Reviewerly/exHarmony huggingface dataset:
Efficient Boolean Keywords Search over Encrypted Cloud Data in Public Key Setting

Searchable public key encryption- (SPE-) supporting keyword search plays an important role in cloud computing for data confidentiality. The current SPE scheme mainly supports conjunctive or disjunctive keywords search which belongs to very basic query operations. In this paper, we propose an efficient and secure SPE scheme that supports Boolean keywords search, which is more advanced than the conjunctive and disjunctive keywords search. We first develop a keyword conversion method, which can change the index and Boolean keywords query into a group of vectors. Then, through applying a technique so-called dual pairing vector space to encrypt the obtained vectors, we propose a concrete scheme proven to be secure under chosen keyword attack. Finally, we put forward a detailed theoretical and experimental analysis to demonstrate the efficiency of our scheme.
Conditional Entropy Coding for Efficient Video Compression

We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we reduce complexity by not performing any form of explicit transformations between frames and assume each frame is encoded with an independent state-of-the-art deep image compressor. We first show that a simple architecture modeling the entropy between the image latent codes is as competitive as other neural video compression works and video codecs while being much faster and easier to implement. We then propose a novel internal learning extension on top of this architecture that brings an additional 10% bitrate savings without trading off decoding speed. Importantly, we show that our approach outperforms H.265 and other deep learning baselines in MS-SSIM on higher bitrate UVG video, and against all video codecs on lower framerates, while being thousands of times faster in decoding than deep models utilizing an autoregressive entropy model.
Space Target Super-resolution Based on Low-complex Convolutional Networks

The images super-resolution is a key technique for space target, which has important research significance and application value. However, the traditional super-resolution algorithms always fail to achieve a good reconstruction effect due to limited information and inaccurate manual design models. Moreover, deep learning algorithms mainly focus on improving reconstruction with the price of high complexity. To address the above issues, we propose an improved space target super-resolution based on low-complex convolutional neural networks. Specifically, we construct a three-layer convolutional neural network by changing network layers and convolution kernels, which reduces the number of parameters and improves the extraction characteristics and reconstruction. Meanwhile, we establish a complete training data set, which solves the problem that the space target images are difficult to obtain. The experimental results demonstrate that the algorithm proposed can achieve the space target super-resolution. Compared with other algorithms, our algorithm obtains better subjective visual effects and objective evaluation indicators.
EFEKTIFITAS PENERAPAN BLENDED LEARNING DALAM MENINGKATKAN MOTIVASI BELAJAR MAHASISWA SASTRA INGGRIS UNIVERSITAS KHAIRUN

Abstract. Blended learning is a learning model that lecturers can apply to optimize the learning outcomes. This study, entitled "The Effectiveness of Blended Learning to Increase Student Motivation" aims to determine the extent of the effectiveness of Blended Learning in increasing the learning motivation on the first semester students of the English Literature Study Program at Khairun University. This research was carried out at the English Literature Study Program a quasi-experimental study designed on using the non-equivalent control group design. A total of 50 on the first semester students were sampled in this study. The sample was divided into 2 classes, experimental class used blended learning and the control class used face-to-face learning, each consisted of 25 students. The learning motivation questionnaire was used as the instrument in collecting the data related to students learning motivation. The results showed that there were significant differences between students learning motivation in the experimental class and students in the control class after had been given different treatments. The researcher also found that the application of Blended Learning was effective in increasing the learning motivation of the first semester students of English Literature Study Program at Khairun University.
Professional Development of Library Specialists in the Context of Implementation of the Federal Project “Creative People”

Mastering of new professional practice-oriented competences by library specialists is necessary for achieving positive changes in the cultural space of civil society. The article reveals prerequisites for the development of competence and qualification training of specialists of domestic libraries, mechanisms for strengthening integration of the professional education system and library information sphere. The Federal project “Creative People” within the framework of the National project “Culture” (2019—2024) is considered as a new model of professional development, an indicator of innovative activity of library specialists and consolidation of professional library community. The article presents the additional professional training programs of Krasnodar State Institute of Culture which have been developed taking into account the current trends in the organization of library activities within the frames of the Federal project “Creative People”. Implementation of library’s mission as a socio-cultural institution of society is impossible without a specialist — subject of innovation, strategist and organizer in the field of project management of activity. From this perspective, there was developed additional professional educational program “Innovative project and grant activities of libraries” tailored to top managers, deputy managers, heads of sectors and departments and methodologists of libraries. The article presents the analysis of intermediate results of fulfilment of the program aimed at acquiring new professional competence in the field of development and management of innovative projects in library-information sphere and participation in grant competitions. The author reveals the content of the program modules: innovative development of modern library, technologies of innovative project activity of modern library, grant activity of libraries. The analysis of specific features of full-time and distance learning forms under the program “Innovative project and grant activities of libraries” allows the author to make conclusion about the degree of its implementation as an interactive discussion platform for specialists of libraries of the Russian Federation.
Innovative Methods in Management and Raising the Quality of Higher Professional Education in Russia

