Reviewerly/exHarmony huggingface dataset:
A Study on Simulated Teaching Design for Improving Authenticity: The Experience of Simulated Teaching and Teaching Practice

To improve the authenticity of the simulated teaching task in Korean, this study focused on the Korean language pre-service teachers’ experience of simulated teaching and teaching practice. The intention of comparing the perception on simulated teaching and teaching practice, which is located between real teaching and simulated teaching, was to gauge the authenticity of the class by experienced people.&#x0D; Third graders of the C University of Education, who experienced both simulated teaching and teaching practice in the same semester, were asked to write selfreflective reports on both experiences, and a qualitative content analysis method with teaching professionalism codes was used to analyze them. The simulated teaching task was designed as a 5-6 people group, choosing one of the five areas of Korean language education. The students’ activities were conducted with fellow pre-service teachers taking on the roles of students.&#x0D; Students and the class dynamics originating from teacher-student interaction were regarded as the key to the gap between simulated teaching and teaching practice. Hence, the simulated teaching task was presented in four stages based on them, in a way that authenticity would gradually increase.&#x0D; Simulated teaching has significance as a complementary means to improve teaching professionalism. This study contributes to systematizing simulated teaching tasks.
Case Study of Process-Oriented Writing Course Development: Focusing on the &lt;Korean Writing&gt; Course at UNIST

This study presents a writing education model based on a process-oriented approach that can be integrated in college writing courses and to confirm the significance of education. This paper introduces the process of developing the course structure and curriculum on &lt;Korean Writing&gt;, a liberal arts subject at Ulsan National Institute of Science and Technology. Next, learner response and the significance of education were examined, focusing on the postscript and review sections of the task activity written by learners in the expressive writing phase, i.e., the first half of class.&#x0D; In the &lt;Korean Writing&gt; course, which consists of two phases: expressive writing and academic writing. Learners write two articles during one semester, learning theory and practice simultaneously. In the expressive writing stage of the first half of this class, which is the focus of this paper, learners learn and practice the process and method of writing and the basic principles of writing experientially. At this time, they practice applying their writing knowledge - which has stayed only at the level of knowledge - to actual writing performance, and they are trained to observe and check their writing process and adjust their writing activities by writing a review of the activities prescribed in all practice courses.&#x0D; The significance of writing instruction presented in this paper is that it enables learners to apply their writing knowledge to actual writing performance, to understand empirically the process and method of writing that they knew only superficially, and to convey the importance of all the stages of writing and to understand the sequence of these steps recursively. Moreover, it helped learners to develop the power to control and direct their writing activities by understanding their own writing process. In this way, it was possible to confirm the importance of providing an actual writing experience in the writing classroom and the usefulness of continuously observing and communicating about learners' writing processes.
The Spatiotemporal Interpretation of Wave ‎Function May ‎Solve the Mystery of Quantum ‎Entanglement

In this paper, the wave function is interpreted as space-time: under any representation, the real part of ‎the wave function ‎represents space and the imaginary part represents time. A wave function represents ‎the quantum entanglement (correlation) ‎between time and space. The unitary evolution process is the ‎mutual transformation of space-time. The imaginary time ‎indicates that it can entangle with space in a ‎nonlocalized way and dominate the integrity (unification) of a quantum, which ‎is the essence of ‎quantum entanglement.‎
Hybrid encryption technique: Integrating the neural network with distortion techniques

This paper proposes a hybrid technique for data security. The computational model of the technique is grounded on both the non-linearity of neural network manipulations and the effective distortion operations. To accomplish this, a two-layer feedforward neural network is trained for each plaintext block. The first layer encodes the symbols of the input block, making the resulting ciphertext highly uncorrelated with the input block. The second layer reverses the impact of the first layer by generating weights that are used to restore the original plaintext block from the ciphered one. The distortion stage imposes further confusion on the ciphertext by applying a set of distortion and substitution operations whose functionality is fully controlled by random numbers generated by a key-based random number generator. This hybridization between these two stages (neural network stage and distortion stage) yields a very elusive technique that produces ciphertext with the maximum confusion. Furthermore, the proposed technique goes a step further by embedding a recurrent neural network that works in parallel with the first layer of the neural network to generate a digital signature for each input block. This signature is used to maintain the integrity of the block. The proposed method, therefore, not only ensures the confidentiality of the information but also equally maintains its integrity. The effectiveness of the proposed technique is proven through a set of rigorous randomness testing.
Design and Development of STLW Android App

