Background Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern...

disclosure of classified public interest information has become one of the major legal challenges in recent years. in the past decades the conflict between the right of people to receive information and principle of sovereignty within the framework of traditional media (direct broadcast satellite) resolved in favor of  principle of free flow of information and the said conflict was replaced by ...

Imputation of missing data is important in many areas, such as reducing non-response bias in surveys and maintaining medical documentation. Estimating the uncertainty inherent in the imputed values is one way of evaluating the results of the imputation process. This paper presents a new method for the estimation of imputation uncertainty, which can be implemented as part of any imputation metho...

Given a noisy or sampled snapshot of an infection in a large graph, can we automatically and reliably recover the truly infected yet somehow missed nodes? And, what about the seeds, the nodes from which the infection started to spread? These are important questions in diverse contexts, ranging from epidemiology to social media. In this paper, we address the problem of simultaneously recovering ...

Environmental, Social, and Governance (ESG) scores measure companies' performance concerning sustainability are organized in three pillars: Governance. These complementary non-financial ESG should provide information about risks. However, the extent of not yet published makes reliability questionable. To explicitly capture on category scores, a new pillar, so-called Missing (M) is proposed adde...