Reviewerly/exHarmony huggingface dataset:
Integration of elements of psychological flexibility in the collective management system in the social sphere

Relevance. The article discusses the features of the process of integrating elements of psychological flexibility as an important component of professional thinking in the collective management system in the social sphere. The relevance of the studied problems lies in the fact that the specifics of the professional activities of social specialists determine the work with a different contingent of people, which requires high mobility of the mental processes of a person in solving complex, often uncertain situations that are characterized by instability and unpredictability is my variability. The purpose of the study is to study the features of the process of integrating elements of psychological flexibility as an important component of professional thinking in the collective management system in the social sphere. The objectives of the study are to identify the specifics of the process of integrating elements of psychological flexibility as an important component of professional thinking in the collective management system in the social sphere and using the results to optimize the management process. As a result of the study, the possibilities were identified for integrating elements of psychological flexibility into the collective management system, whose professional activity lies in the social sphere. The professional thinking of specialists in the social sphere, in our opinion, is based on the following criteria: competence of specialists, personality orientation, psychological flexibility/ rigidity, self-management ability. Flexibility is one of the most important qualities of an effective specialist. In our opinion, flexibility (or the opposite of behavioral rigidity) is a feature that is very difficult to correct. But, one way or another, it is necessary to work on the development of professional thinking and, in our opinion, its main component flexibility. These results were the basis for the development, testing and integration of elements of psychological flexibility in the team management system. The focus was on the question of how the integration of elements of psychological flexibility into the collective management system will contribute to the development of professional thinking of specialists. A series of training sessions was conducted aimed at the development of professional thinking, in particular psychological flexibility, specialists in the social sphere. Innovative coaching and gaming tools, creative tasks for acquiring new skills, brainstorming and other non-standard techniques contribute to the creation of an effective motivational plan for solving problems, which is determined to work with the team [3-5]. Fundamentally important in this cycle is that the specialists gained additional experience of detaching from the usual identification, they found a variety of options for displacement from the usual standard established position. The usual stereo-typing of thinking has disappeared [2, 6, 9].
Building Knowledge Graph in Spark Without SPARQL

Knowledge graphs, powerful assets for enhancing search and various data integration, are being essential in both academia and industry. In this paper we will demonstrate that knowledge graph abilities are much wider than search and data integration. We will do it in a twofold manner: 1) we will show how to build knowledge graph in Spark instead of using SPARQL language and how to explore data in DataFrames and GraphFrames; and 2) we will reveal Spark knowledge graph as a bridge between logical thinking and graph thinking for data mining.
Czech Translation of SQuAD 2.0 and 1.1

The Czech translation of SQuAD 2.0 and SQuAD 1.1 datasets contains automatically translated texts, questions and answers from the training set and the development set of the respective datasets.The test set is missing, because it is not publicly available.The data is released under the CC BY-NC-SA 4.0 license.If you use the dataset, please cite the following paper (the exact format was not available during the submission of the dataset): Kateřina Mackova and Straka Milan: Reading Comprehension in Czech via Machine Translation and Cross-lingual Transfer, presented at TSD 2020, Brno, Czech Republic, September 8-11 2020.
Appendix 2D Sample library resources orientation slides

How to Manage Student Consulting Projects describes the key principles and tools needed by project advisors to manage student consulting projects in an academic setting. The authors highlight different approaches for managing student consulting teams and offer strategies that project advisors can use to improve project performance. The book also provides information for program administrators and deans, as well as project managers in non-academic settings, to help in the development and running of project-based learning.
Statistical entropy component

Complicated processes are characterized by randomness, chaos, and instability. Changes proceeding can be evaluated only using the entropy parameter. Usually, entropy is understood as a characteristic of the system’s dynamics. It was established that entropy has two components—dynamic and static. The separation of the static component in this parameter allowed solving all paradoxes and misunderstandings attributed to entropy. It then became clear that the dynamic component evaluates the influence of regime parameters, while the static component evaluates the extent of changes in the substances used in this process. This property of the static entropy made it possible to develop an unambiguous quality criterion for the evaluation of the efficiency of the results of a complicated process. Examples of practical applications of such parameters are given for the optimization of complicated building process and complex processing of natural resources.
Influence of Information Literacy Competencies on Users of the Saulsville Public Library, South Africa

