Reviewerly/exHarmony huggingface dataset:
Virtual Reality Technologies as a Tool for Development of Physics Learning Educational Complex

The paper describes the project on physics learning. It was implemented using the Unity game engine, Leap Motion package to provide controlling within the laboratory works, and C# programming language in order to define logic between objects of the app. Also, the survey on the efficiency of similar projects and applications is presented. It was conducted among students of the high school. The observations on using of virtual reality technology are also given. The main purpose of the article is to demonstrate and evaluate use of the application in subject’s learning and its efficiency. The paper also raises question on educational tools relevance and modernity.
Traffic Flows Surveying and Monitoring by Drone-Video

For the purposes of planning and managing mobility phenomena, the traffic detection is a widespread practice. In particular, automatic measurements of vehicular traffic on road sections are operated by electronic equipment and different sensors. Some technologies, included in the general class of traffic analysers, also allow to detect other parameters such as vehicle dimension, speed, spacing, headway. Generally, these are most interesting tools for the classification and characterisation of vehicles, but also more expensive and imply expensive work on the road. The paper proposes a short state of the art concerning traffic detection systems with a particular focus on Unmanned aerial systems (UAS). Some experimental results are proposed referring to surveys conducted with the help of drones. The surveys refer to three different contexts: an urban road section, an urban road intersection, a freight terminal. The focus is in particular on the measurement of flow parameters such as flow, density, speed and the classification of vehicles in predefined categories. Moreover, some considerations emerged from the experimental analyses, on the applicative potential of the UAS and on the perspectives for the development of the technological characteristics of these instruments.
Associative Array Architecture to Use Each Change of Series of Changes in Reversible Computing

The energy-saving and quick computations are getting momentum among the researchers across the globe. Reversible computing pays significant contributions from architectural, instructional and logical perspectives in the literature. The present nature of data and complex applications involve many series of changes, however, past data are compared for decision making or backtracking to draw the inference. In this regard, dynamic dispatch with the lexical scoping was combined to access the series of changes that were most recent advancement. However, the loosely coupled data structure is the fundamental drawback to achieve the advantages of reversible computing in all directions. In this work, architecture is proposed that uses the novel associative array in the underlying combination of dynamic dispatch with lexicographic scope. The solution is abstracted as a memory management scheme. The procedural and object orientation paradigm could use such proposed architecture in the programming level. The advancement of the proposed work is supported with results.
Tropical incinerator architecture: Recycling the waste machine through passive smart breathing

Treatment of waste through landfill technique has considered ineffective and caused many damages to both the environment and economic problems. Therefore, the Indonesia government plans to build an Intermediate Treatment Facility equipped with incinerator as the waste machine. The functionalism movement that developed since the beginning of the 20th-century cause services such as incinerator to be designed without understanding and respecting its surrounding environment and its users’ comfort. This research evaluates by designing the recycling of incinerator in the architectural context. The design method uses the principle of passive breathing with the chimney effect which has a smart and straightforward principle of creating a temperature difference between the inlet in the area under the building and the outlet on the roof so that cold air can enter the building, and the hot air inside the building moves up and out of the building. The final design concludes the recommended architectural strategies such as adjusting the orientation of the building towards the arrival of the airflow to increase the area exposed to the wind, keeping the ambient temperature of the building low, especially in areas that have inlets, designing passive strategies in areas exposed to solar radiation according to the needs of inlets and outlets, and organizing the space arrangement in a building to generate the stack effect.
Study on the Integrative Practice of University Teaching Space and Information Platform——Taking Xi’an Eurasia University as an Example

Abstract With the advancement of teaching reform in colleges and universities, the application of information technology in the teaching design and teaching process has become increasingly widespread. In addition to the applications of various software platforms and resources in the teaching process, more and more hardware-based smart classrooms and smart space are also being applied in the new round of teaching space transformation. What this thesis mainly studies is how to realize the integration of the universities’ teaching space and the teaching information platform, so as to further promote the reform and development of teaching mode. Based on the practical case of Xi’an Eurasia University, the study finds that in the process of teaching space transformation, informatization has enabled the connection between physical space and system platform, improving the efficiency and learning experience.
Research on Image Enhancement Algorithm Base on Convolutional Neural Network in Scotopic Vision Environment

