Reviewerly/exHarmony huggingface dataset:
Change Of Student's Position At Transition From Learning Activity To Educational Activity

The article considers the correlation of pedagogical phenomena “learning-cognitive activity” and “educational activity”, as well as the student’s position as the basis for solving the issue of removing the teaching process by the learning process. The following concepts are revealed: educational and cognitive activity, individualized educational activity, educational design, understanding, educational communication, reflection, autonomous cognitive position, and cognitive competence of a student. It is proved that the transition from learning-cognitive to educational activity is ensured by the development by students of universal cognitive procedures as methods of activity: educational design, understanding, educational communication, reflection. The educational results are defined as a set of parameters of the student’s cognitive competence and autonomous cognitive position and their continuity is shown. The idea of developing an independent position of the subject of learning and its subjectivity in teaching on the basis of didactic conditions is highlighted: consideration of the specificity of the essential characteristics of the development of the knowledge tradition in the content of education and changing the essence of pedagogical management of educational and cognitive activity. The characteristic of modern knowledge tradition is given, the purpose of pedagogical management of educational and cognitive activity is shown, the leading principles of pedagogical management of educational and cognitive activity of students for the transition to educational activity are revealed: didactic resonance, flexibility and situationality, eventuality and subsidiarity.
Discrepancies in digital hematopathology diagnoses for consultation and expert panel analysis

Abstract Digital pathology with whole-slide imaging (WSI) has a large potential to make the process of expert consultation and expert panel diagnosis more rapid and more efficient. However, comparison with the current methods is necessary for validation of the technique. In this study, we determined if digital assessment of whole-slide images of hematopathology specimens with a focus on the assessment of lymphoma can be used for consultation and panel diagnostics. Ninety-three histological specimens with a suspicion for lymphoma were assessed both with conventional microscopy and digital microscopy with a wash out period between assessments. A consensus diagnosis was based on full concordance between the pathologists or, in case of discordances, was reached at a joint session at a multi-headed microscope. In 81% of the cases, there was a full concordance between digital and light microscopical assessment for all three pathologists. Discordances between conventional microscopy and digital pathology were present in 3% of assessments. In comparison with the consensus diagnosis, discordant diagnoses were made in 5 cases with digital microscopy and in 3 cases with light microscopy. The reported level of confidence and need for additional investigations were similar between assessment by conventional and by digital microscopy. In conclusion, the performance of assessment by digital pathology is in general comparable with that of conventional light microscopy and pathologists feel confident using digital pathology for this subspecialty.
Simplifying Architecture Search for Graph Neural Network

Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios. To obtain optimal data-specific GNN architectures, researchers turn to neural architecture search (NAS) methods, which have made impressive progress in discovering effective architectures in convolutional neural networks. Two preliminary works, GraphNAS and Auto-GNN, have made first attempt to apply NAS methods to GNN. Despite the promising results, there are several drawbacks in expressive capability and search efficiency of GraphNAS and Auto-GNN due to the designed search space. To overcome these drawbacks, we propose the SNAG framework (Simplified Neural Architecture search for Graph neural networks), consisting of a novel search space and a reinforcement learning based search algorithm. Extensive experiments on real-world datasets demonstrate the effectiveness of the SNAG framework compared to human-designed GNNs and NAS methods, including GraphNAS and Auto-GNN.
An approach to improving the analysis of literature data in Chinese through an improved use of Citespace

Citespace, a visualization-based analysis tool, has been used to analyze the literature data by visualizing the patterns and potential trends of a field. Previous studies show that when used for analyzing the literature in Chinese, Citespace could only conduct very basic analysis, different from its use in analyzing the literature data in English. To address this limitation, this study presents an approach to improving the use of Citespace for effective analysis of literature data in Chinese. The approach employs data-processing and data-analysis scripts in data collection, knowledge map generation, and interpretation steps to improve the accuracy and comprehensiveness of analysis of literature data in Chinese. An empirical evaluation has been conducted to demonstrate the effectiveness of the approach.
Human Activity Recognition-Oriented Incremental Learning with Knowledge Distillation

Recently, a variety of different machine learning methods improve the applicability of activity recognition systems in different scenarios. For many current activity recognition models, it is assumed that all data are prepared well in advance and the device has no storage space limitation. However, the process of the sensor data collection is dynamically changing over time, the activity category may be continuously increasing, and the device has limited storage space. Therefore, in this study, we propose a novel class incremental learning comprehensive solution towards activity recognition with knowledge distillation. Besides, we develop the representative sample selection method to select and update a specific number of preserved old samples. When new activity classes samples arrive, we only need the new classes samples and the representative old samples to preserve the network’s performance for old classes while identifying the new classes. Finally, we carry out experiments using two different public datasets, and they show good accuracy for old and new categories. Besides, the method can significantly reduce the space required to store old classes samples.
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment

