Reviewerly/exHarmony huggingface dataset:
Structure Preserving Image Inpainting Using Edge Priors with Contextual Attention

Deep learning techniques have produced plausible results for both regular and irregular masks for challenging task of image inpainting. Few approaches make use of extra information like edge priors for generator network, which preserves the structure which is blurry and distorted. On the other hand, certain approaches use surrounding patches to flow information in the missing regions, which in some scenarios can lead to erroneous output. Motivated by these approaches, we propose a three-stage architecture, which consists of an Edge Generator, followed by a Multi-Branch Image Generator and a Contextual Attention layer to generate high quality plausible patches in the input hole image. We evaluate the proposed architecture on the ICME 2019 Image Inpainting challenge and places2 dataset. The proposed method out-performs state of the art both quantitatively and qualitatively. This model can process rectangular holes at arbitrary locations.
Extending the Web3D: Design of Conventional GUI Libraries in X3D

Extensible 3D (X3D) modeling language is one of the leading Web3D technologies. Despite the rich functionality, the language does not currently provide tools for rapid development of conventional graphical user interfaces (GUIs). Every X3D author is responsible for building from primitives a purpose specific set of required interface components, often for a single use. We address the challenge of creating consistent, efficient, interactive, and visually appealing GUIs by proposing the X3D User Interface (X3DUI) library. This library includes a wide range of cross-compatible X3D widgets, equipped with configurable appearance and behavior. With this library, we attempt to standardize the GUI construction across various X3D-driven projects, and improve the usability, compatibility, adaptability, readability, and flexibility of many existing applications.
ASSESSMENT OF SECONDARY SCHOOL STUDENTS’ PREFERENCE AMONG E-LEARNING, M-LEARNING AND D-LEARNING DURING COVID-19 PANDEMIC EXPERIENCE IN NIGERIA

This study assessed students’ preference among e-learning, m-learning and d-learning during Covid-19 lockdown experience in Nigeria. The study targeted all SS3 students in all public and private secondary schools in Kaduna metropolis. This is a descriptive survey research and a total of 200 SS3 students were purposively sampled for the study but 194 questionnaire were retrieved for the study. Questionnaire was the instrument used to collect data from the respodents. The administration of the questionnaire was done by the researcher and the data collected were analyzed using frequency counts, percentages and mean statistical analysis. The results of the findings showed that, M-learning is the most preferred learning mode during Covid 19 pandemic lockdown experience in Nigeria followed by D-learning. It was also reported that parts of the challenges encountered in the use of e-learning, m-learning and d-learning include lack of power supply, networked problem, lack of data to access internet, lack of technical staff to fix problem encountered during usage among others. The study equally indicated the possible solutions to the challenges encountered to include provision of facilities, devices and equipment for e-learning, m-learning and d-learning, recruitment of technical staff  to fix the problem encountered during usage and provision of free data to access e-learning, m-learning and d-learning programmes developed for Nigerian students. Based on the findings, it is recommended that the government should aid the school authorities in making funds necessary available for the procurement of devices and equipment needed for e-learning, m-learning and d-learning in Nigerian schools
Library Class and Subject Guides: Creative Commons Licenses: Home

This guide provides information on how to add a Creative Commons License to share your work and how to reuse and remix content that have Creative Commons Licenses.
Design of Low Power Barrel Shifter Architecture by Using Proposed MUX Based CORDIC in CMOS Logic

Shifters usually play an important role in all the operations in VLSI computer systems. For example, barrel shifter is a fundamental block of many computing systems due to its operational operation that it can shift and rotate various multiple bits in one cycle. The barrel shifter can also be replaced for arithmetic and the logical shifters as it uses the rotation of the data. It also provides both the shifting of right and left arithmetically and logically. The design is purely MUX-based, and therefore, designing a MUX for low power to use it as a repetitive block in the barrel shifter will improve its efficiency. In this paper, an 8-bit barrel shifter using multiplexers (MUX) is implemented and is designed in cadence tool for demonstrating in power and its functional operations. The paper analyzes and optimizes the area and power of the barrel Shifter in 180 nm technologies. Cadence Virtuoso tool is used for implementing the block.
MULTI-FUZZY SET RELATIONAL STRUCTURES

In this paper, the concepts of multi-fuzzy set relational structures were introduced and some of their properties were presented. Relations were established on both the class of multi-fuzzy sets and on the power multi-fuzzy set of a given multi-fuzzy set. It is demonstrated that as relation on the latter turn out to be a linear order, relations on the former were only partial orders. Some related results were also presented.
PENINGKATAN HASIL BELAJAR TEMA 2 MUATAN MATEMATIKA MELALUI METODE BILLBOARD RANKING PADA SISWA KELAS 1SDN 18 BATU KUALI

