because all the materials had been delivered well and were veryuseful for all training participants.
AN An Enhanced Differential Homomorphic Model using N-Prime Scheme for Privacy Preservation

In preserving individual privacy in data publishing, several efforts have been made by scholars globally to develop an individual privacy preserving model and hybridized models which harness the strength of the individual model to increase privacy preservation in data Publishing (PPDP). The Differential homomorphic model (DHM) was among the hybridized models developed that combine differential and homomorphic models. Though is one of the state of the art hybridization methods for privacy preservation because of the Differential model and Homomorphic model strengths of the two hybridized models which are the ability to prevent composition problems and database attacks respectively. However, applying this model is challenging because of the high computational complexities due to the modular exponentiation problem available in pailler encryption scheme used in DHM. In this research, an N-PRIME homomorphic encryption scheme was proposed to replace the Pailler encryption scheme in the differential homomorphic model (DHM). The designed model was 51% faster than the existing model (Differential Homomorphic Model) in terms of computation time and 48.5% faster when generating the graphical data set, though the designed model consumed 4% more storage space than the existing model.
Towards an Extractive Summarization for utilizing Learning Content using Deep Learning algorithm: Proposed Framework and Implementation

Decentralized Web (Web3) and Finance (DeFi) have become the main discussion topic in research and industry fields.Cryptocurrencies, as an essential part of DeFi, enjoyed the interest of many stakeholders such as companies, professionals, researchers, and even common citizens eager to benefit from the proposed ecosystems.Although previous research studies focused on establishing price prediction systems using Sentiment Analysis (SA) techniques, the main focus of these studies was the performance of the predictive system rather than the accuracy and efficiency of the used models.In our work, we address two research questions; the predictability of cryptocurrency price based on past social and technical information, and the effect of social features on cryptocurrency price fluctuations using an SA and a Time Series approach.A combination of selected social and technical features was processed and reframed as a prediction problem, then studied to assess the ability of our model to predict the desired price.We noted that there is both an explicit correlation for some considered features and implicit for others, also social features including overall positive and neutral sentiment, and community engagement improved the performance of our model.
Virtual Reality Tourism: Linkage Tourist Intention, Satisfaction, and Quality

This study aims to determine visitor behavioral intention in virtual reality (VR), including the experience with both content and system quality as will as satisfaction as the determinant factors. The data obtained from 350 visitors visiting tourism destinations in greater Bandung region, Indonesia via VR. A self-administered questionnaire was employed using online survey. The analysis was carried out by using Partial Least Square (PLS) modelling. Results of the analysis show that the experience with content quality has an important role in shaping the behavioral intention visiting a tourism destination via VR. Next, there is no significant difference in the moderating role of gender and VR experience on the relationships between variable in the behavioral intention model. This study broadens our understanding of the use of the information success model to assess visitor intention in VR tourism and assists practitioners in developing strategies to build and maintain visitor intention to visit and recommend in VR tourism.
PERFIL EPIDEMIOLÓGICO DA TROMBOSE VENOSA CEREBRAL DURANTE A PANDEMIA DO COVID 19 NO ESTADO DO TOCANTINS

Spasticity is defined as the persistant increase of the muscle resistence to articular moviment.This subtype of hypertonia comes from a lesion in the central nervous system that leads to increase of muscle resitence.Although there's multiple options to treat spasticity, the use of intratecal baclofen is an eficient and low risk therapy that has been used for more than 30 years.The presente article aims to discuss hypertonia, spasticity, and review the main indications and results of the intratecal baclofen use.
Brahmaputra to AI: debashis sinha on practice, body, humanness, and his double release

