t-Exponential Memory Networks for Question-Answering Machines

Recent advances in deep learning have brought to the fore models that can make multiple computational steps in the service of completing a task; these are capable of describ- ing long-term dependencies in sequential data. Novel recurrent attention models over possibly large external memory modules constitute the core mechanisms that enable these capabilities. Our work addresses learning subtler and more complex underlying temporal dynamics in language modeling tasks that deal with sparse sequential data. To this end, we improve upon these recent advances, by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network parameters as latent variables with a prior distribution imposed over them. Our statistical assumptions go beyond the standard practice of postulating Gaussian priors. Indeed, to allow for handling outliers, which are prevalent in long observed sequences of multivariate data, multivariate t-exponential distributions are imposed. On this basis, we proceed to infer corresponding posteriors; these can be used for inference and prediction at test time, in a way that accounts for the uncertainty in the available sparse training data. Specifically, to allow for our approach to best exploit the merits of the t-exponential family, our method considers a new t-divergence measure, which generalizes the concept of the Kullback-Leibler divergence. We perform an extensive experimental evaluation of our approach, using challenging language modeling benchmarks, and illustrate its superiority over existing state-of-the-art techniques

Users’ Attitudes on Mobile Devices: Can Users’ Practices Protect their Sensitive Data?

Smartphones are the most popular personal electronic devices. They are used for all sorts of purposes, from managing bank accounts to playing games. As smartphone apps and services proliferate, the amount of sensitive data stored on or processed by handheld devices rise as well. This practice entails risks, such as violating users’ privacy, stealing users’ identities, etc. Particularly, stealing an unlocked device grants full access to sensitive data and applications. In this survey, we examine whether users adopt some basic practices to protect their sensitive personal data themselves, or is there a need to further strengthen their protection? Our statistical analysis assesses smartphone users’ security attitudes and practices among different age groups. Finally, we investigate the factors that affect the attitude of users with respect to their practices for the protection of personal data.The results of this study, show that while many smartphone users do take some security precautions, a high percentage (24%) of them still ignores security and privacy risks. In addition, 19,1 % of users do not follow any practices to protect their PINs and Passwords