Academically oriented

Full-time study

4 years

prof. Ing. Lukáš Sekanina, Ph.D.

Chairman :
prof. Ing. Lukáš Sekanina, Ph.D.
Councillor internal :
doc. Ing. Jan Kořenek, Ph.D.
prof. Ing. Pavel Václavek, Ph.D.
prof. Ing. Tomáš Vojnar, Ph.D.
prof. Dr. Ing. Jan Černocký
prof. RNDr. Milan Češka, CSc.
prof. Ing. Adam Herout, Ph.D.
prof. Ing. Jan M. Honzík, CSc.
prof. Ing. Tomáš Hruška, CSc.
prof. Dr. Ing. Pavel Zemčík, dr. h. c.
prof. RNDr. Alexandr Meduna, CSc.
prof. Dr. Ing. Zbyněk Raida
prof. RNDr. Josef Šlapal, CSc.
Councillor external :
prof. Ing. Jiří Sochor, CSc. (FI MUNI)
prof.,RNDr. Jiří Barnat, Ph.D. (FI MUNI)

The goal of the doctoral degree programme is to provide outstanding graduates from the master degree programme with a specialised university education of the highest level in certain fields of computer science and information technology, including especially the areas of information systems, computer-based systems and computer networks, computer graphics and multimedia, and intelligent systems. The education obtained within this degree programme also comprises a training and attestation for scientific work.

• Graduates from the doctoral study programme are trained to independently work in research, development, or management.
• They are able to solve and/or to lead teams solving advanced conceptual, research, development, or production problems in the area of contemporary information technology and its applications.
• They can be engaged to work on creative tasks, to lead research and development teams, or to work in management of companies or organizations whenever there are required abilities to work in an independent and creative way, to analyze complex problems, and to propose and realize new and original solutions. Graduates from the doctoral study programme can also teach and/or scientifically work at universities.

FIT graduates in general and FIT doctoral graduates in particular do not have a problem finding employment at scientific, pedagogical or management positions both in Czech Republic and abroad.

• FIT   graduates of the doctoral study are capable of independent scientific, research and management work in the field of Informatics, Computer Technology and Information Technologies. Graduates are ready to solve challenging conceptual, research and development problems. They can independently conduct research, development and production in the field of modern information technology.
• Typically, they work as creative workers at top scientific research workplaces, as leaders of research and development teams and in scientific and pedagogical work at universities. Graduates of this program are also employed in higher functional positions of larger institutions and companies, where the ability to work independently, analyze complex problems and design and implement new, original solutions is required.
• And, last but not least, graduates typically continue as so-called "postdoc" in their academic careers in Czech Republic or abroad.

The requirements that the doctoral students have to fulfil are given by their individual study plans, which specify the courses that they have to complete, their presupposed study visits and active participation at scientific conferences, and their minimum pedagogical activities within the bachelor and master degree programmes of the faculty. A successful completion of the doctoral studies is conditional on the following:

• The student has to pass a doctoral state examination within which he/she has to prove a deep knowledge of methodologies, theories, and their applications in accordance with the state of the art in the areas of science that are given by the courses included in his/her individual study plan and by the theme of his/her future dissertation thesis. The doctoral state examination also encompasses an evaluation of the presumed goals of the future dissertation thesis of the student, of the chosen solution method, and of the so far obtained original results.
• The student has further to prepare and defend his dissertation thesis.

The rules are determined by the directions of the dean for preparing the individual study plan of a doctoral student.  The plan is to be based on the theme of his/her future dissertation thesis and it is to be approved by the board of the branch.

• obligatory doctoral study programme Courses, the total number of courses a student has to complete and their mapping into particular semesters.
• a Research Plan Content (brief descrition of research content - focuse at the intended research area and the doctoral thesis topic
• a Research Plan ( list of research activities focused at the intended research area and the doctoral thesis topic - conferences and seminars to be attended , work to be published)
• teaching duty according to BUT study rules and regulations
• doctoral study schedule

https://www.fit.vut.cz/fit/info/smernice/sm2018-13-en.pdf

Brno university of technology provides studies for persons with health disabilities according to section 21 par. 1 e) of the Act no. 111/1998, about universities and about the change and supplementing other laws (Higher Education Act) as amended, and according to the requirements in this field arising from Government Regulation No. 274/2016 Coll., on standards for accreditation in higher education, provides services for study applicants and students with specific needs within the scope and in form corresponding with the specification stated in Annex III to Rules for allocation of a financial contribution and funding for public universities by the Ministry of Education, Youth and Sports, specifying financing additional costs of studies for students with specific needs.

Services for students with specific needs at BUT are carried out through the activities of specialized workplace - Alfons counselling center, which is a part of BUT Lifelong Learning Institute - Student counselling section.

Counselling center activities and rules for making studies accessible are guaranteed by the university through a valid Rector's directive 11/2017 concerning the status of study applicants and students with specific needs at BUT. This internal standard guarantees minimal stadards of provided services.
Services of the counselling center are offered to all study applicants and students with any and all types of health disabilities stated in the Methodological standard of the Ministry of Education, Youth and Sports.

The study programme builds on both the ongoing follow-up Master's programme in Information Technology and the new follow-up Master's programme in Information Technology and Artificial Intelligence.
Students can also, according to their needs and outside their formalized studies, take courses and trainings related to the methodology of scientific work, publishing and citation skills, ethics, pedagogy and soft skills organized by BUT or other institutions.

1. Advanced algorithms of Video, Image, and Signal processing

   The topic concerns algorithms of image, video, and/or signal processing. Its main goal is to research and in-depth analyze existing algorithms and discover new ones so that they have desirable features and so that they are possible to efficiently implement. Such efficient implementation can be but does not necessarily have to be part of the work but it is important to prepare the algorithms so that they can be efficiently implemented e.g. in CPU, in CPU with acceleration through SSE instructions, in embeded systems, even in combination with FPGA, in Intel Xeon PHI, in extremely low power systems, or in other environments. It is possible to exploit algorithms of artificial intelligence, such as neural networks, especially CNNs The application possibilities of the algorithms are also important and the application can be but does not have to be part of the work. The algorithms/applications of interest include:

   • recognition of scene contents, events, and general semantics of video sequences (such as identification of traffic situations, identification in scenes in moview, action identification, etc.),
   • classification of video sequences using machine learning (AI)through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, object of scene characteristics search, etc.), possibly in combination with object tracking in video using modern methods, 
   • parallel analysis of video and signal (e.g. for detection of coincidence of occurrence of object in video and characteristic signal shape in surveillance applications), fusion of video and sognals,
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" approaches suitable e.g. for mobile technology and/or embedded systems,
   • algorithms of video compression and analysis through frequency or wavelet transforms or similar methods...

   After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

   Collaboration on grant projects, such as TACR, MPO, H2020, ECSEL (possible employment or scholarship).

   Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

2. Advanced Methods of Computational Photography

   The project is concerned with advanced methods of computational photography. The aim is to research new computational photography methods, which comprises software solutions potentially supported by new optics and/or hardware. Our interest is on HDR image and video processing, color-to-grayscale conversions, spectral imaging, and others.
   

   • Further information: http://cadik.posvete.cz/tmo/
   • Contact: http://cadik.posvete.cz/
   • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

   Tutor: Čadík Martin, doc. Ing., Ph.D.

3. Advanced Rendering Methods

   The project is concerned with advanced rendering and global illumination methods. The aim is to research new photorealistic (physically accurate) as well as non-photorealistic (NPR) simulations of interaction of light with the 3D scene. Cooperation and research visits with leading research labs are possible (Adobe, USA, MPII Saarbrücken, Německo, Disney Curych, Švýcarsko, INRIA Bordeaux, Francie).
   
