Reviewerly/exHarmony huggingface dataset:
The Implementation of Simple Form Gamification in Companies

Companies to achieve their goals and vision and mission and create an orderly workatmosphere by making various efforts, including several implemented regulations. Thepurpose of this study is to assess the results of simple gamification implementation,namely by establishing a reward system for obeying the rules and punishing those whoviolating the rules like what PT. ABC. The management team's policy regulations were established as an element of employeeperformance appraisal in the form of behavioral observations and an assessmentcomponent of the results of these actions. To assess the results of the implementationof gamification, direct observation methods were used and random questionnaires weredistributed to obtain responses to the gamification that had been applied. Respondentsare employees who have worked followed by gamification at PT ABC for more than 6months.The results of the gamification implementation at PT. ABC shows the emergence of thedark side of gamification, namely that there are several loopholes in these regulationsthat are used to violate regulations that have been made for several reasons and there isdisappointment felt by some employees who have complied with gamification becausethey did not get the award that had been set
COVID-19에 대응하는 초등교사들의 원격수업 경험과 의미

The purpose of this study is to understand the difficulties of primary school teachers while conducting remote learning, corresponding to COVID-19, and to find out the implications gained while overcoming them. In this study, 6 teachers were interviewed in-depth. There are three main results of the study. First, At the beginning of remote learning, teachers' had very low self-efficacy, but by forming their new teaching system, teachers’ self-efficacy were recovered. Second, The difficulties and dilemma of distance learning were derived as follows. ‘Teacher’s energy exhaustion due to preparation for remote classes, differences in students' willingness to participate, environmental constraints, difficulty in designing classes suitable for grade level', etc. Third, The meanings that teachers found in the process of overcoming the difficulties of distance learning are as follows. ‘the importance of collaborating with colleagues, establishing good relationships with students, planning teaching according to the characteristics of remote learning, creating remote learning environment’ etc. In order for remote learning to take root through this research, we need to create an environment that teachers can concentrate on preparing remote learning, research for internal factors of students to participate in remote learning. Also create courses and teaching methods that can be applied to remote learning and to develop an integrated platform.
EFL Students Engagement in Reading Through Literature Circle

The aim of this study to investigate utilized literature circle method in learning recount text, in using literature circle method on learning recount text, the students expect Engage in learning process. This study used narrative inquiry. This paper is present by words and explanation. This research has conducted in one of the school at Karawang. Subject of this research are a students of XI class, consists of twelve. The data acquire from observation, interview and documentation. The result of this study explain that there are three phase do students engage learning to read in literature circle and students reaction about literature circle. The first, students are facilitated to choose the reading topic. The second, students are encouraged to have discussion session. The last, students are motivated to share their story to group. Learning reading use literature circle method increase students’ motivation, especially for sharing the story to other group and reading comprehension.
Geometric Reconstruction for Visual data Interpretation

Author(s): Lee, Minhaeng | Advisor(s): Fowlkes, Charless C. | Abstract: Reconstruction happens in the human brain every day. When humans watch their surrounding scene, they effortlessly infer dynamic representations of scene geometry from sequences of images. This higher dimensional reconstruction not only helps to interpret the input data but also provides an important basis for performing complex, higher level tasks. In spite of the importance of scene reconstruction, it has been considered a difficult task since the amount of information in the input image (2D) is insufficient to fully reconstruct scene geometry (3D). Performing such a task clearly requires the use of prior knowledge. In this thesis, we explore the advantages of machine learning-based techniques in order to reconstruct geometric information from images or videos. We utilize deep neural networks and probabilistic models and demonstrate their effectiveness in reconstructing geometric information.As the first part of this thesis, we estimate 2D motion flow from video, leveraging constraints of camera ego-motion and scene geometry. From a sequence of images, we first predict relative ego-motion between the input frames, and then reconstruct the camera trajectory. By considering the cycle consistency between 2D motion, depth and camera ego-motion, we train a model to reconstruct scene depth without additional supervision. These processes are trained using a self- supervised end-to-end convolutional neural network (CNN) architecture with motion field driven photometric consistency loss. To minimize accumulated error from imperfect local estimates, we predict relative reliability scores between every connected pair of input frames and then utilize them in global refinement.In the second part, we reconstruct spatio-temporal 4D model from a set of 3D models. From a set of multiple 3D models, we optimize transformation parameters from each model space to global space using a Gaussian mixture to model point observations. We optimize alignment parameters using expectation-maximization algorithm and estimate the temporal extent for each 3D patches by maximizing expected posterior probability over time. This spatial-temporal model allows us to perform object segmentation as well as infer the existence of occluded objects.
A Novel Qualitative Maximum a Posteriori Estimation for Bayesian Network Parameters Based on Computing the Center Point of Constrained Parameter Regions

