Informatics

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Research Topic Summary.

Human-computer interface is the form of communication between a user and computer when the user issues commands for computer in natural form. Such an interface can be implemented using various forms depending on the purpose and user requirements. Natural and user-friendly forms of the implementation shorten time of the user learning to use the implemented tool. Brain – Computer interface is a system that converts the brain activity into the control signals for a computer or some other electronic device. Such a system has large field of application ranging from the disabled persons to playing games. The clear relationship between the brain activity and the commands to muscles is not disclosed yet. Nevertheless, it is possible using mathematical methods to analyse the brain activity and to form the control signals for the external electronic devices. For this purpose, the method is needed to scan and to save the brain activity. The formation of electroencephalogram (EEG) is one of such methods. This is a non-invasive investigation of bioelectrical brain activity. The measured signals of brain activity are non-stationary and non-linear and they are directly dependent of the particular person. Consequently, it is difficult to distinguish the effective control signals. For this purpose, the feature extraction and classification methods are used. The methods of neural networks are dominant among classification methods. The deep neural networks present very good results for image recognition and classification. The signals of brain activity can be presented in the form of image. The purpose of the investigation is to apply the deep neural networks to recognize and classify the signals of brain-computer interface.

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Research Topic Summary.

Artificial Intelligence (AI) has achieved a wide range of capabilities from image recognition to language processing and data analysis. But, the automatic decision made by “black box”- based algorithm is hard to explain and even their designers cannot explain why the algorithm arrived at a specific decision. Quit often, this kind of algorithm for generalizing of training data creates rules that may not fit to another sample of data, i.e., it not generalizing properly or partly generalizing a given data. Therefore, the widespread application of artificial intelligence techniques is limited by the lack of trust of people in algorithmic decisions. Especially when there are no clear and objective criteria and rules for explaining one or the other decision made by the algorithm (e.g. Convolutional Neural Network – a deep learning algorithm). The absence of clear connection between accuracy and interpretability creates a barrier to a human consistently predict the model results. Explainable artificial intelligence (XAI) are all automated decision-making methods and algorithms whose solutions and their access process can be understood and explained by the experts. It involves dealing with interpretable machine learning (iML) models, whose behavior and predictions are understandable to humans. Rationally explained algorithms could be deeper applied in the medical field for the diagnosis of critical life-threatening diseases, cyber-security for the detection of anomalies and other areas of human interest. The main scientific uncertainty of the topic of work is an algorithm, a method or a technique that can provide transparency to black box algorithms. It is not clear what the algorithm should be and how it works, and what form the algorithm will give to transparency. The research will investigate the various artificial intelligence methods to determine the most appropriate method type, structure and parameters. For more details please contact the supervisor of the topic.

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Research Topic Summary.

Factory machinery is prone to failure or breakdown and often this leads to mechanical failures not detectable by a set of common sensors. This thesis hypothesizes on an idea of distinguishing mechanical failures from acoustic features. The novelty aspect comes from understanding that the environment is often very noisy, which requires development of specific intelligent computational model for sound data-based anomaly detection of industrial machinery. Prerequisites: Artificial Intelligence, Object Oriented Programming, signal processing.

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Research Topic Summary.

In current digital era, animations and their usage are an integral part of digital entertainment. Also, motion blending is one of the areas little touched by animation. Research on how deep neural networks could contribute to motion-tuning of animations remains relevant. The aim of the research is to create a computational model that would be able to dynamically adapt and adjust motion animations to a specific situation, by analyzing various features on the fly, like an object’s reaction to force, motion or other vectors in a certain matrix, thus distancing itself from the limitations found in a classic fixed animations. Prerequisites: Artificial Intelligence, Object Oriented Programming, animation methods, game technologies.

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Research Topic Summary.

The idea of this thesis is to investigate a possibility to investigate a model capable of reducing or eliminating posture detection problems related to a limited dataset problem, as well as an issue in data variety, like visibility human torso in the frame, i.e., the occlusion problem, also consideringas dependencies on camera angles, signal clarity and environmental issues such as lighting. Novelty comes from current technological limitations, where current skeleton-based posture prediction and detection methods fail due to the reason of not enough data received by the sensors to infer the full human skeleton or distortions limiting the recreation of that model. Prerequisites: Artificial Intelligence, Object Oriented Programming, signal processing technologies.

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Research Topic Summary.

