Reviewerly/exHarmony huggingface dataset:
NEW APPROACHES TO THE PROMOTION OF SERVICES OF RUSSIAN TOURIST EDUCATION ON FOREIGN MARKETS IN THE FRAMEWORK OF IMPLEMENTATION OF THE NATIONAL PROJECT «EDUCATION»

The article discusses the main directions of the export of educational services using modern distance technologies and digital services. The purpose of this study is to determine the potential of the modern educational industry for the export of tourism education using MOOC platforms and EdTech companies. As the main research methods, a meaningful express analysis and content analysis of open information resources were used, the generalization of the obtained factual materials was carried out using the method of analytical grouping. The article presents the following theoretical and practical results: it is shown that an ecosystem and a new educational reality - the educational industry - have formed in the education system. The main four modes of trade in services form the most common export practices in the field of education, among which cross-border trade in educational services using distance technologies is now coming to the fore. The growth of student mobility around the world, both real and virtual, stimulates educational institutions to expand new formats, including hybrid ones: offline and online education; online formats and EdTech-industries technically ensure the expansion of the export of Russian educational services; MOOC platforms provide infrastructure conditions for universities to implement the concept of “Open Education” and increase export potential. The article shows that Russian EdTech companies do not have any industry specificity of training courses offered on the market; training is also carried out outside the industry tourism context; Among the courses presented on the MOOC platforms, several courses in the field of tourism from foreign universities and only one from a Russian university were identified. Thus, the development of online projects in the “tourist” segment of the Russian education market can become a promising direction for expanding the export of educational services using modern digital technologies and MOOC platforms.
Saturated Post-hoc Optimization for Classical Planning

Saturated cost partitioning and post-hoc optimization are two powerful cost partitioning algorithms for optimal classical planning. The main idea of saturated cost partitioning is to give each considered heuristic only the fraction of remaining operator costs that it needs to prove its estimates. We show how to apply this idea to post-hoc optimization and obtain a heuristic that dominates the original both in theory and on the IPC benchmarks.
A Story of Two Surveys: for the Advancement of Sinhalese Mobile Text Entry Research

This paper presents two surveys: a literature survey on the current progress on Sinhalese mobile text entry research and a user survey on how Sri Lankans experience Sinhalese mobile text entry. The first survey concludes that Sinhalese mobile text entry is limited in scope and size compared to western text entry research. The second survey attempts to bridge this gap by providing deep insight into aspects in Sinhalese mobile text entry such as language switching, using English within Sinhalese e.g. mixed-mode and Singlish, and the popularity of various input modalities, keyboard vendors, and keyboard layouts. This is also the first research publication that unveils the current state-of-the-art in Sinhalese mobile text entry, along with user-preferences such as using autocorrect, glide-typing, and speech. Results from this survey deepens our understanding of the Sinhalese mobile text entry domain resulting in a stronger empirical footing and more innovative Sinhalese mobile text entry solutions.
Gun Detection with Faster R-CNN in X-Ray Images

In this article, Faster R-CNN object detection algorithm is used for automatic weapon detection in X-Ray bag images obtained from security points. In the proposed method, Faster R-CNN model was fed by creating 2 different 3 band images. In the first one, the 3 bands of the image are equalized to the high energy values obtained from the X-Ray device. In the other one, 1st band is equalized to high values, 2nd band is equalized to low values and the 3rd band is equalized to the difference between high energy and low energy values. Performance values of the two different image generation techniques that fed the deep learning model were compared. In the study, X-Ray dataset provided by HTR Company was used. When the results are evaluated, it is seen that Faster R-CNN method can be used in weapon detection with very high performance as a deep learning model and the method of creating images that feed the model has an effect on object detection performance.
Data Mining in Government Social Media

In this paper, data mining is used to uncover hidden knowledge from government social media. Initial experiments on a preliminary data set of 254 reviews show important findings about citizens’ views and emotions towards government services. Data were gathered from the official Facebook page of the U.S. Department of Transportation and were processed with Weka software. J4.8, Apriori and Naive Bayes algorithms were applied in an attempt to analyze the emotions driving citizen's reviews. The results can enable government's ability to understand citizens’ attitudes and increase public support for policymaking.
Az idő szerepe a természet- és a számítástudományban: The role of time in science and in computer science

