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Search results for: SELF-SUPERVISED LEARNING
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Methodology for assessing end-user requirements in the Ella4Life project: elders’ perspectives about self-monitoring
PublicationThe purpose of this paper is to explore elders’ perspectives about self-monitoring and using specially developed sensor technology for measuring health indicators. The qualitative research method is focus-groups with guidelines that were designed for understanding elder’s requirements about monitoring health indicators. We present them two devices: the first sensor is a device for monitoring of cardiac action potential fixed into...
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Minimal number of periodic points of smooth boundary-preserving self-maps of simply-connected manifolds
PublicationLet M be a smooth compact and simply-connected manifold with simply-connected boundary ∂M, r be a fixed odd natural number. We consider f, a C1 self-map of M, preserving ∂M . Under the assumption that the dimension of M is at least 4, we define an invariant Dr(f;M,∂M) that is equal to the minimal number of r-periodic points for all maps preserving ∂M and C1-homotopic to f. As an application, we give necessary and sufficient...
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Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition
PublicationBrain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods....
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Love your mistakes!—they help you adapt to change. How do knowledge, collaboration and learning cultures foster organizational intelligence?
PublicationPurpose: The study aims to determine how the acceptance of mistakes is related to adaptability to change in a broad organizational context. Therefore it explores how knowledge, collaboration, and learning culture (including “acceptance of mistakes”) might help organizations overcome their resistance to change. Methodology: The study uses two sample groups: students aged 18–24 (330 cases) and employees aged >24 (326 cases) who work...
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The awareness of the profession and the self-reflection of the primary, secondary and upper secondary school teachers on their own practice in the light of empirical studies
PublicationThe article presents the issue of awareness of the profession and the self-reflection of the primary, secondary and upper secondary school teachers’ on their own practice. The text refers to data based on empirical studies.
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Technology-Enhanced Environmental Learning: Co-design of Educational Mobile Application Case
PublicationThe process of co-creating an educational mobile application to support environmentally friendly behavior is presented in this paper. The research material consisted of quantitative data collected on the application during the first testing phase by early adopters. The results suggest that the most frequently used features of the app were related to transport and educational activities. While women tended to split their time between...
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Self-Adaptive Mesh Generator for Global Complex Roots and Poles Finding Algorithm
PublicationIn any global method of searching for roots and poles, increasing the number of samples increases the chances of finding them precisely in a given area. However, the global complex roots and poles finding algorithm (GRPF) (as one of the few) has direct control over the accuracy of the results. In addition, this algorithm has a simple condition for finding all roots and poles in a given area: it only requires a sufficiently dense...
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An experimental study of self-sensing concrete enhanced with multi-wall carbon nanotubes in wedge splitting test and DIC
PublicationConcrete is the worldwide most utilized construction material because of its very good performance, forming ability, long-term durability, and low costs. Concrete is a brittle material prone to cracking. Extensive cracking may impact durability and performance over time considerably. The addition of a small amount of carbon nanotubes (CNT) increases the concrete’s overall electrical conductivity, enabling internal structure...
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Optimizing Medical Personnel Speech Recognition Models Using Speech Synthesis and Reinforcement Learning
PublicationText-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons; TTS can help expand the data set. Speech can be synthesized by mimicking different accents, dialects, and speaking styles that may occur in a medical language. Reinforcement Learning (RL), in the...
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Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries
PublicationCatheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified...
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Assessment Of the Relevance of Best Practices in The Development of Medical R&D Projects Based on Machine Learning
PublicationMachine learning has emerged as a fundamental tool for numerous endeavors within health informatics, bioinformatics, and medicine. However, novices among biomedical researchers and IT developers frequently lack the requisite experience to effectively execute a machine learning project, thereby increasing the likelihood of adopting erroneous practices that may result in common pitfalls or overly optimistic predictions. The paper...
