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Wyniki wyszukiwania dla: ACTIVE%20LEARNING
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Vident-real: an intra-oral video dataset for multi-task learning
Dane BadawczeWe introduce Vident-real, a large dataset of 100 video sequences of intra-oral scenes from real conservative dental treatments performed at the Medical University of Gdańsk, Poland. The dataset can be used for multi-task learning methods including:
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„Active learning w praktyce” - 17. Szkolenie certyfikowane 13.12.2022 r.
Kursy Online -
„Active learning w praktyce” - 4. Szkolenie certyfikowane 21.10.2022 r.
Kursy Online -
Piotr Lorens prof. dr hab. inż. arch.
OsobyPiotr Lorens – prof. dr hab. inż. arch., prof. nzw. Politechniki Gdańskiej. Po ukończeniu studiów w 1994 roku podjął pracę w Zakładzie Rozwoju Miasta na Wydziale Architektury Politechniki Gdańskiej. Uzyskawszy Stypendium Fulbrighta wyjechał na staż do USA, gdzie w latach 1996-1997 ukończył Special Program for Urban and Regional Studies na Massachusetts Institute of Technology oraz International Training Program na Harvard University...
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Trends in In Silico Approaches to the Prediction of Biologically Active Peptides in Meat and Meat Products as an Important Factor for Preventing Food-Related Chronic Diseases
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Synthesis of 7-oxo-7h-naphto[1,2,3-de]quinoline derivatives as potential anticancer agents active on multidrug resistant cell lines
PublikacjaOpierając się na naszym wcześniejszym stwierdzeniu, że tetracykliczne analogi antrachinonów z wbudowanym pierścieniem pirydynowym wykazują aktywną cytotoksyczność względem komórek z indukowaną opornością, przeprowadzono syntezę pochodnych 7-oxo-7h-nafto[1,2,3-de]chinoliny (3, 6-8, 10-12, 14,15 i 18) posiadających jeden lub dwa zasadowe łańcuchy boczne i różne podstawniki w pierścieniu pirydynowym, związków o potencjalnym działaniu...
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Probing the binding selected metal ions and biologically active substances to the antimicrobial peptide LL-37 using DSC, ITC measurements and calculations
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Modulation of mRNA and protein levels of CYP1A1, 1A2, and 1B1 in nontumorigenic breast epithelial cells (MCF10A) by cabbage juice and its active components
PublikacjaZbadano wpływ soków z kapusty oraz występujących w nich pochodnych indolowych i sulforafanu na poziom ekspresji enzymów odpowiedzialnych za metabolizm ksenobiotyków w tym estrogenów. Wykazano, że soki z kapusty i izolowane substancje mają podobny wpływ na poziom mRNA i białek enzymatycznych.
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Cloning and characterization of a novel cold-active glycoside hydrolase family 1 enzyme with beta-glucosidase, beta-fucosidase and beta-galactosidase activities.
PublikacjaBackground: Cold-active enzymes, sourced from cold-adapted organisms, are characterized by high catalytic efficiencies at low temperatures compared with their mesophilic counterparts, which have poor activity. This property makes them advantageous for biotechnology applications as it: (i) saves energy costs, (ii) shortens the times for processes operated at low temperatures, (iii) protects thermosensitive substrates or products...
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Modulation of CYP19 expression by cabbage juices and their active components: indole-3-carbinol and 3,30-diindolylmethene in human breast epithelial cell lines
PublikacjaThe aim of this study was to evaluate the effect of white cabbage and sauerkraut juices of different origin and indole-3-carbinol (I3C) and diindolylmethane (DIM) on expression of CYP19 gene encoding aromatase, the key enzyme of estrogen synthesis.Remarkable differences in the effect on CYP19 transcript and protein level were found between the cab- bage juices (in 2.5-25 mL/L concentrations) and indoles (in 2.5-50 lM doses) in...
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A novel cold-active β-D-galactosidase with transglycosylation activity from the Antarctic Arthrobacter sp. 32cB - gene cloning, purification and characterization
PublikacjaA gene encoding a novel β-D-galactosidase from the psychrotolerant Antarctic bacterium Arthrobacter sp. 32cB was isolated, cloned and expressed in Escherichia coli. The active form of recombinant β-D-galactosidase consists of two subunits with a combined molecular weight of approximately 257 kDa. The enzyme's maximum activity towards o-nitrophenyl-β-D-galactopyranoside was determined as occurring at 28 °C and pH 8.0. However, it...
