Studia Informatica. System and information technology
ISSN: 1731-2264 e-ISSN: 3072-4651 DOI: 10.34739 Points: 20
Wydawnictwo Naukowe Uniwersytetu w Siedlcach
Searching form the Model of Human Facial Expressions and its Implementation inMATLAB and Simulink Environments
The publication contains selected research results on the creation of a neural model of human facial expressions along with its implementation in the MATLAB and Simulink environments using the library called Deep Learning Toolbox. The model was generated as an object to recognize selected human faces and emotional states based on visual data recorded on the human face in real time. The study was placed against the background of available literature on the analysis of facial expressions and emotion classification methods. In addition to the concept of the original solution, the assumptions of the research experiment were given, a method for measuring facial expressions was selected for the experimental conditions, and a set of data was developed for training a neural model of the facial expression system with their preparation for ANN learning. Ultimately, various artificial neural networks were trained to model the facial expression system and sensitivity and comparative tests were performed in Simulink to, among others: assess the quality of the model in relation to real data. Very high results of ANN training of the facial expression system model were obtained (MSE on the order of 10−14, R close to 1), as well as relatively high model quality relative the facial expression system, measured, among others, average relative error, the value of which was several percent. However, in terms of prediction effectiveness, because of the obtained results, which are not very high for the assumed high measurement accuracies, the research that has been started is continued.
Copyright (c) 2025 Studia Informatica. System and information technology https://creativecommons.org/licenses/by-nc-nd/4.0
Modeling the National Power System Using Artificial Neural Networks
The purpose of this paper is to investigate and demonstrate the potential of modeling the National Power System (NPS) in Poland. The paper presents the results of research modeling of the NPS system using three different models based on Artificial Neural Networks. Data characterizing the NPS were implemented to build the models. The results measured by Mean Squared Error and determination coefficient were presented and analyzed. The research results and conclusions are presented in the summary.
analysis of selected sizes of the power system
A Vision-Based System for Rapid DXF Generation in Metalworking: Image Processing Pipeline and Economic Feasibility Case Study
Low-volume and custom production jobs in small and medium-sized metalworking enterprises are often declined due to the high fixed costs and time requirements of manual CAD modeling. This paper presents a practical, production-ready desktop application for the automated generation of DXF files from photographs of physical parts, designed to support rapid laser cutting and prototyping workflows. The system combines a user-friendly GUI with a robust computer vision pipeline including adaptive thresholding, contour extraction, geometric scaling, and vector simplification. Unlike generic raster-to-vector tools, it targets the specific needs of SMEs: offline use, closed contours, minimal training, and compatibility with CAM systems. A real-world case study demonstrates the successful replication of an agricultural machine parts, showing ~90% reduction in preparation time and ~45% profit margin on the order. Interviews with industry stakeholders confirm the potential of the proposed solution to recover previously unviable jobs and increase monthly revenues by an estimated 5-7%. This interdisciplinary contribution integrates computer vision and applied economics, offering a scalable solution to improve productivity and digital inclusion in SMEs.
Functioning of an Intelligent System on the Example of the National Power Demand System
This publication includes a concept and research results regarding the search for regularity in the functioning of an intelligent system using the example of a system for forecasting national power demand. The research employs the ARX regression machine learning method to derive models of the functioning of the electricity forecasting system. The study uses actual values of electricity generated by both uniform and non-uniform domestic electricity systems as input quantities, and forecasted national power demand as the output values from January 2023. The main aim of the research was to obtain several hourly models and several monthly models to demonstrate changes in selected system parameters and to show whether, and what changes occur in the direction of increasing the system’s independence, enhancing the system control level, etc. The research methodology also employs, in addition to the machine learning method, the method of control theory and systems. At the same time, an example of obtaining a model of the national system demand for electric power using the ARX machine learning method, which was transformed into state space model, whose matrix elements were used to interpret the correctness of changes in the intelligent system.
