Reviewerly/exHarmony huggingface dataset:
An Approach to Summarize Concordancers’ Lists Visually to Support Language Learners in UnderstandingWord Usages

Concordancers are interactive software that searches for the input word and displays the list of its usages in a corpus.They have been widely used by language learners and educators to analyze word usages.Because naively listing all usages of the word overwhelms users, determining how to summarize the list is important for usability.Previous studies summarized the list by using the surrounding word patterns and showed their frequency; however, such a naive method counts substantially the same usages, such as "the book" and "a book," separately; hence, such a method is not very informative to learners.Here, a novel approach for summarizing the list is proposed.According to the user's input word, the proposed system semantically visualizes each usage of the word using contextualized word embeddings interactively.It is shown that the system responds quickly with intuitive use cases.
Cultivating the Cooperative: PCC, the Music Library Association, and the Music OCLC Users Group

PCC and the Music Library Association (MLA) and Music OCLC Users Group (MOUG) have a long history of coordinating and collaborating on standards, best practices, and training of the music cataloging community. The impact of these collaborations on the formation and structure of the PCC is found throughout the organization, particularly in the PCC funnels, but also in the basic concept of a cooperative cataloging community. This article describes some of the MLA and MOUG cooperative efforts that have benefited and influenced the PCC.
IoU-aware Single-stage Object Detector for Accurate Localization

Due to the simpleness and high efficiency, single-stage object detectors have been widely applied in many computer vision applications . However, the low correlation between the classification score and localization accuracy of the predicted detections has severely hurt the localization accuracy of models. In this paper, IoU-aware single-stage object detector is proposed to solve this problem. Specifically, IoU-aware single-stage object detector predicts the IoU for each detected box. Then the classification score and predicted IoU are multiplied to compute the final detection confidence, which is more correlated with the localization accuracy. The detection confidence is then used as the input of the subsequent NMS and COCO AP computation, which will substantially improve the localization accuracy of models. Sufficient experiments on COCO and PASCAL VOC datasets demonstrate the effectiveness of IoU-aware single-stage object detector on improving model's localization accuracy. Without whistles and bells, the proposed method can substantially improve AP by $1.7\%\sim1.9\%$ and AP75 by $2.2\%\sim2.5\%$ on COCO \textit{test-dev}. On PASCAL VOC, the proposed method can substantially improve AP by $2.9\%\sim4.4\%$ and AP80, AP90 by $4.6\%\sim10.2\%$. Code is available here: {https://github.com/ShengkaiWu/IoU-aware-single-stage-object-detector}.
The Essence and Component Structure of Performing Excellence of Future Teacher of Music in the Process of Popular Singing Training

The article is concentrates on the study of performing excellence of future teachers of music. Popular vocal performances are defined as one of the most popular types ofmusic for various demographics. This makes popular singing training of future teachers of music relevant for public demand. The purpose of this article is to define andsubstantiate the essence, peculiarities and component structure of performing excellence of future teachers of music in the process of teaching popular singing.Performing excellence of future teachers of music in the process of teaching popular singing is defined as an integral professional and personal quality. It is characterized by a high level of performance, is a condition for readiness for a deep and personal comprehension and interpretation of the musical composition and the artistic andfigurative content embodied in it, as well as teacher’s creativity, unique and technically perfect execution on the condition of using specific expressive means, characteristic for popular art performances. Performing excellence is a complex integral phenomenon, which includes four interrelated structural components: motivational, cognitive-technological, operative-creative and reflexive. Each of these components conjoins certain qualities or skills of the future teacher of music into a complex system.
Web Font Optimization for Mobile Internet Users : A performance study of resource prioritization approaches for optimizing custom fonts on the web

According to the HTTP Archive, 75% of websites are using web fonts. Multiple conditions have to be met before modern web browsers like Chrome, Firefox and Safari decide to download the web fonts ne ...
Pengenalan Budaya Komunikasi Bisnis Jepang Melalui Kurasu Katsudo

Introduction of Japanese business communication culture to Japanese learners is one of the important elements in learning Japanese. This introduction can be done through kurasu katsudo 'class activities' to make it easier for students to understand. Kurasu Katsudo is strongly suggested done in various ways and repeatedly by inserting information about cultural elements in the middle of the teaching process. The purpose of this study is to describe Japanese business communication culture through kurasu katsudo. This research is a qualitative descriptive study. Data are collected by library method through several Japanese textbooks. The results of this study indicates that group orientation as one of the Japanese business communication culture can be introduced through the learning of jikoshokai 'self-introduction' and meishi kokan 'exchange business cards'. In addition, hierarchical culture can be taught by the teaching of osewa ni narimashita expression and juju hyogen expression. While the horenso culture can be introduced through learning the N gurai Vru monda sentence pattern.
The Issue of Future Music Teacher’s Professional Mobility in Instrumental Training