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Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning
PublikacjaThis work is part of an effort to develop of a Knowledge-Vision Integration Platform for Hazard Control (KVIP-HC) in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the non-use of personal protective equipment (PPE). The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting...
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The effect of lyophilization on selected biologically active components (vitamin c, catalase, lysozyme), total antioxidant capacity and lipid oxidation in human milk
PublikacjaHuman milk is rich in polyunsaturated fatty acids, as well as lysozyme, vitamin C and other bioactive compounds. The effect of lyophilization on the content of antioxidants (vitamin C and catalase CAT), bactericidal compounds (lysozyme), total antioxidant capacity (TAC) and lipid peroxidation in human milk was investigated in this study. Samples of mature human milk were collected from five healthy women who gave birth on the scheduled...
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The detection of Alternaria solani infection on tomatoes using ensemble learning
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Scheduling Repetitive Construction Processes Using the Learning-Forgetting Theory
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Generation of microbial colonies dataset with deep learning style transfer
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Deep learning-based waste detection in natural and urban environments
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Becoming a Learning Organization Through Dynamic Business Process Management
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Model of distributed learning objects repository for a heterogenic internet environment
PublikacjaW artykule wprowadzono pojęcie komponentu edukacyjnego jako rozszerzenie obiektu edukacyjnego o elementy zachowania (metody). Zaproponowane podejście jest zgodne z paradygmatem obiektowym. W oparciu o komponent edukacyjny zaprojektowano model budowy repozytorium materiałów edukacyjnych. Model ten jest oparty o usługi sieciowe i rejestry UDDI. Komponent edukacyjny oraz model repozytorium mogą znaleźć zastosowanie w konstrukcji zbiorów...
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Digital competence learning in secondary adult education in Finland and Poland
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Didaktische simulationsmodelle fur E-learning in der IK-ausbildung.
PublikacjaPrzedstawiono dydaktyczne modele symulacyjne wykorzystywane w zdalnym kształceniu z zakresu informatyki i technik komunikacyjnych. Pokazano na przykładach zbudowanych symulatorów, w jaki sposób zrealizować lub dostosować modele symulacyjne do zdalnego nauczania. Opisano doświadczenia autorów w wykorzystaniu modeli symulacyjnych w zdalnym nauczaniu.
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Platelet RNA Sequencing Data Through the Lens of Machine Learning
PublikacjaLiquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability...
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Breast MRI segmentation by deep learning: key gaps and challenges
PublikacjaBreast MRI segmentation plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature...
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Divide and not forget: Ensemble of selectively trained experts in Continual Learning
PublikacjaClass-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained all at once using whole task data, which makes them all prone to forgetting and increasing computational burden. To address this limitation,...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublikacjaPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile strengths...
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Meta-Design and the Triple Learning Organization in Architectural Design Process
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublikacjaPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile...
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Building the Learning Environment for Sustainable Development: a Co-creation approach
PublikacjaEducation for sustainable development supports the improvement of knowledge, skills, attitudes and behaviors related to global challenges such as climate change, global warming and environmental degradation, among others. It is increasingly taking place through projects based on information and communication technologies. The effectiveness of the actions taken depends not only on the quality of the project activities or the...
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Agent-Based Population Learning Algorithm for RBF Network Tuning
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An A-Team Approach to Learning Classifiers from Distributed Data Sources
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An A-Team approach to learning classifiers from distributed data sources
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Employing a biofeedback method based on hemispheric synchronization in effective learning
PublikacjaIn this paper an approach to build a brain computer-based hemispheric synchronization system is presented. The concept utilizes the wireless EEG signal registration and acquisition as well as advanced pre-processing methods. The influence of various filtration techniques of EOG artifacts on brain state recognition is examined. The emphasis is put on brain state recognition using band pass filtration for separation of individual...
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Employing Blended E-Learning to Improve Rate of Assignments Handing-In
PublikacjaIt has been observed that students hand in homework assignments at a notably low rate in introductory C programming course. A survey has revealed that the real issue was not student learning but instructor work organization. Based on survey results, the physical course has been complemented with an e-learning component to guide the homework process. Assignment handing-in rate significantly improved, as e-learning allowed the homework...