Modern Application Lifecycle Management Approaches
Effective application lifecycle management (ALM) relies on modern tools and practices that streamline development while ensuring security and stability. This article examines key components of a modern ALM strategy, including DevOps culture, CI/CD pipelines, Infrastructure as Code, containerization, orchestration, monitoring, observability, and the growing role of AI. Special attention is given to the integration of security into the CI/CD process, particularly through automated testing techniques such as Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST). These practices help identify vulnerabilities early in the development cycle, reducing risk and enhancing application reliability.Adopting these technologies involves initial investments in licenses, training, and team restructuring. However, these efforts are rewarded through automation of repetitive tasks, accelerated deployment cycles, improved scalability, and proactive issue detection. AI-driven tools further enhance development efficiency through intelligent code suggestions, predictive analytics, and automated bug detection. By embracing these modern approaches, organizations can achieve a more secure, efficient, and agile development process, better equipped to adapt to a rapidly evolving technological landscape.
Application of the genetic algorithm to the estimation of energy consumption in Poland
This work aims to model and estimate electricity consumption in Poland using a genetic algorithm approach. The genetic model was based on socio-economic data such as GDP, imports and exports, and population growth. The data covered the period from 1990 to 2024. Quadratic and exponential models were tested in this study. The exponential model was demonstrated to be the best fit to the data. The best mean absolute error was obtained for the exponential model, MAE = 0.0276, for a population size of 100, a crossover probability of 0.85, and a mutation probability of 0.02. The projection of electricity consumption in Poland was also demonstrated and compared with the data from the literature. The genetic model forecasts an electricity consumption of 211 TWh in 2040.
The architecture of human mind, and non-von Neumann computer architecture
Structural plasticity in the brain (i.e. rewiring the connectome) may be viewed as mechanisms for dynamic reconfiguration of neural circuits. First order computations in the brain are done by static neural circuits, whereas higher order computations are done by dynamic reconfigurations of the links (synapses) between the neural circuits. Static neural circuits correspond to first order computable functions. Synapse creation (activation) between them correspond to the mathematical notion of function composition. Functionals are higher order functions that take functions as their arguments. The construction of functionals is based on dynamic reconfigurations of function compositions. Perhaps the functionals correspond to rewiring mechanisms of the connectome. The architecture of human mind is different than the von Neumann computer architecture. Higher order computations in the human brain (based on functionals) may suggest a non-von Neumann computer architecture, a challenge posed by John Backus in 1977 [7]. The presented work is a substantial extension and revision of [2].
A Pathfinding Module for the Indoor Navigation System NaviSecure
NaviSecure is an indoor navigation system developed atUniversity of Siedlce. It utilizes a dedicated hierarchical building map and an infrastructure of Bluetooth Low Energy transmitters, as well as hazard detectors such as smoke, flood, and gas sensors. The system facilitates daily navigation and provides emergency assistance while addressing the needs of individuals with disabilities. In this paper we present the pathfinding module of the NaviSecure system, implemented on top of the Neo4j graph database. We introduce the basic concepts behind our approach, we discuss the system architecture and our custom approach to deal with special needs of the users. Finally, we show the results of our experimental evaluation of several graph algorithms, modified to meet NaviSecure’s specific requirements. The results confirm the efficiency of our approach.
Autonomous Navigation for Unmanned Ground Vehicles in Unstructured Terrain
The problem of autonomous navigation for Unmanned Ground Vehicles (UGVs) in unstructured environments is both challenging and crucial for their deployment in real-world applications. Perception is important, as it provides the necessary information for terrain traversability and environmental awareness. In this article, the developed manned-unmanned vehicle designed to carry out autonomous missions in unstructured terrain is presented, along with the system requirements essential for such operations. Challenges related to environmental perception and navigation in unstructured environments are discussed. Achievements in developing AI models capable of interpreting sensor data are highlighted, demonstrating significant progress in the field of autonomous navigation. However, several gaps remain, particularly in the areas of sensor fusion, real-time decision-making, and adaptability to highly dynamic conditions. Development in the field of autonomous systems allows for a wider expansion of the potential applications ofUGVs in various fields, including disaster response, environmental monitoring, and exploration of hazardous environments.