The main goal of this topic is a deep learning based dynamic motion generation model based, for responsive and accurate real-time skeleton animation. This topic’s novelty is a focus on a model to generate enhanced animated movements from scratch, more naturally and interactively by utilizing "on-the-fly" programming animation, combining artificial intelligence, biomechanics, physics and numerous other dynamic features available during run-time. Prerequisites: Artificial Intelligence, Object Oriented Programming, animation programming

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Research Topic Summary.

Any available data can be represented in the form of time series. The ability to interpret the mechanisms governing the evolution of time series and to identify nonlinear models describing the temporary dynamics of time series would help to better understand, analyze and predict different real-world processes. The main research objective is the development of new techniques and algorithms for nonlinear time series analysis. New mathematical algorithms will allow to interpret time series as the carriers of information on the hidden nonlinear time-varying chaotic dynamical systems. Usually, this information is embedded into the additive noise. Algorithms and techniques for the interpretation and possible elimination of noise can be also defined as an important and integral part of the research objectives. New techniques and algorithms for the identification of mathematical models for the prediction, segmentation, fault diagnosis and control of time series will be developed during the doctoral research program. Algorithms based on the principles of artificial intelligence, machine learning, permutation entropy, multiscale attractor embedding, fractional differential equations will be designed and employed for that purpose. At least four papers are planned to be published in International Journals with Impact Factors during the four-year term of doctoral studies.

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Research Topic Summary.

During the past decades, due to the fundamental discoveries in the field of molecular biology, it has become a central subject of biology sciences. Previous focus of ttention has been shifted from the identification of one specific gene to greater opportunities that have become possible by the sequencing of complete genomes. That, in turn, opened the door to the technologies of the so-called post-genomic era. They are often based on the computer analysis of the entire genome. Even the short sequences (genitive case) are used in bioinformatics. Genome of the simple life form (virus) can be very big and can exceed 3.5×105 nucleotides. The number of bacteria genomes varies from 0.5×106 to 10×106 nucleotides. Human genomes have about 3.12×107 nucleotides. It is a big issue to visualize the data of such size or its statistics. Particular maps of genomes are structured in order to simplify the research. One can have a look at the fragment of sequence when operating, but you can’t see the whole, and the attributes that have only particular sequence and visually separate it from the other sequences with different attributes. The short sequences (e.g. genes or proteins) are often compared interdependently, but the results of comparison are hardly interpreted if there are plenty of them. Musical compositions are arranged from the special information units, i.e. sequence of musical notes. Their length in comparison to the length of nucleotides’ sequence is considerably shorter. The biggest classical music composition Ludwig van Beethoven Symphony No. 9 in D minor takes almost 70 minutes and has about 3,8×104 notes. There are also some even bigger compositions, e.g. Frederic Rzewski The Road is supposed to be one of the longest piano solo’s lasting for about 10 hours, or Jacob Mashak Beatus Vir for two pianos takes 11 hours. But even the note sequences with the rank of 103 are not easy to analyse and to compare in large variety of note sequences.

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Research Topic Summary.

Computer vision is an interdisciplinary field that deals with computer algorithms for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding in this context means the transformation of visual images into descriptions of the world that can interface with other action processes and suggest an appropriate action. Machine vision systems are used in a variety of applications, including manufacturing, medicine, traffic monitoring, and security systems. In industrial processes, cameras capture important information, store and archive it, and allow users or software to make decisions based on the image information. Machine vision systems can measure and count products, calculate their weight or volume, and inspect goods at top speed with respect to their predefined characteristics. They automatically extract limited, but crucial, information from huge quantities of data, or they help experts interpreting images by filtering, optimizing, supplementing, or quickly retrieving and making them available. Artificial intelligence algorithms have recently been an integral part of solving computer vision problems. The proposed research will aim to improve existing common methodologies by focusing on services or production real-world processes for greater efficiency and reliability, adapting and improving machine learning methods in Decision trees, K nearest neighbors, Naïve Bayes, Support vector classifiers, Direct propagation neurons networks and Convolutional neural networks. For more details please contact the supervisor of the topic.

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Research Topic Summary.

Recently, graph theory methods are gaining a high interest in finance and economics. The reason is that the financial entities and markets are strongly linked in a globalized world, and graph theory as the framework could be used for describing and modelling the structure of complex financial network. As such, the model to be developed might be used to investigate financial network characteristics such as market stability, network contagion, spillovers, risk sharing among network entities, network shock propagation, etc. The solution to these individual questions depends on the topology of the network and its possible evolution over time. Therefore, the purpose of this work is to develop methods for modelling the financial network and its topology, focusing on financial risk assessment by introducing network characteristics and proposing stress-testing methods.

