282 resultados para Data anonymization and sanitization


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BACKGROUND: There is evidence that children's decisions to smoke are influenced by family and friends. OBJECTIVES: To assess the effectiveness of interventions to help family members to strengthen non-smoking attitudes and promote non-smoking by children and other family members. SEARCH STRATEGY: We searched 14 electronic bibliographic databases, including the Cochrane Tobacco Addiction Group specialized register, MEDLINE, EMBASE, PsycINFO and CINAHL. We also searched unpublished material, and the reference lists of key articles. We performed both free-text Internet searches and targeted searches of appropriate websites, and we hand-searched key journals not available electronically. We also consulted authors and experts in the field. The most recent search was performed in July 2006. SELECTION CRITERIA: Randomized controlled trials (RCTs) of interventions with children (aged 5-12) or adolescents (aged 13-18) and family members to deter the use of tobacco. The primary outcome was the effect of the intervention on the smoking status of children who reported no use of tobacco at baseline. Included trials had to report outcomes measured at least six months from the start of the intervention. DATA COLLECTION AND ANALYSIS: We reviewed all potentially relevant citations and retrieved the full text to determine whether the study was an RCT and matched our inclusion criteria. Two authors independently extracted study data and assessed them for methodological quality. The studies were too limited in number and quality to undertake a formal meta-analysis, and we present a narrative synthesis. MAIN RESULTS: We identified 19 RCTs of family interventions to prevent smoking. We identified five RCTs in Category 1 (minimal risk of bias on all counts); nine in Category 2 (a risk of bias in one or more areas); and five in Category 3 (risks of bias in design and execution such that reliable conclusions cannot be drawn from the study).Considering the fourteen Category 1 and 2 studies together: (1) four of the nine that tested a family intervention against a control group had significant positive effects, but one showed significant negative effects; (2) one of the five RCTs that tested a family intervention against a school intervention had significant positive effects; (3) none of the six that compared the incremental effects of a family plus a school programme to a school programme alone had significant positive effects; (4) the one RCT that tested a family tobacco intervention against a family non-tobacco safety intervention showed no effects; and (5) the one trial that used general risk reduction interventions found the group which received the parent and teen interventions had less smoking than the one that received only the teen intervention (there was no tobacco intervention but tobacco outcomes were measured). For the included trials the amount of implementer training and the fidelity of implementation are related to positive outcomes, but the number of sessions is not. AUTHORS' CONCLUSIONS: Some well-executed RCTs show family interventions may prevent adolescent smoking, but RCTs which were less well executed had mostly neutral or negative results. There is thus a need for well-designed and executed RCTs in this area.

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Despite the compelling case for moving towards cloud computing, the upstream oil & gas industry faces several technical challenges—most notably, a pronounced emphasis on data security, a reliance on extremely large data sets, and significant legacy investments in information technology (IT) infrastructure—that make a full migration to the public cloud difficult at present. Private and hybrid cloud solutions have consequently emerged within the industry to yield as much benefit from cloud-based technologies as possible while working within these constraints. This paper argues, however, that the move to private and hybrid clouds will very likely prove only to be a temporary stepping stone in the industry’s technological evolution. By presenting evidence from other market sectors that have faced similar challenges in their journey to the cloud, we propose that enabling technologies and conditions will probably fall into place in a way that makes the public cloud a far more attractive option for the upstream oil & gas industry in the years ahead. The paper concludes with a discussion about the implications of this projected shift towards the public cloud, and calls for more of the industry’s services to be offered through cloud-based “apps.”