Based on the learning outcomes of Grade 1 students of SDN 18 Batu Kuali. On Thematic Learning My Love Theme found that the learning outcomes of Theme 2 Math Content is still very low. The average student learning outcomes are still below the KKM. The purpose of this study was to describe and obtain information about improving learning outcomes Theme 2 Mathematics content sub-theme 2 likes to exercise through the Billboard Ranking Method in Class 1 SDN 18 Batu Kuali Sawahlunto City. This research is a class action research. Research procedures in this study include planning, action, observation and reflection. This research consisted of two cycles with four meetings. The research subjects consisted of 15 students of Class 1 SDN 18 Batu Kuali. Research data were collected using observation sheets and daily tests. Data were analyzed using percentages. Based on the results of research and discussion that has been stated, it can be concluded that the Billboard Ranking Method can improve learning outcomes Theme 2 Mathematics content sub theme 2 likes to exercise at SDN 18 Batu Kuali. Student learning outcomes from cycle I to cycle II. Student learning outcomes in the first cycle was 57.41 (enough) increased to 84.17 (good) with an increase of 26.76%.
AHP 방법론을 적용한 FMEA 치명도 평가 사례연구

Failure mode and effects analysis(FMEA) is popular approach applied to examine potential failures in equipment designs and maintenance of equipments. Risk Priority Number(RPN) is used as an index for the criticality of fault modes in FMEA and RCM(reliability centered maintenance). Traditional RPN approach does not have much credit as a index for the criticality because it does not reflect their experience and the governing logic is apart from their knowledge. Multiple Criteria Decision Method(MCDM) is a proven approach applied to evaluate multiple conflicting criteria in decision making. MCDM can be applied as a tool for RPN evaluation. This study was carried out to investigate application of Analytical Hierarchy Process(AHP) in evaluating RPN.
LibGuides: Teaching with Primary Sources - Create Your Own Cabinet of Curiosity: Activity

This guide includes details to complete a remote learning exercises in materiality and help gain a sense of the physical nature of objects.
Effect of Strategic Agility Dimensions on Tourism Marketing Performance in Jordan (Case Study on Tourism Companies in the Amman Region)

Strategic agility is even more crucial for multinational organizations such as tourism companies in emerging markets due to higher levels of environmental dynamism and unpredictable market developments. Based on the literature reviewed, the researcher proposed a conceptual framework and examined it to find the role of some of the strategic agility dimensions and its effect on the performance of Jordan's tourism marketing. The proposed model provides a platform to enhance the understanding and the direction to conceptualize a suitable strategic agility in order to improve marketing performance. The study uses the simple random sampling method, and samples were collected from managers and the marketing staff of selected companies. The sample size of the study is 60 respondents. The results of the analysis suggest that a high-level of strategic agility was used in the Jordanian tourism companies. Furthermore, the results also revealed that strategic sensitivity, strategic flexibility, and response rates have a positive influence on marketing performance.
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning

The increasing impact of black box models, and particularly of unsupervised ones, comes with an increasing interest in tools to understand and interpret them. In this paper, we consider in particular how to characterise visual groupings discovered automatically by deep neural networks, starting with state-of-the-art clustering methods. In some cases, clusters readily correspond to an existing labelled dataset. However, often they do not, yet they still maintain an intuitive interpretability. We introduce two concepts, visual learnability and describability, that can be used to quantify the interpretability of arbitrary image groupings, including unsupervised ones. The idea is to measure (1) how well humans can learn to reproduce a grouping by measuring their ability to generalise from a small set of visual examples (learnability) and (2) whether the set of visual examples can be replaced by a succinct, textual description (describability). By assessing human annotators as classifiers, we remove the subjective quality of existing evaluation metrics. For better scalability, we finally propose a class-level captioning system to generate descriptions for visual groupings automatically and compare it to human annotators using the describability metric.
INFLUENCE OF CLASSICAL COGNITIVE THEORIES ON THE LEARNING PROCESS

Classical cognitive learning theories are based on the position of the leading and determining role of cognitive processes in learning, which makes it possible to combine various ideas, approaches and research methods in an eclectic way. The analysis of the cognitive approach in educational psychology in the United States allowed us to establish the main directions in it, which are presented in detail in the article. The described cognitive theories can be successfully applied in modern learning conditions, since the development of active thinking becomes a fundamental task of education in the period of modernization and the search for effective ways to optimize the assimilation of a large amount of material and transformation of theoretical knowledge into practical skills.
Research on Teaching Practice of College English Writing Based on Blended Teaching

