Reviewerly/exHarmony huggingface dataset:
Development of a Basic Liberal Arts Curriculum Related to Coding to Strengthen Core Competencies －Focusing on Python programming basics

Today's university education derives the core competencies of future talent. In addition, in order to strengthen these targeted core competencies, teaching and learning methods and educational evaluation methods are being studied through various approaches. Within this trend, the need for performance evaluation is being emphasized more in a curriculum that requires technical, procedural, and experiential knowledge in order that it might be evaluated in connection with core competencies, such as coding education. Also, the need to develop a rubric that can guarantee the reliability of the results as an objective standard to evaluate performance is growing. This study develops a core competency convergence rubric to strengthen the core competency targeted in the coding-related basic liberal arts curriculum. Furthermore, by actually applying this rubric, we hope to closely examine the appropriateness and effectiveness of this developmental tool. The subjects of this study were 59 students who responded to the questionnaire after taking coding-related courses in the first semester of 2021. The questionnaire prepared, distributed, and obtained in the Google questionnaire environment was used as analysis data. As a result of the quantitative questionnaire analysis, we found that the positive perception among the students was high in terms of their improvement of creativity and convergence thinking, which was achieved by linking the curriculum and the core competencies, along with the effectiveness and suitability of the developed rubric. In the qualitative analysis, we found such effects as: “direction guidance”, “performance standard(standard)”, “improvement of task completion”, “excellence of approach”, “self-inspection tool” and “providing psychological satisfaction”.
Face Age Estimation Based on CSLBP and Lightweight Convolutional Neural Network

As the use of facial attributes continues to expand, research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion, the age estimation of faces is a challenging process.This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability.Improving face age estimation based on Soft Stagewise Regression Network (SSR-Net) and facial images, this paper employs the Center Symmetric Local Binary Pattern (CSLBP) method to obtain the feature image and then combines the face image and the feature image as network input data.Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness.The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.
Comparison of Farmers' Experience in Indonesia on HCI

The purpose of this research is to describe a comparison of the situation of farmers in three regions in Indonesia on human computer interaction (HCI). With the results of this study, it can be mapped how to communicate with them. This research method uses qualitatvie method with primary data collection through interviews to farmers in Bangil, Lembang and Manado, equipped through the observation, and literature review. The results showed in general, they feel unfamiliar with information technology. In Bangil area, the average age of farmers is elderly, so it is very rare on HCI. In Lembang, many farmers are millennial so HCI is most relevant. Manado farmers, half are on HCI and the other half do not touch HCI. The conclusion of this study, the location and age of farmers determine the level on HCI in their agricultural activities.
Item Recommendation Based on Monotonous Behavior Chains

In the field of e-commerce, recommendation systems can accurately provide users with products and services of potential interest, thereby enhancing users' online shopping experience. "Explicit" feedback and "implicit" feedback are mostly studied in two relatively independent research fields. In the actual interaction process between users and commodities, there is a kind of signal between the two Monotonic dependence, that is, sparse and reliable explicit signals must imply dense and noisy implicit signals. In this paper, a special "monotonic behavior chain" structure is proposed to constrain the two signals, and a series of user-commodity interaction behaviors is mapped into a user-commodity multi-stage binary interaction diagram. The two feedback signals were combined and the complete interaction was simulated between the user and the product. Then a depth model framework GAERE was proposed based on the graph auto-encoder, which converts the matrix completion problem of the traditional recommendation system into the problem of graph link prediction. Four realistic data sets were applied to evaluate the effectiveness of the proposed method. The model shows competitiveness on standard collaborative filtering benchmarks. In addition, the application of graph convolutional network was further explored to process graph structure data in recommendation system from the perspective of user behavior intention.
Automatic Pose and Shape Initialization via Multiview Silhouette Images

Automatic pose and shape initialization is the first step to conquer the problem of human tracking, acquiring prior knowledge about the tracking subject. It is crucial for accomplishing a successful tracking. In this paper, we present a simple and effective framework to automatically calibrate the human pose and shape by integrating a data driven shape parameterization into the skeletal animation pipeline and optimizing the template body model against multiview silhouette images to acquire the tracking subject shape and pose information. A PCA based approach is proposed to summarize the space of human body variations in height, weight, muscle tone, gender, body shape. Multiview analysis by synthesis optimization in the hierarchial order is employed to realize pose and shape calibration. Finally, experiments on HumanEvaII and Human3.6m dataset demonstrate our approach is very effective and robust to real world situations.
Passive BB84 decoy-state protocol with a flawed source