Economic and political changes in contemporary society lead to reforms of many social spheres. The system of education in general and higher professional education in particular is no exception. The issue of ensuring the quality of education has recently attracted great attention in all countries of the world. The key problem of reforming education is raising its quality. In this paper, the object of research was the quality of education provided by higher education institutions. The authors have conducted the analysis of models for raising the quality of education and qualimetry of the laboratory work quality based on a survey of teachers and students. The QFD method was described by the example of its application in five universities of the Russian Federation. It has been proved that this method can provide the university with a planning tool with which higher education institutions can provide services that fully meet the requirements of customers (students, parents, employers, the state and society as a whole), thereby ensuring the suitability of the system to meet external requirements – the Fitness-for-Purpose approach (building goals based on the external environment). In addition, it has also been substantiated that the application of this method in the system of higher professional education will ensure the suitability of the system to achieve its own goals - the Fitness-for-Purpose approach (development, improvement, effective management). Thus, the QFD method will make it possible to support of all types of coordinated activities for the leadership and management of higher education institutions in relation to quality, as well as a set of developed regulatory and methodological documents that determine the content, technologies, methods and means of work of all officials, teachers and students to further improve the quality of educational process, thereby ensuring the competitiveness of higher education institutions and effective development on the global market of educational services
Exploring The Family Identity Of Future Educators

The article reveals the nature of the concept "family identity" as a person belonging to a family group, which reflects his commitment to family values and manifests himself at cognitive, affective and behavioral levels. The results of diagnosing the family identity of students, reflecting the hierarchy of family values students, the social roles with which they identify themselves, the types of adaptation and cohesion of the parent families of students. The article identifies various indicators of the formation of family identity: the involvement of a person in the life of the family, the presence of positive emotions and attitudes for the creation of their own family, the expression of interest in family history, help and care about parents and ancestors. The article identifies possible ways to develop the family identity of students: the study of traditional disciplines; the study of special disciplines; conducting research work of students on family pedagogy; conducting educational activities on family issues; organizing joint intergenerational activities, aimed at the formation of value attitude to family. The results of this study can be used in the further designing of the family identity development program and its introduction into the process of studying at the university.
Three-Dimensional Profilometry Utilization on VVER Type Reactors

Abstract The paper is focused on the application of three-dimensional (3D) profilometry on water–water energetic reactor (VVER) type nuclear power plant (NPP) equipment. This method is becoming increasingly used in the power industry and replaces conventional methods such as micrometer measurements. One of the greatest benefits is the accurate recording of the 3D profile of the measured surface and the possibility of its comparison with the production documentation or with the results from previous measurements. Centrum Výkumu Řež, s.r.o. (Research Center Řež) uses 3D laser scanner with a measuring arm. This method was, for example, successfully used for reactor pressure vessel (RPV), steam generator (SG), and bolts. The results are used by the NPP operator for the lifetime management of the primary circuit components.
New Enhanced Symbiotic Organisms Search for Optimal Location and Sizing of Distributed Generation in Radial Distribution System

Large distributed generation (DG) penetration into the power system needs to be accompanied by proper planning to maximize the benefits and minimize the negative effects that may arise on the system.Determining the location and size of DGs in power systems is a complex issue because it involves hundreds or even thousands of buses and lines distribution.Various studies have been conducted to overcome these problems, including by developing existing methods or even discovering new methods.This study deals with location optimization and sizing DG in the radial distribution system to minimize power loss and voltage deviation.The location of the DG is identified using a loss reduction sensitivity factor (LRSF) while the size of the DG is determined using the improved method of symbiotic organisms search (SOS) called New Enhanced SOS (NeSOS).There are two methods developed in the NeSOS, namely random weighted inverse vector (RWIV) and dual-phase parasitism (DPP).DPP consists of classic parasitism (CP) and random weight parasitism (RWP).The NeSOS is programmed under MATLAB software and validated using 26 mathematical benchmark functions.NeSOS also tested on IEEE 33 and IEEE 69 bus test system and compared with other methods.The simulation results show that the convergence rate of NeSOS is 30% faster than SOS.NeSOS also provides an average power loss of 1.53% lower than other methods.
3rd Place Solution to "Google Landmark Retrieval 2020"

