Reviewerly/exHarmony huggingface dataset:
METACREATIVE ABILITIES: SCIENTIFIC APPROACHES AND EXPERIMENTAL INTROSPECTIVE RESEARCH METHODS

Aim. The purpose of our study is based on the urgent need of nowadays in the complex psychological diagnosis of metacreative abilities, which requires the creation of a reliable, valid, standardized methodology for the psychological diagnosis of a given personal construct, which would be based on the modern scientific psychological model of the diagnosed phenomenon (structure, features, etc.) Material and methods. As the methods and techniques of empirical research we use correlation analysis procedure by K. Pearson, questionnaires, interviews, method of expert evaluations. The empirical sample, which actually tests our proposed methodology for compliance with the basic psychometric criteria, includes the students of the first, third and fifth years of full-time studying of the Faculty of Physics and Mathematics, Faculty of Psychology, Economics and Management, Faculty of Technology, Elementary and Professional Education of the State higher educational institution «Donbass State Pedagogical University» (town Slovyansk), a total of 420 people aged between 18 and 30 years (specialization «Music», «Choreography», «Design and Fashion Design», «Basic Design», «Accounting and Taxation», «Management», «Psychology», «Mathematics», «Physics», «IT»). Results. Our methodology is evaluated according to four testing criteria: standardization, norms, reliability and validity. They are the criteria that are used to demonstrate the scientific adequacy of using this technique to measure individual mental differences. Observing the predictive power, it should be noted that during the year between the first and second surveys of the members of our sample by this method, those who had the results above average demonstrated significant creative achievements (artistic creativity, writing of scientific works, the development of advanced technologies of training and work). Finally, assessing constructive validity, we note that this test correlates positively with other relatively valid procedures for measuring creativity (convergent validation), and there is also a negative correlation between the results of the given methodology and the tests that measure conceptually different personality qualities. Conclusions. We have proposed an experimental and introspective technique «Metacreative Abilities» ( EIMCA ) which consists of two blocks – «Awareness in the field of creativity» and «Reflection in the field of creativity». Psychometric evaluation of our methodology was conducted using the criterion of standardization, normativity, reliability and validation with those to prove the scientific adequacy of the use of this technique for measuring the stated individual psychological differences.
Rotation-sensitive and direction-resolved homodyne laser-Doppler vibrometry method for simultaneous measurement of rotational and translational vibrations of a rigid object using a 1D array detector

In this paper, we introduce a new rotation-sensitive and direction-resolved homodyne laser-Doppler vibrometry method for rigid body vibration study that is based on the discrete Fourier-transform of successive 1D profiles of the moving interference fringes recorded with a 1D array detector. By investigating the temporal evolution of the spatial phase distribution of the 1D profiles of the interference fringes, the out-of-plane translational and rotational vibrations of the vibrating object are simultaneously determined. We use a direction-cosine-based approach to establish the theory of the measurements. The merits and limitations of the proposed method is described. We show that the proposed method can be used for detection of both tip and tilt changes and out-of-plane displacement measurements of a rigid body using a couple of parallel 1D array detectors. In addition, we show that the presented method can be also used on optical diffused surfaces by adding three lenses in a corner-like arrangement to the detecting system.
Use of neural networks for forecasting of the exposure of social network users to destructive impacts

Introduction: In social networks, the users can remotely communicate, express themselves, and search for people with similarinterests. At the same time, social networks as a source of information can have a negative impact on the behavior and thinking oftheir users. Purpose: Developing a technique of forecasting the exposure of social network users to destructive influences, based onthe use of artificial neural networks. Results: A technique has been developed and experimentally evaluated for forecasting Ammon’stest results by a social network user’s profile using artificial neural networks. The technique is based on the results of Ammon’s testfor medical students. For training the neural network, a set of features was generated based on the information provided by socialnetwork users. The results of the experiments have confirmed the dependence between the data provided by social network users andtheir psychological characteristics. A mechanism has been developed aimed at prompt detection of destructive impacts or social networkusers’ profiles indicating the susceptibility to such impacts, in order to facilitate the work of psychologists. The experiments haveshown that out of the four investigated types of neural networks, the highest accuracy is provided by a multilayer neural network. Inthe future, it is planned to expand the set of features in order to achieve a better accuracy. Practical relevance: The obtained results canbe used to develop systems for monitoring the Internet environment, detecting the impacts potentially dangerous for mental health ofthe young generation and the nation as a whole.
A Comparative Study of Interactive Segmentation with Different Number of Strokes on Complex Images

