Are Library and Information Science Journals Becoming More Internationalized? A Longitudinal Study of Authors' Geographical Affiliations in 20 LIS Journals from 1981 to 2003

This paper examines journal publications in the field of library and information science (LIS) to assess the level of internationalization in their publications authorship pattern. The international production and communication of scholarly knowledge is crucial to the growth of a discipline. Recent advancement in communication technology and the rise of globalization have led to the hope of a more balanced flow of scientific knowledge. Nevertheless, scholars also cautioned the possibility of a global digital divide and a widening knowledge gap. This study analyzed the geographical affiliations of authors in 20 international LIS journals to track the longitudinal changes in LIS authorship pattern. Findings suggest an increase in the internationalization of LIS authorships over the years. However, the LIS authorship distribution was still highly uneven in 2003 (Gini coefficient = 0.95). Economic power is still found to be a moderate predictor of publication performance. The findings of this study suggest that, at the moment of the writing, there is still room for the LIS field to be more internationalized. Further research is needed to identify the barriers in international scholarly communication and to explore the implications of such a communication pattern on scientific development and global equality

Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing

Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users\u27 sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies

Predicting Alzheimer's risk: why and how?

Because the pathologic processes that underlie Alzheimer's disease (AD) appear to start 10 to 20 years before symptoms develop, there is currently intense interest in developing techniques to accurately predict which individuals are most likely to become symptomatic. Several AD risk prediction strategies - including identification of biomarkers and neuroimaging techniques and development of risk indices that combine traditional and non-traditional risk factors - are being explored. Most AD risk prediction strategies developed to date have had moderate prognostic accuracy but are limited by two key issues. First, they do not explicitly model mortality along with AD risk and, therefore, do not differentiate individuals who are likely to develop symptomatic AD prior to death from those who are likely to die of other causes. This is critically important so that any preventive treatments can be targeted to maximize the potential benefit and minimize the potential harm. Second, AD risk prediction strategies developed to date have not explored the full range of predictive variables (biomarkers, imaging, and traditional and non-traditional risk factors) over the full preclinical period (10 to 20 years). Sophisticated modeling techniques such as hidden Markov models may enable the development of a more comprehensive AD risk prediction algorithm by combining data from multiple cohorts. As the field moves forward, it will be critically important to develop techniques that simultaneously model the risk of mortality as well as the risk of AD over the full preclinical spectrum and to consider the potential harm as well as the benefit of identifying and treating high-risk older patients