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Automated detection of pronunciation errors in non-native English speech employing deep learning
PublicationDespite significant advances in recent years, the existing Computer-Assisted Pronunciation Training (CAPT) methods detect pronunciation errors with a relatively low accuracy (precision of 60% at 40%-80% recall). This Ph.D. work proposes novel deep learning methods for detecting pronunciation errors in non-native (L2) English speech, outperforming the state-of-the-art method in AUC metric (Area under the Curve) by 41%, i.e., from...
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Frequency of use, moral incongruence, and religiosity and their relationships with self-perceived addiction to pornography, internet use, social networking and online gaming
PublicationBackground and Aims Moral incongruence involves disapproval of a behaviour in which people engage despite their moral beliefs. Although considerable research has been conducted on how moral incongruence relates to pornography use, potential roles for moral incongruence in other putative behavioural addictions have not been investigated. The aim of this study was to investigate the role of moral incongruence in self‐perceived...
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Reducing the number of periodic points in the smooth homotopy class of a self-map of a simply-connected manifold with periodic sequence of Lefschetz numbers
PublicationLet f be a smooth self-map of an m-dimensional (m >3) closed connected and simply-connected manifold such that the sequence of the Lefschetz num- bers of its iterations is periodic. For a fixed natural r we wish to minimize, in the smooth homotopy class, the number of periodic points with periods less than or equal to r. The resulting number is given by a topological invariant J[f] which is defned in combinatorial terms and is...
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Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
PublicationThis paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...
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Structural Adaptive, Self-Separating Material for Removing Ibuprofen from Waters and Sewage
Publication-cyclodextrin nanosponge (CDM) was used for the adsorption of ibuprofen (IBU) from water and sewage. The obtained material was characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller (BET), Barrett– Joyner–Halenda (BJH), Harkins and Jura t-Plot, zeta potential, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and elementary analysis (EA)....
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Diagnosis of Damage in a Steel Tank with Self-Supported Roof through Numerical Analysis
PublicationThe safety of civil engineering structures is one of the most important issues of building industry. That is why the assessment of the damage-involved structural response has recently become of major concern to engineers. Among a number of different approaches to diagnosis of damage, the method of measuring the changes in natural frequencies is considered to be one of the most effective indicators of global damage. From the practical...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublicationWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control
PublicationIn this paper, a unified nonlinear modeling and control scheme is presented. A self-structuring Takagi-Sugeno (T-S) fuzzymodel is used to approximate the unknown nonlinear plant based on I/O data collected on-line. Both the structure and theparameters of the T-S fuzzy model are updated by an on-line clustering method and a recursive least squares estimation(RLSE) algorithm. The rules of the fuzzy model can be added, replaced or...
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Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases
PublicationThis paper proposes an idea of Wireless Body Area Networks (WBANs) based on Bluetooth Low-Energy (BLE) standards to recognize and alarm a gesture of touching the face, and in effect, to prevent self-inoculation of respiratory viral diseases, such as COVID-19 or influenza A, B, or C. The proposed network comprises wireless modules placed in bracelets and a necklace. It relies on the received signal strength indicator (RSSI) measurements...
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Minimal number of periodic points for smooth self-maps of simply-connected manifolds
Open Research DataThe problem of finding the minimal number of periodic points in a given class of self-maps of a space is one of the central questions in periodic point theory. We consider a closed smooth connected and simply-connected manifold of dimension at least 4 and its self-map f. The topological invariant D_r[f] is equal to the minimal number of r-periodic points...
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Testing Question Order Effects of Self-perception of Risk Propensity on Simple Lottery Choices as Measures of the Actual Risk Propensity
PublicationUncertainty together with the necessity of making choices inevitably results in risky decisions. For many years now, scientists have been studying notions connected with risk such as risk management, risk perception or risk propensity. While many sophisticated methods regarding measurement of risk propensity have been developed so far, it seems that little attention has been paid to checking whether they are not inherently flawed....
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Fast Machine-Learning-Enabled Size Reduction of Microwave Components Using Response Features
PublicationAchieving compact size has emerged as a key consideration in modern microwave design. While structural miniaturization can be accomplished through judicious circuit architecture selection, precise parameter tuning is equally vital to minimize physical dimensions while meeting stringent performance requirements for electrical characteristics. Due to the intricate nature of compact structures, global optimization is recommended,...