   

   • More information: http://cadik.posvete.cz/

   Tutor: Čadík Martin, doc. Ing., Ph.D.

4. Advanced topics in machine learning

   Machine learning is in the centre of research of artificial intelligence. Many researchers worldwide are dealing with the topics related to machine learning, both in academia and industry. This very dynamic field is characterized with fast transfer of solutions into practical use.

   The topics in this domain are defined by premier scientific conferences, where top-class researchers meet, for example ICML (International Conference on Machine Learning), NeurIPS (Advances in Neural Information Processing Systems), IJCAI (International Joint Conference on AI), COLT (Conference on Learning Theory).

   This thesis will be advised by an external mentor, who will also define its particular topic.

   Interesting research challenges are contained within (but are not limited to) these topics:

   • General Machine Learning (e.g., active learning, clustering, online learning, ranking, reinforcement learning, semi-supervised learning, time series analysis, unsupervised learning)

   • Deep Learning (e.g., architectures, generative models, deep reinforcement learning)

   • Learning Theory (e.g., bandits, game theory, statistical learning theory)

   • Optimization (e.g., convex and non-convex optimization, matrix/tensor methods, sparsity)

   • Trustworthy Machine Learning (e.g., accountability, causality, fairness, privacy, robustness)

   There are many application domains, where advanced machine learning methods can be deployed.

   The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

   Tutor: Bieliková Mária, prof. Ing., Ph.D.

5. Advanced topics in machine learning

   Machine learning is in the centre of research of artificial intelligence. Many researchers worldwide are dealing with the topics related to machine learning, both in academia and industry. This very dynamic field is characterized with fast transfer of solutions into practical use.

   The topics in this domain are defined by premier scientific conferences, where top-class researchers meet, for example ICML (International Conference on Machine Learning), NeurIPS (Advances in Neural Information Processing Systems), IJCAI (International Joint Conference on AI), COLT (Conference on Learning Theory).

   This thesis will be advised by an external mentor, who will also define its particular topic.

   Interesting research challenges are contained within (but are not limited to) these topics:

   • General Machine Learning (e.g., active learning, clustering, online learning, ranking, reinforcement learning, semi-supervised learning, time series analysis, unsupervised learning)

   • Deep Learning (e.g., architectures, generative models, deep reinforcement learning)

   • Learning Theory (e.g., bandits, game theory, statistical learning theory)

   • Optimization (e.g., convex and non-convex optimization, matrix/tensor methods, sparsity)

   • Trustworthy Machine Learning (e.g., accountability, causality, fairness, privacy, robustness)

   There are many application domains, where advanced machine learning methods can be deployed.

   The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

   Tutor: Bieliková Mária, prof. Ing., Ph.D.

6. Analysis of attacks on wireless networks

   Dissertation focuses on the security of wireless local area networks. As part of the solution, student should become familiar with selected wireless networks and their security. The goals of this work: studying the theory of wireless networks, their properties and possibilities of attacks, testing the basic types of attacks, designing a new method of protection, experiments, evaluating the results and proposing the direction of further research.
   Co-supervised by dr. Kamil Malinka.

   Tutor: Hanáček Petr, doc. Dr. Ing.

7. Anomaly Detection in Industrial Communication ICS/SCADA

   SCADA (Supervisory Control And Data Acquisition) and Industrial Control Systems (ICS) communication is intended to control and monitor industrial processes in smart factories, smart grids, etc. Disruption of communication may interrupt important industrial processes or cause a blackout of critical supplies like water or energy. 
   
   To protect SCADA/ICS communication, we can apply advanced SCADA/ICS monitoring that creates normal behavior profiles and detects deviations (anomalies). Communication profiles can be described using statistical methods. We can model communication using automata or create models using machine learning. 
   
   The research aims to describe the behavior of industrial communication and propose a suitable representation using multidimensional profiles. It is then necessary to verify what types of anomalies are covered by these profiles and how the accuracy of detection can be increased. 
   
   The topic is a part of the research project Security monitoring of ICS communication in the smart grid (Bonnet). 
   
   Co-supervisor: Matoušek Petr, Ing., Ph.D., M.A.

   Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

8. Application Detection in Encrypted Communication

   Current internet traffic is mostly encrypted using TLS or DTLS. Detection of network applications communicating over the local network is important for network monitoring and cyber security. There are several approaches how to detect an application with the encrypted network traffic. One approach is based on TLS fingerprinting using the JA3/JA3S method. Another approaches include statistical analysis of encrypted traffic or the application of machine learning. 
   
   A student is expected to research and evaluate current methods for detecting applications in encrypted traffic and to propose a new approach for fast and reliable application detection using advanced methods.
   
   Co-supervisor: Matoušek Petr, Ing., Ph.D., M.A.

   Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

9. Architectures of neural networks for speech and speaker recognition

   The topic aims an the reseach of advanced architectures of neural networks for the tasks of speech and speaker recognition. Although empirically, the results of such NNs are often excellent, our knowledge and understanding of such representations is insufficient. This PhD  topic has an ambition to fill this gap and to study neural representations for speech and text units of different scopes (from phonemes and letters to whole spoken and written documents) and representations acquired both for isolated tasks and multi-task setups.
   
   
   The topic is related to the GACR project of excellence in fundamental research "Neural Representations in multi-modal and multi-lingual modeling", on which we cooperate with colleagues from UFAL MFF Charles University in Prague. 
   
   
   The assignment requires an interest in mathematics, statistics, machine learning and speech processing; experience with Python and its libraries for machine learning is an advantage. 

   Tutor: Burget Lukáš, doc. Ing., Ph.D.

10. Artificial Intelligence in Computer Security

    The rising trend in artificial intelligence usage brings novel cybersecurity approaches on both sides - attacker and defender. The most prominent examples are deepfake usage to counterfeit biometric systems or security analytics, using deep learning for cyber-attacks detection. The goal of this work is to analyze all existing approaches, their properties, and potential applications. The work should then propose novel applications of AI for the problems that were not resolved before while also implementing the most interesting application.
    
    

    Co-supervised by dr. Kamil Malinka.

    Tutor: Hanáček Petr, doc. Dr. Ing.

11. Assessment of mental stress and anxiety from analysis of brain signals

    Problem Statement: Mental stress and anxiety are two of the mental health conditions that often occur together. In such a case, the person is stressed and is not able to control the worry, and it correspondingly affects his/her social and occupational activities. Hence, proper assessment and diagnosis for mental stress and anxiety is required in order for a person to effectively keep taking part in his/her normal daily tasks and activities.

    Issues with Current Solutions: Unfortunately, conventional assessment and diagnostic measures are subjective in nature and are used only when the symptoms are already evident due to advanced stages of mental stress and anxiety. However, mental stress and anxiety do not occur overnight, rather it is a long process. Hence, detection of symptoms is required at early stages of mental stress and anxiety because that may result in a cure or at least it will delay the onset of serious mental health issues associated with them, for example, depression, Generalized Anxiety Disorder etc.

    Challenges: Unlike other diseases where the symptoms like fever and cough allow people to seek help, symptoms at early stages of mental stress and anxiety are not easily identifiable. Hence, the brain needs to be continuously monitored for any sign of change or deterioration in order to detect the symptoms at early stage.

