Reviewerly/exHarmony huggingface dataset:
Not Observed Ontology

Two narratives – natural science and religious, intersect in the area of ​​unobserv­able ontology – an immaterial, transcendental, but real area that paradoxically exists outside and inside ordinary physical space-time. It is assumed that mathe­matical constructs, physical laws, physical constants, quantum objects, and even biological laws can be associated with this area. It is argued that physical laws are not invented by man, but are discovered, since they contain physical con­stants measured in special experimental works. Universal constants were not invented for reasons of convenience – physics accepts them as an inevitable con­sequence of the coincidence of the results of all special measurements. Observa­tional data are presented that indicate an extremely small change in fundamental constants or even their constancy over the entire time of the existence of the Uni­verse, although this interesting problem cannot be considered finally solved. The ontology of quantum objects is considered within the framework of Seval­nikov's polyiontic paradigm, according to which two modes are distinguished – potential and actual. The potential existence of quantum objects is described by the Schrödinger wave function, and the actual one appears during the transition from the spectrum of possible states to the only observable one. It is emphasized that potential being does not belong to the classical space, but is in an unobserv­able ontology. The observed state, on the contrary, is already in ordinary space – time and can be recorded by the device. This determines the existence of a spe­cial transcendental layer of reality, along with the material, which may indicate a certain duality in the structure of the Universe. Then it should be assumed that our Universe is not a universal, but a multiverse – a set of different worlds onto­logically having a different nature. In addition, the polyiontic paradigm leads to the idea that, at the quantum level, matter can be derived from information hid­den in an unobservable ontology.
Digital learning from home and the future of post-pandemic education

Abstract . This paper explains how the learning process from home is carried out during the pandemic era and how it will impact the future of education. The approach in this research is qualitative with the literature review method. But to strengthen the data, this study also uses interviews. In this pandemic era four obstacles were discovered, firstly the readiness of devices and networks, the cost of the internet network and the digital literacy capabilities of parents and teachers. In addition, learning done from home through online during the pandemic era had an impact on the future of  ducation in Indonesia. The learning experience in the pandemic era is increasingly a sign that it is not a single school or college that determines success. The decisive digital era is creativity and digital literacy, second, the class will be more flexible than ever which does not always require students to always come physically to school, and finally the stronger academic inflation, where degrees are no longer a sign of prestige.
MENINGKATKAN BAHASA ANAK DENGAN MEDIA GAMBAR DI TK IT ROBBANI KELOMPOK A UNIT I TRANS ALIAGA KECAMATAN HUTARAJA TINGGI KABUPATEN PADANG LAWAS

This study aims to find out how to improve children's language with image media in IT Kindergarten Robbani Unit I Trans Aliaga District Hutaraja Tinggi Padang Lawas Regency. This type of research is using a Classroom Action Research (CAR). Based on the results of the study, it can be concluded that image media can develop the language skills of learners in kindergarten. This can be seen from the development of the language of learners where in the pre-research cycle it is known that the learners who achieved very well did not exist from all the 11 learners. Then in the first cycle, students who have excellent language skills still do not show results. And in the second cycle increased again to 14 learners or 85% of learners who have reached the standard of assessment that has been set.
SDMN-architektúra az 5G-ben

Due to the exponentially growing number of mobile devices connected to the Internet the current 4G LTE-A mobile network will no longer be able to serve the nearly 5 billion mobile devices.With the advent of the fifth generation, however, the number of cybercrimes may increase.This requires building an architecture that can adequately protect against these attacks.For wired networks, the SDN-type architecture has been introduced for some time.As a result, a similar design concept has emerged, which is called Software Defined Mobile Networks (SDMN).This article describes this technology to help preventing DoS, DDoS attacks, and IP source spoofing.
Pengaruh Model Pembelajaran Learning Cycle 5e Terhadap Self Efficacy Siswa Pada Pembelajaran Tematik Kelas IV SDN Tanjungrejo 5 Kota Malang

This study aims to describe the effect of the Model Learning Cycle 5E on Self efficacy. The background of this writing is the low self efficacy of student in learning. Self efficacy should be owned by every individual in order to be able to complete certain task well. This study used a quantitative approach with an experimental research design using a quasi experimental design. The study population was the fourth grade student of SDN Tanjungrejo 5 Malang, totaling 60 students. The sample consisted of an experimental class of 30 students in class IV A and a control class of 30 students in class IV B with a purposive sampling technique. The instrument used was a self efficacy  questionnaire, to determine the size and level of self efficacy  in students. With the model learning cycle 5E, it is expected to improve self efficacy in students for the better.
InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research

