Privacy Mining from IoT-based Smart Homes

Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elders' privacy might be disclosed from smart homes due to non-fully protected network communication or other reasons. To demonstrate how serious this issue is, we introduce in this paper a Privacy Mining Approach (PMA) to mine privacy from smart homes by conducting a series of deductions and analyses on sensor datasets generated by smart homes. The experimental results demonstrate that PMA is able to deduce a global sensor topology for a smart home and disclose elders' privacy in terms of their house layouts.Comment: This paper, which has 11 pages and 7 figures, has been accepted BWCCA 2018 on 13th August 201

Serum-Free Production of Three-Dimensional Hepatospheres from Pluripotent Stem Cells

Developing renewable human liver tissue from stem cells has been pursued as a potential source of biological material for pharmaceutical and clinical endeavors. At present, two-dimensional differentiation procedures deliver tissue lacking long-term phenotypic and functional stability. Efforts to overcome these limiting factors have led to the development of protocols to generate three-dimensional cellular aggregates. Here we describe a methodology to generate 3D hepatospheres from human pluripotent stem cells using defined and commercially available reagents

Initial Hypotheses for Modeling and Numerical Analysis of Rockfill and Earth Dams and Their Effects on the Results of the Analysis

© 2018 Mohammad Rashidi and Habib Rasouli. Since the behavior of earth dams is unreliable in different stages of construction, impounding, and exploitation, this matter is an unavoidable and essential issue with regard to the serious dangers caused by the failure of these important structures. It is crucial to evaluate the behavior of dams and examine the consistency between the carried out analyses and the behavioral parameters under different conditions in the lifespan of dams due to the uncertainty of the principles and hypotheses which have been adopted to analyze these structures. This objective will be accomplished through the help of correct numerical analyses. A series of hypotheses are adopted to simplify the parametric analyses before starting these analyses. The aim of this research is to develop and discuss these hypotheses. And so, the number of elements and their effects on the results of analyses were examined through the consolidation of unsaturated soil method, the compressible fluid method, correlated analysis, and uncorrelated analysis. It became clear after the numerical analyses that correlated analysis is a more precise method in comparison with the uncorrelated analysis method. However, this method is not economical when it comes to high dams and the replacement method is the uncorrelated analysis. Furthermore, the displacements are not that sensitive to the bulk modulus of water while the maximum settlement of the dam transfers from the middle of the dam's core to a location higher than that the core as the bulk modulus of water increases. However, pore water pressure is very sensitive to the bulk modulus of water

The chick embryo: Hatching a model for contemporary biomedical research

Animal models play a crucial role in fundamental and medical research. Progress in the fields of drug discovery, regenerative medicine and cancer research among others are heavily dependent on in vivo models to validate in vitro observations, and develop new therapeutic approaches. However, conventional rodent and large animal experiments face ethical, practical and technical issues that limit their usage. The chick embryo represents an accessible and economical in vivo model, which has long been used in developmental biology, gene expression analysis and loss/ gain of function experiments. It is also an established model for tissue/ cell transplantation, and because of its lack of immune system in early development, the chick embryo is increasingly recognised as a model of choice for mammalian biology with new applications for stem cell and cancer research. Here, we review novel applications of the chick embryo model, and discuss future developments of this in vivo model for biomedical research. © 2009 Wiley Periodicals, Inc

Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substantially faster than MCMC at practically no compromises in predictive accuracy. In this paper, we address two critical gaps concerning the usage and understanding of VB for MMNL. First, extant VB methods are limited to utility specifications involving only individual-specific taste parameters. Second, the finite-sample properties of VB estimators and the relative performance of VB, MCMC and maximum simulated likelihood estimation (MSLE) are not known. To address the former, this study extends several VB methods for MMNL to admit utility specifications including both fixed and random utility parameters. To address the latter, we conduct an extensive simulation-based evaluation to benchmark the extended VB methods against MCMC and MSLE in terms of estimation times, parameter recovery and predictive accuracy. The results suggest that all VB variants with the exception of the ones relying on an alternative variational lower bound constructed with the help of the modified Jensen's inequality perform as well as MCMC and MSLE at prediction and parameter recovery. In particular, VB with nonconjugate variational message passing and the delta-method (VB-NCVMP-Delta) is up to 16 times faster than MCMC and MSLE. Thus, VB-NCVMP-Delta can be an attractive alternative to MCMC and MSLE for fast, scalable and accurate estimation of MMNL models