The main objective of this android app development is to solve the problem of communication between college and students and it even solves many problems of students. The era of mobile technology opens the windows to the android app. The websites are vanishing and mobile phones are emerging. It's time to change from conventional websites to apps which has become a part of our daily routine. We are introducing the distributed, movable android application for our college and which would be the miniature of our college website. It works not only as a website but also as a small college management software. Our multipurpose application is considering the user as either an Albertian or non- albertian,students,faculties and administration. It gives us more comfort and a better user interface. It acts as an overview about the campus (college history,workshops,library etc.,) and also a feature of application forms which can be taken print. This app is also a navigator for our college location.
Measure-Theoretic Musings Cannot Salvage the Full Bayesian Significance Test as a Measure of Evidence

Abstract Ly and Wagenmakers ( Computational Brain &amp; Behavior :1–8, in press) critiqued the Full Bayesian Significance Test (FBST) and the associated statistic FBST ev: similar to the frequentist p -value, FBST ev cannot quantify evidence for the null hypothesis, allows sampling to a foregone conclusion, and suffers from the Jeffreys-Lindley paradox. In response, Kelter ( Computational Brain &amp; Behavior :1–11, 2022) suggested that the critique is based on a measure-theoretic premise that is often inappropriate in practice, namely the assignment of non-zero prior mass to a point-null hypothesis. Here we argue that the key aspects of our initial critique remain intact when the point-null hypothesis is replaced either by a peri-null hypothesis or by an interval-null hypothesis; hence, the discussion on the validity of a point-null hypothesis is a red herring. We suggest that it is tempting yet fallacious to test a hypothesis by estimating a parameter that is part of a different model. By rejecting any null hypothesis before it is tested, FBST is begging the question. Although FBST may be useful as a measure of surprise under a single model, we believe that the concept of evidence is inherently relative; consequently, evidence for competing hypotheses ought to be quantified by examining the relative adequacy of their predictions. This philosophy is fundamentally at odds with the FBST.
Multicultural Ability in Dental English Impressions of Hypermedia Email: A Specific Educational Paradigm

Given the noteworthy connection between special English education acuities and grade point average (GPA), as a directory of educational success, and settlement, it is essential to support the students’ English education talents. Two-way ANCOVA was engaged to evaluate the knowledge efficiency of the informal AI syllabus with dissimilar learning method (i.e., the series of perform schemes and the set of empirical learning) and masculinity. A cross-sectional appraisal was operated through online review and fractional slightest systems was applied in evaluating the records. Universal English for Dental Purposes (EDP) is educated from the opinion of actual conditions and it implies that education English is absorbed on a specific texts and particular terminology. But what is more significant education EDP terminology trusts on emerging statement services, difficulty-resolving, and choice production (Milosavljevic et al., 2015). The investigation recommends that scholars are of the opinion that more communication is continued through e-mail than might have been possible in communication interaction and more, that the English knowledge helps as valuable framework for more acquiring. On the foundation of the contributors' support and reaction, the probable educational worth of an e-mail may converse action in EDP program is undisputable.&#x0D; The placement-plan reaction provides durable indication of the possible educational profits of the e-mail scheme. Academic communication through e-mail replace to provide a real method to connect the opening between teaching EDP happenings and the energetic firm-changing education setting.
Segmenting water and shadow regions within WorldView imagery using local binary patterns

Many shadow detection algorithms pertaining to remotely sensed imagery exist. Several of these algorithms exploit the spectral characteristics of shadows within imagery to identify shaded regions. However, these algorithms can have problems when water is also present within the imagery because water shares similar spectral characteristics with shadows. Many of these algorithms are applied to small image subsets instead of the image as a whole and frequently are applied to urban environments that require additional use of the normalized difference water index or other features to affect the removal of water from the shadow mask. This diversity of the image scene content coupled with the complexity and wide variety of environmental conditions that satellite imagery can acquire makes reliably separating shadow and water within larger images a complex problem. Thresholding various spectral indices to produce segmentation maps can be challenging when large, complex, and often imbalanced scenes are captured, which may require manual adjustment of algorithmic parameters for different image areas. We present a methodology that makes use of the near-infrared channel using a watershed segmentation algorithm, local binary pattern measurements, and a support vector machine to classify shadow and water within a full WorldView scene. Results are promising with initial accuracies above 97% for both shadow and water.
Convex Trajectory Planning [About This Issue]

