Reviewerly/exHarmony huggingface dataset:
MEDICAL FACE MASK DETECTION USING CNN

The Transmission of Corona virus disease is affected world-wide. Around 126 million peoples are infected with the Covid 19 virus and it caused 2.9 million deaths. There is no special treatments against the virus, wearing face mask has become an effective method to prevent the transmission of disease. In this situation face mask wearing is mandatory for one and each. There is not any information about the automatic detection of face mask wearing conditions. In our paper we proposed a face mask detection using CNN and deep learning technologies. Our proposed algorithm contains several steps that are 1. Import tensor flow, 2.Import keras, 3.Create model, 4.Create hidden layers, 5. Create output layers, 6. Compile 7.Fit and Set. Our method proposes a medical face mask wearing detector with CNN. The input image is tested with our trained ML model and results were the image is masked or not. The door opened for those who are masked. And it will be closed for whom that did not wear mask properly.
A Study on Professional Recognition and Teaching Skills of Pre-Service Childhood Teachers According to the Level of Teaching Efficacy

본 연구는 교수효능감 수준에 따른 에비유아교사의 전문성인식과 교수기술 차이를 살펴보고자 한다. 이를 위해 G, J 지역에 소재한 유아교육과에 재학 중인 예비유아교사 113명을 대상으로 조사하였다. 수집된 자료는 one-way ANOVA를 실시하였으며 집단 간의 차이를 알아보기 위해 Scheffe 사후검증을 실시하였다. 연구결과 첫째, 교수효능감 수준에 따라 예비유아교사의 전문성인식과 하위요인인 전문적지식, 사회봉사, 직업윤리, 사회경제적지위에서 통계적으로 유의한 차이를 나타냈다. 둘째, 교수효능감 수준에 따라 예비유아교사의 교수기술과 하위요인인 수업계획, 수업실행, 수업평가에서 통계적으로 유의한 차이를 나타냈다. 이러한 결과는 예비유아교사의 교수효능감 수준을 향상시킬 수 있는 다양한 방안을 모색하고, 예비유아교사에게 필요한 전문성 역량을 증진시키기 위한 기초자료가 될 것이다.The purpose of this study is to examine the differences in professional recognition and teaching skills of pre-service early childhood teachers according to the level of teaching efficacy. To this end, 113 prospective early childhood teachers attending the Early Childhood Education Department located in the G and J were surveyed. The collected data was analyzed by one-way ANOVA and Scheffe post-test analysis. As a result of the study, first, according to the level of teaching efficacy, there were statistically significant differences in the professional knowledge of pre-school teachers and the sub-factors of professional knowledge, social service, vocational ethics, and socioeconomic status. Second, according to the level of teaching efficacy, there were statistically significant differences in teaching skills and sub-factors such as instruction plans, instructional execution, and instruction evaluations. These results will serve as basic data to explore various ways to improve the level of teaching efficacy of pre-service early childhood teachers and to enhance the professional competency required for pre-service early childhood teachers.
Improved Exemplar based Image Inpainting for Partial Instance Occlusion Handling with K-means Clustering and YCbCr Color Space

The images acquired in real time outdoor environment are often subject to uneven illumination conditions, cloudy weather, lighting conditions.The instances of partial occlusions deteriorate the background modeling of such scenes.Varying illumination outdoor scene set of images with partial occlusions are addressed through this investigative work.There is a need for a restoration method that finds improved subjective perception and execution time.The proposed work focuses on novel amended exemplar model to improve the subjective perception.The exemplar inpainting method is improved through the color quantization with K-means clustering approach in YCbCr color space.Experimental validations and proposed method results show better improvement in qualitative and objective measures than existing methods.The average "Peak Signal to Noise Ratio (PSNR)" as 28.2869 and "Structural SIMilarity index (SSIM)" as 0.9759 of the proposed method has shown better results respectively both visually and with a tradeoff in time.
지속가능경영을 위한 ISO 요구사항 기반 정량적 협력업체 평가모델 구축 _반도체 공정에 한하여

The government announced that it would ask the contractors not only the supplier but also the contractors to take the same responsibilities if they did not observe industrial accident safety measures from 2019. The semiconductor manufacturing process belongs to the representative disaster industry group in which the facility is directly located inside a closed space called clean room. According to previous studies, the semiconductor industry group used checklists for safety management of their suppliers. This study has developed a model for assessing suppliers by constructing a quantitative checklist item through the risk assessment methodology, laws and regulations. The evaluation model of the supplier set up through this study becomes the safety management standard in the semiconductor industry .Furthermore, it is applied to the partner companies in the operation of ISO 14001, 45001, I would like to apply it as a measure of performance management for CSR (Corporate Social Responsibility).
Continuous-time Model-based Reinforcement Learning

Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in continuous-time. To avoid time-discretization approximation of the underlying process, we propose a continuous-time MBRL framework based on a novel actor-critic method. Our approach also infers the unknown state evolution differentials with Bayesian neural ordinary differential equations (ODE) to account for epistemic uncertainty. We implement and test our method on a new ODE-RL suite that explicitly solves continuous-time control systems. Our experiments illustrate that the model is robust against irregular and noisy data, is sample-efficient, and can solve control problems which pose challenges to discrete-time MBRL methods.
USING VIDEO BASED COMPUTER MEDIATED COMMUNICATION IN TEACHING READING

The use of computer/ laptop as a medium of communication is a necessity to fulfill the needs of learning during pandemic to attain learning objectives. The present article aims at showing the implementation of teaching reading though computer mediated communication (CMC), particularly in using video as learning medium to assist students in understanding English text well and to interact in group actively in online learning. The subject of research is 2nd semester students class 01 who are 17 students. The type of research is classroom action research. The methods of collecting data are observation and a questionnaire. In analyzing the data, the researchers used interactive analysis that comprises data display, data reduction, and drawing conclusion. Research findings show that the use of video in CMC assists the students to understand the text being learnt properly and they interacted with their friends to discuss the materials of learning and assignments. The conclusion of the present article is that the use of video in CMC is a solution of learning in the pandemic situation to give visualization of the text being learnt.Keywords: computer mediated communication, teaching reading
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)

We prove that the $\alpha$-expansion algorithm for MAP inference always returns a globally optimal assignment for Markov Random Fields with Potts pairwise potentials, with a catch: the returned assignment is only guaranteed to be optimal for an instance within a small perturbation of the original problem instance. In other words, all local minima with respect to expansion moves are global minima to slightly perturbed versions of the problem. On real-world instances, MAP assignments of small perturbations of the problem should be very similar to the MAP assignment(s) of the original problem instance. We design an algorithm that can certify whether this is the case in practice. On several MAP inference problem instances from computer vision, this algorithm certifies that MAP solutions to all of these perturbations are very close to solutions of the original instance. These results taken together give a cohesive explanation for the good performance of graph cuts algorithms in practice. Every local expansion minimum is a global minimum in a small perturbation of the problem, and all of these global minima are close to the original solution.
Treadmill Assisted Gait Spoofing (TAGS)

In this work, we examine the impact of Treadmill Assisted Gait Spoofing on Wearable Sensor-based Gait Authentication (WSGait). We consider more realistic implementation and deployment scenarios than the previous study, which focused only on the accelerometer sensor and a fixed set of features. Specifically, we consider the situations in which the implementation of WSGait could be using one or more sensors embedded into modern smartphones. In addition, it could be using different sets of features or different classification algorithms, or both. Despite the use of a variety of sensors, feature sets (ranked by mutual information), and six different classification algorithms, Treadmill Assisted Gait Spoofing was able to increase the average false accept rate from 4% to 26%. Such a considerable increase in the average false accept rate, especially under the stringent implementation and deployment scenarios considered in this study, calls for a further investigation into the design of evaluations of WSGait before its deployment for public use.
Optimisation of the nozzle diameter of a nebuliser for salbutamol

In medicine, a nebuliser is an equipment used to achieve medication in the form of spray breathe into lungs. The general methodological principle of nebulisers is to use oxygen or compressed air or ultrasonic powder, as means to breakup medicinal solutions into smaller mister dews, for direct breath from the mouthpiece of the equipment. The utmost normally used nebulisers are jet nebulisers, which is also known as 'atomisers'. Jet nebulisers are associated with compressors that support compressed air to flow at maximum velocity over a liquefied medication to turn into a mister, which is then breathed in by the patient. The objective of this study is to optimise the jet nebuliser in order to deliver medicine in minor sufficient dews to attain a satisfactory effectiveness of medicated aerosol to reach the lungs. This study concentrates on design optimisation of the nozzle by analysis of flow parameters, evaluation of the path of the fluid flow and determination of the nozzle diameter and flow pressure.
LibGuides: University of Vaasa writing guidelines: Frequently asked Questions

The guidelines contain instructions about the layout and contents of theses and other written assignments as well as instructions on how to refer to source literature and other material used in the work.
Towards a Logic of Value and Disagreement via Imprecise Measures

