Reviewerly/exHarmony huggingface dataset:
Current problems of patriotic education of future psychologists of education as the constituent of all-professional training

The current problems of patriotic education of bachelors and undergraduates of psychology and pedagogical education caused by insufficient formation at students of components of patriotic consciousness (cognitive, emotional, behavioural) are considered on the basis of the conducted empirical research. The author’s technique of projective poll allowing to present a general characteristic of these components and level of patriotic consciousness of students according to stages of moral development (according to L. Kolberg)is used for diagnostics. Special attention is paid to a behavioural component of patriotic consciousness of students. The results presented in article allowed to reveal problems of improvement of patriotic education of future psychologists of education for the purpose of formation of their readiness for realization of spiritual education of children and teenagers on the basis of basic national values.
Overview of Research on Health Assessment and Fault Prediction of Complex Equipment Driven by Big Data

Abstract Health evaluation is an important content of health management for complex equipment, and fault prediction is a key link to realize condition-based maintenance. Based on the literature review, this paper gives a feasible modeling method: The health status of complex equipment is expressed as time series by constructing forward and backward LSTM model, and the health evaluation model is constructed and trained based on the deep learning platform TensorFlow. The improved particle filter constraint algorithm is used to optimize LSTM neural network which is to generate PF-LSTM prediction model. The research on health evaluation and fault prediction of complex equipment based on machine learning discussed in this paper puts forward new requirements and development direction for machine learning of complex equipment, and is of great significance for practical implementation of lean intelligent manufacturing, improvement of stability and economic benefits of complex equipment, and reduction of operation and maintenance costs.
Evaluating the Boundaries of Big Data Environments for Machine Learning

Hadoop and Spark are popular open-source Apache projects in the Big Data ecosystem. Due to shortcomings associated with Hadoop MapReduce (Hadoop) Apache Spark had gained prominence in the Big Data environment. However, there is little work aimed at evaluating these two Big Data frameworks to provide understanding for when they could be of most utility for machine learning, for example for when frequently querying large-scale data for input to recommendation systems. To explore the possible best use cases of each platform an experimental analysis between Hadoop and Spark was done and assessed using four criteria in terms of performance, storage, reliability and architecture. Different test environments were created varying memory, cache and volumes of data throughout the experiment, where Impala and Hive were used as query engines on the Hadoop file system against the native Spark query engine. We then conducted analyses along two dimensions. Our outcomes show that Spark performs best with large volumes of data processing compared with other query engines such as Apache Impala and Apache Hive. Findings here suggest that each platform have particular strengths given particular contexts, however, Spark seems to demonstrate most utility overall.
Enhanced Double Random Phase Encoding Systems with Multi-Wavelength Interferometry techniques

Double Random Phase Encoding (DRPE) systems are prone to plain signal and Dirac delta attacks. A recently proposed modular arithmetic based DRPE system has been shown to mitigate this significant weakness, by introducing a time-varying impulse response that is unknown to the receiver. This prevents the attacker characterizing the DRPE system, even if the attacker has access to the encryption unit. In this work, we show that when using specific decryption techniques that are borrowed from the area of Multi-wavelength interferometry, the amount of time-varying noise can be arbitrarily high, provided there is a correlation in the fractional ratios. Simulations shows that the generated noise-like encrypted images have an entropy is near the theoretical limit.
Book Graphics by M.M. Tsekhanovsky. To the 130th Birth Anniversary of the Artist

The purpose of the article is to consider the book design works of the famous graphic artist and animation movie director M.M. Tsekhanovsky (1889—1965), using the complex of bibliological, art historical and historiographic methods, to identify the most characteristic features of his creative method, to reconstruct the important stage of biography of the master, analysing previously unpublished archival materials. Already in the very first illustrative cycles, the artist finds his theme: he gives a clear preference to inanimate heroes, spectacularly and clearly depicts the attributes of modern life, symbols of technological progress. Further, making illustrations to the works of S.Y. Marshak, B.S. Zhitkov, M. Ilyin, he refines his artistry and takes the prominent place among the creators of children’s “manufacturing”, technical, popular science books. He manages to adapt the extremely complex topics to the perception of a child, to fill dry language schemes, drawings or geographical maps with lively, exciting content and vivid imagery. The visual series of such graphic works is not a banal pictorial parallel of the text, its explanation, but it leads an independent party, significantly complements and enriches the content of the book. People appear quite rarely in these graphic series; they are treated very schematically and mechanistically, sometimes they seem to be the creations of the world of things and machines, so close to the artist. However, in the best works of M.M. Tsekhanovsky, and above all, in the illustrations to the famous “Post” by S.Y. Marshak, human images are shown generically, and at the same time individualized, endowed with characteristic, memorable features, clearly inscribed in the plot and rhythmic outline of the dynamic narrative. In the heritage of the master, there are also completely different works, indicating that he could become a remarkable animalist, illustrator of fairy tales or “adult” historical and revolutionary works. However, these sides of his talent were not demanded by the technocratic era of the 1920s. Transition of the artist from book graphics to cartoon animation was in some sense predetermined by the very nature of his talent: M.M. Tsekhanovsky was always interested in the problem of plastic transfer of movement and unification of disparate episodes into a coherent story.
Mining Incrementally Closed Itemsets over Data Stream with the Technique of Batch-Update