Interactive image segmentation is the way to extract an object of interest with the guidance of the user. The guidance from the user is an iterative process until the required object of interest had been segmented. Therefore, the input from the user as well as the understanding of the algorithms based on the user input has an essential role in the success of interactive segmentation. The most common user input type in interactive segmentation is using strokes. The different number of strokes are utilized in each different interactive segmentation algorithms. There was no evaluation of the effects on the number of strokes on this interactive segmentation. Therefore, this paper intends to fill this shortcoming. In this study, the input strokes had been categorized into single, double, and multiple strokes. The use of the same number of strokes on the object of interest and background on three interactive segmentation algorithms: i) Nonparametric Higher-order Learning (NHL), ii) Maximal Similarity-based Region Merging (MSRM) and iii) Graph-Based Manifold Ranking (GBMR) are evaluated, focusing on the complex images from Berkeley image dataset. This dataset contains a total of 12,000 test color images and ground truth images. Two types of complex images had been selected for the experiment: image with a background color like the object of interest, and image with the object of interest overlapped with other similar objects. This can be concluded that, generally, more strokes used as input could improve image segmentation accuracy.
Using Instagram to Improve Students’ Ability and Interest in Writing Descriptive Paragraph

This research aimed at finding out the improvement of the students’ writing ability and interest by using Instagram as media. To find out the improvements, the writer used pre-experimental design with one group pre-test and post-test design and questionnaire. The population of this research was the eleventh-grade students’ at SMA Muhammadiyah 1 Unismuh Makassar, academic year 2017/2018. The sample was class XI IPA taken by purposive sampling technique which consisted of 10 students. It employed five meetings (one meeting for pre-test, three meetings for treatment, and one meeting for post-test and questionnaire). It employed writing test as instrument. The research findings indicated that the application of using Instagram was effective in improving the students’ writing skill in term of content and organization. It was proved by the value of the total t-test was 6.30, which was higher than the value of t-table (1.833). This indicated that (H1) was accepted, and (H0) was rejected. It means that there was a significant difference of the students’ writing skill before and after using Instagram in classroom. The students’ interest in writing also improved after the treatment. It was shown by students’ answers in the questionnaire. Most students gave positive respons toward the use of instagram to improve their interst in writing, even they think that this method can be applied in classroom to attract their attention and interest. Therefore, this research is very useful for learning process as a reference for teachers to teach in classroom.
Communicative practices of young people in social networks

Modern young people more and more often spend their time on social media, which they use for place for friendly chat, or place to spend leisure time, or place for buying things, or space for professional growth, or one of many other reasons. In the process of interaction, youth use various communication practices in social networks, the main of which have been given in the article. Main functions of social media, specifics and classification of user communities, their influence on communicative behavior of users have been described. Instruments of transferring social information on social media: emoji and stickers, photos and pictures (memes included), together with preferences of usage of this instrument by users have been analyzed. It has been proved, that peculiarities of lexis typical for virtual communication along with non-verbal means (such as memes) transform the language of users. The phenomenon of fake (an account hiding the real essence of a user), as well as reasons for creation of fake accounts and their functionality, have been considered. Degree and form of showing aggression in virtual communication have been analyzed. Specifics of content in dependence from typology of social communities, where users are interacting (groups by interest, groups with entertaining content, news communities, official companies groups, e-commerce pages, groups of citizens of a specific city (or workers of a specific place), help groups, blog communities) has been studied. Common regularities of both creation and usage of content in internet communities have been noted.
Edge Detection and Enhancement of Color Images Based on Bilateral Filtering Method Using K-Means Clustering Algorithm

In the recent, object recognition and classification has become an emerging area of research in robotics. In many image processing applications accurate edge detection is very crucial and plays an important role. The continuous and connected edges detection of color images is important in many applications such as satellite imagery and discover cancers in medical images, etc. The detection of these edges is very difficult and most of the edge detection algorithms do not perform well against broken and thick edges in color images. This paper proposed an edge detection and enhancement technique using bilateral method to decrease the broken edges of an optimization model in order to detect the edges. Combining bilateral filtering with convolution mask show all edges that are necessary by analyzing window one-by-one without overlapping. The proposed scheme is applied over the color image for manipulating the pixels to produce better output. The simulation results are performed using both noisy images and noise-free images. For producing the experimental results Standard deviation, Arithmetic mean are calculated. With the use of these parameters’ quality assessment of corrupted and noisy images and the effectiveness of the proposed approach is evaluated.
A fast stream transaction system for real-time IoT applications

