6,111 research outputs found
An analysis of relationship between interpersonal communication and mass media use by foreign students in the United States
Learning an Explicit Hyperparameter Prediction Policy Conditioned on Tasks
Meta learning has attracted much attention recently in machine learning
community. Contrary to conventional machine learning aiming to learn inherent
prediction rules to predict labels for new query data, meta learning aims to
learn the learning methodology for machine learning from observed tasks, so as
to generalize to new query tasks by leveraging the meta-learned learning
methodology. In this study, we interpret such learning methodology as learning
an explicit hyperparameter prediction policy shared by all training tasks.
Specifically, this policy is represented as a parameterized function called
meta-learner, mapping from a training/test task to its suitable hyperparameter
setting, extracted from a pre-specified function set called meta learning
machine. Such setting guarantees that the meta-learned learning methodology is
able to flexibly fit diverse query tasks, instead of only obtaining fixed
hyperparameters by many current meta learning methods, with less adaptability
to query task's variations. Such understanding of meta learning also makes it
easily succeed from traditional learning theory for analyzing its
generalization bounds with general losses/tasks/models. The theory naturally
leads to some feasible controlling strategies for ameliorating the quality of
the extracted meta-learner, verified to be able to finely ameliorate its
generalization capability in some typical meta learning applications, including
few-shot regression, few-shot classification and domain generalization.Comment: 59 pages. arXiv admin note: text overlap with arXiv:1904.03758 by
other author
Secure Healthcare Applications Data Storage in Cloud Using Signal Scrambling Method
A body sensor network that consists of wearable and/or implantable biosensors has been an important front-end for collecting personal health records. It is expected that the full integration of outside-hospital personal health information and hospital electronic health records will further promote preventative health services as well as global health. However, the integration and sharing of health information is bound to bring with it security and privacy issues. With extensive development of healthcare applications, security and privacy issues are becoming increasingly important. This paper addresses the potential security risks of healthcare data in Internet based applications, and proposes a method of signal scrambling as an add-on security mechanism in the application layer for a variety of healthcare information, where a piece of tiny data is used to scramble healthcare records. The former is kept locally whereas the latter, along with security protection, is sent for cloud storage. The tiny data can be derived from a random number generator or even a piece of healthcare data, which makes the method more flexible. The computational complexity and security performance in terms of theoretical and experimental analysis has been investigated to demonstrate the efficiency and effectiveness of the proposed method. The proposed method is applicable to all kinds of data that require extra security protection within complex networks
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