Reviewerly/exHarmony huggingface dataset:
Psychological Determinants of Modern Young Peoples Patriotic Attitude to the Motherland

The article focuses on the problem of developing the young peoples patriotic attitude of towards their home country: it determines the components of patriotic consciousness and messages that contribute to its development. It generalizes the knowledge available in the psychological science and offers a definition of the term «patriotism». It publishes the empirical research data in determining the values hierarchy of the high school students, their level of spiritual and moral development and the predominant components of patriotic consciousness. The results of the research reveal that thespiritual and moral development serves as a factor in forming patriotic consciousness; in the structure of the personal patriotic consciousness of high school students the emotional-sensual component prevails, most of the respondents identify themselves as «convinced patriots». The spiritual and moral development of high school students is characterized by low and medium indicators, which adversely affects their patriotic consciousness. Most of the recipients meditate on patriotism, while only few young people are prepared to act for the good of the Motherland.
Track before detect for radar using stochastic particle flow filters

We have invented a new algorithm that is many orders of magnitude faster than the current state of the art for track before detect problems with radar. We use stochastic particle flow filters to solve this problem for difficult radar applications in dense clutter with closely spaced multiple targets. In some of our applications the density of targets is so high that the radar measurements are often unresolved. Particle filters are particularly good for this application for two reasons: the measurement model is highly nonlinear, and the conditional probability density is extremely multimodal.
Willingness of future teachers to apply innovative productive teaching methods to form a healthy lifestyle in preschoolers

The goal of the study is to investigate the willingness of future teachers to apply innovative productive teaching methods in their direct work. Future teachers took part in the written survey (N 0 =141), students of other specialities and established professionals (N 1 =N 2 =28). Fisher's φ test, Pearson's correlation coefficient, biserial correlation coefficient, Kendall's τ correlation coefficient, Student's t-test (p ≤ 0.01) were used to statistically evaluate the results It is shown that future teachers from the proposed options choose mainly reproductive methods, and freely formulate informative methods. The most significant differences were revealed between the samples of future teachers and “adults” who do not have pedagogical training, which indicates the choice of methods not based on knowledge in the professional sphere, but on the basis of everyday ideas characteristic of this generation. It can be concluded that future teachers are really not ready to apply innovative productive methods in their practice, preferring more traditional informative and reproductive ones. This poses the task of finding ways to introduce into pedagogical practice those methods, theoretical reasoning about which is oversaturated in modern scientific and educational discourse.
Fusion of Global and Local Deep Features Using Bag of Words and VLAD Models for Human Activity Recognition

Human activity recognition is an important and challenging topic for computer vision research community. Action representation using deep learning models are currently the dominant technique compared with other methods. However, supervised convolutional neural networks require large computational and memory resources to optimize their parameters. Recently, a simple unsupervised deep learning architecture Principal Component Analysis Network (PCANet) has emerged as an alternative of Convolutional Neural Networks (CNNs) and has significant accomplishments in various vision applications. Meanwhile, encoding and representation techniques using Bag of Words (BoW) and Vector of Locally Aggregated Descriptors (VLAD) have demonstrated great success for several visual tasks specifically in activity recognition. This work presents a novel human activity recognition technique by combining global and local features of PCANet with BoW and VLAD encoding schemes. Both global and local features are learned by PCANet utilizing selected frames from each action video. After that the dimensionality of these features is decreased via Whitening PCA (WPCA). Then encoding schemes are applied on both features to represent the final descriptors for each action. Ultimately, Support Vector Machines classifier (SVM) is trained for recognition process. Several experiments are conducted on UCF sports dataset to evaluate our method. All experimental results utilizing leave-one-out-cross validation (LOOCV) strategy are satisfactorv and comparable.
Application Research of Artificial Intelligence in Swimming

Artificial intelligence (AI) technology develops rapidly. At present, intelligent technology is still being explored in sports, especially swimming, and there exist some problems such as immature system technology and unclear application. Therefore, it is extremely important to find the combination of AI and swimming, and to optimize intelligent systems. This article about the application of AI to swim at the literature study, video analysis, comparative research and mathematical statistics and other research methods, data of AI in swimming, swimming training the perspectives of the implementation of the way, in order to promote it provides the theory basis for the application of AI in swimming, for AI in the sports training of wider application. At the same time, considering that the development of AI in China is at the stage of perceptual intelligence and machines cannot think and act independently, the research believes that it is necessary to further develop and improve the algorithms related to the collected data of AI.
Encoding Qubit-Qudit States in Photon Polarization and Picosecond Time-Bins