¸e, provides a tutorial on the optimization methods that have proven themselves in both theory and practice to be fast and reliable at producing dynamically feasible trajectories for nonlinear systems subject to nonconvex constraints.The authors and their colleagues are original developers of the methods discussed: lossless convexification and two sequential convex programming algorithms called SCvx and GuSTO.At their core, all three methods leverage a reliable, low-level convex optimizer that solves nonconvex control problems through problem reformulation, linearization, or both.
Virtualization using Docker container

158Many developers are having software packages for supplying them to other different users. These software packages are provided in the environment where all the required settings and dependencies are already available. This scenario gives the opportunity to all users to execute the applications in a similar environment. All the requirements of such users who are in need of providing their software can be fulfilled by Docker. At the same time, it provides the implementation of least level isolation (also known as process sandboxing) to other different processes that are running over the system in which Docker is hosted by utilizing features of Linux’s kernel. Such types of containers can be utilized as an alternative in some scenarios to conventional virtual machines because the overhead involved is negligible in most of the cases. The objective of this chapter is to present an overview of different methods of virtualization keeping in focus the Docker containers with a case study. This virtualization platform is different from the conventional approaches of virtualization, which are based on virtual machines. Docker-based 159projects are emerging from a single command-line tool to the entire operating system. Describing Dockers’ internal mechanism is the focus of this chapter with a demonstration of Docker API.
The Motivation of Chinese Students in Learning Foreign Languages Other Than English

This paper investigates the motivation of Chinese university students in learning Languages other than English (LOTEs) through the lens of Self-determination Theory, exploring the difference in motivational types among different subjects of 75 university students who are English, LOTE and non-language majors who took part in the questionnaire-based study. Students’ motivation was measured using Noels
OUTLIER DETECTION ON HIGH DIMENSIONAL DATA USING MINIMUM VECTOR VARIANCE (MVV)

High-dimensional data can occur in actual cases where the variable p is larger than the number of observations n. The problem that often occurs when adding data dimensions indicates that the data points will approach an outlier. Outliers are part of observations that do not follow the data distribution pattern and are located far from the data center. The existence of outliers needs to be detected because it can lead to deviations from the analysis results. One of the methods used to detect outliers is the Mahalanobis distance. To obtain a robust Mahalanobis distance, the Minimum Vector Variance (MVV) method is used. This study will compare the MVV method with the classical Mahalanobis distance method in detecting outliers in non-invasive blood glucose level data, both at p&gt;n and n&gt;p. The test results show that the MVV method is better for n&gt;p. MVV shows more effective results in identifying the minimum data group and outlier data points than the classical method.
Malaria Detection using Microscopic Image Analysis: A Convolution Neural Network Based Approach

Malaria is a potentially fatal disease which is caused by Plasmodium parasite. These parasites are transmitted to humans through the bites of female Anopheles mosquitoes which play the role of disease vector. Five types of plasmodium cause malaria named P. Falciparum, P. Vivax, P. Ovale, P. Knowlesi, and P. Malariae, Among the Plasmodium parasites, Falciparum and Vivax are particularly lethal to humans. Therefore, early detection of malaria is mandatory to avoid the loss of human life. Different automatic/semi-automatic malaria detection techniques are available in the literature, which reduces the chance of human errors in the prognosis of malaria. In recent years, deep learning-based methods have proven to be effective for object detection. Therefore, such methods have caught the attention of researchers to use for the detection of malarial parasites in human blood. In this paper, we proposed a Convolutional Neural Network (CNN) model, which detects malarial parasites in microscopic images of human blood samples with high accuracy. The proposed model comprises 15 layers. It has 8 convolution layers with ReLu activation function, 4 max-pooling layers, 1 flattening layer, and 2 fully connected layers. The proposed method has been evaluated using various statistical measures against existing state-of-the-art methods. The quantitative measures show the effectiveness of the proposed model. It has a 97.42% testing accuracy, 97.42% sensitivity, 97.41% specificity, 97.70% precision, 97.42% recall , 97.97% F1-score , 97.41% Area Under Curve (AUC), and 94.82% Mathews correlation coefficient.
MISSING CHILD IDENTIFICATION USING DEEP LEARING AND MULTICLASS SVM