Till the beginning of the past century, classic science considered only instant interactions, i.e., laws of nature did not depend on time. Introducing time to science led to the birth of series of “modern sciences”, including special theory of relativity. In modern processors the speed of signal processing approached the speed of light: its logical states depend on the place and time of their evaluation, even within a processing step. Time receives a similar role in computing: transforms the “classsic computing” to “modern computing”.KivonatAz előző evszazad elejeig, a klasszikus termeszettudomany ugy tekintette, hogy a kolcsonhatasok pillanat-szerűek, azaz az idő nem jatszik bennuk szerepet. Az idő bevezetese a fizikaba a “modern fizika”, egyebek kozott: a specialis relativitaselmelet megszuletesehez vezetett. Modern szamitogepeinkben a jelek feldolgozasanak sebessege mar kozel kerult a feny sebessegehez: a logikai allapotok helytől es időtől fuggenek egy feldolgozasi lepes soran. Az idő a szamitastudomanyban is hasonlo szerephez jut: az idő-fuggő logika bevezetese atalakitja modernne a klasszikus szamitastudomanyt.
Learning Richer Features in Deep CNN for Object Detection

Recently developed object detectors employ a Deep Convolutional Neural Network (DCNN) by adding the number of feature layers with a pyramidal shape. Owing the occurrence of Feature Pyramid Network (FPN), the representation of the DCNN's ability was largely improved. Although these object detectors with feature pyramid achieve encouraging results, they have some limitations that didn't fully explore richer information in the deep layers, which is essential for the Object Detection task. In this work, we propose Multi-Level Spatial Pyramid Pooling module to construct more effective feature network for detecting objects of different scales. It is an integration of three pooling schemes of different kernel sizes. Besides, we design novel backbone based Darknet as our feature extractor. First, we fuse multi-level features extracted by new backbone followed by FPN style as the basic features. Then we feed the fused feature to the module to generate richer semantic feature maps at different scales. Finally, we gather up the richer feature maps in the former step for the detectors. To evaluate the effectiveness of the proposed method, experiments are conducted on two major benchmarks, which is PASCAL VOC 2007 and MS COCO dataset, and demonstrates that the ML-SPP module achieve comparable results with high efficiency.
Server Infrastructure Virtualization for Data Centers

Abstract This paper considers cluster systems of data centers containing a definite set of application servers, file servers, data storage systems, and an input-output system that are interconnected by a switching system and communication channels. The goal of research is to increase the efficiency of virtualized cluster systems using a new rational method to distribute virtual machines among the physical elements of data centers. This method is based on an iterative greedy algorithm and a limited enumeration procedure, which improve the performance of a virtualized system due to rational data management. A simulation model for the operation of a virtualized system is developed, and its experimental study is carried out. The approaches presented below determine the required number of cluster nodes in a virtualized system under an unpredictable load rate and an unpredictable level of resources requested by virtual machines.
Upaya Pembentukan Karakter Peserta Didik Di Sekolah Melalui Kegiatan Ekstakurikuler Pramuka

The multi-dimensional crisis that has hit this nation has provided a new discourse to prepare a very crucial step to be operationalized in the life of this nation, especially the next generation, namely character education. Character education is implemented in schools from elementary school to college level. The Scout Movement is an educational movement for young people, voluntary, non-political, open to all, regardless of origin, race, ethnicity, and religion, which organizes scouting through a value system based on Satya and Darma Pramuka. Based on that understanding, schools should organize scouting as a fun educational process for young people, under the responsibility of adult members, which is carried out outside the school and family educational environment, with specific objectives, basic principles, and educational methods. Scouting education as extracurricular activities in primary to secondary schools refers to Permendikbud No.81A of 2013. In addition, there are regulations in Law No. 12/2010, which regulate the implementation of scouting education. In addition, there is a regulation in Law Number 12 the Year 2010, which regulates the implementation of scouting education. In the implementation of the 2013 curriculum, scouting extracurricular activities can be implemented in 3 models, namely: (1) Block system which is implemented at the beginning of school entry; (2) Actualization System, the learning process of each subject into Scouting Education; and (3) Regular System for students who have an interest and interest in becoming a member of the scout. Revitalization of the scout movement needs to be done so that scouting activities can be held in higher quality, attract interest and become the choice of students, and create students with strong characteristics to become future leaders of the nation in various fields of life. In order to support and strengthen these policies, planning of scout extracurricular activities is absolutely necessary.
Automatic Configuration of Benchmark Sets for Classical Planning

The benchmarks from previous International Planning Competitions are commonly used to evaluate new planning algorithms. Since this set has grown organically over the years, it has several flaws: it contains duplicate tasks, unsolvable tasks, trivially solvable domains, and domains with modelling errors. Also, diverse domain sizes complicate aggregating results. Most importantly, however, the range of task difficulty is very small in many domains. We propose an automated method for creating benchmarks that solves these issues. To find a good scaling in difficulty, we automatically configure the parameters of benchmark domains. We show that the resulting benchmark set improves empirical comparisons by allowing to differentiate between planners more easily.
Psychological and pedagogical aspects of teaching students

