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Author(s):  
Tanmayee Parbat

Abstract: Everything stored on the cloud could potentially be a knowledge source used for e-business. Given learners' profiles, desires and feedback on what they have already learned, a new form of personalized e-business emerges, namely Cloud collaborative E-business (CeL). CeL should be able to choose from structured to totally unstructured business material but needs to make them useful for each individual. Existing metadata standards cannot facilitate composition of personalized business paths as a series of business objects. In this paper, we present the structure of CeL Business Objects (CeLLOs), which include an additional set of metadata suitable for each phase of CeL development. Keywords: Cloud collaborative E-business, computing for Education, Electronic Business


Author(s):  
Nazim Faisal Hamed ◽  
Manal Mohammed E. Alhawiti ◽  
Eman Hamed A. Albalawi ◽  
Lena Defallah G. Alzahrani ◽  
Raghad Mohammed E. Alhawiti ◽  
...  

Background: Diabetic ketoacidosis (DKA) is a common emergency and life-threatening illness. Also, if not detected early, early treatment in the emergency room can cause serious complications. The goal of managing type 1 diabetes is to maintain the correct levels of blood sugar, glycated hemoglobin (HbA1c), blood pressure, lipid levels, and body weight while avoiding hypoglycemia. Treatment of type 1 diabetes requires proper insulin treatment, proper nutrition, physical activity, preventive education, and patient self-care Objective: The purpose of this study is to determine parental perceptions of DKA symptoms in children with type 1 diabetes in the Northern Region of Saudi Arabia. Methods: In the Northern Region of Saudi Arabia, a cross-sectional study was conducted from November 2020 to May 2021 among parents with diabetic children at the Diabetes Center in the Northern Region of Saudi Arabia using a pre-designed online questionnaire distributed on social media web-sites to collect data.  Data was analyzed by using statistical package for the social sciences (SPSS, version 23) and results was presented by tabular and graphical presentation according to the study objectives. Results: only 42.9% of our participants responded that they have good knowledge about DKA. 19.2% thought it only occurs in children. 43.3% of our participants knew that DKA is a complication of diabetes due to hyperglycemia. Regarding the source of information about DKA among our participants, our data demonstrated that only 22.9% of our participants got their information about DKA from the doctors, and 31.8% of the participants had the internet as their source of information regarding DKA. In the current study, 14% of the participants said that they had a child had DKA at least one, and 91.6% of them were admitted to the hospital. There was a significant relation with gender, age of the parent, and educational level, while it showed insignificant relation with marital status. Conclusion: In conclusion, knowledge of most of parents of diabetic children about diabetic ketoacidosis is poor. Their main knowledge source is not trustful or adequate. Their main knowledge source is not trustful or adequate. Therefore, we recommend policy makers to held health education to parents and/or caregivers of type 1 diabetic children regarding all aspects of DKA. It must be properly achieved in a structured manner based on a general outline that should include education at the onset of treatment and then repeated based upon an annual assessment of patients’ training needs or upon their own request. Areas of poor knowledge related to diabetes and diabetic ketoacidosis should be emphasized during health education sessions.


2021 ◽  
Author(s):  
Zhaozhen Xu ◽  
Amelia Howarth ◽  
Nicole Briggs ◽  
Nello Cristianini

Every day people ask short questions through smart devices or online forums to seek answers to all kinds of queries. With the increasing number of questions collected it becomes difficult to provide answers to each of them, which is one of the reasons behind the growing interest in automated question answering. Some questions are similar to existing ones that have already been answered, while others could be answered by an external knowledge source such as Wikipedia. An important question is what can be revealed by analysing a large set of questions. In 2017, “We the Curious” science centre in Bristol started a project to capture the curiosity of Bristolians: the project collected more than 10,000 questions on various topics. As no rules were given during collection, the questions are truly open-domain, and ranged across a variety of topics. One important aim for the science centre was to understand what concerns its visitors had beyond science, particularly on societal and cultural issues. We addressed this question by developing an Artificial Intelligence tool that can be used to perform various processing tasks: detection of equivalence between questions; detection of topic and type; and answering of the question. As we focused on the creation of a “generalist” tool, we trained it with labelled data from different datasets. We called the resulting model QBERT. This paper describes what information we extracted from the automated analysis of the WTC corpus of open-domain questions.