The limitation of human visual contrast resolution makes it impossible that people to clearly distinguish the image information obtained in the scotopic vision environment. In view of the low brightness, unclear content and low contrast of the image obtained in the scotopic vision environment, an image enhancement algorithm based on convolutional neural network is proposed. First, the image data sets required for experimental training are captured by image acquisition equipment. Secondly, using the algorithm principle of the GAN network, to construct a convolutional neural network model, and the obtained image is input into an image generation network can get an enhanced image, where the input image includes an image in a scotopic vision environment and its brightness channel image, the image generation network is structured by an autoencoder network, and then according to the characteristics of the image to constructed different loss functions. Theoretical analysis and experimental results show that the enhancement effect of traditional image enhancement algorithm on these images is very limited, while image enhancement using convolutional neural network can ensure the accuracy of the algorithm and achieve a good effect. The method in this paper obviously improves the scotopic vision environment image visual effect, make the image clearer, meets the requirements of people to the naked eye to watch.
A New Multiplication Technique for LWE Based Fully Homomorphic Encryption

We propose a new multiplication technique for LWE based fully homomorphic encryption schemes. Unlike previous schemes, multiplication does not increase the size of the ciphertext. As a result, there is no need for relinearization. The noise associated with it increases only linearly.
The Current State and Challenges of Korean Studies in Australian Universities

Journal of the International Network for Korean Language and Culture 17-2, 75-98. Based on objective or quantitative analysis, personal observations and some anecdotal notes, this study presents the recent developments of Australian universities that offer Korean Studies major programs and their academic activities. Additionally, this study presents an analysis of the current state of Korean Studies programs and their characteristic developments and explores their current and future challenges. Specifically, the study begins by presenting a main statistical index––namely the global standings of the Australian universities that offer Korean Studies––together with information about their global learning programs. It then undertakes analysis of their development in stages and the current state of the Korean language and Korean studies programs, in addition to investigating government support and the appointment of faculty members, approaches to teaching and research, and the recent activities of academic bodies that are related to Korean Studies. The study also briefly highlights the strengths, weaknesses, opportunities and challenges of the Australian universities that offer Korean Studies. The study followingly presents and briefly discusses challenges such as establishing new faculty positions, offering new disciplinary courses, raising early career scholars, and forming digital or virtual research clusters. It concludes with a brief discussion regarding the implications from the experiences of Australian universities for the development of Korean Studies in other regions.(UNSW)
Feature reduction fuzzy C-Means algorithm leveraging the marginal kurtosis measure

The feature reduction fuzzy c-means (FRFCM) algorithm has been proven to be effective for clustering data with redundant/unimportant feature(s). However, the FRFCM algorithm still has the following disadvantages. 1) The FRFCM uses the mean-to-variance-ratio (MVR) index to measure the feature importance of a dataset, but this index is affected by data normalization, i.e., a large MVR value of original feature(s) may become small if the data are normalized, and vice versa. Moreover, the MVR value(s) of the important feature(s) of a dataset may not necessarily be large. 2) The feature weights obtained by the FRFCM are sensitive to the initial cluster centers and initial feature weights. 3) The FRFCM algorithm may be unable to assign the proper weights to the features of a dataset. Thus, in the feature reduction learning process, important features may be discarded, but unimportant features may be retained. These disadvantages can cause the FRFCM algorithm to discard important feature components. In addition, the threshold for the selection of the important feature(s) of the FRFCM may not be easy to determine. To mitigate the disadvantages of the FRFCM algorithm, we first devise a new index, named the marginal kurtosis measure (MKM), to measure the importance of each feature in a dataset. Then, a novel and robust feature reduction fuzzy c-means clustering algorithm called the FRFCM-MKM, which incorporates the marginal kurtosis measure into the FRFCM, is proposed. Furthermore, an accurate threshold is introduced to select important feature(s) and discard unimportant feature(s). Experiments on synthetic and real-world datasets demonstrate that the FRFCM-MKM is effective and efficient.
Optimal Contrast and Size-Invariant Recursive VCS Using Perfect Reconstruction of White Pixels

Visual cryptography is an image secret sharing technique which encrypts the image into n shares and reconstructs the image using k shares from n shares by human visual system. Recursive visual cryptography scheme (RVCS) again encrypts the encrypted shares into sub-shares using recursion. In this paper, we proposed a new model for size-invariant RVCS based on Perfect Reconstruction of White Pixels (PRWP) which enhances the contrast. The size-invariant RVCS based on random basis column pixel expansion with PRWP has been demonstrated based on experimental investigations. We have also reviewed the shortcomings of the existing models and made an experimental analysis between the previous models and the proposed model. From the analysis, we proved that the proposed method enhances the contrast and maintains both security and reliability.
Social and Psychological Features of Hardiness in Personality of Students of Higher Education Institutions

