Conference Papers (KIV)
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Item Current Trends in Automated Test Case Generation(PTI, 2023) Potužák, Tomáš; Lipka, RichardThis paper is a survey of existing literature of the last two decades that deals with test data generation or with tests based on it. This survey is not a systematic literature review and it does not try to answer specific scientific questions formulated in advance. Its purpose is to map and categorize the existing methods and to summarize their common features. Such a survey can be helpful for any teams developing their methods for test data generation as it can be a starting point for the exploration of related work.
Item Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P300 Dataset(Springer, 2023) Khoshkhooy Titkanlou, Maryam; Mouček, RomanEvent-related potential signal classification is a really difficult challenge due to the low signal-to-noise ratio. Deep neural networks (DNN), which have been employed in different machine learning areas, are suitable for this type of classification. UNet (a convolutional neural network) is a classification algorithm proposed to improve the classification accuracy of P300 electroencephalogram (EEG) signals in a non-invasive brain-computer interface. The proposed UNet classification accuracy and precision were 64.5% for single-trial classification using a large P300 dataset of school-aged children, including 138 males and 112 females. We compare our results with the related literature and discuss limitations and future directions. Our proposed method performed better than traditional methods.
Item Tunneling and Replication in Hierarchical DFS(Springer, 2023) Pešička, Ladislav; Matějka, LubošNowadays, distance learning, 5G mobile networks, and 4K cameras in mobile phones generate a large amount of data consumed on demand. The storage systems must fulfill these demands. This chapter presents a counting algorithm to optimize access in the hierarchical distributed file system.Different access strategies, such as tunneling, replication, and reconnection, are discussed and used to optimize file access.Item A generic model of hyperspace curvature preservation for a dynamic radial basis function implicit surface(University of West Bohemia, 2023) Červenka, MartinThe primary objective of the proposed methodology is to be implemented in the domain of computerized muscle modelling. However, preserving shape under different deformation scenarios is crucial across numerous research domains. Consequently, this paper briefly exposes the problem in a broader context. Multiple approaches exist to describe the surface, each with inherent strengths and limitations. In this context, the implicit surface utilized for deformation will be characterized using a collection of radial basis functions (RBFs). Although the choice of RBF may vary depending on the specific domain, this paper employs the widely recognized Gaussian RBF. This selection ensures a smooth and infinitely differentiable surface model, which is advantageous for the intended purpose.Item Genetic-Algorithm-Based Road Traffic Network Division with Improved Refining(IEEE, 2024) Potužák, TomášIn this paper, a method for road traffic network division for distributed and/or parallel road traffic simulation is described. Similarly to its former version, it employs multi-level graph partitioning scheme with graph coarsening, where a genetic algorithm is used for initial partitioning. The version of the method described in this paper (Improved Dividing Genetic Algorithm with Graph Coarsening and Improved Refining – IDGA-GC-IR) incorporates improved refining phase. In order to investigate its performance, it was compared to a third-party division method using testing on five road traffic networks. The tests showed that the IDGA-GC-IR method outperforms the compared third-party method in some important aspects of the division (e.g., number of divided traffic lanes), but is significantly slower.Item A New Highly Efficient Preprocessing Algorithm for Convex Hull, Maximum Distance and Minimal Bounding Circle in E2: Efficiency Analysis(Springer, 2024) Skala, VáclavThis contribution describes an efficient and simple preprocessing algorithm for finding a convex hull, maximum distance of points or convex hull diameter, and the smallest enclosing circle in E2. The proposed algorithm is convenient for large data sets with unknown intervals and ranges of the data sets. It is based on efficient preprocessing, which significantly reduces points used in final processing by standard available algorithms.Item Design of BCI-Based Exoskeleton System for Knee Rehabilitation(ScitePress, 2024) Khoshkhooy Titkanlou, Maryam; Pham, Duc Thien; Mouček, RomanInjuries of the lower limb, particularly the knee, usually require several months of rehabilitation. Exoskeletons are great tools supporting the rehabilitation process; their research and suitable practical use are at the center of interest of researchers and physiotherapists. This paper focuses on designing a brain-computer-interface (BCI)-controlled exoskeleton for knee rehabilitation. It includes reviewing and selecting electroencephalography (EEG) acquisition methods, BCI paradigms, current acquisition devices, signal classification methods and techniques, and the target group of people for whom the exoskeleton will be suitable. Finally, the preliminary proposal of the exoskeleton is provided.Item MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain(INCOMA Ltd., 2023) Pašek, Jan; Sido, Jakub; Konopík, Miloslav; Pražák, OndřejThis work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites. Our multimodal model is trained on question descriptions and source codes in multiple programming languages. We design two new learning objectives to improve duplicate detection capabilities. The result of this work is a mature, fine-tuned Multimodal Question Duplicity Detection (MQDD) model, ready to be integrated into