Social distancing (SD) has been critical in the fight against the novel coronavirus disease (COVID-19). To aid SD monitoring, many technology companies have made available mobility data, the most prominent example bei...
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The use of technology in learning needs to be encouraged starting from the elementary school level. Another thing that needs to be encouraged is local wisdom-based media to support character learning. This study aims ...
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Characteristics such as interactivity, mobility, reaching a higher number of people, learning in real contexts, among others, are considered advantages of using mobile devices in education. M-learning (mobile learning...
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Detection of oil pollution in soil has been carried out using laser-induced breakdown spectroscopy(LIBS). A pulsed neodymium-doped yttrium aluminum garnet(Nd:YAG) laser(1,064 nm, 8 ns, 200 mJ) was focused onto ...
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Detection of oil pollution in soil has been carried out using laser-induced breakdown spectroscopy(LIBS). A pulsed neodymium-doped yttrium aluminum garnet(Nd:YAG) laser(1,064 nm, 8 ns, 200 mJ) was focused onto pelletized soil samples. Emission spectra were obtained from oil-contaminated soil and clean soil. The contaminated soil had almost the same spectrum profile as the clean soil and contained the same major and minor elements. However, a C–H molecular band was clearly detected in the oil-contaminated soil, while no C–H band was detected in the clean soil. Linear calibration curve of the C–H molecular band was successfully made by using a soil sample containing various concentrations of oil. The limit of detection of the C–H band in the soil sample was 0.001 mL/g. Furthermore, the emission spectrum of the contaminated soil clearly displayed titanium(Ti) lines, which were not detected in the clean soil. The existence of the C–H band and Ti lines in oil-contaminated soil can be used to clearly distinguish contaminated soil from clean soil. For comparison, the emission spectra of contaminated and clean soil were also obtained using scanning electron microscope-energy dispersive X-ray(SEM/EDX) spectroscopy,showing that the spectra obtained using LIBS are much better than using SEM/EDX, as indicated by the signal to noise ratio(S/N ratio).
Automatic detection of characteristic patterns of diabetic retinopathy such as hard exudates may help to an early diagnosis. Methods for automatic detection of hard exudates and optic disc are presented. Exudates dete...
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ISBN:
(纸本)9789898425898
Automatic detection of characteristic patterns of diabetic retinopathy such as hard exudates may help to an early diagnosis. Methods for automatic detection of hard exudates and optic disc are presented. Exudates detection involves a preprocessing stage, threshold selection and region growing. For optic disc detection a Bayes classifier is applied followed by mathematical morphology techniques in order to improve the final result. The methods here presented were evaluated using the IMAGERET database, which contains fundus images evaluated by qualified experts. In average, the area of exudates automatically detected overlaped with 60.75% and 63.91% areas defined by each of the two experts. For optic disc detection, sensitivity and specificity were 72.12% and 95.56% respectively.
This full paper describes a complete article in the research category. This work presents a Bibliometric Review that aims to characterize the current scenario of academic research in Artificial Intelligence in educati...