Information literacy competencies indicate the ability of an individual to apply a certain search strategy of information discovery, to understand its design, to value judgement, and to use it in various contents and contexts. The influence of information literacy competencies plays an important role in enabling information users to meet their information needs and prepares them for lifelong learning. The purpose of this study was to examine the influence of information literacy competencies of users in the Saulsville public library, South Africa. The rationale was a lack of awareness and the inability of the users to find their information needs based on the task and decision at hand. The descriptive survey design adopted for this study made use of a questionnaire to collect data from 1 120 registered members of the Saulsville library. Of the 1 120 registered users being the population for the study, the authors purposively used 10 per cent (112 members) of the population as sample size. The findings revealed that users of the Saulsville public library had limited awareness and competencies of information literacy. The users were exposed to some form of library orientation and the use of library resources during their visit to the library. The findings further indicated that the users’ information literacy competencies were affirmed through the quality of information obtained and the satisfaction of library services rendered by library staff. The study recommends the acquisition of new technologies that could enhance users’ interactive information literacy training in public libraries and formal education settings.&#x0D;
A Seismic Event Relative Location Benchmark Case Study

&amp;lt;p&amp;gt;&amp;quot;Precision seismology'' encompasses a set of methods which use differential measurements of time-delays to estimate the relative locations of earthquakes and explosions.&amp;amp;#160; Delay-times estimated from signal correlations often allow far more accurate estimates of one event location relative to another than is possible using classical hypocenter determination techniques.&amp;amp;#160; Many different algorithms and software implementations have been developed and different assumptions and procedures can often result in significant variability between different relative event location estimates.&amp;amp;#160; We present a Ground Truth (GT) database of 55 military surface explosions in northern Finland in 2007 that all took place within 300 meters of each other.&amp;amp;#160; The explosions were recorded with a high signal-to-noise ratio to distances of about 2 degrees, and the exceptional waveform similarity between the signals from the different explosions allows for accurate correlation-based time-delay measurements.&amp;amp;#160; With exact coordinates for the explosions, we can assess the fidelity of relative location estimates made using any location algorithm or implementation.&amp;amp;#160; Applying double-difference calculations using two different 1-d velocity models for the region results in hypocenter-to-hypocenter distances which are too short and the wavefield leaving the source region is more complicated than predicted by the models.&amp;amp;#160; Using the GT event coordinates, we can measure the slowness vectors associated with each outgoing ray from the source region. We demonstrate that, had such corrections been available, a significant improvement in the relative location estimates would have resulted.&amp;amp;#160; In practice we would of course need to solve for event hypocenters and slowness corrections simultaneously, and significant work will be needed to upgrade relative location algorithms to accommodate uncertainty in the form of the outgoing wavefield.&amp;amp;#160; We present this dataset, together with GT coordinates, raw waveforms for all events on six regional stations, and tables of time-delay measurements, as a reference benchmark by which relative location algorithms and software can be evaluated.&amp;lt;/p&amp;gt;
Методологічний інструментарій розширення меж аналітики сценічного мистецтва: парадигмальність

The author attempts to consider the scenic art from the standpoint of the cultural approach, which implements the analysis of the subject in the cultural and historical context and forms the interdisciplinary and universality of cognitive tools. Within the framework of the basic methodological basis in the field of cultural understanding of the performing arts, a categorical tool has been singled out — a cultural paradigm. The author of the study, based on a synthesizing analysis and taking into account the set goal, interprets the cultural paradigm. The cultural paradigm is a kind of historical and cultural matrix consisting of ordered, mobile elements of culture and including the totality characteristic of the historical stage. At the same time, the mentioned matrix is not a constant formation, but a variable that varies from epoch to epoch. Accordingly, these are ideas about the cultural paradigm as about the aesthetic richness of reality of a particular cultural and historical epoch, the totality of all the philosophical and aesthetic programs of historical time - the range of historical and cultural ideas and beliefs taken in their synchronous integrity. The “cultural paradigm” unites mobile elements and reveals the specifics of time cycles that replace each other, which determines the change of cultural paradigms. The change of cultural paradigms is a shift of meanings, the generation of cultural innovations, the repression and replacement of elements of the old cultural paradigm of the new. Theater art as a cultural phenomenon is a peculiar phenomenon that reflects the type and characteristics of culture in a certain cultural and historical epoch represented by the stage language, which is a means of transmitting culturally significant information, objective cultural meanings of a certain epoch, thanks to which culture acquires its legal forms, becoming by reality. The paradigmatic viewpoint can not only productively interpret the historical and cultural dynamics of the evolution of scenic art in the context of a specific area of values, but also specify the prognostic potential of evolution.
Heterogeneous Cooperative Bare-Bones Particle Swarm Optimization with Jump for High-Dimensional Problems