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Development and evaluation of RADA-PDGF2 self-assembling peptide hydrogel for enhanced skin wound healing
PublicationBackground: Wound healing complications affect numerous patients each year, creating significant economic and medical challenges. Currently, available methods are not fully effective in the treatment of chronic or complicated wounds; thus, new methods are constantly sought. Our previous studies showed that a peptide designated as PDGF2 derived from PDGF-BB could be a promising drug candidate for wound treatment and that RADA16-I...
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Modeling lignin extraction with ionic liquids using machine learning approach
PublicationLignin, next to cellulose, is the second most common natural biopolymer on Earth, containing a third of the organic carbon in the biosphere. For many years, lignin was perceived as waste when obtaining cellulose and hemicellulose and used as a biofuel for the production of bioenergy. However, recently, lignin has been considered a renewable raw material for the production of chemicals and materials to replace petrochemical resources....
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Self-Organization of Graft Copolymers and Retortable iPP-Based Nanoporous Films Thereof
PublicationPolyolefins might become inexpensive alternatives to the existing membranes based on polyethersulfone. Here we disclose the production of retortable, well-defined PP-based nanoporous membranes derived from amphiphilic graft copolymer precursors. The graft copolymers, containing a polypropylene backbone and polyester grafts, were obtained by grafting lactones, specifically δ-valerolactone and ε-caprolactone, from well-defined randomly...
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Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples
PublicationIn this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern,...
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Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
PublicationIn this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher...
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Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG
PublicationEEG-based brain-computer interfaces are systems aiming to integrate disabled people into their environments. Nevertheless, their control could not be intuitive or depend on an active external stimulator to generate the responses for interacting with it. Targeting the second issue, a novel paradigm is explored in this paper, which depends on a passive stimulus by measuring the EEG responses of a subject to the primary colors (red,...
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Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
PublicationThis paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublicationLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Optimization of Self-Organized TiO2 Nanotube Geometry on Ti and Ti Alloys Using Fuzzy Logic Reasoning
PublicationThe geometry of self-organized TiO2 nanotubes, obtained by electrochemicalanodization, has been determined by using fuzzy reasoning approach. The efficiency of TiO2nanotubular layer in biomedical applications depends on geometry and available surface area ofnanotubes, which can be determined by their diameter and length. The structure of nanotubesdepends on processing parameters of electrochemical anodization, like applied potential,anodization...
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An algorithmic approach to estimating the minimal number of periodic points for smooth self-maps of simply-connected manifolds
PublicationFor a given self-map f of M, a closed smooth connected and simply-connected manifold of dimension m 4, we provide an algorithm for estimating the values of the topological invariant D^m_r [f], which equals the minimal number of r-periodic points in the smooth homotopy class of f. Our results are based on the combinatorial scheme for computing D^m_r [f] introduced by G. Graff and J. Jezierski [J. Fixed Point Theory Appl. 13 (2013),...
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Note on universal algoritms for learning theory
PublicationW 2001 Cucker i Smale zaproponowali nowe podejście do teorii uczenia się w oparciu o problematykę teorii aproksymacji.W 2005 i 2007 Bivev, Cohen, Dahmen, DeVore i Temlyakov opublikowali dwie prace z teorii uczenia się. W omawianej publikacji uogólniliśmy ich rezultaty jednocześnie upraszczając dowody.
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A consensus-based approach to the distributed learning
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Prototype selection algorithms for distributed learning
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An agent-based framework for distributed learning
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Structure and Randomness in Planning and Reinforcement Learning
PublicationPlanning in large state spaces inevitably needs to balance the depth and breadth of the search. It has a crucial impact on the performance of a planner and most manage this interplay implicitly. We present a novel method \textit{Shoot Tree Search (STS)}, which makes it possible to control this trade-off more explicitly. Our algorithm can be understood as an interpolation between two celebrated search mechanisms: MCTS and random...
