Reviewerly/exHarmony huggingface dataset:
국외 한국어 교원의 교육 및 연구 역량 제고를 위한 역량 중요도-역량 향상도 분석

The purpose of this study is to find out what to focus on in terms of education and research competencies in order to cultivate the professionalism of overseas Korean language teachers, and to seek direction for reinforcement of future training institutes. Technology, Pedagogy, and Content Knowledge(TPACK) survey among overseas participants of Korean language teacher training program was conducted to find out important competencies of Korean language educators and how the improvement of these competencies was achieved by participating in the training program. We studied the importance and improvement competencies from the perspective of TPACK. As a result, in terms of teaching competency, the participants recognized the importance of contents knowledge(CK), teaching knowledge(TK), and content teaching knowledge(CTK), and the average value of technology content teaching knowledge(TCTK) and technology knowledge(TK) was high in terms of capability improvement. As a result of the IPA analysis, ‘concentration’ included teaching knowledge(TK), content knowledge(CK), and content teaching knowledge(CTK), while ‘continuity’ included technology content teaching knowledge(TCTK). In terms of research competencies, the ability to conduct research related to content knowledge, teaching knowledge, and technology all showed high average values of importance competency and improvement competency. Through this study, we identified important areas to pay attention in order to expand education and research competencies, and confirmed that education that combines content knowledge, teaching knowledge, and technology knowledge should be continued at future workshops. (Ewha Womans University, National Institute of Korean Language, Ewha Womans University)
DeepTopPush: Simple and Scalable Method for Accuracy at the Top.

Accuracy at the top is a special class of binary classification problems where the performance is evaluated only on a small number of relevant (top) samples. Applications include information retrieval systems or processes with manual (expensive) postprocessing. This leads to the minimization of irrelevant samples above a threshold. We consider classifiers in the form of an arbitrary (deep) network and propose a new method DeepTopPush for minimizing the top loss function. Since the threshold depends on all samples, the problem is non-decomposable. We modify the stochastic gradient descent to handle the non-decomposability in an end-to-end training manner and propose a way to estimate the threshold only from values on the current minibatch. We demonstrate the good performance of DeepTopPush on visual recognition datasets and on a real-world application of selecting a small number of molecules for further drug testing.
A Framework Assessing the Effect of User Emotions on Touch Gesture Behavioural Biometric Authentication

Smartphone security constitutes a necessary requirement due to the sensitive information they contain and the important tasks they perform. Behavioral biometric technology such as touch gesture authentication is being increasingly researched covering a wide range of privacy and security systems. However, several behavior factors such as emotions and their influence on touch gesture user authentication performance has remained unaddressed. In this paper, we examine the effect of emotions on user behavior that in turn influences the performance of the touch gesture authentication system. To achieve this, we have designed an implicit touch gesture behavioral biometric authentication approach and suitable experiment procedures that will allow us to collect data in different user emotion states (emotional and natural) and conduct a comparative experiment for examining the influence of emotion factor. Android application has been developed to collect data from user input on touchscreen smartphones. The different emotion user data will be induced using film clips emotion elicitation method and categorized based on the discrete emotion dimension.
Sistem Informasi Perpustakaan SMP Yos Sudarso Cigugur Menggunakan Bahasa Pemrograman PHP dan Database MYSQL

SMP Yos Sudarso Cigugur is one of the schools under the auspices of “Yayasan Salib Suci” Fondation. At present there are several problems in the process of library services. this starts from the process of recording member data, recording transactions, recording book data and calculating fines that are still using conventional methods. The purpose of this study is to determine the course of the system, create a system design, test the system created and implement the system created. The purpose of this research is to find and provide solutions to existing problems regarding library services. The method used in this study uses a waterfall, where this method has the characteristics of activities that flow from one phase to the next. The programming language that researchers use is the PHP programming language with MySql database. The results of research conducted by the author shows the system created can help library services at Yos Sudarso Cigugur Middle School. Further research development is expected to have a flexible information system built in a mobile form and integrated with the internet.
Research Guides: Graduate Student Resources & Services: Research Support

