Reviewerly/exHarmony huggingface dataset:
Developing responsible and passionate attitude of military academy cadets to future professional activities

This article describes the concept of «military academy cadets’ responsible attitude to future professional activities» and explains the phenomenon of cadets’ passionarity and passionate attitude to a professional activity. The professional orientation is considered as a result of the professional self-identification of a military man, giving him a positive or negative attitude towards the profession of an officer. It reveals the main factors and conditions for developing value-based, informational, cognitive and behavioral aspects of a responsible attitude towards future professional activities of military higher education institution cadets. The description of the factors and conditions needed for creating a responsible attitude towards future professional activities and the developed ideological world as a component of passionarity among cadets of military universities is accompanied by an interpretation of the modern military education “humanization” concept. The article gives brief relevance analysis of the process of forming a responsible and passionate attitude towards future professional activity among cadets of military universities. The authors make an attempt to study the possibilities of humanitarian disciplines in organizing a special educational environment for its effectiveness. The study reveals the characteristics of game-based technologies that give an impulse to students’ cognitive activity and individualization of the learning process. The research points out the possibilities of integrating pedagogical technologies, ensuring successful learning through cognitive activity of cadets and the formation of their responsible and passionate attitude towards professional activity in the future.
An Efficient Risk Data Learning with LSTM RNN

The use of big risk data for risk management has evolved into a concern within financial services industry in recent years. Whether the quality of big risk data can be relied upon is to be ascertained till 2019. To facilitate the measurement and prediction of data quality, we propose an efficient approach to slide a piece of data from the big risk data and a model to train divergent Long Short-Term Memory (“LSTM”) Recurrent Neural Networks (“RNNs”) with various algorithms. The network is evaluated by the improvement in network run time, prediction accuracy and relevant error. This enables financial institutions to identify potential data risks instantly for earlier mitigation soon.
Deep Neural Networks in Semantic Analysis

This paper presents research of the possibilities of application deep neural networks in semantic analysis. This paper presents the current situation in this area and the prospects for application an artificial intelligence in semantic analysis and trend and tendencies of this science area. For better understanding future tendencies of researches in semantical area we present detailed review of the studies in semantic analysis with using artificial intelligence, studies about a human brain.
Residual Joint Attention Network with Graph Structure Inference for Object Detection

Most object detectors include three main parts, CNN feature extraction, proposal classification, and duplicate detection removal. In this work, focusing on the improvement of the feature extraction, we propose Residual Joint Attention Network, a convolutional neural network using a residual joint attention module which is composed of a spatial attention branch, a channel attention branch, and a residual learning branch within an advanced object detector with graph structure inference. An attention map generated by the joint attention mechanism is used to weight the original features extracted from a specific layer of VGG16 aiming at performing feature recalibration. Besides, the residual learning mechanism is complementary to the joint attention mechanism and keeps good attributes of the original features. Experimental results show that different branches of our residual joint attention module do not contradict each other. By combining them together, the proposed network obtains higher mAP than many advanced detectors including the baseline on VOC dataset.
The International Register of Ideas and Innovations. A Visionary Social Network to Develop Innovation and Protect IP Using Blockchain and Proof-of-Originality Algorithm

The International Register of the Ideas and Innovations (IRI) is a “visionary” and at present an experimental project that wants to protect all over the world the intellectual work leveraging on the blockchain paradigm (BC). IRI protects all the intellectual work not only the traditional ones (patents, trademarks, copyright, industrial design, geographical indication) but also those not protected by traditional offices such as ideas, projects, models, services, artefacts. The starting point is how to generate trust between many and different subjects engaged in the innovation value chain in order to create, build and collaborate in the developing of innovation. For this reason BC is also used to certify the actions, activities and relationships between researchers, inventors, students, innovators, funders, industries, user communities during the creation, construction and marketing of innovation. Detaching from the main BC platforms that need the consensus of the transaction based on a mechanism of proofing (especially regarding the financial area like bitcoin or Ethereum) the core of the project is the Proof-of-Originality algorithm (PoOr) that detects and compares the originality of the proposal. The challenge is to verify how the publication on IRI of an idea, project, …, assumes a “de-facto” protection. If an innovator can protect the idea and if the actions and the relationships that allowed the realization of the innovation are certified, he can share it and this allows the co-construction, the development and at the same time this generates trust for the selling or the search for funding.
Research on Spectrum Fitting and Extraction of Power Grid Communication Based on Big Data

