Reviewerly/exHarmony huggingface dataset:
Psychological and pedagogical problems of the implementation of inclusive education and the directions of their solution

The inclusive education of children requires teachers to have an adequate attitude to a special child in a children's team, the ability to provide him with psychological and pedagogical support and reveal the creative potential of each child. In this regard, the question arises about the need for high-quality training of specialists in the field of pedagogy and psychology for inclusive educational institutions. The qualitative realization of the educational needs of each child, regardless of his physical, mental and intellectual abilities, depends on the psychological and pedagogical competence of specialists engaged in the educational process. And first of all, an important component of the professional competence of specialists here is psychological readiness for pedagogical work with children in an inclusive education. The purpose of the work is to investigate the psychological and pedagogical problems of the implementation of inclusive education and the directions of their solution.
Scaling Up Instance Annotation via Label Propagation

Manually annotating object segmentation masks is very time-consuming. While interactive segmentation methods offer a more efficient alternative, they become unaffordable at a large scale because the cost grows linearly with the number of annotated masks. In this paper, we propose a highly efficient annotation scheme for building large datasets with object segmentation masks. At a large scale, images contain many object instances with similar appearance. We exploit these similarities by using hierarchical clustering on mask predictions made by a segmentation model. We propose a scheme that efficiently searches through the hierarchy of clusters and selects which clusters to annotate. Humans manually verify only a few masks per cluster, and the labels are propagated to the whole cluster. Through a large-scale experiment to populate 1M unlabeled images with object segmentation masks for 80 object classes, we show that (1) we obtain 1M object segmentation masks with an total annotation time of only 290 hours; (2) we reduce annotation time by 76x compared to manual annotation; (3) the segmentation quality of our masks is on par with those from manually annotated datasets. Code, data, and models are available online.
Data analysis for flood monitoring using Machine Learning and Internet of Things

Flood is one of the most common natural disasters that are causing damages tolife and the country’s economy. This study focuses on Flood monitoring using DataAnalysis &Internet of Things (IoT). This design incorporates certain important elementslike Water level flowing in the dam, humidity, and the water flow based on the rainfallrate. These parameters are closely monitored for predicting and releasing water from thedam at a moderate rate in advance to avoid flood issues faced by the public who are livingnear the dams. The sensor aggregates and collects the data from various sources that arecontrolled by the Wireless Sensor Network (WSN) from the controller. The sensorinformation can be analyzed and the system creates an alert to the public communitythrough the siren and alert message using a smart communication device. The design ofthe automated system is to be optimized for the control of the shutter position. The failureof the opening and closing of gates has resulted in water scarcity and its wastage. Manualgate results in human errors. Hence this system is controlled by the machine learningmechanism to follow the servo motors and the electronic control to monitor the flow ofwater according to the water level at the given time. Thereafter, the data stored in the cloudis further used for the prediction. The system can be implemented through mobile/webapplications.
A Broad Study on the Transferability of Visual Representations With Contrastive Learning

Tremendous progress has been made in visual representation learning, notably with the recent success of self-supervised contrastive learning methods. Supervised contrastive learning has also been shown to outperform its cross-entropy counterparts by leveraging labels for choosing where to contrast. However, there has been little work to explore the transfer capability of contrastive learning to a different domain. In this paper, we conduct a comprehensive study on the transferability of learned representations of different contrastive approaches for linear evaluation, full-network transfer, and few-shot recognition on 12 downstream datasets from different domains, and object detection tasks on MSCOCO and VOC0712. The results show that the contrastive approaches learn representations that are easily transferable to a different downstream task. We further observe that the joint objective of self-supervised contrastive loss with cross-entropy/supervised-contrastive loss leads to better transferability of these models over their supervised counterparts. Our analysis reveals that the representations learned from the contrastive approaches contain more low/mid-level semantics than cross-entropy models, which enables them to quickly adapt to a new task. Our codes and models will be publicly available to facilitate future research on transferability of visual representations.
Pembuatan video tutorial pengetahuan produk aplikasi SilviPlus dengan teknik motion graphic di PT KST

