Reviewerly/exHarmony huggingface dataset:
Masked Face Recognition and Liveness Detection Using Deep Learning Technique

Face recognition has been the most successful image processing application in recent times. Most work involving image analysis uses face recognition to automate attendance management systems. Face recognition is an identification process to verify and authenticate the person using their facial features. In this study, an intelligent attendance management system is built to automate the process of attendance. Here, while entering, a person’s image will get captured. The model will detect the face; then the liveness model will verify whether there is any spoofing attack, then the masked detection model will check whether the person has worn the mask or not. In the end, face recognition will extract the facial features. If the person’s features match the database, their attendance will be marked. In the face of the COVID-19 pandemic, wearing a face mask is mandatory for safety measures. The current face recognition system is not able to extract the features properly. The Multi-task Cascaded Convolutional Networks (MTCNN) model detects the face in the proposed method. Then a classification model based on the architecture of MobileNet V2 is used for liveness and mask detection. Then the FaceNet model is used for extracting the facial features. In this study, two different models for the recognition have been built, one for people with masks another one for people without masks.
Robust Adversarial Training for Detection of Adversarial Samples

The advent of machine learning in our lives provides much needed convenience and speed, but this also comes with some unwanted danger. Since the process is getting governed by machine learning models, which are prone to adversarial samples which could lead to catastrophic events like car crashes in autonomous cars, adversarial attacks range from one pixel attack to fast gradient sign method attack. Potential defences against these attacks vary from denoisers to adversarial learning. A system is proposed for robust adversarial learning to protect against real-life danger which could be averted if such systems are implemented and further advanced.
E-Voting using Blockchain Technology

Blockchain is a decentralized, digital, and consensus-based secure information storage system. This article gives an overview of how to use the blockchain to construct a secure and trustworthy electronic voting system. The main goal of this paper is to investigate the most recent state of blockchain-based voting research while attempting to foresee the accompanying potential issues future developments. This paper's technique is as follows: a methodical approach to review and following an overview of the blockchain's core structure and properties in relation. We present a conceptual definition of e-voting. Desired e-voting application based on blockchain symmetrical improvements in asymmetrical cryptography are also important. We gathered and examined a number of research papers from scientific databases that advocated for the use of the blockchain architecture in voting systems. These articles suggested that blockchain-based voting systems may offer alternatives to standard e-voting. The most pressing challenges were divided into five categories: general, integrity, coin-based, privacy, and consensus. As a consequence of this study, it was discovered that blockchain systems can help solve some of the issues that now plague election systems. We are attempting to design a system that respects the essential and most significant aspects of a voting system, such as transparency, secrecy, security, and decentralisation, while also providing the extra benefit of mobility.
Bert-GCN: multi-sensors network prediction

With the application of neural network technologies such as GCN and GRU in sensor networks, the accuracy and robustness of multi-sensor prediction have been greatly improved. GCN effectively uses the spatial characteristics of the sensor network, and GRU effectively uses the temporal characteristics of the sensor network, so the PROPOSED T-GCN model has achieved excellent results. However, there are still shortcomings: i) The prediction is only for a single sensor feature, and multiple features cannot be trained at the same time. ii) Only the connections between sensors are considered, while the connections between multiple features of sensors are ignored. iii) Modeling for multiple features leads to the deepening of the model from 2d to 3D, resulting in slow model training and poor learning effect. To solve the above problems, this paper proposed the Bert-GCN model. Bert pre-training was added on the basis of the original GCN-GRU model to effectively improve the learning effect of multiple features of a single sensor.
THE SEMANTIC CONTENT OF THE GREAT VICTORY IN THE FORMATION OF STUDENT YOUTH VALUES

The article discusses the problem of updating the meaning content of sociological education in Russian universities. The theoretical foundations for the study of meanings were laid down by the classics of this science and developed throughout the 20th century. This article offers recommendations for planning and conducting seminars on the topic "meanings of the Great Victory", the goals of which are the development of the categories "social value", "social meaning", "social symbol", "culture", and "interethnic unity".
University Students' Experiences and Attitudes Toward Online Learning During the Pandemic Covid-19

