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    Wyświetlanie 1-14 z 14
    Tytuł:
    Theoretical Concepts, Sources and Technical Background of E-learning
    Autorzy:
    Kapounová, Jana
    Kostolányová, Kateřina
    Pavlíček, Jiří
    Powiązania:
    https://bibliotekanauki.pl/articles/28765724.pdf
    Data publikacji:
    2006-03-31
    Wydawca:
    Wydawnictwo Adam Marszałek
    Tematy:
    Information and Communication Technology (ICT)
    programmed learning
    teaching machines
    courseware
    e-learning
    Learning Management System (LMS)
    learning object
    Opis:
    The topic Theoretical Concepts, Sources and Technical Background of E-learning is discussed in a project of the Czech Science Foundation. A research team from three Czech universities (University of Ostrava, Charles University in Prague and University of West Bohemia in Pilsen) is working on the project. Its aim is to summarise theoretical concepts, to analyse sources of content, to assess methodological background and to search for technical solutions how to transfer some titles of current courseware into electronic version and to evaluate the efficiency of procedure. The methodology of transformation can help authors of study materials (not only e-learning), they may benefit from old instructional programmes.
    Źródło:
    The New Educational Review; 2006, 8; 97-106
    1732-6729
    Pojawia się w:
    The New Educational Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel approach of voterank-based knowledge graph for improvement of multi-attributes influence nodes on social networks
    Autorzy:
    Pham, Hai Van
    Duong, Pham Van
    Tran, Dinh Tuan
    Lee, Joo-Ho
    Powiązania:
    https://bibliotekanauki.pl/articles/23944825.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    video surveillance
    deep learning
    moving object detection
    Opis:
    Recently, measuring users and community influences on social media networks play significant roles in science and engineering. To address the problems, many researchers have investigated measuring users with these influences by dealing with huge data sets. However, it is hard to enhance the performances of these studies with multiple attributes together with these influences on social networks. This paper has presented a novel model for measuring users with these influences on a social network. In this model, the suggested algorithm combines Knowledge Graph and the learning techniques based on the vote rank mechanism to reflect user interaction activities on the social network. To validate the proposed method, the proposed method has been tested through homogeneous graph with the building knowledge graph based on user interactions together with influences in realtime. Experimental results of the proposed model using six open public data show that the proposed algorithm is an effectiveness in identifying influential nodes.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 165--180
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Moving object detection for complex scenes by merging BG modeling and deep learning method
    Autorzy:
    Lin, Chih-Yang
    Huang, Han-Yi
    Lin, Wei-Yang
    Ng, Hui-Fuang
    Muchtar, Kahlil
    Nurdin, Nadhila
    Powiązania:
    https://bibliotekanauki.pl/articles/23944823.pdf
    Data publikacji:
    2023
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    video surveillance
    deep learning
    moving object detection
    Opis:
    In recent years, many studies have attempted to use deep learning for moving object detection. Some research also combines object detection methods with traditional background modeling. However, this approach may run into some problems with parameter settings and weight imbalances. In order to solve the aforementioned problems, this paper proposes a new way to combine ViBe and Faster-RCNN for moving object detection. To be more specific, our approach is to confine the candidate boxes to only retain the area containing moving objects through traditional background modeling. Furthermore, in order to make the detection able to more accurately filter out the static object, the probability of each region proposal then being retained. In this paper, we compare four famous methods, namely GMM and ViBe for the traditional methods, and DeepBS and SFEN for the deep learning-based methods. The result of the experiment shows that the proposed method has the best overall performance score among all methods. The proposed method is also robust to the dynamic background and environmental changes and is able to separate stationary objects from moving objects. Especially the overall F-measure with the CDNET 2014 dataset (like in the dynamic background and intermittent object motion cases) was 0,8572.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 151--163
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Content Repository in Object Oriented data model
    Autorzy:
    Dobrowolski, D.