a Stack Overflow search system, where it can help users find answers for already answered questions. Alongside the MQDD model, we release two datasets related to the software engineering domain. The first Stack Overflow Dataset (SOD) represents a massive corpus of paired questions and answers. The second Stack Overflow Duplicity Dataset (SODD) contains data for training duplicate detection models.Item Towards Automatic Medical Report Classification in Czech(ScitePress, 2023) Přibáň, Pavel; Baloun, Josef; Martínek, Jiří; Lenc, Ladislav; Prantl, Martin; Král, PavelThis paper deals with the automatic classification of medical reports in the form of unstructured texts in Czech. The outcomes of this work are intended to be integrated into a coding assistant, a system that will help the clinical coders with the manual coding of the diagnoses. To solve this task, we compare several approaches based on deep neural networks. We compare the models in two different scenarios to show their advantages and drawbacks. The results demonstrate that hierarchical GRU with attention outperforms all other models in both cases. The experiments further show that the system can significantly reduce the workload of the operators and thus also saves time and money. To the best of our knowledge, this is the first attempt at automatic medical report classification in the Czech language.Item Comparative Analyses of Multilingual Sentiment Analysis Systems for News and Social Media(Springer, 2023) Přibáň, Pavel; Balahur, AlexandraIn this paper, we present evaluation of three in-house sentiment analysis (SA) systems originally designed for three distinct SA tasks, in a highly multilingual setting. For the evaluation, we collected a large number of available gold standard datasets, in different languages and varied text types. The aim of using different domain datasets was to achieve a clear snapshot of the level of overall performance of the systems and thus obtain a better quality of an evaluation. We compare the results obtained with the best performing systems evaluated on their basis and performed an in-depth error analysis. Based on the results, we can see that some systems perform better for different datasets and tasks than the ones they were designed for, showing that we could replace one system with another and gain an improvement in performance. Our results are hardly comparable with the original dataset results because the datasets often contain a different number of polarity classes than we used, and for some datasets, there are even no basic results. For the cases in which a comparison was possible, our results show that our systems perform very well in view of multilinguality.Item Global Optimisation for Improved Volume Tracking of Time-Varying Meshes(Springer, 2023) Dvořák, Jan; Hácha, Filip; Váša, LiborProcessing of deforming shapes represented by sequences of triangle meshes with connectivity varying in time is difficult, because of the lack of temporal correspondence information, which makes it hard to exploit the temporal coherence. Establishing surface correspondence is not an easy task either, especially since some surface patches may have no corresponding counterpart in some frames, due to self-contact. Previously, it has been shown that establishing sparse correspondence via tracking volume elements might be feasible, however, previous methods suffer from severe drawbacks, which lead to tracking artifacts that compromise the applicability of the results. In this paper, we propose a new, temporally global optimisation step, which allows to improve the intermediate results obtained via forward tracking. Together with an improved formulation of volume element affinity and a robust means of identifying and removing tracking irregularities, the procedure yields a substantially better model of temporal volume correspondence.Item Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks(ELRA and ICCL, 2024) Šmíd, Jakub; Přibáň, Pavel; Pražák, Ondřej; Král, PavelIn this paper, we introduce a novel Czech dataset for aspect-based sentiment analysis (ABSA), which consists of 3.1K manually annotated reviews from the restaurant domain. The dataset is built upon the older Czech dataset, which contained only separate labels for the basic ABSA tasks such as aspect term extraction or aspect polarity detection. Unlike its predecessor, our new dataset is specifically designed to allow its usage for more complex tasks, e.g. target-aspect-category detection. These advanced tasks require a unified annotation format, seamlessly linking sentiment elements (labels) together. Our dataset follows the format of the well-known SemEval-2016 datasets. This design choice allows effortless application and evaluation in cross-lingual scenarios, ultimately fostering cross-language comparisons with equivalent counterpart datasets in other languages. The annotation process engaged two trained annotators, yielding an impressive inter-annotator agreement rate of approximately 90%. Additionally, we provide 24M reviews without annotations suitable for unsupervised learning. We present robust monolingual baseline results achieved with various Transformer-based models and insightful error analysis to supplement our contributions. Our code and dataset are freely available for non-commercial research purposes.Item Evaluation of Approximate Reflectional Symmetry(SCITEPRESS – Science and Technology Publications, Lda., 2024) Maňák, Martin; Podgorelec, David; Kolingerová, IvanaWhen an object can be split by a plane into two symmetrical parts, one being the mirrored image of the other, the object has a reflectional symmetry with respect to that plane. The symmetry is often only approximate and not necessarily global. Many algorithms exist for the detection of symmetries and there are various applications utilizing symmetrical properties. Yet there are not so many ways to measure the amount of approximate reflectional symmetry. In this paper, we