Reviewerly/exHarmony huggingface dataset:
The Application of Robotics in the Field of Service from the Investment Perspective

Just as the industrial revolution, which brings significant change and boom to the development of productivity, the robots in the service industry are highly possible to adjust and regenerate the current business.However, many technological workers are still experimenting with applying artificial intelligence to the services, lowering the cost of the service robots.The paper aims to determine the investment opportunity by exploring the role of service robots will play in the future.This paper uses PEST to offer the general situation of the current service robotics industry.It finds out the specific problems that existed in the immature industry and market.Finally, the paper sheds light on a few solutions and analyzes venture capital and optimal funding for technological startups.For the other researchers, it is better to investigate the overall potential value given by the service robots and people's expectations of service robots.In this way, the view toward the investment opportunity will be more comprehensive and incisive.
Comparison of Plain and Dense Skip Connections on U-Net Architecture for Change Detection

In recent years, identifying changes in multitemporal images in terms of land use and land cover is significant in a variety of applications including urban planning.CNN architectures are one of the most extensively utilised methods for change detection.The aim of this research is to investigate two types of skip connections that may be incorporated into CNN architecture to determine if they can improve the effectiveness of change detection during the CNN learning process.In this paper, we adopt the U-Net architecture to train the change detection model.We also modify the U-Net skip connection's path to include the dense skip connection and compare the modified U-Net with the original U-Net, which uses the plain skip connection.We also test the trained model with our collected local dataset in Cyberjaya to see how well it can anticipate changes in our location.The results of this study show that a U-Net with dense skip connections produces the best results and optimises change detection.It will help researchers understand how important the skip connection is to the model's performance.
Face Mask Detection Using Deep Learning

It is vital to remain vigilant during pandemic COVID-19.Wearing a face mask is one of the crucial steps that people must take to ensure that they are a step away from spreading and infecting the virus.However, controlling and monitoring people in a densely crowded place is tough.Hence, a face mask detection system in public area is needed to remotely monitor if one is wearing a face mask or vice versa.In this study, two face masks datasets are downloaded from GitHub with 3834 images and 11800 colour images.Data pre-processing steps are carried out before the classification, which includes image resizing, converting images into array and label encoding.Two deep learning models, MobileNetV2 and VGG19, are developed for detection and evaluation.The experimental results performed by MobileNetV2 outperformed the VGG19 with achieving accuracy of 98.96% and 99.55% on Dataset 1 and Dataset 2 respectively.
Features of systematic preparation for the All-Russian Olympiad of schoolchildren in English: traditions and innovations

Relevance.The study of the features of preparation for the All-Russian Olympiad of schoolchildren is timely and relevant for a number of reasons, including the constantly growing interest to this intellectual project on the part of students and teachers, as well as the high importance of the Olympiad movement to increase the intellectual potential of the nation.Aim.The article aims to reveal the features of the modern Olympiad competition in English, as well as to show the systemic nature of preparation for the Olympiad.Procedure and methods.Among the main methods of research are the analysis of the publication activity of modern authors on the declared topic, content analysis of the content of tasks of previous years, the study of official documents published by the central subject-methodological commission.Scientific novelty and/or theoretical and/or practical significance.The article clarifies the term "Olympiad", as well as indicates the features of training schoolchildren at the current stage.Results of the study.The systemic nature of preparation for the Olympiad tours and an understanding of the evolution of the competition allows us to achieve high-quality results.Conclusion.The Olympic movement is one of the main drivers of improving the quality of education in the country, participation in the Olympiad conceptually allows us to educate schoolchildren who can think independently and creatively.
CNN-Based Traffic Sign Recognition

Traffic signs are a crucial part of maintaining driver and pedestrian safety on the road since they are being designed to provide essential information and alerts of potential hazards.With the rapid development of Advanced Driver Assistance Systems (ADAS), traffic sign recognition is also becoming much of a concern.However, due to real-world variations such as lighting conditions, occlusion, weather factors, motion blur and colour fading, there are still some failures in traffic sign recognition that cannot be perfectly resolved.Therefore, we implement image enhancement techniques and a pre-trained convolutional neural network for traffic sign recognition in this paper.Our proposed model uses the pre-trained VGG19 model as the baseline model and changes the fully connected layer and classifier of the VGG19 model.The experimental results demonstrate the effectiveness of applying image enhancement.Our proposed model was able to outperform the traditional machine learning method but did not surpass other deep learning methods.
Automatic Face Mask Detection and Violation of Social Distancing Application