Introducing parameter constraints has become a mainstream approach for learning Bayesian network parameters with small datasets. The QMAP (Qualitative Maximum a Posteriori) estimation has produced the best learning accuracy among existing learning approaches. However, the rejection-acceptance sampling employed in the QMAP algorithm for determining average BN(Bayesian Network) parameter values is time-consuming, particularly when the number of parameter constraints is large. This paper proposes a new analytical approach that enhances the learning efficiency of the QMAP algorithm without reducing its learning accuracy by treating the average value of the parameters as the center point of the constrained parameter region, which is a much more efficient method than the rejection-acceptance sampling method employed in the traditional QMAP algorithm. First, a novel objective function is designed and a constrained objective optimization model is constructed based on parameter constraints. Second, the constructed model is employed to obtain the center point of the constrained parameter region based on its boundary points, and the average parameter value is the average of all boundary points. The results obtained from a large number of simulation experiments with four benchmark Bayesian networks demonstrate that of the parameter learning accuracy of the proposed algorithm is slightly better than that the original QMAP algorithm under specific conditions, and the computational efficiency is substantially increased under all conditions.
“CASE-STUDY METHOD IN STUDENTS TRAINING”

The article discusses the specifics of using the case study method in teaching students. A specific mechanism is proposed, the logic of the development of such projects, the use of business incubator infrastructure.The essence of such a Case is that students should talk aloud about their intentions, the teacher should see the logic of their actions. In addition, in the event that any of the actions that are relevant to the case cannot be graphically depicted, the teacher can say the result of such an action. Such teaching methods help not only master the theory, but also test students their knowledge in practice in a situation as close to real as possible.&#x0D;
Abstraction-Guided Policy Recovery from Expert Demonstrations

Behavior cloning is a method of automated decision-making that aims to extract meaningful information from expert demonstrations and reproduce the same behavior autonomously. It is unlikely that demonstrations will exhaustively cover the potential problem space, compromising the quality of automation when out-of-distribution states are encountered. Our approach RECO jointly learns both an imitation policy and recovery policy from expert data. The recovery policy steers the agent from unknown states back to the demonstrated states in the data set. While there is, per definition, no data available to learn the recovery policy, we exploit abstractions to generalize beyond the available data and simulate the recovery problem. When the most appropriate abstraction for the given data is unknown, our method selects the best recovery policy from a set generated by several candidate abstractions. In tabular domains, where we assume an agent must call to a human supervisor for help if it is in an unknown state, we show how RECO results in drastically fewer calls without compromising solution quality and with relatively few trajectories provided by an expert. We also introduce a continuous adaptation of our method and demonstrate the ability of RECO to recover an agent from states where its supervised learning-based imitation policy would otherwise fail.
A Method to Improve the Security of Information Diffusion in Complex Networks— Node Trust Value Management Mechanism

In a sensing field-based placement of nodes the communication happens from the data acquisition points to the control center which is the receiver of data acquisition. In this project an algorithm ...
Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality

Medical entity extraction (EE) is a standard procedure used as a first stage inmedical texts processing. Usually Medical EE is a two-step process: named entityrecognition (NER) and named entity normalization (NEN). We propose a novelmethod of doing medical EE from electronic health records (EHR) as a single-step multi-label classification task by fine-tuning a transformer model pretrainedon a large EHR dataset. Our model is trained end-to-end in an distantly supervisedmanner using targets automatically extracted from medical knowledge base. Weshow that our model learns to generalize for entities that are present frequentlyenough, achieving human-level classification quality for most frequent entities.Our work demonstrates that medical entity extraction can be done end-to-endwithout human supervision and with human quality given the availability of alarge enough amount of unlabeled EHR and a medical knowledge base.
Intervention Force-based Imitation Learning for Autonomous Navigation in Dynamic Environments