2021 ◽  
pp. 172-192
Author(s):  
Keun Lee

Chapter 8 explores how Huawei was able to emerge as the leader in the telecommunications system sector, overtaking the incumbent Swedish giant Ericsson. It answers this question by focusing on whether a latecomer firm trying to catch up uses technologies similar to or different from those of the forerunners. The study investigated patents by Huawei and Ericsson and found that Huawei relied on Ericsson as a knowledge source in its early days but subsequently reduced this reliance and increased its self-citation ratio to become more independent. The results of mutual citations, common citations, and self-citations provided strong evidence that Huawei caught up with or overtook Ericsson by taking a different technological trajectory. Huawei developed its technologies by relying on more recent and scientific knowledge; in terms of citations to scientific articles and citation lags, Huawei extensively explored basic research and up-to-date technologies to accomplish its technological catch-up. This study suggests that leapfrogging by exploring a new technological path is a possible and viable catch-up strategy for a latecomer. Moreover, Huawei’s case re-confirms the hypothesis that catch-up in technological capabilities tends to precede that in market share. Huawei overtook Ericsson in terms of quantity and quality of patents before annual sales. In summary, the results suggest that Huawei’s catch-up with Ericsson in the telecommunications equipment market is owing not only to its cost advantage, the large domestic market, or the Chinese government’s support but also more importantly to its technological leapfrogging based on its technological strength and independence.


2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Jorge Vivaldi ◽  
Horacio Rodríguez

AbstractEven though many NLP resources and tools claim to be domain independent, their application to specific tasks is restricted to some specific domain, otherwise their performance degrade notably. As the accuracy of NLP resources drops heavily when applied in environments different from which they were built a tuning to the new environment is needed. This paper proposes a method for automatically compile terminologies from potentially any domain. The proposed method takes as reference the set of domains defined by Magnini, the Multilingual Central Repository (a resource based on WordNet 3.0) together with DBpedia, an open knowledge source that had proven to be reliable for restricted domains. Using the method described in this article, we have produced a big set of reliable terminologies for 164 domains and 2 languages totalling 635,527 terms. The proposed method has been applied to English and Spanish languages but it is potentially applicable to any language that has its own a DBpedia evolved enough. The obtained results have been intensively evaluated in several ways.


2021 ◽  
Author(s):  
Rui Liu

Abstract This article conducts a textual and reception analysis of George Jamieson’s translation of Qing marriage law with the aim of probing a translational encounter between traditional Chinese law and British anthropology. Approaching a Qing clause against marriage between persons of the same family name as an object of anthropological study, Jamieson annotated his rendition with rich paratexts to orient it under the concept of exogamy. After reflecting upon predecessors’ theories, he advanced his own by restructuring existing anthropological constructs. Taking his translation as a knowledge source, Jamieson further highlighted the existence of an endogamous limit upon the exogamy rule; this observation was absorbed by Henry Maine to strengthen his argument that exogamy and endogamy were not oppositional in agnatic societies. As revealed in Jamieson’s interaction with British anthropologists, he proved himself more than a translator of Qing marriage law but also a contributor to nineteenth-century British anthropology.


First Monday ◽  
2021 ◽  
Author(s):  
Besiki Stvilia

This paper introduced a synthesized theoretical framework of online news quality assurance. The framework includes conceptual models of quality evaluation, value assessment, and intervention. The framework also provides typologies of user activities, information agents, and the relationships among them. The framework is grounded in prior frameworks of information quality and the analysis of two cases of large-scale online news aggregators: Google News and Facebook News. The framework can be used as a knowledge source to guide the design and evaluation of quality assurance processes of online news providers and aggregator ecosystems.


Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 837
Author(s):  
Olzhas Kozbagarov ◽  
Rustam Mussabayev ◽  
Nenad Mladenovic

This article presents a new conceptual approach for the interpretative topic modeling problem. It uses sentences as basic units of analysis, instead of words or n-grams, which are commonly used in the standard approaches.The proposed approach’s specifics are using sentence probability evaluations within the text corpus and clustering of sentence embeddings. The topic model estimates discrete distributions of sentence occurrences within topics and discrete distributions of topic occurrence within the text. Our approach provides the possibility of explicit interpretation of topics since sentences, unlike words, are more informative and have complete grammatical and semantic constructions inside. The method for automatic topic labeling is also provided. Contextual embeddings based on the BERT model are used to obtain corresponding sentence embeddings for their subsequent analysis. Moreover, our approach allows big data processing and shows the possibility of utilizing the combination of internal and external knowledge sources in the process of topic modeling. The internal knowledge source is represented by the text corpus itself and often it is a single knowledge source in the traditional topic modeling approaches. The external knowledge source is represented by the BERT, a machine learning model which was preliminarily trained on a huge amount of textual data and is used for generating the context-dependent sentence embeddings.


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