Reviewerly/exHarmony huggingface dataset:
Social partnership at the stage of professional education of a teacher of social minded school

The subject of this research is the problem of social partnership, which determines modern social processes, interactions, and spaces. The priorities and standards of modern educational policy actualize its significance. The purpose of the work is to consider the professional education of the future teacher from the standpoint of social partnership and its design in the socially oriented space of a social minded school. Using the methods of dynamic and contextual analysis of the educational system, theoretical analysis of literature, review analysis of the experience of foreign countries (France, Finland, Germany, Poland): 1) the definition of “social partnership” is theoretically formulated; 2) the concepts of “social minded school” and “socially oriented educa-tion” are substantively justified; 3) the social and educational components of these phenomena are noted, which allow them to become a unifying principle and a field of active social interaction between the state and society. We emphasize the practical importance of professional training of a future teacher for successful work in a new social minded school due to the wide possibilities of social partnership between the state, society and the interested civil community, as well as the re-source opportunities and innovative potential of the university where future teacher studied.
Subject Guides: Math : Online Calculators

This guide brings together resources to help you learn and practice math skills, from basic operations to advanced topics.
Investigating N2pc effects with the use of Prior Knowledge and Novel Information in Visual Search

Previous research has shown that visual search efficiency is affected when using prior knowledge (e.g., looking for semantically-related objects; Wu et al., 2013, see also Telling et al., 2009) and novel information (e.g., using patterned sequences vs. random sequences to guide attention; Zhao et al., 2013). How might the use of prior knowledge and novel information impact search efficiency when both can be utilized during search? Across two experiments, this study used a visual search paradigm (2-item search array) with known real-life objects (food vs. toys) and statistical patterns with novel rune symbols as proxies for prior knowledge and novel information, respectively. Rune patterns were presented in either structured or random sequences in the locations of upcoming target and distractor objects in the search array. Experiment 1 (behavioral study, n=31) found that participants were marginally faster at detecting items in patterned locations, compared to random locations (replicating Zhao et al., 2013). However, this effect was not present when distractors that were semantically related to the target appeared (i.e., no interaction between use of prior knowledge and novel information). Experiment 2 (ERP with behavioral measures, n=12) revealed that the behavioral results largely replicated those from Experiment 1. Interestingly, the N2pc ERP component (marker of target selection) was twice as large when targets were in the random location, compared to when the targets were in the pattern location, which is the opposite pattern of the behavioral results. Although this N2pc amplitude difference was not statistically significant (p=.287) with 12 participants, data collection is ongoing until the pre-registered number of 20 participants. These results suggest that novel sequences may impact behavior and neural responses to search tasks differently, and in some cases may not interact with prior knowledge.
Access to digital materials on research output: Experience of Federal University Libraries users in northern states of Nigeria

This study examined access to digital materials on research output in some selected federal university libraries users in northern Nigeria. To achieve the goals, three objectives guided the study. Quantitative method and survey design were adopted to elicit data from lecturers and ICT librarians using close-ended questionnaire. A multi-stage sampling technique was used in selecting respondents from each institution. The data collected were analysed descriptively and the results were presented using frequency counts, mean and aggregate mean scores in tables. The findings revealed that access to digital materials influenced research output of library users with particularly to number of journal articles, conference papers presented, books, and chapters in books published. The choice of metadata type is determined by the number of modules such as administrative, technical, and preservation so as to resolve militating technical factors, promote information availability, interactive nature of technology, and portability of digital content. It could be concluded that access to digital materials and use immensely contributed to research output and metadata types, technical factors moderate access, adoption, use and research output in the selected federal universities in northern Nigeria. It is recommended that access and use of digital materials be prioritised for service effectiveness and efficiency on digital platforms supported by the libraries in the zone. Choice of metadata types should be based on possessing modules that support administrative, structural, technical, transformative procedures and preservation.Keywords: Access; Digital materials; Research output; Experience; Federal University ; Library users; Northern Nigeria
Voice for the Paralytic Victims

Disability is something that is a physical or mental condition that limits a person’s movements, senses or activities. One such disability in which a person loses muscle function in part of your body is called Paralysis. According to the study, there are nearly 1 in 50 people living with Paralysis which is 5.4 million people in the whole world population and technical advancement in the fields where they concentrate on disabilities in communication is nil. This application helps in providing communication between patient and the caretaker/nurse. A patient can easily send messages by just small movements of accelerometer which takes the angle of tilt as the input and decides which messages to be sent based on that. Arduino is used for detecting the input as per the programming done for the direction of accelerometer and output will be displayed on the LCD screen. Along with the real time reminders, an emergency buzzer is added to simplify the work of the nurse. So, this project is simple yet an effective way to solve the communication issue of disabled people.
Modelling aging

