Many college students believe that typing lecturenotes on computers produces better notes and higher achievement than handwritten lecturenotes on paper. The few studies investigating computer versus longhand note ta...
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Many college students believe that typing lecturenotes on computers produces better notes and higher achievement than handwritten lecturenotes on paper. The few studies investigating computer versus longhand note taking yielded mixed note-taking and achievement findings. The present study investigated computer versus longhand note taking but permitted note takers to revise or recopy notes during pauses interspersed throughout the lecture. Moreover, the present study analyzed notes recorded while a lecture was ongoing and following revision pauses to determine if lecture ideas and images were recorded completely or partially. Findings did not support the belief that computers aid note taking and achievement and, instead, favored longhand note taking and revision. computer and longhand note takers recorded a comparable number of complete and partial ideas in notes while the lecture was ongoing, but longhand note takers recorded more lecture images. Among note revisers, longhand note takers added three-times-as-many complete ideas to their notes during revision as computer note takers-an important finding because note completeness predicted achievement. Achievement results showed that longhand note takers who revised notes scored more than half a letter grade higher on a lecture posttest than computer note takers who revised notes. Present findings suggest that college instructors should provide students with revision pauses to improve note taking and achievement and encourage students to record and revise notes using the longhand method. Finally, regarding the computer versus longhand note-taking debate, the need to investigate further the interplay between note-taking medium and lesson material is discussed.
Prolonged maintenance of poor sitting posture can have detrimental effects on human health. Thus, maintaining a healthy sitting posture is crucial for individuals who spend long durations sitting. The recent Vision Tr...
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People with disabilities encounter barriers and are thus restricted in their participation in social and societal areas, among that science. As a rising field of research, citizen science has lately been fostered to g...
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ISBN:
(纸本)9783031608742;9783031608759
People with disabilities encounter barriers and are thus restricted in their participation in social and societal areas, among that science. As a rising field of research, citizen science has lately been fostered to gain new insights in fields of research, especially application oriented research. As citizen science experiences strong expectations in research fields connected to civil life, the article argues that citizen science must enable full participation of people with disabilities. To grant people with disabilities' access to these areas of life, participatory approaches should be the state of the art. Due to this, the article will start with a discursive discussion of different participatory approaches of various contexts. Thereby the different theoretical references, as (Participatory) Action Research, (Community-Based) Participatory Research, Practice Research, are presented and located into the methodological frame. After this, the focus is on the Citizen science project "Incluscience - Disability Mainstreaming in science and practice" in which participatory approaches are being prepared in a target strand and bundled into a so-called "Citizen science Toolbox".
Foreground segmentation is an important part of computer vision and has many useful applications, such as tracking objects and detecting anomalies. However, deep learning models often struggle with the difficult task ...
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Foreground segmentation is an important part of computer vision and has many useful applications, such as tracking objects and detecting anomalies. However, deep learning models often struggle with the difficult task of preserving small details when extracting features, which is crucial for accurately segmenting edges. To overcome this challenge, we have developed a new segmentation network with a cascading architecture. This innovative approach gradually incorporates finer details into higher-level features, continuously improving the segmentation results. We have thoroughly evaluated our method using the challenging CDnet2014 dataset, and it has shown exceptional performance with an impressive F-measure of 0.9868. This research demonstrates the potential of cascading networks to greatly improve foreground segmentation in computer vision applications, and it is completely original work.
Certifying software-based systems is a time-consuming and expensive task that requires much manual human effort. We introduce Online Certification, a partly automated version of the certification process, where partic...
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ISBN:
(纸本)9783031744976;9783031744983
Certifying software-based systems is a time-consuming and expensive task that requires much manual human effort. We introduce Online Certification, a partly automated version of the certification process, where participants provide the necessary information dynamically. All information is cryptographically signed to ensure integrity and authorization, and a system of certificates allows for fine-grained delegation of competencies. The requirements for certification, as well as the information needed to fulfill them, are represented in a subset of first-order logic. Consequently, validation is performed using automated logic reasoning. Compared to existing approaches, Online Certification enhances flexibility and agility. In cases where automatic generation of certification data is not possible, human certification processes can be integrated.
Extension and restriction of derivations are long-known operations in the framework of graph transformation. In this paper, we continue the study of extension and restriction on the higher level of the adhesive catego...
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We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/...
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Diffusion Models have become increasingly popular in recent years and their applications span a wide range of fields. This survey focuses on the use of diffusion models in computer vision, specially in the branch of i...
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ISBN:
(纸本)9783031539657;9783031539664
Diffusion Models have become increasingly popular in recent years and their applications span a wide range of fields. This survey focuses on the use of diffusion models in computer vision, specially in the branch of image transformations. The objective of this survey is to provide an overview of state-of-the-art applications of diffusion models in image transformations, including image inpainting, super-resolution, restoration, translation, and editing. This survey presents a selection of notable papers and repositories including practical applications of diffusion models for image transformations. The applications are presented in a practical and concise manner, facilitating the understanding of concepts behind diffusion models and how they function. Additionally, it includes a curated collection of GitHub repositories featuring popular examples of these subjects.
Myopic Maculopathy is the leading cause of legal blindness in patients with pathologic myopia. Automated myopic maculopathy diagnosis is of vital importance to early treatment and progression slowdown. However, the sc...
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There has been a growing need to devise processes that can create comprehensive datasets in the world of computer Algebra, both for accurate benchmarking and for new intersections with machine learning technology. We ...
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ISBN:
(纸本)9783031690693;9783031690709
There has been a growing need to devise processes that can create comprehensive datasets in the world of computer Algebra, both for accurate benchmarking and for new intersections with machine learning technology. We present here a method to generate integrands that are guaranteed to be integrable, dubbed the LIOUVILLE method. It is based on Liouville's theorem and the Parallel Risch Algorithm for symbolic integration. We show that this data generation method retains the best qualities of previous data generation methods, while overcoming some of the issues built into that prior work. The LIOUVILLE generator is able to generate sufficiently complex and realistic integrands, and could be used for benchmarking or machine learning training tasks related to symbolic integration.
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