Daniel Kostrzewa

• Doctor of Philosophy
• Assistant at Silesian University of Technology

This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection and the Mona Lisa TSP Challen...

The Invasive Weed Optimization algorithm (IWO) is an optimization method inspired by dynamic growth of weeds colony. The authors of the present paper have modified the IWO algorithm introducing a hybrid strategy of the search space exploration. The goal of the project was to evaluate the modified version by testing its usefulness for numerical func...

The paper addresses the problem of quality estimation of the search space exploration strategy. The strategy is used to find a satisfying solution to the join ordering problem, which constitutes a crucial part of the database query optimization task. The method of strategy verification is based on the comparison of the execution time for the soluti...

The Invasive Weed Optimization algorithm (IWO) is an optimization metaheuristic inspired by dynamic growth of weeds colony. The authors of the present paper have expanded the strategy of the search space exploration of the IWO algorithm introducing a hybrid method along with a concept of the family selection applied in the phase of creating individ...

The goal of the project was to adapt the idea of the Invasive Weed Optimization (IWO) algorithm to the problem of predetermining the progress of distributed data merging process and to compare the results of the conducted experiments with analogical outcomes produced by the evolutionary algorithm. The main differences between both compared algorith...

Super-resolution is aimed at reconstructing high-resolution images from low-resolution observations. State-of-the-art approaches underpinned with deep learning allow for obtaining outstanding results, generating images of high perceptual quality. However, it often remains unclear whether the reconstructed details are close to the actual ground-trut...

Super-resolution stands as one of the most prominent research areas in computer vision, aiming to augment the resolution of digital images. The majority of current state-of-the-art techniques rely on deep neural networks. While many of these are tailored for grayscale or natural color images, only a fraction are specifically designed for hyperspect...

Time signature detection is a fundamental task in music information retrieval, aiding in music organization. In recent years, the demand for robust and efficient methods in music analysis has amplified, underscoring the significance of advancements in time signature detection. In this study, we explored the effectiveness of residual networks for ti...

The possibility of recommendations of musical songs is becoming increasingly required because of the millions of users and songs included in online databases. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using three basic factors based on genre classification made b...

The need for enhancing image spatial resolution has motivated the researchers to propose numerous super-resolution techniques, including those developed specifically for hyperspectral data. Despite significant advancements in this field attributed to deep learning, little attention has been given to evaluating the practical value of super-resolved...

The AIOPEN project will combine and extend the existing platform framework Automated Service Builder (ASB), Open Interoperable Platform for Unified Access & Analysis of EO Data (EOPEN) and EO Exploitation Platform Common Architecture (EOEPCA) with Artificial Intelligence (AI) and Machine Learning (ML) capabilities. The resulting platform, AIOPEN, r...

The Meter2800 dataset is an important contribution to Music Information Retrieval (MIR) research, as it is the first dataset to include audio files specifically designed for time signature detection. By combining audio files from three renowned datasets and including additional tracks, we have created a comprehensive and diverse collection of 2800...

Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. This is inherent to remote sensing, including Sentinel-2 satellite images that are available free of charge at a high revisit frequency, but whose spatial resolution is li...

Federated learning is a distributed machine learning method that is well-suited for the Industrial Internet of Things (IIoT) as it enables the training of machine learning models on distributed datasets. One of the most important advantages of using Federated Learning for Automated Guided Vehicles (AGVs) is its capability to optimize resource consu...

Tempo and time signature detection are essential tasks in the field of Music Information Retrieval. These features often affect the perception of a piece of music. Their automatic estimation unlocks many possibilities for further audio processing, as well as supporting music recommendation systems and automatic song tagging. In this article, the ma...

Insufficient spatial resolution of satellite imagery, including Sentinel-2 data, is a serious limitation in many practical use cases. To mitigate this problem, super-resolution reconstruction is receiving considerable attention from the remote sensing community. When it is performed from multiple images captured at subsequent revisits, it may benef...

Industrial IoT systems, such as those based on Autonomous Guided Vehicles (AGV), often generate a massive volume of data that needs to be processed and sent over to the cloud or private data centers. The presented research proposes and evaluates the approaches to data aggregation that help reduce the volume of readings from AGVs, by taking advantag...

Detecting anomalies in telemetry data captured on-board a spacecraft is critical to ensure its safe operation. Although there exist various techniques for automatically detecting point, contextual, and collective anomalies from time-series data, quantifying their performance remains under-researched. In this paper, we thoroughly validate our approa...

Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melod...

This paper presents research on one of the most challenging branches of music information retrieval – musical instruments identification. Millions of songs are available online, so recognizing instruments and tagging them by a human being is nearly impossible. Therefore, it is crucial to develop methods that can automatically assign the instrument...

