Academic literature on the topic '280115 Expanding knowledge in the information and computing sciences'

In Fog Computing, fog colonies are formed by nodes cooperating to provide services to end-users. To enable efficient operation and seamless scalability of fog colonies, decentralized control over participating nodes should be promoted. In such cases, autonomous Fog Nodes operate independently, sharing the context in which all colony members provide their services. In the paper, we explore different techniques of context diffusion and knowledge sharing between autonomous Fog Nodes within a fog colony, using ECTORAS, a publish/subscribe protocol. With ECTORAS, nodes become actively aware of their operating context, share contextual information and exchange operational policies to achieve self-configuration, self-adaptation and context awareness in an intelligent manner. Two different ECTORAS implementations are studied, one offering centralized control with the existence of a message broker, to manage colony participants and available topics, and one fully decentralized, catering to the erratic topology that Fog Computing may produce. The two schemes are tested as the Fog Colony size is expanding in terms of performance and energy consumption, in a prototype implementation based on Raspberry Pi nodes for smart building management.

Purpose The purpose of the study was to investigate factors promoting innovation and application of internet of things in academic and research information organizations. Design/methodology/approach Quantitative research design involved survey of selected academic and research information organizations in public and private chartered institutions. Information professionals, digital content managers, information systems and technologists that normally consume big data and technological resources were involved in the process of data collection using structured questionnaire and content analysis. Information organizations and information practitioners were selected from public and private academic and research institutions. Findings Innovation of internet of things has increasingly transformed and changed academic and research information organizations as the source of knowledge in addition to expanding access to education, data, information and communication anywhere anytime through hyperconnectivity and networking. Internet of things technologies such as mobile of things, web of things, digital information systems and personal devices are widely applied by digital natives in academic and research information organizations. Mobilization platform and devices is the single biggest provider of data, information and knowledge in academic and research organizations. Modern trends in education and knowledge practices in academic institutions and information organizations depends upon internet of things, digital repositories, electronic books and journals, social media interfaces, multimedia applications, information portal hubs and interactive websites, although challenges regarding inadequate information communication technology infrastructure and social computing facilities still persist. Research limitations/implications Information organizations in public and private chartered academic and research institutions were adopted in the study. Respondents handling and supporting information management, planning and decision-making provided the necessary data. Information professionals, digital content managers, information systems and technologists are proactively involved in data and information analytics. Practical implications Academic and research information organizations are powerhouses that provide knowledge to support research, teaching and learning for sustainable development and the betterment of humanity and society. Innovation of internet of things and associated technologies provides practical aspects of attaining sustainable information development practices in the contemporary knowledge society. Internet of things technologies, principles of economies of scale and investment and customer needs entail that information organizations and practitioners should provide appropriate and smart systems and solutions. Social implications Modern academic and research information organizations have the social corporate responsibility to offer technological innovations to heighten access to knowledge and learning in academic and research institutions. Economically, innovation and application of internet of things provide unlimited access to big data and information in organizations all the time anywhere anytime. Originality/value Data management is a growing phenomenon that information practitioners need to fully understand in the digital economies. Information professionals need to embrace and appreciate innovation and application of internet of things technologies whose role in sustainable development practices is critical in academic and research organizations.

