Reviewerly/exHarmony huggingface dataset:
PSYCHOLOGICAL LEADERSHIP TRAINING OF CRISIS MANAGEMENT

The necessity of delivering psychological bases of crisis management to leadership is substantiated and psychological recommendations for effective management of leaders have been offered. The author’s vision of the socio-psychological components of the managerial competence of a modern manager is being highlighted: it suggests the introduction into the system of training managers of various categories of psychological training as an innovative technology of forming leaders’ practical skills and capabilities of efficient personnel management. The results of an experimental study of the influence of training on the dynamics of personal and professionally significant qualities of leaders are being presented. A significant positive impact of leadership training on the personal sphere of leaders, increasing of leadership potential and the development of individual psychological qualities that are essential for a modern manager, has been established. Over time, after the training, the level of volitional, leadership qualities has been increased, such as leadership role, self-discipline, purposefulness, and the assessment of their business and personal qualities given by subordinates increases. Psychological training of leadership in the form of training contributes to the development of such necessary qualities of crisis management as stress resistance and the ability for emotional self-regulating.
Computer Simulation of the Path and Control of an Intelligent Mobile Robot in Python

Artificial intelligence has long been a scientific study. Mathematicians, physicists, neurobiologists, psychologists, programmers, philosophers, engineers have concentrated their efforts on their development. In the words of the famous French mathematician Jules Henri Poincare «Science is a graveyard of hypotheses». The article covers in detail current issues on artificial intelligence systems, mobile robot control systems, virtual 3D modeling. Intelligent control systems significantly expand the capabilities of robots to master increasingly complex operations. Using a methodology, the authors have developed methods for modeling the motion of an intelligently controlled mobile robot in three different computer programs. The authors conducted the study in the Python computer environment, and the C++ programming language was also used. In the work, the coordinates of motion x, y, z and distance were calculated. This distance was calculated by the total number of steps, and the optimal trajectory was realized by selecting the movement. Using a laser remote sensor (rangefinder) of the mobile robot, a virtual 3D model was modeled in a computer navigation control program environment based on the algorithm of an intelligently controlled mobile robot. In addition, the material discusses the relevance of using a microcontroller control system for the intelligent robot. The topic under consideration will be of interest to specialists in science, engineering, and other fields.
Dictionary Learning-Based Reinforcement Learning with Non-convex Sparsity Regularizer

Spare representations can help improve value prediction and control performances in Reinforcement Learning (RL), by capturing most essential features from states and ignoring unnecessary ones to avoid interference. However, existing sparse coding-based RL methods for control problems are optimized in the neural network methodology, which can not guarantee convergence. To this end, we propose a dictionary learning-based RL with the non-convex sparsity regularizer for RL control. To avoid the black-box optimization with the SGD, we employ the dictionary learning model in RL control, guaranteeing efficient convergence in control experiments. To obtain accurate representations in RL, we employ the non-convex $$\ell _p$$ norm (0 < p < 1) beyond the convex $$\ell _1$$ norm as the sparsity regularizer in dictionary learning-based RL, for capturing more essential features from states. To obtain solutions efficiently, we employ the proximal splitting method to update the multivariate optimization problem. Hence, the non-convex sparsity regularized dictionary learning-based RL is developed and validated in different benchmark RL environments. The proposed algorithm can obtain the best control performances among compared sparse coding-based RL methods with around 10% increases in reward. Moreover, the proposed method can obtain higher sparsity in representations in different environments.
HiSA: Facilitating Efficient Multi-Agent Coordination and Cooperation by Hierarchical Policy with Shared Attention

While numbers of partially observable agents improve their policies throughout decentralized training, the performance of multi-agent systems under this setting suffers from severe non-cooperation and non-stationary. Most previous works attempt to introduce communication into the training and optimization to facilitate cooperation between agents, but the noise message brought by communication may lead to misunderstandings during complex tasks and even lead to catastrophic failure of long-term training. To alleviate the above dilemma, in this paper, we propose the Hierarchical Structure with Shared Attention Mechanism (HiSA), a novel communication-based approach, to facilitate the efficiency and robustness of coordination and cooperation in multi-agent reinforcement learning (MARL). HiSA can not only resist the negative impact of noise in communication, but also effectively utilize attention as communication tool to build efficient cooperative hierarchical policies. Experimental results demonstrate that HiSA significantly outperforms existing communication-based MARL methods especially in the long-term complex cooperation scenarios with isomorphic agents.
Parameter Investigation in Low Computing Cost Model-Based EfficientDet for UAV Object Detection