The article focuses on the subject of professional mobility formation in a future music teacher during instrumental training. The author analyzes certain principal peculiaritiesof instrumental training of students of departments of musical education and institutes of art in higher school, determines the aims and goals of instrumental performance.Forming professional mobility of a future music teacher is directly connected with the structure of professional training of students of departments of musical education, determined by the specificity of musical education and combines psychological and pedagogical research directions. Professional activity of music teacher is multifunctional and has specific features. Music teachers simultaneously play several roles: teach children bases of musical art, give assistance in fostering spiritual values of a person, promotes musical art as a musician. This versatility of professional activity becomes basis for professional mobility of a future music teacher.
강릉원주대학교 복사-위성연구소에서 실외 비교관측을 통한 전천일사계 교정

Although the technology for the observation of solar radiation is rapidly developing worldwide, in Korea the guidelines for comparing observations of solar radiation are only now under preparation. In this study, a procedure for intercomparison observations of solar radiation was established which accounts for meteorological and geographical conditions. The intercomparisons among observations by national reference pyranometers were carried out at the Asia Regional Radiation Center, Japan, in 2017. Recently, the result of the calibration of the reference pyranometer of the Korean Meteorological Administration (KMA) has been reported. Using the KMA pyranometer as a reference, comparisons between observations and calibrations were carried out for the standard (B to J) pyranometers of the KMA, and for the reference (A) and the standard pyranometers of the Gangneung-Wonju National University. The intercomparisons were carried out between October 24 and October 25, 2018. The sensitivity constants were adjusted according to the results of the data analysis performed on October 24. On October 25, a post-comparison observation was also performed, and the data of the participating pyranometers were verified. The sensitivity constants were calculated using only data corresponding to a solar radiation of 450.0Wm−2 or higher. The B and I pyranometers exhibited a small error (±0.50Wm−2 ), and the applied sensitivity constants were in the range 0.08-0.16 μV (Wm−2 )−1 . For the C pyranometer, the adjustment of the sensitivity constant was the largest, i.e., -0.16 μV (W m−2 ) -1 . As a result, the nine candidate pyranometers could be calibrated with an average error of 0.06Wm−2 (0.08%) with respect to the KMA reference, which falls within the allowed tolerance of ±1.00% (or ±4.50Wm−2 ).
Deep Adaptive Wavelet Network

Even though convolutional neural networks have become the method of choice in many fields of computer vision, they still lack interpretability and are usually designed manually in a cumbersome trial-and-error process. This paper aims at overcoming those limitations by proposing a deep neural network, which is designed in a systematic fashion and is interpretable, by integrating multiresolution analysis at the core of the deep neural network design. By using the lifting scheme, it is possible to generate a wavelet representation and design a network capable of learning wavelet coefficients in an end-to-end form. Compared to state-of-the-art architectures, the proposed model requires less hyper-parameter tuning and achieves competitive accuracy in image classification tasks
CAPTURING STUDENT TEACHERS’ TECHNOLOGICAL PEDAGOGICAL AND CONTENT KNOWLEDGE: USING A PEDAGOGICAL TOOL CALLED TECHNOLOGY CONTENT REPRESENTATION AND VIDEO REFLECTION

CAPTURING STUDENT TEACHERS’ TECHNOLOGICAL PEDAGOGICAL AND CONTENT KNOWLEDGE: USING A PEDAGOGICAL TOOL CALLED TECHNOLOGY CONTENT REPRESENTATION AND VIDEO REFLECTION
Learning from i.i.d. data under model miss-specification

Virtually any model we use in machine learning to make predictions does not perfectly represent reality. So, most of the learning happens under model misspecification. In this work, we present a novel analysis of the generalization performance of Bayesian model averaging under model misspecification and i.i.d. data using a new family of second-order PAC-Bayes bounds. This analysis shows, in simple and intuitive terms, that Bayesian model averaging provides suboptimal generalization performance when the model is misspecified. In consequence, we provide strong theoretical arguments showing that Bayesian methods are not optimal for learning predictive models, unless the model class is perfectly specified. Using novel second-order PAC-Bayes bounds, we derive a new family of Bayesian-like algorithms, which can be implemented as variational and ensemble methods. The output of these algorithms is a new posterior distribution, different from the Bayesian posterior, which induces a posterior predictive distribution with better generalization performance. Experiments with Bayesian neural networks illustrate these findings.
Adversarial VC-dimension and Sample Complexity of Neural Networks