    Chromiak, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/106182.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    content repository
    e-learning
    classification of e-learning content
    standards
    content management
    prospective object-oriented database
    Opis:
    The need for creating content repository stores for e-learning systems grows as the number of available materials increases. Moreover, along with the number of courses, the problem of describing them in a unified form appears. While there are standards used for strict classification of e-learning content, the store model still seems to be based on preservative relational databases approach. In this paper we introduce an idea to represent the e-learning content management information in the well organized object-oriented form based on a prospective object-oriented database.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2013, 13, 1; 17-27
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An AI & ML based detection & identification in remote imagery: state-of-the-art
    Autorzy:
    Hashmi, Hina
    Dwivedi, Rakesh
    Kumar, Anil
    Powiązania:
    https://bibliotekanauki.pl/articles/2141786.pdf
    Data publikacji:
    2021
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    convolutional neural network
    remote sensed imagery
    object detection
    artificial intelligence
    feature extraction
    deep learning
    machine learning
    Opis:
    Remotely sensed images and their allied areas of application have been the charm for a long time among researchers. Remote imagery has a vast area in which it is serving and achieving milestones. From the past, after the advent of AL, ML, and DL-based computing, remote imagery is related techniques for processing and analyzing are continuously growing and offering countless services like traffic surveillance, earth observation, land surveying, and other agricultural areas. As Artificial intelligence has become the charm of researchers, machine learning and deep learning have been proven as the most commonly used and highly effective techniques for object detection. AI & ML-based object segmentation & detection makes this area hot and fond to the researchers again with the opportunities of enhanced accuracy in the same. Several researchers have been proposed their works in the form of research papers to highlight the effectiveness of using remotely sensed imagery for commercial purposes. In this article, we have discussed the concept of remote imagery with some preprocessing techniques to extract hidden and fruitful information from them. Deep learning techniques applied by various researchers along with object detection, object recognition are also discussed here. This literature survey is also included a chronological review of work done related to detection and recognition using deep learning techniques.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 3-17
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Systematic analysis and review of video object retrieval techniques
    Autorzy:
    Ghuge, C. A.
    Prakash, V. Chandra
    Ruikar, Sachin D.
    Powiązania:
    https://bibliotekanauki.pl/articles/2050246.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    video object retrieval
    computer vision
    deep learning
    fuzzy-based techniques
    machine learning
    query-based techniques
    graph-based techniques
    Opis:
    Video object retrieval is a promising research direction, developing in the recent years, and the current video object retrieval strategies are used for visualizing, digitizing, modeling, and retrieving the objects especially in graphics and in architectural design. The research performed led to the design of proficient video object retrieval techniques. Yet, although, a number of algorithms had been devised for tracking objects, the problems persist in enhancing the performance, for instance – with regard to non-rigid objects. In this review article we provide a detailed survey of 50 research papers presenting the suggested video object retrieval methodologies, based on approaches such as deep learning techniques, graph-based techniques, query-based techniques, feature-based techniques, fuzzybased techniques, machine learning-based techniques, distance metric learning-based technique, and also other ones. Moreover, analysis and discussion are presented concerning the year of publication, employed methodology, evaluation metrics, accuracy range, adopted framework, datasets utilized, and the implementation tool. Finally, the research gaps and issues related to various proposed video object retrieval schemes are presented for guiding the researchers towards improved contributions to the video object retrieval methods.
    Źródło:
    Control and Cybernetics; 2020, 49, 4; 471-498
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep Learning for Small and Tiny Object Detection: A Survey
    Przegląd metod uczenia głębokiego w wykrywaniu małych i bardzo małych obiektów
    Autorzy:
    Kos, Aleksandra
    Belter, Dominik
    Majek, Karol
    Powiązania:
    https://bibliotekanauki.pl/articles/27312454.pdf
    Data publikacji:
    2023
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    Deep Learning
    Small Object Detection
    Tiny Object Detection
    Tiny Object Detection Datasets
    Tiny Object Detection Methods
    uczenie głębokie
    wykrywanie małych obiektów
    wykrywanie bardzo małych obiektów
    zbiory danych bardzo małych obiektów
    metody wykrywania bardzo małych obiektów
    Opis:
    In recent years, thanks to the development of Deep Learning methods, there has been significant progress in object detection and other computer vision tasks. While generic object detection is becoming less of an issue for modern algorithms, with the Average Precision for medium and large objects in the COCO dataset approaching 70 and 80 percent, respectively, small object detection still remains an unsolved problem. Limited appearance information, blurring, and low signal-to-noise ratio cause state-of-the-art general detectors to fail when applied to small objects. Traditional feature extractors rely on downsampling, which can cause the smallest objects to disappear, and standard anchor assignment methods have proven to be less effective when used to detect low-pixel instances. In this work, we perform an exhaustive review of the literature related to small and tiny object detection. We aggregate the definitions of small and tiny objects, distinguish between small absolute and small relative sizes, and highlight their challenges. We comprehensively discuss datasets, metrics, and methods dedicated to small and tiny objects, and finally, we make a quantitative comparison on three publicly available datasets.