The COVID-19 pandemic had a significant influence on university education and organization of lecturing.Most universities throughout the world had not been prepared for the new circumstances, and this ultimately affected both the organization and the quality of lectures.Universities were obliged to follow trends and implement new strategies in lecturing in order to support their students where help was most required, and furthermore, in order to maintain the quality of their experiences and education.The objective of this research was to gain insight into how students reacted to the new pandemic situation and whether they are satisfied with online teaching so that universities could use the results to adapt and conduct online teaching in the future.The research found that students considered online learning had affected their efficiency, productivity, and level of motivation in the process of learning.Apart from these adversities regarding online teaching and learning, the survey found that the students' attitude toward the implementation of technology in university education would improve the process of online lecturing and examination and enhance the present situation.
Assessment of the Status of Health and Fitness of Students of Thanh Hoa University of Culture, Sports and Tourism, Vietnam

A study on the current state of health and fitness of students at Thanh Hoa University of Culture, Sports, and Tourism has provided scientific and practical evaluation data according to 3 health contents, physical fitness, and physical education subject scores. The research results have proved that the health and fitness status of students majoring in Culture and Tourism at Thanh Hoa University of Culture, Sports and Tourism is at an average level, ensuring the requirements for studying. ; However, the fitness test results of many male and female students are still weak when compared with the regulations on physical fitness standards of the Ministry of Education and Training and the physical standards of Vietnamese people.
On Controlling Fallback Responses for Grounded Dialogue Generation

Dialogue agents can leverage external textual knowledge to generate responses of a higher quality.To our best knowledge, most existing works on knowledge grounded dialogue settings assume that the user intention is always answerable.Unfortunately, this is impractical as there is no guarantee that the knowledge retrievers could always retrieve the desired knowledge.Therefore, this is crucial to incorporate fallback responses to respond to unanswerable contexts appropriately while responding to the answerable contexts in an informative manner.We propose a novel framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable contexts in an endto-end manner.Since no existing knowledge grounded dialogue dataset considers this aim, we augment the existing dataset with unanswerable contexts to conduct our experiments.Automatic and human evaluation results indicate that naively incorporating fallback responses with controlled text generation still hurts informativeness for answerable context.In contrast, our proposed framework effectively mitigates this problem while still appropriately presenting fallback responses to unanswerable contexts.Such a framework also reduces the extra burden of the additional classifier and the overheads introduced in the previous works, which operates in a pipeline manner. 1
A new intelligent construction management platform

Focusing on the project construction unit and construction process, through "BIM + GIS" and other advanced technologies, real-time, accurate, real and complete monitoring and management of "human, machine, material, method and environment" and other elements and processes, so as to realize the interconnection, collaborative sharing and comprehensive display of post, project and enterprise data, and build a platform based on BIM Technology and with progress as the main line With economy as the core and project as the carrier, all participants cooperate, multi-level linkage, management pre control, and integrate an efficient digital, online and intelligent new construction management platform to ensure the smooth realization of project quality, safety, progress and cost construction objectives.
ABOUT SOME ASPECTS OF STEM EDUCATION

The article deals with the contemporary situation in education sphere both in our country and abroad, describing the modern way of learning by implementing the recent methods of education.It is noted in the 21st century it is essential to provide children with the skills and knowledge they'll need to be successful innovators.The article analyzes the main aspects of a learning strategy STEM which provides students mainly with an interdisciplinary approach to learning.Both the advantageous and disadvantageous issues of this learning approach are revealed in the article.The peculiarities of STEAM learning method have also been emphasized.The article notes the specific practices of applying STEM/STEAM learning in the USA, European Union and Ukraine, specified by educational and professional training programs.Special emphasis is put upon the perspectives of these approaches to education.It is determined that STEM educational program focuses on science, technology, engineering and mathematics.The aim is to help better prepare students in these areas of learning and create practical applications for how these lessons apply to the real world.Lately there has been a growing emphasis on STEAM that is a new educational discipline that engages students not only about the subjects of Science, Technology, Engineering and Math but also the Arts which is the ability to think, discuss, dispute the problematic issues, to encourage and develop inquiry, curiosity, ability to find solutions to a problem, creativity in the finding of the necessary solutions, both practical skills and appreciation for collaborations.It is opposed to traditional models of teaching and aims to bring the disciplines together.In the course of the study, it has been discovered that modern educational system faces lots of challenges and needs to be improved by implementing an interdisciplinary approach to learning such as STEM and SREAM education that promote not only skills such as critical thinking, problem solving, higher-order thinking, but also behavioral competencies such as perseverance, adaptability, cooperation, organization, and responsibility.
Investigating The Polemic of Emergency Remote Teaching for Very Young Learners