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ISBN:
(数字)9798350351507
ISBN:
(纸本)9798350363067
This full paper describes a complete article in the research category. This work presents a Bibliometric Review that aims to characterize the current scenario of academic research in Artificial Intelligence in education to support educators in the teaching and learning processes in Special Education in different contexts, becoming a tool with potential use in cases of students with Autism Spectrum Disorder (ASD). In this sense, this work aims to contribute to a better understanding of research associated with the area of Artificial Intelligence aimed at educational processes involving students with autism. To do this, we used the EndNote reference manager, an online tool designed to support researchers in conducting Literature Reviews, following the bibliometric protocol proposed by Guedes and Borschiver (2005). From the definition of search strings, (“artificial intelligence”) AND (“autism” OR “ASD” OR “autism spectrum disorder”) AND (“education” OR “teaching”), scientific databases were explored like Web of science (WoS), Scopus, ERIC, Emerald, Scielo, Portal CAPES, and IEEE, to locate existing studies on the topic, between 2019 and 2024. As a result, 298 articles were mapped and 20 scientific works were selected that address the aforementioned specific theme, written by 89 authors belonging to 50 institutions in 19 countries on four continents, enabling the creation of a significant theoretical basis. By analyzing the information contained in scientific publications in this specific field, it was possible to infer that research is divided into distinct areas of concentration: 1) the exploration of algorithms and data analysis based on Artificial Intelligence for analytical-predictive issues in the field of special education; 2) the use of robots in the teaching and learning processes of students with autism; 3) the development of personalized educational intervention models for learning pedagogical, social and communication skills. The data show that research on
In this work we combine the Internet-Based Information Consumer Theory(IBICT) and the Blogger-Centric Contextual Advertising(BCCA). The efficiency of the assignment of personal ads to any blog page relies in the a...
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In this work we combine the Internet-Based Information Consumer Theory(IBICT) and the Blogger-Centric Contextual Advertising(BCCA). The efficiency of the assignment of personal ads to any blog page relies in the ability to understand or to direct the consumers’ desires. Hence, IBICT’s framework supports the understanding of the consumers’ desire while BCCA directs the assignment of personal ads.
Complexity and dynamism of day-to-day activities in organizations are inextricably linked, one impacting the other, increasing the challenges for constant adaptation on the way to organize work to address emerging dem...
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Complexity and dynamism of day-to-day activities in organizations are inextricably linked, one impacting the other, increasing the challenges for constant adaptation on the way to organize work to address emerging demands. Market is demanding systems that are aware of organizations processes and able to evolve and adapt to new situations in everyday working activities. We argue that flexibility in processes could be managed in real time, by PAIS (Process-aware Information Systems), using context information collected in the work environment. This paper proposes CGAdapt, a context management architecture approach that aims to improve and automate dynamic process adaptation. We explain how process adaptation may occur in real time through an existent scenario using this proposal and discuss the value of context-awareness to reason about process alternative adaptations in a goal oriented approach.
Dynamism of day-to-day activities in organizations is inextricably linked and there is a variety of information, insight and reasoning being processed between people and systems, in carrying out a business process. We...
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Dynamism of day-to-day activities in organizations is inextricably linked and there is a variety of information, insight and reasoning being processed between people and systems, in carrying out a business process. We argue that flexibility in processes could be managed in real time, using context information collected in the work environment. This paper proposes a context management architecture approach that aims to improve and automate dynamic process adaptation. We explain how process adaptation may occur in real time and discuss a scenario for this proposal.
Dealing with complex networks is often a challenge due to the high computational cost in analyzing a huge amount of data. Partitioning methods can decrease the complexity of large structures by reducing them to smalle...
ISBN:
(数字)9781728180861
ISBN:
(纸本)9781728180878
Dealing with complex networks is often a challenge due to the high computational cost in analyzing a huge amount of data. Partitioning methods can decrease the complexity of large structures by reducing them to smaller, less connected parts. Also, the data splitting allows the use of multiprocessing to accelerate the execution of data procedures with simultaneity and parallelism. In this paper, we propose a new parallel partitioning algorithm with a focus on assisting in community detection in social networks. The algorithm uses a subtree-splitting strategy, as well as boundaries defined, in order to cut the network into n balanced subnetworks. Our proposal stands out for the focus on aiding density-based approaches, such as the NetSCAN clustering algorithm, considering two particulars: (i) keeping the partitions connectivity, and; (ii) allowing node overlapping between partitions. Experiments were carried out with different instances intending to investigate the partitions obtained and evaluate our proposal. Furthermore, the algorithm performance analysis in a large network is employed, sequential and parallel implementations are compared in terms of execution time and memory consumption. Evidence was provided that the proposed algorithm is able to split an extensive data set into balanced partitions with optimistic performance results.
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