Computational Thinking (CT) is a set of logical-operational cognitive skills or processes of reasoning, based on Computer Science. Abstraction, pattern recognition, algorithmic reasoning, and decomposition are examples of some of these skills that form the four pillar of CT. Some researchers have considered these skills as useful, and even mandatory to to cognitive development of the schoolchildren. In this paper, we present practical aspects and the possible contributions of CT in the development of competence of reading and interpreting English texts. Didactic interventions were carried out in high school classes of a public school, supported by the Bring Your Own Device (BYOD) approach, in which the students used their own smartphones. During these interventions, the students developed concept maps and podcasts, performed online exercises and the traditional exam, all of that composed the set of evaluation instruments. It was possible to understand that the CT skills are intrinsically present and contributed to the development of the reading and writing skills in English. According to testimonials, we highlight that the BYOD approach provided new conceptions and perspectives on the use of electronic equipment in function of the students’ learning.ResumoO Raciocínio Computacional (RC) é um conjunto de habilidades ou processos cognitivos lógico-operacionais de raciocínio, fundamentadas na Ciência da Computação. Abstração, reconhecimento de padrões, raciocínio algorítmico e decomposição são exemplos de algumas dessas habilidades que formam os quatro pilares do RC. Alguns pesquisadores consideram essas habilidades úteis, e até mesmo fundamentais, para o desenvolvimento cognitivo dos estudantes. Nesse sentido, este relato de experiência tem por objetivo apresentar aspectos práticos e possíveis contribuições do RC no desenvolvimento da competência de leitura e interpretação de textos de diferentes naturezas na disciplina de língua inglesa. Para isso, realizaram-se intervenções didáticas em uma turma do ensino médio de uma escola pública, apoiadas na abordagem Bring Your Own Device ou, simplesmente, BYOD, em que os estudantes usaram seus próprios aparelhos celulares. Durante o desenvolvimento das intervenções, os estudantes construíram mapas conceituais e podcasts, realizarem exercício online e a tradicional prova, os quais compuseram o conjunto de instrumentos avaliativos do bimestre. Por meio dessas intervenções, foi possível identificar como as habilidades do RC estiveram intrinsecamente presentes e contribuíram para o desenvolvimento da competência de leitura e escrita em língua inglesa, elencada pelos Parâmetros Curriculares Nacionais. Conforme relatos, além da articulação didática com o RC, a abordagem BYOD proporcionou à professora e aos estudantes novas concepções e perspectivas sobre o uso de equipamentos eletrônicos em função da aprendizagem deles mesmos.Palavras-chave: Raciocínio computacional, Ensino de inglês, Mobile learning, Educação em computação.Keywords: Computational thinking, English teaching, Mobile learning, Computer science education.ReferencesALBERTA Education. School Technology Branch. Bring your own device: a guide for schools. 2012. Disponível em:http://education.alberta.ca/admin/technology/research.aspx. Acesso em: 01 fev. 2017.ALLAN, Walter; COULTER, Bob; DENNER, Jill; ERICKSON, Jeri; LEE, Irene; MALYN-SMITH, Joyce; MARTIN, Fred. Computational thinking for youth. White Paper for the ITEST Learning Resource Centre na EDC. Small Working Group on Computational Thinking (CT), 2010. Disponível em: http://stelar.edc.org/publications/computational-thinking-youth. Acesso em: dez 2017.ARAÚJO, Ana Liz; ANDRADE, Wilkerson; GERRERO, Dalton Serey. Pensamento Computacional sob a visão dos profissionais da computação: uma discussão sobre conceitos e habilidades. In: Anais dos Workshops do VI Congresso Brasileiro de Informática na Educação. v. 4, n 1, 2015. p. 1454-1563.ARMONI, Michal. Computing in schools: On teaching topics in computer science theory. ACM Inroads, v. 1, n. 1, p. 21-22. 2010. DOI=http://dx.doi.org/10.1145/1721933.1721941BARBOSA, Márcio Lobo; ALVES, Álvaro Santos; JESUS, José Carlos Oliveira; BURNHAM, Teresinha Fróes. Mapas conceituais na avaliação da aprendizagem significativa. In: Anais do XVI Simpósio Nacional de Ensino de Física, v. 14, 2005, p. 1-4.BELL, Tim; WITTEN, Ian; FELLOWS, Mike. Ensinando Ciência da Computação sem o uso do computador. Computer Science Unplugged, 2011.BOCCONI, Stefania; CHIOCCARIELLO, Augusto; DETTORI, Giuliana; FERRARI, Anusca; ENGELHARDT, Katja. Developing computational thinking in compulsory education Implications for policy and practice. European Commission, JRC Science for Policy Report. 2016.BRASIL, Ministério da Educação. Secretaria da Educação Básica. PCN+ ensino médio: Orientações educacionais complementares aos parâmetros curriculares nacionais, Brasília: MEC. 2002. Disponível em: http://portal.mec.gov.br/seb/arquivos/pdf/linguagens02.pdf. Acesso em: set 2017.BRASIL. Ministério da Educação (MEC). Base Nacional Comum Curricular. 2017. Disponível em: http://basenacionalcomum.mec.gov.br/. Acesso em: set 2017.BRITANNICA, Encyclopaedia. Phenol: Encyclopaedia Britannica Online Academic Edition. Encyclopædia Britannica Inc. 2012. Disponível em: https://www.britannica.com/. Acesso em: 01 fev. 2017.BROOKSHEAR, J-Glenn. Ciência da Computação: uma visão abrangente. Porto Alegre, Bookman Editora, 2005.CHARLTON, Patricia; LUCKIN, Rosemary. Computational thinking and computer science in schools. What The Research Says’ Briefing, v. 2. 2012. [s.p.]CHIOFI, Luiz Carlos; OLIVEIRA, Marta Regina Furlan de. O uso das tecnologias educacionais como ferramenta didática no processo de ensino e aprendizagem. In: Anais da III Jornada de Didática - Jornada de Didática: Desafios para a Docência e II Seminário de Pesquisa do CEMAD. Londrina, 2014. [s.p.]COMPUTER AT SCHOOL. Computational Thinking: a guide for teachers. Hodder Education - the educational division of Hachette UK Digital Schoolhouse, 2015. Disponível em: https://community.computingatschool.org.uk/resources/2324/single. Acesso em: 01 set 2017.CORREIA, Paulo Rogério Miranda; SILVA, Amanda Cristina; ROMANO JÚNIOR, Jerson Geraldo. Mapas conceituais como ferramenta de avaliação na sala de aula. Revista Brasileira de Ensino de Física, v. 32, n. 4, p. 4402-4408. 2010.COSTA, Giselda dos Santos. Mobile learning: explorando potencialidades com o uso do celular no ensino-aprendizagem de língua inglesa como língua estrangeira com alunos da escola pública. 2013. 201f. Tese (Doutorado em Letras). Faculdade de Letras. Universidade Federal de Pernambuco. Recife. 2013.CSIZMADIA, Andrew; SENTANCE, Sue. Teachers’ perspectives on successful strategies for teaching Computing in school. In: IFIP TCS. 2015. Disponível em: <http://community.computingatschool.org.uk/files/6769/original.pdf>. Acesso em março 2018.CSIZMADIA, Andrew; CURZON, Paul; DORLING, Mark; HUMPHREYS, Simon; NG, Thomas; SELBY, Cynthia; WOOLLARD, John. Computational thinking: A guide for teachers. Computing at Schools, 2015. Disponível em: https://community.computingatschool.org.uk/files/8550/original.pdf>. Acesso em: 26 out. 2017.DIAS, Reneildes; JUCÁ, Leina; FARIA, Raquel. High Up: ensino médio. Cotia, SP: Macmillan, 2013.GOOGLE FOR EDUCATION. What is Computational Thinking? Computational Thinking for Educators. 2015. Disponível em: <https://computationalthinkingcourse.withgoogle.com/unit?lesson=8&unit=1. Acesso em: set 2017.LEE, Irene; MARTIN, Fred; DENNER, Jill; COULTER, Bob; ALLAN, Walter; ERICKSON, Jeri; MALYN-SMITH, Joyce; WERNER, Linda. Computational thinking for youth in practice. ACM Inroads, v. 2, n. 1, 2011. p. 32-37.LIUKAS, Linda. Hello Ruby: adventures in coding. New York: Feiwel & Friends, 2015.LU, Zhao.; YING, Lu. Application of Podcast in Teaching and Learning Oral English for Non-English Majors. In: International Conference on Computational and Information Sciences, Shiyang, 2013. p. 1935-1938. doi: 10.1109/ICCIS.2013.506MANNILA, Linda; VALENTINA, Dagiene; DEMO, Barbara; GRGURINA, Natasa; MIROLO, Claudio; ROLANDSSON, Lennart; SETTLE, Amber. Computational thinking in K-9 education. In: Proceedings of the working group reports of the 2014 on innovation & technology in computer science education conference. ACM, 2014. p. 1-29.MOREIRA, Antonio Marco. 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Data mining is the procedure of extracting interesting knowledge from enormous amounts of data contained in databases, including such patterns, associations, changes, deviations, and prominent structures. Soft Computing Methods such as fuzzy logic, artificial neural network, etc. aims to uncover the potential for error and inaccuracy in order to accomplish scalability, durability, and reduced methods. In today's information age, the Web is the most common distribution medium. Due to its popularity on the Internet, it is widely used in commercial, entertainment, and educational purposes. Web Intelligence (WI) is engaged with the scientific study of the Web's new areas. It is a new area of computer science that integrates artificial intelligence with sophisticated information technology in the framework of the Web, expanding well outside each one of them. In online applications, data mining gives a plethora of possibilities. The biggest concern is figuring out how to identify relevant hidden patterns for improved application. Soft computing techniques such as neural networks, fuzzy logic, support vector machines, and genetic algorithms are used in evolutionary computation to solve this problem. We look at how soft computing approaches may be used to build web intelligences in this research.