Emergency Remote Teaching (ERT) has been implemented in Indonesia at all levels of education since March 2020 to prevent the spread of coronavirus.Immediate changes and adjustments are required to transform face to face learning into online learning.In addition, challenges emerge during this process.To investigate the challenges during the implementation of Emergency Remote Teaching for very young learners, a narrative inquiry research was applied.In-depth interview was held to five respondents consisted of 2 teachers and 3 parents to collect the data.The researcher also observed the online teaching and learning process conducted via WhatsApp group.Then, the data were categorized into two sides, teachers' side and parents' side, since it is undeniable that online learning for very young learners intensively involved not only the teachers but also the parents.From the teachers' side, the challenges dealt with materials preparation, online class management, and learning achievement.From parents' side, the problems comprised the time management and learning environment.This paper recommends that teachers need to continuously develop their professional skills so that they can adapt to the rapid change of education system.
VIVÊNCIAS NA IMPLEMENTAÇÃO DO PRÉ-NATAL MASCULINO EM UMA UNIDADE DE SAÚDE: RELATO DE EXPERIÊNCIA

This is an experience report, carried out between October and December 2021, at the basic health unit (UBS) Enaldo Torres, located in the municipality of Patos, Paraíba, in which the Maguerez Arch method was used to construction of the research, addressing the difficulties of implementing male prenatal care at this UBS.In the care provided by the basic health unit, the absence of parents in prenatal consultations is notorious, which motivated the team at UBS Enaldo Torres to discuss the reasons that justify this absence.It was noticed that most parents were not available to attend appointments with their partners due to work.Another difficulty encountered was the fact that they had a troubled relationship, some partners did not live with the women and did not maintain contact, others were imprisoned.Male prenatal care is an important strategy to encourage men's participation in health services and, consequently, contributes to the expansion of their health.In this sense, consultations allow parents to develop knowledge about their role in relation to paternity and self-care, through health promotion, prevention and diagnosis of diseases.
External Information Security Resource Allocation with the Non-cooperation of Multiple Cities

Abstract The external information security resource allocation method is proposed considering the non-cooperation of multiple cities. In this method, the effects of different influence factors, for example, city size, probability of intrusion by illegal users and propagation probability of one-time intrusion on resource allocation is explored. Through the simulation experiment, the proposed conclusions are conveniently and clearly verified.
Development of Electronic Student Worksheet Problem Based Learning Model Based on Articulate Storyline Application in Class IV SD Negeri 043935 Simpang Katepul School Year 2020/2021

The problem in this research is the unavailability of Electronic Student Worksheet that is able to attract the learning interest of fourth grade elementary school students.The purpose of this study was to determine the development process, feasibility and effectiveness of the Electronic Student Worksheet Problem Based Learning Model Based on the Articulate Storyline Application which was developed on Theme 6 Ideals -My Goals, Sub-theme 1 I and My Dreams, learning 1 Class IV SD Negeri 043935 Simpang Katepul School Year 2020/ 2021, based on the Expert (Validator) and product trial results.This research is development research (R&D) using the Borg and Gall model.The steps in this study are limited to 6 stages, namely: 1) Potential and Problems, 2) Data Collection, 3) Product Design, 4) Design Validation, 5) Design Revision, 6) Product Trial.Validation was carried out by material experts, design experts, educational practitioners and field trials.Based on the results of the validation of the Electronic Student Worksheet seen in the assessment of material experts, media experts and educational practitioner experts, the feasibility percentages were 94.2%, 80% and 75%, respectively.So the Electronic Student Worksheet developed was feasible to be tested.Based on the results of product testing, it can be seen that the value of students before using the Electronic Student Worksheet Problem Based Learning Model Based on the Articulate Storyline Application was 54.3% and after using the Electronic Student Worksheet Problem Based Learning Model Based on the Articulate Storyline Application was 77.6%.The results of students' answers in using the product have an average value above the minimum completeness criteria, which is 70, so it can be categorized as "Very Good".So it can be said that the Electronic Student Worksheet Model Problem Based Learning Based on Articulate Storyline Applications is effectively used in the fourth grade of elementary school.Based on the results of the description above, it can be concluded that the Electronic Student Worksheet Model Problem Based Learning Based on the Articulate Storyline Application which was developed on Theme 6 : My Dreams, Sub-theme 1 : Me and My Dreams, First Learning is suitable for use in the learning process of fourth grade students of SD Negeri 043935 Simpang Katepul School Year 2020/2021.
Colorectal Polyp Segmentation Based on HarDNet-CPS