Unmanned Aerial Vehicles (UAV) are equipped with high-resolution cameras. Previous researches novel object detection models for UAV images, but available models took high computational cost to retrain. Models that not trained using the UAV image will result in poor performance. EfficientDet object detection model is significantly lower in computing cost. In this paper, the author wants to investigate epochs and optimisation function impact to the performance of the low computing cost model-based EfficientDet on UAV images. An object detection system on UAV imagery is trained using conventional computer will be designed to detect 10 classes object in Visdrone dataset using the D0 version of the EfficientDet model. After the data is obtained then preprocessing will be carried out in the form of annotation conversion. Next, model training process will be trained from 10 to 50 epochs. In each training, the model will be tested so as to produce a validation value. The last validation value will be analysed as performance benchmark. As a result, our proposed model surpassed state-of-the-art models in Average Recall score with 2.1 % ARmax1 for Visdrone by only using 50 epochs or less.
Recognition of radar in-pulse modulation based on radar de-noising Linknet

Intra-pulse modulation pattern recognition of radar signals is an important aspect of modern electronic reconnaissance warfare. For radar intra-pulse modulated signals with low signal-to-noise ratio (SNR), traditional machine learning and deep learning networks do not preprocess the signals properly, resulting in low recognition accuracy. To solve this problem, this paper proposes a semantic segmentation network-based method to improve the recognition rate of modulation patterns by preprocessing the semantics of signal time-frequency maps. The experimental results show that the method can effectively recognize 12 radar modulation patterns at low SNR, and the pattern recognition accuracy is as high as 98.6% for signals with SNR of -10db.
Experimental multimodal user interface

This paper describes the motivation of experimental multimodal user interface development and its usage in the smart/virtual environment. Using the transformation method, the independence of the user interface design from the particular implementation is guaranteed. The proposed experimental multimodal user interface for cooking activity is proof of the applicability of the transformation method. The proposal has been implemented for the graphical, voice and gestural user interface. This experimental interface can also be used when deployed in a virtual school environment.
Degradable Tracking System based on Hardware Multi-Model Estimators

Sensing systems onboard unmanned vehicles operate in an environment of constrained computational resources. A cyber-attack may primarily aim to degrade these computing devices and, ultimately, incapacitate the sensing system itself. To prepare a prototype tracking system for degradation, this paper proposes distributed hardware implementation of a Multiple Model estimator on two FPGA units and, after an attack, adaptation of the estimator by leveraging Dynamic Partial Reconfiguration of the single surviving FPGA. The method ensures that the most likely models of the estimator are loaded on to the fabric of the surviving FPGA with minimal interruption.
Joint Modulation Format Identification and OSNR Monitoring Based on Stokes Vector Distribution Features

We proposed a joint modulation format identification and OSNR monitoring method. Identification accuracies are all 100% and the mean absolute errors of OSNR monitoring are 0.067-dB, 0.118-dB, 0.17-dB, 0.327-dB, 0.345-dB for PDM-QPSK/-8QAM/-16QAM/-32QAM/-64QAM, respectively.
Model-Based Security Assessment on the Design of a Patient-Centric Data Sharing Platform

The architectural design of a healthcare data sharing system must cope with security requirements especially when the system integrates different data sources and patient-centric features. The design choices come with different risks, where vulnerabilities and threats highly depend on how the system components interact and depend on each other to operate as well as how it handles the external connections. This paper focuses on security aspects arising early in the design phase of a patient-centric system. The system presents a blend of emergent technologies such as novel authentication methods, blockchain for access control, and a data lake for patient metadata storage and retrieval based on access rules. We exploit a model-based approach to tackle security assessment using attack-defense trees (ADtrees) formalism and other support diagrams altogether as a way to model and analyse potential attack paths to the system and its countermeasures. The modelling approach helps creating a framework to support the attack vectors analysis and the proposal of appropriate defense mechanisms within the system architecture.
A Safe Approach to Sensitive Dropout Data Collection Systems by Utilizing Homomorphic Encryption