This article highlights the psychological and pedagogical aspects of student learning and discusses the tools required in the process from the student and the teacher. Understanding of the events that take place in the pedagogical process. One of the problems of secondary specialized educational institutions is the low quality of general education of the first year, the lack of the formation of general educational skills, skills of organizing independent work, mental activity, insufficient development of the communicative culture. At the same time, the teacher must know a large number of them - in certain cases, combine or use only one - the choice depends on the teacher's competence.
The Mediation Effect of Organizational Culture between Knowledge Management Processes and Creative Thinking: A Case of COVID 19 Healthcare Workers in Northern Iraq

Employees have to be more creative and knowledgeable during crisis times, because they are among the most important intangible assets of an organization However, employees should also be supported by the managerial parts to increase the capacity of intangible assets This study focuses on identifying whether organizational culture (OC) plays a mediating role between knowledge management processes (KMP) and creative thinking (CT) in the healthcare industries in Northern Iraq A Quantitative approach has been used in this study, and 400 healthcare workers are considered for collecting data 100% response rate is reached Factor analysis, structural equation modelling, and path analysis techniques were used for identifying the mediation effect between the dependent and independent variables The results indicate that positive relationships have been found among the variables However, the correlation between KMP and CT was found to have insignificant direct effects as the p-value was found to be greater than the significant value The correlation between KMP and OC, as well as that between OC and CT were found to be positive and significant In addition, OC was found to have a mediating role in the relationship of KMP and CT as revealed in the hypotheses test The study suggested that the management put a measure in place to promote CT through enhancing KMP Some of the directions were suggested for future research in the study
Video Smoothening Using Features Matching

Video recorded using hand held camera or mounted on any vibrating platform make unintentional and some intentional camera motion. Their unintentional hand motions cause certain video instability. The algorithm uses Global Motion Vector (GMV) array for auto-video stabilization using methodical Speeded Up Robust Features (SURF) technique. The algorithm auto detects the unstabilized features and compensates it using the GMV array. The efficiency of the proposed algorithm is calculated based on the experiments on different videos.
Quantum Radar: A Brief Analytical Study

The quantum computing began with the concern of Richard Feynman to simulate the non-classical physical theories, Shor's algorithm showed the power of the quantum mechanics when solved a classical problem with it, these facts push the technology to discover and implement the quantum illumination, this discovery made a huge change in sensing theory, moreover, the implemented such as phenomena with a classical radar, make a play role in detecting strategies in sensing the stealth objects (whatever be an aircraft or drones), even more, the quantum radar can detect in an efficient way and more accuracy the natural debris outside the atmosphere, also, the quantum radar can work even though, in nosy, foggy, and high-temperature environment, all these features make the quantum radar promising emerging technology deserve to develop and enhanced in theoretical and technical ways. this paper provided a short analysis study to the most common types of quantum radar showing their cons and pros and could be used either in civil or military fields.
CODE SWITCHING USED BY ENGLISH TEACHER AT ISLAM TIRTAYASA JUNIOR HIGH SCHOOL (EIGHT GRADE)

Objective of this research is to explain the use of code switching by English teachers in classroom during learning activities. This research uses qualitative approach with a case study design, the research subject is an English teacher at Islam Tirtayasa Junior High School. Collecting data in this study using data display, data reduction and drawing conclusions. The focus of this research is the use of code switching by English teachers in online classroom during the learning process and the sub focus of the research is the types of code switching used by the English teacher and the reasons for using code switching. The result of this research is to find the type of code switching usage and the reasons for using code switching by English teachers in online classroom during the learning process. The conclusion is there are three types of code switching used by English teachers during the learning process in the classroom. Namely: First, inter sentential code switching, which is 64 times. Second, Intra-sentential code switching 31 times. Third, Tag switching 10 times. Then some reasons why English teachers use code switching, namely: First, Talking about Particular topic. Second is Interjection. Third is Repitition for Clarification. Fourth is Expressing for Group Identity
Sentiment Analysis on Tweets

Sentiment analysis can be defined as a process of analyzing user reviews, opinions, emotions, sentiments, attitude regarding various entities such as products, services, organization, key issues. The Web is place where anybody can express their opinions or post reviews about various entities. Social Media is actually influencing the user’s point of view or individual opinions. It has now become an essential part of digital marketing. Sentiment Analysis is a process of identifying and aggregating opinions of various about any issues or products. Monitoring Social Media activities has now become a prime concern for politicians to understanding their social image. In this paper we are going to analyze the tweets of various users regarding a political leader, which showcases the responses or opinions of voters pre and post implementation of Government policy.
An Experiment on Mizar Adjectives with Extra Visible Arguments

