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This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting. To address this situation, we develop a general defensive few-shot learning (DFSL) framework to answer the following two key questions: (1) how to transfer adversarial def...

This paper considers collection and processing of data by solar-powered servers operating in an Internet of Things (IoT) network. Specifically, these servers aim to cooperatively maximize the amount of data collected from devices and computed over multiple time slots. To achieve this aim, they must consider computation deadline, time-varying energy arrivals at sensor devices and other servers. To this end, this paper outlines a mixed integer linear program (MILP), which can be used to optimize the sensing time of sensor devices, offloading decision of each server, and the number of virtual mac...

Robotic process automation can streamline business workflows by eliminating tedious manual tasks without requiring you to completely re-engineer legacy systems.

A text semantic classification is an essential approach to recognising the verbal intention of online learners, empowering reliable understanding and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as CNN and RNN, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to semantics is often considerably varied, requiring the attachment of weights to these elements to inc...

This work discuss the task of automatically detecting satire instances in short articles. It is the study of extracting the most optimal features by using a deep learning architecture combined with carefully handcrafted contextual features. It is found that a few sets can perform well when they are used independently, but the others not so much. However, even the latter sets become very useful after the combination process with the former sets. This shows that each of the feature sets are significant. Finally, the combined feature sets undergoes the classification using well-known machine lear...