Reviewerly/exHarmony huggingface dataset:
Developing Society Integrity and Cultural Code by Means of Social and Cultural Activity as Factor of Ensuring National Security

Trends of developing relations in today’s society, spread of digital technologies and restrictions in movement during the last year showed a rising diffusion of society, split of opinions and weakening of the cultural code of the Russian population. The article studies sources of these changes and searches for solutions based on means of social and cultural activity. It is well known that cultural code is formed for decades and it is the social and cultural sphere that can concentrate this process and direct it to strengthening of national self-identification, rise in the rate of patriotism, education and wellbeing of the population. The process of shaping the civil society in the post-soviet period is based on pursuing cultural policy financed at the expense of the country budget, in contrast to western countries. This aspect makes us think about the necessity to change means and methods of social and cultural activity with due regard to processes of society digitalization. At the same time cultural policy acts as a significant section of the system of national security, as a way of shaping civil society, keeping cultural values and traditions. The research showed that non-linear interaction of such tensors, as sustainable development, corporate social responsibility, national security, digitalization, technologization of society can seriously affect the processes of diffusion of society integrity, destruction of its cultural code and sovereignty of state as it is. Preserving the cultural code as a foundation of national culture and overcoming problems of society diffusion are possible in case the state participation in reforming approaches to pursuing cultural policy is extended. A separate aspect is developing the HR potential of cultural institutions of the future oriented to advanced technologies, continuous learning the achievements of psychology as a science, which can create patriotic feelings in society promoting values of cultural inheritance as a foundation of national identification.
User Profiling on Universal Data Insights tool on IBM Cloud Pak for Security

User profiling is one of the most important research topics where organizations endeavour to establish profiles of user activities to detect or predict potential abnormal behaviours. Previous researches have mainly focused on detecting and identifying static activities through social media. A universal analysis based on streaming settings to monitor user activities continuously is missing. This paper proposes a framework for user profiling based on UDI platforms to address this issue. Our framework consists of three main steps: simulating realistic scenarios for user activities, proposing and extracting potential features, and applying machine learning models on simulated datasets. Our experimental results show that selected machine learning algorithms can distinguish most abnormal behaviours correctly. LODA, RRCF, and LSCP algorithms achieve the highest performance among all algorithms. Tree-based algorithms such as Isolation Forest acquire the best results when considering small datasets and speed. Furthermore, machine learning algorithms’ performance demonstrates the high quality of our simulated datasets.
Cryptographic Pseudo-Random Bit Generator Based on New Combination Discrete Chaotic Systems

Traditional 1-D discrete chaotic systems are not suitable to use directly in PRBG design for their cryptographic usage as their structures are simple and have predictability. Pseudo-random sequences have wide applications in image and video encryption, hash functions, spread spectrum communications, etc. In chaos-based cryptography, chaotic systems have been regarded as an important pseudorandom source in the design of pseudo-random bit generators due to its inherent properties of sensitive dependence on initial conditions and parameters. In order to improve the dynamism and features of standard logistic map, a 1-D discrete combination chaos model is proposed in this paper. The chaos model enables to construct new chaotic systems with combination of logistic map and Trigonometric functions. The performance analysis shows that the new systems are more complex and better than the original Logistic map. Further, we also propose to present a new pseudo-random bit generator based on new log-tan chaotic system and log-cot chaotic system. The randomness and other statistic analysis show that our pseudo-random bit generator has good randomness features, satisfy the linear complexity and balancedness requirements well.
Value orientations of vkontakte users with different strategies for meanings transferring

The paper presents the results of a study of strategies for transferring the meanings and the value orientations of young people in social networks. The Internet is so firmly rooted in our everyday life that we can no longer imagine our life without it. It is penetrating into an increasing number of human life spheres, becoming the environment in which communication, educational and work processes, leisure and shopping take place. Its hard not to notice that the youth audience is especially interested in the virtual environment. The Internet and, in particular, social networks are becoming the environment that influences the formation and development of society, the dissemination of ideas, news, trends. On the Internet, one can observe both the amazing consolidation of users who are able to create a news agenda, and the disunity of many contradictory judgments, meanings and forms of their presentation. The purpose of this study is to identify the strategies for the translation of meanings that Internet users resort to when commenting on posts on social networks, and to study the value orientations of young people using various strategies for the translation of meanings. The study includes the authors questionnaire, the method of diagnosing M. Rokichs value orientations, methods of mathematical statistics (H-Kruskal-Wallis criterion, 22-criterion). Terminal and instrumental values characteristic of the 6 strategies of meanings transferring have been determined; the strategies most often used by users with abstract and concrete terminal values are revealed. The research helps to understand how the transferring of meanings takes place in a network and according to which characteristics of the value sphere users with different strategies for the meanings transferring differ.
Distributed entanglement generation from asynchronously excited qubits