The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales -- from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research.
Space and Time Dissociate in the Construction of the Visual Now

How are events occurring at different times and places integrated into a unified experience of what is happening now? We report experiments that sequester and dissect the visual now. In particular, we consider how a moment of visual experience combines events occurring at different times and locations. Our first two experiments rely on a Rapid Serial Visual Presentation (RSVP) paradigm, because serial error distributions in the paradigm generally suggest temporal uncertainty in visual experience. In each trial an RSVP stream of letters appeared at the fovea. Participants reported the letter in the stream that they perceived simultaneously with a transient peripheral cue. In Experiment 1, participants made errors, sometimes reporting letters from the stream that actually appeared before or after the cue. The pattern of errors suggests that the cue arrived in visual experience faster than the foveated RSVP stream. Critically, eccentric cues produced more before errors than parafoveal cues. In other words, eccentric cues appear to arrive in experience faster than parafoveal cues. These effects cannot be explained by Posner-like attention shifts, which predict the opposite pattern (errors on letters that appear after the cue). In Experiment 2, the cue was presented between two letters in the RSVP, producing results that conceptually replicate Experiment 1. Finally, in Experiment 3, we sought to further understand how eccentricity influences temporal experience.Participants reproduced the durations of discs that appeared at either eccentric or more foveal positions. Peripheral discs were reliably reported as lasting longer than foveated ones, given thesame objective duration. Collectively, these results suggest that space and time dissociate as events are stitched into a moment of experience
Ontological implications in the learning of social distancing of covid-19 pandemic

The world pandemic situation affects all economics sector and specially education;the objective was to show new models of learning to mitigate the negative impacts of the pandemic Qualitative and reflexive methodology is used from grounded theory, this paper proposes to the student, teacher and Higher Education Institution (HEI) from its ontology in teaching process with its features and B-learning method relation It is concluded the relevance participation of HEI and government as fundamental actors to alleviate the negative impacts of COVID-19 © 2020, Universidad del Zulia All rights reserved
Evaluation of Handwritten Arithmetic Equations using Convolution Neural Networks

This paper aims to develop a user interactive assistance system that evaluates the handwritten arithmetic equations based on handwriting recognition algorithms. Although recognizing handwritten characters and symbols is generally easy for anyone but recognizing them is difficult for a machine. By following a deep learning approach, this challenge can be solved by designing a system that recognizes the operands and operators. Being able to solve handwritten arithmetic equations through the model will bring faster and accurate results. The model will identify pictures of handwritten arithmetic equations and will be able to emit the corresponding characters into a list and evaluates the results.This includes digit classification which involves feature extraction and classification. For this purpose, computer vision is used to input the image and obtain contours, Convolutional NeuralNetwork(CNN) is the algorithm used to build the model, which does feature extraction and classify the operators and operands and develop a model with 92.6% accuracy.Keywords - Computer Vision (CV), Feature extraction, Contour extraction, Convolutional Neural Network (CNN), TensorFlow, Training model, Keras, ReLu, Softmax.
Slideback / Thomas Kahn, chant [acc. instr.]

Comprend : Twinkling star (3 min 05 s) - Once again (3 min 15 s) - Blame and regret (3 min 08 s) - Dream maker (3 min 44 s) - Go back home (6 min 11 s) - The ice of silence (4 min) - My closest friend (4 min 15 s) - Lil' boy (3 min 31 s) - Hear me (4 min 02 s)
A comparison of regimens for training face matching

