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Search results for: human learning
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INVESTIGATION OF THE LOMBARD EFFECT BASED ON A MACHINE LEARNING APPROACH
PublicationThe Lombard effect is an involuntary increase in the speaker’s pitch, intensity, and duration in the presence of noise. It makes it possible to communicate in noisy environments more effectively. This study aims to investigate an efficient method for detecting the Lombard effect in uttered speech. The influence of interfering noise, room type, and the gender of the person on the detection process is examined. First, acoustic parameters...
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The Cultures of Knowledge Organizations: Knowledge, Learning, Collaboration (KLC)
PublicationThis book focuses on seeing, understanding, and learning to shape an organization’s essential cultures. The book is grounded on a fundamental assumption that every organization has a de facto culture. These “de facto cultures” appear at first glance to be serendipitous, vague, invisible, and unmanaged. An invisible and unrecognized de facto culture can undermine business goals and strategies and lead to business failures. The authors...
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The use of machine learning for face regions detection in thermograms
PublicationThe aim of this study is to analyse the methods of detecting characteristic points of the face in thermographic images. As part of the implementation an extensive analysis of scientific publications covering similar issues both for the analysis of images made in visible light and thermographic images was carried out. On the basis of this analysis, 3 models were selected and then they were implemented and tested on the basis of...
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Detecting Lombard Speech Using Deep Learning Approach
PublicationRobust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks...
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Transfer learning in imagined speech EEG-based BCIs
PublicationThe Brain–Computer Interfaces (BCI) based on electroencephalograms (EEG) are systems which aim is to provide a communication channel to any person with a computer, initially it was proposed to aid people with disabilities, but actually wider applications have been proposed. These devices allow to send messages or to control devices using the brain signals. There are different neuro-paradigms which evoke brain signals of interest...
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Assessment of student language skills in an e-learning environment
PublicationThis article presents the role of various assessment structures that can be used in a VLE. e-Learning language courses offer tutors a wide range of traditional and computer-generated formative and summative assessment procedures and tools. They help to evaluate each student’s progress, monitor their activities and provide varied support, which comes from the tutor, the course structure and materials as well as other participants....
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Preferred Benchmarking Criteria for Systematic Taxonomy of Embedded Platforms (STEP) in Human System Interaction Systems
PublicationThe rate of progress in the field of Artificial Intelligence (AI) and Machine Learning (ML) has significantly increased over the past ten years and continues to accelerate. Since then, AI has made the leap from research case studies to real production ready applications. The significance of this growth cannot be undermined as it catalyzed the very nature of computing. Conventional platforms struggle to achieve greater performance...
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From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition
PublicationRecently gathered image datasets and the new capabilities of high-performance computing systems have allowed developing new artificial neural network models and training algorithms. Using the new machine learning models, computer vision tasks can be accomplished based on the raw values of image pixels instead of specific features. The principle of operation of deep neural networks resembles more and more what we believe to be happening...
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Model of distributed learning objects repository for a heterogenic internet environment
PublicationW 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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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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Meta-Design and the Triple Learning Organization in Architectural Design Process
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Building the Learning Environment for Sustainable Development: a Co-creation approach
PublicationEducation 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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DentalSegmentator: robust deep learning-based CBCT image segmentation
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Deep learning-based waste detection in natural and urban environments
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-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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Becoming a Learning Organization Through Dynamic Business Process Management
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Digital competence learning in secondary adult education in Finland and Poland
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Cognitive and Psychological Reverberations of Remote Learning on Students at the University of Saida
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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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Didaktische simulationsmodelle fur E-learning in der IK-ausbildung.
PublicationPrzedstawiono 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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Antenna Optimization Using Machine Learning with Reduced-Dimensionality Surrogates
PublicationIn modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM)...
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Divide and not forget: Ensemble of selectively trained experts in Continual Learning
PublicationClass-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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Employing Blended E-Learning to Improve Rate of Assignments Handing-In
PublicationIt 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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Employing a biofeedback method based on hemispheric synchronization in effective learning
PublicationIn 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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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublicationWe 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
PublicationMusic 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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Playback detection using machine learning with spectrogram features approach
PublicationThis 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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Machine Learning in Multi-Agent Systems using Associative Arrays
PublicationIn 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
PublicationThese 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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Processes of enhancing the intelligence of Learning Organizations on the basis of Competence Centers
PublicationThe 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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Deep learning-based waste detection in natural and urban environments
PublicationWaste 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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Organizational Wisdom: The Impact of Organizational Learning on the Absorptive Capacity of an Enterprise
PublicationPurpose: 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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Analysis of Learning Outcomes in Medical Education with the Use of Fuzzy Logic
PublicationThe 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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Breast MRI segmentation by deep learning: key gaps and challenges
PublicationBreast 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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Designing acoustic scattering elements using machine learning methods
PublicationIn 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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Machine learning applied to acoustic-based road traffic monitoring
PublicationThe 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-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-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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Platelet RNA Sequencing Data Through the Lens of Machine Learning
PublicationLiquid 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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Noise profiling for speech enhancement employing machine learning models
PublicationThis 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
PublicationThe 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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Leadership, culture, intellectual capital and knowledge processes for organizational innovativeness across industries: the case of Poland
PublicationPurpose – This study aims to present the overview of intellectual capital creation micro-mechanisms concerning formal and informal knowledge processes. The organizational culture, transformational leadership and innovativeness are also included in the investigation as ascendants and consequences of the focal relation of intellectual capital and knowledge processes. Design/methodology/approach – Based on a sample of 1,418 Polish...
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Jacek Rumiński prof. dr hab. inż.
PeopleWykształcenie i kariera zawodowa 2022 2016 2002 1995 1991-1995 Tytuł profesora Habilitacja Doktor nauk technicznych Magister inżynier Prezydent RP, dziedzina nauk inżynieryjno-technicznych, dyscyplina: inzyniera biomedyczna Politechnika Gdańska, Biocybernetyka i inżyniera biomedyczna, tematyka: „Metody wyodrębniania sygnałów i parametrów z różnomodalnych sekwencji obrazów dla potrzeb diagnostyki i wspomagania...
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The Role of Dopaminergic Genes in Probabilistic Reinforcement Learning in Schizophrenia Spectrum Disorders
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Confirmation Bias in the Course of Instructed Reinforcement Learning in Schizophrenia-Spectrum Disorders
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Efficient sampling of high-energy states by machine learning force fields
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Application of creative problem-solving methods in remote learning. Bibliometric analysis
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