Ever since worldwide COVID-19 epidemic, social distancing has been prioritised and advised to practice everywhere on a daily basis.It has been and will continue to be one of the most efficient and effective measures to fight the pandemic and saving lives.In Malaysia, social distancing is a proactive action to minimize the spread of COVID-19 among human beings.No matter we are staying indoor or outdoor, a tolerable distance among each other will save ourselves and others.However, there are a lot of constraints to govern the violation of social distancing manually.In this research, an automatic detection of the violation of social distancing is proposed to create a healthier environment.The proposed research could be conducted in public places or confined places.Particularly, a security camera combined with another camera is used to monitor the social distancing in a confined area.Social distancing violation will be detected using Euclidean distance analysis.
Neural Network-Based Cryptography: A Primary Study on the Performances and Techniques

Cybersecurity is getting more and more important in this big data era.Neural Network-based cryptosystem which is a complex system and deserve more research study for improvement.This article will discuss and analyse existing neural network-based cryptography performance and techniques.The in-depth analysis will examine the effectiveness of older structure as well as the effects initiated against them.Several advanced techniques will be discussed that reliant on generative adversarial networks (GANs) to contend with one another in pursuit of a common goal such as efficient encryption for communication.At the end of the paper, it will review a few decent neural network methods best applied on cryptography.
Comparison of Deep Learning Methods for Underwater Image Enhancement

Underwater image enhancement is an important process in image processing due to the images often suffering from severe degradation causes by the nature of light and underwater environment.The purpose of this research is to study the existing methods and algorithms for enhancing underwater images.In this paper, we compared 3 different deep learning-based methods (i.e.Water-Net, Shallow-UWnet, Deep Learning and Image Formation Model) for underwater image enhancement.Furthermore, we proposed an enhancement method based on white balance, adaptive gamma correction, sharpening and multi-scale fusion technique.The result of our proposed method is fed into the deep learning-based models.A real-world dataset which is the Underwater Image Enhancement Benchmark (UIEB) dataset is used for the model training and testing.Experimental results show that our proposed method improves the colour hue, image clarity and achieves higher scores in terms PSNR, SSIM and UIQM metrics.
Quantitative Analysis of Intelligent Speech System Applied to English Speech Assessment

The intelligent speech system has been widely used in English classrooms due to its advantages of pronunciation standard, abundant resources and convenient operation.This paper uses the video analysis method to deeply analyze the English classroom teaching based on intelligent speech technology.In order to fully understand the application of intelligent speech system to English classroom teaching, this study combines the existing classroom teaching coding system and the functional characteristics of intelligent speech system, draws on TIMSS and LPS analysis methods, and constructs a coding table of classroom teaching activities, and analyzes the selected video recordings.Make observations and records, objectively analyze the teaching effectiveness of the intelligent voice system, and summarize and summarize the characteristics and laws of the application of the intelligent voice system in the classroom teaching process, so as to better improve the application effect and role of classroom teaching.
Dynamic Hand Gesture Recognition Based on Deep Learning for Muslim Elderly Care

Gesture recognition for elderly care is an approach to classify gestures performed by the elderly to convey specific messages.Nursing homes or caretakers are often hired to take care of senior citizens and are responsible to keep them safe.Hence, this study will be useful to assist caretakers in providing needs requested by the elderly when they are absent.A collection of dynamic hand gesture recognition (HGR) datasets consisting of ten gesture classes with 630 videos is used to build the models.The ten gestures will represent requests for help to perform daily activities namely eating, toileting and dressing.Specifically for this research, we have infused Islamic hand gestures such as gestures to perform prayers and read the Quran.In this paper, we adopted the action recognition pre-trained model into the HGR by using CNN RNN and Transformer with CNN models.The result of this study shows that Transformer with CNN model has higher accuracy in recognizing hand gestures compared to CNN-RNN model.
Objectivity and Subjectivity Classification with BERT for Bahasa Melayu

This research present the notion of subjectivity and objectivity in Bahasa Melayu language.Word2Vec and BERT word embedding models are created for the purpose of subjectivity classification and sentiment classification.Two types of embeddings are developed (Word2Vec and BERT) with Wikipedia data as objectivity dataset, Twitter data as subjectivity dataset and combination of both datasets.A pre-trained BERT embedding model called Bert-Base-Bahasa-Cased is used as a reference.First, the datasets are fed into every embedding model to be embedded as vectors.The subjectivity classification and sentiment classification are carried out via 70:30 train-test splits.Both classification tasks are carried out using Logistic Regression, Random Forest, and Double Layer Neural Network classifiers.Logistic Regression on Bert-Base-Bahasa-Cased model achieved the highest result of 99.95% in subjectivity classification and 74.30% in sentiment classification.
Email Plugin Suite for k-Resilient Identity-Based Encryption and Searchable Encryption