Colorectal cancer (CRC) is one of the top three cancers in the world, and the disease is initially caused by the deterioration of polyps that are regarded as the predecessor of CRC. Many people die from the disease every year, so clinical specialists pay high attention to the polyp. It is great significant to improve the accurate recognition of polyp areas for clinicians to find the lesion area. However, using current approaches the segmentation of polyp images from a endoscope has not achieved the best effect. This paper proposed a new model which is called Harmonic Densely Connected Network for Colorectal Polyp Segmentation (HarDNet-CPS). The new architecture for the model is based on the backbone of Harmonic Densely Connected Network-68. In the architecture the branches with multi-scale channel attention (MS-CAM) are cleverly embedded to extract features from channel attentions, the pyramid feature fusion technique is proposed to adaptively spatial feature fusion (ASFF) in the decoding area, and then output is obtained through aggregation module. The mean Dice in Kvasir and CVC-ClinicDB data sets both have broken through 0.9 for the first time. HarDNet-CPS has advantages such as good robustness, and high accuracy, revealing the effectiveness of automated polyp detection. It will bring great help to clinical diagnosis and treatment.
Pipeline recognition in side scan sonar image using adaptive network based fuzzy inference system (ANFIS) classifier

In applications of Autonomous Underwater Vehicle Navigation the automatic detection of underwater objects in sonar images is the most important part. A process of analyzing multi-beam sonar images for localization and underwater object detection is a new framework presented in this research. The main idea of the project is based on Pipeline Detection. Morphological filtration method is used for the more efficient form of Denoising and segmentation of the image. Image quality assessment features and image quality enhancement features such as GMSD, PSNR, SSIM, MSE, along with Bicubic interpolation and super resolution is also implemented for better image classification. The image is been preprocessed with filtration technique but also with image segmentation process such as Fuzzy-C means segmentation and further been sent to Adaptive Neuro Fuzzy Interference System (ANFIS). Classification which has good generalisation ability to accurately spot pipeline. The proposed framework which can figure out the position accurately and detect objects is been experimented by exploiting the image processing technique.
Development of Interface for Visualizing the Center of Gravity of Caregiver's Body Segment

Today, the population is aging not only in Japan but also on a global scale. Along with this, the number of people who need long- term care is increasing. Nurses need skills as caregivers, and in the future, the time will come when many people not limited to nurses will also need those skills. In the current textbooks for nurses, the movements that should be conscious of in nursing care movements are indicated by arrows. However, these arrows are drawn by the intuition and experience of the textbook author and are not based on kinematic data. In this study, we develop an interface for visualizing kinematic data focusing on caregiver’s movement. For this purpose, we develop a system calculating centers of gravity of three body segments, trunk, right foot and left foot. Three-dimensional coordinates and trajectories of centers of gravity are shown on front, side and top view planes. The interface makes the movement of a skilled caregiver visually easy to understand.
Distinguishing In-Groups and Onlookers by Language Use

Inferring group membership of social media users is of high interest in many domains.Group membership is typically inferred via network interactions with other members, or by the usage of in-group language.However, network information is incomplete when users or groups move between platforms, and ingroup keywords lose significance as public discussion about a group increases.Similarly, using keywords to filter content and users can fail to distinguish between the various groups that discuss a topic-perhaps confounding research on public opinion and narrative trends.We present a classifier intended to distinguish members of groups from users discussing a group based on contextual usage of keywords.We demonstrate the classifier on a sample of community pairs from Reddit and focus on results related to the COVID-19 pandemic.
Edge Detection and Graph Neural Networks to Classify Mammograms: A Case Study With A Dataset from Vietnamese Patients

Mammograms are breast X-ray images and they are used by doctors, among other purposes, as an effective means of detecting breast cancer. Screening mammography is crucial since it allows doctors to understand better the situation and have suitable intervention. The classification of medical modalities is a prerequisite for development of computer-aided diagnosis tools in healthcare, and various techniques have been proposed to automatically classify from mammography images. Though there have been several tools developed, they have been mostly validated with data collected from Western women. Based on our initial investigations, breast anatomy in Vietnamese women differs from that of Western women, due to denser breast tissue. In this paper, to tackle the issue of detecting mammograms, we propose MammoGNN -- a novel approach using the synergy between image processing techniques and graph neural networks. To validate the conceived tool, we curated a mammogram dataset from 2,351 Vietnamese women. MammoGNN obtains a maximum accuracy of 100% on independent and shuffle test sets for both classification of BI-RADS scores and breast density types. The experimental results also demonstrate that our proposed approach outperforms different baselines. We anticipate that the proposed approach can be deployed as a non-invasive pre-screening tool to assist doctors in performing their diagnosis activities.