Abstract Passive generation of quantum states in quantum key distribution (QKD) is an elegant solution to utilise an internal source of quantum randomness of the transmitter’s scheme. It can be profitable, for example, in setups running at high repetition rates. However, the original analysis of passive protocol doesn’t consider real-life effects of a laser source, therefore possibly breaches the unconditional security of the scheme. Here we take into account the influence of various laser imperfections and make a refined comparison between active and passive setups. We show that the new bound on the secret key rate of the passive protocol stays similar to the one of an active scheme, thus proving passive generation to be a practical means of constructing QKD systems.
A Survey on Data Security Challenges and Their Solutions in Cloud Computing

As technology increases rapidly, the demand for cloud computing is also growing day by day due to its features like on-demand services, cost efficiency, and rapid elasticity. The work which was traditionally done on-premise, now performed remotely across the internet or off-premise. And with increasing popularization of cloud computing the security becomes a major concern. Because user stores their confidential data on the cloud so they require high-security policies from cloud service providers to keep user’s data safe and confidential. In this paper, we are going to explain some major threats associated with data in cloud computing at all stages of the data life cycle and also discuss some solutions to emerge from the threats and these solutions are discussed based on structural properties of the cloud storage system.
Guided Random Projection: A Lightweight Feature Representation for Image Classification

Modern neural networks [e.g., Deep Neural Networks (DNNs)] have recently gained increasing attention for visible image classification tasks. Their success mainly results from capabilities in learning a complex feature mapping of inputs (i.e., feature representation) that carries images manifold structure relevant to the task. Despite the current popularity of these techniques, they are training-costly with Back-propagation (BP) based iteration rules. Here, we advocate a lightweight feature representation framework termed as Guided Random Projection (GRP), which is closely related to the classical random neural networks and randomization-based kernel machines. Specifically, we present an efficient optimization method that explicitly learns the distribution of random hidden weights instead of time-consuming fine-tuning or task-independent randomization configurations. Further, we also report the detailed mechanisms of the GRP with subspace theories. Experiments were conducted on visible image classification benchmarks to evaluate our claims. It shows that the proposed method achieves reasonable accuracy improvement (more than 2%) with moderate training cost (seconds level) compared with other randomization methods.
A New Technique Research on Data Mining

Classification is a basic problem in the field of data mining. It is one of the key steps that intelligent systems take when extracting meaningful information from complex and massive data. This paper introduces a new classification approach based on the theory of human vision from the perspective of bionics. The experimental results show that the new algorithm is efficient for the classification.
Learning the Features ignored by Classification Models using Staged Modeling Variational Autoencoder

Classification networks usually only save the specified factor associated with labels. We propose a probabilistic graphical model (PGM) to learn the features ignored by arbitrary classification networks using staged modeling. To implement the staged PGM, we introduce Staged Modeling Variational Autoencoder (SMVAE), in which the first stage can apply arbitrary classification models to encode the specified factor, then optimizing the Evidence Lower Bound (ELBO) given optimal specified factor to compress the features ignored at the first stage into the unspecified factor. Besides, SMVAE can learn the disentangled unspecified factors unsupervised by further decomposing the ELBO given the optimal specified factor. At last, we introduce Adain based on Infusion Training in SMVAE to learn more details to reconstruct data. Detailed experiments are given to evaluate the disentanglement and performance of SMVAE.
Plurals and Second-Order Logic

Abstract While plural logic can be interpreted in monadic second-order logic, the full system of second-order logic cannot be interpreted in plural logic. This means it is formally possible to eliminate plural logic in favor of monadic second-order logic. However, a number of philosophical considerations militate against such an elimination. The conclusion of this chapter echoes that of the preceding ones: although the two systems can occasionally be used for similar purposes, the notions they represent are different and must be kept apart.
Using Corpus Methods to Triangulate Linguistic Analysis Edited By JesseEgbert and PaulBaker. Routledge, 2020. 300 pp., ISBN: 9780367777050. 96£

