22,176 research outputs found
A critical cluster analysis of 44 indicators of author-level performance
This paper explores the relationship between author-level bibliometric
indicators and the researchers the "measure", exemplified across five academic
seniorities and four disciplines. Using cluster methodology, the disciplinary
and seniority appropriateness of author-level indicators is examined.
Publication and citation data for 741 researchers across Astronomy,
Environmental Science, Philosophy and Public Health was collected in Web of
Science (WoS). Forty-four indicators of individual performance were computed
using the data. A two-step cluster analysis using IBM SPSS version 22 was
performed, followed by a risk analysis and ordinal logistic regression to
explore cluster membership. Indicator scores were contextualized using the
individual researcher's curriculum vitae. Four different clusters based on
indicator scores ranked researchers as low, middle, high and extremely high
performers. The results show that different indicators were appropriate in
demarcating ranked performance in different disciplines. In Astronomy the h2
indicator, sum pp top prop in Environmental Science, Q2 in Philosophy and
e-index in Public Health. The regression and odds analysis showed individual
level indicator scores were primarily dependent on the number of years since
the researcher's first publication registered in WoS, number of publications
and number of citations. Seniority classification was secondary therefore no
seniority appropriate indicators were confidently identified. Cluster
methodology proved useful in identifying disciplinary appropriate indicators
providing the preliminary data preparation was thorough but needed to be
supplemented by other analyses to validate the results. A general disconnection
between the performance of the researcher on their curriculum vitae and the
performance of the researcher based on bibliometric indicators was observed.Comment: 28 pages, 7 tables, 2 figures, 2 appendice
Alexandria: Extensible Framework for Rapid Exploration of Social Media
The Alexandria system under development at IBM Research provides an
extensible framework and platform for supporting a variety of big-data
analytics and visualizations. The system is currently focused on enabling rapid
exploration of text-based social media data. The system provides tools to help
with constructing "domain models" (i.e., families of keywords and extractors to
enable focus on tweets and other social media documents relevant to a project),
to rapidly extract and segment the relevant social media and its authors, to
apply further analytics (such as finding trends and anomalous terms), and
visualizing the results. The system architecture is centered around a variety
of REST-based service APIs to enable flexible orchestration of the system
capabilities; these are especially useful to support knowledge-worker driven
iterative exploration of social phenomena. The architecture also enables rapid
integration of Alexandria capabilities with other social media analytics
system, as has been demonstrated through an integration with IBM Research's
SystemG. This paper describes a prototypical usage scenario for Alexandria,
along with the architecture and key underlying analytics.Comment: 8 page
Probabilistic cluster labeling of imagery data
The problem of obtaining the probabilities of class labels for the clusters using spectral and spatial information from a given set of labeled patterns and their neighbors is considered. A relationship is developed between class and clusters conditional densities in terms of probabilities of class labels for the clusters. Expressions are presented for updating the a posteriori probabilities of the classes of a pixel using information from its local neighborhood. Fixed-point iteration schemes are developed for obtaining the optimal probabilities of class labels for the clusters. These schemes utilize spatial information and also the probabilities of label imperfections. Experimental results from the processing of remotely sensed multispectral scanner imagery data are presented
Multiple multimodal mobile devices: Lessons learned from engineering lifelog solutions
For lifelogging, or the recording of one’s life history through digital means, to be successful, a range of separate multimodal mobile devices must be employed. These include smartphones such as the N95, the Microsoft SenseCam – a wearable passive photo capture device, or
wearable biometric devices. Each collects a facet of the bigger picture, through, for example, personal digital photos, mobile messages and documents access history, but unfortunately, they operate independently and unaware of each other. This creates significant challenges for the practical application of these devices, the use and integration of their data and their operation by a user. In this chapter we discuss the software engineering challenges and their implications for individuals working on integration of data from multiple ubiquitous mobile devices drawing on our experiences working with such technology over the past several years for the development of integrated personal lifelogs. The chapter serves as an engineering guide to those considering working in the domain of lifelogging and more generally to those working with multiple multimodal devices and integration of their data
Does Context Matter for the Relationship between Deprivation and All-Cause Mortality? The West vs. the Rest of Scotland
One of the assumptions that is often made in modeling the relationship between deprivation and mortality is that this relationship will remain the same across space. There is little justification presented in the literature as to why the deprivation-mortality relationship will be homogenous across space. The homogeneity of this relationship over space is an empirical question and most of the published literature does not formally test this relationship. Using postcode data for Scotland (UK), this study addresses this research gap and tests the hypothesis of spatial heterogeneity in the relationship between area-level deprivation and mortality. Research into health inequalities frequently fails to recognise spatial heterogeneity in the deprivation-health relationship, assuming that global relationships apply uniformly across geographical areas. In this study, exploratory spatial data analysis methods are used to assess local patterns in deprivation and mortality. A variety of spatial regression models are then implemented to examine the relationship between deprivation and mortality. The hypothesis of spatial heterogeneity in the relationship between deprivation and mortality is rejected. Implications of the homogeneity of the deprivation-mortality relationships for addressing health inequities are discussed in light of the inverse care law.
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