Imitation learning is a data-driven approach that has proven to be successful in building autonomous navigation systems. One of the key tasks in imitation learning is collecting data, but data only collected by humans cannot include many types of data, such as deviating from the target path. If we use a model trained with such data, the deviation will accumulate, and returning to the target path will be difficult. Related studies have demonstrated the importance of correction data, and two types of solutions have been proposed: data augmentation and online sampling. In this paper, we propose a new online sampling method for acquiring correction data that is safe and effective, which uses a device that detects the force applied a steering wheel and accelerator pedal during an intervention. Autonomous navigation experiments are conducted using small vehicles to follow a specified path in static and dynamic environments. Our experimental results show that we can successfully separate intervention data from all collected data using intervention force and that using intervention data for model training is effective for improving the route-following. Also, our model can perform obstacle avoidance and generate appropriate control signals when encountering dynamic objects, such as pedestrians, at specific locations.
The Application of Online Caricature Momentcam as A Media for Biography Text Writing About Jokowi’s Outstanding Character By Senior High School Students

This research aims at examining the application of online caricature application MomentCam on student’s biography text writing about Jokowi’s outstanding characters. This research employed a qualitative method. The data in this research was in the form of students’ writing results from the application of MomentCam. The subject of this research was a tenth grade students of senior high school. The research particularly aims at identifying (1) students’ biography writing skills outcomes that explain positive and outstanding characters of Jokowi by using MomentCam online caricature application and (2) the description of positive and outstanding characters of Jokowi within students’ biography text writing by using MomentCam online caricature applicationThis research confirms that the utilization of MomentCam online caricature application is successful in assisting students’ writing ability, particularly in writing biography text which consisted outstanding characteristics of Jokowi.
Relationship between Fifth Grade Students’ Reading Interest and Reading Ability

The purpose of this study was to determine the relationship between reading interest and students' reading comprehension ability. Research approach using quantitative with correlational study method. The subjects of this study were students of class VA and VB at SD Negeri Karya Bhakti totaling 90 students. The sample was taken by 47 students, then this study was obtained using a five-scale questionnaire for the Reading Interest and Reading Comprehension Ability variables using tests. Therefore, from the data obtained, it was found that a moderate relationship between reading interest and reading comprehension ability was seen from the accumulated calculation results, which was 30% such as using the time to read books in the library, while 70% is influenced by other factors such as students who are motivated by the teacher to increase reading interest so that students can to read a comprehension and other factors are also influenced from outside of students, namely when doing tasks, students are guided and assisted by parents. . Based on the results of the research above, it can be concluded that there is a positive relationship between Reading Interest and Reading Comprehension Ability in class VA and VB students of SD Negeri Karya Bhakti Odd semester of the 2019/2020 academic year.
a The Influence of Learning Models to Students’ Writing Skill of The Explanation Text in Viii Grade at Junior High School (SMP)

Learning model is standardization for developing the one's skill and in the interpersonal situations. This study aims to determine the effect of learning models to the test results of the students’ writing skill of the explanation text in class VIII grade at junior high school. This research is an ex-post-facto research. The population of this research is all students of VIII grade at SMP of the 2019/2020 academic year. The sample in this study were students of class VIIIA and VIIIB SMP N 22 Semarang with the amount of 64 students who were taken by cluster random sampling, namely by choosing two classes randomly. Data collection techniques were questionnaires for student learning model data and tests to obtain data on the results of the skill to write explanation text for class VIIIA and VIIIB students. The results showed that (1) students with auditory learning models had the highest average score rather than the others who use visual and other learning models. (2) the learning model in this study has a significant effect on the results of the students; skill to write explanation text. (3) The learning model in this study gives e a large effective contribution 89.9% to the results of the students’ writing skill of explanation text; this proved that the learning model is one of the important factors for the students' achievement in writing skill of explanation text.
A Bibliometric Analysis and Visualisation of Research Trends in Nano diagnostic

Material engineering and or Nanotechnology had revolutionizedthe Nano diagnostic segment by improving the quality of images, detection,and by offering help in treatments.Thebibliometric analysis had been conducted to understand the active authors, organizations, journals, and countries involved in the research domain of “Nano diagnostic”.All published articles related to “Nano diagnostic” from “Scopus”, were analyzed using the VOS viewer to develop analysis tables and visualization maps.This article had set the objective to consolidate the scientific literature regarding “Nano diagnostic”and also to find out the trends related to the same.The most active journal in this research domain wasthe International Journal of NanoMedicine. The most active country was the United States of America. The leading organizations engaged in the research regarding Nano diagnostics were the Harvard University of USA and Shivaji University of India. The most active authors who had made valuable contributions related to Nano diagnostics were Li Z, and Wang L.
Analyzing Attention Mechanisms through Lens of Sample Complexity and Loss Landscape