Aiming at the problems of poor power spectrum fitting and extraction of current signals, such as poor power grid communication, a power spectrum fitting and extraction technology of power grid communication signals based on big data technology is proposed. By extracting the parameter characteristics of the power grid communication signal, the power wave frequency of the signal is calculated, and the power spectrum fitting value is calculated according to the wave frequency fluctuation characteristics, so that the power spectrum fitting extraction of the power grid communication signal is finally realized. Finally, the experiment proves that the power spectrum fitting and extraction technology of power grid communication signals based on big data technology is obviously improved compared with the traditional method and fully meets the research requirements.
Recognition of connected components in the problem of restoring a three-dimensional model from projection images

This article describes one of the ways to solve the problem of recognizing the components of the image connectivity in the drawing field in the problem of synthesizing a three-dimensional model. This task is a fragment of the information technology of processing archives of drawing, design and technological documentation on paper.
GPU and CUDA in Hard Computing Approaches: Analytical Review

Hard computing, i.e., conventional computing, requires an exactly expressed analytical model. At the time of implementation of hard computing techniques, each time exact results are guaranteed. The fundamental premises and standards behind hard computing are precision, certainty, and rigor. The main problem of hard computing is the time consumption of different algorithms such as kriging interpolation algorithm, Smith–Waterman algorithm, LZW compression, and many more. GPU provides an efficient way to do massively parallel operations such as taking the square root of all values in a region of memory quickly. The operational frequency of GPU is slower than CPU but has more cores than the CPU. This is the main reason why the above algorithms perform better on GPU architecture compared to the CPU. CUDA-based implementation of several hard computing techniques on GPU gives more significant results in terms of time. This article provides an overview of the current literature of CUDA and GPU-based implementation of several hard computing techniques.
Linear congruent method on computer based test for new student admission

Abstract The new admissions examination is an early process in advanced education. Through the entrance examination, the school can select students with the best potential. The exam results can be used as a standard of candidate ability which can further be utilized as a determination of content, methods, and learning strategies. The purpose of this study is to design a system using Linear Congruent Method (LCM) algorithm on the Computer Based Test (CBT) for the new admissions exam. The system build using Waterfall software development life cycle method. The designation of the LCM algorithm is based on consideration of the conformity of the LCM algorithm to be used in the randomization of questions on CBT. The result of experiment shows the ability of LCM to randomize the question and answer of new student admission test is running well, so that the exam process can avoid the cheating of examinees, and on the other hand the assessment can do objectively, quickly, and accurately.
Improved Cuckoo Search Algorithm Based on Firefly Mechanism

Cuckoo Search (CS) algorithm has the characteristics of few parameters, easy realization and strong exploration ability. However, CS algorithm relies heavily on the random step in the process of exploration and lacks communication with excellent individuals in the process of optimization, which leads to slow convergence speed of CS algorithm. Firefly algorithm (FA) achieves the goal of optimizing by constantly approaching individuals who are better than themselves. Inspired by the firefly algorithm, Our search algorithm introduces the firefly mechanism into the CS algorithm to increase the information exchange between particles to improve the convergence speed of the algorithm. CS-FA has no random term of FA, because cuckoo search algorithm has powerful global exploring ability. The simulation results show that our algorithm CS-FA has better optimization ability and faster convergence speed than CS algorithm, FA and also PSO algorithm.
Development and quality analysis of a learning media electrical motor installation on the Android platform, for vocational students

The purpose of this study is to design a learning media electrical motor installation (EMI) application with quality standardization. The research was conducted with the research and development approach. The product discussed is two applications that can be used on smartphones on the Android platform. The two applications have the same content but different interface designs, so that comparisons could be made to obtain quality products. Data was collected through a questionnaire, offline tests (direct testing), and online tests (cloud testing). Quality analysis was carried out with reference to ISO 25010. This study shows that the the first application (IML-WA) is higher quality than the second application (IML-BBM): an analysis of functional suitability and compatibility aspects scored the applications as equal in quality, while the usability and performance efficiency aspects show that IML-WA received a higher mean rank score than IML-BBM.
A Robust Facial Landmark Detector with Mixed Loss