International Journal of Applied LinguisticsEarly View BOOK REVIEW Using Corpus Methods to Triangulate Linguistic Analysis Edited By Jesse Egbert and Paul Baker. Routledge, 2020. 300 pp., ISBN: 9780367777050. 96£ Mehrdad Vasheghani Farahani, Corresponding Author Mehrdad Vasheghani Farahani submittingpapers@yahoo.com mv64nyjo@studserv.uni-leipzig.de orcid.org/0000-0001-7665-2054 Department of Applied, Linguistics and Translation Studies, Leipzig University, Leipzig, Germany Correspondence Mehrdad Vasheghani Farahani, Department of Applied Linguistics and Translation Studies, Leipzig University, Germany. Email: submittingpapers@yahoo.com; mv64nyjo@studserv.uni-leipzig.deSearch for more papers by this author Mehrdad Vasheghani Farahani, Corresponding Author Mehrdad Vasheghani Farahani submittingpapers@yahoo.com mv64nyjo@studserv.uni-leipzig.de orcid.org/0000-0001-7665-2054 Department of Applied, Linguistics and Translation Studies, Leipzig University, Leipzig, Germany Correspondence Mehrdad Vasheghani Farahani, Department of Applied Linguistics and Translation Studies, Leipzig University, Germany. Email: submittingpapers@yahoo.com; mv64nyjo@studserv.uni-leipzig.deSearch for more papers by this author First published: 18 September 2021 https://doi.org/10.1111/ijal.12391Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Light Field Image Super-Resolution via Mutual Attention Guidance

Deep learning-based methods have prompted light field image super-resolution to achieve significant progress. However, most of them ignore aligning different sub-aperture features of light field image before aggregation, resulting in sub-optimal super-resolution results. We aim to propose an efficient feature alignment method for sub-aperture feature aggregation. To this end, we develop a mutual attention mechanism for sub-aperture feature alignment and propose a mutual attention guidance block (MAG). MAG achieves the mutual attention mechanism between the center feature and surrounding feature with the center attention guidance module (CAG) and the surrounding attention guidance module (SAG). CAG aligns the center-view feature with the surrounding-view feature and generates the refined surrounding-view feature, while SAG aligns the refined surrounding-view feature with the original surrounding-view feature to implement bidirectional center-view, and surrounding view features alignment. Based on MAG, we build a Light Field Mutual Attention Guidance Network (LF-MAGNet) constructed by multiple MAGs in a cascade manner. Experiments are performed on commonly-used light field image super-resolution benchmarks. Our method achieves superior qualitative and quantitative results to other state-of-the-art methods, which demonstrate the effectiveness of our LF-MAGNet.
Teaching Reform of Data Structure Course Based on Rain Classroom and Classroom Ideology under OBE Concept

From the perspective of blended teaching reform, the goal of teaching reform integrating OBE and classroom ideology is discussed, the supporting relationship between learning content and ability literacy is elaborated, and the teaching case design is carried out with rain classroom tools. Taking the "Huffman Tree" as an example, a hybrid teaching design based on OBE, curriculum ideology and rain classroom is proposed. Finally, the effectiveness of teaching is evaluated from the results of performance comparison.
유학생의 서술형 답안 평가 결과 분석 - 한국어 교육 전공자를 중심으로 -

This study analyzed the characteristics of the examination essays of international students and statistically verified which of the characteristics of the examination essays influenced the evaluation. Firsty international student essays showed that the higher the linguistic accuracy, the longer the total length, the richer the semantic structure, the higher the coherence, while the less formality of the style. Second, the higher the linguistic fluency, the longer the answer and the richer the semantic structure. Third, the total amount of text was correlated with all of the richness, accuracy of content, and coherence of contents, and strong correlation between richness and coherence of contents. In addition, the factors influencing the evaluation were ‘average number of propositions per topic’ indicating the richness of contents and ‘morpheme numbers’ indicating the amount of text. After interviewing the evaluator and the learner, the evaluators found that linguistic accuracy was not a decisive factor but showed a lack of knowledge of the major if it was below a certain level. In addition, it was found that the precise use of the terminology and the use of the coagulation device are considered important factors. (Yonsei University)
Water Body Extraction of Hyperspectral Remote Sensing Image Based on Combining Residual U-Net and Conditional Random Field

Aiming at the problem that the training difficulty of neural network model increases with the increase of the number of network layers due to the disappearance of gradient, a method by combining residual U-Net and conditional random field for water body extraction from hyperspectral remote sensing image is proposed. A deeper model based on the U-Net semantic model by adding residual units is constructed, and then a conditional random field is connected after the output of the model to further optimize the output category and refine the image segmentation results. As experimental data, the Zhuhai-1 hyperspectral satellite image is selected to training and validation for the proposed method. The experimental results show that, compared with the water index method, support vector machine, U-Net semantic model and residual U-Net model, the proposed method has the best effect in water body extraction from the hyperspectral remote sensing image.
Mathematical Modeling of Motion Control of Air Manipulation System and Its Stabilization

