Reviewerly/exHarmony huggingface dataset:
Image Denoising Based on Dictionary Learning of Mean Corrected Atoms

Image denoising is an important pre-processing step in image processing. Different algorithms have been proposed in past few decades with varying denoising performances. In recent years, the image-based sparse representation can better characterize the essential features of images. Image sparse representation based on adaptive overcomplete dictionary can satisfy the sparsity and image noise separability, and has been successfully applied to image denoising, forming a kind of image denoising algorithms based on sparse representation. K-singular Value Decomposition algorithm is currently the most representative, the most widely applied adaptive learning dictionary image denoising algorithm. Based on the K-Singular Value Decomposition algorithm, this paper proposes an image denoising algorithm based on dictionary learning of mean correction atoms. In the dictionary initialization process, the dictionary learning scheme of mean corrected atoms is proposed to effectively suppress noisy atoms. The experimental results show that this algorithm can obtain better image restoration quality than these similar algorithms.
A Forest from the Trees: Generation through Neighborhoods

In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors). Although human learning makes seamless use of both learned perceptual features and instance recall, current generative learning paradigms only make use of one of these two components. Take, for instance, flow models, which learn a latent space that follows a simple distribution. Conversely, kernel density techniques use instances to shift a simple distribution into an aggregate mixture model. Here we propose multiple methods to enhance the latent space of a flow model with neighborhood information. Not only does our proposed framework represent a more human-like approach by leveraging both learned features and memories, but it may also be viewed as a step forward in non-parametric methods. In addition, our proposed framework allows the user to easily control the properties of generated samples by targeting samples based on neighbors. The efficacy of our model is shown empirically with standard image datasets. We observe compelling results and a significant improvement over baselines. Combined further with a contrastive training mechanism, our proposed methods can effectively perform non-parametric novelty detection.
Substantiation of the professional competence structure of biology teacher at specialized school

The article deals with the concept of professional competence of the Biology teacher at profession-oriented school.On the basis of the analysis of educational and professional programs of Biology teacher training at the institutions of higher education in Ukraine, such components as integral, general and professional are distinguished in the structure of their professional competence.At the intersection of general competences (provide the opportunity for professional pedagogical activity) and professional (reflect the features of the field of Biology, modern methods and teaching Biology techniques), specialized professional competence of the Biology teacher at upper secondary school is formed.According to the author, it is a dynamic integrated system of theoretical knowledge, practical skills, emotions, motives, attitudes, values and experience of the individual, which provides successful implementation of daily professional educational activities in terms of profile organization of training.Motivational-value, cognitive, activity and personality-reflexive components are distinguished, criteria and indicators of their formation are determined in the structure of the professional competence of the teacher at the specialized school.The survey results of Biology teachers of Zhytomyr region, students of bachelor's and master's degrees of higher education, different universities of Ukraine are presented in the paper.The study confirmed the positive perception of the specialized education introduction by the pedagogical community and the lack of internal motivation and willingness of teachers to implement it.The results of the experiment ascertaining stage demonstrated the necessity for continuous professional training of Biology teachers to work in a specialized upper secondary school; introduction of changes in the system of teacher training in institutions of higher and postgraduate pedagogical education.Study of the cognitive and operational activity components of the professional competence of bachelors, masters, and Biology teachers revealed that among all categories of respondents, specialized knowledge about the organization of specialized education in upper secondary school is the worst formed; realization of scientific, research, career guidance work; methods of differentiated teaching in classes of various specializations and profiles.It is difficult to work with creative, gifted students, organize their participation in competitions, contests, research projects.Students, according to self-assessment, consider their own biological, research, methodological knowledge and skills to be formed insufficiently.Moreover, at the levemaster's degreel quantity of individuals with sufficient and high level of this component of professional
Hubungan Kreativitas dengan Hasil Belajar Bahasa Indonesia

The purpose of this study was to describe creativity with the learning outcomes of Indonesian students in class X SMAN 4 Payakumbuh. The method used was the correlation method. Determination of the sample is done by cluster random sampling or randomly, then which amaunted to 20 students. The results found that there was a significant relationship between student learning creativity and Indonesian language learning outcomes because the results of hypothesis testing proved that tcount was greater than ttable namely, 4.244&gt; 1.734. Conclusion, there is a significant relationship between learning creativity with Indonesian language learning outcomes &#x0D; Keywords: Creativity, Learning Outcomes
Image Completion with Large or Edge-Missing Areas