introduce a method for the evaluation of approximate symmetry for objects represented as a point cloud. The method consists of three parts - a relative symmetry distance for measuring the amount of approximate reflectional symmetry, a plot of relative errors, and visualization of errors. This method offers a way how to compare different objects by the amount of symmetry and improves understanding of the symmetrical properties of objects, both quantitatively and visually.Item A Predictor for Triangle Mesh Compression Working in Tangent Space(SCITEPRESS – Science and Technology Publications, Lda., 2024) Vaněček, Petr; Hácha, Filip; Váša, LiborTriangle mesh compression has been a popular research topic for decades. Since a plethora of algorithms has been presented, it is becoming increasingly difficult to come up with significant performance improvements. Some of the recent advances in compression efficiency come at the cost of rather steep implementation and/or computational expense, which has profound consequences on their practicality. Ultimately it becomes increasingly difficult to come up with improvements that are reasonably easy to implement and do not harm the computational efficiency of the compression/decompression procedure. In this paper, we analyze a combination of two previously known techniques, namely using the local coordinates for expressing compression residuals and weighted parallelogram prediction, which were not previously investigated together. We report that such approach outperforms industry standard Draco on a large set of test meshes in terms of rate/distortion ratio, while retaining beneficial properties such as simplicity and computational efficiency.Item UWB at KSAA-RD shared task: Computing the meaning of a gloss(Association for Computational Linguistics, 2023) Taylor, StephenTo extract the ‘meaning’ of a gloss phrase, we build a list of sense-IDs for each word in the phrase which is in our vocabulary. We choose one sense-ID from each list so as to maximise cosine similarity of all the IDs in the chosen subset. We take the meaning of the phrase in semantic space to be the weighted sum of the embedding vectors of the Ids in the chosen subset.Item A New Algorithm for the Closest Pair of Points for Very Large Data Sets Using Exponent Bucketing and Windowing(Springer, 2023) Skala, Václav; Martinez, Alejandro Esteban; Martinez, David Esteban; Moreno, Fabio HernandezIn this contribution, a simple and efficient algorithm for the closest-pair problem in E1 is described using the preprocessing based on exponent bucketing and exponent windowing respecting accuracy of the floating point representation. The preprocessing is of the O(N) complexity. Experiments made for the uniform distribution proved significant speedup. The proposed approach is applicable for the E2 case. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG..Item Detection of Local Symmetry Polylines of Polygons Based on Sweeping Paradigm(SCITEPRESS – Science and Technology Publications, Lda., 2024) Safko, Martin; Lukač, Luka; Žalik, Borut; Kolingerová, IvanaSymmetry is a fundamental property of many objects of interest. In this work, we identify polylines satisfying the local reflection symmetry of polygons and we describe an algorithm based on a sweep-line paradigm to efficiently compute the polylines by scanning through a polygon at various angles.Item Use of Spiking Neural Networks over Augmented EEG Dataset(IEEE, 2023) Hrabík, Václav; Mouček, RomanThe relatively small size of EEG datasets impacts the use of traditional and spiking neural networks as EEG data classifiers. Since getting a larger number of EEG recordings requires much laborious laboratory work, using data augmentation methods and techniques seems beneficial. This paper deals with the experiments with, in particular, spiking neural networks over the augmented P300 dataset. Augmentation methods for EEG data are shortly presented; generative adversarial network models and sliding windows of various sizes are used to augment the original P300 dataset. The classification results over the original and augmented P300 datasets are compared, noting that classification accuracy increased by almost 27%.Item Findings of the Second Shared Task on Multilingual Coreference Resolution(Association for Computational Linguistics, 2023) Žabokrtský, Zdeněk; Konopík, Miloslav; Nedoluzhko, Anna; Novák, Michal; Ogrodniczuk, Maciej; Popel, Martin; Pražák, Ondřej; Sido, Jakub; Zeman, DanielThis paper summarizes the second edition of the shared task on multilingual coreference resolution, held with the CRAC 2023 workshop. Just like last year, participants of the shared task were to create trainable systems that detect mentions and group them based on identity coreference; however, this year’s edition uses a slightly different primary evaluation score, and is also broader in terms of covered languages: version 1.1 of the multilingual collection of harmonized coreference resources CorefUD was used as the source of training and evaluation data this time, with 17 datasets for 12 languages. 7 systems competed in this shared task.Item Automatic Sleep Stage Classification by CNN-Transformer-LSTM using single-channel EEG signal(IEEE, 2023) Pham, Duc Thien; Mouček, RomanSleep stage classification plays a crucial role in diagnosing sleep disorders and understanding sleep physiology. In recent years, automated models based on machine learning and deep learning have gained attention for sleep stage classification. This paper uses the single-channel EEG signal to present an automatic sleep stage classification system using a combination of Convolutional Neural Network (CNN), Transformer, and Long Short-Term Memory (LSTM) models. Experimental evaluation of the ISRUC sleep datasets S1 and S3 demonstrates the effectiveness of the proposed model. It achieves accuracies of 80.37% and 82.40%, respectively, achieving competitive performance compared to state-of-the-art models.