Currently incremental mining techniques can be divided into two groups: direct-update technique and batch-update technique. Mining closed item sets is one of the core tasks of data mining. In addition, advances in hardware technology and information technology have created huge data streams in recent years. Therefore, mining incrementally closed item sets over data streams with the batch-update technique is necessary. Incremental algorithms are always associated with an intermediate structure such as tree, lattice, table… In the previous study, the author proposed an intermediate structure which is a linear list called constructive set. In this paper, an incremental mining algorithm based on the constructive with the batch-update technique is proposed in order to mine data streams.
The image series forgery detection algorithm based on the camera pattern noise analysis

In the paper, the image series forgery detection algorithm based on the analysis of camera pattern noise is proposed. Distribution characteristics of the camera pattern noise are obtained by extracting the noise component of images from the non-tampered image series. A noise residual of a forgery image is compared with the camera pattern noise. We compare various noise filtering algorithms to choose the one that achieves the best performance of the proposed method. The proposed algorithm is tested both on examples of copy-move forgeries and forgery fragments which were inserted from an image not included in the image series.
A Novel Blurring based Method for Video Compression

Compression is an essential processing step for multimedia storage, distribution and transmission in terms of optimum resource utilization. Non-customary compression methods have become an important area for research due saturation of traditional coding based compression techniques. In this paper, we propose a novel spectrum based compression technique to further reduce the data footprint with satisfactory quality metric for the cases of video. Proposed method is based on principle that `Any information that can be restored, can be compressed'. We blur the individual frames with a given Point Spread Function (PSF) to increase the DC component and compress using standard MPEG coder. To recover the video, we perform frame by frame deconvolution using the known blur PSF. Results obtained show further improvement of 20 to 30% in achieved compression with respect to original MPEG compressed video with satisfactory quality of recovered input.
On a New Distance Approximation for an Implicit Polynomial Manifold Fitting

The application of a new approximate point-to-algebraic manifold distance formula is suggested to the geometric approach to curve fitting and surface reconstruction using implicit polynomial manifolds. A brief overview of the fitting methods features for implicit algebraic manifolds is given. To illustrate the possibilities of a new approximate point-to-manifold distance formula, the equidistant curves of the exact distance, Samson’s distance and the present formula are given. A four-step algorithm for implicit algebraic manifold fitting is proposed, using one of the algebraic fitting methods at the initial step, the present approximate formula for the distance finding to calculate the geometric criterion of approximation quality and an optimization method for updating the value of the vector of coefficients of the manifold. The first results of the proposed algorithm on test data are briefly characterized. In conclusion, the tasks and directions for further research are described.
Frequent Semantic Patterns for Document Relevance Ranking

Modelling user interest has been a challenge for improving the performance of information filtering systems (IFs). Currently, there have been term-based, phrase-based, and pattern-based approaches in modelling user interest [2, 5, 13]. Patterns have been said to convey more specific and relevant information in modelling user’s interest [5]. However, the existing patterns such as frequent and closed patterns are all generated based on their statistical features such as frequency. But their semantic meaning was ignored. This study proposes a new information filtering model named as Frequent Semantic Patterns for Document Relevance Ranking, shorted as FSPnIF. In particular, a new type of patterns, called frequent semantic pattern (FSP), is proposed to represent user’s interest. The patterns are representative as they are generated from the top highly frequent words in the training corpus. These patterns also convey semantic meanings because they are verified by meaningful concepts in ontology. A new method to measure document relevance based on FSPs is also proposed to filter relevant documents in IFs. The model was evaluated in IFs using RCV1 and R8 datasets. The results of extensive experiments show that the new proposed model significantly outperformed all the state-of-the-art baseline models according to five main evaluating measures.
A Novel Small Vehicle Detection Method Based on UAV Using Scale Adaptive Gradient Adjustment

