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Search results for: CO-TRAINING
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Magdalena Szuflita-Żurawska
PeopleHead of the Scientific and Technical Information Services at the Gdansk University of Technology Library and the Leader of the Open Science Competence Center. She is also a Plenipotentiary of the Rector of the Gdańsk University of Technology for open science. She is a PhD Candidate. Her main areas of research and interests include research productivity, motivation, management of HEs, Open Access, Open Research Data, information...
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Justyna Signerska-Rynkowska dr inż.
PeopleI am currently an assistant professor (adjunct) at Gdansk University of Technology (Department of Differential Equations and Mathematics Applications). My scientific interests include dynamical systems theory, chaos theory and their applications to modeling of biological phenomena, especially to neurosciences. In June 2013 I completed PhD in Mathematics at the Institute of Mathematics of Polish Academy of Sciences (IMPAN) (thesis...
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Classification of Music Genres Based on Music Separation into Harmonic and Drum Components . Klasyfikacja gatunków muzycznych wykorzystująca separację instrumentów muzycznych
PublicationThis article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector...
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Podejście środowiskowe w dydaktyce projektowania Sustainable approach in teaching of design
PublicationThe aim of the paper is to assess the inclusion of academic teaching design to the process of sustainable development and proposals for an integrated approach in training architects. The article presents an original interpretation of environmental design in the field of architecture and urban planning. The significance of the environmental perspective is presented in the context of growing spatial and social fragmentation. The...
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Paweł Rościszewski dr inż.
PeoplePaweł Rościszewski received his PhD in Computer Science at Gdańsk University of Technology in 2018 based on PhD thesis entitled: "Optimization of hybrid parallel application execution in heterogeneous high performance computing systems considering execution time and power consumption". Currently, he is an Assistant Professor at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Poland....
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To Survive in a CBRN Hostile Environment: Application of CAVE Automatic Virtual Environments in First Responder Training
PublicationThis paper is of a conceptual nature and focuses on the use of a specific virtual reality environment in civil-military training. We analyzed the didactic potential of so-called CAVE automatic virtual environments for First Responder training, a type of training that fills the gap between First Aid training and the training received by emergency medical technicians. Since real training involves live drills based on unexpected situations,...
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Implementing SP4CE Learning Rooms concept and AUTODESK online certification in the preparation of a new generation of engineers.
PublicationIn academia, educators do not always cope with rapidly changing technologies. Yet keeping up with new trends is essential to graduates’ success in a competitive job market. In the article, the author will answer the question of how Autodesk University Open Educational Resources and Certiport exams including GMetrix can enhance students’ academic progress and prepare them for future career. The concept of co-operation between Authorized...
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Methods of Cyclist Training in Europe
PublicationThe following study aims to address the issue of cyclist training methodologies. Recent European bicycle accident statistics reveal a troubling upward trend. A potential solution to mitigate such incidents involves providing cyclists with comprehensive training encompassing traffic regulations and interactions with fellow road users. We conducted a comparative analysis of the cycling education approaches and cyclist training systems...
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Automatic music genre classification based on musical instrument track separation / Automatyczna klasyfikacja gatunku muzycznego wykorzystująca algorytm separacji dźwięku instrumentó muzycznych
PublicationThe aim of this article is to investigate whether separating music tracks at the pre-processing phase and extending feature vector by parameters related to the specific musical instruments that are characteristic for the given musical genre allow for efficient automatic musical genre classification in case of database containing thousands of music excerpts and a dozen of genres. Results of extensive experiments show that the approach...
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TRWAŁOŚĆ PROJEKTU ERASMUS+ SP4CE - STUDIUM PRZYPADKU
PublicationProjekt ERASMUS+ Partnerstwo Strategiczne na Rzecz Kreatywności i Przedsiębiorczości (ang. Strategic Partnership for Creativity and Entrepreneurship - SP4CE) dotyczył wdrażania i upowszechniania innowacyjnych rozwiązań wzmacniających współpracę europejską w dziedzinie kształcenia i szkolenia zawodowego. Działania projektowe były związane z promowaniem innowacyjnych praktyk w edukacji oraz szkoleniach poprzez wspieranie spersonalizowanych...
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Exploration of Creativity Techniques in Software Engineering in Training-Application-Feedback Cycle
PublicationCreativity research has proposed about a hundred and fifty creativity techniques. The question is whether they can be applied in software engineering for creativity training or directing creativity in software projects. This paper aims at answering this question via a quasi-experiment conducted in Training-Application-Feedback cycle in which participants express their opinions about selected creativity techniques after training...