Adversarial attacks during the testing phase of neural networks pose a challenge for the deployment of neural networks in security critical settings. These attacks can be performed by adding noise that is imperceptible to humans on top of the original data. By doing so, an attacker can create an adversarial sample, which will cause neural networks to misclassify. In this paper, we seek to understand the theoretical limits of what can be learned by neural networks in the presence of an adversary. We first defined the hypothesis space of a neural network, and showed the relationship between the growth number of the entire neural network and the growth number of each neuron. Combine that with the adversarial Vapnik-Chervonenkis(VC)-dimension of halfspace classifiers, we concluded the adversarial VC-dimension of the neural networks with sign activation functions.
On filter convergence of nets in uniform spaces

In this paper, we introduce F-convergent and Fst-fundamental nets in uniform spaces and study some their properties.
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection

Deep learning demands a huge amount of well-labeled data to train the network parameters. How to use the least amount of labeled data to obtain the desired classification accuracy is of great practical significance, because for many real-world applications (such as medical diagnosis), it is difficult to obtain so many labeled samples. In this paper, modify the unsupervised discriminant projection algorithm from dimension reduction and apply it as a regularization term to propose a new semi-supervised deep learning algorithm, which is able to utilize both the local and nonlocal distribution of abundant unlabeled samples to improve classification performance. Experiments show that given dozens of labeled samples, the proposed algorithm can train a deep network to attain satisfactory classification results.
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty

Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.
Client-side Vulnerabilities in Commercial VPNs

Internet users increasingly rely on commercial virtual private network (VPN) services to protect their security and privacy. The VPN services route the client's traffic over an encrypted tunnel to a VPN gateway in the cloud. Thus, they hide the client's real IP address from online services, and they also shield the user's connections from perceived threats in the access networks. In this paper, we study the security of such commercial VPN services. The focus is on how the client applications set up VPN tunnels, and how the service providers instruct users to configure generic client software. We analyze common VPN protocols and implementations on Windows, macOS and Ubuntu. We find that the VPN clients have various configuration flaws, which an attacker can exploit to strip off traffic encryption or to bypass authentication of the VPN gateway. In some cases, the attacker can also steal the VPN user's username and password. We suggest ways to mitigate each of the discovered vulnerabilities.
영유아 대상 놀이프로그램 관련 연구동향 분석

The purpose of this study is to analyze the research trends of play programs by analyzing the papers on play programs for early childhood published in domestic academic journals, and to help develop and implement various early childhood play programs in the future. For this purpose, 59 domestic academic papers on play programs for early childhood enrolled from 1996 to May 2019 were analyzed according to the yearly research trends, play programs development, implementation process and evaluation methods. As a result of the analysis, research trends by year of play programs have been increasing since 2010. but they decreased briefly from 2013 to 2014 and have shown a steady increase from 2015. Second, according to an analysis of the development trends of play programs, physical play was the most common type of play, followed by role play, traditional play and tool play. The development purpose of the program was the emotional and social development program. More than 90% of the types of play program development have been verified from the development of play programs to the implementation and effectiveness of the programs. Third, according to the analysis of the program implementation process, the participants were 5 years of age and the number of participants was 11∼20 persons. The total number of sessions of the play program was 11∼20 times, the operation period was 5∼8 weeks, and the number of sessions per week was 1 time the most frequently. Fourth, as a result of analyzing how to evaluate a play program, most of the quantitative research(experimental studies) were conducted in advance using post-comparison to see if the effects of the program were shown to the participants after the program was conducted.
Implementing a Protocol Native Managed Cryptocurrency

Previous work presented a theoretical model based on the implicit Bitcoin specification for how an entity might issue a protocol native cryptocurrency that mimics features of fiat currencies. Protocol native means that it is built into the blockchain platform itself and is not simply a token running on another platform. Novel to this work were mechanisms by which the issuing entity could manage the cryptocurrency but where their power was limited and transparency was enforced by the cryptocurrency being implemented using a publicly mined blockchain. In this work we demonstrate the feasibility of this theoretical model by implementing such a managed cryptocurrency architecture through forking the Bitcoin code base. We discovered that the theoretical model contains several vulnerabilities and security issues that needed to be mitigated. It also contains architectural features that presented significant implementation challenges; some aspects of the proposed changes to the Bitcoin specification were not practical or even workable. In this work we describe how we mitigated the security vulnerabilities and overcame the architectural hurdles to build a working prototype.
협력학습이 한국어 자기효능감과 수업 몰입에 미치는 영향

