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    Wyświetlanie 1-12 z 12
    Tytuł:
    Individuation, reliability, and the mass/count distinction
    Autorzy:
    Sutton, P. R.
    Filip, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/103819.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
    Tematy:
    mass distinction
    mount distinction
    probabilistic semantics
    individuation
    reliability
    semantic learning
    information theory
    context-sensitivity
    Type Theory with Records
    Opis:
    Counting in natural language presupposes that we can successfully identify what counts as one, which, as we argue, relies on how and whether one can balance two pressures on learning nominal predicates, which we formalise in probabilistic and information theoretic terms: individuation (establishing a schema for judging what counts as one with respect to a predicate); and reliability (establishing a reliable criterion for applying a predicate). This hypothesis has two main consequences. First, the mass/count distinction in natural language is a complex phenomenon that is partly grounded in a theory of individuation, which we contend must integrate particular qualitative properties of entities, among which a key role is player by those that rely on our spatial perception. Second, it allows us to predict when we can expect the puzzling variation in mass/count lexicalization, cross- and intralinguistically: namely, exactly when the two learning pressures of individuation and reliability conflict.
    Źródło:
    Journal of Language Modelling; 2017, 5, 2; 303-356
    2299-856X
    2299-8470
    Pojawia się w:
    Journal of Language Modelling
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamic semantic description and search methods for heterogeneous learning resources
    Autorzy:
    Lai, Xiaocong
    Pan, Ying
    Jiang, Xueling
    Powiązania:
    https://bibliotekanauki.pl/articles/2173685.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    heterogeneous data
    learning resources
    semantic description
    semantic search
    dane niejednorodne
    zasoby edukacyjne
    opis semantyczny
    wyszukiwanie semantyczne
    Opis:
    Learning resources are massive, heterogeneous, and constantly changing. How to find the required resources quickly and accurately has become a very challenging work in the management and sharing of learning resources. According to the characteristics of learning resources, this paper proposes a progressive learning resource description model, which can describe dynamic heterogeneous resource information on a fine-grained level by using information extraction technology, then a semantic annotation algorithm is defined to calculate the semantic of learning resource and add these semantic to the description model. Moreover, a semantic search method is proposed to find the required resources, which calculate the content with the highest similarity to the user query, and then return the results in descending order of similarity. The simulation results show that the method is feasible and effective.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e139434
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Is a quantifier mismatch a problem for L1 Japanese learners of English?
    Autorzy:
    Nehls, Paul N.
    Aramaki, Kodai
    Fujii, Tomohiro
    Powiązania:
    https://bibliotekanauki.pl/articles/40226622.pdf
    Data publikacji:
    2023
    Wydawca:
    Katolicki Uniwersytet Lubelski Jana Pawła II
    Tematy:
    quantifier
    learning problem
    L2 acquisition
    semantic mismatch
    truth value judgement task
    Opis:
    After identifying a linguistic difference between the English quantifier most and the Japanese quantifier hotondo ‘most’ we set out to find if the semantic difference between the two would constitute a learning problem for Japanese second language (L2) learners of English. The difference we hypothesized between the two is that English most is considered “more than half,” while hotondo is “nearly all.” As this semantic difference is not explicitly taught in a classroom environment, acquisition by learners would need to take place through experiencing most in contexts where they might receive contextual clues. An examination of a corpus indicated that contextual clues towards such a semantic difference would be unavailable or rarely available. Two sets of experiments (Experiments 1 and 2) were conducted using the Truth Value Judgment Task methodology. The results of Experiment 1 showed that L2 speakers treated most as meaning “nearly all” but that the level of learner proficiency has an effect. The upper intermediate L2 learner group (Experiment 1a) behaved more like the L1 English speaker group (Experiment 1b) than the lower proficiency L2 group (Experiment 1c). Experiment 2, testing Japanese L1 speakers on their interpretation of Japanese hotondo ‘most,’ revealed that while a majority of participants treated hotondo as “almost all,” there was, somewhat unexpectedly, a group of speakers who interpreted hotondo to mean “more than half.” Therefore, although the possibility cannot completely be eliminated that the result of Experiment 1a is due to L1 transfer, if some Japanese learners of English can unlearn the incorrect meaning, then some prior, if not innate, knowledge that makes the process possible must be available to them.
    Źródło:
    Linguistics Beyond and Within; 2023, 9; 133-146
    2450-5188
    Pojawia się w:
    Linguistics Beyond and Within
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multimedia Mathematical Communication in a Diverse Group of Students
    Autorzy:
    Brzostek-Pawłowska, Jolanta
    Powiązania:
    https://bibliotekanauki.pl/articles/307926.pdf
    Data publikacji:
    2019
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    efficiency of communication
    learning mathematics
    mathematical formula notations
    semantic readout of formulas
    Opis:
    The article tackles the problem of improving mathematical communication in a group of students with different visual impairment levels, under the guidance of a group leader or a teacher. Visually impaired persons face a problem while learning mathematics. The said problem results from the specific nature in which mathematical content (formulas, function graphs, geometrical figures and projections of solids) is recorded and presented. The effectiveness of learning mathematics is boosted when students work in a group moderated by a leader. This requires them to share documents, with the leader being able to keep track of the individual work of each participant, and with the group discussing specific solutions. In order for a visually impaired student to be able to participate in and contribute to the work of the group, either remotely or locally, all participants must use universal IT tools that support visually impaired students without complicating the work of others. This paper presents interactive multimedia solutions developed under two research projects carried out by the author. The said solutions support communication in mathematics. Results of qualitative surveys on new solutions are presented, confirming their usefulness and the measurable impact they exert on the efficiency of the group’s work concerning mathematical problems.