Nowadays, most of the companies use their preferred email platform to perform their daily tasks such as exchanging of messages and sharing of documents.However, it is quite often to hear the news that someone's email account is being hacked.The malicious intention of the hackers is to hijack the users' personal information or company documents.Thus, cryptography plays an important role to provide more a secure email platform.Identity-based encryption (IBE) is a public key cryptosystem that uses a unique identifier such as email address or IP address to generate the user public key and it relies on a trusted third party to generate the corresponding user private key.As compared to conventional public key infrastructure, users now no longer need to distribute their public keys to others.Meanwhile, searching over encrypted emails could be a challenging task.In order to overcome this, the concept of public key encryption with keyword search (PEKS) was introduced to enable searching for the encrypted keywords without revealing anything about the original email.This research focuses on implementing and integrating both IBE and PEKS with encryption and searching functionalities into an email platform via an email plugin.More precisely, this project is to design an email plugin suite using k-resilient identity-based encryption and k-resilient searchable encryption in order to provide a more secure email platform.The performance analysis shows promising result.
The Implementation of Augmented Reality for WATER Introduction

The use of technology in education is growing rapidly, some of which use augmented reality technology.Augmented reality is one type of technology use not only in education but also in other sectors because it is one technology that is quite useful in facilitating various human activities.However, the reality of the world of education today is that there are still many teachers who have not implemented and innovated the use of technology in learning.This study presents the implementation of augmented reality in wayang learning (WATER).Using augmented reality technology that can create a more real learning atmosphere so that children's interest in loving wayang culture appears.In implementing augmented reality applications, Unity3D and Vuforia tools are supported.This research was conducted by making a prototype of puppet learning media in the classroom.The results obtained, the learning media looks more attractive and the students spend more time with the application to explore the world of the puppet.
A Residual CNN Model for ICD Assignment

International Classification of Diseases (ICD) has been used as a standardized way of classifying a diagnosis or a medical procedure.ICD has also been employed to keep track of illness progression and treatment purposes.However, the assignment methods often require manual input of medical professionals and therefore time consuming and prone to human errors.By automating the assignment of ICD-9 codes to clinical notes we can effectively save time and human resources.In this light, this study proposed a residual convolution neural network leveraging label co-occurrence to measure label correlation and a label attention mechanism to capture label-dependent features.The model was fine-tuned by changing its hyper-parameters which have included dropout probabilities, CNN kernel size and its output size.The empirical findings suggested that the model has outperformed conventional approaches with 93.6% for Micro-AUC, 91.8% for Macro-AUC, 70.0%Micro-F1, and 64.6% for Macro-F1.
Development of Web-Based Personnel Information System on My-HK Unesa

Services in the field of staffing related to proposed promotions and positions, study tasks and study permits, pensions, and data archive management that are conducted manually require energy and a long time.The use of information systems is very necessary to support acceleration, convenience, and comfort in providing services.However, staffing services at the State University of Surabaya are conducted manually.The purpose of this study is to develop a staffing information system on my-HK Unesa based on the web and conduct an effectiveness test.The method used in this study is research and development (R&D) with a system development method, namely waterfall.The result of this study is the My-HK Unesa information system that has successfully run and functioned according to requirement analysis.The effectiveness test has been conducted using the End User Computing Satisfaction (EUCS) method through questionnaires with respondents of employee staff, lecturers, and administrative staff.The results showed that from the aspect of content by 84.58%, accuracy by 87.78%, format by 91.87%, ease of use by 91.04%, and timelines by 88.13%.So, in general, respondents are very satisfied with the development of the My-HK Unesa information system.Thus, the results of the development of the My-HK Unesa information system can be implemented by evaluating and improving to provide effective and efficient services.
QRCode Recognition on Flutter Framework Mobile Application Implemented on Entrance Security System

As the advancement of technologies becomes more progressive and efficient, State Polytechnic of Malang would like to start digitalizing most of the student attendance system by using mobile-based QR code scanning for student identification.They aim to improve their student attendance system to be more effective and versatile than the previous system.A contactless attendance system in offline events is urgently needed in the pandemic.The manual attendance system still uses papers and signatures can support the spread of the virus.The mobile-based QR code attendance system is expected to minimize direct and indirect physical contact between the event organizers and the attendee, thus minimizing the widespread of the virus.Besides, a paperless attendance system can help the environment by decreasing paper consumption, contributing to 42% of the global wood harvest.The previous student attendance system within the campus was still using a standalone, third-party QR code scanner that was connected to a laptop.After the student's QR code had been scanned, the student's data would be displayed instantly on a laptop web page.This cycle is inefficient since it requires two devices, and the QR code scanner cannot instantly display the scanned barcode data.Moreover, the device itself is bulky, thus making it less mobile than smartphones.To overcome those issues, the mobile-based QR code attendance system can be the solution, as it would only utilize one device for scanning and displaying the data instead of two.Furthermore, this system can be used on different occasions and events everywhere, from wedding invitation management to an academic graduation ceremony.Seven out of ten participants agree that this application helps to reduce the possibility of attendance fraud and attendance abuse, two out of ten are still unsure about it, and 1 out of 10 denies the statement.The optimal brightness for the smartphone screen used to display the QR code is between 25% and 50%.Moreover, the optimal distance of the scan for the smartphone screen used to display the QR code is between 10cm to 30cm.
Development of Digital Literacy Performance Items to Improve Digital Electronics Competence of Vocational School Students