    Solution: The solution lies in the development of an objective and quantitative method that can detect mental stress and anxiety at an early stage. Perception of mental stress and anxiety originates in the brain; therefore, this research investigates the neurophysiological features extracted from brain electroencephalogram (EEG) signal to measure mental stress and anxiety at early stage. This will require development of method for extraction of features as well as pattern recognition approach to provide a solution. The EEG dataset is already available for this project.

    Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

12. Automatic speech recognition in air-trafic communication

    
    Automatic speech recognition (ASR) is among popular machine learning tasks already well exhibited by industry in various applications. 
    
    Recent advances in deep learning enabled for the use of speech technologies
    in areas where very also low error-rates are expected, such as in recognition of air-traffic communication (i.e. an intensive use of spoken language between controllers and pilots). 
    
    The objective of this thesis is to focus on unsupervised training of ASR components by exploiting auxiliary information provided by 
    other resources, such as radar data or ADS-B communication (surveillance technology in which an aircraft determines its position via satellite navigation or other sensors and periodically broadcasts it). Possible application of ASR can be expected to (i) support real-time voice communication, or (ii) to automatically process and transcribe 
    large data audio archives.   

    Tutor: Motlíček Petr, doc. Ing., Ph.D.

13. Brain Biometrics

    Problem Statement: Biometrics technology enables recognition of a person based on certain unique characteristic of human body. It is the primary tool for identification of a person and it has various applications including banking, security, immigration etc. Hence, it is critical to have a foolproof biometrics method.

    Issues with Current Solutions: Fingerprints and face identification are commonly used in today's world for verifying a person's identity.  Some other biometrics technologies include voice recognition, gait recognition, iris recognition and retina-based pattern recognition. However, issues like ageing affects the recognition of a person based on these technologies. In addition, some of them are easy to replicate and hence a person's identity can be hacked.

    Challenges: Ageing is one of the main challenges for any biometric method. In addition, physical damage to the body as well as replication of the biometric identifier are the main challenges.

    Solution: This research will investigate the brain biometrics using the human electroencephalogram (EEG) brain signals. It will involve identifying a person's unique brain activity patterns and it has the advantage that it is invisible from outside and hence cannot be replicated or cloned. Development of a brain biometric method will involve identification of a unique pattern from the brain signals that can be used as an identifier for the person.

    Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

14. Brain Biometrics

    Problem Statement: Biometrics technology enables recognition of a person based on certain unique characteristic of human body. It is the primary tool for identification of a person and it has various applications including banking, security, immigration etc. Hence, it is critical to have a foolproof biometrics method.

    Issues with Current Solutions: Fingerprints and face identification are commonly used in today's world for verifying a person's identity.  Some other biometrics technologies include voice recognition, gait recognition, iris recognition and retina-based pattern recognition. However, issues like ageing affects the recognition of a person based on these technologies. In addition, some of them are easy to replicate and hence a person's identity can be hacked.

    Challenges: Ageing is one of the main challenges for any biometric method. In addition, physical damage to the body as well as replication of the biometric identifier are the main challenges.

    Solution: This research will investigate the brain biometrics using the human electroencephalogram (EEG) brain signals. It will involve identifying a person's unique brain activity patterns and it has the advantage that it is invisible from outside and hence cannot be replicated or cloned. Development of a brain biometric method will involve identification of a unique pattern from the brain signals that can be used as an identifier for the person.

    Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

15. Computer Vision in Traffic Monitoring
    • Research and development of computer vision algorithms.
    • Focus on videos from traffic surveillance cameras.
    • Research of algorithms avoiding user input.
    • Collection and synthesis of suitable data sets.
    • Implementation of experimental prototypes.
    • Design and prototyping of applications.

    Tutor: Herout Adam, prof. Ing., Ph.D.

16. Create a 3D model of head from 2D photos of diverse origins

    The aim of the thesis is to create 3D face model from 2D photos of diverse origins, namely:
    

    • Getting acquainted with creating a 3D model from 2D photos.
    • Specify options - photo size and resolution, photo age, head position, B & W / color photography, and other parameters to create a 3D model from this data.
    • Design and implementation of the algorithm for determining the parameters from the previous point to determine the usability of the input data and enable the creation of a 3D model.
    • Design and implementation of a 3D model creation algorithm from usable input data.
    • Implementation of experiments and summary of achieved results.
    Participation on major international conferences and publishing in scientific or scientific journals is expected. Foreign internship is possible and strongly supported. This thesis will be dealt with in cooperation with the Police of the Czech Republic.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

17. Cyber Threads in DNS Communication

    Majority of network communication today is encrypted. One of the remaing plain-text communication is the DNS that is essential for most network operations. In the past years the attackers misuse DNS communication to redirect internet connections or for DDoS attacks. DNS communication is also misused by malware for transporting stealed data over a hidden channel. Domains generated by DGA algorithms are also misused by botnets for C&C communication.
    
    In this project, a student is expected to study typical classes of DNS cyber attacks based on MITRE ATT&CK classification and to propose a method for DNS cyber threats detection using machine learning and artificial intelligence techniques.
    
    Co-Supervisor: Ing. Petr Matoušek, Ph.D., M.A.
    
    References:
    [1] Daihes Y., Tzaban H., Nadler A., Shabtai A. (2021) MORTON: Detection of Malicious Routines in Large-Scale DNS Traffic. In: Bertino E., Shulman H., Waidner M. (eds) Computer Security - ESORICS 2021. ESORICS 2021. Lecture Notes in Computer Science, vol 12972. Springer, Cham. https://doi.org/10.1007/978-3-030-88418-5_35
    [2] M. Grill, I. Nikolaev, V. Valeros and M. Rehak, "Detecting DGA malware using NetFlow," 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015, pp. 1304-1309, doi: 10.1109/INM.2015.7140486.
    [3] S. Torabi, A. Boukhtouta, C. Assi and M. Debbabi, "Detecting Internet Abuse by Analyzing Passive DNS Traffic: A Survey of Implemented Systems," in IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 3389-3415, Fourthquarter 2018, doi: 10.1109/COMST.2018.2849614.

    Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

18. Deep Neural Networks in Image Analysis with Small Training Data Set

    Deep convolution networks have been a clear trend of machine learning for image analysis in recent years. However, in tasks with a very small and specific data set, where it is not enough to use data augmentation or GAN concepts, their usage is still problematic.
    
    The goal of the dissertation thesis is to explore, analyze and design new architectures of deep convolutional networks and approaches to their learning for image analysis tasks in which the size of the annotated data set is extremely small or is gradually growing. For learning neural networks it is possible to use unannotated data or partially annotated data in the form of a limited user input.
    
    Proposed methods will be applied in the projects on which the supervisor participates.

    Tutor: Španěl Michal, Ing., Ph.D.

19. Detection mechanism of moving objects (lasers, drones) by revealing their position or broadcast sources

    The aim of the work is to create a detection mechanism of moving objects (lasers, drones) with revealing their position or broadcast sources, namely:
    
    

    • Introduction to video processing.
    • Determination of drone and laser beam detection options in video sequence data.
    • Design and implementation of an algorithm for detecting drone or laser beams in an image.
    • Design and implementation of an algorithm for calculating the position of the drone or determining the source of laser radiation.
    • Execution of experiments and summary of achieved results.
    Participation in major international conferences and publication in professional or scientific journals is expected. An internship abroad is possible and strongly supported. This work will be solved in cooperation with the Czech Army.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

20. Development of retention classification for Long Term Memory (LTM)

    Problem Statement: Learning and memory are two fundamental inter-related cognitive processes. Learning is the change in behavior because of an experience; while memory is the ability to store and recall learned experience (which deals with Long Term Memory). Therefore, it is vital to assess the capabilities of long-term memory of an individual and provide relevant classification (ability to remember, speed of recall).