This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19. We describe the development of a proof-of-concept prototype tool, InfoForager, which uses AMR to conduct a semantic search, targeting the meaning of the user question, and matching this to sentences in medical documents that may contain information to answer that question. This work was conducted as a sprint over a period of six weeks, and reveals both promising results and challenges in reducing the user search time relating to COVID-19 research, and in general, domain adaption of AMR for this task.
Similarity study of clinical data

Abstract The classification of patients is very important for accurately determining the patient’s disease and subsequent drug use. More and more clinical data provide a clue for the classification of various diseases. We use related software to generate simulation data based on ICD10 and propose a specific algorithm to classify patients, and find patients belonging to other categories in the classified patient group, which provides a theoretical basis for the improvement of various classification algorithms for diseases.
The Implementation of Color Vowel Chart to Enhance the EFL Students’ Pronunciation At SMPN 1 Majauleng

This research aims to find out the color vowel chart in improving the second year students’ pronunciation of SMPN 1 Majauleng. The method of this research is pre-experimental method with two class pre-test and post-test design. The population of this research is the second year students of Color Vowel Chart in academic year 2017/2018, it consists of 40 students which divided in two classes. This research uses random samping and two class as the sample is ten class women VIII B and ten class Men VIII C. The result of the research indicates that there is a significant difference between pre-test and post-test where the mean score of pre-test is 78 and the mean score of post-test 85. It indicates that there is a significant difference before and after teaching by using color vowel chart method in students’pronunciation.
Electronic Record Authenticity in Evidence

With the adoption of the legislator to the principle of functional equivalent, the individuals dealing with electronic transactions became more confident in the electronic record as a tool used to save their procedures that are conducted through the electronic means , and where it has become possible to present electronic record as an evidence in the lawsuit , the judge no longer has the right to refuse the electronic record as an evidence as long as it includes the necessary conditions, and is provided on an electronic mean "which is the reason for which the court rejected the electronic record as evidence "By referring to the Electronic Transactions Law, we find that the legislator graded in the authenticity of the electronic record, by the difference in the type of signature associated with the electronic record until , we reach the state in which the electronic record is not associated with a signature .Therefore, a research will be conducted regarding the authenticity of each record separately, indicating the extent of the evidential power it has, in addition to the research for the adequacy of the provisions that the legislator applied to the electronic record and to what extent the electronic record as a means of.proof is able to dispense with the traditional means of evidence.Until we come to a conclusion that the legislator granted the electronic record th evidential power that it granted to the traditional means, by referring the determination of its evidential power to the general rules.However, we sometimes find that such a matter is not commensurate with the nature that the electronic record enjoys, especially when searching in the electronic commercial record .This study has been divided into two requirements.The first requirement discusses the cases in which the authenticity of the electronic record is presumed and the second requirement discusses the cases in which the authenticity of the electronic record is not presume
ERCS: An efficient and robust card recognition system for camera-based image

Cards Recognition Systems,(CRSs) are representative computer vision-based applications. They have a broad range of usage scenarios. For example, they can be used to recognize images containing business cards, personal identification cards, and bank cards etc. Even though CRSs have been studied for many years, it is still difficult to recognize cards in camera-based images taken by ordinary devices, e.g., mobile phones. Diversity of viewpoints and complex backgrounds in the images make the recognition task challenging. Existing systems employing traditional image processing schemes are not robust to varied environment, and are inefficient in dealing with natural images, e.g., taken by mobile phones. To tackle the problem, we propose a novel framework for card recognition by employing a Convolutional Neutral Network (CNN) based approach. The system localizes the foreground of the image by utilizing a Fully Convolutional Network (FCN). With the help of the foreground map, the system localizes the corners of the card region and employs perspective transformation to alleviate the effects from distortion. Text lines in the card region are detected and recognized by utilizing CNN and Long Short Term Memory,(LSTM).To evaluate the proposed scheme, we collect a large dataset which contains 4,065 images in a variety of shooting scenarios. Experimental results demonstrate the efficacy of the proposed scheme. Specifically, it is able to achieve an accuracy of 90.62% in the end-to- end test, outperforming the state-of-the-art.
Performance Analysis of Semi-Supervised Learning Methods under Different Missing Label Patterns

