Reviewerly/exHarmony huggingface dataset:
코로나-19(COVID-19)로 인한 대학 미용교육에서 대면수업과 온라인수업의 차이에 관한 연구

This paper study the difference of online and offline classes in college beauty education caused by COVID-19 to respond the post- Corona era, and to use it as an opportunity for educational innovation. For this study, the questionnaire was approved by the Dongnam Health University Institutional Bioethics Committee on February 19, 2021 (1044371 202102-HR-001-01), and a survey was conducted using the Naver office form to 448 students and graduates of the beauty-related departments of colleges in the metropolitan area who experienced online and offline classes due to COVID-19 from April 1st to April 18th, 2021. For statistical methods, frequency analysis, paired t-test, reliability analysis, and X2-test were used. The result are as follows; First, the difference in class satisfaction was significantly higher in online class at theoretical subjects, but in offline class at practical subjects. Second, the difference in immersion was significantly higher in offline classes for both theoretical and practical subjects. Third, the interaction between professor-students and student-student was significantly greater in offline classes in both theory and practice subjects. In conclusion, it is better to use online classes appropriately for theoretical subjects and offline classes for practical subjects.
Target based Document in Birds Eye Architecture Using Enhanced Md5 Algorithm

De-duplication process has progressed into the primary storage area where more spatial data exist, in either case increasing the efficiency of the usable storage capacity. In parallel another industry trend is the use of cloud resources to provide a utility model for software and infrastructure services. Cloud resources aid in decreasing the provisioning time for applications and infrastructure, while increasing the scalability to meet the elastic nature of application and infrastructure requirements. This paper proposed to Enhanced MD5 Algorithm and Birds eye architecture of De-duplication Based Mathematical Model process. Experimental results are exposed to better performance to Proposed Enhance Algorithm.
Continuous-variable Gate Teleportation and Bosonic-code Error Correction

We examine continuous-variable gate teleportation using entangled states made from pure product states sent through a beam splitter. We show that such states are Choi states for a (typically) nonunitary gate, and we derive the associated Kraus operator for teleportation, which can be used to realize non-Gaussian, nonunitary quantum operations on an input state. With this result, we show how gate teleportation is used to perform error correction on bosonic qubits encoded using the Gottesman-Kitaev-Preskill (GKP) code. This result is presented in the context of deterministically produced macronode cluster states, generated by constant-depth linear optical networks, supplemented with a probabilistic supply of GKP states. The upshot of our technique is that state injection for both gate teleportation and error correction can be achieved without active squeezing operations---an experimental bottleneck for quantum optical implementations.
Modern Health Care System Using Internet of Things - Challenges and Prospective Solutions

Health is a sustainable development area that is one of the main topics of the world. The Internet of Things is a process that enables various devices and sensors to interact and exchange information. Internet of Things is a technology that has gained widespread popularity due to its ability to monitor and control various devices such as mobile platforms and healthcare systems. Due to the increasing medical costs, public healthcare has been considered an important area of concern. The emergence of Wireless Sensor Networks (WSNs) has led to the creation of new technologies that can help improve the quality of life. The emergence of Internet of things has brought about various advantages for the healthcare industry. Its continuous growth is being utilized for various applications such as remote monitoring of patients, diagnosis and treatment of diseases, and many more. In this paper, we discussed the various components of IOT that are used in healthcare monitoring systems. They are also considered as the building blocks for the future of healthcare monitoring system
Do Parents Have Trust in Teachers? :Focused on the Parents of underachieving high school students

The purpose of this research was to investigate how much the parents of underachieving high-school students trust in their school teachers. In order to accomplish this purpose, the data for the research was gathered through the interviews with seventeen parents. These data were analyzed using qualitative data analysis. The results of this study were as follows: First, there is little contact between the parents of underachieving high-school students and their school teachers. Second, there is a disjunction between the parents’ expectation for their school teachers and their performance in career guidance, teaching, and life guidance. Third, school organization (overcrowded class, departmental system, teachers’ overloaded work), school culture(cultural of entrance, parents’ reliance on private education, denial of the school parents as subject of education), and educational policies(complexity and frequent change of the system of college entrance exam, Personal Information Act, lack of school autonomy) had an undue effect on the parents of underachieving high-school students’ trust in their school teachers. In conclusion, parents need to have a great deal of opportunity to meet their school teachers. High school teachers need to have an expertise in career guidance, teaching, and life guidance. Some innovations in school organization, school culture, and educational policies are required for the parents of underachieving high-school students to have a lot of trust in their school teachers.
Analisis Investasi Pembangunan Proyek Spam (Sistem Penyediaan Air Minum) Kota Bandar Lampung