    Issues with Current Solutions: Currently, the existing studies focus on short term memory. However, the corresponding studies related to long-term memory are much smaller in number. The main reason is that an experiment involving short term memory can be completed very quickly but an experiment involving long-term memory requires considerable time and effort.

    Challenges: Over the last few decades, researchers and educationalists have put in considerable efforts in developing content that can provide effective learning. Both 2D and 3D audio-visual content and now virtual reality content is being developed. The challenge lies in relating such content with the long-term human memory that could lead to effective learning.

    Solution: This research plans to utilize 2D and 3D educational content and develop a corresponding long-term memory classification and grading method. The method will be based on learning and measuring human memory performance by analyzing the neurophysiological effects (from brain EEG data) using 3D and virtual reality contents during learning and memorization process. The EEG dataset is already available for this project.

    Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

21. Diagnosis and Treatment Selection for Depression using EEG data

    Problem Statement: This research deals with depression or to be more specific - unipolar depression or Major depressive disorder (MDD). MDD is a common mental illness that may impede daily life functioning, hence creating problems during individual social and personal activities. Hence, it is critical to have the right diagnosis as well as treatment selection for MDD patients.

    Issues with Current Solutions: Currently, subjective questionnaires are used by clinicians to diagnose MDD. However, there are chances of misdiagnosis because of similar symptoms of bipolar disorder and schizophrenia. In addition, the treatment selection is done based on the experience of a clinician who prescribes an anti-depressant and then checks its efficacy for 4 to 6 weeks before deciding to either continue with same medicine or change to a new medicine. This is a repetitive process till the right medicine is found for the patient.

    Challenges:  Treatment management for major depressive disorder (MDD) has been a challenge including diagnosis and treatment selection because of similarities in symptoms of various diseases. Further, MDD is gender specific with different brain patterns for males and females.

    Solution: This research deals with the development of diagnostic and treatment selection method for MDD (Unipolar Depression). It will be based on objective measures of brain activities from EEG signal that can detect unipolar depression. The solution will be based on a machine learning approach inherently based on feature extraction from the recorded EEG data of the patients. This method will complement and assist psychiatrists in diagnosis of unipolar depression. The EEG dataset is already available for this project.

    Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

22. Embedded Systems for Video/Signal Processing

    The topic focuses embedded image, video and/or signal processing. Its main goal is to research capabilities of "smart" and "small" units that have such features that allow for their applications requiring smyll, hidden, distributed, low power, mechanically or climatically stressed systems suitable of processing of some signal input. Exploitation of such systems is perspective and wide and also client/server and/or cloud systems. The units themselves can be based on CPU/DSP/GPU, programmable hardware, or their combination. Smart cameras can be considered as well. Applications of interest include:

    • classification of images or objects using machine learning (AI) using traditional methods or through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, etc.),
    • parallel analysis of signal(s) and video (e.g. for robust detection of occurrence of object in industrial or surveillance applications),
    • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" (with focus on the technlogy) suitable e.g. for mobile technology and/or embedded systems,
    • other similar topics can be individually consulted and considered.

    A possibility exists in collaboration on grant projects, especially the newly submitted TAČR, H2020, ECSEL ones (potentially employment or scholarship possible).

    Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

23. Generate damage to synthetic fingerprints and analyze their quality

    The aim of this work is to generate various damages into synthetic fingerprints and analysis of their quality. The work will consist of:
    

    • Familiarization with biometric fingerprint recognition, synthetic fingerprint generators, various damages in fingerprints and fingerprint quality estimation methods.
    • Design and implementation of algorithms for inserting (simulating) different types of damage into synthetic fingerprints.
    • Design and implementation of methods for fingerprint quality estimation, ideally combined with existing methods.
    • Realization of experiments and summary of achieved results.
    Participation on major international conferences and publishing in scientific or scientific journals is expected. Foreign internship is possible and strongly supported. This thesis will be dealt with in cooperation with the Police of the Czech Republic.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

24. Graph Neural Networks for 3D Shape Analysis

    Deep convolution networks have been a clear trend of machine learning for image analysis in recent years. Neural networks can also be used for 3D image analysis, where the network works with 3D convolutions. However, this approach is problematic because of its huge memory and computational requirements.
    
    The aim of the dissertation thesis is to explore, analyze and design new architectures of Graph Neural Networks and approaches to their learning for 3D object shape recognition tasks, where the data set consists of various 3D data representations - e.g. 3D meshes, voxel representation, etc.
    Proposed methods will be applied in the projects on which the supervisor participates.

    Tutor: Španěl Michal, Ing., Ph.D.

25. Human-Drone Interaction in Specific Scenarios

    The use of drones in a number of specific situations is an important trend today not only in industry, but especially in security operations. This trend brings new problems in the effective communication of man with the drone, ie how to increase the pilot's efficiency, reduce his cognitive load, increase his orientation in a complex environment or when cooperating with multiple drones, etc.
    
    The aim of this work is to explore new possibilities in the field of effective and intuitive use of sensory data and their fusion with other available data sources in the interaction of the pilot with the drone. The solution requires:
    

    • analyze the needs for effective human-drone cooperation, especially specific situations for visualization and precise control,
    • to suggest solutions based on sensory data and other available data analys and fusion,
    • prepare experimental solutions,
    • perform experiments with users and evaluate these experiments.
    The technologies that the researcher should analyze and use experimentally include, in particular, the processing of sensory data and their fusion, visualization and interaction using advanced technologies such as mixed reality, gestures, etc. and autonomous controls and methods.
    
    The researcher will have at his disposal an experimental platform (drone with a Jetson computer unit), a device for interaction (glasses for AR, etc.) and a DroCo SW platform for data integration and visualization, which is being developed in the laboratories of the Robo@FIT research group.
    
    
    
    Participation in relevant international conferences and publication in professional or scientific journals is expected. Furthermore, cooperation with security forces, especially rescue services and the Police of the Czech Republic. Last but not least, participation in relevant domestic or foreign projects.
    
    
    
    
    More information after a personal meeting.

    Tutor: Beran Vítězslav, doc. Ing., Ph.D.

26. Human-Robot Interaction in Collaborative Environment

    The trend of the manufacturing industry is the introduction of collaborative robots into production, which allows for closer human-robot cooperation. The aim is to streamline production by using robots for repetitive activities and workers for complex activities, their robotization would be too expensive and not very scalable. This trend brings new problems in how to communicate effectively with robots: to have an idea of the state of the robot and its understanding of the situation, and to control and program the robot easily and naturally.
    
    The aim of this work is to explore new possibilities of human-robot communication using modern technologies and devices. The solution requires:
    

    • Analyze user needs and available technologies and for information visualization,
    • suggest ways of interaction for selected tasks,
    • prepare experimental solutions,
    • perform experiments with users and evaluate these experiments.
    Technologies suitable for interaction that the researcher should analyze and use experimentally include, in particular, augmented reality (projected or in a mobile device), contactless technologies (sensing of human activities and condition by cameras or depth sensors, muscle activity sensors), haptic and audio feedback apod.
    
    The researcher will have at his disposal an experimental workplace for close human-robot cooperation, which is being developed in the laboratories of the Robo@FIT research group.
    
    Participation in relevant international conferences and publication in scientific journals is expected. Furthermore, cooperation with industrial partners in the field. Last but not least, participation in relevant domestic or foreign projects.
    
    More information after a personal meeting.

    Tutor: Beran Vítězslav, doc. Ing., Ph.D.