This is a guide to resources and services in the UO Libraries especially relevant for graduate students.
The effectiveness of literacy stimulation model based on multisensory development of the results of DDST

Abstract This research aims to study the effectiveness of literacy stimulation model based on multisensory development of the results of DDST in PAUD Aster 36, Jember. This type of research is Quasi-experimental design which is a form of experimental research that uses a control group but the control group unfunctionally to control external variables that can influence it. DDST assessment was carried out before and after treatment both in children who were given literacy stimulation or not. The research instrument used was the REEDA sign observation sheet with SOP tools. Respondent distribution data included gender, toddler age, toddler parent education, toddler parent age, toddler mother's work, toddler body weight, toddler head circumference and toddler upper arm circumference size. The result is the development of toddler overall assessment using DDST before treatment belongs to the normal category with different percentages. Development of toddler overall assessment using DDST After treatment was included in the normal category, but there was no statistical difference (p-value = 0.083) between the control and treatment groups.
Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data.

Normalizing flows and generative adversarial networks (GANs) are both approaches to density estimation that use deep neural networks to transform samples from an uninformative prior distribution to an approximation of the data distribution. There is great interest in both for general-purpose statistical modeling, but the two approaches have seldom been compared to each other for modeling non-image data. The difficulty of computing likelihoods with GANs, which are implicit models, makes conducting such a comparison challenging. We work around this difficulty by considering several low-dimensional synthetic datasets. An extensive grid search over GAN architectures, hyperparameters, and training procedures suggests that no GAN is capable of modeling our simple low-dimensional data well, a task we view as a prerequisite for an approach to be considered suitable for general-purpose statistical modeling. Several normalizing flows, on the other hand, excelled at these tasks, even substantially outperforming WGAN in terms of Wasserstein distance---the metric that WGAN alone targets. Overall, normalizing flows appear to be more reliable tools for statistical inference than GANs.
The Effect of Models of Reading Instruction on Reading Comprehension, Reading Self-efficacy, and Reading Anxiety

This study compared the effect of four reading models on reading comprehension, foreign language reading anxiety (FLRA), and reading self-efficacy. In order to do so, 184 female Iranian senior high school EFL students at intermediate English reading level were selected through convenience sampling in three high schools and one language institute in Zanjan. The participants were in four intact groups. Each group was randomly assigned to one of the treatment conditions— ‘Direct Activities Related to Texts’ (DARTs), Peer-Assisted Learning Strategies (PALS), ‘Read, Ask, and Put into your own words’ (RAP), and ‘Title, Headings, Introduction, Each first sentence, Visuals, End of each part, Summary’ (THIEVES) models. These models were taught for eight sessions. Data were collected using the reading comprehension part of the Michigan Test of English Language Proficiency (MTELP), Foreign Language Reading Anxiety Scale (FLRAS), and Reading Self Efficacy Questionnaire (RSEQ). The collected data were analyzed using three one-way ANCOVA procedures. The results showed that the four models did not significantly differ in terms of their effect on foreign language reading anxiety and reading self-efficacy. However, there was a significant difference between the effect of THIEVES and RAP on reading comprehension in favor of RAP. Besides, only RAP and PALS improved reading self-efficacy. Moreover, DARTs, THIEVES, and RAP improved reading comprehension and decreased reading anxiety, whereas PALS increased reading anxiety and negatively affected reading comprehension. The theoretical and pedagogical implications of the findings are also discussed.
A Power Big Data Processing Method for Heterogeneous Data Sources

Abstract With the advent of the era of electric power big data, the volume of electric power data will grow at an unprecedented speed. In this paper, we propose a construction scheme of electric power big data service platform. The platform is positioned as a data sharing, analysis and application platform. Data acquisition, storage, preprocessing, visualization and other aspects can be realized. This paper analyzes the demand of platform construction and designs the platform architecture. At the same time, we introduce the implementation way of big data platform from the aspects of overall platform architecture, functional architecture and technical architecture. And the power big data mining algorithm based on Apriori algorithm is introduced. It can be seen that the platform can provide a reliable analysis and mining platform for data mining in power industry.
Specialization of Microcomputers for Targeted Use