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Processes of enhancing the intelligence of Learning Organizations on the basis of Competence Centers
PublikacjaThe process of organizational learning and proper knowledge management became today one of the major challenges for the organization acting in the knowledge-based economy. According to the observations of the authors of this paper the demand for formalization of knowledge management processes and organizational learning is particularly evident in research institutions, established either by the universities, or the companies. The...
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Machine Learning in Multi-Agent Systems using Associative Arrays
PublikacjaIn this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance...
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Data augmentation for improving deep learning in image classification problem
PublikacjaThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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Self-Supervised Learning to Increase the Performance of Skin Lesion Classification
PublikacjaTo successfully train a deep neural network, a large amount of human-labeled data is required. Unfortunately, in many areas, collecting and labeling data is a difficult and tedious task. Several ways have been developed to mitigate the problem associated with the shortage of data, the most common of which is transfer learning. However, in many cases, the use of transfer learning as the only remedy is insufficient. In this study,...
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Organizational Wisdom: The Impact of Organizational Learning on the Absorptive Capacity of an Enterprise
PublikacjaPurpose: In this article, we analyze the concept of organizational wisdom, indicating its key elements and verifieng the relationships between them. Design/Methodology/Approach: The study was conducted at Vive Textile Recycling Sp. z o.o in Poland. Empirical data was collected from 138 managers using the PAPI technique. Structural equation modelling (SEM) was performed to test the research hypotheses. Additionally, the significance...
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Playback detection using machine learning with spectrogram features approach
PublikacjaThis paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on different data partitions in respect...
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Analysis of Learning Outcomes in Medical Education with the Use of Fuzzy Logic
PublikacjaThe national curricula of the EU member states are structured around learning outcomes, selected according to Bloom’s Taxonomy. The authors of this paper claim that using Bloom’s Taxonomy to phrase learning outcomes in medical education in terms of students’ achievements is difficult and unclear. This paper presents an efficient method of assessing course learning outcomes using Fuzzy Logic.
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Designing acoustic scattering elements using machine learning methods
PublikacjaIn the process of the design and correction of room acoustic properties, it is often necessary to select the appropriate type of acoustic treatment devices and make decisions regarding their size, geometry, and location of the devices inside the room under the treatment process. The goal of this doctoral dissertation is to develop and validate a mathematical model that allows predicting the effects of the application of the scattering...
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Noise profiling for speech enhancement employing machine learning models
PublikacjaThis paper aims to propose a noise profiling method that can be performed in near real-time based on machine learning (ML). To address challenges related to noise profiling effectively, we start with a critical review of the literature background. Then, we outline the experiment performed consisting of two parts. The first part concerns the noise recognition model built upon several baseline classifiers and noise signal features...
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Machine learning applied to acoustic-based road traffic monitoring
PublikacjaThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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MACHINE LEARNING APPLICATIONS IN RECOGNIZING HUMAN EMOTIONS BASED ON THE EEG
PublikacjaThis study examined the machine learning-based approach allowing the recognition of human emotional states with the use of EEG signals. After a short introduction to the fundamentals of electroencephalography and neural oscillations, the two-dimensional valence-arousal Russell’s model of emotion was described. Next, we present the assumptions of the performed EEG experiment. Detail aspects of the data sanitization including preprocessing,...
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Deep learning-based waste detection in natural and urban environments
PublikacjaWaste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic waste detection are hardly comparable due to the lack of benchmarks and widely accepted standards regarding the used metrics and data. Those problems are addressed in this article by...
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Machine learning applied to acoustic-based road traffic monitoring
PublikacjaThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublikacjaWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
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Discovering Rule-Based Learning Systems for the Purpose of Music Analysis
PublikacjaMusic analysis and processing aims at understanding information retrieved from music (Music Information Retrieval). For the purpose of music data mining, machine learning (ML) methods or statistical approach are employed. Their primary task is recognition of musical instrument sounds, music genre or emotion contained in music, identification of audio, assessment of audio content, etc. In terms of computational approach, music databases...
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DentalSegmentator: robust deep learning-based CBCT image segmentation
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NAIS-Native American and Indigenous Studies Association
Czasopisma