The student's fault is not the only cause of dropping out of school. Often, cases of dropping out of school are only associated with too general problems. However, sensitive issues that can be detrimental to certain parties in this regard, such as the institution's reputation, are usually not made public. To overcome this, an in-depth analysis of these cases is needed for proper handling. Many risks are associated with creating a single repository for this sensitive information. Therefore, some encryption is required to ensure data is not leaked. However, encryption at rest and in transit is insufficient as data leakage is a considerable risk during processing. In addition, there is also a risk of abuse of authority by insiders so that no single entity is allowed to have access to all data. Homomorphic encryption presents a viable solution to this challenge. Data may be aggregated under the security provided by Homomorphic Encryption. This method makes the data available for computation without being decrypted first and without paying the risk of having a single repository.
Lossy Compression of Three-Channel Remote Sensing Images with “Color” Component Downscaling

Multichannel systems of remote sensing provide a huge amount of data useful for different applications. However, such images occupy a large space that poses problems of processing, storage, transmission, and management. Lossy compression is widely used to decrease the size of data. In lossy compression, one has to provide a reasonable trade-off between compression ratio (CR) and introduced losses or quality of compressed data. Quality can be characterized in various ways including traditional criteria as peak signal-to-noise ratio (PSNR) or some others as well as criteria that describe efficiency of solving the final tasks of remote sensing as, e.g., probability of correct classification. In this paper, we concentrate on classification of three-channel images that can be either color images or three components of multi- or hyperspectral data acquired, e.g., by Sentinel-2 sensor. In lossy compression of color images, downscaling of color components is often applied to increase CR without essential loss of quality. The goal of this paper is to study the influence of such downscaling on classification accuracy for three-channel remote sensing data. The compression method based on atomic functions is considered since this method allows easy control of compressed image quality and its providing. The neural networks trained for distorted-free images are applied for image classification. Analysis is carried out for four images of different complexity. Based on it, practical recommendations are given.
Sentiment Analysis in the Rest-Mex Challenge

In this paper, we describe our participation in the Rest-Mex 2022 forum for the Sentiment Analysis task. The objective of the task was to create a model capable of predicting the polarity of the sentiment expressed by a tourist’s opinion, as well as the type of attraction visited. For this task, we followed two different approaches: a lexicon-based approach and a Machine Learning approach. In the lexicon-based approach, we use a dictionary with words that have a numerical value that specifies the association with some emotions or attractions. We trained a logistic regression model for the Machine Learning approach to predict sentiment polarity and attractions. Our proposal obtained a combined score for both tasks of 0.85, which is only 0.03 away from the best reported result.
The study of intelligent poetry writing robots

In order to solve the problem that the content of the poem is inconsistent with the writing intention of the poem at this stage and is not enough to express the author's mood and mood, the Encoder-Decoder model, Transformer, python programming language, and the pycharm development tool are used to realize a poetry writing robot system, and the automatic writing of the poetry writing robot is completed, and only one line needs to be entered to automatically generate a poem that is neat and tidy, and the function of a good mood is automatically generated. The central idea and theme of the written poems are more clear, the content and creative mood expressed in the story are more unified, and the poetry writing system designed to protect traditional culture in the literary industry and promote the development of cultural treasures - ancient poetry has been widely used.
Ontology-Based Design of Hybrid Problem Solvers

The creation of problem solvers for intelligent computer systems based on the OSTIS Technology is discussed from an ontological perspective in this study. It is now possible to describe the ideas of a problem-solving model and a problem solver on the basis of the formal interpretation of terms like action, problem, class of actions, class of problems, method, and skill. The collected results will increase the effectiveness of the component approach to problem solving and automation tools for problem solving.
Metric Learning on Complex Projective Spaces

Shape analysis of landmarks is a fundamental problem in computer vision and multimedia. We propose a family of metrics called Fubini-Study distances defined in the complex projective space based on the seminal work of Kendall [11] for metric learning to measure the similarity between shape representations which are modeled directly by the equivalence classes of the 2D landmark configurations. Experiments conducted on the face landmarks for facial expression recognition demonstrate the competitiveness of the proposed method with respect to state-of-the-art approaches. A comparison with the metric defined in the Euclidean space has also been explored, proving that the Fubini-Study metric is more effective and discriminative than the Euclidean metric in identifying facial deformation.
Intelligent Technologies for Projective Thinking and Research Management in the Knowledge Representation System