With the rapid development of modern educational technology, English teaching mode has been unprecedentedly influenced. Blended teaching, a dynamic educational activity, reconstructs the English teaching mode. This thesis explores the practical feasibility of the blended teaching model in the college English writing, so as to provide references for teachers in other colleges and universities.
Learning from the Field

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation S. Mark Young; Learning from the Field. Journal of Management Accounting Research 1 September 2020; 32 (3): 1–5. doi: https://doi.org/10.2308/jmar-10754 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest Search
High-speed Parallel Feature Extraction Algorithm of Wind Tunnel Image Based on GPU

Abstract In wind tunnel test, the serial feature extraction algorithm of wind tunnel image based on CPU is too slow to meet the execution speed requirements of wind tunnel test. To solve this problem, a high-speed parallel feature extraction algorithm of wind tunnel image based on GPU is proposed. The proposed algorithm is optimized in parallel from the CUDA kernel level and CUDA stream level to speed up feature extraction execution. In image pre-processing, a pixel is processed by a CUDA thread to achieve parallelization. In feature extraction, the image segmentation is introduced to parallelize 8-connected boundary tracking algorithm. And a CUDA thread is used to process an image block to parallelize feature extraction process. Furthermore, the proposed algorithm uses CUDA stream to asynchronous data transmission and data processing to achieve CUDA stream level parallelism. To verify the efficiency and effectiveness of the GPU-based algorithm, comparative experiment between CPU and GPU is conducted. The experimental results show that the performance of GPU-based parallel algorithm is far better than that of CPU-based serial algorithm. The GPU-based parallel algorithm can greatly improve the execution speed of feature extraction while ensuring the accuracy of feature data.
Classification of Breast Cancer and Determination of Related Factors with Deep Learning Approach

Aim: In this study, it is aimed to classify breast cancer and identify related factors by applying deep learning method on open access to breast cancer dataset. Materials and Methods: In this study, 11 variables related to open access to breast cancer dataset of 569 patients shared by the University of Wisconsin were used. The deep learning model for classifying breast cancer was established by a 10-fold cross-validation method. The performance of the model was evaluated with accuracy, sensitivity, specificity, positive/negative predictive values, F-score, and area under the curve (AUC). Factors associated with breast cancer were estimated from the deep learning model. Results: Accuracy, specificity, AUC, sensitivity, positive predictive value, negative predictive value, and F-score values obtained from the model were 94.91%, 91.47%, 0.988, 96.90%, 95.42%, 95.14%, and 96.03%, respectively. In this study, when the effects of the variables in the dataset on breast cancer were evaluated, the three most important variables were obtained as area mean, concave points mean and symmetry mean, respectively. Conclusion: The findings of this study showed that the deep learning model provided successful predictions for the classification of breast cancer. Also, unlike similar studies examining the same dataset, the importance values of cancer-related factors were estimated with the help of the model. In the following studies, breast cancer classification performances can give more successful predictions thanks to different deep learning architectures and ensemble learning approaches.
Scientometric modelling experience for the detection of research fronts in geophysics

This report presents a bibliometric approach to identifying research fronts in scientific organizations, based on a multiplex analysis of the flow of publications of authors from the analyzed organization. A model for more accurate subject clustering of publications of a scientific organization based on the GRNTI classification is proposed and tested using the example of geophysics research area. Based on metadata from the publication clusters allocated by GRNTI, algorithms for compiling query profiles in a scientometric database for further detection of research fronts are presented.
What if Neural Networks had SVDs

Various Neural Networks employ time-consuming matrix operations like matrix inversion. Many such matrix operations are faster to compute given the Singular Value Decomposition (SVD). Previous work allows using the SVD in Neural Networks without computing it. In theory, the techniques can speed up matrix operations, however, in practice, they are not fast enough. We present an algorithm that is fast enough to speed up several matrix operations. The algorithm increases the degree of parallelism of an underlying matrix multiplication $H\cdot X$ where $H$ is an orthogonal matrix represented by a product of Householder matrices. Code is available at this http URL .
PENGARUH PERBEDAAN NILAI MASK KIRSCH PADA REPRESENTASI VISUAL CITRA DIGITAL