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Vietnam has a unique culture which is revealed in the way that people have built and designed their traditional housing. Vietnamese dwellings reflect occupants’ activities in their everyday lives, while adapting to tropical climatic conditions impacted by seasoning monsoons. It is said that these characteristics of Vietnamese dwellings have remained unchanged until the economic reform in 1986, when Vietnam experienced an accelerated development based on the market-oriented economy. New housing types, including modern shop-houses, detached houses, and apartments, have been designed in many places, especially satisfying dwellers’ new lifestyles in Vietnamese cities. The contemporary housing, which has been mostly designed by architects, has reflected rules of spatial organisation so that occupants’ social activities are carried out. However, contemporary housing spaces seem unsustainable in relation to socio-cultural values because they has been influenced by globalism that advocates the use of homogeneous spatial patterns, modern technologies, materials and construction methods. This study investigates the rules of spaces in Vietnamese houses that were built before and after the reform to define the socio-cultural implications in Vietnamese housing design. Firstly, it describes occupants’ views of their current dwellings in terms of indoor comfort conditions and social activities in spaces. Then, it examines the use of spaces in pre-reform Vietnamese housing through occupants’ activities and material applications. Finally, it discusses the organisation of spaces in both pre- and post-reform housing to understand how Vietnamese housing has been designed for occupants to live, act, work, and conduct traditional activities. Understanding spatial organisation is a way to identify characteristics of the lived spaces of the occupants created from the conceived space, which is designed by designers. The characteristics of the housing spaces will inform the designers the way to design future Vietnamese housing in response to cultural contexts. The study applied an abductive approach for the investigation of housing spaces. It used a conceptual framework in relation to Henri Lefebvre’s (1991) theory to understand space as the main factor constituting the language of design, and the principles of semiotics to examine spatial structure in housing as a language used in the everyday life. The study involved a door-knocking survey to 350 households in four regional cities of Vietnam for interpretation of occupancy conditions and levels of occupants’ comfort. A statistical analysis was applied to interpret the survey data. The study also required a process of data selection and collection of fourteen cases of housing in three main climatic regions of the country for analysing spatial organisation and housing characteristics. The study found that there has been a shift in the relationship of spaces from the pre- to post-reform Vietnamese housing. It also indentified that the space for guest welcoming and family activity has been the central space of the Vietnamese housing. Based on the relationships of the central space with the others, theoretical models were proposed for three types of contemporary Vietnamese housing. The models will be significant in adapting to Vietnamese conditions to achieve socioenvironmental characteristics for housing design because it was developed from the occupants’ requirements for their social activities. Another contribution of the study is the use of methodological concepts to understand the language of living spaces. Further work will be needed to test future Vietnamese housing designs from the applications of the models.

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Our paper approaches Twitter through the lens of “platform politics” (Gillespie, 2010), focusing in particular on controversies around user data access, ownership, and control. We characterise different actors in the Twitter data ecosystem: private and institutional end users of Twitter, commercial data resellers such as Gnip and DataSift, data scientists, and finally Twitter, Inc. itself; and describe their conflicting interests. We furthermore study Twitter’s Terms of Service and application programming interface (API) as material instantiations of regulatory instruments used by the platform provider and argue for a more promotion of data rights and literacy to strengthen the position of end users.

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Environmental issues continue to capture international headlines and remain the subject of intense intellectual, political and public debate. As a result, environmental law is widely recognised as the fastest growing area of international jurisprudence. This, combined with the rapid expansion of environmental agreements and policies, has created a burgeoning landscape of administrative, regulatory and judicial regimes. Emerging from these developments are increases in environmental offences, and more recently environmental crimes. The judicial processing of environmental or ‘green’ crimes is rapidly developing across many jurisdictions. Since 1979, Australia has played a lead role in criminal justice processing of environment offences through the New South Wales Land and Environment Court (NSW LEC). This article draws on case data, observations and interviews with court personnel, to examine the ways in which environmental justice is now administered through the existing court structures, and how it has changed since the Court’s inception.

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What are the information practices of teen content creators? In the United States over two thirds of teens have participated in creating and sharing content in online communities that are developed for the purpose of allowing users to be producers of content. This study investigates how teens participating in digital participatory communities find and use information as well as how they experience the information. From this investigation emerged a model of their information practices while creating and sharing content such as film-making, visual art work, story telling, music, programming, and web site design in digital participatory communities. The research uses grounded theory methodology in a social constructionist framework to investigate the research problem: what are the information practices of teen content creators? Data was gathered through semi-structured interviews and observation of teen’s digital communities. Analysis occurred concurrently with data collection, and the principle of constant comparison was applied in analysis. As findings were constructed from the data, additional data was collected until a substantive theory was constructed and no new information emerged from data collection. The theory that was constructed from the data describes five information practices of teen content creators. The five information practices are learning community, negotiating aesthetic, negotiating control, negotiating capacity, and representing knowledge. In describing the five information practices there are three necessary descriptive components, the community of practice, the experiences of information and the information actions. The experiences of information include information as participation, inspiration, collaboration, process, and artifact. Information actions include activities that occur in the categories of gathering, thinking and creating. The experiences of information and information actions intersect in the information practices, which are situated within the specific community of practice, such as a digital participatory community. Finally, the information practices interact and build upon one another and this is represented in a graphic model and explanation.