The purpose of the study was to explore the effects of collaboration activities on Korean learner’s Self-efficacy and Student flow. The participants of the study were 42 students from Korean language class who belonged to two different groups : One group is with collaboration activity and the other group is with general teacher-centered class. Data analysis was completed using descriptive statistic analysis, correlation analysis, and hierarchical regression analysis with SPSS 21.0. In addition, in order to examine the mediating effects of Self-efficacy and flow Major findings from the study were as follows: First, In Self-efficacy part a collaboration activities class proved to be more effective than teacher-centered class. Self-efficacy for group work, refers to the learner’s capability to successfully perform group tasks. It is known as the variable that participation in the group learning process. Second, in Student flow part, a collaboration activities class proved to be more effective than teacher-centered class, but the average is meaningless. Flow is a sensation that people experience when they work with complete focus. Flow has been validated in numerous studies as a critical component of learning.Third, self-efficacy also had significant direct influence on student flow as well as significant indirect effect via collaboration class. The degree of effect of self-efficacy on student flow was higher than the effect of teacher-centered group. (Kosin University, Busan University Of Foreign Studies)
Analysis on the Reform of Course Assessment in Chinese Universities

Although there are many similarities between the course assessment methods in Chinese and foreign universities, there are also a number of differences. The course assessment in foreign universities can improve students' learning enthusiasm, motivate the class participation, and foster students' professional skills and abilities. Drawing on the experience of the course assessment in foreign universities, Chinese universities should give the teachers enough rights to choose the course assessment method independently and guide the teachers to distribute diverse examination methods flexibly in the teaching process. The reform of course assessment should guide the students to arrange their studying rationally, to improve their theoretical knowledge, practical abilities and relearning abilities effectively. Introduction
A Systematic Mapping Study of Utility-Driven Models and Mechanisms for Interclouds or Federations

Cloud computing is a dynamic paradigm that applies utility driven models at all layers in providing elastic services to the users and also facilitating the processes of the cloud provider. Utility-driven models and mechanisms for cloud federation play a very significant role on the cloud, hence worth researching on. However, the issue of finding out a similar study in models and mechanics for Interclouds or federation is an arduous task for most researchers. Systematic mapping studies delivers an outline of all that had been completed in a specific discipline. The objective is to carry out a systematic mapping study of utility driven models and mechanics for Interclouds or federation. Selected results showed that articles on environment had more in relation to metric with 2.78%, articles on design had more in terms of tool with 13.89%, articles on architecture had more in terms of model with 23.15%, articles on challenges had more in terms of method with 9.26%, and articles on policy had more in terms of tool with 6.48%. However, there were no articles on utility driven model and mechanisms for inter clouds or federation on the aspects of policy, architecture, design and challenges that consider metric. This study has identified research gaps in utility driven models and mechanics for Interclouds or federation which ought to inspire enthusiasm for further investigations by the scholars and industry experts.
Association of Multi-dimensional factors with Employee Satisfaction and Attrition using SEM Analysis

The study is to verify employee satisfaction significance with Attrition in reference to employees working in non-listed IT companies, using multi-variants like income, attitude, comfort, caution, HR policies, bench resources, the overall process, poaching, work culture etc. grouped under the head of Personal factors, Organizational Factors and Behavioral factors. Workers perform better with employee satisfaction, otherwise, they just leave the organization. There are various factors enhancing employee satisfaction. The findings are reliable as the study is evaluated using SEM analysis which is the latest analysis. The output result of this paper is to help Human Resource / Personnel managers to get equipped with knowledge of the relevance of employee satisfaction and turnover. The solution from the study is to help and motivate the Human Resource managers to build a bigger team and better growth to benefit the employers.&#x0D; Keywords: Organizational factor, Behavioral, Personal factors.
Tifinagh-IRCAM Handwritten character recognition using Deep learning.

In this paper, we exploit the benefits of the deep learning approach to design an efficient system of Amazigh handwritten recognition. Indeed, this approach has proved a greater efficiency in the various domains, especially recognition tasks. However, to take full advantage of this approach it's necessary to construct an adequate dataset of training and testing that represent faithfully the concerned problem. To this end, we have prepared our dataset of 102 writers each one contains 33 characters of IRCAM-Tifinagh. Inspired by the MNIST database, the set of characters is size-normalized and centered in a fixed-size image. The resulting is a grey level image of size 28x28, where the black color is the non-color of the character. The number of images produced after this preprocessing step is 3,366.