&lt;p&gt;An article regarding the composition process of Debashis Sinha, a musician and composer. The conversation circled around the material included in the double releases Brahmaputra and Adeva_v000_04 on the Establishment label (the article contains links to the releases). &lt;/p&gt; &lt;p&gt;Sinha talks about the recording process, using AI and how his compositional practice unfolded in the making of these releases.&lt;/p&gt; &lt;p&gt;https://cdm.link/2023/03/brahmaputra-and-ai-debashis-sinha-interview/&lt;/p&gt;
Money Laundering and the Privacy Design of Central Bank Digital Currency

This paper studies the implications of money laundering for the optimal design of central bank digital currency (CBDC). I build a general equilibrium framework to explicitly allow money laundering by agents and income audits by a government. I find that as long as CBDC offers less anonymity than cash, introducing CBDC will decrease money laundering. However, if CBDC is "cash-like" in the sense that it still offers a relatively high level of anonymity but a low interest rate, then introducing CBDC will decrease the output from not only agents who launder money but also agents who do not. If CBDC is instead "deposit-like" in the sense that it offers low anonymity but a high interest rate, then introducing CBDC can increase aggregate welfare without lowering output. Furthermore, introducing deposit-like CBDC needs not increase the funding costs of banks or decrease bank lending.
Discrete cosine transformation unwrapping algorithm based on phase gradient smoothing correction

At present, in the shear speckle interference technique for vehicle tire defect detection, the key is to accurately reflect the real phase of object motion when there are interference factors. Thus, this study proposes a discrete cosine transformation (DCT) unwrapping algorithm based on phase gradient smoothing correction, and analyzes the feasibility of the proposed method through several experiments and numerical simulation calculations. The proposed method replaces abnormal phase gradients caused by noise with calculated weighted moving average phase gradients, bringing phases closer to real values. Finally, the DCT unwrapping method is used to obtain the final solution of phase. A phase recovered using the proposed method is closer to the real value than that recovered using the traditional DCT unwrapping algorithm. Compared to the traditional DCT unwrapping algorithm, the root mean square error accuracy of the proposed method is improved by 10 to 100 times. And after many experiments to record the average running speed, it can be obtained that the time-consumption of the two algorithms only differs by ∼0.1 to 0.3 s.
Empowering Adult Inmates with Communication Needs in an East Malaysia Prison

Communication skills are essential in our daily life because we communicate to pass information from one person to another. However, to have effective communication in a correctional environment, like prisons, is not easy. There are many distractions and barriers to communicate effectively in prisons. Although prison education should be seen as a way to educate inmates to successfully integrate in society upon their release, inmates face difficulties in getting enough reading materials, content and learning tools to enhance their communication skills. This may negatively affect their integration in society and employability after serving their sentences. Therefore, this study aimed to elicit the communication needs of the adult inmates in one of the local prisons in East Malaysia. We employed a textography research approach, where we combined textual analysis and an ethnographic method (interview), to investigate contexts, texts and practices of the prison studied. The findings showed that the inmates need to learn verbal and non-verbal communication, new technologies for communication and simple English conversations. This study is pertinent in providing insights for the development of suitable teaching and learning modules for prison learning institutions.
Web-Based Knowledge Management System Application to Improve Employee Activities

The need for information is very high in the current 4.0 era, every organization needs fast, accurate and detailed information. Currently there is documentation at the Indonesian Medical Council Secretariat regarding knowledge on how to handle employee problems which can help the employee's own performance so that it has an impact on secretariat activities that feel ineffective. Based on the results of the analysis that has been carried out, there is a lot of important knowledge at the Indonesian Medical Council Secretariat whose function is to support activities in Health Institutions. The methodology used in this research is to use Beccera Fernandez which was developed by Beccera and Sabherwal. The data collection methods used were observation, interviews, literature study, while the Knowledge Management System implementation used the Knowledge Management Process, and the KMS design used the Unified Modeling Language (UML) modeling. The programming languages used in building the system are PHP and MySQL. The concept of knowledge sharing among employees was applied as a result of a study conducted at the Indonesian Medical Council Secretariat. This is implemented on the website so that individual employee knowledge is not simply lost.