This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.
Corrections to “Automatic and Accurate Expansion of Abbreviations in Parameters”

Presents corrections to author information in the above named paper.
SWD: Low-Compute Real-Time Object Detection Architecture

In recent years, deep learning-based object detection has been researched hot spot due to its powerful learning ability in dealing with occlusion, scale transformation, and background switches, etc. There are many state-of-art object detectors like SSD, SSH, YOLO, and RCNN that have been invented in recent years. These architectures are highly complex, work on deep learning framework, requiring high computing power, which restricts their practical adaptability for low-cost applications and low-computes devices. In this work, a novel real-time object detector architecture, sliding window detector (SWD) based on a sliding window technique, has been proposed. SWD works on a deep learning framework and can execute on a low-compute device. In the proposed SWD architecture, the classifier network is optimized. The fully connected layer of the classifier trained on N classes is replaced by a convolutional layer, which generates N heat-maps. These heat-maps are used to localize and classify the object. SWD simulation on an Intel i5 CPU with 20 FPS shown mAP 0.85 for PKLOT data-set.
Recommending in changing times

Recommender systems today face major challenges in keeping up with dynamic customer preferences. Disruptions or sudden changes in the environment affect customer preferences drastically and render historical data ineffective for modeling. With businesses relying heavily on Machine Learning(ML) based recommender systems for catering to customer preferences, the accuracy of timely recommendations gains prime significance.
Building a Social App

This chapter goes into more detail on getting content from Office 365 to start building a social app. This social app will incorporate several functionalities as described in the different scenarios at the beginning of the book.
Comparison of Different DC Motor Speed Controllers

Control of DC motor is a popular practice, consequently, the controller of the DC motor speed is essential. The main aim of motor speed control is to keep the motor rotation at the present speed, and to drive a machine at the speed of demand. Process mathematical model has been designed utilizing plant data i.e. firstly without controller and secondly with three controllers independent from each other. Linear Proportional Integral Derivative (LPID) and Nonlinear Proportional Integral Derivative (NPID) are presented here. In addition, the speed of a DC motor is controlled using Fuzzy Logic Controller (FLC). It has two inputs: one is the speed error and the other one is the change in the speed error. Center of Gravity (COG) is the method utilized for defuzzification. The results of the controllers are compared to each other and to the one with no control in in order to clarify the features of the presented controllers. The ability of the proposed FLC is explored by the numerical simulations in controlling the speed of DC motor to a desired value within fast time. After a comparative evaluation of the results done between DC motor without FLC and with FLC system in terms of characteristics, utilizing MATLAB/Simulink, it has been found out that FLC has more efficiency and effectiveness than the system without this controller.
Video Tagging and Recommender System Using Deep Learning

In today’s digital era, video tagging has gained its importance to acknowledge the fact that searching for appropriate videos is a waste of time. In this paper, we address the problem of finding relevant videos in a very fast and efficient way by developing an automatic video tagging framework. Instead of using a separate MPEG-7 descriptor, we have applied a deep learning model, convolution neural network (CNN) with in-built color descriptors. First, frames are extracted from the videos at a definite interval of time, second the extracted frames are then processed for duplication removal providing a comparative systematic study of techniques and methods for video tagging, and last, the system takes in queries from the user, videos are sort with respect to the queries, and it recommends videos accordingly. The proposed final system includes a detailed description of the techniques used and the underlying architecture to support them. An accuracy of 91.7% is achieved.
An Identification Method of Question Subjects Based on Word Embedding and LSTM

