Reviewerly/exHarmony huggingface dataset:
대학 비대면 실시간 온라인 화상강의에서 학습 몰입 및 학습 성과에 영향을 미치는 요인

The study aims to examine of factors affecting the relationship between Korean university students'' learning flow and learning outcomes, and the correlations among these factors under the real-time online learning conditions. The participants, 49 college students from a university in Busan, were asked to complete a questionnaire regarding their perception of the given factors. The collected data was then analyzed by using correlation analysis and regression analysis. The results were as follows: first, significant correlations were discovered among learners'' participation in non-face-to-face online real-time classes, self-directed learning, communication between learners, sharing of information, learning flow, and digital tool use. On the other hand, no correlation was reported between self-directed learning and communication specifically. Second, regarding factors affecting learning flow and learning performance, the relationship between digital tool use and communication was found to be statistically significant, while that between self-directed learning and sharing information was not. The results of this study cannot be definitive due to its limitations, but it might still show the relationships among factors involved in online real-time classes. A further look at the design and operation of online classes conducted by different instructors might reveal a truer picture of what is going on under these conditions.
On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors

Predicting the states of dynamic traffic actors into the future is important for autonomous systems to operate safelyand efficiently. Remarkably, the most critical scenarios aremuch less frequent and more complex than the uncriticalones. Therefore, uncritical cases dominate the prediction. In this paper, we address specifically the challenging scenarios at the long tail of the dataset distribution. Our analysis shows that the common losses tend to place challenging cases suboptimally in the embedding space. As a consequence, we propose to supplement the usual loss with aloss that places challenging cases closer to each other. This triggers sharing information among challenging cases andlearning specific predictive features. We show on four public datasets that this leads to improved performance on the challenging scenarios while the overall performance stays stable. The approach is agnostic w.r.t. the used network architecture, input modality or viewpoint, and can be integrated into existing solutions easily. Code is available at this https URL
중국에서 한국어 무크 발전 방향성 연구 - 한국어 무크의 구축 및 사용 상황에 관한 조사를 기반으로 -

This study aims to explore the pathways of developing Korean MOOC in China by using the method of survey research. Firstly, this paper investigates the construction of Korean MOOC on 15 MOOC platforms. The results show that Korean MOOC is relatively underdeveloped both in quality and in quantity compared with MOOCs of other disciplines. Secondly, this paper does a questionnaire survey on the use of Korean MOOC `among Korean learners and researchers in 18 universities and research institutions of 19 regions. Most of the respondents show an inadequate satisfaction toward the status quo of Korean MOOC. Many problems exist in Korean MOOC such as unspecified course objectives, unsystematic course content, non-interactive teaching process, and uni-dimensional evaluation method. Based on the surveys, combined with relevant research findings and learner requirements, the paper proposes the pathways of developing Korean MOOC, namely, the hierarchization of course objectives, the systematization of course content, the interaction of teaching process, and the diversification of evaluation.
SIGNIFICANCE OF ACIDIC POLAR SPECIES ASSESSED BY FT-ICR MS TO REVEAL GEOCHEMICAL INFORMATION OF OILS FROM THE SERGIPE-ALAGOAS BASIN

Summary In this work, we investigate in detail the acidic polar composition of five oil samples from distinct fields of the Sergipe-Alagoas Basin (Brazil) by FT-ICR MS using ESI(-). We aim to present the significance of acidic species to reveal geochemical information of petroleum fluids and to reconstruct the maturity and depositional environment of the source rocks by extracting features of biodegradation. The heteroatom class distribution of the oil samples shows great variability, with a higher abundance of N and O2 classes, followed by the O1, NO, and NO2 classes. The biodegradation was successfully assessed by the A/C ratio and SA index based on the O2 compounds, classifying the samples in four levels of biodegradation (from non to heavy biodegraded). The maturation was assessed by the distribution of the N compounds, showing that samples have reached the peak of the oil window, but the heavy biodegraded oil was misleading evaluated with higher maturity. The depositional environment was evaluated using the C27/C28 (DBE 4) ratio and the suggested Isoprenoidyl phenol index, differentiating the lacustrine oil samples from the marine evaporitic ones. However, care should be taken in the assessment of highly biodegraded oils since the acidic compounds are considerably affected.
Plop, plonk, plunk: The physics of dunking something into water

The dynamics of the air pockets created from dunking something into liquid has applications ranging from the food industry to military operations.
LOCALIZATION OF SOIL MONITORING DATA PROCESSING