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Multiscaled Hybrid Features Generation for AdaBoost Object Detection
PublicationThis work presents the multiscaled version of modified census features in graphical objects detection with AdaBoost cascade training algorithm. Several experiments with face detector training process demonstrate better performance of such features over ordinal census and Haar-like approaches. The possibilities to join multiscaled census and Haar features in single hybrid cascade of strong classifiers are also elaborated and tested....
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Minimizing Distribution and Data Loading Overheads in Parallel Training of DNN Acoustic Models with Frequent Parameter Averaging
PublicationIn the paper we investigate the performance of parallel deep neural network training with parameter averaging for acoustic modeling in Kaldi, a popular automatic speech recognition toolkit. We describe experiments based on training a recurrent neural network with 4 layers of 800 LSTM hidden states on a 100-hour corpora of annotated Polish speech data. We propose a MPI-based modification of the training program which minimizes the...
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Reliable Microwave Modeling By Means of Variable-Fidelity Response Features
PublicationIn this work, methodologies for low-cost and reliable microwave modeling are presented using variable-fidelity response features. The two key components of our approach are: (i) a realization of the modeling process at the level of suitably selected feature points of the responses (e.g., S-parameters vs. frequency) of the structure at hand, and (ii) the exploitation of variable-fidelity EM simulation data, also for the response...
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Language material for English audiovisual speech recognition system developmen . Materiał językowy do wykorzystania w systemie audiowizualnego rozpoznawania mowy angielskiej
PublicationThe bi-modal speech recognition system requires a 2-sample language input for training and for testing algorithms which precisely depicts natural English speech. For the purposes of the audio-visual recordings, a training data base of 264 sentences (1730 words without repetitions; 5685 sounds) has been created. The language sample reflects vowel and consonant frequencies in natural speech. The recording material reflects both the...
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Training Services in Small and Medium-sized Enterprises: Evidence from Poland
PublicationIn the knowledge-based economy knowledge and skills are becoming more and more significant for the success of companies. This applies also to firms from small and medium-sized enterprises (SMEs) sector. As large companies in many cases posses special divisions devoted to trainings, they normally have no problems with updating the knowledge and skills of their employees. The situation is different with regard to SMEs, which often...
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Biometric identity verification
PublicationThis chapter discusses methods which are capable of protecting automatic speaker verification systems (ASV) from playback attacks. Additionally, it presents a new approach, which uses computer vision techniques, such as the texture feature extraction based on Local Ternary Patterns (LTP), to identify spoofed recordings. We show that in this case training the system with large amounts of spectrogram patches may be difficult, and...
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Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
PublicationIn this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, and mass of product) and dependent variables (energy consumption and size reduction) were established. For energy consumption,...
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Food Classification from Images Using a Neural Network Based Approach with NVIDIA Volta and Pascal GPUs
PublicationIn the paper we investigate the problem of food classification from images, for the Food-101 dataset extended with 31 additional food classes from Polish cuisine. We adopted transfer learning and firstly measured training times for models such as MobileNet, MobileNetV2, ResNet50, ResNet50V2, ResNet101, ResNet101V2, InceptionV3, InceptionResNetV2, Xception, NasNetMobile and DenseNet, for systems with NVIDIA Tesla V100 (Volta) and...
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Active Learning Based on Crowdsourced Data
PublicationThe paper proposes a crowdsourcing-based approach for annotated data acquisition and means to support Active Learning training approach. In the proposed solution, aimed at data engineers, the knowledge of the crowd serves as an oracle that is able to judge whether the given sample is informative or not. The proposed solution reduces the amount of work needed to annotate large sets of data. Furthermore, it allows a perpetual increase...
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Texture Features for the Detection of Playback Attacks: Towards a Robust Solution
PublicationThis paper describes the new version of a method that is capable of protecting automatic speaker verification (ASV) systems from playback attacks. The presented approach uses computer vision techniques, such as the texture feature extraction based on Local Ternary Patterns (LTP), to identify spoofed recordings. Our goal is to make the algorithm independent from the contents of the training set as much as possible; we look for the...
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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublicationIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...
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Process of Medical Dataset Construction for Machine Learning-Multifield Study and Guidelines
PublicationThe acquisition of high-quality data and annotations is essential for the training of efficient machine learning algorithms, while being an expensive and time-consuming process. Although the process of data processing and training and testing of machine learning models is well studied and considered in the literature, the actual procedures of obtaining data and their annotations in collaboration with physicians are in most cases...