Facial landmark detection is one of the most important tasks in face image and video analysis. Existing algorithms based on deep convolutional neural networks have achieved good performance in public benchmarks and practical applications such as face verification, expression analysis, beauty applications and so on. However, the performance of a facial landmark detector degrades significantly when dealing with challenging facial images in the presence of extreme appearance variations such as pose, expression, occlusion, etc. To mitigate these difficulties, we propose a robust facial landmark detection algorithm based on coordinates regression in an end-to-end training fashion. By using the soft-argmax function, the network weights can be optimised with a mixed loss function. The online pose-based data augmentation technology is used to effectively solve the data imbalance problem and improve the robustness of the proposed method. Experiments conducted on the 300-W and AFLW datasets demonstrate that the performance of the proposed algorithm is competitive to the state-of-the-art heatmap regression algorithms, in terms of accuracy. Besides, our method achieves real-time speed on 300-W with 68 landmarks, which runs at 85 FPS on a Tesla v100 GPU.
Mutually independent feature flow: An approach to produce fixed complexity latent space for improved performance and decreased parameter count

Deep neural networks are integral part of every recent breakthrough in learning systems. Much of the share of this success goes to new and powerful computational devices, which lead to bigger and deeper models with higher learning capacity. Recently, a lot of new approaches have been developed to make it more convenient to train deeper models, and overcome the training problems like exploding and vanishing gradients. In this paper, we use a very unorthodox approach and propose novel mutually independent feature flow method for efficient architecture design. This approach decomposes the traditional bigger and continuously widening graphical model to fixed width graphs. It is achieved by decomposing the wider layers into conditionally independent branches with smaller width. Resulting model is a constrained parametric graph with fixed size and lower depth kernels. The process leads to huge reduction in model parameters in comparison to their traditional counterparts. This approach also provides low dimensional parameter space for gradient flow leading to faster convergence along with improved accuracy. Proposed approach is tested in three different styles of CNN architectures generally used in computer vision community. Performance is tested on four benchmark datasets of different complexity. Proposed approach not only provides huge reduction in model parameters but also leads to increased model accuracy.
ECG cloud monitoring and analysis information system

Abstract The article shows the possibility of implementing a technical solution for a dynamic cloud-based ECG monitoring system. It can conduct continuous remote monitoring of the dynamics of the human circulatory system state, aiming at quick detecting diseases of the circulatory system and its adjacent diseases. The system can perform 24/7 measurement of the health state of the circulatory system using a mobile cardiograph that sends received ECG data to the server where analysis and processing are performed. It can be used as patients who do not have the opportunity to visit a specialist in person, as well as in hospitals. The system is recommended for use in sports facilities for monitoring the health of athletes and life-threatening events.
Class-Level Adaptation Network with Self Training for Unsupervised Domain Adaptation

Deep learning has been widely used in various tasks. However, in real-world scenarios, obtaining various dataset with labels is time-consuming and difficult. Most models are trained in simulated scenarios, and such models degrade in real-world scenarios. Unsupervised domain adaptation is a branch of transfer learning, which utilizes a large number of labeled source domain data to improve the performance of the model in the target domain with limited or missing labels through knowledge transfer. However, most of the previous work neglected the category information when aligning the distribution between source and target domains, which led to the emergence of negative transfer. To address this problem, we propose the class-level adaptation network (CLAN) optimizing a novel metric which makes the centers of each class in source and target domains are close. Specifically, the class center of the source domain is generated by the labels of the source samples, while the class center of the target domain without any label is generated by the high confidence pseudo labels of the target samples. Technically, CLAN matches each target sample to the nearest center in the source domain and assigns an example a high confidence pseudo label by considering a threshold. Extensive experiments indicate that the combination of the aforementioned two models can achieve state-of-the-art performance on the Office-31 and digital domain adaptation benchmarks.
Empirical Adaptation of Control Parameters in Differential Evolution Algorithm

Proper settings of two control parameters in differential evolution (DE) algorithm play a crucial role when solving various optimisation problems. There are a lot of investigated adaptive mechanisms sampling the values of these parameters differently. In the proposed adaptive approach, efficient and inefficient settings of both DE control parameters are considered. A sampling of the control parameters is performed using the performance of parameters’ settings achieved in a preliminary experimental study. The proposed approach is applied to a frequently used adaptive jDE algorithm. The statistical analysis of the results provides better information about the efficiency of the proposed technique. More proper settings in the adaptation of DE control parameters provide better performance than a more general approach when solving CEC 2017 problems.
Cross-LingualWord Embeddings for Morphologically Rich Languages