    Źródło:
    Journal of Telecommunications and Information Technology; 2019, 2; 92-103
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Segmentation of bone structures with the use of deep learning techniques
    Autorzy:
    Krawczyk, Zuzanna
    Starzyński, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2128158.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    semantic segmentation
    U-net
    FCN
    ResNet
    computed tomography
    technika deep learning
    głęboka nauka
    segmentacja semantyczna
    tomografia komputerowa
    Opis:
    The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136751, 1--8
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Segmentation of bone structures with the use of deep learning techniques
    Autorzy:
    Krawczyk, Zuzanna
    Starzyński, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2173574.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    semantic segmentation
    U-net
    FCN
    ResNet
    computed tomography
    technika deep learning
    głęboka nauka
    segmentacja semantyczna
    tomografia komputerowa
    Opis:
    The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136751
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Semantic Segmentation of Diseases in Mushrooms using Enhanced Random Forest
    Autorzy:
    Yacharam, Rakesh Kumar
    Sekhar, Dr. V. Chandra
    Powiązania:
    https://bibliotekanauki.pl/articles/31339414.pdf
    Data publikacji:
    2023
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
    Tematy:
    mushroom diseases
    semantic segmentation
    computer aided
    Machine Learning
    significant feature extraction
    Random Forest classifier
    Opis:
    Mushrooms are a rich source of antioxidants and nutritional values. Edible mushrooms, however, are susceptible to various diseases such as dry bubble, wet bubble, cobweb, bacterial blotches, and mites. Farmers face significant production losses due to these diseases affecting mushrooms. The manual detection of these diseases relies on expertise, knowledge of diseases, and human effort. Therefore, there is a need for computer-aided methods, which serve as optimal substitutes for detecting and segmenting diseases. In this paper, we propose a semantic segmentation approach based on the Random Forest machine learning technique for the detection and segmentation of mushroom diseases. Our focus lies in extracting a combination of different features, including Gabor, Bouda, Kayyali, Gaussian, Canny edge, Roberts, Sobel, Scharr, Prewitt, Median, and Variance. We employ constant mean-variance thresholding and the Pearson correlation coefficient to extract significant features, aiming to enhance computational speed and reduce complexity in training the Random Forest classifier. Our results indicate that semantic segmentation based on Random Forest outperforms other methods such as Support Vector Machine (SVM), Naïve Bayes, K-means, and Region of Interest in terms of accuracy. Additionally, it exhibits superior precision, recall, and F1 score compared to SVM. It is worth noting that deep learning-based semantic segmentation methods were not considered due to the limited availability of diseased mushroom images.
    Źródło:
    Machine Graphics & Vision; 2023, 32, 2; 129-146
    1230-0535
    2720-250X
    Pojawia się w:
    Machine Graphics & Vision
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning-based framework for tumour detection and semantic segmentation
    Autorzy:
    Kot, Estera
    Krawczyk, Zuzanna
    Siwek, Krzysztof
    Królicki, Leszek
    Czwarnowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2128156.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    medical imaging
    tumour detection
    semantic segmentation
    image fusion
    technika deep learning
    głęboka nauka
    obrazowanie medyczne
    wykrywanie guza
    segmentacja semantyczna
    połączenie obrazu
    Opis:
    For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136750, 1--7
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning-based framework for tumour detection and semantic segmentation
    Autorzy:
    Kot, Estera
    Krawczyk, Zuzanna
    Siwek, Krzysztof
    Królicki, Leszek
    Czwarnowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2173573.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    medical imaging
    tumour detection
    semantic segmentation
    image fusion
    technika deep learning
    głęboka nauka
    obrazowanie medyczne
    wykrywanie guza
    segmentacja semantyczna
    połączenie obrazu
    Opis:
    For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136750
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Koncepcja Elastycznego Systemu Sieciowych Usług Rekomendacyjnych
    Conception of the Flexible System of Network Recommendation Services Model
    Autorzy:
    Marciniak, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/290892.pdf
    Data publikacji:
    2010
    Wydawca:
    Polskie Towarzystwo Inżynierii Rolniczej
    Tematy:
    usługi rekomendacyjne
    uczenie maszynowe
    elastyczne systemy informacyjne
    semantyczna integracja danych
    recommendation services
    machine learning
    flexible information systems
    semantic data integration
    Opis:
    W pracy przedstawiono koncepcję elastycznego systemu sieciowych usług rekomendacyjnych. Usługi rekomendacyjne są rodzajem usług informacyjnych świadczonych również przez systemy doradztwa rolniczego. Zakłada się, że algorytm tworzący ranking rekomendowanych produktów będzie powstawał w wyniku maszynowego uczenia na zweryfikowanych danych. Aplikacja modelująca taki algorytm może być wykonana w technologii typowej dla sieciowych baz wiedzy. Problemem jest to, że aktualne systemy sieciowych usług informacyjnych są niewystarczająco odporne na zakłócenia w dostępie do danych, z których syntetyzowana jest usługa. Stąd pomysł uelastycznienia systemu poprzez semantyczną integrację zasobów sieciowych.