The generation of GHZ states calls for simultaneous excitation of multiple qubits. The peculiarity of such states is reflected in their nonzero distributed entanglement which is not contained in other entangled states. We study the optimal way to excite three superconducting qubits through a common cavity resonator in a circuit such that the generation of distributed entanglement among them could be obtained at the highest degree in a time-controllable way. A non-negative measure quantifying this entanglement is derived as a time function of the quadripartite system evolution. We find that this measure does not stay static but obtains the same maximum periodically. When the qubit-resonator couplings are allowed to vary, its peak value is enhanced monotonically by increasing the greatest coupling strength to one of the qubits. The period of its peak to peak revival maximizes when the couplings become inhomogeneous, thus qubit excitation becoming asynchronous, at a relative ratio of 0.35. The study demonstrates the role of asynchronous excitations for time-controlling multi-qubit systems, in particular in extending entanglement time.
Measurement of Film Structure Using Time-Frequency-Domain Fitting and White-Light Scanning Interferometry

A new technique is proposed for measuring film structure based on the combination of time- and frequency-domain fitting and white-light scanning interferometry. The approach requires only single scanning and employs a fitting method to obtain the film thickness and the upper surface height in the frequency and time domains, respectively. The cross-correlation function is applied to obtain the initial value of the upper surface height, thereby making the fitting process more accurate. Standard films (SiO2) with different thicknesses were measured to verify the accuracy and reliability of the proposed method, and the three-dimensional topographies of the upper and lower surfaces of the films were reconstructed.
Research on the Trajectory Planning and Control Technology of Industrial Robots Guided by Computer Visualization

Abstract With the popularization of computers, the development of all walks of life has become more rapid. This article mainly analyzes the mechanical structure design of the Cartesian coordinate robot system, it also studies the motion control.
Reconstructing Image From Noisy Radon Projections Using Shearlet Transform

The Radon transform on the two-dimensional space, called X-ray transform, is reviewed. Some methods to reconstruct an X-ray image from its sinogram are investigated. Important details are lost when the shearlet transform is used to retrieve an image from its noisy Rdadon data. We present a new theorem to relate the shearlet and Radon transforms to come up with this challenge. An optimum threshold to use is obtained. The algorithm of fast finite shearlet transform is improved, and numerical results show the notable superiority of the proposed method over other existing algorithms.
Towards a universal cognitive tool

Data visualization leverages human visual system to enhance cognition, it helps a person quickly and accurately see the trends, outliers, and patterns in data. Yet using visualization requires a viewer to read abstract imagery, estimate statistics, and retain information. These processes typically function differently for those with Intellectual and Developmental Disabilities (IDD) and have created an inaccessible barrier for them to access data. Preliminary findings from our graphical perception experiment suggest that people with IDD use different strategies to reason with data and are more sensitive to the design of data visualization compared with non-IDD populations. This article discusses several implications from that study and lays out actionable steps towards turning data visualization into a universal cognitive tool for people with varying cognitive abilities.
Leadership lessons for engineers