Vehicle detection based on UAV video is a typical small object detection task. In recent years, multi-scale prediction framework has become one of key steps for small object detection. However, the performances of existing methods are still not satisfactory for small object detection. In this paper, inspired by that the scale of object has an impact on gradient descent in the deep learning process, we choose the intersection over union (IOU) as the evaluation metric to analyze the relationship between scale of objects and gradient. We have shown that the gradient adjustment methods should satisfy some rules and thus we propose a new gradient adjustment formula based on our analysis. In addition, we built a mixed small vehicle dataset based on UAV videos for better evaluation of small vehicle detection. In the comparison with existing methods, our proposed method has achieved better results. The performance of our method reveals the potential of scale adaptive gradient descent method.
Irrigation network information system for irrigation area management (Case study in Tapango Polewali District, West Sulawesi)

Abstract Success in implement irrigation area management activities is highly dependent on data availability. The existence of a realtime, fast, and accurate information is very important. The data and information needed must be easily accessible to interested parties. Development of an irrigation network information system is carried out by converting data into coordinate form using ArcGIS software. Visual studio software is used to display all data that has been processed. Spatial-based irrigation network information systems built using ArcGIS and Visual Studio software can easily obtain information about channel conditions, rating curves, channel profiles and channel photos. the availability of spatial-based irrigation network information systems facilitates the management of irrigation networks.
An Adaptive Heuristic for Scheduling Dynamic and Fuzzy Jobs on Elastic Clouds

We consider a special workflow scheduling problem in a cloud-based workflow management system in which tasks have the fuzzy processing times, fuzzy due dates, linearly dependency, stochastic arriving times and executed on elastic cloud resources with multiple price structurings. A triangle fuzzy number based problem model is formulated. Two metrics are explored: the cost and the satisfaction degree. In order to improve the solutions, the price structuring of VMs can be adjusted according to their real utilization. An adaptive and iterated heuristic framework is presented to schedule tasks event by event which mainly consists of the task collecting and the fuzzy task scheduling. The proposed methods are calibrated through detailed designs of experiments and sound statistical techniques. The proposed algorithm is compared to existing methods for related problems. Experimental results show that the proposal is robust and effective for the problems under study.
Revision-Aware Caching for Hybrid Cloud Render Farm

Render farm is a bunch of networked servers dedicated to render image in a distributed or parallel fashion. The capacity of in-house render farm has an upper bound but a rendering workload in real world tends to highly fluctuate. Thus execution of overloaded jobs on a remote cloud in a seamless way is very attractive to build cost-effective rendering infrastructures. However, extending a render farm from a cluster to a public cloud is not straightforward. The most critical obstacle is data synchronization between render farms. A simple approach to overcome this issue is that rendering data are uploaded to or downloaded from public cloud. This kind of configuration is used by most cloud-based rendering services. Despite its simplicity, data synchronization by an explicit data transferring imposes a heavy burden on a user and incurs unnecessary data transmission as well as startup latency. A more elaborated data synchronization technique armed with an on-demand fetch and cooperative caching can ease the burden and improve a rendering throughput by exploiting rendering workload characteristics. Our proposed revision-aware caching is that rendering data identifiable with additional revisions are cached on cloud and are fetched from on-premises revision-aware repository or a cache pool of peer cloud node. We proved the performance of our scheme via experiments based on synthetic workload and real workload.
Head and Tail Technique for Hashing Passwords

This research developed a head and tail (HT) technique that added complexity on implementing MD5 and SHA-1 algorithm for hashing passwords using fragmentation and concatenation processes. The Head and Tail technique uses multiple hashing on password plain text and bit manipulation on combined password digest to produce a 512-bit hash. Frequency (Monobit) Test gives an average computed p-value of 0.72 when HT is applied to MD5 and 0.73 to SHA-1 while Frequency Test within the block resulted to an average p-value of 0.854 and 0.806 for MD5 with HT and SHA-1 with HT respectively. On the other hand, in the run test, an average p-value of 0.40 is achieved for MD5 with HT and an average p-value of 0.54 for SHA-1 with HT. The HT technique has greatly improved the randomness of the password digest.
Motion Detection Using a Hybrid Texture-Based Approach