The value of a mask used in a digital image edge detection has a significant difference. Similarly,the mask value on the Kirsch edge detection. There are eight types of masks that can be used. Thevariety of mask values on the Kirsch edge detection will certainly be a problem for users, becausethey have to do trials one by one to find out how the visual representation of a digital image thatthey process using the Kirsch mask. Therefore it is necessary to conduct an in-depth study of theinfluence of the different Kirch mask values. By entering the value of each mask into a convolutionprocess, it can be seen how the effect of each mask is used. The purpose of this research is to helpusers to determine which Kirsch mask values they should use to get the right visual representationof digital images as needed. The main point of the results of this study is the certainty that all linepatterns in the image of the results are directly proportional to the positive edge detection valuescontained in the Kirsch mask value.Keywords - Edge detection, Kirsch mask, visual representation, digital image
The Teacher-Student Chatroom Corpus.

The Teacher-Student Chatroom Corpus (TSCC) is a collection of written conversations captured during one-to-one lessons between teachers and learners of English. The lessons took place in an online chatroom and therefore involve more interactive, immediate and informal language than might be found in asynchronous exchanges such as email correspondence. The fact that the lessons were one-to-one means that the teacher was able to focus exclusively on the linguistic abilities and errors of the student, and to offer personalised exercises, scaffolding and correction. The TSCC contains more than one hundred lessons between two teachers and eight students, amounting to 13.5K conversational turns and 133K words: it is freely available for research use. We describe the corpus design, data collection procedure and annotations added to the text. We perform some preliminary descriptive analyses of the data and consider possible uses of the TSCC.
Information system model for implementation and maintenance of stochastic control over a nonlinear object on manifolds

Abstract The paper presents a problem statement for a robust stochastic regulator synthesis based on the principles of control on manifolds, a solution algorithm of a new information system and its structure maintaining the synthesis algorithm for a stochastic discrete object. The object under study is presented as a system of stochastic, difference nonlinear equations. The mathematical tools of the classical method of analytical design of aggregated regulators are used, which were developed earlier for a deterministic nonlinear object with a complete description. The robust stochastic nonlinear regulator ensures the following characteristics of the control system: a) minimum deviation of the output variable; b) minimum dispersion of the target invariant; c) minimum average value of the control quality functional.
반성적 저널에 나타난 (예비) 유아교사들의 그림책 읽어주기 활동에 대한 반성적 사고 및 의미 분석

Objective: This study aimed to show and analyze the flow, content, and meaningof (prospective) early childhood educators reflective thoughts entailed in the processof planning,implementing and evaluating storybook reading by analyzing reflectivejournals written by technical college students enrolled in an advanced major curriculum after they completed a storybook reading assignment.Methods: The subjects of this study were 39 technical college students enrolledin an advanced major curriculum that participated in a 15-week-long ‘storybook seminar’ course. This study analyzed journals on storybook reading written bythe students as an assignment for the storybook seminar course.Results: As a result, four categories of selecting a storybook, planning the readingof the selected storybook, actual reading of the storybook and subsequent activitieswere extracted according to the implementation process of storybook reading.Through this series of experiences, (prospective) early childhood educators hada childt-centered view of reading picture books, changed the method of readingpicture books, re-recognized the importance of early childhood literature education,and developed a will to develop professionalism through reflective thinking.Conclusion/Implications: This study finds its meaning in showing the reflectivethinking that occur within early childhood educators that plan, implement andevaluate storybook reading
Research Guides: Studio Resources to support Media & Design during COVID-19: Recommended Software

Resources for supporting the UT community working on media and design remotely during this crisis.
Security and Risk Management

In this chapter, you will learn tips on the basics of security, including the governing principles, control frameworks, threats, risks, and legal issues concerning security. Understand these concepts as they apply in day-to-day work of a cybersecurity professional.
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization

Deterministic-policy actor-critic algorithms for continuous control improve the actor by plugging its actions into the critic and ascending the action-value gradient, which is obtained by chaining the actor's Jacobian matrix with the gradient of the critic with respect to input actions. However, instead of gradients, the critic is, typically, only trained to accurately predict expected returns, which, on their own, are useless for policy optimization. In this paper, we propose MAGE, a model-based actor-critic algorithm, grounded in the theory of policy gradients, which explicitly learns the action-value gradient. MAGE backpropagates through the learned dynamics to compute gradient targets in temporal difference learning, leading to a critic tailored for policy improvement. On a set of MuJoCo continuous-control tasks, we demonstrate the efficiency of the algorithm in comparison to model-free and model-based state-of-the-art baselines.