Deep Neural Networks were first developed decades ago, but it was not until recently that they started being extensively used, due to their computing power requirements. Since then, they are increasingly being applied to many fields and have undergone far-reaching advancements. More importantly, they have been utilized for critical matters, such as making decisions in healthcare procedures or autonomous driving, where risk management is crucial. Any mistakes in the diagnostics or decision-making in these fields could entail grave accidents, and even death. This is preoccupying, because it has been repeatedly reported that it is straightforward to attack this type of models. Thus, these attacks must be studied to be able to assess their risk, and defenses need to be developed to make models more robust. For this work, the most widely known attack was selected (adversarial attack) and several defenses were implemented against it (i.e. adversarial training, dimensionality reduc tion and prediction similarity). The obtained outcomes make the model more robust while keeping a similar accuracy. The idea was developed using a breast cancer dataset and a VGG16 and dense neural network model, but the solutions could be applied to datasets from other areas and different convolutional and dense deep neural network models.
A useful taxonomy for adversarial robustness of Neural Networks

Adversarial attacks and defenses are currently active areas of research for the deep learning community.
3D image augmentation using neural style transfer and generative adversarial networks

In this paper, we take the very first step in using Neural Style Transfer and Generative Adversarial Networks for the task of 3D image augmentation. With this approach, more data may be generated for object recognition and visualization purposes without having to fully reconstruct 3D objects. To the best of our knowledge, this is the first report that describes image augmentation in the 3D domain using Integral Imaging and Deep Learning. The author assumes that readers have some knowledge regarding Deep Learning.
Information And Communication Technologies In Additional Art Education Of Children

The relevance of the study is due to the need for competent psychological and pedagogical support for the use of information and communication technologies in the additional art education of children of preschool and primary school age. The purpose of the article is to systematize and determine the developmental capabilities of information and communication technologies in additional art education of preschool and young children, as well as to identify principles and conditions for the successful implementation of these opportunities in the artistic and creative development of children as part of a multi-artistic approach. The main research method was a survey of teachers of children's art schools and parents of children of preschool and primary school age, there was subsequent analysis and systematization of data. Empirical data on preferences in the use of information and communication technologies by additional education teachers with the aim of developing children's artistic creativity are presented. The problem of the scientific substantiation of the choice of digital educational resources and information and communication technologies for the artistic and creative development of preschool children and primary school children in the modern information space is disclosed. The means of information and communication technologies, the most popular among teachers of additional art education, are found out, the conditions conducive to the successful artistic and creative development of children are identified: increasing the information, communication and technological competence of participants in the educational process, coordinated pedagogical interaction, the formation of cultural and family values.
Fused Text Recogniser and Deep Embeddings Improve Word Recognition and Retrieval

Recognition and retrieval of textual content from the large document collections have been a powerful use case for the document image analysis community. Often the word is the basic unit for recognition as well as retrieval. Systems that rely only on the text recogniser (OCR) output are not robust enough in many situations, especially when the word recognition rates are poor, as in the case of historic documents or digital libraries. An alternative has been word spotting based methods that retrieve/match words based on a holistic representation of the word. In this paper, we fuse the noisy output of text recogniser with a deep embeddings representation derived out of the entire word. We use average and max fusion for improving the ranked results in the case of retrieval. We validate our methods on a collection of Hindi documents. We improve word recognition rate by 1.4 and retrieval by 11.13 in the mAP.
Competencias del docente en educación online en tiempo de COVID-19: Universidades Publicas de Honduras

Es el centro de educacion superior que rige la tarea de gestion y promocion del conocimiento, con el fin del desarrollo de profesionales, es por ello que la labor del docente es imperante en la formacion de estos En el presente articulo se analizan las competencias de los docentes, propias o adquiridas en el campo digital, para el empleo de plataformas y recursos educativos como sistemas de gestion de aprendizaje y contenido e incorporando otros como la mensajeria instantanea y videoconferencia que no han sido creados con este fin Con una metodologia de caracter cuantitativo se describe y relacionan las variables de la investigacion realizada por la Universidad Nacional Autonoma de Honduras;sobre las necesidades y abordaje de metodos de ensenanza-aprendizaje utilizados por los docentes en el primer periodo de clases durante la pandemia declarada por la Organizacion Mundial de la Salud Los resultados reflejan la conversion de un entorno presencial a uno digital en este periodo de emergencia a nivel mundial por parte de los docentes de esta institucion, mostrando resiliencia y adaptabilidad al cambio Se concluye que el docente universitario desarrolla sus competencias digitales y adecua sus genericas de manera satisfactoria al nuevo escenario educativo mundial Alternate abstract:It is the higher education center that governs the task of knowledge management and promotion, in order to develop professionals, which is why the work of the teacher is prevalent in their training This article analyzes the competencies of teachers, their own or acquired in the digital field, for the use of educational platforms and resources such as learning and content management systems and incorporating others such as instant messaging and videoconferencing that have not been created for this purpose Using a quantitative methodology, the variables of the research carried out by the National Autonomous University of Honduras are described and related;on the needs and approach of teaching-learning methods used by teachers in the first period of classes during the pandemic declared by the World Health Organization The results reflect the conversion of a classroom environment to a digital one in this period of emergency worldwide by the teachers of this institution, showing resilience and adaptability to change It is concluded that university teachers develop their digital skills and adapt their generic skills in a satisfactory way to the new world educational scenario
Refining Co-operative Competition of Robocup Soccer with Reinforcement Learning