Innovation and technology have brought benefits in everyday life that have an impact on all aspects of people's lives, including how to obtain and convey information. Multimedia is one of the media for delivering information that is often used today. This time the author focuses on the SilviPlus application which is a Point of Sale application designed according to the needs of MSMEs. T he SilviPlus application strongly supports MSME business players in Indonesia to carry out digital transformation in terms of sales and bookkeeping transactions, so that changes will be faster from cash transactions to cashless transactions. This study aims to provide education about the SilviPlus application to business users so that they can understand the use of the application easily. The results of this study are 6 video motion graphic products made with Adobe Illustrator, Adobe After Effects. After testing the product, it was found that 77% stated very good for the products produced, as many as 22% said good, while the other 1% stated enough for the products produced.
LibGuides: DBA - Management and Instruction: Databases

This LibGuide is intended to provide a library research home base for students enrolled in the Benson School of Business DBA program for Management and Instruction.
LEARNING AMID COVID-19: MEDICAL STUDENTS INVOLVEMENT IN PATIENT EDUCATION &amp; DISEASE AWARENESS

The COVID-19 pandemic has nurtured meaningful transformations in educational contexts; even highly traditional contexts were open about transforming approaches and methodologies. Medical professionals' education relies heavily on presence-based interaction with patients, for example, to develop community activities such as patient education, which needed to continue despite the school lockdown. As part of an intervention, medical students designed a patient education and disease awareness campaign through social media. The objective of this study was to assess the transformation of patient education and disease awareness strategies to the new remote format. The methodology considered a quantitative approach with a descriptive, and crosssectional design. The sample consisted of 34 medical students enrolled in an endocrine diseases course. Each team designed the material for the campaign based on the literature research they performed. Three tutors evaluated each team using a 5-point Likert scale addressing: disease, posts, survey, and overall assessment. Analysis of results included mean, standard deviation, and linear regression. With a correlation coefficient of 0.745 and a standard error of 0.21, the study found a high correlation between variables for each team. The results show that the transformation of this educational experience was key to offer an engaging educational learning experience for medical students. The campaigns held on social media platforms received a favorable response, as the engagement through comments and likes from their followers show, which helped on tunning the contents to relate closely to the specific audience. Keywords: higher education, educational innovation, professional education, COVID-19 pandemic adaptations, patient education
Model of realization of personalized learning of students of higher education institution

In the article, the author considers the organization of personalized learning of students of higher education by designing a pedagogical model of education. Personalized learning improves the educational process, as the student can build their own educational trajectory according to their individual needs, interests, preferences, and abilities. The proposed pedagogical model reveals the structure and content of personalized education of higher education institutions, the central figure of which is the student, his educational needs, interests, and abilities. We have identified the purpose of personalized learning, the principles and main stages that reflect the dynamics of its operation, as well as the pedagogical conditions for effective implementation of the proposed model. We have identified the basic principles of personalized learning. These are the principle of consciousness and activity of the student, accessibility and comprehensibility of the content of education, flexibility and adequacy of the educational process, the principle of consistency and dosage of educational content, social participation of participants in the educational process, control and self-control. The implementation of the model should be carried out in stages. These processes include the processes of initiation, diagnosis, and design, integration, with the latter process occurring cyclically. Pedagogical conditions are the main condition for the effectiveness of personalized learning. In the process of implementing personalized learning, it is advisable to adhere to the normative, socio-psychological, and organizational-pedagogical conditions of the educational process. The development of the model made it possible to identify certain characteristics of personalized learning. In particular, it allows to significantly increase the motivation to learn, to build an individual educational trajectory, to adapt the learning process in accordance with the student's interactions and through the mechanisms of electronic environment systems.
Segmentation and Other Image Operations

Computational methods for the analysis of medical images is an active area of research. Tasks include image segmentation and registration. Advances in machine learning, including techniques such as deep learning, are being applied to quantitative imaging and in clinical decision support systems. Image segmentation, or the task of delineating a region of interest, is a common task with many applications in radiology, oncology, and radiation therapy. Image registration to establish correspondences between pairs or sets of images is fundamental to multi-modal image fusion, longitudinal monitoring of patients, and population studies. This chapter reviews example applications for these image operations and considers technical approaches to achieve these tasks.
Improved Fuzzy Modeling of Thyroid Disease Detection using Interval Type 2 Fuzzy Techniques

Fuzzy Systems are the managers for the modeling environment uncertainty for real time decision making. Type 1 fuzzy systems are much interpretable but less accurate than the type 2 and Interval Type 2 Fuzzy Systems (IT2FS). The paper introduces an experimental analysis to address the interpretability quantification and accuracy measurement in all types of fuzzy implementations. The experiment is carried out on the Thyroid dataset which leads to predict the level of Thyroid in the patients.
A Classification method of Fake News based on Ensemble Learning