Human performance in matching unfamiliar faces to images is known to be poor, with far-reaching implications in a variety of workplace settings. Luckily, expertise for recognizing and matching particular individuals is highly trainable — through exposure, we can learn to distinguish even identical twins. Popular training methods ask participants to repeatedly perform a matching task or recognition task using photographs of the target identities, with feedback given after each trial. However, by limiting the training set to only original photographs of the target individuals, many of these training tasks are too easy and result in inefficient perceptual learning, with performance quickly hitting the ceiling. Here, we compare the effectiveness of more challenging face-training regimens that focus on the differences among a set of target faces to be learned. To accomplish this, we make use of recent advances in machine learning that provide the capability to encode photographs into a learned face space and then generate photorealistic morphs that interpolate between mid-level features of the depicted individuals, such as the taper of the chin and bushiness of the eyebrows. In particular, we used the StyleGAN neural-network architecture to generate several challenging variants of the Glasgow Face Matching Test (GFMT). In each variant, we morph each face towards the centroid of the set of all GFMT faces in the learned face space. We then empirically compare the efficacy of these GFMT variants for training face matching, measuring transfer of learning from the trained set to the original faces.
Long-term memory guides resource allocation in working memory

Working memory resources are incredibly limited yet every day we must maximize the goal-relevant information we encode from our environment. Working memory, however, is not in complete isolation. Prior knowledge in long-term memory can aide in maximizing the information we encode. Here we used a full-report procedure in a visual working memory paradigm to examine the influence of prior information on resource allocation in working memory. For each trial, six colored circles appeared at a random angle about a fixed radius. The location of five of the six colors was drawn from a uniform distribution (“non-prior” items), while the remaining color was drawn from a von Mises distribution with a standard deviation of 20 degrees (“prior” item). The color and mean of the prior distribution were randomly determined for each participant at the beginning of the experiment and remained constant. Participants first completed a training phase where they were explicitly shown and tested on both the color and location of the prior distribution. For the remainder of the experiment, participants reported the locations of all six colors in any order on each trial. To verify participants maintained the prior information, they were tested on the color and location of the prior item at the end of each run. We found that participants allocated fewer resources to the prior items, as evidenced by 1) a bias to report the prior item later in the response sequence (typically 4th of 6 positions), and 2) a decrease in precision for reports of non-prior items that appeared near the prior location. Together these findings show that participants are using strategies to prioritize encoding items with no prior information. These results give us a better understanding of how working memory may rely on long-term memory to strategically encode information from our environment.
표현용 어휘집 사용과 쓰기: 한국어 중급 학습자를 대상으로

The experiment was conducted on 40 intermediate Korean language learners in order to suggest the necessity of a level-specific dictionary that can help with the writing activities of Korean language learners who are learning Korean as a foreign language. An intermediate vocabulary book was created for the experiment. The experimental group (20) performed the writing task referring to the intermediate vocabulary book for five weeks, and the control group (20) performed the writing task referring to the bilingual dictionary (Naver dictionary). After a five-week experiment, syntactic proficiency was measured based on the final test and the extensive writing of free writing. The experimental group showed better fluency, complexity, and accuracy than the control group. Through this, we saw the possibility of using a level-specific dictionary to improve the writing ability of Korean learners. (Sookmyung Women’s University)
Influence of outstanding object memories on gist representation in visual long-term memory

Recent evidence suggests that visual information in memory can be stored as a hierarchy of representations from feature to object to gist of many objects (Brady & Alvarez, 2011; Brady et al., 2011, 2018). Memory for individual objects’ color is biased toward the gist in long-term memory, that is, mean color of a list of learned objects (Brady et al., 2018). Here we asked whether individual objects with outstanding features can affect the gist memory. Evidence from ensemble summary statistics suggests that salient or outstanding objects would either disproportionately influence the gist (Kanaya et al., 2018) or would be completely excluded from the gist (Haberman & Whitney, 2010). In our experiment, we compared color gist-based bias in long-term memory for categories with and without outliers using the continuous report paradigm (Wilken & Ma, 2004; Zhang & Luck, 2008). The experiment consisted of five identical blocks. In each block, participants were asked to memorize 40 images of real-world objects from four different categories, such as armchairs, scarves, or notebooks. Individual object colors for each category were generated from a normal distribution with a randomly chosen mean from a CIE Lab color wheel. In half of the categories across all blocks, one object was a color outlier: Its color was located fairly at “tails” of the categorical normal distribution. The participants were then tested with 2AFC for recognition memory with old and new exemplars from the same categories. The participants had to choose an old exemplar and report its color using a color wheel. Color reports were analyzed using the mixture models. Our results showed that individual memories were biased towards the categorical mean, with outliers excluded from the mean (gist) computation. This supports the Bayesian integration model as an adaptive mechanism in visual long-term memory (Brady, Schacter, & Alvarez, 2018).
Effect of Service Quality and Collection Quality on Use Decisions through Library Image as Intervening Variables at the University Library North Sumatra