27. Image and video quality assessment metrics

    The project deals with image and video quality assessment metrics (IQM). The aim is to explore new ways how to incorporate human visual system properties into IQM. In particular, we will consider perception of HDR images, and utilization of additional knowledge (in form of metadata, 3D information, etc.) about the tested scenes using machine learning (e.g. neural networks).
    

    • Further information: http://cadik.posvete.cz/iqm/
    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland)

    Tutor: Čadík Martin, doc. Ing., Ph.D.

28. Image Processing using Neural Networks

    The project is concerned with advanced methods of image processing. The aim is to research new methods using machine learning, in particular deep convolutional neural networks.
    

    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

    Tutor: Čadík Martin, doc. Ing., Ph.D.

29. Improvement of cyberthreat detection

    This topic focuses on the process and techniques used for cyberthreat detection, obtaining and representation of threat data. The goal of this dissertation thesis is to study the current techniques and propose a solution that effectively improves the process and its results usable for malware detection and threat intelligence.
    
    

    Co-supervised by  dr. Kamil Malinka.

    Tutor: Hanáček Petr, doc. Dr. Ing.

30. Information Extraction from the WWW

    The topic of identifying and extracting specific information from documents on the Web has been the subject of intensive research for quite a long time. The basic obstacles that make this problem difficult are the loose structure of HTML documents and absence of meta-information (annotations) useful for recognizing the content semantics. This missing information is therefore compensated by the analysis of various aspects of web documents that include especially the following:

    • Document HTML code (DOM)
    • Document Text (Keyword Search, Statistical Text Analysis, Natural Language Processing Methods)
    • Visual organization (page content layout, visual properties)

    A background knowledge about the target domain and the commonly used presentation patterns is also necessary for successful information extraction. This knowledge allows a more precise recognition of the individual information fields in the document body.

    Current approaches to information extraction from web documents focus mainly modeling and analyzing the documents themselves; modeling the target information for more precise recognition has not yet been examined in detail in this context. The assumed goals of the dissertation are therefore the following:

    • Analysis of existing domain models such as UML class diagrams, E-R diagrams or ontology.
    • Extending these models with the specification of recognizing particular data in documents (e.g. regular expressions, advanced text classification).
    • Design of information extraction methods based on a comparison of the structure of the information presented in the document and the expected structure of the target information.

    Experimental implementation of the proposed methods using existing tools and experimental evaluation on real-world documents available on the WWW is also an integral part of the solution.

    Tutor: Burget Radek, doc. Ing., Ph.D.

31. Information Extraction from the WWW

    The topic of identifying and extracting specific information from documents on the Web has been the subject of intensive research for quite a long time. The basic obstacles that make this problem difficult are the loose structure of HTML documents and absence of meta-information (annotations) useful for recognizing the content semantics. This missing information is therefore compensated by the analysis of various aspects of web documents that include especially the following:

    • Document HTML code (DOM)
    • Document Text (Keyword Search, Statistical Text Analysis, Natural Language Processing Methods)
    • Visual organization (page content layout, visual properties)

    A background knowledge about the target domain and the commonly used presentation patterns is also necessary for successful information extraction. This knowledge allows a more precise recognition of the individual information fields in the document body.

    Current approaches to information extraction from web documents focus mainly modeling and analyzing the documents themselves; modeling the target information for more precise recognition has not yet been examined in detail in this context. The assumed goals of the dissertation are therefore the following:

    • Analysis of existing domain models such as UML class diagrams, E-R diagrams or ontology.
    • Extending these models with the specification of recognizing particular data in documents (e.g. regular expressions, advanced text classification).
    • Design of information extraction methods based on a comparison of the structure of the information presented in the document and the expected structure of the target information.

    Experimental implementation of the proposed methods using existing tools and experimental evaluation on real-world documents available on the WWW is also an integral part of the solution.

    Tutor: Burget Radek, doc. Ing., Ph.D.

32. Intelligent data analysis

    Thanks to the deployment of new technologies in our daily lives, a huge amount of data of various types is constantly being generated. The created datasets need to be processed efficiently - the information contained in them often supports the correctness and accuracy of decision-making processes. The development of data analysis methods is therefore an important part of IT research. For various reasons, traditional processing methods are not generally applicable, so new approaches need to be sought. They are mostly based on artificial intelligence and machine learning.

    Intelligent data analysis has great potential for solving current research tasks in many areas, including the energy domain. This field is undergoing great changes - renewable energy sources, batteries and electromobility convert the nature of the electricity grid and the classic one-way centralized network is becoming a two-way distributed network. This fact raises a number of research questions and problems to solve. Research topics include:

    - optimal management of microgrids (small energy networks with renewable energy sources) enabling energy sharing between customers,

    - predicting customer consumption and renewable production needed for efficient microgrid management,

    - disaggregation of energy consumption into consumption of individual appliances, which will allow a better understanding of the nature of consumer consumption,

    - investigation of anomalies and / or extreme values in the consumption or production of energy from renewable sources.

    However, data analysis in a broader sense can be of interest - it is possible to focus on the tasks of prediction, clustering, classification or detection of anomalies in different domains.

    
    

    Relevant publications: 

    • Rozinajova, V., Bou Ezzeddine, A., Grmanova, G., Vrablecova, P., Pomffyova, M.(2020): Intelligent Analysis of Data Streams, Published in: Towards Digital Intelligence Society: A Knowledge-based Approach, Publish date 22 December 2020

    • Kloska M., Rozinajova V. (2021): Towards Symbolic Time Series Representation Improved by Kernel Density Estimators. In: Hameurlain A., Tjoa A.M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems L. Lecture Notes in Computer Science, vol 12930. Springer, Berlin, Heidelberg.

    Tutor: Rozinajová Věra, Doc., Ph.D.

33. Internet of Things Security Analysis

    Dissertation focuses on the security of IoT systems. The goals of this work: studying the theory of IoT systems, their properties and possibilities of attacks, testing the basic types of attacks, designing a new method of protection, experiments, evaluation of results and design of further research.
    Co-supervised by dr. Kamil Malinka.

    Tutor: Hanáček Petr, doc. Dr. Ing.

34. Machine Learning for Information Identification on the Web

    Although there are technologies that allow publishing data on the WWW in machine-readable form (such as JSON-LD, RDFa, etc.), a large amount of structured data is still published on the web in the form of plain HTML/CSS code, which greatly limits the possibilities of their further use.
    
    Recently, new machine learning methods (especially deep learning methods) are gaining importance, which show interesting results, e.g., in recognizing important entities in weakly structured or unstructured data (e.g., text or images). However, the area of web document processing has not received much attention from this perspective. Existing works deal with the identification of simple data items and neglect structured data and more complex usage scenarios.
    
    The goal of this topic is to analyze and develop web content models suitable as input for machine learning and, at the same time, machine learning methods suitable for recognizing structured data in web documents.

    Tutor: Burget Radek, doc. Ing., Ph.D.

35. Machine learning in security

    Množstvo problémov týkajúcich sa bezpečnosti je potrebné riešiť prostredníctvom metód založených na umelej inteligencii a s pomocou strojového učenia. Medzi aktuálne výskumné problémy v oblasti informačnej bezpečnosti patrí analýza údajov veľkých bezpečnostných logov, detekcia širokej škály anomálií pri sieťovej komunikácii, detekcia škodlivého správania. 