Analysis of Information Technology Adoption in Polish Agriculture
This study explores the adoption of information technology (IT) in Polish agriculture, focusing on its impact on efficiency, profitability, and farmers’ perceptions of associated costs. A survey conducted among 106 farmers from all Polish provinces assessed their use of IT tools, including farm management software and automatic machine navigation systems. The results highlight the varying degrees of IT adoption based on farm size, with larger farms demonstrating greater integration of these technologies. Farmers’ views on the cost-effectiveness of these systems were mixed, indicating room for improvement in affordability and functionality. The study underscores the potential of IT to enhance agricultural practices but notes the barriers faced by smaller farms in adopting such technologies.
Analysis of the Possibilities of the Evolutionary Algorithm to Improve the Neural Model of the TGE S.A. Day Ahead Market System Using Selected Programming Environments
The article contains selected research results regarding the analysis of the possibility of using the Evolutionary Algorithm to improve neural models of intelligent systems using selected programming environments. Choosing an appropriate programming language is one of the basic activities in the process of implementing complex algorithms, which include methods of artificial neural networks and evolutionary algorithms. Due to the fact that the object of the researchwas an intelligent Day Ahead Market system operating on the Polish Power Exchange and the modeling methods were artificial neural networks and evolutionary algorithms, it was decided to use very high-level programming languages such as Python, Matlab and C# for implementation and associated development environments. It turned out, among other things, that each of these languages and programming environments has its advantages and disadvantages, but all of them are very useful due to their useful syntax and rich included libraries. A thorough analysis of the implementation shows, among other things, that the choice of programming language affects the efficiency, speed and quality of the obtained implementations of system models. Against this background, the advantages and disadvantages of individual programming languages are shown, especially in the context of implementing evolutionary algorithms. The research results indicate directions for selecting an appropriate programming language and the associated programming environment for system modeling using artificial neural networks and evolutionary algorithms. In addition, the method of analysis, as well as the method of modeling and implementation was shown on the example of a specific system, which was the Day Ahead Market system of TGE S.A.
Selected Aspects of Neural-Evolutionary Modeling of Prices on the Day Ahead Market of TGE S.A.
Modeling in the context of Artificial Intelligence (AI) is using mathematics to describe, analyze, and predict real-world systems. Building models that can simulate or predict various aspects of reality is a key issue that is the subject of many studies. The quality of models depends on many elements, starting from the architecture of the neural network itself, through the selection of teaching data in terms of the size of the sets, and the number of factors influencing the choice of the network itself. Modifications of the network training methods themselves also play an important role, e.g. through the use of Evolutionary Algorithms (AE). The paper focuses on several selected aspects related to the quality of modeling based on prices on the Day Ahead Market (DAM). The influence of network architecture factors, network type, number of training data, and Evolutionary Algorithms on the improvement of the model quality measured by the Mean Squared Error (MSE) and the coefficient of determination (R2) were considered.
Data Collection and Analysis in Social Anxiety VR Therapies
Virtual reality (VR) therapy has emerged as a promising approach for treating social anxiety disorder by allowing controlled exposure to anxiety-provoking social situations in a safe virtual environment. Collecting and analyzing physiological data from participants during VR therapy sessions is crucial for monitoring their anxiety levels, tailoring the therapy, and assessing treatment outcomes. This article discusses the challenges and solutions for collecting and analyzing rapidly generated physiological data, such as galvanic skin response, heart rate, and respiration rate, in the context of social anxiety VR therapies. It explores techniques for real-time data streaming, storage in efficient file formats like Apache Parquet, and subsequent analysis of the collected data. The article also examines the role of data analysis in improving therapy protocols, personalizing treatment, and advancing our understanding of the physiological responses associated with social anxiety. By leveraging modern data engineering practices, VR therapy platforms can unlock valuable insights and enhance the effectiveness of exposure-based interventions for social anxiety disorder.
