Reviewerly/exHarmony huggingface dataset:
Hubungan antara Percaya Diri dengan Motivasi Berprestasi pada Ekstrakurikuler Paskibra di Surabaya

The study aims to determine the correlation between self confidence and achievement motivation of Paskibra extracurricular in Surabaya. The type of research is a quantitative type of correlational research. The subjects were 99 junior and senior high school students who joined the Paskibra extracurricular team in Surabaya. Measuring instruments in this study used 2 scales, the achievement motivation scale and the self confidence scale. Data analysis uses the Product Moment analysis method. Product Moment analysis results showed that the variable of self confidence with achievement motivation correlates very significantly. It shows that hypothesis proposed by the researcher is accepted. Keywords: Achievement motivation, Self Confidence, Paskibra.
LibGuides: Teaching Elementary- For Beginners: Helpful Reads

In this lib guide, you will discover helpful tips, tricks, and information for beginning teachers. There is no need to reinvent the wheel. I have provided resources for classroom management, movement, Math websites, ELA websites, articles, Ted Talks, and, This section has a variety of journals, articles, and books regarding teaching.
Bi-projection-based Foreground-aware OmnidirectionalDepth Prediction

Due to the increasing availability of commercial 360-degree cameras, accurate depth prediction for omnidirectional images can be beneficial to a wide range of applications including video editing and augmented reality.Regarding existing methods, some focus on learning high-quality global prediction while fail to capture detailedlocal features. Others suggest integrating local contextinto the learning procedure, they yet propose to trainon non-foreground-aware databases. In this paper, weexplore to simultaneously use equirectangular and cube-map projection to learn omnidirectional depth predictionfrom foreground-aware databases in a multi-task manner.Experimental results demonstrate improved performancewhen compared to the state-of-the-art.
One-Class Self-Attention Model for Anomaly Detection in Manufacturing Lines

In this paper, we present our case study on anomaly detection in manufacturing lines. More specifically, our goal is to explore machine learning and deep learning models, including our designed architectures, on detecting different types of irregularities in data that is collected from a manufacturing system in operations. We focus on four types of sensors which measure air pressures and water flows of a liquid injection process, and positions and torques of a transportation motor. The system works in a cyclic nature - the collected data can be divided into cycles with similar patterns, each of which form a data instance in this anomaly detection task. Since procuring labeled data with actual anomalies is costly, we simulate four types of abnormal patterns to examine the models’ behaviors in each case. The tested models include One-Class Support Vector Machine, Isolation Forest, Deep Auto-Encoders, and Deep One-Class Classifier. We further empirically design two deep learning architectures that are called One-Class Self-Attention (OCSA) models. OCSA integrates self-attention mechanisms with the one-class classifier training objective to incorporate the representation capacity of the former and the modeling capability of the latter. Our experimental study shows that our proposed designs consistently achieve the highest or competitive performances in both detection rates and in running times in a majority of tests.
Digitalization and Employee Engagement in Nepalese Banking Sector

The objective of the present study is to analyse the impact of digitalization on employee engagement in the Nepalese banking sector. Digital usefulness, security, privacy, digital efficacy, and technology support are independent variables, and employee engagement is a dependent variable. Using the Convenient sampling technique, 384 employees were selected, and a questionnaire survey was carried out to collect data in Kathmandu Vally of Nepal. Using F-test and t-test, the study found that digital usefulness, security, privacy, digital efficacy, and technical support significantly impact employee engagement in the Nepalese banking sector.
DDB-EDM to FaBiO: The Case of the German Digital Library.

Cultural heritage portals have the goal of providing userswith seamless access to all their resources. This paper introduces initialefforts for a user-oriented restructuring of the German Digital Library(DDB). At present, cultural heritage objects (CHOs) in the DDB aremodeled using an extended version of the Europeana Data Model (DDBEDM), which negatively impacts usability and exploration. These challenges can be addressed by exploiting ontologies, and building a knowledge graph from the DDB’s voluminous collection. Towards this goal, analignment of bibliographic metadata from DDB-EDM to FRBR-AlignedBibliographic Ontology (FaBiO) is presented.
Advertising Slogan: A Creative Element Undergoing Frequent and Erratic Metamorphosis

Slogan happens to be a true extract of the entire gamut of advertising message. In common parlance, advertisements and ad campaigns are identified by their slogans. Technically, it is a brief, repeatable, and memorable positioning statement. The creative design of slogans has varied with the time. It has been seen in a numerous creative form during its peregrination. The present paper attempts to bring to the limelight an erratic and plural metamorphosis witnessed by this buzzword – slogan.
Contextual Information based Network with High-Frequency Feature Fusion for High Frame Rate and Ultra-Low Delay Small-Scale Object Detection