A tree of data analysis for the formation and preprocessing, storage and protection of data based on Big Data and Blockchain technologies has been developed. The developed algorithm allows for the classification of data on the state of the field, split testing of data, forecasting and machine learning for the implementation of differential irrigation with sprinklers.
The Usage of QR Codes among Young Generation in Johor

QR code is define as an identification mark for a type of matrix barcode. Based on the previous research, there is still lack of research conducting the IS Success Model by doing the qualitative method in Malaysia. The purpose of the study is to discuss the usage of QR code among the young generation in Johor. This research used the qualitative method with semi-structured interviews for the data collection. In this study, the researcher focused on the young generation in Johor among the range of age from 18 to 30 years old for the interview sessions. Around 15 respondents from a population of 3.76 million peoples were selected to take part in the interviews. The type of interviews used in this study is semi-structured. The reason of using QR codes were  experience new things, social media, making online payment, request new friends, save information, questionnaire and attendance of classes. QR codes were beneficial to others by provided detail information easily, connect the online and offline media, cashless payment, and record attendance. The challenges were speed of internet connection, apps installation, inconvenient for elderly people, management of shops, security risks and cybercrime. QR codes can be innovated in future that widely used in the medical field, new marketing resources and improved ability of connection. They can use QR codes to punch cards instead of fingerprints that reduced system error.
BITCOIN CRYPTO CURRENCY PREDICTION USING MACHINE LEARNING

The goal of this paper is to determine how well the Bitcoin volume per USD can be predicted. The Bitcoin Price Index contains price information. The work is accomplished to varying degrees of success by employing the Bayesian optimised recurrent neural network (RNN) and the Long Short Term Memory (LSTM) network. LSTM achieves a maximum accuracy of 52% and an RMSE of 8%. The popular ARIMA model for time series is used to compare with in-depth learning models. In-depth offline learning methods outperform ARIMA's poor performance forecast, as expected. Finally, both in-depth learning models are marked on both GPU and CPU, with GPU training time improving CPU implementation by 67.7%. Bitcoin, Deep Learning, GPU, Recurrent Neural Network, Long-Term Memory, ARIMA are index terms.
Sentiment analysis of Korean movie reviews using XLM-R

Sentiment refers to a person's thoughts, opinions, and feelings toward an object. Sentiment analysis is a process of collecting opinions on a specific target and classifying them according to their emotions, and applies to opinion mining that analyzes product reviews and reviews on the web. Companies and users can grasp the opinions of public opinion and come up with a way to do so. Recently, natural language processing models using the Transformer structure have appeared, and Google's BERT is a representative example. Afterwards, various models came out by remodeling the BERT. Among them, the Facebook AI team unveiled the XLM-R (XLM-RoBERTa), an upgraded XLM model. XLM-R solved the data limitation and the curse of multilinguality by training XLM with 2TB or more refined CC (CommonCrawl), not Wikipedia data. This model showed that the multilingual model has similar performance to the single language model when it is trained by adjusting the size of the model and the data required for training. Therefore, in this paper, we study the improvement of Korean sentiment analysis performed using a pre-trained XLM-R model that solved curse of multilinguality and improved performance.
The Role of Digitalization in the Process of Teaching a Foreign Language and in the Formation of a Foreign Language Competence of Future Specialists

Globalization and the development of digital technologies affect not only the political, economic development of the country, but also higher education. The modernization of the higher education field led to the digitalization of educational disciplines. The importance of foreign language proficiency is highly stressed in conditions of modern society development. The ability to communicate in a foreign language is one of the most important indicators of today specialist's competitiveness. Digitalization plays a significant role in teaching and provides a greater opportunity to improve foreign language skills. A competent organization of a foreign language teaching process with the use of digital technologies will contribute to increasing students' motivation and cognitive activity, the development of their creative abilities and skills to use a foreign language. Modern digital technologies used in a foreign language teaching also individualize the learning process, create conditions for self-education and self-development, and form the foreign-language competence of future specialists.
Cognitive Communications and Networking Technology Infusion Study Report

As the envisioned next-generation SCaN Network transitions into an end-to-end “system of systems” with new enabling capabilities, it is anticipated that the introduction of machine learning, artificial intelligence, and other cognitive strategies into the network infrastructure will result in increased mission science return, improved resource efficiencies, and increased autonomy and reliability. This enhanced set of cognitive capabilities will be implemented via a “space cloud” concept to achieve a service-oriented architecture with distributed cognition, de-centralized routing, and shared, on-orbit data processing. The enabling cognitive communications and networking capabilities that may facilitate the desired network enhancements are identified in this document, and the associated enablers of these capabilities, such as technologies and standards, are described in detail.
The Regulation of Human Germline Genome Modification in Europe