In an effort to meet the needs of drinking water for the community, the city government of Bandar Lampung pioneered the development of a Drinking Water Supply System (SPAM), the authors reviewed the analysis of investment in the construction of the Bandar Lampung City SPAM project. The purpose of this study was to determine the feasibility of a SPAM development project assessed from financial parameters. In this study, the feasibility of the economic aspect was carried out by reviewing the following assessment parameters, namely Net Present Value (NPV), Benefit Cost Ratio (BCR), Internal Rate of Return (IRR) and Payback Period (PP). The data used are detailed data on SPAM cost budget, SPAM working drawings and water consumption levels, as well as data from BI in the form of interest rates and inflation rates. From the analysis, it can be concluded that the inflation rate in Indonesia is based on a projection for 25 years, namely between 4% -10% per year, the BI rate based on projections in 2018-2057 ranges from 6% -12% per year, the total investment in SPAM development is Rp. .750,000,000,000.00, from the existing scenario it is concluded that the Bandar Lampung SPAM development project is not financially feasible. However, SPAM development is economically feasible because it increases water availability and the coverage of existing services and also expands regional development in the city of Bandar Lampung. Keywords: analiysis , SPAM, scenario
Quintessential Aspects of Machine Learning Algorithms used for Animal Species Classification

Species classification is an essential step of many biological researches. Manual processing of images for species identification and classification is highly time consuming. Manual processing is error prone also. Due to high noise level of input images preprocessing is also a challenging task. This paper mathematically analyses some of the algorithms which can be best utilized for species classification. This paper studies the working theo- ries of those algorithms and also discusses the applicability in species classification area. These machine learning algorithms are highly capable of making significant contribution to the animal species classification area.
“Home Spa” Project – Change of Children’s Knowledge and Skills after Participating in the Project

The subject of the research was to check whether students’ knowledge and skills regarding cleaning products and cosmetics will increase as a result of their participation in the 4-hour- long project entitled the “Home Spa”. For this purpose, research was conducted before and after the “Home Spa” classes, on 65 aged 7–10. As part of the laboratory, students studied the physicochemical characteristics of soaps and cosmetics received from available foodproducts. A practical summary of children’s activities was a box full of cosmetics, while the theoretical added value was the increase in students’ knowledge of the chemicals present in their daily lives. The article discusses only part of the research on the physico-chemical properties of soap. Research results show that the hypothesis – “Home Spa“ project will increase students’ knowledge of soap properties’ – has been confirmed. But the increase in does not seem sufficient. It seems that the most important achievement of the project is the satisfaction of students with this kind of activity.
PERSEPSI SISWA TENTANG LAYANAN PENGUASAAN KONTEN DALAM MENINGKATKAN PRESTASI BELAJAR SISWA DI SMP NEGERI 3 MUARA BUNGO

This research is motivated by students who have low learning achievement such as: have no desire to learn, are more interested in helping their parents and playing with their friends, school assignments are only simple work, there is no effort in doing the work or assignments given. The purpose of this study was to see students' perceptions of content mastery services in improving student achievement. This research method is descriptive quantitative. The population in this study were students of class VIII at SMP N 3 Muara Bungo totaling 227 people, the sample obtained was 90 people who were selected by simple random sampling technique. The instrument used is a questionnaire. The data were analyzed using percentages. The research findings show that mastery of content in improving student achievement is of good quality (83%), to clarify several indicators can be divided, namely indicators: (1) motivating learning is of good quality (83%) , (2) attitudes and study habits are of good quality (83%), and (3) improving learning skills are of good quality (82%). The implication of this research is that it can be used as an analysis of student needs to help direct BK teachers in providing services at SMP Negeri 3 Muara Bungo.
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling

Neural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity only by exploiting contextual features, but rarely do they consider incorporating syntactic structure clues. In this work, we propose using linguistic annotation, i.e., part-of-speech (POS), to guide the text generation. In detail, we introduce POS Guided Softmax to explicitly model two posterior probabilities: (i) next-POS, and (ii) next-token from the vocabulary of the target POS. A POS Guided Sampling strategy is further proposed to address the low-diversity problem by enriching the diversity of POS. Extensive experiments and human evaluations show that, compared with existing state-of-the-art methods, our POS Guided Softmax and Sampling (POSG) can generate more diverse text while maintaining comparable quality.
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding

Automatic news recommendation has gained much attention from the academic community and industry. Recent studies reveal that the key to this task lies within the effective representation learning of both news and users. Existing works typically encode news title and content separately while neglecting their semantic interaction, which is inadequate for news text comprehension. Besides, previous models encode user browsing history without leveraging the structural correlation of user browsed news to reflect user interests explicitly. In this work, we propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) to enhance news and user representation learning. CNE equipped with bidirectional LSTMs encodes news title and content collaboratively with cross-selection and cross-attention modules to learn semantic-interactive news representations. SUE utilizes graph convolutional networks to extract cluster-structural features of user history, followed by intra-cluster and inter-cluster attention modules to learn hierarchical user interest representations. Experiment results on the MIND dataset validate the effectiveness of our model to improve the performance of news recommendation. Our code is released at https://github.com/Veason-silverbullet/NNR.
Understanding of Comorbidities Using Modeling Techniques on EHR

Abstract Comorbidities refer to the existence of numerous, co-occurring diseases in medicine. The course of one comorbidity is typically extremely dependent on the course of the other condition due to their co-occurrence, and therapies can have major spill-over effects. Despite the high occurrence of comorbidities among patients, there is no complete statistical framework for modelling comorbidity longitudinal dynamics. We propose a probabilistic approach for studying comorbidity dynamics in patients over time in this paper. This approach is a non-homogenous transition technique/mechanism using Hidden Markov Model called as coupled-HMM. Clinical research influenced the design of our coupled-HMM: (1) It accounts for different disease stages (acute, stable) in disease progression by providing clinically meaningful latent phases. (2) It simulates a relationship between the trajectories of comorbidities and the dynamics of capturing co-evolution. (3) The transition mechanism takes into account between-patient heterogeneity (e.g., risk factors, treatments). Based on 675 health trajectories, we assessed our proposed Coupled-HMM, which investigates the concomitant evolution of diabetes mellitus and chronic liver disease. We find that our Coupled-HMM provides a superior fit when compared to competing models without coupling. We also assess the spill-over impact, or the amount to which diabetic therapies are linked to a shift in chronic liver disease from an acute to a stable condition. Immediate application in treatment planning and clinical research becomes possible as a result of our approach in context of comorbidities.
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods

We study private synthetic data generation for query release, where the goal is to construct a sanitized version of a sensitive dataset, subject to differential privacy, that approximately preserves the answers to a large collection of statistical queries. We first present an algorithmic framework that unifies a long line of iterative algorithms in the literature. Under this framework, we propose two new methods. The first method, private entropy projection (PEP), can be viewed as an advanced variant of MWEM that adaptively reuses past query measurements to boost accuracy. Our second method, generative networks with the exponential mechanism (GEM), circumvents computational bottlenecks in algorithms such as MWEM and PEP by optimizing over generative models parameterized by neural networks, which capture a rich family of distributions while enabling fast gradient-based optimization. We demonstrate that PEP and GEM empirically outperform existing algorithms. Furthermore, we show that GEM nicely incorporates prior information from public data while overcoming limitations of PMW^Pub, the existing state-of-the-art method that also leverages public data.
Pemanfaatan Aplikasi Google Classroom dan Video Pembelajaran Sebagai Media Belajar Dalam Upaya Meningkatkan Motivasi Siswa Sekolah Dasar di Masa Pandemi Covid-19

The existence of the COVID-19 pandemic virus in Indonesia has caused all activities to be carried out from home. In order to reduce the spread of the COVID-19 pandemic, the government changed the education system to be bold. So that in the world of education all teachers must be able to create learning media that can be operated effectively by students from home. This research was carried out using a qualitative research methodology with a descriptive method, the aim of this research is to find out the benefits of the google classroom application and learning videos as learning media in an effort to increase student learning motivation during the Covid-19 pandemic. Based on the results of the study by referring to relevant journals and articles, it was stated that the use of the google classroom application and video learning as a learning medium had a positive effect of 86.30% in increasing students' learning motivation at Tarumajaya 2 Elementary School. So it can be said that the use of google classroom and video learning at Tarumajaya 2 Elementary School is going well.
Learned Construction Grammars Converge Across Registers Given Increased Exposure

This paper measures the impact of increased exposure on whether learned construction grammars converge onto shared representations when trained on data from different registers. Register influences the frequency of constructions, with some structures common in formal but not informal usage. We expect that a grammar induction algorithm exposed to different registers will acquire different constructions. To what degree does increased exposure lead to the convergence of register-specific grammars? The experiments in this paper simulate language learning in 12 languages (half Germanic and half Romance) with corpora representing three registers (Twitter, Wikipedia, Web). These simulations are repeated with increasing amounts of exposure, from 100k to 2 million words, to measure the impact of exposure on the convergence of grammars. The results show that increased exposure does lead to converging grammars across all languages. In addition, a shared core of register-universal constructions remains constant across increasing amounts of exposure.
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection

Deep networks have shown remarkable results in the task of object detection. However, their performance suffers critical drops when they are subsequently trained on novel classes without any sample from the base classes originally used to train the model. This phenomenon is known as catastrophic forgetting. Recently, several incremental learning methods are proposed to mitigate catastrophic forgetting for object detection. Despite the effectiveness, these methods require co-occurrence of the unlabeled base classes in the training data of the novel classes. This requirement is impractical in many real-world settings since the base classes do not necessarily co-occur with the novel classes. In view of this limitation, we consider a more practical setting of complete absence of co-occurrence of the base and novel classes for the object detection task. We propose the use of unlabeled in-the-wild data to bridge the non co-occurrence caused by the missing base classes during the training of additional novel classes. To this end, we introduce a blind sampling strategy based on the responses of the base-class model and pre-trained novel-class model to select a smaller relevant dataset from the large in-the-wild dataset for incremental learning. We then design a dual-teacher distillation framework to transfer the knowledge distilled from the base- and novel-class teacher models to the student model using the sampled in-the-wild data. Experimental results on the PASCAL VOC and MS COCO datasets show that our proposed method significantly outperforms other state-of-the-art class-incremental object detection methods when there is no co-occurrence between the base and novel classes during training.
Code-Mixing in Student Interaction of Japan UKM Members in State Polytechnic of Bali

Code-Mixing often occurs in a place where there are various ethnicities, tribes, languages, and various cultures. One of them is at the State Polytechnic of Bali. This study aims to describe the form of code-mixing that appears in the interactions of Students Extracurricular Unit of Japan called UKM Jepang members of the State Polytechnic of Bali and explain the motives for using code-mixing in the interactions. The data used in this study is the result of the interaction of students who are members of the UKM Jepang, State Polytechnic of Bali, indicated to cause Code-Mixing both offline and online. Furthermore, the research approach used in this study is a qualitative approach with the type of research being descriptive research. The results showed that the form of Code-Mixing that occurred in students of Japanese UKM members of the State Polytechnic of Bali occurred in mixing nouns, verbs, adjectives, and adverbs. The motives that cause the emergence of code-mixing include the consideration of the interlocutor, namely members of UKM Jepang who both understand Japanese vocabulary. Besides, some special terms in Japanese are considered more appropriate to be conveyed by students on certain topics related to Japan, and they deliberately mix the code to make the conversation more interesting. On the other hand, Some Japanese vocabulary has no meaning that can be spoken in conversation in Indonesian, which causes students to use the term and become a new 'vocabulary' in Indonesian. They accidentally did lexical borrowing to meet the language barrier and cause code-mixing.
THE CONTINUING PROFESSIONAL DEVELOPMENT OF TEACHERS FOR SCHOOLS’ INOVATIVE TEACHING METHODS

The professional development of teachers is necessary not only to meets the standards of job realization, but also to improve their activities in response to changes in pedagogical science and practice. This report presents the results of the research project "Current status and trends for the application of innovative methods in school’s activities" durring the National Program "Young Scientists and Postdoctoral Students" in 2019. The aim of the study is to indicate the teachers’ attitude to the application of innovative teaching methods in their practice in school.
PENERAPAN METODE SIMPLE ADDICTIVE WEIGHTING (SAW) UNTUK MENENTUKAN KELAYAKAN PENERIMA BEASISWA KARTU INDONESIA PINTAR (KIP) KULIAH

AbstractThis review means to deliver a choice emotionally supportive network utilizing the Simple Addicitive Weighting (SAW) technique to decide the qualification of KIP College grant beneficiaries. The foundation of this exploration is that in deciding the beneficiaries of the KIP Lecture grants, STT Pagar Alam actually utilizes the ordinary framework, to be specific planned understudies gather documents and step through different exams, then, at that point, understudy information will be contrasted and the measures individually. Choice emotionally supportive network is a PC based data framework to help people in settling on a choice. The improvement strategy utilized is Waterfall which comprises of examination, plan, coding, testing and upkeep/support. The Simple Addictive Weighting (SAW) strategy will be applied in settling on this choice emotionally supportive network. This strategy was picked on the grounds that this technique decides the weight an incentive for each characteristic, then, at that point, continues with positioning which will choose the best option from various other options. This choice emotionally supportive network was made utilizing the PHP programming language with the codeigniter 3 structure. Testing this framework utilizing discovery testing with a score of 3.6 from master survey and 3.8 from the execution of beta restatement with substantial classifications so a framework for carrying out the Simple Addictive Weighting technique can be delivered. SAW) to decide the qualification of the beneficiaries of the KIP College grants.