Since 2015, microcomputers have appeared in the information environment, which are a compact system unit with minimal functionality without peripherals. The article published the results of the analysis of the use of 6 different microcomputers in various fields of activity. The purpose of the study is to determine the limiting factors affecting the efficiency of the targeted use of microcomputers. It has been established that for scientific and educational presentations, office and trading activities, it is currently advisable to use fanless microcomputers with a perforated case and an internal WiFi antenna, at least 4 GB of operational and 64 GB of permanent memory, and a microSD (TF) memory card slot, at least 128 GB, NTFS file system), Intel HD Graphics, USB3.0 and HDMI interfaces. Based on comparative experiments, methodological recommendations were created on optimizing the configuration of the hardware-software environment of microcomputers in stationary and mobile conditions. The problems of major updates to Windows 10, as well as the compatibility of Microsoft Store software and third-party manufacturers, are analyzed. It is recommended to specialize individual microcomputers for working with 32-bit applications; accounting and cryptographic programs; as well as conducting presentations with their video. Options for optimal configuration of the Start menu of the Windows 10 desktop are suggested. It is concluded that specialization in the hardware-software configuration of modern microcomputers allows you to increase the efficiency of using single devices and their paired systems in accordance with BYOD (Bring Your Own Device).
Analysis on Landscape Design Based on Digital Media Technology

Abstract With the rapid development of Internet technology, the traditional English teaching mode is no longer suitable for the current classroom teaching, which requires colleges and universities to gradually meet the needs of personalized and differentiated learning. In the 21st century, colleges and universities must carry out modern English teaching mode, which can better carry out modern technology education, such as micro class, MOOC class, flipped class and so on. Through online learning platform, students can better meet the interaction of English teaching, which is more conducive to English teaching. This paper first analysis the current situation of English teaching mode. Then, this paper analysis the problems in English classroom teaching. Finally, this paper puts forward the development trend of English teaching mode.
Deep Transformation-Invariant Clustering

Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space. This learning process naturally fits in the gradient-based training of K-means and Gaussian mixture model, without requiring any additional loss or hyper-parameters. It leads us to two new deep transformation-invariant clustering frameworks, which jointly learn prototypes and transformations. More specifically, we use deep learning modules that enable us to resolve invariance to spatial, color and morphological transformations. Our approach is conceptually simple and comes with several advantages, including the possibility to easily adapt the desired invariance to the task and a strong interpretability of both cluster centers and assignments to clusters. We demonstrate that our novel approach yields competitive and highly promising results on standard image clustering benchmarks. Finally, we showcase its robustness and the advantages of its improved interpretability by visualizing clustering results over real photograph collections.
ФАЛЬКЛАРЫЗМ У ДЗЕЙНАСЦI АМАТАРСКАГА КАЛЕКТЫВУ

This article is devoted to the problem of the revival of folk traditions in the activities of amateur folklore groups, which is a kind of folklorism. By folklorism, the author understands any authentic folklore use outside it’s natural environment, including perfomances.
The Speech Acts of the Police Investigator on a Banking Criminal Act in the Police Investigation Report: A Pragmatic Study

The interrogation by the police through an investigator is an important stage in revealing the facts of the criminal acts perpetrated by suspects. This is needed due to suspects’ tendency to hide, cover up the facts, and delegate the crimes committed to other parties. Therefore, there is a need for an effective communication strategy to reveal the hidden truth. This is important because these facts play a major role in making a public prosecutor's indictment in the trial process up to when a court ruling is obtained. This study therefore aimed to interrogate the facts of language, especially the speech acts used by the police investigator on banking crime suspect. The data used were obtained from the text of the 2015 Police Investigation Report of the resort police of Malang City, East Java, Indonesia. The data of 61 speech question texts were obtained by researching the forms and functions of the investigator’s speech acts to the suspect and classified into three phases including opening, deepening of the criminal materials, and closing. The results showed the combination of commissive-asking and directive-commanding to be the most frequently used followed by a verdictive speech act. Their functions are to gather information, clarify, insinuate, humiliate, suppress, challenge, and test. The questions proposed by the investigator formed a unity of coherence to make the suspect guilty. Moreover, a persuasive approach was used with consideration for the cultural perspective by avoiding direct attack and by gradually asking questions to obtain a successful confession from the suspect.
Harmonic attentive multimodal neural network for movie recommendation