It is proposed to address existing methodological issues in the educational process with the development of intellectual technologies and knowledge representation systems to improve the efficiency of higher education institutions. For this purpose, the structure of relational database is proposed, it will store the information about defended dissertations in the form of a set of attributes (heuristics), representing the mandatory qualification attributes of theses. An inference algorithm is proposed to process the information. This algorithm represents an artificial intelligence, its work is aimed at generating queries based on the applicant preferences. The result of the algorithm's work will be a set of choices, presented in ranked order. Given technologies will allow applicants to quickly become familiar with known scientific results and serve as a starting point for new research. The demand for co-researcher practice in solving the problem of updating the projective thinking methodology and managing the scientific research process has been justified. This article pays attention to the existing parallels between the concepts of technical and human sciences in the framework of their convergence. The concepts of being (economic good and economic utility) and the concepts of consciousness (humanitarian economic good and humanitarian economic utility) are used to form projective thinking. They form direct and inverse correspondences of technology and humanitarian practice in the techno-humanitarian mathematical space. It is proposed to place processed information from the language of context-free formal grammar dissertation abstracts in this space. The principle of data manipulation based on formal languages with context-free grammar allows to create new structures of subject areas in terms of applicants' preferences.It is believed that the success of applicants’ work depends directly on the cognitive training of applicants, which needs to be practiced psychologically. This practice is based on deepening the objectivity and adequacy qualities of obtaining information on the basis of heuristic methods. It requires increased attention and development of intelligence. The paper studies the use of heuristic methods by applicants to find new research directions leads to several promising results. These results can be perceived as potential options in future research. This contributes to an increase in the level of retention of higher education professionals.
SchemaDB: A Dataset for Structures in Relational Data

In this paper we introduce the SchemaDB dataset; a collection of relational database schemas in both sql and graph formats. Databases are not commonly shared publicly for reasons of privacy and security, and so the corresponding schema for these databases are often not available for study. Consequently, an understanding of database structures in the wild is lacking, and most easily found examples of schema found publicly belong to common development frameworks or are derived from textbooks or engine benchmarks. SchemaDB contains 2,500 samples of relational schema found in public code repositories which have been standardised to MySQL syntax. We provide our gathering and transformation methodology, summary statistics, structural analysis, and discuss potential downstream research tasks in several domains.
FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion

We propose an accurate and lightweight convolutional neural network for stereo estimation with depth completion. We name this method fully-convolutional deformable similarity network with depth completion (FCDSN-DC). This method extends FC-DCNN by improving the feature extractor, adding a network structure for training highly accurate similarity functions and a network structure for filling inconsistent disparity estimates. The whole method consists of three parts. The first part consists of fully-convolutional densely connected layers that computes expressive features of rectified image pairs. The second part of our network learns highly accurate similarity functions between this learned features. It consists of densely-connected convolution layers with a deformable convolution block at the end to further improve the accuracy of the results. After this step an initial disparity map is created and the left-right consistency check is performed in order to remove inconsistent points. The last part of the network then uses this input together with the corresponding left RGB image in order to train a network that fills in the missing measurements. Consistent depth estimations are gathered around invalid points and are parsed together with the RGB points into a shallow CNN network structure in order to recover the missing values. We evaluate our method on challenging real world indoor and outdoor scenes, in particular Middlebury, KITTI and ETH3D were it produces competitive results. We furthermore show that this method generalizes well and is well suited for many applications without the need of further training. The code of our full framework is available at: https://github.com/thedodo/FCDSN-DC
Development of a Portal to Help Organize Temporary Wireless Networks

The use of wireless communication networks for temporary use has already become a popular practice and shows good results. Such networks can both cover an area where there was no network and be used for load balancing, for example, during large-scale events. There are brilliant technical solutions for this class of tasks. But the design and deployment of such networks requires an individual approach and the work of highly qualified specialists. Therefore, it is possible only in large-scale projects where an appropriate budget can be allocated for network design. It is worth noting that often the organization of the network is necessary precisely in sparsely populated areas to provide communication for small–scale events - expeditions, tourist routes, sports events. The article discusses the main problems faced by participants when trying to design small-scale, temporary, fast-deployable wireless networks. A solution in the form of a web portal has been proposed, which would allow combining a scientific approach to building models, background information, the experience of participants, operational communication in case of emergencies.