    Jednou z aktuálnych výziev umelej inteligencie, je učenie bez potreby označenej dátovej sady. Dnešné modely väčšinou dosahujú vynikajúce výsledky, ale veľa krát ich dosahujú vďaka veľkým množstvám označených trénovacích vzorov. Existujú ale problémy, pri ktorých by vytvorenie takejto dátovej sady bolo veľmi nákladné, alebo sa vytvoriť vôbec nedá. Prístupy učenia posilňovaním (reinforcement learning) umožňujú učiť agenta vykonávať akcie tak, aby dosiahol svoj cieľ, pričom sa snaží maximalizovať svoje odmeny. Takýto agent nepotrebuje označenú dátovú sadu, postačí mu len vonkajšie ohodnotenie jeho správania (odmena za dobré rozhodnutia, trest za zlé).

    
    

    Výzvou je interpretovateľnosť strojového učenia pri riešeniach bezpečnosti a dôvery.

    
    

    Súvisiace publikácie:

    • CHUDÁ, Daniela - KRÁTKY , Peter - BURDA, Kamil. Biometric Properties of Mouse Interaction Features on the Web. In Interacting with Computers : The Interdisciplinary Journal of Human-Computer Interaction. Vol. 30, iss. 5 (2018), s. 359-377.  https://doi.org/10.1093/iwc/iwy015 

    • P. Lacko, V. Kvasnička. Mixture of Expert Used to Learn Game Play. In: Kůrková V., Neruda R., Koutník J. (eds) Artificial Neural Networks - ICANN 2008, Lecture Notes in Computer Science, vol 5163. Springer. https://doi.org/10.1007/978-3-540-87536-9_24

    
    

    Výskum bude doktorand vykonávať v rámci Kempelenovho inštitútu inteligentných technológií (KInIT, https://kinit.sk) v Bratislave v spolupráci s priemyselnými partnermi alebo výskumníkmi zo svetovo uznávaných výskumných skupín. Predpokladá sa kombinovaná (externá) forma štúdia a pracovný pomer na plný úväzok v KInIT.

    Tutor: Chudá Daniela, doc. Mgr., Ph.D.

36. Machine learning with human in the loop

    The models created in machine learning can only be as good as the data on which they are trained. Researchers and practitioners thus strive to provide their training processes with the best data possible. It is not uncommon to spend much human effort in achieving upfront good general data quality (e.g. through annotation). Yet sometimes, upfront dataset preparation cannot be done properly, sufficiently or at all. 
    

    In such cases the solutions, colloquially denoted as human-in-the-loop solutions, employ the human effort in improving the machine learned models through actions taken during the training process and/or during the deployment of the modes (e.g. user feedback on automated translations). They are particularly useful for surgical improvements of training data through identification and resolving of border cases. This is also directly related to explainability and interpretability of models.

    
    

    Human-in-the-loop approaches draw from a wide palette of techniques, including active and interactive learning, human computation, crowdsourcing (also with motivation schemes of gamification and serious games), and collective intelligence. Each of these fields (or combination thereof) presents opportunities for new discoveries. They border on computer science disciplines such as data visualization, user experience (usability in particular) and software engineering.

    
    

    The domains of application of machine learning with human-in-the-loop are predominantly those with a lot of heterogeneity and volatility of data. Such domains include online false information detection, online information spreading (including spreading of narratives or memes), auditing of social media algorithms and their tendencies for disinformation spreading, support of manual/automated fact-checking and more.

    
    

    Relevant publications:

    • M. Tomlein, B. Pecher, J. Simko, I. Srba, R. Moro, E. Stefancova, M. Kompan, A. Hrckova, J. Podrouzek, and M. Bielikova. An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes. In Fifteenth ACM Conference on Recommender Systems (pp. 1-11). 2021, September. https://dl.acm.org/doi/10.1145/3460231.3474241 

    • J. Šimko and M. Bieliková. Semantic Acquisition Games: Harnessing Manpower for Creating Semantics. 1st Edition. Springer Int. Publ. Switzerland. 150 p. https://link.springer.com/book/10.1007/978-3-319-06115-3 

    
    

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with industrial partners or researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

    Tutor: Šimko Jakub, doc. Ing., PhD.

37. Machine learning with limited (labelled) data

    Ubiquity of computing is nowadays generating vast datasets potentially usable for training machine learned models. Yet, these data mostly come not annotated, i.e. without labels necessary for their use for supervised (most conventional) training methods (e.g. classification). Conventional acquisition of proper labels using human force is a costly endeavour. The result is that proper labels are either unavailable, few or of poor quality.

    To circumvent the issue, many approaches are emerging and are presently researched by many researchers: meta-learning, transfer-learning, weakly supervised learning, zero/one-shot learning, semi-supervised learning. Each of these fields (or combination thereof) presents opportunities for new discoveries. Orthogonal to this, explainability and interpretability of models is an important factor to consider and advances in this regard are welcome (generally in AI and particularly in the mentioned approaches).

    There are several domains of applications, where research of methods and models for addressing small labelled data can be applied. These include (but are not limited to) false information detection, auditing of social media algorithms and their tendencies for disinformation spreading, and support of manual/automated fact-checking.

    Relevant publications:

    • B. Pecher, I. Srba, and M. Bielikova. Learning to Detect Misinformation Using Meta-Learning. Presented in PhD Forum by B. Pechcer at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML-PKDD 2020.

    • M. Pikuliak, M. Simko, M. Bielikova. Cross-Lingual Learning for Text Processing: A Survey Expert Systems With Applications or its open access mirror. Expert Systems with Applications. Vol. 165, 2021. https://doi.org/10.1016/j.eswa.2020.113765 

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with industrial partners or researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

    Tutor: Bieliková Mária, prof. Ing., Ph.D.

38. Modern Algorithms of Computer Graphics

    The topic concerns algorithms of computer graphics and image synthesis. Its main goal is to research new algorithms so that their features and application possibilities are better understood so that they are improved or newly created.  If suitable, it is possible to work on various platforms, includeing parallel CPUs, such as x86/64, ARM, Xeon PHI, GPU, etc. or other cores in CUDA, OpenCl, VHDL, etc. Algorithms of interest include:

    • rendering using selected computer graphics methods (such as ray tracing, photon mapping, direct rendering of "point clouds", etc.),
    • modeling of scenes and redering using artificial intelligence, including image synthesis using neural netowrks (especially CNNs),
    • processing and rendering of "lightfield" images, their acquisition, or possibly compression, reconstruction of 3D scenes from images and/or video, eventually also fusing with other sensors, such as LIDAR or RADAR,
    • modern algorithms of geometry suitable for applications in cpmputer graphics and perhaps also 3D printing,
    • emerging algorithms of 3D synthesis, holography, wavelet transform, frequency transform, etc.

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, H2020, ECSEL possible (employment or scholarship).

    Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

39. Modern Methods of Computer Vision
    • Use of modern and promising approaches to computer vision, especially methods of machine learning and convolutional neural networks
    • Identification of promising open problems
    • Design and development of non-traditional modifications of existing approaches
    • Experimental evaluation, use of existing data sets and collection of new ones

    Tutor: Herout Adam, prof. Ing., Ph.D.

40. Modern Methods of Computer Vision
    • Use of modern and promising approaches to computer vision, especially methods of machine learning and convolutional neural networks
    • Identification of promising open problems
    • Design and development of non-traditional modifications of existing approaches
    • Experimental evaluation, use of existing data sets and collection of new ones

    Tutor: Herout Adam, prof. Ing., Ph.D.

41. Multimodal analysis for assessment of mental health

    Problem Statement: The importance of mental health has increased significantly over the past decade. However, the methods for the assessment of mental health issues at early stages are still in their infancy compared to the availability of corresponding methods for early assessment of physical health issues. Hence, it is required that due research is done to develop methods for early assessment of abnormalities leading to mental health problems.