The purpose of this study is to describe a MSc course aimed at preparing the elite of engineering education, providing knowledge, developing skills and experience that will allow graduates of Master degree programs not only to find a job, but also to build a successful professional career and a happy life consciously. The presented masters course introduces the best practices of leadership development from the rector of KNRTU-KAI, Albert Gilmutdinov (20122021) (based on his book Leadership Lessons), advanced experience in the development of leadership skills from Russian and foreign universities, as well as a unique American teaching methodology aimed at the peculiarities of training engineering personnel. The study reveals the problems of training modern engineering specialists: engineering student negative attitudes (I cannot learn English, it is too difficult to apply for a grant, an interesting job can only be found by acquaintance); inability of young people to answer questions about how to find a job in their life, how to work hard and not get tired, how to become successful and at the same time maintain good relations with people. The team of authors of the course believes that the results of the implementation of the Leadership Lessons project can be of professional interest not only among masters students, but also among all students of engineering schools, increasing the personal activity of students, their demand in the labor market, as well as strengthening ties between employers and the university.
Research on underwater image enhancement algorithm based on improved DCP

Abstract All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has better contrast to meet the requirements of underwater image observation.
Lung CT image registration based on end-to-end unsupervised learning

Medical image registration (MIR) is an important issue of medical image research field, and the purpose of MIR is to establish a spatial correspondence between two medical images. Previous MIR methods based on supervised deep learning usually employ ground-truth or manual landmarks to guide network training. Since ground-truth and manual landmarks are difficult to obtain in practice, the implementation of MIR methods based on supervised deep learning is practically challenging. Currently, most of the MIR methods based on unsupervised deep learning utilize patch-based to learn the local spatial transformation from one image patch to another corresponding patch. Although the patch-based method has satisfactory performance locally, it would generate grid-like artifacts that occur on the edge of the patches during the patch fusion. In this paper, we design a fully convolutional network (FCN), in which the input of the network is a pair of images and the output is the displacement field. The displacement field is applied to warp the moving image to the fixed image through the spatial transformer network (STN) and achieve end-to-end image registration. There is no any supervision information applied in the proposed method. We evaluate the performance of the proposed method using the public DIR-Lab dataset. The average TRE value obtained is 2.03 (1.42), and the registration time is less than one second. The results demonstrate that the method we designed has satisfactory registration capability.
Procedural Game Map Generation using Multi-leveled Cellular Automata by Machine learning

The concept of Procedural Content Generation (PCG) has been intensively applied in the game industry for its capability of producing infinite game maps without any human effort. Innumerable games, such as Minecraft, Terraria, and No Man's Sky, successfully employed this technique to create unpredictable yet playful gaming experiences. While randomness is essential to adding engaging elements to a game, complete randomness may hurt the outcome of map generations by making a chaotic scene. To address this issue, this paper introduces an effective way of "tweaking" the randomness to generate flexible, endless, natural-looking game maps by machine learning.
Graph Analysis-based Recommendation via Entity Embeddings Using Wikipedia

This paper presents graph analysis-based recommendation via entity embeddings using Wikipedia. Conventional recommendation models using Graph Convolutional Network (GCN) suffer from the limits of performance since they only use information about user-item interactions. Therefore, we focus on Wikipedia, which has the semantics of items and relationships among them, as side information and introduce its information into the latest GCN model. Specifically, we set entity embeddings from Wikipedia to initial item embeddings of GCN. The main contribution is to overcome the limits of recommendation performance by considering external knowledge about items. The experiments on open datasets show the efficacy of our method.
Assessment of students’ self-regulation in the process of foreign languages acquisition

In recent years, the problem of self-regulation of educational activity has become an urgent subject of psychological and pedagogical research. This is due to the fact that self-regulation is one of the most important factors that ensure the effectiveness of students’ educational performance, especially while studying distantly. This paper is devoted to the description of the methodology for assessing students’ self-regulation, successfully tested in the process of teaching foreign languages at Peter the Great St. Petersburg Polytechnic University.
Design of crew appointment and dismissal system based on face recognition

The registration of crew dismissal and appointment refers to the record of dismissal and appointment of Chinese crew members when they disembark ships in ports, and the information is an important part of the crew’s service qualifications. It is related to the crew’s promotion examination and job promotion, but the dismissal and appointment registration is not perfect. On the basis of discussing the application of face recognition technology, proposes the application of face recognition technology to crew management, and designs the crew appointment and dismissal system. The main body of the system is the shipping company, crew and captain. It can effectively solve the problem of forging the service qualifications in the "Crew Service Book" in the registration of appointment and dismissal.
Social Media Named Entity Recognition Based On Graph Attention Network