Existing image completion methods are mostly based on missing regions that are small or located in the middle of the images. When regions to be completed are large or near the edge of the images, due to the lack of context information, the completion results tend to be blurred or distorted, and there will be a large blank area in the final results. In addition, the unstable training of the generative adversarial network is also prone to cause pseudo-color in the completion results. Aiming at the two above-mentioned problems, a method of image completion with large or edge-missing areas is proposed; also, the network structures have been improved. On the one hand, it overcomes the problem of lacking context information, which thereby ensures the reality of generated texture details; on the other hand, it suppresses the generation of pseudo-color, which guarantees the consistency of the whole image both in vision and content. The experimental results show that the proposed method achieves better completion results in completing large or edge-missing areas.
Building a Knowledge Base Shell Based on Exploring Text Semantic Relations from Arabic Text

This research aims at focusing on building a knowledge base shell which task is to automatically build the knowledge base component from text sources.The proposed research aims at considering the Arabic text sources as one of the main challenges in due to the difficulty in processing Arabic text that suffers from the high inflection, shortcuts, and the varieties in letters' representation.The research restored the confidence in the term frequency method and the research adopts the semantic relations network representation.The research applied the experiment for building the weeds' identification facts' part in the knowledge-based system.The experiment applied the proposed framework on two sources, the precision, recall, and f-score have been measured which resulted in an average of 91.2%, 91.1%, and 91% respectively.The evaluation metrics provide a promising perspective in the proposed approach with more focus on the bottlenecks for enhancements.
Integration Of Educational Models Of Digital And Classical Universities

The analysis of the relevance of the educational models of the University of NTI 20.35 and the classical university is carried out on the example of Yaroslav-the-Wise Novgorod state university, their main differences and possible ways to integrate and create a common model. The educational experiment took place in the form of a joint intensive program on the basis of NovSU on 09.12.2018 in the areas of the NTI markets: Neuro, Aero and Technet. The analysis of factors contributing to the updating of the coordination of conflicting systems of digital and classical education in the Novgorod region is presented. The article describes the step-by-step organization of the stages of introducing the value foundations of the University of NTI into the structure of the educational activity of Novgorod State University, preparation from the development of the model through the creation of a developing environment to the protection of student projects. The methodology of integrating the individual educational trajectory of the student on the digital platform of the University 20.35 and the project activities of students to develop projects for the NTI markets in the educational plans of the university are fully presented. Considerable attention is paid to the risks analyzed during the design of the intensive course and taken into account in its implementation. The success criterion for the implemented integrated educational model is a lot of attention from the academic community of the country.
Academic and Independent Drawing During the First Year of Study in the System of Training Future Designers

The aim of the article is to consider the most efficient way to organize the learning process of the academic subject "Drawing" during the first year of study in the system of training designers. This process should fully meet the requirements of educational reform, where an academic drawing would be directly connected with creative activity and would contribute to the formation of knowledge and skills necessary for a designer. The methodology of the research is to find the most efficient way of organization of the learning process where classroom work, self-studying work and homework could be effectively combined with scientifically grounded educational-methodological system of professional art training. The scientific novelty lies in the balanced structuring of the tasks in the curriculum of the subject "Drawing" for both classroom work with an academic focus, and self-studying work with a creative focus which will enhance the effectiveness of teaching the subject in the face of limiting the number of academic hours. Conclusions. The subject "Drawing" still remains the basis of artistic education in the system of training of designers, but it requires some changes in the ways teaching and methodical process are organized, which are caused by the specific character of the subject closely related to the designer’s work.
Sistem Peramalan Kebutuhan Hidup Layak Minimum (Kapita/Bulan) Kota Bandung

The importance of inflation forecasting is used as a reference for estimating the Need for Living (KHL). If inflation can be predicted with high accuracy, it can certainly be used as the basis for government policy making in anticipating future economic activity. This study aims to produce a model of inflation data forecasting system, the forecasting results can be used as a reference for determining the Decent Living Needs (KHL) of a single worker in one month. The data used in forecasting is inflation data for January 2011 - December 2017 while the KHL data for the food and beverage category is obtained from the food price portal. The method used in this study is the SARIMA-SES hybrid method. In forecasting the inflation rate where the data is in the form of time series, the SARIMA-SES hybrid method can show more accurate forecasting results than using a single method. Based on the comparison of the overall forecasting model and by combining the SARIMA (1,0,1) (1,0,1) 12 and SES models with alpha 0,6 the smallest error value with MAD value 0,114, MSE 0,017 and 0,39% for MAPE. From these results, it was gathered that inflation forecasting in Bandung City using the SARIMA-SES hybrid method has a high accuracy value so that the results of the KHL value calculation with the forecasting inflation value approach the actual value. From these values can be used as a reference for the decision making of a single worker in fulfilling their life needs one month.
DIALOGIC INTERACTIVE SPEAKING SKILLS ASSESSMENT IN THE EXPERIENTIAL TEACHING OF TECHNICAL ENGLISH TO TERTIARY SCHOOL STUDENTS