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QUT’s new metadata repository (data registry), Research Data Finder, has been designed to promote the visibility and discoverability of QUT research datasets. Funded by the Australian National Data Service (ANDS), it will provide a qualitative snapshot of research data outputs created or collected by members of the QUT research community that are available via open or mediated access. As a fully integrated metadata repository Research Data Finder aligns with institutional sources of truth, such as QUT’s research administrative system, ResearchMaster, as well as QUT’s Academic Profiles system to provide high quality data descriptions that increase awareness of, and access to, shareable research data. In addition, the repository and its workflows are designed to foster smoother data management practices, enhance opportunities for collaboration and research, promote cross-disciplinary research and maximize existing research datasets. The metadata schema used in Research Data Finder is the Registry Interchange Format - Collections and Services (RIF-CS), developed by ANDS in 2009. This comprehensive schema is potentially complex for researchers; unlike metadata for publications, which are often made publicly available with the official publication, metadata for datasets are not typically available and need to be created. Research Data Finder uses a hybrid self-deposit and mediated deposit system. In addition to automated ingests from ResearchMaster (research project information) and Academic Profiles system (researcher information), shareable data is identified at a number of key “trigger points” in the research cycle. These include: research grant proposals; ethics applications; Data Management Plans; Liaison Librarian data interviews; and thesis submissions. These ingested records can be supplemented with related metadata including links to related publications, such as those in QUT ePrints. Records deposited in Research Data Finder are harvested by ANDS and made available to a national and international audience via Research Data Australia, ANDS’ discovery service for Australian research data. Researcher and research group metadata records are also harvested by the National Library of Australia (NLA) and these records are then published in Trove (the NLA’s digital information portal). By contributing records to the national infrastructure, QUT data will become more visible. Within Australia and internationally, many funding bodies have already mandated the open access of publications produced from publicly funded research projects, such as those supported by the Australian Research Council (ARC), or the National Health and Medical Research Council (NHMRC). QUT will be well placed to respond to the rapidly evolving climate of research data management. This project is supported by the Australian National Data Service (ANDS). ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative.

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This paper describes the use of property graphs for mapping data between AEC software tools, which are not linked by common data formats and/or other interoperability measures. The intention of introducing this in practice, education and research is to facilitate the use of diverse, non-integrated design and analysis applications by a variety of users who need to create customised digital workflows, including those who are not expert programmers. Data model types are examined by way of supporting the choice of directed, attributed, multi-relational graphs for such data transformation tasks. A brief exemplar design scenario is also presented to illustrate the concepts and methods proposed, and conclusions are drawn regarding the feasibility of this approach and directions for further research.

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Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.

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Open the sports or business section of your daily newspaper, and you are immediately bombarded with an array of graphs, tables, diagrams, and statistical reports that require interpretation. Across all walks of life, the need to understand statistics is fundamental. Given that our youngsters’ future world will be increasingly data laden, scaffolding their statistical understanding and reasoning is imperative, from the early grades on. The National Council of Teachers of Mathematics (NCTM) continues to emphasize the importance of early statistical learning; data analysis and probability was the Council’s professional development “Focus of the Year” for 2007–2008. We need such a focus, especially given the results of the statistics items from the 2003 NAEP. As Shaughnessy (2007) noted, students’ performance was weak on more complex items involving interpretation or application of items of information in graphs and tables. Furthermore, little or no gains were made between the 2000 NAEP and the 2003 NAEP studies. One approach I have taken to promote young children’s statistical reasoning is through data modeling. Having implemented in grades 3 –9 a number of model-eliciting activities involving working with data (e.g., English 2010), I observed how competently children could create their own mathematical ideas and representations—before being instructed how to do so. I thus wished to introduce data-modeling activities to younger children, confi dent that they would likewise generate their own mathematics. I recently implemented data-modeling activities in a cohort of three first-grade classrooms of six year- olds. I report on some of the children’s responses and discuss the components of data modeling the children engaged in.

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For the evaluation, design, and planning of traffic facilities and measures, traffic simulation packages are the de facto tools for consultants, policy makers, and researchers. However, the available commercial simulation packages do not always offer the desired work flow and flexibility for academic research. In many cases, researchers resort to designing and building their own dedicated models, without an intrinsic incentive (or the practical means) to make the results available in the public domain. To make matters worse, a substantial part of these efforts pertains to rebuilding basic functionality and, in many respects, reinventing the wheel. This problem not only affects the research community but adversely affects the entire traffic simulation community and frustrates the development of traffic simulation in general. For this problem to be addressed, this paper describes an open source approach, OpenTraffic, which is being developed as a collaborative effort between the Queensland University of Technology, Australia; the National Institute of Informatics, Tokyo; and the Technical University of Delft, the Netherlands. The OpenTraffic simulation framework enables academies from geographic areas and disciplines within the traffic domain to work together and contribute to a specific topic of interest, ranging from travel choice behavior to car following, and from response to intelligent transportation systems to activity planning. The modular approach enables users of the software to focus on their area of interest, whereas other functional modules can be regarded as black boxes. Specific attention is paid to a standardization of data inputs and outputs for traffic simulations. Such standardization will allow the sharing of data with many existing commercial simulation packages.