Named entity recognition is a basic task of natural language processing. With the development of the Internet, social media has become the main way of information transmission and sharing with friends, but social media texts are often short and informal, resulting in low entity recognition accuracy. In this paper, we use the Graph Attention Network (GAT) for the first time in the task of Chinese social media named entity recognition to generate the representation of the nodes in the sentence selection parsing tree, and capture the information of the sentence itself through the self-attention layer. In order to reduce the impact of Chinese word segmentation errors, we use a combination of character vectors and word vectors, and incorporate part of speech information for training. We integrate the above two points into the classic named entity recognition model BiLSTM-CRF model to form a new model to conduct research on named entity recognition of Chinese social media text. Experimental results show that our model achieves competitive results.
Learning initial trajectory using sequence-to-sequence approach to warm start an optimization-based motion planner

In recent years, optimization-based motion planners have shown that they can provide a fast, smooth, and locally optimal trajectory even for a higher dimension planning problem. Their convergence rate depends on the given initial trajectory. The proper selection of an initial trajectory is crucially important: if it is not within the basin of attraction of the optimum, it will take longer to convergence or even get stuck in local minima. This paper presents a neural network-based initial trajectory predictor, which utilizes the power of the sequence-to-sequence (Seq2Seq) learning method to predict a good initial trajectory for an optimization-based motion planner even in an unseen environment. The proposed model learns the mapping between the tasks and the optimal trajectories from a database. Given a start and a goal configuration of a manipulator along with the environment information in the form of a voxel grid, the proposed model predicts a good initial trajectory, which was learned from previously seen situations. The learned model is evaluated in a 6 degree of freedom (DOF) manipulator planning in two different environments. The results show that by using the predicted initial trajectory, there is a significant improvement in the convergence rate and the planning time of an optimization-based motion planner, even in an unseen environment.
Optimization for overfitting problems in spam email classification based on parameter adjusting

Spam email classification can effectively help us to reject useless information. Different machine learning methods have been proposed to deal with spam email classification problems. However, exploring the key parameters adjusting in the machine learning methods for spam email classification is still insufficient. Therefore, in this paper, we adjusted the key parameters of three classical machine learning methods, including SVM, Random Forest and Logistic Regression in the specific spam email classification tasks. Many tests have been done to evaluate the adjusting operation for different classifiers. The results show that bigger C value of Logistic Regression is, the higher accuracy would be. However, if C value is too big and the model would be overfitting. The amount and the depth of trees may influence the accuracy of Random Forest, where if the amount is bigger and the accuracy would be higher under a limitation. In addition, if the depth of trees is too big, although the accuracy of the model is high, and the model would be overfitting. The Linear kernel function in Logistic Regression has the best performance in the spam email classification task. Our research has a great significance to show how to adjust the parameters for classifiers in the specific classification tasks.
Traffic Analysis of High Throughput Traffic on Tor

Tor is a popular anonymity network used by millions to access internet services while maintaining their privacy. The main concept behind Tor is that a user can build a "circuit" of routers called relays, where each relay carries the client’s traffic to the next relay, without any single relay knowing the full extent of the path. Thus, anonymity is achieved, because no single relay can trace the client to the destination.However, a paper written in 2005 titled "Low-Cost Traffic Analysis of Tor" by Steven J. Murdoch and George Danez is demonstrated that a traffic analysis attack was possible against the Tor network. Any attacker could monitor the load on a relay in the Tor network by calculating the round trip time (RTT) to the relay, and when the RTT spiked, it was clear that the relay was being used. With this information, attackers could trace the path of a client in the Tor network and de-anonymize them.This paper was written in 2005, when Tor was still young. At the time of Murdoch and Danezis’ paper, the entire Tor network consisted of just fifty relays. As of the time of writing, there were well over 7,000 relays in the network, so the ecosystem of Tor is radically different. With all of the increased traffic, it is highly required to determine if this type of attack was still valid, and would not be masked by other traffic.Our results indicate that if a victim is downloading or streaming a large file as fast as Tor will allow them to, a decrease in bandwidth and an increase in round trip time (RTT) is usually observable on each relay in the circuit. This research work has also discovered the Tor guard relays, a special subset of relays that clients will pick as the first hop in their circuit are very susceptible to this kind of attack.