Intelligent production requires maximum downtime avoidance since downtimes lead to economic loss. Thus, Industry 4.0 (today’s IoT-driven industrial revolution) is aimed at automated production with real-time decision-making and maximal uptime. To achieve this, new technologies such as Machine Learning (ML), Artificial Intelligence (AI), and Autonom...

The classification of music genres is essential due to millions of songs in online databases. It would be nearly impossible or very costly to do this job manually. That is why there is a need to create robust and efficient methods that automatically help to do this task. In this paper, music genre recognition is implemented by exploiting the potent...

This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). The results of the r...

Active collision avoidance has become an important task in space operations nowadays, and hundreds of alerts corresponding to close encounters of a satellite and other space objects are typically issued for a satellite in Low Earth Orbit every week. Such alerts are provided in the form of conjunction data messages, and only about two actionable ale...

This paper presents research on music genre recognition. It is a crucial task because there are millions of songs in the online databases. Classifying them by a human being is impossible or extremely expensive. As a result, it is desirable to create methods that can assign a given track to a music genre. Here, the classification of music tracks is...

This paper presents a comparison of the use of several selected optimization algorithms. They were applied to determine the parameters of classifiers. The value of these parameters should have a significant impact on the quality of the classification, and their determination is not a trivial process. This article checks how selected optimization al...

The paper presents the data dimensionality reduction in the classification process, with a special presentation of using the ability of features weighting by determining the level of importance of a given attribute in the data vector. This reduction was implemented using the Forest Optimization Algorithm (FOA) and the use of a classifier allowing t...

This paper presents a comparison of a few chosen outlier detection methods and test quality of classification, both before and after the procedure of removing outliers. Using a few selected methods of outlier detection on several selected data sets, the process of elimination of atypical data was carried out. Atypical data may be of various nature....

Super-resolution (SR) reconstruction is a process aimed at enhancing the spatial resolution of images, either from a single observation, based on the learned relation between low and high resolution, or from multiple images presenting the same scene. SR is particularly important, if it is not feasible to acquire images at the desired resolution, wh...

The capabilities of super-resolution reconstruction (SRR)---techniques for enhancing image spatial resolution---have been recently improved significantly by the use of deep convolutional neural networks. Commonly, such networks are learned using huge training sets composed of original images alongside their low-resolution counterparts, obtained wit...

Super-resolution reconstruction (SRR) is aimed at increasing image spatial resolution from multiple images presenting the same scene or from a single image based on the learned relation between low and high resolution. Emergence of deep learning allowed for improving single-image SRR significantly in the last few years, and a variety of deep convol...

Super-resolution reconstruction (SRR) is a process aimed at enhancing spatial resolution of images, either from a single observation, based on the learned relation between low and high resolution, or from multiple images presenting the same scene. SRR is particularly valuable, if it is infeasible to acquire images at desired resolution, but many im...

This book constitutes the refereed proceedings of the 15th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2019, held in Ustroń, Poland, in May 2019. It consists of 26 carefully reviewed papers selected from 69 submissions. The papers are organized in topical sections, namely big data and cloud computing; arch...

The article presents the author’s algorithm of dimensionality reduction of used data set, realized through Greedy Backward Feature Elimination. Results of the dimensionality reduction are verified in the process of classification for 2 selected data sets. These data sets contain the data for the realization of the multiclass classification. The art...

Super-resolution reconstruction (SRR) consists in enhancing image spatial resolution given a single image or a bunch of images presenting the same scene at lower resolution. Potential benefits of applying SRR to satellite imagery are evident, as it may enhance the capacities of images characterized with lower resolution, shorter revisit times, and...

Super-resolution reconstruction (SRR) allows for enhancing image spatial resolution from low-resolution (LR) observations, which are assumed to have been derived from a hypothetical high-resolution image by applying a certain imaging model (IM). However, if the actual degradation is different from the assumed IM, which is often the case in real-wor...

Super-resolution reconstruction (SRR) allows for producing a high-resolution (HR) image from a set of low-resolution (LR) observations. The majority of existing methods require tuning a number of hyper-parameters which control the reconstruction process and configure the imaging model that is supposed to reflect the relation between high and low re...

Super-resolution reconstruction (SRR) consists in enhancing image spatial resolution given a single image or a bunch of images presenting the same scene. Potential benefits of SRR are evident, when images of high resolution are required, but are unavailable due to technological limitations or economic reasons. Obviously, this is inherent to satelli...

