317,810 research outputs found
Big Data Privacy Context: Literature Effects On Secure Informational Assets
This article's objective is the identification of research opportunities in
the current big data privacy domain, evaluating literature effects on secure
informational assets. Until now, no study has analyzed such relation. Its
results can foster science, technologies and businesses. To achieve these
objectives, a big data privacy Systematic Literature Review (SLR) is performed
on the main scientific peer reviewed journals in Scopus database. Bibliometrics
and text mining analysis complement the SLR. This study provides support to big
data privacy researchers on: most and least researched themes, research
novelty, most cited works and authors, themes evolution through time and many
others. In addition, TOPSIS and VIKOR ranks were developed to evaluate
literature effects versus informational assets indicators. Secure Internet
Servers (SIS) was chosen as decision criteria. Results show that big data
privacy literature is strongly focused on computational aspects. However,
individuals, societies, organizations and governments face a technological
change that has just started to be investigated, with growing concerns on law
and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions
and the only consistent country between literature and SIS adoption is the
United States. Countries in the lowest ranking positions represent future
research opportunities.Comment: 21 pages, 9 figure
ShenZhen transportation system (SZTS): a novel big data benchmark suite
Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets
Collaborative Representation based Classification for Face Recognition
By coding a query sample as a sparse linear combination of all training
samples and then classifying it by evaluating which class leads to the minimal
coding residual, sparse representation based classification (SRC) leads to
interesting results for robust face recognition. It is widely believed that the
l1- norm sparsity constraint on coding coefficients plays a key role in the
success of SRC, while its use of all training samples to collaboratively
represent the query sample is rather ignored. In this paper we discuss how SRC
works, and show that the collaborative representation mechanism used in SRC is
much more crucial to its success of face classification. The SRC is a special
case of collaborative representation based classification (CRC), which has
various instantiations by applying different norms to the coding residual and
coding coefficient. More specifically, the l1 or l2 norm characterization of
coding residual is related to the robustness of CRC to outlier facial pixels,
while the l1 or l2 norm characterization of coding coefficient is related to
the degree of discrimination of facial features. Extensive experiments were
conducted to verify the face recognition accuracy and efficiency of CRC with
different instantiations.Comment: It is a substantial revision of a previous conference paper (L.
Zhang, M. Yang, et al. "Sparse Representation or Collaborative
Representation: Which Helps Face Recognition?" in ICCV 2011
Analyse the risks of ad hoc programming in web development and develop a metrics of appropriate tools
Today the World Wide Web has become one of the most powerful tools for business promotion and social networking. As the use of websites and web applications to promote the businesses has increased drastically over the past few years, the complexity of managing them and protecting them from security threats has become a complicated task for the organizations. On the other hand, most of the web projects are at risk and less secure due to lack of quality programming. Although there are plenty of frameworks available for free in the market to improve the quality of programming, most of the programmers use ad hoc programming rather than using frameworks which could save their time and repeated work. The research identifies the different frameworks in PHP and .NET programming, and evaluates their benefits and drawbacks in the web application development. The research aims to help web development companies to minimize the risks involved in developing large web projects and develop a metrics of appropriate frameworks to be used for the specific projects. The study examined the way web applications were developed in different software companies and the advantages of using frameworks while developing them. The findings of the results show that it was not only the experience of developers that motivated them to use frameworks. The major conclusions and recommendations drawn from this research were that the main reasons behind web developers avoiding frameworks are that they are difficult to learn and implement. Also, the motivations factors for programmers towards using frameworks were self-efficiency, habit of learning new things and awareness about the benefits of frameworks. The research recommended companies to use appropriate frameworks to protect their projects against security threats like SQL injection and RSS injectio
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Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing
Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies
A Compendium of Core Lexicon Checklists
Core Lexicon (CoreLex) is a relatively new approach assessing lexical use in discourse. CoreLex examines the specific lexical items used to tell a story, or how typical lexical items are compared with a normative sample. This method has great potential for clinical utilization because CoreLex measures are fast, easy to administer, and correlate with microlinguistic and macrolinguistic discourse measures. The purpose of this article is to provide clinicians with a centralized resource for currently available CoreLex checklists, including information regarding development, norms, and guidelines for use
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