Reinforcement learning (RL) has been widely applied in RoboCup soccer games because of its great potential in enhancing the performance for the model-free competitive scenarios. In recent years, researchers have made a lot of efforts on reducing the input size of RL in order to speed up the training process of the RoboCup soccer agents. In this work, we proposed an improved DQN algorithm named Hierarchical Movement Grouped Deep-Q-Network (HMG-DQN). The algorithm can be trained when actions are in high hierarchy of movement groups, especially in the co-operative competition scenarios, such as 2v1 and 3v2 break-throughs. We conducted the experiments on a simulation platform based on RoboCup SPL rules, and the results showed that our improved algorithm has significantly improved the winning rate compared with DQN.
Reducing the Collision Damage Done to the Tips of Steel Needles during Integrated Piercing by Using Shape Optimization with Feature Selection

Recent research has shown that, during integrated piercing, the yarn tension can displace the needles from the centers of the holes in the piercing template. To reduce the damage done to the needle tips when the needles and the hole walls collide when the needle displacement is too large, this paper proposes a method for optimizing the needle shape that satisfies the strength constraint while targeting minimum needle displacement. First, the optimal objective function and strength constraint conditions for the tip displacement of the integrated puncture needle are established, which are affected by many factors. Then, the feature selection method of machine learning is used to reduce the dimensionality of the objective function after data reduction, and the feasible region of key features is reduced to avoid falling into the local best point in the optimization iteration. Finally, machine vision is used to measure experimentally the tip displacement of a needle array. The results show that the average tip displacement is reduced by 18.16–31.42% and the collision risk is reduced from 43.14% to 5.54%. It shows that the optimization method of needle shape based on feature selection is effective for reducing needle tip impact damage during integrated piercing.
Conversations: The Open Libraries Project – Interview with Chris Freeland

In this installment of “Conversations,” Chris Freeland, director for the Open Libraries project with the Internet Archive, discusses his work with the Open Libraries project and the National Emergency Library.
Relationships of Ambiguity Tolerance, Anxiety, Nursing Competency, and Satisfaction with Simulation Training

본 연구는 간호대학생의 모호함의 관용, 불안, 시뮬레이션 간호역량, 시뮬레이션 학습만족도 간 관련성을 파악하고 시뮬레이션 교육 전후 모호함의 관용 수준을 비교하기 위해 시도되었다. 일 대학 간호대학생 95명을 대상으로 구조화된 설문지를 이용하여 대상자의 모호함의 관용, 불안을 측정한 후 8주간 시뮬레이션 기반 교육을 진행하였다. 코스가 끝난 후에 모호함의 관용, 간호역량, 시뮬레이션 학습만족도를 측정하였다. 수집된 자료는 기술통계, 대응 표본 t 검정, 카이제곱 검정, 상관관계 분석을 시행하였다. 연구결과, 간호대학생의 모호함을 견디는 능력은 65점 중 36.76±5.13으로 중간 수준이었다. 시뮬레이션 교육에서 학생의 불안 수준과 시뮬레이션 학습만족도는 음의 상관관계(r=-.21, p=.048)가 있었고 시뮬레이션 교육이 끝난 후 측정한 모호함의 관용 수준과 시뮬레이션 간호역량은 양의 상관관계(r=.24, p=.021)가 있었다. 8주간의 시뮬레이션교육 전후로 모호함의 관용 수준은 37.71±5.71점으로 유의한 변화가 있었다(t=-2.06, p=.042). 시뮬레이션 학습만족도는 시뮬레이션 간호역량과 강한 상관관계(r=.42, p<.001)를 보여서 시뮬레이션 교육의 설계 및 운영의 중요성을 확인하였다. 개인의 심리적 특성이 시뮬레이션 기반 교육의 학습 성과와 관련이 있으므로 추후 이를 고려하여야 할 것이다.This study was conducted to identify the relationships of ambiguity tolerance, anxiety, nursing competency, and satisfaction with simulation training. A total of 95 senior nursing students currently studying at a university participated in the study in 2016. Students completed a questionnaires before and after 8-week simulation training course. Data were analyzed by descriptive statistics, Pearson’s correlation, Chi-square, and paired t-test. As a result of the study, nursing students ambiguity tolerance was moderate. The higher the learner s anxiety, the lower the satisfaction with simulation training, and the higher the level of ambiguity tolerance, the higher the nursing competency. Satisfaction with simulation training showed a strong correlation with nursing competencey, which confirmed the importance of simulation education design and operation. The 8-week simulation training has developed the ambiguity tolerance of students, which can be helpful in making decisions in actual clinical practice that are unfamiliar and complex to resolve. The findings suggest that the psychological factors of learners should be taken into account to maximize achievement in high fidelity simulation training.
Imitation Learning with Sinkhorn Distances