Attention mechanisms have advanced state-of-the-art deep learning models in many machine learning tasks. Despite significant empirical gains, there is a lack of theoretical analyses on their effectiveness. In this paper, we address this problem by studying the sample complexity and loss landscape of attention-based neural networks. Our results show that, under mild assumptions, every local minimum of the attention model has low prediction error, and attention models require lower sample complexity than models without attention. Besides revealing why popular self-attention works, our theoretical results also provide guidelines for designing future attention models. Experiments on various datasets validate our theoretical findings.
L, LC and LCL Filter Choosing by C-Tool Application

Energy efficiency is crucial for sustainable development. Among the aspects related to energy efficiency in electricity, the power quality is a key topic. Active power filters (APF) are an efficient solution to improve the power quality. Designing APF requires solid knowledge about the means to evaluate the power quality and about the equipment available on the market. This paper presents a software application, called “C-tool”, which is capable to indicate the right elements available for the construction of the L, LC and LCL filters, included in the structure of APFs. This application was developed in the context of the project: „Knowledge Transfer Regarding the Energy Efficiency Increase and Intelligent Power Systems” (acronym CRESC-INTEL). This paper belongs to a series of articles aiming to present the C-tool application to the scientific community.
Scale-invariant image feature transformation technology

Р. Г. ЗацерковнийУкраїнська академія друкарства, вул
Hukuk öğrencilerinin Covid-19 pandemisi sırasında çevrimiçi mesleki yabancı dil kursu öğretimine ilişkin görüşleri

This study examined Law students’ perceptions about integrating technology and online instruction of Professional Foreign Language Course during COVID-19 pandemic. For this aim, 91 third-year and 86 fourth-year students were selected to participate in this study. They were required to answer an open-ended questionnaire regarding their insights about online education and its differences with traditional education. By collecting and analysing the qualitative data, it was revealed that 77% of the participants had negative attitudes towards online education. These participants reported that online education was challenging for them. The most challenging factors for the participants of this study were the lack of self-reliance and self-confidence, being stressful about losing family members, lack of immediate feedback from their instructor, being interrupted by their family members, Lack of concentration because of distraction by phone and other online applications, having technical problems, being stressful because of financial problems, and lack of group work. The findings also illustrated that 33% of the participants had positive attitudes towards online education of their course and found it satisfying means of education. Most of these participants stated that under favour of online education they could feel themselves as an independent learner, did not waste their time and money, had more contact with their instructor, also, online education brought technological facilities to their course, and finally they felt stress-free and confident.
CRYPTOCURRENCIES IN THEORY OF MONEY AND CREDIT: AN AUSTRIAN PERSPECTIVE

Theories of money and credit can be divided into two general categories: commodity theory of money and credit theory of money. Both categoriestheoretically question the acceptance of cryptocurrencies as money. The present study aimed to provide a new interpretation of the Theory ofMoney and Credit in relation to both theories mentioned above. First, the functions of public trust was taken as a commodity, which is consistentwith Karl Mengers views and the subjective theory of value. Based on this approach, the definitions of the credit theory of money will be acceptablewith a new interpretation. This new interpretation also involves an extension of Hayek's definition of money, which is consistent with Mises'definition of money. Then, the concept of intertemporal preferences, with an emphasis on the barter root of money based on Mises regressiontheorem, was used to show that cryptocurrencies can be accepted as money as far as they serve as private currency and suit the computability ofdynamics of Underlying Economic Realities with intertemporal preferences. Therefore, the main criterion for defining money is how it affectsintertemporal preferences
Asynchronous Modeling: A Dual-phase Perspective for Long-Tailed Recognition

This work explores deep learning based classification model on real-world datasets with a long-tailed distribution. Most of previous works deal with the long-tailed classification problem by re-balancing the overall distribution within the whole dataset or directly transferring knowledge from data-rich classes to data-poor ones. In this work, we consider the gradient distortion in long-tailed classification when the gradient on data-rich classes and data-poor ones are incorporated simultaneously, i.e., shifted gradient direction towards data-rich classes as well as the enlarged variance by the gradient fluctuation on data-poor classes. Motivated by such phenomenon, we propose to disentangle the distinctive effects of data-rich and data-poor gradient and asynchronously train a model via a dual-phase learning process. The first phase only concerns the data-rich classes. In the second phase, besides the standard classification upon data-poor classes, we propose an exemplar memory bank to reserve representative examples and a memory-retentive loss via graph matching to retain the relation between two phases. The extensive experimental results on four commonly used long-tailed benchmarks including CIFAR100-LT, Places-LT, ImageNet-LT and iNaturalist 2018 highlight the excellent performance of our proposed method.