Education is the main thing that must be owned, because education is the root of the civilization of a nation. Education is a basic requirement for everyone to be able to face the challenges ahead. To obtain education, many media can be used, including through the library. Libraries as a source of required information, where libraries include several activities ranging from collecting, storing, processing and maintaining books and other publications both printed and digital which are arranged in such a way according to a certain arrangement. One type of library that can support, expedite and enhance the quality of the implementation of the teaching and learning process is a university library. The purpose of this study was to analyze the effect of service quality and collection quality on the decision to use through library images as an intervening variable at the University of North Sumatra Library. The population of this research is students of USU S2 and S3 who have an active period in lectures with a total sample of 363 respondents. The sampling method used was cluster sampling. Data analysis was carried out through PLS-SEM using the SmartPLS 3.0 program. The results showed that service quality, collection quality and library image directly had a positive and significant effect on the decision of use, then indirectly the service quality and collection quality had a positive and significant effect on the decision to use through library images.
Fast Evaluation and Approximation of the Gauss-Newton Hessian Matrix for the Multilayer Perceptron.

We introduce a fast algorithm for entry-wise evaluation of the Gauss-NewtonHessian (GNH) matrix for the multilayer perceptron. The algorithm has aprecomputation step and a sampling step. While it generally requires $O(Nn)$work to compute an entry (and the entire column) in the GNH matrix for a neuralnetwork with $N$ parameters and $n$ data points, our fast sampling algorithmreduces the cost to $O(n+d/\epsilon^2)$ work, where $d$ is the output dimensionof the network and $\epsilon$ is a prescribed accuracy (independent of $N$).One application of our algorithm is constructing the hierarchical-matrix(\hmatrix{}) approximation of the GNH matrix for solving linear systems andeigenvalue problems. While it generally requires $O(N^2)$ memory and $O(N^3)$work to store and factorize the GNH matrix, respectively. The \hmatrix{}approximation requires only $\bigO(N r_o)$ memory footprint and $\bigO(Nr_o^2)$ work to be factorized, where $r_o \ll N$ is the maximum rank ofoff-diagonal blocks in the GNH matrix. We demonstrate the performance of ourfast algorithm and the \hmatrix{} approximation on classification andautoencoder neural networks.
Comparing Word Recognition by Humans and Deep Neural Networks

We compare word recognition by deep neural networks (DNN) and humans, asking whether the effects of increased pooling in the network can model crowding in human vision. We focus our experiments on a "Convolutional Recurrent Neural Network" CRNN [Shi et al. 2016], a popular model for word recognition. We study efficiency and crowding of the network on word recognition. To measure efficiency, we assess the network's performance in recognizing random 4-letter words in mono-space font at various contrast levels on a white noise background. We find that the network has a lower efficiency than the human observer: in our experiments, we found that the network has roughly one tenth of the 3\% efficiency that the humans attain[Pelli et al., 2003]. Letter crowding in human vision results in a minimum threshold spacing, independent of letter size. Crowding is usually explained as inappropriately large pooling for the task at hand. We studied how the network's size and spacing thresholds would be affected by changing its pooling from 2 to 32. The network with modified pooling was trained as specified by the original authors. We measured word recognition accuracy as a function of letter size and spacing. For humans tested at any given eccentricity, there are two regimes, one limited by crowding, and one limited by acuity[Song et al. 2014]. In the crowding regime, the threshold size is inversely related to spacing ratio. In the spacing regime, the threshold is independent of the spacing ratio. In the network, our manipulation revealed only one regime for all pooling values: a slope of -0.3 for a log-log plot of acuity vs spacing ratio, unlike the human data, which has slopes of -1(crowding limited) and 0(acuity limited). Based on these results, we believe that there are important limitations in how well this network models human reading.
Research on Teaching Reform of Applied Statistics Course Based on Network Cloud Platform under the Background of Innovation and Entrepreneurship Education

Abstract Under the background of innovation and entrepreneurship education, colleges and universities pay more and more attention to the teaching process reform, which requires strengthening the teaching reform. The traditional teaching mode can’t meet the needs of personnel training. Therefore, colleges and universities must remember to reform their own teaching mode of innovation and entrepreneurship education, which will enhance the ability of application-oriented personnel training. First of all, this paper analyzes the necessity of teaching reform of Applied Statistics. This article briefly describes some problems in the reform of applied statistics course teaching reform based on the network cloud platform.