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Some aspects of blended-learning education
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E-learning in tourism and hospitality: A map
PublicationThe impact of information and communication technologies (ICT) on tourism and hospitality industries has been widely recognized and investigated as a one of the major changes within the domains in the last decade: new ways of communicating with prospective tourists and new ways of purchasing products arisen are now part of the industries’ everyday life. Poor attention has been paid so far to the role played by new media in education...
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Employees’ self-expansion, work conditions, work engagement and productive behaviours: study 1&2
Open Research DataIn the following studies conducted in Poland, we examined the importance of workplace self-expansion and found that it is a significant mediator between job resources (e.g. compensation and benefits, job tasks) and work engagement (Study 1) as well as task-oriented engagement (Study 2). At the same time, our findings prove that job demands (e.g. role...
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Propulsion and Maneuvering Systems’ Characteristics of the U.S. Flagged Great Lakes Self-Unloading Bulk Carriers
PublicationPaper contains an overview of the propulsion and maneuvering systems’ characteristics of United States flagged Great Lakes self - unloading bulk carriers. A contrast between the importance of the transport task carried by those vessels to their low number and considerable age suggests the need to review and understand their complexity as well as complexity of their operations in order to provide suitable energy- and operational...
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Isolation and Self-Association Studies of Beta-Lactoglobulin
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Multifrequency self-optimizing narrowband interference canceller
PublicationThe problem of cancellation of a nonstationary sinusoidal interference, acting at the output of a linear stable plant, is considered. It is assumed that disturbance is a multifrequency narrowband signal, and that system output is contaminated with wideband noise. It is not assumed that the reference signal is available. Two disturbance cancelling schemes are proposed, one for disturbances with unrelated frequency components, and...
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Modelling gene expression of a self-regulating protein
PublicationWe analyze a model of gene transcription and protein synthesis. We take into account the number of sites on the protein’s promoter at which the protein’s dimers can bind blocking transcription of protein mRNA.
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Coupledfield modelling of interferometric hydrophone with self supportedmandrel.
PublicationW pracy przedstawiono nowatorską konstrukcję światłowodowego interferometrycznego przetwornika ciśnienia do zastosowań w hydrofonach. Przetwornik ten składa się z cewki światłowodowej zalanej w żywicy epoksydowej. Zaprezentowano technikę modelowania właściwości przetwornika, wykorzystującą opis przy pomocy parametrów równoważnego materiału ortotropowego i Metodę Elementów Skończonych. Przestawiono wyniki statycznego modelowania...
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Energy optimisation in resilient self-stabilizing processes
PublicationW pracy rozważa się rozproszony model obliczeń, w którym struktura systemu jest reprezentowana przez graf bezpośrednich połączeń komunikacyjnych. W tym modelu podajemy nowy samostabilizujący algorytm kolorowania grafów oparty na konstrukcji drzewa spinającego. Zgodnie z naszą wiedzą jest to pierwszy algorytm z gwarantowaną wielomianową liczbą ruchów, który dokładnie koloruje grafy dwudzielne.
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Self-stabilizing algorithm for edge-coloring of graphs
PublicationReferat ten poświęcony jest kolorowaniu grafów w modelu rozproszonym.Podano samostabilizujący się algorytm kolorowania krawędzi grafu wraz z dowodem poprawności oraz oszacowaniem jego czasu działania.
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Self-assessment of competencies of students and graduates participating in didactic projects – Case study
PublicationAim/purpose: the aim of this article is to examine the opinions of students and graduates of the faculty of economics of a technical university as regards their selfassessment of their preparation for entering the modern labour market. All the respondents participated during their studies in didactic projects aimed at improving their competencies taking into account the expectations of potential employers. Design/methodology/approach:...
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Speed estimation of a car at impact with a W-beam guardrail using numerical simulations and machine learning
PublicationThis paper aimed at developing a new method of estimating the impact speed of a passenger car at the moment of a crash into a W-beam road safety barrier. The determination of such a speed based on the accident outcomes is demanding, because often there is no access to full accident data. However, accurate determination of the impact speed is one of the key elements in the reconstruction of road accidents. A machine learning algorithm...