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Feature Reduction Using Similarity Measure in Object Detector Learning with Haar-like Features
PublicationThis paper presents two methods of training complexity reduction by additional selection of features to check in object detector training task by AdaBoost training algorithm. In the first method, the features with weak performance at first weak classifier building process are reduced based on a list of features sorted by minimum weighted error. In the second method the feature similarity measures are used to throw away that features...
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Massive Open Online Courses (MOOCs) in hospitality and tourism
PublicationThe tourism industry, interesting and challenging, faces structural human resource problems such as skills shortages and staff turnover, seasonality and a high percentage of small to medium enterprises whose employees have limited time for training or education. Large tourism enterprises often span countries and continents, such as hotel chains, airlines, cruise companies and car rentals, where the employees need similar training...
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Performance improvement of NN based RTLS by customization of NN structure - heuristic approach
PublicationThe purpose of this research is to improve performance of the Hybrid Scene Analysis – Neural Network indoor localization algorithm applied in Real-time Locating System, RTLS. A properly customized structure of Neural Network and training algorithms for specific operating environment will enhance the system’s performance in terms of localization accuracy and precision. Due to nonlinearity and model complexity, a heuristic analysis...
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Computer-Supported Polysensory Integration Technology for Educationally Handicapped Pupils
PublicationIn this paper, a multimedia system providing technology for hearing and visual attention stimulation is shortly presented. The system aims to support the development of educationally handicapped pupils. The system has been presented in the context of its configuration, architecture, and therapeutic exercise implementation issues. Results of pupils’ improvements after 8 weeks of training with the system are also provided. Training...
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Adaptive CAD-Model Construction Schemes
PublicationTwo advanced surrogate model construction techniques are discussed in this paper. The models employ radial basis function (RBF)interpolation scheme or artificial neural networks (ANN) with a new training algorithm. Adaptive sampling technique is applied withrespect to all variables. Histograms showing the quality of the models are presented. While the quality of RBF models is satisfactory, theperformance of the ANN models obtained...
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Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers
PublicationThis chapter presents the application of new information technology in education for the training of air traffic controllers (ATCs). Machine learning, multi-criteria decision analysis, and text analysis as the methods of artificial intelligence for ATCs training have been described. The authors have made an analysis of the International Civil Aviation Organization documents for modern principles of ATCs education. The prototype...
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Factors Affecting the Effectiveness of Military Training in Virtual Reality Environment
PublicationIn this paper, we explored the factors influencing the effectiveness of military trainings performed in a virtual reality environment. The rationale for taking up the topic is the fact that such trainings are often conducted under specific operational procedures. These procedures may create rigorous frameworks for all elements of the learning environment, including the teacher’s performance. Therefore, to ensure the most conducive...
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Real and Virtual Instruments in Machine Learning – Training and Comparison of Classification Results
PublicationThe continuous growth of the computing power of processors, as well as the fact that computational clusters can be created from combined machines, allows for increasing the complexity of algorithms that can be trained. The process, however, requires expanding the basis of the training sets. One of the main obstacles in music classification is the lack of high-quality, real-life recording database for every instrument with a variety...
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A review of emotion recognition methods based on keystroke dynamics and mouse movements
PublicationThe paper describes the approach based on using standard input devices, such as keyboard and mouse, as sources of data for the recognition of users’ emotional states. A number of systems applying this idea have been presented focusing on three categories of research problems, i.e. collecting and labeling training data, extracting features and training classifiers of emotions. Moreover the advantages and examples of combining standard...
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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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Recognizing emotions on the basis of keystroke dynamics
PublicationThe article describes a research on recognizing emotional states on the basis of keystroke dynamics. An overview of various studies and applications of emotion recognition based on data coming from keyboard is presented. Then, the idea of an experiment is presented, i.e. the way of collecting and labeling training data, extracting features and finally training classifiers. Different classification approaches are proposed to be...
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Musical Instrument Identification Using Deep Learning Approach
PublicationThe work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related to musical instrument identification. It focuses on tasks performed, input type, algorithms employed, and metrics used. The paper starts with the background presentation, i.e., metadata...
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Color-based Detection of Bleeding in Endoscopic Images
PublicationIn this paper a color descriptor designed for bleeding detection in endoscopic images is proposed. The development of the algorithm was carried out on a representative training set of 36 images of bleeding and 25 clear images. Another 38 bleeding and 26 normal images were used in the final stage as a test set. All of the considered images were extracted from separate endoscopic examinations. The experiments include color distribution...