This project revolves around Small Histories, an online web-based software system for the uploading and sharing of life stories: http://www.smallhistories.com. I created Small Histories to explore the ways in which the internet can facilitate the urge to tell, share and compare one’s personal history and, by doing so, generate an online network of interlinked personal narratives connected to historical times, events and places. The project originated with a personal event: the tracing of my biological Israeli father in 1997 and my subsequent explorations of my Israeli and German family histories. The stories I encountered in these explorations differed, depending on who was telling them. The Small Histories system was a response to the potential of the burgeoning internet to represent such differing viewpoints, and to generate new forms of encounters with the past. Since then the system has developed in tandem with the internet, especially the explosive growth over recent years of what has been called social software. Conceptually, this project explores the fast-evolving social internet as a setting for auto/biographical narrative practice and how this overlaps with and changes accepted notions of performance, community formation, identity construction and acts of memory. As a framework for these investigations, I propose that the internet is a catalyst without precedent for the production of performances of reconstruction, where fragments of the past are dug up, collected, assembled and presented as an imaginative reconstruction of ‘what used to be’, in an attempt to re-establish a lost sense of roots, identity and belonging; a coherent narrative of identity in an era of fragmentation.

In this chapter, Big Data provide large-volume, complex structure, heterogeneous and irregular growing data sets include multiple and autonomous different resources. In this chapter, With the growing improvement of networking sites, image information storing capacity become big issue too, Big Data concept are most growing expanding in all technical area and knowledge engineering domains, including physical, medical and paramedical sciences. Here a data-driven method consist demand-driven aggregation of information and knowledge mining and analysis, user interest prototyping, security and privacy aspects has been presented.