The article discusses the outcomes of the research on dialogic interactive skills assessment in teaching Technical English to tertiary school students. The authors propose to implement dialogic collaborative interaction as both the medium of instruction and an alternative assessment tool. Theoretically, the study relies on the assumption that Technical English speaking skills acquisition is carried out through different types of communicative interaction and collaborative dialogic interaction in particular. The procedure proposed for speaking skills assessment has been primarily targeted at sustaining oral collaborative interaction along with assessing students’ communicative competence. The research represented relies on the experimental teaching English speaking skills to the 1st year Bachelor’s students (n=84) at Igor Sikorsky Kyiv Polytechnic Institute. The assessment criteria include conformity of utterances to the topic; speaking tempo; relative grammatical, lexical and phonetic accuracy; cohesion and coherence; compliance with the stylistic and etiquette conventions of English; diversity of grammatical and lexical devices for reaching mutual understanding between the dialogue participants; the interlocutor’s speaking initialisation; reaching consensus; and background knowledge deduction. The results of the research confirm that the assessment procedure introduced contributes to boosting oral production if the techniques of supporting collaborative dialogic interaction are applied by the interlocutors. Collaborative dialogue positively affects the development of students’ communicative competence and acquisition of such skills as perceiving, understanding and decoding the content of oral texts, identifying the communicative purpose of interlocutor’s utterances through analysing verbal and non-verbal means; defining the style and genre of utterances and aligning them with stylistic registers in English.
Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations

Learning compact representations for shapes (binary images) is important for many applications. Although neural network models are very powerful, they usually involve many parameters, require substantial tuning efforts and easily overfit small datasets, which are common in shape-related applications. The state-of-the-art approach, ShapeOdds, as a latent Gaussian model, can effectively prevent overfitting and is more robust. Nonetheless, it relies on a linear projection assumption and is incapable of capturing intrinsic nonlinear shape variations, hence may leading to inferior representations and structure discovery. To address these issues, we propose Infinite ShapeOdds (InfShapeOdds), a Bayesian nonparametric shape model, which is flexible enough to capture complex shape variations and discover hidden cluster structures, while still avoiding overfitting. Specifically, we use matrix Gaussian priors, nonlinear feature mappings and the kernel trick to generalize ShapeOdds to a shape-variate Gaussian process model, which can grasp various nonlinear correlations among the pixels within and across (different) shapes. To further discover the hidden structures in data, we place a Dirichlet process mixture (DPM) prior over the representations to jointly infer the cluster number and memberships. Finally, we exploit the Kronecker-product structure in our model to develop an efficient, truncated variational expectation-maximization algorithm for model estimation. On synthetic and real-world data, we show the advantage of our method in both representation learning and latent structure discovery.
Neuro-fuzzy inference system based face recognition using feature extraction

Human face recognition (HFR) is the method of recognizing people in images or videos. There are different HFR methods such as feature-based, eigen-faces, hidden markov model and neural network (NN) based methods. Feature extraction or preprocessing used in first three mentioned methods that associated with the category of the image to recognize. While in the NN method, any type of image can be useful without the requirement to particular data about the type of image, and simultaneously provides superior accuracy. In this paper, HFR system based on neural-fuzzy (NF) has been introduced. In the NN system, backpropagation (BP) algorithm is used to update the weights of the neurons through supervised learning. Two sets of the image have been used for training and testing the network to identify the person. If the test image matches to one of the trained sets of the image, then the system will return recognized. And if the test image does not match to one of the trained sets of the image, then the system will return not recognized. The feature extraction methods used in this paper is Geometric moments and Color feature extraction. The recognition rate of 95.556 % has been achieved. The experimental result illustrations that the association of two techniques that provide better accuracy.
Continuous speech segmentation using local adaptive thresholding technique in the blocking block area method