In India a countless number of children are reported missing every year. Among the missing child cases a large percentage of children remain untraced. This paper presents a novel use of deep learning methodology for identifying the reported missing child from the photos of multitude of children available, with the help of face recognition. The public can upload photographs of suspicious child into a common portal with landmarks and remarks. The photo will be automatically compared with the registered photos of the missing child from the repository. Classification of the input child image is performed and photo with best match will be selected from the database of missing children. For this, a deep learning model is trained to correctly identify the missing child from the missing child image database provided, using the facial image uploaded by the public. The Convolutional Neural Network (CNN), a highly effective deep learning technique for image based applications is adopted here for face recognition. Face descriptors are extracted from the images using a pre-trained CNN model VGG-Face deep architecture. Compared with normal deep learning applications, our algorithm uses convolution network only as a high level feature extractor and the child recognition is done by the trained SVM classifier. Choosing the best performing CNN model for face recognition, VGG-Face and proper training of it results in a deep learning model invariant to noise, illumination, contrast, occlusion, image pose and age of the child and it outperforms earlier methods in face recognition based missing child identification. The classification performance achieved for child identification system is 99.41%. It was evaluated on 43 Child cases.
Consistent Experience Replay in High-Dimensional Continuous Control with Decayed Hindsights

The manipulation of complex robotics, which is in general high-dimensional continuous control without an accurate dynamic model, summons studies and applications of reinforcement learning (RL) algorithms. Typically, RL learns with the objective of maximizing the accumulated rewards from interactions with the environment. In reality, external rewards are not trivial, which depend on either expert knowledge or domain priors. Recent advances on hindsight experience replay (HER) instead enable a robot to learn from the automatically generated sparse and binary rewards, indicating whether it reaches the desired goals or pseudo goals. However, HER inevitably introduces hindsight bias that skews the optimal control since the replays against the achieved pseudo goals may often differ from the exploration of the desired goals. To tackle the problem, we analyze the skewed objective and induce the decayed hindsight (DH), which enables consistent multi-goal experience replay via countering the bias between exploration and hindsight replay. We implement DH for goal-conditioned RL both in online and offline settings. Experiments on online robotic control tasks demonstrate that DH achieves the best average performance and is competitive with state-of-the-art replay strategies. Experiments on offline robotic control tasks show that DH substantially improves the ability to extract near-optimal policies from offline datasets.
Research on the Influence of Multiple Factors on the Deformation of the Detection Institutions

The detection institution in this paper has higher requirements on precision and belongs to precision instrument. In order to improve the detection accuracy, the paper analyzes the deformation of the detection institution from many aspects, including the environmental temperature, the heat generated by the operation of the mechanism, and the structural stiffness. After determining the factors affecting the deformation of the institution, finite element analysis and experimental data analysis are carried out by software. According to the results of the analysis, the optimization scheme is designed, and the structure is optimized continuously to improve the accuracy of the detection mechanism.
Analysis on Teachers' Performance and Goal Perception of Innovative Education District Projects Using Cluster Analysis: Focusing on the case of Incheon Education Innovation District

The purpose of this study is to understand the actual status of teachers' performance perception of innovative education district projects, verify the effect of participation experience on teachers' perception, and analyze goal perception according to the characteristics of performance perception. To this end, a survey was conducted on 950 teachers in seven areas of the Incheon Education Innovation District. As a result of the study, first, the school area-linked vision and planning, sustainability, and educational cooperation system were derived as four sub-factors of teachers' performance perception of innovative education district projects. Second, as a result of analyzing the difference in performance perception according to the experience of participation in the innovative education district project, the perception of teachers with experience of participation was higher than that of teachers without participation experience. Third, based on the characteristics of teachers' performance perception of innovative education districts, it was classified into four clusters, such as; 'a cluster with very high overall perception', 'a cluster with very low overall perception', 'a cluster with high overall perception'. and 'a cluster with low overall awareness'. Fourth, according to the teacher's performance recognition cluster, the level of goal recognition showed a significant difference, and the higher the performance recognition, the higher the goal recognition. Based on the research results, policy and practical measures were proposed for the development of sustainable innovative education districts and business operation.