Supporting the Development of Spatial Orientation Skills of Blind People Using Binaural Sounds in VSR Game
Spatial orientation skills are crucial for the comfortable everyday functioning of blind individuals and affect their physical and mental well-being. Developing spatial orientation is usually carried out through traditional training courses without the support of electronic tools. Training them in a natural city environment, even under the supervision of an instructor, is often a very stressful process for the student. This paper proposes a solution for a simplified city space simulator created as a game using 3-dimensional binaural sounds. This simulator can be used at home to develop basic spatial orientation skills for blind people. While feedback from testing by three blind users was positive, the limited scope of this initial evaluation underscores the need for developing more advanced applications supporting the teaching of spatial orientation to blind users.
This paper offers a detailed and comprehensive analysis of the various cyber threats that target machine learning models and applications. It begins by characterizing basic classifiers and exploring the objectives of intentional attacks on typical classifiers, providing a foundational understanding of the threat landscape. The paper then thoroughly examines the vulnerabilities that machine learning systems face, alongside the methods for detecting, countering, and responding to these cyber threats. Special attention is given to specific types of threats, including attacks on machine learning models, adversarial attacks, poisoning attacks, and backdoor attacks. The paper also addresses critical issues such as attacks on data protection mechanisms, replay attacks, denial of service attacks, learning model theft, malware, and breaches in data privacy. Each of these threats is analyzed in detail, with a focus on their potential impact and the strategies that can be employed to mitigate them. In its conclusion, the paper provides recommendations on regulatory measures and best practices to safeguard machine learning models and applications against these evolving cyber threats. These recommendations emphasize the necessity for a robust regulatory framework to ensure the security, reliability, and integrity of machine learning systems in an increasingly digital and interconnected world.
Quantum Inspired Evolutionary Algorithm based on Day Ahead Market of the Polish Electricity Power Exchange
The paper discusses the essence of the method and selected results of the quantum-inspired implementation of the so-called System Evolutionary Algorithm on the example of the neural model of the Day-Ahead Market of the Polish Electricity Exchange Market S.A. First, the appropriate Perceptron Artificial Neural Network was designed and implemented in the MATLAB and Simulink environment, in which the Day-Ahead Market model was taught. Then it was assumed that the obtained parameters of the neural model, i.e. weights and biases, are quantum-encoded numbers, the values of whichwere corrected by the quantum-inspired Evolutionary Algorithm. Finally, a hybrid model was obtained in the form of an Artificial Neural Network with weights and biases corrected by a quantum inspired Evolutionary Algorithm. As a result of the conducted research, the relative error improvement was obtained from the level for different hours of the day from -0.11% ÷ 0.12% to the level from 0.04% ÷ 0.05%, i.e. by an order of magnitude. Moreover, the improvement of the quantum-inspired evolutionary algorithm measured by the fitness metric formulated as MSE error was achieved from a value of 0.990366 to a value of 0.990375.
Dependence of Spectrogram from Graph Spectral Clustering in Text Document Domain onWord Distribution Models
Analysis of the shape of a Laplacian spectrogram is a new line of research used in graph spectral clustering. More precisely, we observed that (properly normalized) plots of the eigenvalues of sub-Laplacians characterizing different groups of documents differ in their shape. Thus, by computing the distance between these plots, we can solve the problem of clustering and classifying new observations. This idea is developed in a number of our papers and as such, can be considered a pioneering approach to cluster analysis. In an attempt to answer why it is so useful, in this paper we consider the hypothesis that the shape of a spectrogram could be attributed to the writing style of the authors of the document group in the cluster. We explore this hypothesis for several models of word distribution. In particular, we assume that the writing style is reflected in the word distribution of texts of an author or a group of them. We check if changing of distribution parameters of a widely accepted log-normal word distribution model changes in fact the Laplacian eigenvalue spectrogram in such a way as to distinguish between document groups. We found that in fact variation of each of the distribution parameters leads to distinct groups of documents. These findings justify the usage of Laplacian spectrograms to distinguish (cluster or classify) groups of documents.