With1 the advent of the next generation of computing, news is available in various environments anytime, anywhere. This is a positive aspect of rapid information sharing, but information with unclear sources was produced in a news format and quickly spread to the public through social network services. The concept of fake news, which began to draw attention as of the 2016 U.S. presidential election, is now causing many economic and social damage around the world. As a result, IT and the industry are paying attention to classifying fake news and active research is ongoing. Therefore, identifying fake news and obtaining accurate information is a very important area in the information age. In this paper, after analyzing the Fake News Dataset of the ISOT, an Information Security and Object Technology, two methods of weighting were used. Based on this, Soft Voting Classifier, an ensemble method that showed the highest performance when using TF-IDF values as weight, is proposed as a fake news classification model.
Interesting Places Finder Mobile Application

Malaysia is one of the countries that is known for its beautiful places. Tourists who come to Malaysia will always be attracted to their interesting places. There are many beautiful places that can be found in Malaysia. In this project, an effective way has been explored to meet the demand of interesting places by developing interesting places finder mobile application. The main objective of this project is to design a mobile application finder for interesting places that eases the users. Moreover, the objective of this project is also to develop an interesting places finder mobile application that enables users to find the type of places that they want easily by using Android Studio software instead of using maps. Mobile application for Android OS will be developed. The methodology that has been used to develop this project is Agile Development Methodology. Survey questions are one of the sources data to get users’ requirements. User interfaces will be designed and prototype will be developed. Testing will be conducted to the targeted users to know users’ opinion regarding interesting places finder mobile application. The last part of this project, the results and recommendations will be shared for future improvement.
DIGITAL TRANSFORMATION IN ACADEMIC INSTITUTIONS: ELECTRONIC DOCUMENT FLOW BASED ON LOW-CODE TECHNOLOGIES

A significant increase in information flows in modern educational organizations, an increase in the dynamics of development of higher educational institutions requires the introduction and more effective use of information and telecommunication technologies both in the educational process and in the management activities of higher educational institutions. In essence, we are talking about the digital transformation of the activities of academic institutions. Digital transformation is designed to optimize all business processes for healers and, in particular, workflow processes, which will reduce the number of documents created, reduce the time it takes to find documentary information necessary for employees’ activities, and increase the effectiveness of control of management decisions. These issues take on particular weight in the context of distance learning during a pandemic. The article deals with the issues of modeling business processes of electronic document management for the selection, coordination and approval of topics and scientific supervisors in the framework of the design of a diploma/course in higher education institutions during online training. The practical implementation of these business processes and the corresponding document flow is carried out on the basis of low-code technology. As a tool for implementing the software, it is proposed to use the low-code Microsoft Power Platform, which includes the following modules: 1) Power Apps – for quick creation of mobile applications and portals; 2) Power Automate – module of automation of business processes and routine tasks; 3) Power BI – allows you to collect, process and visualize data from hundreds of data sources; 4) Power Virtual Agent – designed to create chatbots. All of these modules are part of the cloud platform Microsoft 365.
The Implementation of Describing Picture Strategy in Improving Students’ Speaking Skill

The purpose of this research was to know whether or not describing picture strategy can improve students‟ speaking skill at the seventh grade of SMP Anugerah Tanjungpinang. The method of this research was Classroom Action Research (CAR). There were two instrument used to collect the data, test and observation checklist. The subject of this research was seventh grade student of SMP Anugerah Tanjungpinang consist of 8 students. The researcher started with pre-cycle by giving the students pre-test mean score in pre-test was 39.3 and no student passed the criteria. After finished with pre-cycle, the researcher continued to apply CAR started with cycle one. In this cycle, the researcher taught the student about describing picture strategy. The mean score of the students‟ posttest one was 70.6 and 62.50% students has passed criteria. There was huge improvement in this cycle one but there is no significant, and the researcher continue to the second cycle. In cycle two, the researcher did the same like cycle and the students‟ mean score was 86.8 and 87.50% students passed the criteria in percentages. The researcher realized that students‟ improvement had achieved researcher‟s target of improvement so it can be concluded that implementation of describing picture strategy was succeed to improve students‟ speaking skill at the seventh grade students of SMP Anugerah Tanjungpinang
Boosting Monocular Depth Estimation With Lightweight 3D Point Fusion