We introduce a qubit-qutrit information system realized by two degrees of freedom in a single photon: polarization and picosecond time-bin. We demonstrate creation and reconstruction of states in this framework using quantum state tomography.
An Overview of Internet of Things Security from a Modern Perspective

The number of devices connected to the Internet and among themselves is increasing. Internet of Things (IoT) is a large Web that creates interconnection of devices and objects to the Internet, making an immense sea of information and data. This concept is fundamental in business strategy; however, as security risks increase with so many “things” connected, the security challenges become immense, making these devices vulnerable to cyber-attacks. It is possible to suggest some solutions for protecting networks that are often used also to transmit information from these devices. Since IoT enables connections, information security must be treated with a focus on ensuring greater efficiency in operational processes and security of user data. Thus, this paper aims to provide an updated review of the security in the Internet of Things showing and approaching its particularities, through a concise background, categorizing and synthesizing the potential of technologies and best security practices and strategies focused on this topic.
Did British colonial rule in Africa foster a legacy of corruption among local elites

Corruption is considered one of the main contributors to Africa’s development challenges, but less is known about the determinants of corruption and why the levels of corruption vary between countries. A new study finds that the legacy of colonial rule and its influence on local elites (chiefs) may be part of the explanation. The authors present empirical evidence that British colonial rule in Africa may have fostered the corruption of chiefs and undermined society’s trust in them.
Inferring Logical Clauses for Answering Complex Multi-hop Open Domain Questions

We enable a conventional Question Answering (Q/A) system with formal reasoning that relies on clauses learned from texts and also on the complex question decomposition into simple queries that can run against various sources, from local index to intranet to the web. We integrate the reasoning, multi-hop querying and machine reading comprehension (MRC) so that a value can be extracted from a search result of one simple query and substituted into another simple query, till the answer is obtained via the recomposition of simple queries. The integrated approach boosts the Q/A performance in a number of domains and approaches the state-of-the-art for some of them, designed to train a deep learning (DL) Q/A system to answer complex, multi-hop queries. We assess the contribution of two reasoning components: ontology building from text, and entity association, as well as multi-hop query decomposer and MRC and observe that these components are necessary and complement each other answering complex questions. We apply a similar multi-hop framework to the problem of a natural language (NL) access to a database. The conclusion is that the proposed architecture with the focus on formal reasoning is well suited for industrial applications where performance is guaranteed in cases of no or limited training sets.
A Multi-Objective Learning Method for Building Sparse Defect Prediction Models

Software defect prediction constructs a model from the previous version of a software project to predict defects in the current version, which can help software testers to focus on software modules with more defects in the current version. Most existing methods construct defect prediction models through minimizing the defect prediction error measures. Some researchers proposed model construction approaches that directly optimized the ranking performance in order to achieve an accurate order. In some situations, the model complexity is also considered. Therefore, defect prediction can be seen as a multi-objective optimization problem and should be solved by multi-objective approaches. And hence, in this paper, we employ an existing multi-objective evolutionary algorithm and propose a new multi-objective learning method based on it, to construct defect prediction models by simultaneously optimizing more than one goal. Experimental results over 30 sets of cross-version data show the effectiveness of the proposed multi-objective approaches.
Optimal Camera Placement in a Virtual Environment

Camera placement in a virtual environment consists of positioning and orienting a 3D virtual camera so as to respect a set of visual or cinematographic properties defined by the user. Carrying out this task is difficult in practice. Indeed, the user has a clear vision of the result he wants to obtain in terms of the arrangement of the objects in the image. In this chapter, the authors identify three areas of research that are relatively little covered by the literature dedicated to camera placement and which nevertheless appear essential. On the one hand, existing approaches offer little flexibility in both solving and describing a problem in terms of visual properties, especially when it has no solution. They propose a flexible solution method which computes the set of solutions, maximizing the satisfaction of the properties of the problem, whether it is over constrained or not. On the other hand, the existing methods calculate only one solution, even when the problem has several classes of equivalent solutions in terms of satisfaction of properties. They introduce the method of semantic volumes which computes the set of classes of semantically equivalent solutions and proposes a representative of each of them to the user. Finally, the problem of occlusion, although essential in the transmission of information, is little addressed by the community. Consequently, they present a new method of taking into account occlusion in dynamic real-time environments.
Colour Image Denoising using Curvelets and Scale Dependent Shrinkage