This study aims to analyze and synthesize the development of performance items to improve digital literacy instruments using classical Test Theory and Item Response Theory by looking at the fit model index results on digital literacy instruments of Electrical Engineering vocational students.Research products produce a set of digital literacy instruments that can be used as a tool to measure student performance in using digital literacy instruments.This study uses quantitative methods, on digital literacy instruments to measure the performance of Electrical Engineering vocational students.The steps of instrument preparation begin with designing several variables, developing indicators, arranging instrument grids, compiling several indicators, making improvements to some suggestions from experts, validating theories by experts, and refining, and writing instruments consisting of several items and validation theoretically and empirically.The number of performance items is 39 items, and the number of samples is 638 students.The number of items about the ability of digital electronics theory is 40 items.The study produced eight indicators, namely LD1-LD8.Normality testing using Q-plot shows normal results by observing diagonal lines, spreading evenly, and not forming patterns.The X-LD variable produces LD1 (0.052), LD2 (0.761), LD3 (0.701), LD4 (0.693), LD5 (0.688), LD6 (0.744), LD7 (0.621), and LD8 (0.624), so it can be said that the 8 indicators are normal or valid.Variable y-KT has 1 indicator is not normal or Invalid, namely KT1 (0.490 < 0.5) and 3 indicators are valid or normal with the results of KT2 (0.644 > 0.5), KT3 (0. 703 > 0.5) and KT4 (0.748 > 0.5).Cronbach's alpha variable values are X-LD (0.829 > 0.5) and Y-KT (0.679 > 0.5).The value of the path coefficient is 0.092 and the p-value is 0.005 so that the hypothesis testing can be expressed as significant and H1 is accepted.Fit model index on SRMS obtained 0.079 < 0.080, so the data can be expressed better.
The Digitization of Voluntary Disclosure Program

This study aims to understand the perceptions of research informants on the implementation of a digital-based voluntary disclosure program (VDP).This research is qualitative research that uses transcendental phenomenology to analyze the interview results.This study interviewed four research informants who had experience participating in and not participating in VDP as well as an informant who had knowledge of VDP.The results of the study are divided into three perceptions.The first perception is related to VDP fairness, the aspect of fairness does not cover all taxpayers, considering that VDP's goal to increase tax revenue is prioritized on the momentum of post-pandemic economic recovery.The second perception is related to government centered which prioritizes the ease of data collection for tax authorities.The third perception regarding the implementation of digital-based VDP, the digitization of E-Form has not paid attention to the level of readiness of understanding technology and infrastructure for individual taxpayers, so there is a tendency to use the services of other parties.This research contributes practically to both taxpayers and tax authorities.For taxpayers as a consideration in utilizing digital-based tax services and for tax authorities as a reference for future policies.
Research on Curriculum Assessment and Evaluation Method in Blended Learning

In view of the shortcomings of the course assessment and evaluation methods in the traditional teaching mode, this paper carries out the reform of the assessment and evaluation methods based on the advantages of the full record of the online learning process in the blended learning mode, designs the course assessment and evaluation system under the hybrid teaching mode, and puts forward a proposal that the way of companion evaluation of subjective questions can greatly increase the participation of students in learning and promote the improvement of learning effect accordingly.Finally, the functional requirements of the online teaching platform are analyzed according to the evaluation system, which can provide some reference for the curriculum construction and implementation of the online and offline hybrid teaching mode.
The Analysis of Student’s Learning Outcomes Based on OBE Model: Taking An Introduction to Business as an Example

Student's learning outcomes is the driving force in OBE education system, and it needed to be analysed in a scientific approach.This article takes the course An Introduction to Business as an example, and analyses student's learning outcomes under the guidance of OBE model.Through qualitative and qualitative analysis of students' formative and summative assessment scores, the framework of analysing student's' learning outcomes is constructed, which sheds light on how such an analysis approach may be best integrated into the OBE model.