Continuous speech is a form of natural human speech that is continuous without a clear boundary between words. In continuous speech recognition, a segmentation process is needed to cut the sentence at the boundary of each word. Segmentation becomes an important step because a speech can be recognized from the word segments produced by this process. The segmentation process in this study was carried out using local adaptive thresholding technique in the blocking block area method. This study aims to conduct performance comparisons for five local adaptive thresholding methods (Niblack, Sauvola, Bradley, Guanglei Xiong and Bernsen) in continuous speech segmentation to obtain the best method and optimum parameter values. Based on the results of the study, Niblack method is concluded as the best method for continuous speech segmentation in Indonesian language with the accuracy value of 95%, and the optimum parameter values for such method are window = 75 and k = 0.2.
Subject Review: Comparison between RSA, ECC &amp; NTRU Algorithms

Encryption technology is one of the main means of protecting information security and includes many forms in terms of digital signatures, authentication, system security, confidentiality and data integrity and other functions. Therefore, Encryption and decryption are the best solution to protect data, especially not only sensitive data and protect them from attackers who aim to steal information. In this paper we use asymmetric encryption algorithms such as (RSA, Elliptic Curve, NTRU) that are used to send data over the network to provide security to users. We work on a review of the algorithms mentioned because of their importance in the encryption side and will give a summary of the performance of its work.
Using convolutional neural networks for breast cancer diagnosing

During the last few years, Convolutional Neural Networks (CNN) have been widely used in Computer-Aided Detection and the medical image analysis. The main idea of this paper is to modify CNN’s architectures to achieve the better sensitivity and the precision for detecting breast cancer at an early stage compared to existing methods. For this purpose, several factors were considered before CNN training such as the data processing, model, dataset, etc. In the proposed model the following hyperparameters were the following: the dropout rate 0,2, epoch 38 and batch size 33. Besides the hyperparameters, two fully connected layers in the modified model were used. An average recall (sensitivity) in the recent works was 74%. The precision and recall of proposed model for breast cancer classification were 66,66% and 85,7%, respectively.
Investigation of accessory elements of representative petrologic radioactivity carriers at Stara Planina, Serbia

The present study deals with the abundance of the most common accessory elements of radioactivity carriers on the Stara Planina mountain at four defined locations. All investigated locations have increased radioactivity, greater than 200 cps, up to 1250 cps. In all examined samples the following elements were detected and their concentrations were determined: Zr, Rb, W, Mo, Sn, Zn, Cu, As, Sb, Ba, Ni, Cr, V, and Ti. The analysis showed that depending on the sample, elevated concentrations of all detected elements except titanium were found. As all localities containing the listed detected elements are located near watercourses, all the present elements, and in particular, those with elevated concentrations can be relatively easily transferred to the environment by water action. Likewise, there is the possibility of eolic erosion from the investigated deposits and tailing dams whereby these chemical elements and/or radionuclides would be distributed to areas away from the primary sources of natural radioactivity. These have a high risk of spreading and therefore have harmful or radioecological effects on the environment. The study indicated a need for adequate monitoring, and risk assessment of the examined locations, which could prevent the distribution of these elements further into the environment. [Projects of the Serbian Ministry of Education, Science and Technological Development, Grant no. III 43009 and Grant no.
The Use Of Digital Media As An Instrument Of Higher Economic Education

Economic education nowadays is characterized by a growing trend to digitalization. The importance of e-learning in the educational system and the development of digital economy cannot be over-emphasized. A literature review results of some previous studies on e-learning from different parts of the world are presented. The paper is aimed at exploring the necessity and prospects of digital education and e-learning of foreign languages towards promoting best practices in higher education. It was discovered that effective communication between lecturers and students is enhanced through e-learning which has the potential to make learning more interesting, interactive and to be realized faster and more efficiently than existing learning methods. The study is focused on the role of digital technologies and educational platforms in teaching foreign languages at economic universities. The paper reflects real needs and priorities within foreign language teaching at the Saratov Socio-Economic Institute of Plekhanov Russian University of Economics. The aim of this article specifically is to focus on the role of digital technologies and educational platforms in teaching foreign languages at the economic universities. The main research methods are the analysis of scientific works concerning digital teaching. The investigation is carried on the basis of the educational platform Moodle. Results show that students are in general positively inclined towards e-learning and would be willing to take online courses. However, the majority of students do not want the full integration of Information Technology into the study process, meaning that the traditional learning methods combined with IT are preferred.