With the widespread use of image processing and computer vision applications, effective denoising methods are highly sought after, prompting the development of a variety of algorithms under different assumptions on noise and signal properties. However, most of these techniques are developed to deal with grayscale images, and are typically extended to colour images by processing each RGB channel separately. In this paper, we extend the curvelet power shrinkage algorithm, introduced previously for grayscale images, to colour image denoising, by applying the proposed method in the luminance/opponent-colour YCbCr colour space to take into consideration image inter-channel dependencies. The performance of the proposed algorithm on colour images corrupted by additive white Gaussian noise is evaluated in terms of both objective and subjective measures, and the obtained results show our method to be competitive to other methods including curvelet domain hard thresholding and MSt-SVD.
Distributed Multi-object Tracking and Registration with LMB Filter in Multistatic Radar Systems

The following topics are dealt with: radar signal processing; radar imaging; radar detection; Doppler radar; synthetic aperture radar; object detection; target tracking; radar clutter; radar tracking; array signal processing.
Formation of digital competencies of future life safety teachers

The article discusses the actual problems of the formation of future teachers’ digital competencies, which are an important aspect of professional success. The positive aspects of education digitalization are shown. The task of improving the process of developing digital competencies of future teachers studying in the profiles of Life Safety and Physical Education is being solved. The formation of future teachers’ digital competencies of the life safety basics is considered from the standpoint of a metasubject approach. It is shown that the formation of digital competencies of future life safety teachers is a process of purposeful pedagogical influence on a student in order to develop a set of skills, abilities and knowledge that allows using digital technologies effectively to solve professional problems at all levels of education. The conditions for the formation of digital competencies are determined. The formation of the components of digital competence among practicing teachers of life safety is considered. It is shown that the formation of digital competencies of future life safety teachers is possible implementing specialized disciplines, educational and industrial practices that include the organization of pedagogical activities in a digital environment from the standpoint of health preservation, creating a comfortable educational environment.
Webspam Detection Using Classification Algorithms and Optimizing the Performance of Classifiers by Selecting the Features

Web spam is one among the main problems of search engines because it reduces the standard of the online page. Web spam also effects economically because spammers / attackers provide an oversized free ad data or websites on the search engines that results in a rise within the web traffic. There are certain ways to tell apart such spam pages and one among them is using classification techniques. Relative examination of web spam detection using classification algorithms like, Random Forest and LAD Tree, C4.5 and Naive bayes is presented here during this paper. Analyses were completed on highlight sets of all around acknowledged dataset WEB SPAM UK-2007 utilizing WEKA. When classification was refrained from feature selection some classifier were high on false positive rate and time taken to create model but when feature selection was applied to datasets results were optimized and Random Forest outperformed on all the datasets altogether parameters that were selected.
Training of future teachers for work with children under the conditions of preschool inclusive education

The objective of this study is to identify areas of training of teachers, as well as a set of skills needed to work in inclusive institutions, taking into account the criteria of readiness of teachers. The study involved generalization, abstraction, grouping, and survey methods. Grouping the components of readiness of future teachers for preschool inclusive education allowed to identify several basic components: motivational, cognitive, value-content and operational-activity. Also was identified conceptual criteria for the readiness of future teachers, to assess the importance of each of them through a survey. The results of the study have several limitations, in particular insufficiently covered practical component of training of preschool teachers, as well as the limited number of research methods used in this article.
Predictive analysis of <scp>real‐time</scp> strategy games: A graph mining approach

Abstract Machine learning and computational intelligence have facilitated the development of recommendation systems for a broad range of domains. Such recommendations are based on contextual information that is explicitly provided or pervasively collected. Recommendation systems often improve decision‐making or increase the efficacy of a task. Real‐time strategy (RTS) video games are not only a popular entertainment medium, they also are an abstraction of many real‐world applications where the aim is to increase your resources and decrease those of your opponent. Using predictive analytics, which examines past examples of success and failure, we can learn how to predict positive outcomes for such scenarios. The goal of our research is to develop an accurate predictive recommendation system for multiplayer strategic games to determine recommendations for moves that a player should, and should not, make and thereby provide a competitive advantage. Herein we compare two techniques, frequent and discriminative subgraph mining, in terms of the error rates associated with their predictions in this context. As proof of concept, we present the results of an experiment that utilizes our strategies for two particular RTS games. This article is categorized under: Application Areas &gt; Data Mining Software Tools Technologies &gt; Prediction Commercial, Legal, and Ethical Issues &gt; Fairness in Data Mining
Improving elasticity in cloud with predictive algorithms