    The work presents the conception of the flexible system of network recommendation services. Recommendation services are a kind of information services provided also by agricultural consultancy services. It is assumed that the algorithm creating the ranking of recommended products will be formed as a result of machine learning on the basis of verified data. The application modelling such an algorithm can be created in a technology which is characteristic of network knowledge bases. The problem is that current systems of network information services are insufficiently resistant to interruptions of access to data from which the service is synthesized. Consequently, an idea of making the system more flexible through semantic integration of network resources was created.
    Źródło:
    Inżynieria Rolnicza; 2010, R. 14, nr 5, 5; 159-164
    1429-7264
    Pojawia się w:
    Inżynieria Rolnicza
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Technology of creating a system of multilevel tasks as a means of monitoring student learning achievement
    Technologia kreacja systemu zadań wielopoziomowych jako środka monitorowania osiągnięcia edukacyjne uczniów
    Autorzy:
    Bachііeva, Larysa
    Powiązania:
    https://bibliotekanauki.pl/articles/38119185.pdf
    Data publikacji:
    2021
    Wydawca:
    Wyższa Szkoła Techniczna w Katowicach
    Tematy:
    multilevel task
    logical-semantic structure
    levels of learning of educational information
    zadania wielopoziomowe
    struktura logiczna
    struktura semantyczna
    poziomy przyswajania informacji edukacyjnej
    Opis:
    The paper presents the technology of creating a system of multilevel tasks in order to monitor the achievement of learning outcomes. It involves the consistent implementation of such stages: identification of sources of educational and scientific information, their study and selection; creation of didactic materials (logical and semantic structure and the plan of the topic presentation); determining the level of achievement for each concept of the topic; development of tasks using the kit. The implementation of the presented technology allows for the achievement of the following results: the formation of the actual content of education, the determination of the required level of learning of information; implementation of informational, formative and corrective monitoring functions by means of a system of multilevel tasks.
    W artykule przedstawiono technologię kreacji systemu wielopoziomowych zadań przedmiotu dyscypliny akademickiej, w celu monitorowania osiąganych efektów kształcenia. Zakłada ona sekwencyjną realizację takich etapów: identyfikacja źródeł informacji edukacyjnej i naukowej, ich badanie i selekcja, tworzenie materiałów dydaktycznych (struktura logiczna i semantyczna oraz zarys tematu), określenie poziomu asymilacji dla każdej koncepcji tematu, opracowanie zadań z wykorzystaniem konstruktora. Wdrożenie przedstawionej technologii umożliwia osiągnięcie następujących rezultatów: ukształtowanie rzeczywistych treści kształcenia, określenie wymaganego poziomu przyswajania informacji, realizacja funkcji monitoringu informacyjnego, formatywnego i korekcyjnego, za pomocą systemu wielopoziomowych zadań.
    Źródło:
    Zeszyty Naukowe Wyższej Szkoły Technicznej w Katowicach; 2021, 13; 159-172
    2082-7016
    2450-5552
    Pojawia się w:
    Zeszyty Naukowe Wyższej Szkoły Technicznej w Katowicach
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Single target tracking algorithm for lightweight Siamese networks based on global attention
    Autorzy:
    Wang, Zhentao
    He, Xiaowei
    Cheng, Rao
    Powiązania:
    https://bibliotekanauki.pl/articles/2173664.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    target tracking
    Siamese network
    semantic information
    training strategy
    feature fusion
    deep learning
    śledzenie celu
    sieć syjamska
    informacja semantyczna
    strategia szkolenia
    fuzja funkcji
    głęboka nauka
    Opis:
    Object tracking based on Siamese networks has achieved great success in recent years, but increasingly advanced trackers are also becoming cumbersome, which will severely limit deployment on resource-constrained devices. To solve the above problems, we designed a network with the same or higher tracking performance as other lightweight models based on the SiamFC lightweight tracking model. At the same time, for the problems that the SiamFC tracking network is poor in processing similar semantic information, deformation, illumination change, and scale change, we propose a global attention module and different scale training and testing strategies to solve them. To verify the effectiveness of the proposed algorithm, this paper has done comparative experiments on the ILSVRC, OTB100, VOT2018 datasets. The experimental results show that the method proposed in this paper can significantly improve the performance of the benchmark algorithm.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e139961
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-12 z 12

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