This paper presents the extended processing of adjectives with visible arguments in the Mizar system. The proposed enhancement is compared with the current implementation by presenting the results of a case study based on refactoring selected Mizar Mathematical Library (MML) theories.
Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm

Finite mixtures normal regression (FMNR) models are widely used to investigate the relationship between a response variable and a set of explanatory variables from several unknown latent homogeneous groups. However, the classical EM algorithm and Gibbs sampling to deal with this model have several weak points. In this paper, a non-iterative sampling algorithm for fitting FMNR model is proposed from a Bayesian perspective. The procedure can generate independently and identically distributed samples from the posterior distributions of the parameters and produce more reliable estimations than the EM algorithm and Gibbs sampling. Simulation studies are conducted to illustrate the performance of the algorithm with supporting results. Finally, a real data is analyzed to show the usefulness of the methodology.
Issues of formation of vocational value orientations of cadets of departamental educational institutions of the Ministry of Internal Affairs of Russia

Analyzed are principal issues of the process of formation of vocational and value orientations of cadets and students of educational institutions of the Ministry of Internal Affairs of Russia under modern conditions. Researched are regularities of that process and highlighted are problems in the course of its realization. Also presented is the survey of existing scientific literature, connected with examining problem.
Modelling of a tunnel dryer with a bond graph approach

In this research work, a bond graph model of a tunnel dryer is performed. The tunnel dryer is sustained by a centrifugal blower. A set of heating resistances are placed inside the tunnel dryer. A detailed model of the centrifugal blower is built. Special importance is attributed to the modelling of the convection between heating resistances and internal air. The resulting model is an energetic lumped approach to simulate the temperature at the output of the tunnel dryer. Data temperature acquired from an experimental tunnel dryer are confronted to the simulated one to validate the model. The simulated results match correctly the measured one.
Color-compressive bilateral filter and nonlocal means for high-dimensional images

We propose accelerated implementations of bilateral filter (BF) and nonlocal means (NLM) called color-compressive bilateral filter (CCBF) and color-compressive nonlocal means (CCNLM). CCBF and CCNLM are random filters, whose Monte-Carlo averaged output images are identical to the output images of conventional BF and NLM, respectively. However, CCBF and CCNLM are considerably faster because the spatial processing of multiple color channels are combined into a single random filtering process. This implies that the complexity of CCBF and CCNLM is less sensitive to color dimension (e.g., hyperspectral images) relatively to other BF and NLM methods. We experimentally verified that the execution time of CCBF and CCNLM are faster than the existing “fast” implementations of BF and NLM, respectively.
Quantified Reflection Calculus with One Modality.

This paper presents the logic QRC$_1$, which is a strictly positive fragment of quantified modal logic. The intended reading of the diamond modality is that of consistency of a formal theory. Predicate symbols are interpreted as parametrized axiomatizations. We prove arithmetical soundness of the logic QRC$_1$ with respect to this arithmetical interpretation. Quantified provability logic is known to be undecidable. However, the undecidability proof cannot be performed in our signature and arithmetical reading. We conjecture the logic QRC$_1$ to be arithmetically complete. This paper takes the first steps towards arithmetical completeness by providing relational semantics for QRC$_1$ with a corresponding completeness proof. We also show the finite model property, which implies decidability.
Research on Postgraduate Examination Question Extraction Algorithms Based on Multilayer Filtering and Dynamic Probability Model

In view of the characteristics of postgraduate education, such as more courses, quicker content updating, flexible examination methods, and multiple courses compounding questions, a random test question extraction algorithm based on multi-layer filtering model and dynamic probability model is proposed, which strengthens the pertinence of the test question extraction process and improves the efficiency of the algorithm. The results show that the parameters can better approximate the set requirements, the distribution of the problem is reasonable, the versatility is good, and can meet the needs of personalized questions.
Cooperative Vehicle Tracking using SMC-PHD Integrated with Interacting Multiple Models

Recently, multi-target tracking has played an important role in the perception of autonomous driving. V2V communication vehicle collaboration becomes a solution. However, the environmental impact of high-precision GNSS (Global Navigation Satellite System) greatly affects the tracking accuracy. In this paper, an improved IMM-SMCPHD is proposed to solve this problem, and it is verified by simulation that it is improved than the basic SMCPHD.
LibGuides: Keep Calm and Study On: BUBeWell, Be Online

LibGuide serves as a platform to support Butler University students, faculty and staff during remote learning through a variety of resources to engage students academically and socially.