Artificial Text Generation from Simple Language Model
A modification of the Gale-Shapley algorithm used to assign Polish pupils to schools
Allocation algorithms based on rankings are utilized in various situations, such as selecting appropriate tasks for workers, scheduling doctors’ appointments, or assigning children to schools.Amodified version of the standard Gale-Shapley algorithm is introduced and employed in Poland’s secondary school recruitment process. This algorithm relies on students’ school results and their priority lists. During the recruitment process, students are assigned to a school on the basis of their performance and preference list. However, this approach disadvantages weaker students, as they are only considered towards the end of the process. Consequently, they are often assigned to schools with a lower priority on their lists or, in some cases, remain unassigned. The proposed modification aims to improve opportunities for students to plan their educational future more fairly, considering their individual achievements.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
Scheduling using Convolutional Neural Network in GPU environment
Graphics processing units (GPU) have become the foundation of artificial intelligence. Machine learning was slow, inaccurate, and inadequate for many of today’s applications. The inclusion and utilization of GPUs made a remarkable difference in large neural networks. The numerous core processors on a GPU allow machine learning engineers to train complex models using many files relatively quickly. The ability to rapidly perform multiple computations in parallel is what makes them so effective; with a powerful processor, the model can make statistical predictions about very large amounts of data. GPUs are widely used in machine learning because they offer more power and speed than CPUs. In this paper, we show the use of GPU for solving a scheduling problem. The results show that this idea is useful, especially for large optimization problems.
Selected applications of models based on quantum computing
The work discusses mathematical models using paradigms developed for quantum phenomena, including the vector-matrix Hilbert space and quantum gates for linear transformations of states to build a day-ahead market model (DAM). The article presents the most popular directions in the development of models using tools used in the description of quantum phenomena to present the methodology of conducting this type of calculation. Mathematical structures such as Hilbert space and operations on this space were used to build a quantum-inspired Artificial Neural Network. The work presents the proposition of implementation of DAM system to build a Neural Network Model with usage quantum phenomena based on Hilbert space. The concept of quantum processing based on quantum circuits was also noticed due to its large use and the development of implementing tools as a supplement to the entire area of development of building models based on quantum computing.
Smart Village as an action system supporting people with special information and communication needs
The paper presents selected results of theoretical research regarding the concept of a smart village as an intelligent operating system oriented to supporting people with special information and communication needs. Such a system is a developing system of socio-technical nature, which in its area enables the cooperation of its inhabitants as a local society. The presented idea of Smart Village is therefore based on the combination of modern tools and information and communication technologies to improve the quality of life and raise the standard of public services for citizens with much better use of resources and with less negative impact on the environment. It was pointed out that before proceeding with the design of the Smart Village as an intelligent operating system, it is important to identify the needs and capabilities of subsystems in a specific village and commune, and then, on this basis, design a model of an operating system supporting people with special information and communication needs as a hybrid system. The concept of Smart Village as a system of action is therefore very important for the development of rural areas, both because of new opportunities to create new jobs, as well as from the point of view of the quality of life and work in the countryside. More and more attention is paid to these problems, especially in terms of practical aid programs, but so far no one has defined a smart village system. For these reasons, this study is pioneering and awaited due to the need to develop a modeling method and possible subsequent implementation of the Smart Village system model in system practice.