After putting forward a formal account of value disagreement via imprecise measures, I develop a logic of value attribution and of (dis)agreement based on (exact) truthmaker semantics.
A Survey of Data Driven Methodologies for Mitigating Cyber Attack in Online Environment

A large number of users use social networking as a platform for sharing their official and personal information. A spammer will utilize these social platforms for their benefit by flooding malicious links, unwanted information and others. In social networking platform, detecting a spammer is a critical and challenging tasks. For the detection and defending of cyber-attacks analysis of social and internet traffic is fundamental job. Automated approaches that use machine learning are replacing traditional approaches for detection of spammers. This revolution has speeded up by the large datasets that takes the help machine-learning models which gives exceptional performance. In data-driven prototype environment, a review on cyber traffic in social networks and Internet is presented by considering the common parameters like correlation, collective indication, similarity. This work also gives an analysis on classification of network applications or network host and Tweets or users by sharing the goals of security. This article also gives a new methodology of research for data-driven cyber security and its application in social network and Internet traffic study.
Performance Efficiency: Meet the Demand Spikes

Scaling up or down on demand to meet changing performance requirements is one of the major drivers of cloud adoption. This is in stark contrast to the on-premises approach where the planning has to be done up front to meet peak performance demands. As we discussed in Chapter 2, over-provisioning is an anti-pattern that should be avoided in the cloud to optimize the costs. How do you strike a balance between the performance demands and cost optimization? This chapter will explore the paradigms for designing environments to meet varying performance demands, while ensuring you stay within the budgeted cloud usage.
INCREASING THE EFFICIENCY OF TEACHING AT A SCHOOL FOR CHILDREN WITH HEARING DISABILITIES BY CONDUCTING INTEGRATED CLASSES OF PHYSICS AND INFORMATICS

This article describes the results of experimental research work on studying the impact of integrative approach to the quality of teaching children with hearing disabilities. Examples of establishing interdisciplinary relationships with various academic disciplines from the personal practice of the doctoral student are given and their methodological significance are substantiated. The process of integrating computer science lessons with other academic disciplines, combining practical orientation, new technologies, and the establishment of interdisciplinary connections, is substantiated. It is argued that this approach is justified as an interesting and promising area of pedagogical activity in a special school. The methodology of increasing the cognitive activity of students with the help of computer modeling of physical processes and phenomena is given, the efficiency of applying such methodology at a special school is determined. Options of applying information technologies at integrated classes are considered.
Deep Learning vs. Super Pixel Classification for Breast Masses Segmentation

Computer-assisted diagnosis of breast mass detection and segmentation is becoming more and more of a necessity given the exponential growth of mammograms performed each year. However, the identification of masses on mammography images is not a trivial task, especially for dense breasts. During the last decade, several works have contributed to the improvement of the mammographic masses segmentation; nevertheless, the emergence of deep learning (DL) caused a huge success in image processing due to its performance and its ability to generalize across diverse data. All these observations put scientists and researchers to wonder whether traditional learning methods are still relevant in the segmentation of this type of biomedical images. In this chapter, a comparative study of different segmentation techniques is carried out on INBREAST database, between DL approaches with the Convolutional Neural Networks and a traditional machine learning technique by the super-pixel-based segmentation. The experimental segmentation results of the most common DL networks, namely, Unet, FPN and LinkNet, show that the Unet network outperforms on the task of breast mass segmentation in comparison with FPN and LinkNet. Therefore, the traditional super-pixel-based segmentation method has shown less efficient breast mass segmentation results. This work has shown that DL methods present promising performances in comparison with traditional methods.
A Computer Memory Based on Cold Atoms and Light

Merging ideas from neuroscience, machine learning, and quantum technology, researchers propose a new information-storage device.
SISTEM INFORMASI PENDAFTARAN IMUNISASI POSYANDU ANGGREK DI DESA TANJUNG MEDAN BERBASIS WEB