    W ostatnich latach, dzięki rozwojowi metod uczenia głębokiego, dokonano znacznego postępu w detekcji obiektów i innych zadaniach widzenia maszynowego. Mimo że ogólne wykrywanie obiektów staje się coraz mniej problematyczne dla nowoczesnych algorytmów, a średnia precyzja dla średnich i dużych instancji w zbiorze COCO zbliża się odpowiednio do 70 i 80 procent, wykrywanie małych obiektów pozostaje nierozwiązanym problemem. Ograniczone informacje o wyglądzie, rozmycia i niski stosunek sygnału do szumu powodują, że najnowocześniejsze detektory zawodzą, gdy są stosowane do małych obiektów. Tradycyjne ekstraktory cech opierają się na próbkowaniu w dół, które może powodować zanikanie najmniejszych obiektów, a standardowe metody przypisania kotwic są mniej skuteczne w wykrywaniu instancji o małej liczbie pikseli. W niniejszej pracy dokonujemy wyczerpującego przeglądu literatury dotyczącej wykrywania małych i bardzo małych obiektów. Przedstawiamy definicje, rozróżniamy małe wymiary bezwzględne i względne oraz podkreślamy związane z nimi wyzwania. Kompleksowo omawiamy zbiory danych, metryki i metody, a na koniec dokonujemy porównania ilościowego na trzech publicznie dostępnych zbiorach danych.
    Źródło:
    Pomiary Automatyka Robotyka; 2023, 27, 3; 85--94
    1427-9126
    Pojawia się w:
    Pomiary Automatyka Robotyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Formation of a linguistic competence of pre-school children in the language learning developmental environment
    Autorzy:
    Spiridonova, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/443127.pdf
    Data publikacji:
    2011
    Wydawca:
    ADVSEO
    Tematy:
    EARLY LANGUAGE TEACHING
    LINGUISTIC COMPETENCE
    ENVIRONMENTAL APPROACH
    LANGUAGE LEARNING DEVELOPMENT ENVIRONMENT (LLDE)
    OBJECT ORIENTATED ACTIVITY
    Opis:
    The problem of early language education is discussed in the paper. The components of the linguistic competence of pre-school children are described. Definitions of such concepts as the environmental approach and the Language Learning Developmental Environment (LLDE) are given. The article also focuses on the LLDE components illustrating theoretical data with practical examples. The most efficient means of early language learning and of forming the linguistic competence of pre-school children are analyzed.
    Źródło:
    General and Professional Education; 2011, 3; 41-51
    2084-1469
    Pojawia się w:
    General and Professional Education
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine learning versus human-developed algorithms in image analysis of microstructures
    Autorzy:
    Piwowarczyk, Adam
    Wojnar, Leszek
    Powiązania:
    https://bibliotekanauki.pl/articles/103967.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Menedżerów Jakości i Produkcji
    Tematy:
    image analysis
    object detection
    neural networks
    machine learning
    analiza obrazu
    detekcja obiektów
    sieci neuronowe
    uczenie maszynowe
    Opis:
    Automatic image analysis is nowadays a standard method in quality control of metallic materials, especially in grain size, graphite shape and non-metallic content evaluation. Automatically prepared solutions, based on machine learning, constitute an effective and sufficiently precise tool for classification. Human-developed algorithms, on the other hand, require much more experience in preparation, but allow better control of factors affecting the final result. Both attempts were described and compared.