Due to the recent prevalence of IoT (Internet of Things) technologies, various IoT devices connect to the Internet and continuously send their generated data to remote processing computers such as video data or sensor data. The transaction rate is one of the main factors to improve the performance of some IoT applications. For instance, in surveillance systems, the probability to catch a thief increases as the processing computer analyzes the video with a higher transaction rate. To improve the transaction rate, some methods reduce the transaction time between a processing computer and stream data sources under a static transaction interval. However, the transaction rate can be further improved by changing the transaction interval dynamically depending on the transaction time. In this paper, we propose a method to improve the transaction rate by changing the transaction interval dynamically. In our proposed method, a processing computer sometimes changes the transaction interval to be the same length as the average transaction time. Moreover, our proposed method adopts a progressive quality improvement (PQI) approach to reduce the transaction time. We measured the transaction rate of our proposed method by both a simulator and an implemented system. We confirmed that our proposed method can improve the transaction rate by 4.4 times and the transaction time by 21% at least compared with the conventional method. Moreover, we confirmed that the average frame rate increases 22% compared with a simple method in a real situation.
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this article we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneity in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.
Superpixel Segmentation Based on Global Similarity and Contour Region Transform

This letter proposes a new superpixel segmentation algorithm based on global similarity and contour region transformation. The basic idea is that pixels surrounded by the same contour are more likely to belong to the same object region, which could be easily clustered into the same superpixel. To this end, we use contour scanning to estimate the global similarity between pixels and corresponded centers. In addition, we introduce pixel's gradient information of contour transform map to enhance the pixel's global similarity to overcome the missing contours in blurred region. Benefited from our global similarity, the proposed method could adherent with blurred and low contrast boundaries. A large number of experiments on BSDS500 and VOC2012 datasets show that the proposed algorithm performs better than traditional SLIC.
Applied R in the Classroom

Abstract R, with support from a number of add‐on packages, is an excellent teaching tool that can greatly enhance learning of exploratory data analysis (EDA) and linear regression. This article illustrates using R along with the packages tidyverse , GGally , esquisse and lindia to walk through an example of basic EDA and linear regression that might be used in an introductory class along with code examples for every step. Teaching with R can give learners control of their analysis, lower the intimidating coding barrier, and provide a platform on which to learn modeling and analysis.
Generalized Net Model of Common Internet Payment Gateway with Intuitionistic Fuzzy Estimations

A payment gateway is a service that authorizes and processes debit/credit card or other payments for online merchants it facilitates these transactions by encrypting sensitive data and transferring it between a payment portal (a website or a mobile device) and the bank/front end processor. In this paper we examine the transaction process, even though it takes only a few seconds, several steps are accomplished during that brief window of time. Here we present a generalized net model with intuitionistic fuzzy estimations of such an internet payment gateway.
Learning the $k$ in $k$-means via the Camp-Meidell Inequality

Determining $k$ , the number clusters, is a key challenge in $k$ -means clustering. We propose an approach, named Automatic $k$ -means ( $k$ -means-auto), which judges a $k$ value to be right, if at least 88.89% of $\pmb{D}$ is less than twice its standard deviation, where $\pmb{D}$ is a vector of length $\pmb{N}$ (number of points) comprising the distance of each point to its cluster centroid. The bound is derived from Camp-Meidell's inequality for unimodal and symmetrical distributions. First in this paper, we show that $k$ -means' equal-variance Gaussian cluster assumption induces Gaussian fit for $\pmb{D}$ . Thus, if $\pmb{D}$ is Gaussian, then all clusters are Gaussian, and the underlying $k$ value is appropriate. We chose to test $\pmb{D}$ for unimodality and symmetry instead of Gaussian fit, as a means of relaxation, since clusters in real data are hardly perfectly Gaussian. The proposed approach is fast since $\pmb{D}$ does not have to be computed afresh: it is already available as a by-product of the clustering process. On 10 well-known datasets, $k$ -means-auto finds the true $\pmb{k}$ in 6 cases and comes very close ( $\pm 1$ cluster) in 3 other cases. Compared with two well-known methods, gap statistics and silhouette, it outperforms the former and competes closely (statistically insignificant difference at 95% confidence) with the latter. Over all the cases, the running time range for $k$ -means-auto, gap and silhouette are 0.02s-0.44s (factor of 22), 0.31s-13.63s (factor of 108) and 0.02s-70.78s (factor of 3539) respectively. Thus, $k$ -means-auto surpasses the other two methods in efficiency.
Sci-tech libraries within the knowledge management system: From information specialist’s viewpoint