In this study, we analyze the performance of semi-supervised learning methods under different missing label patterns and missing label proportions. Some semi-supervised learning methods make several assumptions about the missingness mechanism or data characteristics to promote performance improvement compared to supervised techniques. On the other hand, some works do not even consider the underlying patterns of missing labels. To investigate the behavior of these methods or to verify their assumptions, we constructed partially labeled datasets virtually through simulating different missingness patterns over fully labeled datasets. We analyze the performance of support vector machines with self-training (SVM-ST) and Gaussian mixture models with semi-supervised expectation maximization (GMMSSEM). We also compare these methods with their supervised counterparts in terms of performance. Results show that missing label patterns and proportions have significant effects on performance.
Implemented to Be Shared: the WoPoss Annotation of Semantic Modality in a Latin Diachronic Corpus

The SNSF project A World of Possibilities [WoPoss] aims at tracking the evolution of modal meanings in the diachrony of the Latin language. Passages expressing modal notions of ‘necessity’, ‘possibility’ and ‘volition’ are annotated following a pipeline that combines both automatic and fine-grained manual annotation. Texts are first gathered from different online open access resources to create the initial dataset. Owing to the heterogeneity of formats and encodings, the files are regularized before the implementation of the automatic annotation of linguistic features. They are then uploaded to the annotation platform [INCEpTION] which has been previously customized for the fine-grained manual annotation [Bermudez Sabel, in press]. This second type of annotation is carried out following the WoPoss guidelines [Dell’Oro, 2019] and it also involves checking the automatic annotation for curation. In a third stage the files are automatically enriched with metadata. In this paper, we focus on the first two phases of the workflow – i.e. the gathering and automatic annotation of the texts and the fine-grained manual annotation –, which have been the core tasks of the WoPoss team in the first year of the project (2019-2020).
Effects of 24-hour Sleep Deprivation on Exercise Desire and Stress Hormones

The purpose of this study was to confirm the effect of 24-hour sleep deprivation on exercise desire and stress hormone changes. To this end, male college students attending a university in Gyeonggi-do were divided into an experimental group, who was deprived of sleep for 24 hours, and a control group, who led daily life. In addition, since the psychological state of the study subjects could indirectly affect hormones, the effect of the mediator was controlled as much as possible through the questionnaire for personal problems, diseases, and recent psychological stress states. Based on this, the following conclusions were drawn.<BR> First, the experimental group and the control group showed a significant interaction effect in the physical exercise desire, and the psychological exercise desire also showed a significant interaction effect. In addition, it showed a significant interaction effect in overall exercise desire.<BR> Second, among the stress hormones of the experimental group and the control group, cortisol had a significant interaction effect, and norepinephrine had a significant interaction effect. On the other hand, in growth hormone, there was a tendency toward the mean, but no statistically significant interaction effect was observed.
Licensing business data in Academia: Negotiating and balancing needs

Reading licenses can be a challenge with the dry complicated language, but licensing business data is an even bigger challenge. Data providers tend to be less familiar with the academic arena than other business vendors, and they can be much more protective of their content. This article covers the basics of reviewing business data licenses including areas of potential trouble and examples of some language found in licenses. It covers topics like accessing the data, who the users are, vendor requirements to be in compliance, and the difference in how faculty may expect to access the data and how that may conflict with the vendor’s expectations. It also provides basic tips on how to modify and negotiate the license.
PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN ALGORITMA C4.5 (STUDI KASUS DI UNIVERSITAS PERADABAN)

Students are an important parameter in implementing a study program, many students have different graduation rates, are on time and are not on time. This is an obstacle for the University in general because the number of graduations each year is one of the factors that will be assessed for applying for accreditation. This study aims to determine the level of accuracy of the C4.5 algorithm in predicting student graduation, using the C4.5 decision tree algorithm method. Student data used as attributes are IPK, credits, age, gender. The results of the analysis in this study indicate that the C4.5 algorithm can predict with an accuracy value of 88.07% with a precision of 91.79% and a recall of 95.34%.
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations

One of the major downsides of Deep Learning is its supposed need for vast amounts of training data. As such, these techniques appear ill-suited for NLP areas where annotated data is limited, such as less-resourced languages or emotion analysis, with its many nuanced and hard-to-acquire annotation formats. We conduct a questionnaire study indicating that indeed the vast majority of researchers in emotion analysis deems neural models inferior to traditional machine learning when training data is limited. In stark contrast to those survey results, we provide empirical evidence for English, Polish, and Portuguese that commonly used neural architectures can be trained on surprisingly few observations, outperforming n-gram based ridge regression on only 100 data points. Our analysis suggests that high-quality, pre-trained word embeddings are a main factor for achieving those results.
Kekuatan Transversal Termoplastik Nilon, dan Campuran Resin Akrilik Polimerisasi Panas dan Serat Kaca