This is an excerpt from Andrea Boggio, Cesare P.R. Romano, Jessica Almqvist (eds.), Human Germline Genome Modification and the Right to Science: A Comparative Study of National Laws and Policies, Cambridge University Press, 2019. It is the version sent to the publisher, before editing. Please, cite to the published book. This chapter discusses the present regulatory framework of the modification of the human genome in general, and, when applicable, of germline cells, in Europe. It covers both standards set by the European Union and the Council of Europe, including the Oviedo Convention.
Extended Sprague–Grundy theory for locally finite games, and applications to random game-trees

The Sprague-Grundy theory for finite games without cycles was extended to general finite games by Cedric Smith and by Aviezri Fraenkel and coauthors. We observe that the same framework used to classify finite games also covers the case of locally finite games (that is, games where any position has only finitely many options). In particular, any locally finite game is equivalent to some finite game. We then study cases where the directed graph of a game is chosen randomly and is given by the tree of a Galton-Watson branching process. Natural families of offspring distributions display a surprisingly wide range of behavior. The setting shows a nice interplay between ideas from combinatorial game theory and ideas from probability.
Enhancement of Key Based Encryption Technique in Delegation System

Cloud computing plays a major role in technology. Data moved to cloud due to larger size. Data in cloud is becoming a high risk. Data stored in cloud start from virtual machine to share resources. Enhancement of key based encryption technique in delegation system provide high security to users therefore making to minimize risks. Data stored in cloud using CP-ABE under certain access policies. One time MAC is combined with symmetric encryption to develop KEM/DEM model for hybrid encryption ABE and again they can be combined with Diffie Helman key exchange method to produce enhanced security.
Autofictional Strategies in Michele Mari’s Leggenda Privata

Leggenda privata (2017) is the latest in a series of autobiographical writings by Michele Mari. The novel is the factual narration of the author's childhood and adolescence, documented by family photos, but is also the narration of a fictional and fantastic situation of writing, where the reader is involved in live recording. In particular, the narrative framework is structured almost like a personal essay that engages dialogically the norms and potentials, opportunities and risks of a theory of autobiography. The purpose of this article is to explore the limits, the difficulties, indeed the impossibility of autobiographical writing in Leggenda Privata , which instead verges towards a complex autofictional strategy. The analysis is focused on the narrative framework and the onomastics, and the consequent theoretical questions that they raise, namely the definition of the literary genre and the identification of the subject. As the search for a proper term to identify this new form of writing confirms that Mari’s novel does not easily fit into the norms of the autobiographical genre, the multitude of names attributed to the protagonist reveal the difficulty of narrating a unitary identity for the subject.
Multi-headed Neural Ensemble Search.

Ensembles of CNN models trained with different seeds (also known as Deep Ensembles) are known to achieve superior performance over a single copy of the CNN. Neural Ensemble Search (NES) can further boost performance by adding architectural diversity. However, the scope of NES remains prohibitive under limited computational resources. In this work, we extend NES to multi-headed ensembles, which consist of a shared backbone attached to multiple prediction heads. Unlike Deep Ensembles, these multi-headed ensembles can be trained end to end, which enables us to leverage one-shot NAS methods to optimize an ensemble objective. With extensive empirical evaluations, we demonstrate that multi-headed ensemble search finds robust ensembles 3 times faster, while having comparable performance to other ensemble search methods, in both predictive performance and uncertainty calibration.
Recognizing Multimodal Entailment

How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web.With ever-larger amounts of unstructured and limited labels, organizing and reconciling information from different sources and modalities is a central challenge in machine learning.
Growth Factor: an Important Factor in Determining the Fate of Outbreaks

Introduction: Epidemic curves are a type of time series data consisting of the number of events that occur over a period of time. The time unit in this data can be a day, a week, or a month, etc.&#x0D; Methods: In the current letter, the authors tried to explain the growth factor and its effect on epidemic curves by using some literature.&#x0D; Results: In the outbreaks setting, the number of cases can increase with different patterns. When the number of cases is increasing exponentially, it means that the number of cases is increasing at a certain speed, which is determined by a factor called an exponential growth factor. When this factor is greater than one, it means that the cases are increasing exponentially, and when this coefficient is equal to 1, it means that we have reached an inflection point that we will face a change in the growth rate of the cases.&#x0D; Conclusion: Some factors such as reducing the contact between infected and healthy people, run the social distancing program, and so on can have an effective role in decreasing epidemic growth factor and controlling the epidemic.
Nauka ili pseudonauka – primer istraživanja telepatije