Image retrieval is a fundamental problem in computer vision. This paper presents our 3rd place detailed solution to the Google Landmark Retrieval 2020 challenge. We focus on the exploration of data cleaning and models with metric learning. We use a data cleaning strategy based on embedding clustering. Besides, we employ a data augmentation method called Corner-Cutmix, which improves the model's ability to recognize multi-scale and occluded landmark images. We show in detail the ablation experiments and results of our method.
Generate High Resolution Images With Generative Variational Autoencoder

In this work, we present a novel neural network to generate high resolution images. We replace the decoder of VAE with a discriminator while using the encoder as it is. The encoder is fed data from a normal distribution while the generator is fed from a gaussian distribution. The combination from both is given to a discriminator which tells whether the generated image is correct or not. We evaluate our network on 3 different datasets: MNIST, LSUN and CelebA dataset. Our network beats the previous state of the art using MMD, SSIM, log likelihood, reconstruction error, ELBO and KL divergence as the evaluation metrics while generating much sharper images. This work is potentially very exciting as we are able to combine the advantages of generative models and inference models in a principled bayesian manner.
Commentary: The Ethics of Realism in Virtual and Augmented Reality

GENERAL COMMENTARY article Front. Virtual Real., 20 August 2020 | https://doi.org/10.3389/frvir.2020.00006
Knowledge-Preserving Certain Answers for SQL-like Queries

Answering queries over incomplete data is based on finding answers that are certainly true, independently of how missing values are interpreted. This informal description has given rise to several different mathematical definitions of certainty. To unify them, a framework based on "explanations", or extra information about incomplete data, was recently proposed. It partly succeeded in justifying query answering methods for relational databases under set semantics, but had two major limitations. First, it was firmly tied to the set data model, and a fixed way of comparing incomplete databases with respect to their information content. These assumptions fail for real-life database queries in languages such as SQL that use bag semantics instead. Second, it was restricted to queries that only manipulate data, while in practice most analytical SQL queries invent new values, typically via arithmetic operations and aggregation. To leverage our understanding of the notion of certainty for queries in SQL-like languages, we consider incomplete databases whose information content may be enriched by additional knowledge. The knowledge order among them is derived from their semantics, rather than being fixed a priori. The resulting framework allows us to capture and justify existing notions of certainty, and extend these concepts to other data models and query languages. As natural applications, we provide for the first time a well-founded definition of certain answers for the relational bag data model and for value-inventing queries on incomplete databases, addressing the key shortcomings of previous approaches.
Evaluation of the Hough and RANSAC methods for the detection of circles in biological tests

In the analysis of in vitro biological samples is very common to use Petri dishes, in which the samples are cultured. Likewise, in the study of mouse behavior and learning, the Morris test is employed, which consists of placing the animal in a circular pool to observe its behavior within it. In both cases, usually, the detection of each circle in images and videos is done manually, which makes the process long, tedious and imprecise. Currently, there are several image processing methods that allow the detection of lines, circles and other geometric shapes; the most well-known being the technique based on the Hough transform, which allows the detection of geometric shapes that can be expressed by a mathematical equation, and the Random Sample Consensus (RANSAC), a robust estimation algorithm that allows a mathematical model to be found from data contaminated with numerous values that do not fit the model. The precise location of the circle's position is very important, as it can seriously affect the detection and counting of samples on the Petri dishes and the measurement of mouse paths in the Morris test. Therefore, in this paper we evaluate and present the results obtained with these two techniques in synthetic images, for the detection of Petri dishes in biological images and the circular pool in Morris' test videos, measuring their computational efficiency and the error in the location of the circles.
Improving the performance of processing special computing tasks using an asynchronous actor model