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Background Non-fatal health outcomes from diseases and injuries are a crucial consideration in the promotion and monitoring of individual and population health. The Global Burden of Disease (GBD) studies done in 1990 and 2000 have been the only studies to quantify non-fatal health outcomes across an exhaustive set of disorders at the global and regional level. Neither effort quantified uncertainty in prevalence or years lived with disability (YLDs). Methods Of the 291 diseases and injuries in the GBD cause list, 289 cause disability. For 1160 sequelae of the 289 diseases and injuries, we undertook a systematic analysis of prevalence, incidence, remission, duration, and excess mortality. Sources included published studies, case notification, population-based cancer registries, other disease registries, antenatal clinic serosurveillance, hospital discharge data, ambulatory care data, household surveys, other surveys, and cohort studies. For most sequelae, we used a Bayesian meta-regression method, DisMod-MR, designed to address key limitations in descriptive epidemiological data, including missing data, inconsistency, and large methodological variation between data sources. For some disorders, we used natural history models, geospatial models, back-calculation models (models calculating incidence from population mortality rates and case fatality), or registration completeness models (models adjusting for incomplete registration with health-system access and other covariates). Disability weights for 220 unique health states were used to capture the severity of health loss. YLDs by cause at age, sex, country, and year levels were adjusted for comorbidity with simulation methods. We included uncertainty estimates at all stages of the analysis. Findings Global prevalence for all ages combined in 2010 across the 1160 sequelae ranged from fewer than one case per 1 million people to 350 000 cases per 1 million people. Prevalence and severity of health loss were weakly correlated (correlation coefficient −0·37). In 2010, there were 777 million YLDs from all causes, up from 583 million in 1990. The main contributors to global YLDs were mental and behavioural disorders, musculoskeletal disorders, and diabetes or endocrine diseases. The leading specific causes of YLDs were much the same in 2010 as they were in 1990: low back pain, major depressive disorder, iron-deficiency anaemia, neck pain, chronic obstructive pulmonary disease, anxiety disorders, migraine, diabetes, and falls. Age-specific prevalence of YLDs increased with age in all regions and has decreased slightly from 1990 to 2010. Regional patterns of the leading causes of YLDs were more similar compared with years of life lost due to premature mortality. Neglected tropical diseases, HIV/AIDS, tuberculosis, malaria, and anaemia were important causes of YLDs in sub-Saharan Africa. Interpretation Rates of YLDs per 100 000 people have remained largely constant over time but rise steadily with age. Population growth and ageing have increased YLD numbers and crude rates over the past two decades. Prevalences of the most common causes of YLDs, such as mental and behavioural disorders and musculoskeletal disorders, have not decreased. Health systems will need to address the needs of the rising numbers of individuals with a range of disorders that largely cause disability but not mortality. Quantification of the burden of non-fatal health outcomes will be crucial to understand how well health systems are responding to these challenges. Effective and affordable strategies to deal with this rising burden are an urgent priority for health systems in most parts of the world. Funding Bill & Melinda Gates Foundation.

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This work identifies the limitations of n-way data analysis techniques in multidimensional stream data, such as Internet chat room communications data, and establishes a link between data collection and performance of these techniques. Its contributions are twofold. First, it extends data analysis to multiple dimensions by constructing n-way data arrays known as high order tensors. Chat room tensors are generated by a simulator which collects and models actual communication data. The accuracy of the model is determined by the Kolmogorov-Smirnov goodness-of-fit test which compares the simulation data with the observed (real) data. Second, a detailed computational comparison is performed to test several data analysis techniques including svd [1], and multi-way techniques including Tucker1, Tucker3 [2], and Parafac [3].

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This paper describes an approach to investigate the adoption of Web 2.0 in the classroom using a mixed methods study. By using a combination of qualitative or quantitative data collection and analysis techniques, we attempt to synergize the results and provide a more valid understanding of Web 2.0 adoption for learning by both teachers and students. This approach is expected to yield a better holistic view on the adoption issues associated with the e-learning 2.0 concept in current higher education as opposed to single method studies done previously. This paper also presents some early findings of e-learning 2.0 adoption using this research method

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This paper describes the implementation of the first portable, embedded data acquisition unit (BabelFuse) that is able to acquire and timestamp generic sensor data and trigger General Purpose I/O (GPIO) events against a microsecond-accurate wirelessly-distributed ‘global’ clock. A significant issue encountered when fusing data received from multiple sensors is the accuracy of the timestamp associated with each piece of data. This is particularly important in applications such as Simultaneous Localisation and Mapping (SLAM) where vehicle velocity forms an important part of the mapping algorithms; on fast-moving vehicles, even millisecond inconsistencies in data timestamping can produce errors which need to be compensated for. The timestamping problem is compounded in a robot swarm environment especially if non-deterministic communication hardware (such as IEEE-802.11-based wireless) and inaccurate clock synchronisation protocols are used. The issue of differing timebases makes correlation of data difficult and prevents the units from reliably performing synchronised operations or manoeuvres. By utilising hardware-assisted timestamping, clock synchronisation protocols based on industry standards and firmware designed to minimise indeterminism, an embedded data acquisition unit capable of microsecond-level clock synchronisation is presented.