In this era of globalization, computers are needed in all aspects of life, including in the world of health. Just like the development of information systems. Exactly whether or not information depends on the information obtained. If the information produced is incomplete or incorrect, then it can be said that decision making will become incorrect and misdirected. Posyandu as a public health service, especially for baby's health, still has many weaknesses. In addition to requiring a long time in registration, the accuracy is also less acceptable. Registration of immunization is one of the initial processes before immunization. This immunization is one of the programs of the Posyandu, which is a way to prevent children from getting defects or deadly diseases in a cost effective manner. With the support of existing information technology, data processing currently carried out can be replaced with a web-based system. Data collection methods used are observation, interview, and literature study. The programming used is website programming using MySQL and PHP databases. This study produced a web-based infant immunization registration information system for the Melati Posyandu Labuhanbatu Selatan Red Water Village. Hopefully the existence of this information system can improve the work process of Posyandu cadres in handling the registration of babies who will be immunized.
Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.
PENINGKATAN HASIL BELAJAR MENULIS RESENSI BUKU DENGAN MODEL COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC)

The ability to write a student review of SMP Negeri 244 is low. Learners do not understand the writing of reviews. The purpose of research to improve learning outcomes to write reviews. The writer applies cooperative integrated reading and composition (CIRC) learning model. This model is an integrated composition of reading and writing skills. This is Classroom Action Research (PTK). The stages of each cycle are planning, execution / action, observation, and reflection. Each cycle is held in two meetings. The subjects students class 9F SMP Negeri 244 Jakarta odd semester of 2018/2019 as many as 35 people. The study was conducted from August to November 2018. After applying cooperative integrated reading and composition (CIRC) learning model, the learners' learning achievement was 79,00 (83% mastery). In the second cycle the average score of learning achievement is 88.00 (100% mastery). Research with CIRC can improve the ability to write book reviews
Tools for course preparation in metric competence format

Currently, the determination of the quality of training is the most important task. In our work, we use the definition of quality monitoring according to ABC-abilities, which are defined by N.K.Nuriyev and L.N. Zhurbenko. According to them, competence in any field is invariantly supported by the triad of abilities -A, B, C- of a certain level of development, i.e. ABC abilities and knowledge, as auxiliary means, where A, B, C are formalization, constructive and performing abilities, respectively. The course editor in the form of a written application is developed in the form of a web-based student training program that allows to evaluate and visualize the level of ABC - a student's abilities. Based on these results, a decision is made on the trajectory of further education.
Preserving EMR Records Using Blockchain

In recent years, the technological advancements has been booming but in the medical field the structure being used to store patient data is still outdated. Patients carry hard copy medical report with them when going for an appointment. When people usually talk about blockchain, they usually think about financial, banking and crypto markets, but we can also use blockchain technology for decentralized healthcare data storage. Blockchain used in healthcare is called as heath blockchain.EMRs crave attention and work to match up with the technological advancements in ways that would make sharing of EMR between. In today’sworld EMRs can be stored and retrieved safely using Block Chain-based implementation which store data in blocks. These blocks are connected to each other using cryptography technology where each block is unique and it stored the data in a secure manner. Thus, securing the process of storing and retrieving EMRs.
특허정보를 이용한 국내 개발 신약의 기술혁신 특성 분석

BACKGROUNDS For efficient new drug development, the importance of open innovation is increasing in the pharmaceutical industry. This study aims to evaluate the quality of innovation by analyzing the source of innovation through case analysis of new drugs developed in Korea, and analyzing the time gap between the first-in-class drugs and domestically developed drugs. METHODS Product patents were identified using the K-Orange Book presented by the Ministry of Food and Drug Safety for new drugs developed in Korea included in the Drug approval report 2019. The source of innovation was analyzed from a identified product patent, and the quality of innovation was evaluated by comparing the timing of approval between first-in-class drug and domestically developed new drug. RESULTS Among the new drugs developed in Korea, 24 substances for which product patents could be identified were analyzed. Of the total, 67% were In-house developed drugs, 33% were external technology introduction, and half of the external technology were introduced from foreign pharmaceutical companies. Moreover, domestically developed new drugs were released on the market more than 10 years after the approval of first-in-class drugs on average. CONCLUSION As a result of case analysis of new drugs developed in Korea, domestic pharmaceutical companies still have enough room for development in terms of the quality of innovation. This study shows the current status of innovation on new drug development in the pharmaceutical industry. We expect this study to be used as a basis for discussion for policy approaches on the pharmaceutical industry.
Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis.Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus' vocabulary due to their bag-of-words representation.Although many studies have examined the effect of preprocessing techniques on vocabulary size and accuracy, none have examined how these methods affect a model's run-time.To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets.We show that some individual methods can reduce run-time with no loss of accuracy, while some combinations of methods can trade 2-5% of the accuracy for up to a 65% reduction of run-time.Furthermore, some combinations of preprocessing techniques can even provide a 15% reduction in run-time while simultaneously improving model accuracy. 1