In this paper, we propose enhancing monocular depth estimation by adding 3D points as depth guidance. Unlike existing depth completion methods, our approach performs well on extremely sparse and unevenly distributed point clouds, which makes it agnostic to the source of the 3D points. We achieve this by introducing a novel multi-scale 3D point fusion network that is both lightweight and efficient. We demonstrate its versatility on two different depth estimation problems where the 3D points have been acquired with conventional structure-from-motion and LiDAR. In both cases, our network performs on par with state-of-the-art depth completion methods and achieves significantly higher accuracy when only a small number of points is used while being more compact in terms of the number of parameters. We show that our method outperforms some contemporary deep learning based multi-view stereo and structure-from-motion methods both in accuracy and in compactness.
Pose-attention: a novel baseline for person re-identification

This paper proposes a novel baseline for deep person ReID methods by introducing human pose-based attention mechanism. Benefiting from deep convolutional network, there has been great progress of person re-identification (ReID) in recent years, which aims at retrieving the same person identities from images captured by different cameras. Most of existing methods focus on designing complex network structures to achieve higher scores on public datasets, but few works pay attention to baseline design. A strong baseline is crucial in experiments and could make the elaborated proposed methods more convincing. The present study makes use of a pre-trained human pose estimator to extract human key-point information. Then, we propose a novel manner to fuse pose information with global feature from Resnet50, which could lead the network concentrate more on discriminative key-point feature areas. Our work could achieve 94.8% rank-1 accuracy & 87.4% mean average precision (mAP) on Market1501, and outperform all other existing baselines that only use Resnet50 to our best knowledge. What’s more, experiment results also suggest that with the help of pose information, our work could naturally be robust against misalignment and occlusion problems.
ADMISSIBILITY OF COMPUTER EVIDENCE IN ISLAMIC LAWAND COMMON LAW: A PRELIMINARYANALYSIS

Computer technology and its various applications have come to pervade all walks of life — be it social media, economy, higher studies, e-commerce etc., Common law principles on computer evidence are going through rapid change due to change in technologies and increasing use of electronic evidence in court. Islamic law also lays down very strong evidential principles for electronic evidence. Electronic evidence is treated as documentary and circumstantial evidence. In both the cases (when it is documentary or circumstantial evidence), it is admissible in civil cases. But its use is limited in criminal cases. That is, ×udEd and QiIEI cases cannot be decided solely on the basis of electronic evidence, unless corroborated by other evidences. Islamic law does not provide rules for electronic evidence, but it does provide important general principles, that can be used to test the standards advocated by modern law. The present research shall explore the principles of Islamic law for electronic evidence in the areas of documentary (al-KitEbah) and circumstantial evidence (QarEnah). Then KitEbah or QarEnah, are discussed to ascertain standards of SharE‘ah in civil and criminal cases.
한국어 온라인 수업에서 상호작용 촉진 방안 -협동학습에서 상호의존성 요인을 중심으로-

The purpose of this study is to provide a qualitative analysis of interdependence factors based on cooperative learning for Chinese students who have not actively participated class interaction, and to derive factors to promote interaction in the online learning environment. The method of the study analyzed problems that hinder interaction of foreign students through the experience log related to colloquial interactions in the ZOOM online learning environment of five Chinese students, and derived factors that should be considered to promote interaction in online classes centered on the interdependence of cooperative learning. As a result, items that could promote Korean interaction could be categorized into group communication interdependence, psychological environment interdependence, discourse context interdependence, and sub-items that facilitate interaction among group members, resource-role interaction, learning community formation, Korean speaking anxiety, Korean ability to performance, and participation differences. The bottom line is to design a joint class for learners to actively participate in the Korean language online class designed for Chinese students, to guide foreign learners, to provide background knowledge or key information related to major subjects, to understand new class culture, and to provide emotional support. (Jillye Middle School)
ELECTRONIC PANIC ALARM SYSTEM

Health issues are very common nowadays due to our lifestyle.In some of the conditions we can't even help ourselves in that situation like a panic attack or something.In that situation if we need help we need a more easier way to find help, this device does the same for us.In this paper we will discuss all about the device, how it works, what are the uses of it etc.