Lidé na fakultě
doc. RNDr. Ing. Marcel Jiřina, Ph.D.

2023, ICAT-EGVE 2023: International Conference on Artificial Reality and Telexistence & Eurographics Symposium on Virtual Environments, Saarbrücken, Eurographics Association), p. 95-104), ISBN 978-3-03868-218-9, ISSN 1727-530X

In this research, we compared the precision and ease of use of hand-tracking with one and three optical sensors. We created two test scenes that simulated real-life scenarios, one focusing on smoothness and intuitiveness and the other one focusing on precision and tracking range. We conducted tests with 25 participants and measured the precision and effectiveness of their work with basic user interface elements and 3D objects. This research showed that using multiple optical sensors for hand-tracking greatly improves the precision of the tracking, widens the tracking range and provides more smooth interaction. On average, 80% of the users preferred using three sensors for the interaction because it allowed more users to finish the tasks (74% on average) in a shorter time (15% on average) and with more precise results (43% on average) compared to same tasks done with just one sensor.

Stať ve sborníku z mezinár. konf.

2022, Mezinárodní Masarykova konference pro doktorandy a mladé vědecké pracovníky, Hradec Králové, Akademické sdružení MAGNANIMITAS), p. 1083-1090), ISBN 978-80-87952-37-5

Příspěvek se zabývá problematikou radikalizačního procesu v kontextech terorismu a případů extrémního násilí. Představuje projekt bezpečnostního výzkumu podporovaný ministerstvem vnitra České republiky Detekce radikalizace v kontextu ochrany obyvatelstva a měkkých cílů před násilnými incidenty. Textu informuje o metodě detekce indikátorů radikalizace u potencionálně rizikových osob za pomocí souboru indikátorů a softwarové aplikace

Stať ve sborníku z mezinár. konf. česky

2021, Proceedings of 7th IEEE International Conference on Virtual Reality, Beijing, IEEE), p. 19-25), ISBN 9781665423090, ISSN 2331-9569

Finding a simple and precise way to control the virtual environment is one of the goals of a lot of human-computer interaction research. One of the approaches is using a Leap Motion optical sensor, which provides hand and finger tracking without the need for any hand-held device. However, the Leap Motion system currently supports only one sensor at a time. To overcome this limitation, we proposed a set of algorithms to combine the data from multiple Leap Motion sensors to increase the precision and the usability of hand tracking. First, we suggested a way how to improve the calibration of the current hand pose alignment proposed by Leap Motion. Then, we proposed an approach to fuse the tracking data from multiple Leap Motion sensors to provide more precise interaction with the virtual world. For this, we implemented our very own algorithm for computing the confidence level of the tracking data that can be used to distinguish which Leap Motion sensor detects the tracked hands best. We implemented those algorithms into our MultiLeap library. We also created two demo scenes that we used to validate the correctness of our work - one for evaluation of the fusing algorithms and one for mimicking the interaction with control panels in a helicopter cockpit.

Stať ve sborníku z prestižní konf.

2021, Proceedings of 2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), Beijing, IEEE), p. 77-83), ISBN 978-1-6654-3225-2

We present a concept that provides hand tracking for virtual and extended reality, only with the use of optical sensors, without the need for the user to hold any physical controller. In this article, we propose five new algorithms further to extend our previous research and the possibilities of the hand tracking system whilst also making it more precise. The first algorithm deals with the need to calibrate the tracking system. Thanks to the new approach, we improved tracking precision by 37% over our previous solution. The second algorithm allows us to compute the precision of the hand tracking data when multiple sensors are used. The third algorithm further improves the computation of hand tracking data confidence by correctly handling the edge cases, for example, when the tracked hand is at the edge of the sensor's field of view. The fourth algorithm provides a new way to fuse the hand tracking data by using only the hand tracking data with the highest hand tracking data confidence. The fifth algorithm deals with the issue when the optical sensor misclassifies the hand chirality.