    Źródło:
    Quality Production Improvement - QPI; 2019, 1, 1; 412-416
    2657-8603
    Pojawia się w:
    Quality Production Improvement - QPI
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Utilizing relevant RGB-D data to help recognize RGB images in the target domain
    Autorzy:
    Gao, Depeng
    Liu, Jiafeng
    Wu, Rui
    Cheng, Dansong
    Fan, Xiaopeng
    Tang, Xianglong
    Powiązania:
    https://bibliotekanauki.pl/articles/329725.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    object recognition
    RGB-D image
    transfer learning
    privileged information
    rozpoznawanie obiektu
    obraz RGB-D
    uczenie maszynowe
    informacja poufna
    Opis:
    With the advent of 3D cameras, getting depth information along with RGB images has been facilitated, which is helpful in various computer vision tasks. However, there are two challenges in using these RGB-D images to help recognize RGB images captured by conventional cameras: one is that the depth images are missing at the testing stage, the other is that the training and test data are drawn from different distributions as they are captured using different equipment. To jointly address the two challenges, we propose an asymmetrical transfer learning framework, wherein three classifiers are trained using the RGB and depth images in the source domain and RGB images in the target domain with a structural risk minimization criterion and regularization theory. A cross-modality co-regularizer is used to restrict the two-source classifier in a consistent manner to increase accuracy. Moreover, an L2,1 norm cross-domain co-regularizer is used to magnify significant visual features and inhibit insignificant ones in the weight vectors of the two RGB classifiers. Thus, using the cross-modality and cross-domain co-regularizer, the knowledge of RGB-D images in the source domain is transferred to the target domain to improve the target classifier. The results of the experiment show that the proposed method is one of the most effective ones.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 611-621
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Syntactic and pragmatic aspects of object clitic production in Polish learners of L2 Italian
    Autorzy:
    Tedeschi, Roberta
    Powiązania:
    https://bibliotekanauki.pl/articles/571952.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Warszawski. Wydział Neofilologii
    Tematy:
    Italian clitic pronouns, object clitic pronouns, Polish clitic pronouns, context clitic left dislocation, CLLD, Italian language learning, Polish-Italian comparison
    Opis:
    This paper presents the results of a new study investigating object clitic production by Polish learners of L2 Italian in different syntactic environments. Object clitic pronouns have different distributional properties in Italian and Polish. Additionally, Polish is less restrictive than Italian in allowing object drop in pragmatically felicitous contexts. New findings indicate the presence of object clitic omissions in language production, especially in the context of clitic left dislocation (CLLD). It is proposed that Italian CLLD constructions are particularly demanding for Polish learners, since both syntactic and discourse-pragmatic requirements support clitic omission in their native language.
    Niniejszy artykuł przedstawia wyniki pracy badawczej poświęconej produkcji zaimków klitycznych w funkcji dopełnienia przez osoby uczące się języka włoskiego jako drugiego języka (L2). Zaimki klityczne w tych dwóch językach nie zawsze występują na tym samym miejscu w zdaniach. Ponadto w języku polskim dopełnienia domyślne są bardziej dopuszczalne. Najnowsze wyniki wskazują na występowanie pominięcia zaimków litycznych w funkcji dopełnienia w produkcji językowej, zwłaszcza w kontekście przesunięcia dopełnienia w lewo (Clitic Left Dislocation, CLLD). Ponadto sugerują, ze konstrukcje CLLD stanowią szczególne wyzwanie dla polskojęzycznych osób uczących się języka włoskiego, gdyż wymogi syntaktyczne i pragmatyczne wspierają pominięcie zaimków klitycznych w języku polskim.
    Źródło:
    Acta Philologica; 2016, 48; 87-96
    0065-1524
    Pojawia się w:
    Acta Philologica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multiscaled hybrid features generation for AdaBoost object detection
    Autorzy:
    Dembski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/333917.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    object detection
    machine learning
    biometrics
    AdaBoost classifier
    high resolution images
    detekcja obiektów
    uczenie maszynowe
    biometria
    klasyfikator AdaBoost
    obrazy wysokiej rozdzielczości
    Opis:
    This 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. The high resolution example images were used in detector training process.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 75-82
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fast multispectral deep fusion networks
    Autorzy:
    Osin, V.
    Cichocki, A.
    Burnaev, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/200648.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multispectral imaging
    data fusion
    deep learning
    convolutional network
    object detection
    image segmentation
    obrazowanie wielospektralne
    fuzja danych
    uczenie głębokie
    sieci splotowe
    wykrywanie obiektów
    segmentacja obrazu
    Opis:
    Most current state-of-the-art computer vision algorithms use images captured by cameras, which operate in the visible spectral range as input data. Thus, image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions, e.g. during nighttime. Another significant limitation of such systems is high demand for computational resources, which makes them impossible to use on low-powered embedded systems without GPU support. This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors. First, we analyze existing methods of combining data from different spectral ranges for object detection task. Based on the analysis, an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data. This architecture is based on the single shot multi-box detection algorithm. Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems. This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems. Thus, the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks, as well as a modification of detection algorithms for working on embedded systems.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 875-889
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of computer vision and image analysis technics
    Autorzy:
    Rybchak, Z.
    Basystiuk, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/411187.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    computer vision
    image recognition
    object recognition
    machine learning
    computer with high-level understanding
    digital images processing
    scene reconstruction
    wizja komputerowa
    rozpoznawanie obrazów
    rozpoznawanie obiektów
    systemy uczące się
    cyfrowe przetwarzanie obrazów
    Opis:
    Computer vision and image recognition are one of the most popular theme nowadays. Moreover, this technology developing really fast, so filed of usage increased. The main aims of this article are explain basic principles of this field and overview some interesting technologies that nowadays are widely used in computer vision and image recognition.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 79-84
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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