The topicality and significance of the study is determined by the fact that life and activity of a person in contemporary socio-economic conditions are inextricably connected with the constant impact of adverse social, political, economic and other factors on him. These factors are accompanied by the emergence of various kinds of difficult life situations, as a result of which stress develops in a person. In the student milieu, stress arises under the influence of difficulties associated with both the learning process and life circumstances. The paper presents the results of an empirical study of socio-psychological features of hardiness in students of higher educational institutions. The methods of research comprised: theoretical analysis, empirical research (testing), qualitative analysis of the obtained data, mathematical and statistical data processing (SPSS Statistics, Excel). Testing included the following psycho-diagnostic tools: the author’s questionnaire for assessing stressful situations; McCrae-Costa Five-Factor Personality Questionnaire (The Big Five dimensions); “Hardiness test”; Self-attitude technique. The validity and reliability of the study results was ensured by the initial methodological positions, the use of proven tools, sample representativeness of the research, as well as mathematical processing of data with the use of a software package for statistical analysis. As a result of the study, it was proved that emotion-focused ways of overcoming stress associated with a decrease in tension and a change in attitude to emerging difficulties prevail among the students of both the experimental and control groups. A high level of stress was found in the majority of subjects. In order to reduce this stress, it is necessary for students to form special skills to overcome it. In addition, an analysis of the psychological factors of students’ hardiness made it possible to determine the main areas of their psychological support in overcoming difficult life situations (development of qualities that form the core of students’ personal adaptive potential, the formation of their social competency, enrichment of social networks, as well as learning ways to find and accept social support).
A system design for elastically scaling transaction processing engines in virtualized servers

Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of redesigning the engine, which has led to the introduction of several, new, cloud-based transaction processing systems mainly focusing on: (i) the transaction coordination protocol, (ii) the data partitioning strategy, and, (iii) the resource isolation across multiple tenants. As a result, standalone OLTP engines cannot be easily deployed with an elastic setting in the cloud and they need to migrate to another, specialized deployment. In this paper, we focus on workload variations that can be addressed by modern multi-socket, multi-core servers and we present a system design for providing fine-grained elasticity to multi-tenant, scale-up OLTP deployments. We introduce novel components to the virtualization software stack that enable on-demand addition and removal of computing and memory resources. We provide a bi-directional, low-overhead communication stack between the virtual machine and the hypervisor, which allows the former to adapt to variations coming both from the workload and the resource availability. We show that our system achieves NUMA-aware, millisecond-level, stateful and fine-grained elasticity, while it is not intrusive to the design of state-of-the-art, in-memory OLTP engines. We evaluate our system through novel use cases demonstrating that scale-up elasticity increases resource utilization, while allowing tenants to pay for actual use of resources and not just their reservation.
End-to-End Nested Multi-Attention Network for 3D Brain Tumor Segmentation

Utilizing the powerful feature learning ability of deep learning, researchers have proposed a variety of effective methods for brain tumor segmentation in three-dimensional medical images. However, the existing approaches often need to be processed in stages pipeline, without considering the anatomical nested structural characteristics of brain tumors, thus affecting the accuracy and efficiency of tumor segmentation. In this paper, we propose the Nested Multi-Attention mechanism Network (NMA-Net) tailored for brain tumors, which can effectively connect the sub-segmentation tasks of different organizations, and can directly conduct end-to-end training. By using the segmentation result of the tumor peripheral tissue as a kind of soft attention to the tumor segmentation task, it can make the subsequent network focus more on the region of interest, which makes it possible to obtain more accurate segmentation results. Besides, we transform multi-class segmentation tasks into multiple binary sub-segmentation tasks. Experiments on the BraTS’2017 Challenge Dataset show that the proposed NMA-Net framework is very suitable for organ tissue segmentation with nested anatomical structures. Here, our single-view model achieves the best segmentation performance compared with the exiting approaches, and the multi-view fusion model also achieves the state-of-the-art performance on the TC and ET sub-regions.
Introduction to GNU Octave

We hope you have gone through the table of contents and the preface. If not, we highly recommend you do so. This is the very first chapter of this book and we welcome you to the exciting journey of learning GNU Octave.
3D Fractal Interpolation Functions