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Research Topic Summary.

Artificial intelligence is one of the hot topics of the early 21st century in the physical and technological sciences. Large-scale investors such as pension funds, banks and investment companies have been using artificial intelligence principles for some time, however, most of techniques they use are limited to machine learning or artificial neural networks. The resulting solutions are difficult to explain and interpret. Moreover, the worst thing is that it is often it is nearly impossible to verify if the resulting solution is exactly what we aspired and whether it is optimal. Because investment decisions usually depend on many factors, such as expected return, risk, ethics, investor's risk profile and personal characteristics, all of these must be taken into account when developing an autonomous system or robo-advisor. Quite often such problems are quite easily solved by conventional methods, however, as the amount of information and number of criteria increases, the complexity of the task can become unbeatable even for supercomputers. It is therefore necessary to develop new methods based on artificial intelligence that are understandable and explainable and that their results can be validated. Therefore, the main task is: to create artificial intelligence based methods that are explainable, transparent and interpretable for most of the investment decision makers.

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Kita informacija.

Related ongoing projects: • „A FINancial Supervision and TECHnological Compliance training programme – FIN-TECH“, Nr. H2020-ICT-2018-2, 2019–2021, https://www.fintech-ho2020.eu/ . • „Fintech and Artificial Intelligence in Finance – Towards a transparent financial industry“, Cost Action 19130, 2020-2024, https://fin-ai.eu/ • DyMoDiF – dynamic models for digital finance, Czech Science Foundation, 2019–2023

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Research Topic Summary.

Optimization methods and algorithms are an important constitutive part of the computer science. In particular, the heuristic algorithms play a very significant role, just to mention local search (LS), tabu search (TS), genetic algorithms (GA), and their numerous combinations (hybrids). The high efficiency of the combined heuristic algorithms by solving various optimization problems was empirically confirmed several decades ago. The demand of such algorithms was stimulated by the growth of research and development (R&D) along with the newly arising tasks. This has become even more actual in recent years. The further development of heuristic algorithms is continued by not only combining different algorithms, but also by exploiting and expanding the inner structure (architecture) of the algorithms. One of the promising directions in this area is the use of so-called hierarchically-structured (hierarchicity-based) (or simply hierarchical) heuristic (HH) algorithms. The central idea behind the HH algorithms is the multiple adoption/utilization (reuse) of the well-known heuristics (like LS, TS, GA). This in connection with what is known as self-similarity  this means that an object (in our case, algorithm) is exactly or approximately similar to constituent parts of itself. The principle of self-similarity is the universal principle; so, we conjecture that for both the computational methods and algorithms algorithms, this principle may also be deeply inherent and important. This idea is not very new, and some examples of hierarchical-like algorithms have been already investigated (e.g., iterated/hierarchical local search, master-slave genetic algorithms, etc.). Still, this is an area of active and progressing research. Further computational studies are required for accelerating research and to reveal higher potential and advantages of the HH algorithms.

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Research Topic Summary.

Aim of research - to create the methodology for efficiency analysis of data systems and customer segmentation based on data science and artificial intelligence and confirmed by testing and demonstration of software tools developed for its application. Research tasks: 1. Develop the concept and methods of data systems efficiency and customer segmentation; 2. Develop tools and models for big data mining and machine learning (artificial intelligence); 3. Develop the concept for optimization of data systems with their users’ behaviour in mind. 4. Combine data science application, i.e. data mining and machine learning, with optimization concepts and demonstrate this as a unified methodology for the data systems efficiency. For further information please contact the supervisor of the topic.

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Research Topic Summary.

The objective of the research is creation of methodology and test calculations based on machine learning and information integration for the most accurate disease prediction and health care. Tasks: 1. Review and compare possibilities of information integration methods and algorithms as well as application of related software and machine learning methods intended for health care. 2. Define information integration and smart systems accuracy criteria and their evaluation procedures in the context of data relevant for health care. 3. Develop and demonstrate the information integration tools which usage increase the accuracy and / or reduce the risk of incorrect decisions. 4. Perform the pilot studies of smart systems intended for health care and create methodology for effective application of these systems. For further information please contact the supervisor of the topic.