Abstract Using the subject of the question can locate the question area, narrow the scope of the query, and provide users with better answers. The question text is usually short text. Therefore, in view of its sparse features and irregular structure, this paper proposes an identification method of question subjects based on word embedding and LSTM (IQS-WE-L), and uses question set on the MadSci website for experimentation, which has three subjects. We firstly use the Word2vec to train the Wikipedia database to generate a dictionary. Then based on word vectors, we propose four feature extraction methods: W2V, W2V-TFIDF, W2V-c-TFIDF and W2V-c, which formalizes the text features into vectors through word embedding and other features. Finally, we build an LSTM network for classification training to identify the subject of the question and quantitative evaluate effect of four feature extraction methods we proposed. Experimental data shows that the method proposed in this paper can effectively identify the subject of the question. When classifying the subject of the question, the F1 value can reach a maximum of 0.9339.
An Approach to Improving the Effectiveness of Data Augmentation for Deep Neural Networks

The following topics are dealt with: learning (artificial intelligence); security of data; mobile computing; Internet; computer aided instruction; feature extraction; health care; Internet of Things; neural nets; and cloud computing.
The Impact of Overhead Information on Fronthaul Signal Compression

This work aims to analyze the impact of the overhead generated by the signal compression techniques on fronthaul transmission. Specifically, this work analyzes the impact of the overhead generated by the coefficients of the distortion function approximation model of the Adaptive non Linear Distortion (AnLD) method. The results of this analysis show that the effective compression rate of AnLD method may reduce from 2:1 to 1.1:1, due to the increase of overhead information. In addition, the results also show that transmission rates on the fronthaul network may increase by up to 81.6%, compared to the ideal scenario, limiting the transmission capacity of the fronthaul network. Thus, we concluded that in order to achieve a better performance of AnLD method, it is necessary to limit the amount of overhead information in such a way that ensure a low distortion of the compressed signal, as well as a moderate transmission rate on fronthaul network.
Tablet Computer Truly Help Students to Master Mathematical Knowledge?

With the deepening of education reform, more and more teachers suggest their students in the classroom apply the tablet computers to do mathematics learning. Does it work? Especially in the aspect of mastering mathematics knowledge. This study chose 152 Junior high school students as participants and make a survey. The results indicated that the tablets computer has a positive influence on students' understanding of mathematics knowledge, directly applying mathematical knowledge to solve difficult problems and comprehensively applying mathematical knowledge to solve general mathematical problems. While it has a negative influence on students' applying knowledge to solve simple problems. Therefore, it is suggested that tablet computers should be used with traditional teaching methods in practical mathematics teaching.
Pedestrian Detection: Unification of Global and Local Features

Effective and precise detection of pedestrian serves as a key to a number of applications in the domain of computer vision such as smart cars, video surveillance, robotics, and security. This paper presents the combination of feature extraction and classification. We present a thorough study on the type of features fit for pedestrian detection. The features are obtained by concatenating global shape feature histogram of oriented gradients (HOG) with global color and local texture features. We investigate our proposed method with respect to their receiver operator characteristics (ROC) and detection error trade-off (DET) performance. For classification part, we use the standard support vector machines (SVM) with linear kernel. We test our proposed method on the benchmark dataset for pedestrian detection: Institut National de Recherche en Informatique et en Automatique (INRIA) Pedestrian Dataset. The dataset contains pedestrians and non-pedestrians captured over a varying environment. Our proposed method performs best with respect to other algorithms presented in this study and gives a miss rate of 5.80%.
Recommender-Systems.com: A Central Platform for the Recommender-System Community

We introduce Recommender-Systems.com (RS_c) as a central platform for the recommender-systems community. RS_c provides regular news on important events in the community as well as curated lists of recommender-system resources including datasets, algorithms, jobs, software, and learning materials. Based on a survey with 28 participants – mostly authors at the RecSys 2019 conference – 91% agree that RS_c could be a major contribution to the community. Participants consider it currently particularly difficult to find best practice guidelines (45%); researchers, freelancers and employers (45%); and curated lists of state-of-the-art algorithms, software, and datasets (36%). Notably, only 19% consider it (very) easy to find material relating to diversity, equality and anti-discrimination.