Abstract The use of third-party ready-made solutions to perform special computing tasks (SCT): processing text arrays in a natural language, the use of cryptography methods in data transmission networks, machine learning in solving applied problems, scientific computations and many other ones is a common practice in modern development of software systems. However, the use of third-party ready-made software solutions often leads to their poor optimization for parallel and distributed operation and to the difficulty in vertical and horizontal scaling, impossibility of modifying a solution, e.g., due to its legal protection, etc. In order to solve these problems, an asynchronous actor model can be used that facilitates developing software for processing special computing tasks. In addition, an asynchronous actor model allows parallel processing of input tasks, provides asynchronous operation, and extends its functionality to third-party ready-made solutions, that it is impossible or forbidden to modify to meet user’s or optimization requirements. Methods, algorithms, and the entire logic of asynchronous actor operation involve transitions of actors from one state to another under the effect of input messages and returning the results of processed tasks to the frame system. This allows setting an automatic grammar and for using methods and approaches for developing software systems based on finite-state automata. The asynchronous actor operation scheme is represented by a directional state graph, which allows using the mathematical apparatus of the finite-state automaton theory (the Mealy model) to describe the same. The developed model is convenient for designing and organizing the SCT processing using computer systems. The use of the asynchronous actor model makes it possible, in special operation modes, to increase the performance of poorly optimized SCT without modifying the same, as compared to task processing without using the asynchronous actor model.
Methodology to Develop Military Students’ Infographic Competence

The research objective includes development and presentation of the author’s original methodology to develop military students’ infographic competence in the process of foreign language teaching. The article considers the problem of using infographics while teaching a foreign language to military students with the purpose to develop their infographic competence, the essence and specificity of which are revealed. The researcher’s attention is focused on identifying structural components of a methodological system for formation of military students’ infographic competence. Scientific originality of the study lies in the fact that the author develops a unified structural model of her original methodological system, which allows achieving optimal organization of foreign language teaching focused on forming military students’ infographic competence. The research findings justify efficiency of the proposed methodology.
Research on Improving the Quality of Online Teaching Based on Big Data under the COVID-19 Coronavirus Epidemic

Abstract In 2020, the global COVID-19 outbreak came out of the blue and caught people and businesses by surprise, including the education sector. In order to ensure the “suspension of classes”, schools at all levels in China implement online teaching. How to ensure the quality of online teaching is an important issue. Based on the big data of online teaching, it is of great practical significance to analyze the factors affecting the quality of online teaching, so as to improve the path of improving the quality of online teaching. With the help of big data analysis, the index system of online teaching quality improvement is constructed and applied. Through questionnaire survey and SPSS software analysis, it can be concluded that the teaching quality of online teaching can be improved from three aspects: the behavior level of school, the learning level of students and the behavior level. Therefore, the following Suggestions are put forward: improve the hardware environment, perfect the teaching organization, strengthen the teaching supervision, strengthen students’ mastery of knowledge and skills, strengthen students’ active inspection and team learning. It is hoped that the implementation of such recommendations will further improve the quality of online teaching in the context of COVID-19.
Estimation of the Support Parameters of a Uniform PDF and the Cramér-Rao-Leibniz Lower Bound

This letter is focused on the problem of estimating the two parameters of a uniform distribution - its support boundaries - and the application of the Cramér-Rao-Leibniz Lower Bound, a replacement for the Cramér-Rao Lower Bound when the latter does not hold.
One Weight Bitwidth to Rule Them All

Weight quantization for deep ConvNets has shown promising results for applications such as image classification and semantic segmentation and is especially important for applications where memory storage is limited. However, when aiming for quantization without accuracy degradation, different tasks may end up with different bitwidths. This creates complexity for software and hardware support and the complexity accumulates when one considers mixed-precision quantization, in which case each layer's weights use a different bitwidth. Our key insight is that optimizing for the least bitwidth subject to no accuracy degradation is not necessarily an optimal strategy. This is because one cannot decide optimality between two bitwidths if one has a smaller model size while the other has better accuracy. In this work, we take the first step to understand if some weight bitwidth is better than others by aligning all to the same model size using a width-multiplier. Under this setting, somewhat surprisingly, we show that using a single bitwidth for the whole network can achieve better accuracy compared to mixed-precision quantization targeting zero accuracy degradation when both have the same model size. In particular, our results suggest that when the number of channels becomes a target hyperparameter, a single weight bitwidth throughout the network shows superior results for model compression.