High frame rate and ultra-low delay small-scale object detection plays an important role in factory automation for its timely and accurate reaction. Although many CNN based detection methods have been proposed to improve the accuracy of small object detection for the low resolution and large gap between the object and the background, it is difficult to achieve a trade-off between accuracy and speed. For the pursuit of ultra-low delay processing by utilizing FPGA, this paper proposes: (A) IoU and distance based loss function, (B) Contextual information with high temporal correlation based parallel detection, (C) High frequency feature fusion for enhancing low-bit networks. The proposed methods achieve 45.3 % mAP for test sequences, which is only 0.7 % mAP lower compared with the general method. Meanwhile, the size of the model has been compressed to 1.94 % of the original size and reaches a speed of 278 fPs on FPGA and 15 fPs on GPU.
Review and Prospect on the Construction and Development of National Experimental Teaching Center for Materials Science and Engineering

Taking the Experimental Teaching Center of Materials Science and Engineering of Wuhan University of Technology as an example, this paper introduces the construction and development process of undergraduate laboratories in colleges and universities in detail. The development of the laboratories has experienced the stages of adjustment and reconstruction, standardized management, and experimental teaching system reform. Now it is in the stage of information construction and experimental teaching mode reform. Experimental teaching focuses on the student-centered and results-oriented teaching concept, and comprehensively improves students’ comprehensive ability and professional quality.
Digital Media VR Design Recommendation Analysis Relying on LDA Model

Abstract With the gradual use of VR technology on a global scale, it has gradually begun to be used in data multimedia teaching, involving more areas. This technology is conducive to improving teaching quality and teaching efficiency. In order to further improve the recommendation effect of digital digital media, this paper uses the design in the LDA model for digital multimedia synthesis and scheduling and decision-making. According to the data information of the feature mapping, combined with the LED model used, the user characteristic behavior of the digital media recommendation is carried out for data. Cluster analysis is used to obtain comprehensive data information of digital media user behavior, and digital media recommendation optimization is performed according to the obtained entropy weight.
THE EVALUATION OF SCHOOL LITERACY MOVEMENT (GLS) IN SDN 11 DESA GEDANG, SUNGAI PENUH CITY

This article presents the result of evaluation study of School Literacy Movement (GLS) program located in elementary school (SDN) 11 Desa Gedang, Sungai Penuh City. Regarding this program has been implemented since 2016, the evaluation is considered to be necessity. The research conducted with CIPP research design has found: (i) on the level of contexts, there is some strong relevance between literacy culture that has been lived by its school before and GLS program; (ii) on the level of input component, the teachers are qualified to fulfill the program and they are very helped by a competent and professional head of school library. (iii) on the process component, the habitation works well, but the school has some problems with the development and innovation progress. (iv) on the product, GLS implies on many things such as the students national exam scores are improved, the students final scores are always above KKM, it is inducing students parents awareness about importance of literacy, and also it is improving students achievement on various competition.
Document image binarization using difference of concatenated convolutions

Binarization is the most important stage in historical document image processing. Efficient working of character and word recognition algorithms depend on effective segmentation methods. Segmentation algorithms in turn depend on images free of noises and degradations. Most of these historical documents are illegible with degradations like bleeding through degradation, faded ink or faint characters, uneven illumination, contrast variation, etc. For effective processing of these document images, efficient binarization algorithms should be devised. Here a simple modified version of the Convolutional Neural Network (CNN) is proposed for historical document binarization. AOD-Net architecture for generating dehazed images from hazed images is modified to create the proposed network.The new CNN model is created by incorporating Difference of Concatenation layer (DOC), Enhancement layer (EN) and Thresholding layer into AOD-Net to make it suitable for binarization of highly degraded document images. The DOC layer and EN layer work effectively in solving degradation that exists in the form of low pass noises. The complexity of working of the proposed model is reduced by decreasing the number of layers and by introducing filters in convolution layers that work with low inter-pixel dependency. This modified version of CNN works effectively with a variety of highly degraded documents when tested with the benchmark historical datasets. The main highlight of the proposed network is that it works efficiently in a generalized manner for any type of document images without further parameter tuning. Another important highlight of this method is that it can handle most of the degradation categories present in document images. In this work, the performance of the proposed model is compared with Otsu, Sauvola, and three recent Deep Learning-based models.
Geometric Data Augmentation Based On Feature Map Ensemble