For visualizing one, two- and/or three-dimensional data, reconstruction mechanism is generally considered. In the present study, I have examined the application of iterated function systems in interpolation. I have also presented new fractal interpolation derivations for three-dimensional scalar data. The interpolations can indicate uncertainty of the data, allow tenability from the data, represent the data statistically at different scales, and may also more accurately facilitate data analysis.
Secure Outsourced Numerical Solution of Algebraic Equations

Numerical methods are designed to provide numerical solutions of algebraic equations, because there are not analytical solutions for algebraic equations whose degrees are larger than four. In cloud computing, outsourcing numerical solutions to the cloud raises privacy issues due to the privacy sensitive coefficients. In this paper, we propose an effective privacy-preserving computation outsourcing protocol for the common numerical solutions of algebraic equations. We first present a protocol that can securely evaluate Newton iterative method with the preservation of the private coefficients, by relying on somewhat homomorphic encryption. We then provide two implementations of our protocol based on the two popular somewhat homomorphic encryption schemes, i.e., the Brakerski-Gentry-Vaikuntanathan (BGV) scheme and the CKKS scheme. We conduct experiments to compare the two implementations and analyze the security and effectiveness. The experimental results show that the proposed scheme is effective in practice, and the CKKS-based implementation is general better than the BGV-based implementation.
Design of Intelligent Mobile Robot Control System Based on Gesture Recognition

At present, technologies such as smart home and intelligent wearable devices are developing, bringing convenience and enjoyment to our daily lives, while also avoiding a series of potential hidden dangers. This study aims to design an intelligent mobile robot control system based on gesture recognition which allows people to control the forward, backward and steering of the mobile robot through simple gestures. For gesture recognition, this paper uses the MPU6050 accelerometer to detect and recognize human gestures. The system adopts the control chip STM32 MCU to collect the acceleration data. The results of the experiments have shown that the proposed gesture recognition device can control the mobile robot maneuvering well and complete the function of forward and backward and steering.
A Novel Approach to File Deduplication in Cloud Storage Systems

In view of file deduplication in client-server task partitioning, this paper proposes a pre-filtering algorithm and a PIA algorithm. Firstly, the pre-filtering algorithm is based on Bloom filter and used to determine whether or not there are duplicate files on the server by the Bloom filter and objective attributes of files. The non-duplication files are directly uploaded to the cloud storage system. Secondly, for the potential duplicate files with the process of pre-filtering, and a further comparison is made with the PIA algorithm to determine whether it is a duplicate file. Once the non-duplication files are uploaded to the cloud storage system, the incomplete digest calculation in the client-side is completed by the server-side. Compared with the full-file digest algorithm of FastDHT component in the FastDFS, experimental evaluation shows that the PIA algorithm has the advantages of lower algorithm time, lower CPU usage.
Estimation of the Solar Resources in Kyrgyzstan Using the Meteonorm Computer Program

There are four actinometric stations (AS) in Kyrgyzstan located in the north-eastern part of the country, for which there is no information in open sources for the last 40 years. Databases (DBs) formed on the basis mathematical modeling and results of satellite sensing of the Earth's atmosphere and surface can be used to estimate the solar resources in the other regions of the country. Among the developed global and regional DBs in the field of solar energy, the Swiss Meteonorm DB is most widely used around the world, the adequacy of which can be verified by comparison with the data of actinometric stations. The long-term monthly solar radiation (SR) data contained in the Meteonorm DB are compared with the data of seven actinometric stations of Kyrgyzstan and neighboring countries from two available DBs: the World Radiation Data Center (WRDC) and the Scientific and Applied Reference Book on the Climate in the Soviet Union (SARB). The considered DBs are briefly described. It is shown based on the obtained analysis results that the values of data contained in the Meteonorm DB are higher than the data contained in the WRDC and SARB databases compiled using information from the actinometric stations. Moreover, this exceeding varies not only within the year, but also depending on the terrain latitude. An analysis of the long-term variability of the total SR according to the data of three ASs in the territory of countries neighboring with Kyrgyzstan from the WRDC DB has not revealed any trend of its change. The methods used for the newly developed mathematical model to restore the long-term average annual and monthly values of the total SR in Kyrgyzstan from the Meteonorm DB with taking the terrain latitude into account are described. An updated theoretical potential of solar energy in Kyrgyzstan is presented, and it is shown that the long-term average annual arrival of total solar energy varies insignificantly: from 1656 to 1856 kW-h/m2. Its average value is 1756 kW-h/m2.