Singh, Rimjhim Padam Sharma, Poonam Madarkar, JitendraMotion analysis plays an important role in various real-time applications like object detection, human–computer interaction, surveillance systems, human detection and tracking, event monitoring, etc. Background subtraction that aims at separating the motion regions from the static portions lays the foundation of all such applications. Most of the background subtraction techniques developed to date explore colour features of pixels, either individually or in a spatio-temporal manner. Many other techniques exploit texture characteristics of pixels, while a few have been developed that employ a combination of both texture and colour characteristics for extracting motion-related information from frames. But most of the efficient background modelling techniques demand extensive use of hardware and computation. In this paper, we propose a hybrid sample consensus-based foreground segmentation technique that fuses similarity-based binary patterns of pixels with YCbCr colour space. The core of a pixel-based technique has been reconstructed to obtain drastically refined results.
Advanced Methods to Extract Value from Scientific Datasets

In these days the scientific community is times bigger comparing with the previous centuries. The need of powerful tools to aggregate and analyse the information about articles, books and other publications becomes greater with each published paper. In this paper we describe a solution for understanding and leveraging this data. The platform integrates the following requirements: data aggregation, data analysis (visualizations, dashboards, graphs), complex and simple searches, and support for data export. The platform brings value to its users due to various reasons such as quick identification of relevant data and in depth analysis on the provided input. Another key feature is the granularity, application being easily configurable for rigorous use cases: just one author, a group of authors or an entire scientific field.
Nonlinear Principal Component Analysis Based Deep Associative Memory

In order to improve associative memory in artificial brain for the fuzzy photo and amnesia, we apply nonlinear principal component analysis (NLPCA) based deep learning to improve the problem of associative memory on capability. That is, the proposed NLPCA based deep associative memory neural networks can improve auto and bidirectional associative memory on capability problem in this paper. We proposed the seven-layered multi-layer perceptron neural network based on NLPCA and used a TensorFlow module with python to implement the proposed method. The proposed method used back-propagation to replace Hebbian learning for bidirectional associative memory. Finally, we presented seven-layered multi-layer perceptron neural networks under NLPCA with 0~9 numbers in patterns for the proposed method.
Data Mining: The Classification Method to Predict the Types of Motorcycle Spare Parts to be Restocked

Abstract The research intends to create an application which is able to analyse sales data in a motorcycle company to predict the types of spare parts which should be stocked. This prediction is crucial since problems are often encountered while restocking. For instance, when there have been some imprecisions occurring in deciding regarding the types of spare parts to restock, the spare parts accumulate. It can cause inefficiency in terms of storage, the products quality deteriorates due to having been stored for too long, and sometimes the best-selling products are not available in the warehouse. This application is developed with Naïve Bayes Classifier (NBC) method which has a high accuracy in predicting future occurrences. This method works by calculating the probability value in each attribute class and determining the optimal probability value. From the test results, 4500 training data with 200 sample test data has 90% similarity with the results of the restock decision without application. For 500 test data, the similarity was 96%. It is proven that this method has a high accuracy so that it can help the decision makers solved the company problem in predicting the types of motorcycle parts to be restocked.
The Form of High-Performance Computing: A Survey

Abstract Computational technology is an important thing that needed in the whole of human life. Along with the development of computer technology, there are a significant data created and wait to be processed; we will get more advantages if it well-processed, if not we will see the data explosion, the data is just a useless stack. Research mitigates the other research paper and makes a conclusion and a report by PRISMA method. The development of computer technology will support the development of an organization. Considering any factors in choosing the right and suitable high-performance computer resource due to our organization is a must. The existing high-performance computing forms are Supercomputer, Grid Computing, Cluster Computing, and Cloud Computing. Based on this work, the last form of high-performance computing, Cloud Computing is one of the most used forms thus we mitigate its advantages and disadvantages. Recently, to process this vast data, we must have a high-performance computer and to provide it, this is not a cheap resource.
School environment as a prerequisite for acculturation of migrant children (foreign students) using School № 36, in the city of Perm, Russia, as an example

Consideration of the problem of migrants in education is extremely relevant. To date, the name "multi-ethnic school" is assigned to this type of national Russian school, the contingent of which is up to 15% of the "monoethnic" students who speak Russian at an insufficient level to become the language of instruction in the school. The main result of school education for us is its compliance with the goals of the advanced development of young people. Foreign students in the process of delving into the peculiarities of the Russian language initially see it as a game and thanks to this approach find playful ways to learn. In the future, this skill is subconsciously extrapolated to the rest of their activities. They turn out to be more receptive and adaptive to the changing "rules of the game" than those who were initially immersed in its atmosphere and gradually mastered its rules. On a concrete example, it is considered how the school environment is capable of realizing the conceptual idea of multicultural education.