It is not always easy to draw a dividing line between true scientific theo- ries, erroneous but verifiable scientific theories and pseudo-scientific ones that are impos- sible to verify. As an example, this paper presents researches on various phenomena of extra-sensory perception, precognition, clairvoyance, distance communication, premonition for receiving SMS messages, phone calls or e-mails, which are classified in the domain of telepathy research. They were conducted within different paradigms, with diverse re- search methodologies and approaches, from anecdotal experiences, case studies and intui- tive reasoning, to careful recording of data and their statistical processing. In recent years, researches have been conducted using state-of-the-art “brain-to-brain” technology, which is used to register brain activities of the research participants. Telepathy has not remained uninteresting even to some modern physicists who find in Einstein’s theory of relativity and the laws of quantum physics the possibility of postulating a new quantum model of telepathy. This new, unconventional approach to the research of telepathy is conditioned by the paradigm shift in the research of this phenomenon and significant shifts from the pseudo-scientific to the scientific approach. This paper aims to point out the necessity of setting a clear demarcation line between scientific and pseudo-scientific research and building the ability of critical thinking, which is a condition for differentiating science from pseudoscience. Using a specific example of the phenomenon of telepathy, we underlined the importance of research in the development of scientific knowledge, but the kind of research that meets the reproducibility and verifiability criteria that clearly differentiate science from pseudoscience.
PENGEMBANGAN MEDIA BUSY BOOK UNTUK PEMBELAJARAN KETERAMPILAN BERBICARA SISWA KELAS I SDN 16 BANDA ACEH

Interviews with SD Negeri 16 Banda Aceh class teachers revealed that the learning media used in grade I primary schools were less varied, less interesting, and the use of busy book media had never been used for students' speaking skills, with the development of busy book media expected to produce instructional media that were interesting and fun as well as being a solution for various problems in the learning process. Based on this case, the Busy Book media can be said to be valid used in the learning process in the first grade of elementary school. This has led researchers to conduct research under the title Development of Busy Book Learning Media on Speaking Skills of Class I Students in SD Negeri 16 Banda Aceh. The formulation of the problem in this study is: how to develop a valid Busy Book learning media used for learning speaking skills of first grade students of SD Negeri 16 Banda Aceh? This study aims to produce a valid busy book learning media used for learning the speaking skills of grade 1 students at SD Negeri 16 Banda Aceh. This research is included in research and development (R&D). This study adapted the development procedure from Thiagarajan, a 4D (four-D) device development model. Data collection techniques used by researchers are questionnaire questionnaires for media experts, namely from the aspect of content, graphics and depth of the media. Research data were analyzed using quantitative and qualitative analysis techniques. The results of the study in the form of validator 1 assessment results get an average value of 3.9 validity and the assessment category is Valid validator 2 assessment results get an average value of 3.6 and the assessment category is Valid and validator 3 obtained an average value of 4.03 and the assessment category was Valid used. Thus, the Busy Book media can be said to be valid to be used for learning the speaking skills of grade 1 students at SD Negeri 16 Banda Aceh.
Predicting the Global Impact of Authors from the Learning Analytics Community — A Case Study grounded in CNA

Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the International Conference of Learning Analytics & Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact.
Text Analytics an Approach to AI

Text analytics supports organizations in managing unstructured information, identifying connections and relationships in information, and in extracting relevant entities to improve knowledge management activities. For the past decade, the amount of text messages sent monthly has increased by more than 7,700%. Younger generations overwhelmingly prefer texting to phone calls. And this is often scratching the surface, as there are many other sorts of textual data: support tickets, insurance application forms, healthcare records, product descriptions, and plenty of others. Extracting meaning out of this text is an incredibly difficult task since texts may have different contexts and formats. Textual data is sometimes remarked as unstructured data because it doesn’t have a transparent storage format or a predefined data model. Sure, you could put a sentence into an Excel cell. But how would that facilitate you to study it? The applications of text analysis are far and wide, from simple automation to advanced interactions between the person inputting the data and also the system they interact with. A fundamental example of that is a chatbot. This paper emphasis on how text analytics is a new approach to Artificial Intelligence.