Imitation learning algorithms have been interpreted as variants of divergence minimization problems. The ability to compare occupancy measures between experts and learners is crucial in their effectiveness in learning from demonstrations. In this paper, we present tractable solutions by formulating imitation learning as minimization of the Sinkhorn distance between occupancy measures. The formulation combines the valuable properties of optimal transport metrics in comparing non-overlapping distributions with a cosine distance cost defined in an adversarially learned feature space. This leads to a highly discriminative critic network and optimal transport plan that subsequently guide imitation learning. We evaluate the proposed approach using both the reward metric and the Sinkhorn distance metric on a number of MuJoCo experiments. For the implementation and reproducing results please refer to the following repository https://github.com/gpapagiannis/sinkhorn-imitation.
Deep learning and video quality analysis: towards a unified VQA

Video makes up 80% of internet traffic today and is still rising. Most of it is meant for human consumption. But for 40 years, the video coding industry has been using mean-squared error-based PSNR, effectively the most basic full reference (FR) video quality measure, as the main tool for assessing video quality despite long known poor correlation to subjective video ratings. Moreover, in the encoder, the sum of absolute differences (SAD) is used instead of MSE to save multiplications. Meanwhile, many current video applications such as YouTube do not have access to a pristine reference and have had to develop ad hoc methods to attempt to monitor the volumes of video in their servers in a challenging no reference (NR) setting. For this, they have in part leaned on the Gaussianity of natural scene statistics (NSS), and evaluating how video distortions affect or alter those statistics to create a measure of quality. An entire cottage industry has sprung up to create both full-reference and no-reference video quality assessment (FR-, NR-VQA) measures, that can adequately meet the needs for monitoring and stream selection, in the massive worldwide video services industry. These two fields have so far gone their separate ways, as there seemed no sensible way to bring them under one roof. In this paper, we attempt a first synthesis of FR and NR VQA, which we simply call FastVDO Quality (FVQ). It incorporates all the lessons learned from the Video Multi-Assessment Fusion (VMAF) algorithm introduced by Netflix in 2016, the NSS-based assessment concepts developed by Univ. of Texas and Google to treat the NR case, culminating in the algorithms VIIDEO and SLEEQ, as well as our own research over the past several years in using learning-based methods in VQA. We provide some early indications that this approach can bear fruit for both NR and FR-VQA and may even offer state-of-the-art results in each field.
How Organizational Management Affect Employees’ Motivation and Service Quality: A Study of Kentucky Fried Chicken (KFC)

This study aims to determine how organizational management affects organization’s employees, their motivation and service quality they deliver to the customers. This research was conducted on a world-famous Restaurant Chain - Kentucky Fried Chicken. KFC is a subsidiary of Yum! Brands, the world’s most popular chicken restaurant chain. KFC has been serving Finger Lickin’ Good Chicken since 1930 and has settled itself in 118 countries all over the world with a total of 18,875 restaurants. KFC is recognized for its high customer satisfaction. It is proven that how management treats their workers can affect their worker’s motivation and company’s service quality. A positive attitude of management towards workers might result in higher motivation which contributes to greater performance along with high quality of service. The study comprises of both primary and secondary data to obtain the relevant information & findings.
Independence and D-separation in Abstract Argumentation

We investigate the notion of independence in abstract argumentation, i.e., the question of whether the evaluation of one set of arguments is independent of the evaluation of another set of arguments, given that we already know the status of a third set of arguments. We provide a semantic definition of this notion and develop a method to discover independencies based on transforming an argumentation framework into a DAG on which we then apply the well-known d-separation criterion. We also introduce the SCC Markov property for argumentation semantics, which generalises the Markov property from the classical acyclic case and guarantees the soundness of our approach.