Stať ve sborníku z prestižní konf.

2020, Biomedical Journal of Scientific & Technical Research, 28 (4), p. 21762-21765), ISSN 2574-1241

Each patient has his/her own individual characteristics. It is given by his/her main diagnosis and its detailed description (e.g., in oncology by TNM classification, grading, histological type etc.), comorbidities, results of genetic markers, biomarkers and many other factors. Standard patient care is usually based on the results of large clinical trials. This approach ignores the patient’s individuality. Using patient profiling based on tumor characteristics, genetic markers and biomarkers, it is possible to identify the drug with the highest clinical benefit for small patient groups where, according to the guidelines, treatment choice is no longer defined only by diagnosis and TNM classification. This procedure belongs to the area of personalized medicine. An important methodology is how to find similar patients to a newcomer and how to evaluate clinical benefits and risks of different types of therapies in this group of similar patients. The main focus is on overall survival, but time to progression and adverse reactions to treatment are also important. Based on extensive patient data available in electronic medical records (hospital information systems), similar cases to a newcomer can be identified and some statistics for these similar patients can be provided.

Článek v odborném recenzovaném periodiku

2020, Computational Science - ICCS 2020, Cham, Springer), p. 225-239), ISBN 978-3-030-50422-9, ISSN 0302-9743

Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction. Variational Autoencoder with Arbitrary Conditioning (VAEAC) and Generative Adversarial Imputation Network (GAIN) were researched as representatives of generative models, while the denoising autoencoder (DAE) represented non-generative models. Performance of the models is compared to traditional methods k-nearest neighbors (k-NN) and Multiple Imputation by Chained Equations (MICE). Moreover, we introduce WGAIN as the Wasserstein modification of GAIN, which turns out to be the best imputation model when the degree of missingness is less than or equal to 30%. Experiments were performed on real-world and artificial datasets with continuous features where different percentages of features, varying from 10% to 50%, were missing. Evaluation of algorithms was done by measuring the accuracy of the classification model previously trained on the uncorrupted dataset. The results show that GAIN and especially WGAIN are the best imputers regardless of the conditions. In general, they outperform or are comparative to MICE, k-NN, DAE, and VAEAC.

Stať ve sborníku z prestižní konf.

2020, PRESENCE: Virtual and Augmented Reality, 29, p. 37-90), ISSN 1054-7460

Virtual reality has been with us for several decades already, but we are still trying to find the right ways to control it. There are a lot of controllers with various purposes and means of input, each with its advantages and disadvantages, but also with specific ways to be handled. Our hands were the primary means of input for human-computer interaction for a long time. However, now we can use movements of our eyes, our feet or even our whole body to control the virtual environment, interact with it, or move from one place to another. We can achieve this with various controllers and wearable interfaces, like eye tracking, haptic suits or treadmills. There are numerous devices that we can choose from for every category, but sometimes it can be hard to pick the one that suits our intentions best. This article summarises all types of user interface controllers for virtual reality, with their main pros and cons and their comparison.

Článek v periodiku excerpovaném SCI Expanded

2020, ESANN 2020 - Proceedings, Louvain la Neuve, Ciaco - i6doc.com), p. 13-18), ISBN 978-2-87587-074-2

One task that is often discussed in a computer vision is the mapping of an image from one domain to a corresponding image in another domain known as image-to-image translation. Currently there are several approaches solving this task. In this paper, we present an enhancement of the UNIT framework that aids in removing its main drawbacks. More specifically, we introduce an additional adversarial discriminator on the latent representation used instead of VAE, which enforces the latent space distributions of both domains to be similar. On MNIST and USPS domain adaptation tasks, this approach greatly outperforms competing approaches.