Cloud computing is an innovation that is of expanding request nowadays. Here, resources are multiplexed from physical machines to virtual machines through virtualization technology. Cloud computing gives different sorts of administrations to clients. In Cloud Computing, the supplier progressively distributes the resources. Doing as such, the service provider ought to have some information about the future asset needs. They can be determined utilizing the load prediction calculations. A calculation named long short-term memory (LSTM) neural system is utilized to analyze the load, which is proficient as far as both expanding and diminishing need of resources. The predicted results of the LSTM model is helpful for optimizing the service response time and also fulfils the Service Level Agreement (SLA) contracted by the user.
Strong Overall Error Analysis for the Training of Artificial Neural Networks Via Random Initializations

Although deep learning based approximation algorithms have been applied very successfully to numerous problems, at the moment the reasons for their performance are not entirely understood from a mathematical point of view. Recently, estimates for the convergence of the overall error have been obtained in the situation of deep supervised learning, but with an extremely slow rate of convergence. In this note we partially improve on these estimates. More specifically, we show that the depth of the neural network only needs to increase much slower in order to obtain the same rate of approximation. The results hold in the case of an arbitrary stochastic optimization algorithm with i.i.d.\ random initializations.
A saliency-guided clothing attribute recognition method by fusing salient prior information

Convolutional neural network (CNN) based clothing attribute recognition has been applied in many clothing-related applications, such as recommendation system and clothes retrieval. However, the outperformance of existing recognition approaches is limited on account of the requirement of manual spatial prior information, such as landmarks and bounding box. In this paper, we innovatively transfer existing instance-irrelative knowledge to our proposed method where salient prior information is employed to assist the attribute prediction to avoid such laborsome annotations. Concretely, we first propose a saliency-guided method composed of salient object detection network (SOD-N) and clothing attribute recognition network (CAR-N). SOD-N provides the saliency map as the prior information guiding CAR-N to focus on the valid region. Furthermore, a new learnable fusion module is designed in CAR-N to aggregate the high-level features from the deep salient and clothing features. That can strengthen the ability of CAR-N to represent the correlative clothing attributes, resulting in further improving the final performance. The effectiveness of our method is demonstrated by the experimental results on clothing- related datasets, which improves among 1% to 10% mAP on each attributes category and almost 5% mean AP.
Variance: Secure Two-Party Protocol for Efficient Asset Comparison in Bitcoin

Secure multiparty protocols are useful tools for parties wishing to jointly compute a function while keeping their input data secret. The millionaires' problem is the first secure two-party computation problem, where the goal is to securely compare two private numbers without a trusted third-party. There have been several solutions to the problem; however, these solutions are either insecure in the malicious model or cannot verify the validity of inputs. In this paper, we introduce Variance, a privacy-preserving two-party protocol for solving Yao's millionaires' problem in a Bitcoin setting, in which each party controls several Bitcoin accounts (single and multi signature addresses) and they want to find out who owns more bitcoins without revealing (1) how many accounts they own or the addresses associated with their accounts, (2) the balance of any of their accounts, and (3) their total wealth of bitcoins while assuring the other party that they are not claiming more bitcoin than they possess. We utilize zero knowledge proofs to provide a solution to the problem, and subsequently prove that Variance is secure against active adversaries in the malicious model.
Improved Constructive Neural Network Learning with Local Polynomial Estimator

The constructive feed-forward neural network (CFN) learning has been regarded as a very scalable learning scheme for large-scale regression. However, it may suffer from the saturation issue, which limits its wide range of applications in practice. In this paper, we propose an improved CFN learning method via borrowing the well-known idea of local polynomial estimator from statistics to alleviate the saturation problem, and further exploiting the recently proposed fast polynomial kernel learning (FPL) scheme for fast implementation of the local polynomial estimator. Thus, the proposed method in this paper is called CFN-FPL for short. A series of experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method. The numerical results show that the proposed method can effectively alleviate the saturation issue suffered by CFN and thus achieve better learning performance than CFN, but increase a little more computational cost. Moreover, we also discuss some refined techniques in the practical implementation of the proposed method, such as adopting the K-means method to adaptively generate the center points used in the method. The performance of the proposed method, particularly the stability to the tuned polynomial order, can be further improved in some extent via adopting such data-dependent generation scheme.