Comparative Analisis of Digital Filters for Proper Positioning of User Mobile Device Using BLE Transmitters and Multilateration Technique
Nowadays, traveling without systems supporting navigation and guidance to the destination is hard for us to imagine. The outdoor positioning techniques used in these solutions have been mastered mainly by global positioning systems. However, assisting the navigation of users inside large public buildings still needs to be improved. It matters even more for persons with special needs, such as visually impaired, seniors, or people in wheelchairs. Determining the correct position of the user inside the complex space of a multi-floor building is a big challenge for such persons. Several methods can help in this matter. For example, technologies such as RFID, WIFI networks, image recognition, or lidar are used. However, the best solution to this problem is using infrastructures of low energy transmitters (BLE), called beacons. Then, construct an appropriate map to determine the user’s position to help guide the user to a destination. Nevertheless, to designate such a position, we need to know each transmitter’s signal strength and coordinates. Because of the physical properties of radio waves, the data collected from such transmitters are often inaccurate. This paper compares two methods, the Kalman filter and particle filter, to improve the quality of signal strength data received from BLE transmitters. As a result, the recommendation of the Kalman filter as the best method to improve the quality of these data and use it in the developed applications supporting indoor navigation in large buildings is provided.
Spectral Clustering Versus Watanabe Theorem
We devote this paper to a special case of Graph Spectral Clustering of graphs with identical distances between nodes. This study is motivated by the special theorem presented by Watanabe which claims that given all derivable attributes are taken into account, all distinct objects are at the same distance. As the multi-view clustering becomes popular, the mentioned Watanabe theorem may imply serious problems for recovering the intrinsic structure of the collection of objects. We show that Graph Spectral Clustering should not be affected in the most favourable case that is block structure of similarity matrix in theory, but in practice the underlying k-means algorithm introduces up to 20% error rate in assignment of elements to clusters.
Forearm vein pattern recognition using features from visible and NIR images
Forearm vein recognition is one of many available methods used for identification. However, forearm veins can be considered more secure compared to other biometric traits because the veins are inside the human body and therefore not easily manipulated. Veins possess several properties that make a good biometric feature for personal identification: 1) they are difficult to damage and modify; 2) they are difficult to simulate using a fake template; and 3) vein information can represent the liveness of person. Features were extracted from each pair of visible and NIR images. For the visible images, feature extraction was done using the Gabor filter. For the NIR forearm images, a crossing number was used to extract properties of the veins e.g. bifurcation. We present the results of the recognition of forearm veins patterns that show the suitability of the method for biometric identification purposes.
WPad BVI - a multilingual universal knowledge exchange tool with support for visually impaired users
Nowadays, users need to work simultaneously with many files and folders on their computer and various resources on the Internet, including several software pieces. It means that they have many windows open simultaneously, and they must continuously switch between different opened applications such as web browsers, text editors, or audio-visual programs. These problems are also crucial to blind or visually impaired users, for whom this issue is even more difficult. Due to the nature of their work, i.e., they do not use the mouse and cannot see the video and graphic content, they have to remember, at each moment, which programs they have open and which they no longer use. In this paper, we propose an approach that consists of universal software, where, on the one hand, the user can collect and view data; on the other hand, he can produce ready-made output packages in simple web pages. This solution consists of two main parts: an offline softwareWPad and the online - Personal Knowledge Information System (PIKS). We propose a simplified and more automated version of an earlier developed WPad software system called WPad for BVI, which visually impaired persons and other beginners can use. The solution was tested by two BVI users (one BVI student and one BVI teacher) and several non-disabled teachers and students from Poland and Slovakia. Feedback from users was collected that will be used to improve the system further.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0
Selection of the programming environment for neural modelling of the power and electricity demand generation systems in terms of unmanned factories
The work concerns the selection of the programming language and environment for the needs of neural modeling of the power and electricity demand generation system in terms of uninhabited factories. Therefore, the main goal of the conducted research is to obtain the best possible Artificial Neural Network, i.e. to teach it a model of a real system, which is a system for generating demand for power and electricity based on numerical data on parts of the power system operation in terms of uninhabited factories. The learning capabilities of artificial neural networks were checked by comparing the MSE error and the Regression Index R2. In each of the examined programming languages and related programming environments, i.e. Matlab, Python and Wolfram, an Artificial Neural Network with the same structure and properties was designed and implemented, i.e. with the same number of input and output neurons, the number of hidden layers and the number of neurons in them, the activation function of neurons and the learning method. In addition to the ANN training of the system model, testing and validation as well as comparative studies were carried out.