Heat cured acrylic resin which is often used as a base for dentures has the disadvantage of being easily broken, the use of flexible denture materials made of nylon thermoplastic has become a new innovation to increase strength. Aim this research to evaluate transversal strength between 1 wt% glass fiber added heat cured acrylic resin and nylon thermoplastic. Thirty two specimens (n=16) with size 65x10x2,5 mm were divided into 2 groups (Nylon termoplastic, and 1 wt% glass fiber added to heat cured acrylic resin). Transversal strength was tested using Torsee’s Electronic System Universal Testing Machine with a load cell of 50 kgf and a test speed of 0.1 mm / second. Statistical data analyzed with unpaired t-test. Results, transversal strength mean value of glass fiber 1 wt% added heat cured acrylic resin was 104,03±9,29 MPa, while nylon termoplastic was 313,89±27,44 MPa. There was statistically significant transversal strength difference between both of groups (p <0.05). Conclusion, It can be concluded that nylon termoplastic had higher transversal strength than 1 wt% glass fiber added heat cured acrylic resin.
A study of distortion correction algorithms based on aspheric fisheye lens design

A design method of aspheric fisheye lens has been proposed in this paper, based on the requirements of automobile surround view system. The study has designed a kind of ultra-wide-angle fisheye lens, which only consists of a spherical glass lens and three aspherical plastic lenses. The maximum diameter of imaging aperture is 15.3 mm; the working distance behind is 2.158 mm; the total length of system is 11.44 mm; the focal length is 0.97 mm; the viewing angle is 210°, and the modulation transfer function (MTF) curve is 0.35 at 60 lp/mm. Furthermore, a kind of a distortion correction algorithm for fisheye lens has been created, which calculates the position of the ideal image point with the actual image point and the obtained distortion curve and distortion model. The algorithm can correct the distorted image taken by a fisheye lens to an image without distortion, which is suitable for the human eye. The algorithm, which is simple and effective, has been applied to the automobile surround view system. It has been verified to be accurate and reasonable, after the comparison is made between the real image taken by a fisheye lens and the corrected image.
A Learning Approach for Optimizing Robot Behavior Selection Algorithm

Algorithms are the heart of each robotics system. A specific class of algorithm embedded in robotics systems is the so-called behavior – or action – selection algorithm. These algorithms select an action a robot should take, when performing a specific task. The action selection is determined by the parameters of the algorithm. However, manually choosing a proper configuration within the high-dimensional parameter space of the behavior selection algorithm is a non-trivial and demanding task. In this paper, we show how this problem can be addressed with supervised learning techniques. Our method starts by learning the algorithm behavior from the parameter space according to environment features, then bootstrap itself into a more robust framework capable of self-adjusting robot parameters in real-time. We demonstrate our concept on a set of examples, including simulations and real world experiments.
KEMAMPUAN MENGANALISIS STRUKTUR DAN KEBAHASAAN TEKS EKSPLANASI PADA SISWA KELAS Xl SMK PGRI 2 BANDAR LAMPUNG TAHUN PELAJARAN 2018/2019

The study aims to find out and describe the ability to analyzing explanatory text students of class XI semester 1 SMK PGRI 2 Badndar Lampung. The approach of this research is quantitative and the research type is descriptive. Instrument of this research is performance test. The population of this study amounted to 270 students and sample of this study was set at 29 students in one class. The sampling technique used is cluster random sampling. The results showed the ability to analyzying explanatory text students of class XI semester 1 SMK PGRI 2 Bandar Lampung in the category of sufficient. This showed that students could analyze the structure and linguistic explanatory text but were incomplete. Student constraints in analyzing explanatory texts, namely for the first aspect students experience difficulties when analyzing interpretations where students have not been able to find interpretations correctly. In the second aspect students experience difficulties in analyzing the linguistic of explanatory texts especially on the aspects of material and relational verbs, this is due to a lack of understanding of the students regarding these aspects.
A Politeness Language Study of Karampuang Culture in Sinjai, South Sulawesi

The aims of this research is to explore and to represent the form and the characteristics of linguistic politeness, in buginese Karampuang Sinjai, South Sulawesi society. The method employed was descriptive method. The source of data divided into two categories, primary data will be collected and analyzed as an object of the people in Karampuang Sinjai. Secondary data that was some literature related to the object analyzed. The result of this research show the characteristics and the forms of linguistic politeness in Karampuang Sinjai, South Sulawesi.&#x0D;
Методы работы с плохо различимыми цветными изображениями

The paper is devoted to description of contemporary methods of revealing of faded rock art images, especially made with red ochre. They are the methods of pigment maps and DStretch plugin for ImageJ. The methods are to be compared and the perspectives of the approaches are discussed.