Many research institutions have been developing their knowledge management systems (KMS). This process may be facilitated if the existing sci-tech libraries are taken as basic element of this system, while the libraries’ task will be to transform itself from the institution providing information support into a knowledge managing entity. Today the structure of sci-tech libraries comprises the relevant book and journal collections, functioning electronic library systems and in-house digital collections of external and their own resources. Merging the traditional and electronic libraries into knowledge management system will produce a synergetic effect. Managing in-house databases, sci-tech libraries are potentially able to manage sci-tech, intellectual products and intellectual property which makes a functional KMS fragment. Managing both external and in-house scholarly and information resources, building single information space on organizational intranet-portal, perfectly chosen software — all these make the main conditions of successful incorporation of sci-tech libraries in the knowledge management system.
Real-Time Generic Object Tracking via Recurrent Regression Network

Deep neural networks have achieved great success in visual tracking by learning a generic representation and leveraging large amounts of training data to improve performance. Most generic object trackers are trained from scratch online and do not benefit from a large number of videos available for offline training. We present a real-time generic object tracker capable of incorporating temporal information into its model, learning from many examples offline and quickly updating online. During the training process, the pre-trained weight of convolution layer is updated lagging behind, and the input video sequence length is gradually increased for fast convergence. Furthermore, only the hidden states in recurrent network are updated to guarantee the real-time tracking speed. The experimental results show that the proposed tracking method is capable of tracking objects at 150 fps with higher predicting overlap rate, and achieves more robustness in multiple benchmarks than state-of-the-art performance.
National society for histotechnology workload study

The National Society for Histotechnology (NSH) Quality Management Committee conducted a Workload Study in response to numerous requests by NSH society members for current information examining productivity and staffing in the clinical histology laboratory. Data collection was conducted between October 11 and 9 November 2018 of 15,848 individuals with deliverable email addresses taken from the NSH database (3.6% net email bounce rate). Survey productivity questions were segmented into key areas including grossing, embedding, microtomy and ancillary duties. From the responses, comparisons of laboratory demographics and productivity were examined by institution type and size. This report presents the data collected from the NSH Workload Study.
Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD

Distributed stochastic gradient descent (SGD) is essential for scaling the machine learning algorithms to a large number of computing nodes. However, the infrastructures variability such as high communication delay or random node slowdown greatly impedes the performance of distributed SGD algorithm, especially in a wireless system or sensor networks. In this paper, we propose an algorithmic approach named Overlap-Local-SGD (and its momentum variant) to overlap the communication and computation so as to speedup the distributed training procedure. The approach can help to mitigate the straggler effects as well. We achieve this by adding an anchor model on each node. After multiple local updates, locally trained models will be pulled back towards the synchronized anchor model rather than communicating with others. Experimental results of training a deep neural network on CIFAR-10 dataset demonstrate the effectiveness of Overlap-Local-SGD. We also provide a convergence guarantee for the proposed algorithm under non-convex objective functions.
Integrating an Intelligent Tutoring System into an Adaptive E-Learning Process

With the emergence of new technologies, a new way of learning called “e-learning” has appeared, based on interactive training. This helps learners to develop their skills, while making the learning process independent of time and place. Artificial intelligence (AI) techniques in education were tested to increase learning experience. AI researchers look for domain applications, beginning to apply their ideas by tracking students on a course. Intelligent systems apply artificial intelligence techniques to meet the needs of their users; these methods allow the performance of an e-learning system to be increased by analyzing and adapting the profile of learning behavior and learning styles. Therefore, the objective of this chapter is to present the requirements for integrating AI into e-learning systems. The results showed significant advantages when including AI techniques, specifically intelligent tutoring system, in the learning process.