    Issues with Current Solutions: Unlike physical health parameters, the mental health is assessed through a number of subjective parameters. Hence, there is lack of objective and quantitative methods for mental health assessments. In addition, the patients seek help when their mental health problem is at advanced stage. So, there is lack of continuous monitoring for mental health issues.

    Challenges: Many of the abnormalities related to mental health issues are subtle in nature and are related to behavior and other changes in facial expressions, speech and handwriting. In addition, there are changes in cortisol levels, skin conductance, heart rate variability and breathing rate. Hence, there are multiple modalities that should be included for measuring and quantifying any abnormalities related to mental health.

    Solution: Every modality has its pros and cons. For example, in neuroimaging, functional magnetic resonance imaging has high spatial resolution (in mm) and low temporal resolution (in seconds) while electroencephalogram has low spatial resolution (in cm) and high temporal resolution (in milliseconds). Combining both of them will result in high spatial as well as high temporal resolution. This research deals with the assessment of abnormalities leading to mental health problems by utilizing multimodal approach. The various modalities may include, but not limited to, electroencephalogram (EEG) brain signals, facial videos, speech audios, handwriting and text from social media. The physiological parameters from various modalities include, but not limited to, the heart rate, breathing rate, dominant emotion, fatigue and stress. Dominant emotion can be classified as positive or negative and then sub-classified as sad, happy, angry etc. Data mining and data fusion techniques will be developed for this multimodal analysis. The corresponding multimodal data is available for this project.

    Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

42. Natural language processing

    Continuous increase of data available world-wide emphasizes the need of its automatic processing and understanding. Particular challenges are posed by the heterogeneous and unstructured nature of text content provided in natural language. Natural language processing (NLP) ranks among the most prospective subfields of artificial intelligence with great potential for innovative applications affecting everyday life.

    Recent advances in neural networks and machine learning allowed to push efficiency and scope of natural language understanding and generation forward. Yet, there remain many research challenges related to particular subtasks, application domains and languages. Further research and various resulting phenomena exploration is necessary. Special attention is drawn by the issues of interpretability and transparency of NLP models or by novel paradigms of learning addressing the problem of low-resource languages.

    Particularly interesting challenges include, but are not limited to:

    • Low-resource language processing

    • Transfer/multilingual learning

    • Fairness, interpretability, transparency, explainability and/or robustness for NLP

    • Domain-specific information extraction, text classification

    • Visual grounding of natural language, image captioning, multimodal data processing

    • Deep learning for NLP

    Relevant publications:

    • M. Pikuliak, M. Šimko, M. Bieliková. Cross-lingual learning for text processing: A survey. Expert Systems with Applications, 2021. https://doi.org/10.1016/j.eswa.2020.113765

    • P. Korenek, M. Šimko. Sentiment analysis on microblog utilizing appraisal theory. World Wide Web, 2014. https://doi.org/10.1007/s11280-013-0247-z

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT) in Bratislava in cooperation with industrial partners or researchers from highly respected research units from abroad. A combined (external) form of study and full employment at KInIT is expected.

    Tutor: Šimko Marián, doc. Ing., Ph.D.

43. Presentation attack detection mechanism for biometric system based on fingerprints / face / handwriting

    The aim of the work is to create a reliable detection mechanism of the presentation attack on the technology of fingerprints, faces and handwriting, namely:
    
    

    • Introduction to biometric fingerprint, face and handwriting systems.
    • Design and implementation of a presentation attack detection algorithm for biometric fingerprint, face and handwriting technology.
    • Execution of experiments and summary of achieved results.
    Participation in major international conferences and publication in professional or scientific journals is expected. An internship abroad is possible and strongly supported. This work will be solved in cooperation with the Police of the Czech Republic.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

44. Recommender and adaptive web-based systems

    The recommender systems are an integral part of almost every modern Web application. Personalized, or at least adaptive, services have become a standard that is expected by the users in almost every domain (e.g., news, chatbots, social media, or search).

    Obviously, personalization has a great impact on the everyday life of hundreds of million users across many domains and applications. This results in a major challenge - to propose methods that are not only accurate but also trustworthy and fair. Such a goal offers plenty of research opportunities in many directions:

    • Novel machine learning approaches for adaptive and recommender systems

    • Off-policy learning

    • Explaining recommendations

    • Fairness and justice in recommendations

    • Biases in the recommendations

    There are several application domains where these research problems can be addressed, e.g., search, e-commerce, news, and many others.

    Relevant publications:

    • M. Kompan, P. Gaspar, J. Macina, M. Cimerman, M. Bielikova. Exploring Customer Price Preference and Product Profit Role in Recommender Systems. In IEEE Intelligent Systems int. Journal, 2021. https://doi.org/10.1109/MIS.2021.3092768 

    • M. Svrcek, M. Kompan, M. Bielikova. Towards Understandable Personalized Recommendations: Hybrid Explanations. In Computer Science and Information Systems. Vol. 16, No. 1, 2019. https://doi.org/10.2298/CSIS171217012S 

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT) in Bratislava in cooperation with industrial partners or researchers from highly respected research units from abroad. A combined (external) form of study and full employment at KInIT is expected. 

    Tutor: Bieliková Mária, prof. Ing., Ph.D.

45. Recommender and adaptive web-based systems

    The recommender systems are an integral part of almost every modern Web application. Personalized, or at least adaptive, services have become a standard that is expected by the users in almost every domain (e.g., news, chatbots, social media, or search).

    Obviously, personalization has a great impact on the everyday life of hundreds of million users across many domains and applications. This results in a major challenge - to propose methods that are not only accurate but also trustworthy and fair. Such a goal offers plenty of research opportunities in many directions:

    • Novel machine learning approaches for adaptive and recommender systems

    • Off-policy learning

    • Explaining recommendations

    • Fairness and justice in recommendations

    • Biases in the recommendations

    There are several application domains where these research problems can be addressed, e.g., search, e-commerce, news, and many others.

    Relevant publications:

    • M. Kompan, P. Gaspar, J. Macina, M. Cimerman, M. Bielikova. Exploring Customer Price Preference and Product Profit Role in Recommender Systems. In IEEE Intelligent Systems int. Journal, 2021. https://doi.org/10.1109/MIS.2021.3092768 

    • M. Svrcek, M. Kompan, M. Bielikova. Towards Understandable Personalized Recommendations: Hybrid Explanations. In Computer Science and Information Systems. Vol. 16, No. 1, 2019. https://doi.org/10.2298/CSIS171217012S 

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT) in Bratislava in cooperation with industrial partners or researchers from highly respected research units from abroad. A combined (external) form of study and full employment at KInIT is expected. 

    Tutor: Kompan Michal, doc. Ing., PhD.

46. Reconstruction of damaged surfaces of CD/DVD/BR/HDD for forensic purposes

    This thesis is actually not supported by any grant. Anyway, this thesis will be dealt with in cooperation with the Police of the Czech Republic. Thus, we can expect support to get financed by a granted project.
    
    Moreover, foreign internship is possible and strongly supported.
    
    The aim of the thesis is reconstruction of damaged CD/DVD/BR/HDD surfaces, consisting of:
    

    • Getting to know all possible ways of recording on CD/DVD/BR/HDD media.
    • Usage of advanced scanning technology and techniques of surfaces - both at and out of the faculty.
    • Design and implementation of an application for composition of gained images into a single complex - as much automated as possible.
    • If gained complex is sufficiently good then the application must be able to provide restored data - e.g. for CD an ISO image file should be created (missing information will be filled with "smart" values).
    • Implementation of experiments and summary of achieved results.
    Participation on major international conferences and publishing in scientific or scientific journals is expected.
    