Deep convolutional networks have become the mainstream in computer vision applications. Although CNNs have been successful in many computer vision tasks, it is not free from drawbacks. The performance of CNN is dramatically degraded by geometric transformation, such as large rotations. In this paper, we propose a novel CNN architecture that can improve the robustness against geometric transformations without modifying the existing backbones of their CNNs. The key is to enclose the existing backbone with a geometric transformation (and the corresponding reverse transformation) and a feature map ensemble. The proposed method can inherit the strengths of existing CNNs that have been presented so far. Furthermore, the proposed method can be employed in combination with state-of-the-art data augmentation algorithms to improve their performance. We demonstrate the effectiveness of the proposed method using standard datasets such as CIFAR, CUB-200, and Mnist-rot-12k.
Detecting Single Photons with Superconducting Nanowires

In the past decades, generating single photons on demand with well defined quantum states and detecting them after photon-photon or photon-matter interaction are central to the area of quantum optics and quantum information science. The ability to detect light efficiently at the single photon level offers unprecedented opportunities for a wide range of applications, including long-distance quantum key distribution, light detection and ranging, photonic quantum computing, weak light detection for astronomy and bio-imaging.
Enhancement of classification techniques using principal component analysis and class imbalance handling methods in credit card defaulter detection

The following research reveals the significance of modified classification in estimating new trends. Rigorous evaluation of different classification algorithms viz. logistic regression, decision tr...
PLASTIC POSSIBILITIES OF CERAMICS AND METALS AS AN INTRODUCTION TO CREATE CONTEMPORARY JEWELRY

The field of experimentation and integration in using materials and techniques has become one of the things that helped the artist and the designer to succeed in the artwork in which he has a deep vision through the relationships and installations of the aesthetics of the material. As well as, the expression of design and technical abilities that help the artist to develop his senses and innovative abilities to realize material heritage elements and a manifestation of artistic, historical and social life. The two researchers believe that the integration between ceramics and metals leads to the development of innovative thinking and enrich the expression in the artwork. Therefore, the study aims to explore the possibility of between ceramics and metals in the manufacture of jewellery is inspired by Islamic art with unconventional modern ideas and visions. The field of experimentation and pairing in using materials and techniques has become one of the things that helped the artist and the designer to succeed in the Artwork in which he has a deep vision through the relationships and installations of the aesthetics of the material. As well as, the expression of design and technical abilities, that help the artist to develop his senses and innovative abilities to realize new formative concepts.
An Investigation of the Complexity of Bitcoin Pricing

This chapter aims to investigate whether it is possible to combine existing research regarding specific attributes of Bitcoin and other cryptocurrencies into one model of Bitcoin price explanation. To do so, an extensive literature review is conducted to explore the publications available. The literature review results in a list of variables used to explore various research areas regarding Bitcoin. The most popular variables (such as the amount of web searches regarding Bitcoin, the gold price or security of blockchain technologies) are selected and combined into a regression model. Even though the coefficient estimates for the Google Trends index, the mean transaction fee, the number of Bitcoin wallets, the security breach dummy variable and the lagged Bitcoin price are reported as significant, statistical testing indicates severe issues with the model. The chapter therefore concludes with the finding that research regarding Bitcoin is not advanced enough and that its pricing mechanisms are too complex to build a sum-of-the-parts model. For future research the exploration of an advanced model with measures implemented to counteract mentioned issues is suggested.
Object Contour in Medical Images Based on Saliency Map Combined with Active Contour Model

Medical imaging is useful in the diagnosis and treatment of diseases. A wide range of pathologies have been discovered by identifying abnormalities of the object’s boundary in the medical image. Almost tokens of illness are shown in salient regions of objects, such as the intensity of color, connection together, etc. Therefore, the contour is a pressing-issue in the cutting-edge diagnose. This paper proposed a method for detecting contour of the object in medical images based on the saliency map combined with the active contour model. The proposed method detected salient regions of the medical image and activated the boundary of objects by energy reducing. The proposed method includes four steps: firstly, the pre-processing is applied for input images by the median filter. Secondly, the threshold of intensity is divided by super-pixels. Thirdly, the outstanding map is made by clustering the above super-pixels. Finally, the active contour model is built in salient regions of concerned about. We test results from DICOM images dataset, which includes high-quality and low-quality medical images. The accuracy of the proposed method is 97.52%, more than the results of the other methods, 5.0%.
Hatenography On Twitter During the Covid-19 Pandemic in Indonesia: Hate Speech Case Against Anies Baswedan