Abstract A multimodal neural network based on harmonic self-attention was proposed for movie recommendation. This method can deal with the multi-source data and learn representations of users and items well. The multimodal neural network mainly consists of three sub-networks, ResNet, Bert, and LSTM. In addition, the harmonic self-attention mechanism can explore the user’s preference from the perspective of time. Experimental results show that compared with other three latest recommendation methods, HSMNN shows better performance in terms of HR and NDCG.
Defeats and Defects of Spanish Cyberdiplomacy in the Arab World

This case study aims to reveal the linguistic deficiencies in Spanish Embassies’ Facebook messaging in the Arab world.
The best writing on mathematics 2017 by Mircea Pitici (ed.), pp. 224, £20.00, ISBN 978-0-691-17863-9, Princeton University Press (2018)

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Pedagogy of Fundamentals and Diatonic Harmony

The earliest levels of the undergraduate music theory core might be some of the more challenging courses to teach. Because students enter the undergraduate theory core with a variety of backgrounds, experiences, and knowledge, instructors face the challenge of inspiring some students with new material while keeping the more experienced students involved. How can educators make this material both relevant and engaging for all students? Teaching the lower levels of written theory is more than just memorization of patterns and rules; it is an opportunity to engage students in creative music making from the very first day with an introduction that helps them understand why a certain element of music works. By participating in engaging and creative methods of learning scales, key signatures, intervals, triads, harmonic function, and voice leading, students are immersed in a music experience that is more than just printed notes on the page.
Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%
LibGuides: LEP Orientation Guide: Study Spaces

This guide is created for participants of the Leaders in Education Programme. It gives an introduction to the relevant library resources, facilities and services.
Study on Learning Strategies of English Language Based on Multimedia Computer-Aided

Abstract This paper introduces the problems and opportunities of English language learning in the period of high development of computer technology, and highlights the importance of English language ability. After comparing with the traditional English language ability learning, the author draws many advantages of multimedia computer aided English language learning, and draws relevant conclusions.
New Adaptive Regularization Parameter Selection Based on Expected Patch Log Likelihood

Digital images have been applied to various areas such as evidence in courts. However, it always suffers from noise by criminals. This type of computer network security has become a hot issue that can’t be ignored. In t... | Find, read and cite all the research you need on Tech Science Press
Experiment Planning Method for Selecting Machine Learning Algorithm Parameters

The paper deals with the problem of selecting the parameters of machine learning algorithms. The selection of the most important factors affecting the success of the machine learning algorithm can be done conducting a full factorial experiment. A comparison of the methods of organizing a full-factor experiment is given and the choice of the planning method for the Box-Wilson experiment is substantiated, which can significantly reduce the number of experiments to be carried out when building a model that relates the factors to the target parameter. The Box-Wilson experiment planning method is implemented in software. The results of software modeling obtained for real problems are given. These results confirm the reduction in time spent on optimizing model hyperparameters.
A Framework to Improve Quality of a Java System by Performing Refactoring

Software quality is a key pillar that helps provides high reliability for software. Refactoring is one of several methods that helps in improving the quality by mitigating design issues of software without affecting its external behavior. Several automated and/or semi-automated refactoring tools/approaches are available in the literature. However, they lack considering the role of human expertise and/or they may produce side effects that can further negatively affect the quality. Therefore, this paper considers proposing a more mature framework for Java systems that consumes less time and effort. The proposed framework systematically identifies design issues and then prioritise them using the expertise of developers. Further, it applies different refactorings to improve quality. The framework is tested on standard Java software systems and corresponding quality is evaluated for determining its suitability. The obtained results show the effectiveness and practicality of the proposed quality improvement framework.