The chapter provides a solution to the problem of synthesizing kinematic and dynamic models of the UAV manipulator, describes the structure of the UAV Manipulator Modeling software system, which provides modeling of control and movement of the UAV manipulator and its stabilization under the influence of disturbances. The output data is movement graphs of the center of mass of manipulator, trajectory graphs of each link of the manipulator, graphs of the angular velocity and angular acceleration of the links. These graphs allow you to evaluate the stability of the manipulator when working under the influence of disturbances and without it. When designing the motion control system of the UAV manipulator, it is necessary to calculate the dynamics of the manipulator, determine the equations of motion for each link of the manipulator and calculate the parameters of the manipulator controller.
University graduate satisfaction profile as an education quality criterion

Research background: Despite a fairly long tradition of measuring students’ assessment of the quality of obtained university education, there are still many unresolved issues in this area. The aim of the study: to theoretically substantiate the concept of a profile of graduates’ satisfaction with university educational programs including several parameters. The satisfaction profile serves as a criterion of the quality of education, at the same time reflecting students’ subjective assessment of the learning outcomes received at the university that they correlate with their expectations at the stage of admission. The concept is proposed in association with the monitoring of university graduates’ satisfaction with educational programs developed and tested as part of the research work at the Russian State Social University. Methods: graduate satisfaction is assessed using quantitative (questionnaires, the results of which are processed in SPSS) and qualitative (interviews) research methods. The proposed and tested method for the assessment of university graduates’ satisfaction with educational programs can serve as a basis for the development of university systems for monitoring graduates’ satisfaction with educational programs and education quality management and the satisfaction profile can become a universal criterion of education quality assessment not only in Russian but also in foreign universities. Results and novelty: The novelty and significance of the study results lie in the fact that the rating of educational process factors significant for graduates’ satisfaction provides information that can be used for successfully placing the direction of training in the market of educational services. Moreover, two ratings of the importance of the identified factors are constructed. The first one reflects the respondents’ direct assessment that can be used in advertising and information campaigns in the market of educational services. The second rating shows indirect assessment providing a more adequate image of the real impact of various factors on graduates’ satisfaction with educational programs; the results of this rating can be used in the intra-university quality management.
Determination of fracture toughness using the compression fracture technique

Many engineering materials of importance can be mechanically characterized as brittle or quasi-brittle solids. Examples include many hard polymers, ceramic composites, and low ductility metals. Fracture toughness is the measure of crack extension as a function of applied load and the resistance of the deforming material to the advance of cracking. In this investigation we use digital image correlation (DIC) for observing and studying the process of macroscopic crack initiation and propagation, and applied linear elastic fracture mechanics (LEFM) to determine the fracture toughness of these materials. We will address issues such as loading configuration for stable crack growth, diagnostics for identifying crack initiation and quantifying the extent of crack growth, and scheme for extracting the stress intensity factor at the moving crack tip. The technique described in this report has been applied to many different materials, but for the purpose of illustrating the application of the technique and data processing scheme, we choose the following materials: graphite and beryllium as materials described in this paper. This technique can be used at any rate with the only limitation being the resolution and rate at which images can be captured.
Medical Image Denoising in MRI Reconstruction Procedure

With the rapid development of computer technology, deep learning can be used in nearly every field, and it always has the potential to achieve a high efficiency performance. Specifically, in the field of medical images, it makes doctors possible to distinguish and diagnose diseases in a more accurate way. Medical images like any other form of imaging techniques are affected by noise and artifacts. There are many types of noise, such as quantum, random, electric, and gaussian noise, etc. The presence of noise affects image clarity and may obstruct the recognition and analysis of diseases. The traditional image denoising method has much more limitation when came to medical images, and the results cannot meet some specific medical image standards. Hence, denoising of medical images in deep learning can be an important technique for further medical image processing. In this work, we conducted a deep learning method, which is a sparse dilated convolution neural network based on compressed sensing, for medical image denoising.
Asset market equilibria in cryptocurrency markets : Evidence from a study of privacy and non-privacy coins

©2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)