    Tutor: Kolář Dušan, doc. Dr. Ing.

47. Reliable matching of 2D face with 3D face projection

    The aim of this work is to create a reliable matching of 2D face with 3D face projection, namely:
    

    • Introduction to work with 3D model and axes of rotation
    • Design and implementation of the algorithm for determining the correct rotation of the 3D head model according to the 2D face layout
    • Design and implementation of 2D face matching algorithm with 3D face projection with the same parameters
    • Conduct experiments and summarize the results
    Participation in important international conferences and publication in research or scientific journals is expected. Foreign internship is possible and strongly supported. This work will be solved in cooperation with the Police of the Czech Republic.

    Tutor: Drahanský Martin, prof. Ing., Ph.D.

48. Security Analysis of IoT Communication

    
    Internet of Things is a communication platform that interconnects different types of devices in smart homes, smart buildings, smart hospitals, traffic control systems, etc. IoT devices produce a large amount of monitoring data that include both network status data and application data. By longitudinal monitoring we can create a profile of the IoT device and then observe deviation in its behavior which is important for failure detection or identification of cyber attacks.
    
    The topic focuses on security analysis of IoT communication.  The goal of this project is to propose a automated method for creating IoT device profiles based on monitoring data and demonstrate how these profiles can be exploited for anomaly detection.
    
    
    Co-supervisor: Matoušek Petr, Ing., Ph.D., M.A.

    Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

49. Self-operating Computer Networks

    
    The idea of developing a smart method supporting a network design, monitoring, and management is not new. However, recent advancements in the AI domain and the introduction of P4 for network device programmability creates a promising environment to achieve this goal.
    In this context, self-awareness means the ability of a network to observe its state and improve its behavior to meet the stated objectives [1]. Though the primary goal of each network is to deliver data packets, there are also other important non-functional requirements, notably, QoS, security, reducing energy consumption. 
    
    Project Goal
    
    This work aims to design a network system able to observe the situation and by applying an AI approach to decide about the next behavior to keep providing reliable function even when under cyberattack. The project assumes P4 as the technology enabler to achieve the expected goals.
    
    Research Approach
    
    The research consists of the following major steps:
    

    • Survey the existing approaches to cognitive/self-aware networking.
    • Identify the suitable AI/ML algorithms for sensed data processing and behavior alteration decisions.
    • Experiment with proposed approaches within the created testbed environment that involves P4 devices.
    • Perform critical evaluation of the achieved results.
    
    References:
    
    
    [1] Gelenbe, E., Domanska, J., Frohlich, P., Nowak, M. P., & Nowak, S. (2020). Self-Aware Networks That Optimize Security, QoS, and Energy. Proceedings of the IEEE, 108(7), 1150-1167. https://doi.org/10.1109/JPROC.2020.2992559

    Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

50. Semantic Web Technology Application for Document Analysis

    Semantic web technology allows the representation of information and knowledge for the purpose of its further sharing, for example, in computer applications. Available knowledge databases, such as DBPedia or Wikidata, contain a great amount of useful information and facts. On the current web, however, most of the new information is published in the form of documents most often in HTML, whose further processing is problematic mainly due to their free structure and the absence of explicit information about the meaning of individual parts of the content. There exist two ways to overcome this gap between the classical and the semantic web:
    

    • Use the structured knowledge available in semantic repositories as a background knowledge to refine web document analytics and to identify new information in their content, and, on the other hand
    • Integrate web documents as an information source into knowledge databases.

    To achieve both these goals, it is necessary to analyze the capabilities of existing ontological models regarding the modelling of the target domains and mapping these descriptions on the content of real-world web pages and documents. Possible applications include, but are not limited to:

    • Recognizing the topic of an entire document or its individual parts by keywords analysis and mapping to concepts in a semantic database.
    • Recognizing structured data in documents based on their alignment with existing domain models - ontologies.
    • Adding and updating information in semantic databases from web sources.

    Experimental implementation of the proposed methods using the existing tools and experimental evaluation on real-world data and documents is also an integral part of the solution.

    Tutor: Burget Radek, doc. Ing., Ph.D.

51. Speech recognition and data processing for ATC-pilot communication in aviation

    The topic of the work is focused on speech recognition and data processing for ATC-pilot communication in aviation. It will cover all components of automatic speech recognition (ASR), i.e. data processing, acoustic model, vocabulary (including special aviation terminology) and language model.
    
    The topic is related to the European project Horizon 2020 ATCO2, and will be elaborated in cooperation with project partners. 
    
    The assignment requires an interest in mathematics, statistics, machine learning and speech processing, the advantage is proficiency in Python and its libraries for machine learning.

    Tutor: Černocký Jan, prof. Dr. Ing.

52. Speech recognition and data processing for ATC-pilot communication in aviation

    The topic of the work is focused on speech recognition and data processing for ATC-pilot communication in aviation. It will cover all components of automatic speech recognition (ASR), i.e. data processing, acoustic model, vocabulary (including special aviation terminology) and language model.
    
    The topic is related to the European project Horizon 2020 ATCO2, and will be elaborated in cooperation with project partners. 
    
    The assignment requires an interest in mathematics, statistics, machine learning and speech processing, the advantage is proficiency in Python and its libraries for machine learning.

    Tutor: Černocký Jan, prof. Dr. Ing.

53. Visual Geo-Localization and Augmented Reality on Mobile Devices

    The project deals with geo-localization of mobile devices in unknown environments using computer vision and computer graphics methods. The aim is to investigate and develop new image registration techniques (with geo-localized image database or 3D terrain model). The goal is an efficient implementation of proposed methods on mobile devices as well as search for additional applications in the area of image processing, computational photography, and augmented reality.
    

    • Further information: http://cadik.posvete.cz/locate/
    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

    Tutor: Čadík Martin, doc. Ing., Ph.D.

54. Voice biometry

    The objective of this PhD thesis focuses on the voice biometry, i.e. advanced person identification from voice content. 
    
    Recent advances in deep learning give a potential to significantly increase performance of person identification systems from various type of biological signals. This thesis primarily aims to advance speaker identification technologies for applicability in law enforcement, while taking into account apriori knowledge provided by end-users. Typical case is a combination of intercepted voice calls with call detail records (i.e. network built from telephone numbers). 
    
    PhD thesis will also address known problems of machine learning algorithms such as "bias" provided by training data.

    Tutor: Motlíček Petr, doc. Ing., Ph.D.

55. Voice deepfake

    
    Recent advances in Deep Learning enabled for the automatic generation of hyper-realistic fake media content known as Deepfakes. 
    While this technology has positive potential for entertainment applications, its malicious use has been already reported to harm citizens and the 
    society at large by producing obscene content, broadcasting fake news to undermine elections or de-stabilise politics and improving social 
    engineering to commit financial fraud. The severity of the problem calls for the urgent development of Deepfake detection technologies for media companies, multimedia news providers and Law Enforcement Agencies. Also as Deepfakes encompasses several categories of synthetically altered or generated media, a secondary objective is to identify the type of manipulation and the specific technology employed for that purpose, this is referred to as attribution.
     
    The objective of this PhD thesis focuses on the "voice" part of the media content and aims to develop "voice" Deepfake detection and attribution 
    algorithms. 
    Novel research directions in one class modeling, spatio-temporal learning (i.e. combination with semantic knowledge), few-shot learning, 
    and adversrial training will be explored. 

    Tutor: Motlíček Petr, doc. Ing., Ph.D.