Stať ve sborníku z prestižní konf. (Scopus)

2019, Computational Science – ICCS 2019, Springer, Cham), p. 207-220), ISBN 978-3-030-22744-9

In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values can be either on the level of individual instances or on the level of entire features. Both situations have a negative impact on the usability of the model on such a dataset. This paper focuses on the scenario where entire features are missing which can be understood as a specific case of transfer learning. Our aim is to experimentally research the influence of various imputation methods on the performance of several classification models. The imputation impact is researched on a combination of traditional methods such as k-NN, linear regression, and MICE compared to modern imputation methods such as multi-layer perceptron (MLP) and gradient boosted trees (XGBT). For linear regression, MLP, and XGBT we also propose two approaches to using them for multiple features imputation. The experiments were performed on both real world and artificial datasets with continuous features where different numbers of features, varying from one feature to 50%, were missing. The results show that MICE and linear regression are generally good imputers regardless of the conditions. On the other hand, the performance of MLP and XGBT is strongly dataset dependent. Their performance is the best in some cases, but more often they perform worse than MICE or linear regression.

Stať ve sborníku z prestižní konf.

2019

Souhrnná výzkumná zpráva obsahuje popis analýzy dostupných dat a jejich použitelnost pro behaviorální analýzu osob, dále obsahuje podrobný popis zkoumaných algoritmů a vyhodnocení jejich kvality a škálovatelnosti, a to nejen základních algoritmů, ale i jejich kombinaci (ensemblů), se kterými dále pracujeme. Následně je také pospána výsledná webová aplikace, která je hlavním softwarovým výstupem prezentující výsledky algoritmů a umožňující vlastní exploratorní analýzu uživatelem.

Výzkumná zpráva v češtině

2018, Data Management Technologies and Applications, Cham, Springer International Publishing), p. 3-26), ISBN 978-3-319-94809-6, ISSN 1865-0929

In practice we often encounter classification tasks. In order to solve these tasks, we need a sufficient amount of quality data for the construction of an accurate classification model. However, in some cases, the collection of quality data poses a demanding challenge in terms of time and finances. For example in the medical area, we encounter lack of data about patients. Transfer learning introduces the idea that a possible solution can be combining data from different domains represented by different feature spaces relating to the same task. We can also transfer knowledge from a different but related task that has been learned already. This overview focuses on the current progress in the novel area of asymmetric heterogeneous transfer learning. We discuss approaches and methods for solving these types of transfer learning tasks. Furthermore, we mention the most used metrics and the possibility of using metric or similarity learning.

Stať ve sborníku z prestižní konf. (Scopus)

2017, Proceedings of the 6th International Conference on Data Science, Technology and Applications, Porto, SciTePress - Science and Technology Publications), p. 17-27), ISBN 978-989-758-255-4

One of the main prerequisites in most machine learning and data mining tasks is that all available data originates from the same domain. In practice, we often can’t meet this requirement due to poor quality, unavailable data or missing data attributes (new task, e.g. cold-start problem). A possible solution can be the combination of data from different domains represented by different feature spaces, which relate to the same task. We can also transfer the knowledge from a different but related task that has been learned already. Such a solution is called transfer learning and it is very helpful in cases where collecting data is expensive, difficult or impossible. This overview focuses on the current progress in the new and unique area of transfer learning - asymmetric heterogeneous transfer learning. This type of transfer learning considers the same task solved using data from different feature spaces. Through suitable mappings between these different feature spaces we can get more data for solving data mining tasks. We discuss approaches and methods for solving this type of transfer learning tasks. Furthermore, we mention the most used metrics and the possibility of using metric or similarity learning.

Stať ve sborníku z prestižní konf.

2017

Cílem projektu bylo navázat na předchozí rozvojový projekt CESNETu 527/2014, který se zabýval analýzou phishingových zpráv, získáváním phishingových zpráv (včetně česky psaných) a návrhem vhodných algoritmů pro jejich detekci, a implementovat (napojit) navržené algoritmy do sítě CESNETu. Zde byly dále testovány a zpětná vazba z reálného provozu byla zohledňována při úpravě algoritmů, resp. Jejich dolaďování a doučování.

Technická zpráva v češtině