The Covid-19 pandemic's handling was not merely a health problem but created a discourse battle in the social media space. The policies decided by the central and local governments often provoke public reactions in the form of criticism to hate speech. DKI Jakarta Governor Anies Baswedan has one of the governments figures whose policies have often been attacked by criticism and hate speech. This study aimed at analyzing the trend of Anies Baswedan's phrases. The phrases related to the handling of COVID-19 in netizen conversations in the Twitter space and collecting patterns of hate speech about Anies Baswedan in pro-government and contra-government accounts connected with the policies of handling COVID-19. This study used a virtual ethnography method against hate speech involving written verbal messages and communication objectives from Twitter users (influencers) who commented on Anies Baswedan's policies in dealing with Covid-19. The captured data come from two groups of accounts, namely government supporter and opposition. Data exploration used https://exportcomments.com/ software for comments, while trend data and public psychology (sentiment and perception) used Intelligence Socio-analytics (ISA) from March-August 2020. Data were taken from 42,393 total replies and 1,010,007 total posts for ISA. Qualitative data processing used Nvivo 12 plus to arrange phrases and speech patterns. The result showed that Anies Baswedan associated with COVID-19 had significantly increased since march 2020 and decreased in the following months. The tweets about Anies Baswedan had built public psychology in anticipatory emotions and anger, neutral and positive sentiments. The majority public's perception of Anies Baswedan is disgusting. Pro-government accounts had made Anies Baswedan the theme of conversation in their tweets. The narrative that was built was to criticize and or belittle the governor's policies and actions. Tweets with harmful contents (insulting, condescending, disgusting, swearing, and accusations) were the dominant model, triggering replies in the same pattern, but with more crude, ironic, innocent satirical content using nouns or animal names and other, more sarcastic terms. © 2021. RIGEO. All Rights Reserved.
Unsupervised Image Generation with Infinite Generative Adversarial Networks

Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little supervision. Generative Adversarial Networks (GANs) as an implicit approach have achieved great successes in this direction and therefore been employed widely. However, GANs are known to suffer from issues such as mode collapse, non-structured latent space, being unable to compute likelihoods, etc. In this paper, we propose a new unsupervised non-parametric method named mixture of infinite conditional GANs or MIC-GANs, to tackle several GAN issues together, aiming for image generation with parsimonious prior knowledge. Through comprehensive evaluations across different datasets, we show that MIC-GANs are effective in structuring the latent space and avoiding mode collapse, and outperform state-of-the-art methods. MICGANs are adaptive, versatile, and robust. They offer a promising solution to several well-known GAN issues. Code available:github.com/yinghdb/MICGANs.
FURTHER TRAINING AS A FACTOR OF INCREASING THE COMPETITIVENESS AND EFFICIENCY OF WORK AT TECHNICAL UNIVERSITY

Currently, the higher education system continues to rearrange and modernize the educational process. Federal State Educational Standards of new generation require strict consideration of interconnected competency-based approach when training students. On graduating from the University, the alumni are expected to acquire all the necessary competences responsible for the lifelong improvement of his or her skills, as well as to have the ability for further training and the opportunity to retrain in the future. Employers» requirements and expectations are often seen as too high: being young, a specialist should have an appropriate higher education, work in the related areas, quickly retrain and expand his or her creative potential, and be capable to perform the duties of a manager. The labour market is witnessing a steady growth in the number of knowledge-intensive professions and professions requiring PC skills, in particular, with various software complexes. Further training mainly aims at ensuring the mobility of specialists in the labour market. It provides the opportunity for students to expand their list of competencies or get a new one within a short period of time and meets all the requirements of modern society. The development and implementation of adults» further training programs is seen to be an urgent problem. This results in the highest demand on further training. It should be up-to-date, modern and include technological components of the educational process. Further training centres founded at higher education institutions have all the necessary facilities to provide the required level of education. Generally, adult education is provided on-the-job, which requires application of digital technology in the learning process. The article considers organization and outlooks of further training development at technical university applying distance learning module.
Photon and photon-added intelligent states of coupled parametric oscillators

We study a quantum system of coupled oscillators subject to a periodic time dependence of its parameters. Using the Floquet-Lyapunov theory we derive linear non-Hermitian integrals of motion of the system and relate their covariance matrix to that of the canonical observables. The operator integrals allow us to construct states that saturate the Robertson uncertainty relation (intelligent states) for canonical operators and the corresponding photon-added states. We found explicit expressions for the wave function, Wigner function and covariance matrices of the latter.