The article presents the results of the optimization process of classification for five selected data sets. These data sets contain the data for the realization of the multiclass classification. The article presents the results of initial classification, carried out by dozens of classifiers, as well as the results after the process of adjusting par...

The conception of classification is one of the major aspects in data processing. Conducted research present comparison of chosen classifiers’ results of classification for a few data sets. All data were chosen from these available on UCI Machine Learning Repository web site. During realization of research, the optimization process of the results of...

This book constitutes the refereed proceedings of the 13th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2017, held in Ustroń, Poland, in May/June 2017. It consists of 44 carefully reviewed papers selected from 118 submissions. The papers are organized in topical sections, namely big data and cloud computing...

The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version inspired by dynamic growth of weeds colony. The authors of the present paper have modified the exIWO algorithm introducing a set of both deterministic and non-deterministic strategies of individuals’ selection. The goal of...

The conception of storing and managing data directly in RAM appeared some time ago but in spite of very good efficiency, it was impossible to massive implementation because of hardware limitations. Currently, it is possible to store whole databases in memory as well as there are some mechanisms to organize pieces of data as in-memory databases. It...

This book constitutes the refereed proceedings of the 12th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2016, held in Ustroń, Poland, in May/June 2016. It consists of 57 carefully reviewed papers selected from 152 submissions. The papers are organized in topical sections, namely artificial intelligence, dat...

------------------------------------------------------------------------------- BDAS 2015: 11th International Conference Beyond Databases, Architectures and Structures IEEE technically co-sponsored Ustron near Krakow, Poland (transfer from Krakow provided by organizers) May 26-29, 2015 http://www.bdas.pl --------------------------------------------...

This book constitutes the refereed proceedings of the 11th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2015, held in Ustron, Poland, in May 2015. This book consists of 53 carefully revised selected papers that are assigned to 8 thematic groups: database architectures and performance; data integration, stor...

This book constitutes the refereed proceedings of the 10th IEEE International Conference Beyond Databases, Architectures, and Structures, BDAS 2014, held in Ustron, Poland, in May 2014. This book consists of 56 carefully revised selected papers that are assigned to 11 thematic groups: query languages, transactions and query optimization; data wareh...

The authors summarize the several years research on the join ordering problem presenting a method based on the exIWO metaheuristic which is characterized by both the hybrid strategy of the search space exploration and three variants of selection of individuals as candidates for next population. The nub of the problem was recalled along with details...

The authors present a heuristic method of feature selection for gait mocap data, based on the exIWO metaheuristic which is characterized by both the hybrid strategy of the search space exploration and three variants of selection of individuals as candidates for next population. The proposed method was evaluated by the accuracy of person re-identifi...

This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challe...

The authors present results of the research aiming at human identification based on gait motion capture data. Tensor objects were chosen as the appropriate representation of data. High-dimensional tensor samples were reduced by means of the multilinear principal component analysis (MPCA). For the purpose of classification the following methods from...

The authors present results of the research on human recognition based on the video gait sequences from the CASIA Gait Database. Both linear (principal component analysis; PCA) and non-linear (isometric features mapping; Isomap and locally linear embedding; LLE) methods were applied in order to reduce data dimensionality, whereas a concept of hidde...

The authors present results of the research aiming at human identification based on gait motion capture data. Second-order tensor objects were chosen as the appropriate representation of data. High-dimensional tensor samples were reduced by means of the multilinear principal component analysis (MPCA). For the purpose of classification the following...

Streszczenie. Oszacowanie selektywności zapytania jest istotnym elementem pro-cesu uzyskiwania optymalnego planu wykonania tego zapytania. Wyznaczenie selek-tywności wymaga użycia nieparametrycznego estymatora rozkładu wartości atrybutu, na ogół histogramu. Wykorzystanie wielowymiarowego histogramu jako reprezentacji łącznego rozkładu wielowymiarow...

Streszczenie. Artykuł stanowi próbę oceny jakości autorskiej strategii eksploracji przestrzeni poszukiwań dla problemu określenia kolejności realizacji złączeń w zapy-taniu adresowanym do bazy danych. Jakość strategii zostanie oceniona przez porów-nanie czasów wykonania zapytania uzyskanych w systemie SQL Server 2008: z jed-nej strony przez realiza...

Streszczenie. Prezentowane zagadnienie stanowi kontynuację badań poświęco-nych zastosowaniu algorytmu IWO do realizacji zadania istotnego dla dziedziny roz-proszonych baz danych – określenia planu przebiegu procesu scalania danych rozpro-szonych. W niniejszym opracowaniu zaproponowano modyfikację ważnej części al-gorytmu IWO, jaką jest metoda penet...