Cross-lingual word embedding models learn a shared vector space for two or more languages so that words with similar meaning are represented by similar vectors regardless of their language.Although the existing models achieve high performance on pairs of morphologically simple languages, they perform very poorly on morphologically rich languages such as Turkish and Finnish.In this paper, we propose a morpheme-based model in order to increase the performance of crosslingual word embeddings on morphologically rich languages.Our model includes a simple extension which enables us to exploit morphemes for cross-lingual mapping.We applied our model for the Turkish-Finnish language pair on the bilingual word translation task.Results show that our model outperforms the baseline models by 2% in the nearest neighbour ranking.
Face Recognition Implementation System As A Media Access To Restricted Room With Histogram Equalization And Fisherface Methods

The face is one of the easiest way to identify individual and to distinguish. Therefore, the face recognition system is usually needed in the security system in the restricted rooms of the company. This research is to minimize all fraudulent actions such as theft of the company data. In this final project, the method used is histogram equalization and fisherface. The main step in this security system is that the user's face will be taken using a webcam. Then the process of face recognition uses the histogram equalization and the fisherface method using a PC (Personal Computer). Fisherface is a combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods. When an RFID sensor matches the employee data, the camera will capture and the process of face recognition will be run. After that the face data will be matched with the existing face data. When the both is match, the PC sends a command to the Arduino microcontroller to open the solenoid door lock. So that the security systems through face recognition can be more effective than conventional security systems. The test result of the face recognition system which has been done in this Final Project, has a success rate of 88.33% which was obtained from 120 times of experiments consisted of 12 poses. The test success level of the security system with 3 correspondents was 88.33%.
Evolution Analysis of Adaptive Distance-Preserving Level Set for Image Segmentation Based on Depth Learning

Abstract Image segmentation technology is the most basic part of computer vision and the basis of all other image processing methods. The quality of image segmentation technology will affect the effect of subsequent processing to a large extent. In the research and application of computer vision, image segmentation is often the first step and plays a very important role in the whole process. Compared with image classification and target detection, Distance-Preserving level set method involves two cross-cutting areas of image recognition and natural language processing, which is very challenging. Handling the information interaction between the machine and the real world is the first step in the study of artificial intelligence. The goal of computer vision research is to make the computer have the ability to perceive and understand image information. In the future, the focus of the work will be on improving the generalization ability of the model so that the model can perform well in a variety of tasks.
New statistical method for generating sequences of pseudo-random numbers

Abstract The paper describes the algorithm of the precision generator (hereinafter the P-generator) of random variables. Its feature is the ability to reproduce samples of large and small amounts of data with high accuracy knowing only the type of distribution. The created program module, which is based on the algorithm described in the work, is capable of generating a sample according to four distribution laws: normal, exponential, Laplace and Pareto. The proposed statistical method is based on calculating the number of points using priori known density distribution formula of the selected law. Further generation of the found number of points is implemented using the built into C# programming language random numbers generator. Computational experiments, which are given in the work, demonstrate the accuracy of the algorithm and also allow one to conduct a comparative analysis with existing alternatives (such popular mathematical and statistical packages as Mathcad and Excel).
Power and Bitcoins : a critical realism perspective

In this study, through the lens of critical realism to power, we take a close look into the power dynamics behind the Bitcoin protocol in relation to its Cypherpunk philosophical underpinnings. We focus on some of its main components that can be seen as constraining structures, and we discuss how these structures generate constraining mechanisms that restrict users’ power to act, further reinforcing other entities' power over them. In doing so, we illustrate that the Bitcoin Protocol, as it is used today, is in tension with the principles on which it was developed. In addition, we show that power, instead of beingdecentralised and distributed to the many, it has merely shifted from traditional actors to what can be seen as newcomers or atypical regulators. In line with the paradigm of critical realism, we note that the identified mechanisms and structures we discuss in this paper, are those that we were able to observethrough our subjective lens; others may exist but may require different contextual conditions to be activated and observed.