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Towards Scalable Simulation of Federated Learning
PublicationFederated learning (FL) allows to train models on decentralized data while maintaining data privacy, which unlocks the availability of large and diverse datasets for many practical applications. The ongoing development of aggregation algorithms, distribution architectures and software implementations aims for enabling federated setups employing thousands of distributed devices, selected from millions. Since the availability of...
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The impact of training and neurotrophins on functional recovery after spinal cord transection: cellular and molecular mechanisms contributing to motor improvement
PublicationBeneficial effects of locomotor training on the functional recovery after complete transection of the spinal cord indicate that in chronic spinal animals spontaneous recovery processes are enhanced and shaped by the training. The mechanisms of that use-dependent improvement are still not fully understood. This review tackles three aspects of this issue: (1) neurochemical attributes of functional improvement...
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublicationThis paper presents an approach to bee detection in video streams using a neural network classifier. We describe the motivation for our research and the methodology of data acquisition. The main contribution to this work is a comparison of different color models used as an input format for a feedforward convolutional architecture applied to bee detection. The detection process has is based on a neural binary classifier that classifies...
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Train the trainer course
PublicationThis chapter presents the concept, evaluation and evaluation results for the train the trainer. This concept of train the trainers is prepared within Workpackage 5 of EU-funded project: MASTER BSR (Erasmus+ Strategic Partnership Programme). Due to the nature of adult learning the content is designed for the use of participatory methods (involved, active). This method uses various techniques of active learning e.g. group work,...
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Creating new voices using normalizing flows
PublicationCreating realistic and natural-sounding synthetic speech remains a big challenge for voice identities unseen during training. As there is growing interest in synthesizing voices of new speakers, here we investigate the ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC) modes to extrapolate from speakers observed during training to create unseen speaker identities. Firstly, we create an approach for TTS...
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Development and test of fex, a fingers extending exoskeleton for rehabilitation and regaining mobility
PublicationThis paper presents the design process of an exoskeleton for executing human fingers' extension movement for the rehabilitation procedures and as an active orthosis purposes, together with its first clinical usability tests of a robotic exoskeleton. Furthermore, the Fingers Extending eXoskeleton (FEX) is a serial, under-actuated mechanism capable of executing fingers' extension. FEX is based on the state-of-art FingerSpine serial...
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Immersive Technologies that Aid Additive Manufacturing Processes in CBRN Defence Industry
PublicationTesting unique devices or their counterparts for CBRN (C-chemical, B-biological, R-radiological, N-nuclear) defense relies on additive manufacturing processes. Immersive technologies aid additive manufacturing. Their use not only helps understand the manufacturing processes, but also improves the design and quality of the products. This article aims to propose an approach to testing CBRN reconnaissance hand-held products developed...
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Neural network training with limited precision and asymmetric exponent
PublicationAlong with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage...
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Towards bees detection on images: study of different color models for neural networks
PublicationThis paper presents an approach to bee detection in videostreams using a neural network classifier. We describe the motivationfor our research and the methodology of data acquisition. The maincontribution to this work is a comparison of different color models usedas an input format for a feedforward convolutional architecture appliedto bee detection. The detection process has is based on a neural...
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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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PROJEKTOWANIE STANOWISK LABORATORYJNYCH WSPIERAJĄCYCH PROCES SZKOLENIA PRAKTYCZNEGO KADR MORSKICH DZIAŁU MASZYNOWEGO W ŻEGLUDZE MIĘDZYNARODOWEJ, PRZYBRZEŻNEJ I KRAJOWEJ
PublicationWithin the article a design offer of the Department of Ship and Power Plants of the Faculty of Ocean Engineering And Ship Technology at the Gdansk University of Technology has been presented. The offer concerns designing laboratory stations which might stand for the equipment of a didactic base of maritime educational centers i.e. maritime (naval) academies and schools as well as maritime affairs' professional training centers,...
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Computer-assisted assessment of learning outcomes in the laboratory of metrology
PublicationIn the paper, didactic experience with broad and rapid continuous assessment of students’ knowledge, skills and competencies in the Laboratory of Metrology, which is an example of utilisation of assessment for learning, is presented. A learning management system was designed for manage, tracking, reporting of learning program and assessing learning outcomes. It has ability to provide with immediate feedback, which is used by the...
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Training of Deep Learning Models Using Synthetic Datasets
PublicationIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
PublicationThis paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS). In a classical approach, audio features are usually extracted from fixed regions of speech such as the syllable nucleus. We propose an attention-based deep learning model that automatically de...