An indoor campus navigation system for users with disabilities
While supporting navigation outside buildings is currentlywell solved through GPS-based systems, only a few solutions support indoor navigation and user positioning in large buildings. In this paper, we propose a solution to support such navigation by creating a dedicated hierarchical map of the building and associating it with the infrastructure of Bluetooth Low Energy (BLE) transmitters installed in the building. Then a user equipped with a smartphone can easily read the signals of these transmitters and, thanks to the map, can determine their position in space.We use our method in a system that supports navigation and user safety, emphasizing users with special needs, which we are implementing on our academic campus. We describe the idea, architecture, and implementation of this system.
Universal structural map for indoor navigation in university campus
Mapmodeling is an important aspect of indoor navigation. It involves creating a digital map of the indoor environment that can be used for navigation purposes. The map can be created using various techniques such as laser scanning, photogrammetry, and computer vision. Once the map is created, it can be used to develop navigation algorithms that can help users navigate the indoor environment. In this paper, we propose a solution based on CAD files. These models can be used for a variety of purposes, including indoor navigation. There are many CAD applications available, including AutoCAD, SketchUp, and SolidWorks, among others. It is a relatively cheap method to model any indoor environment from scans of plans or CAD files. CAD files are the most accurate way to build a digital indoor map because these files can include 2D or 3D designs and usually contain important location information (e.g. floor level) within the layer properties. Moreover, we can map the CAD annotation to the following feature classes if they conform to the indoors model: types of rooms, types of doors, etc. We propose a solution based on DXF format files. We developed the parser to transfer the necessary data from the CAD files to the navigation system. It consists of processing existing maps from the CAD format to the appropriate structure, supplementing it with the data of BLE transmitters, and saving it as a graph suitable for determining routes and guiding the user along the route. We use our method in a system that supports navigation and user safety, emphasizing users with special needs, which we are implementing on our academic campus.
Towards explainability of hashtags in the light of Graph Spectral Clustering methods
Hashtags constitute an indispensable part of modern social media world. As more and more hashtags are invented, it becomes a necessity to create clusters of these hashtags. Nowadays, however, the clustering alone does not help the users. They are asking for justification or expressed in the modern AI language, the clustering has to be explainable. We discuss a novel approach to hashtag explanation via a measure of similarity between hashtags based on the Graph Spectral Analysis. The application of this similarity measure may go far beyond the classical clustering task. It can be used to provide with explanations for the hashtags. In this paper we propose such a novel view of the proposed hashtag similarity measure.
Application of artificial neural networks and NEAT algorithm to control a quadcopter
We present a framework for building ArtificialNeuralNetworks (ANNs) able to control a quadcopter and perform basic maneuvers, like hover or following waypoints. In this approach, we make use of the Neuroevolution of Augmenting Topologies (NEAT) algorithm which is aimed at creating the network structure and the weights in result of evolutionary computations. In order to evaluate fitness of individuals, we use physics based, realistic simulation engine Gazebo, where each individual controls a drone in a simulated environment. Our approach is aimed at using one of existing, popular protocols used to remotely control drones, and train ANNs able to imitate signals received from a radio controller operated by a human pilot. Thus, contrary to the most of other approaches, our autonomous controller cooperates with standard drone software. Our ultimate goal is to train ANNs able to control a real-world quadcopter and perform advanced tasks autonomously. Not only such ANNs should be able to perform the maneuvers correctly, but they should be small enough to transfer them into a quadcopter’s limited memory. In this paper we report the first stage of our project - a successful development and deployment of the ANNs distributed training framework, and choosing the activation function for further research.