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When realized on computational devices with finite quantities of memory, feedforward artificial neural networks and the functions they compute cease being abstract mathematical objects and turn into executable programs generating concrete computations. To differentiate between feedforward artificial neural networks and their functions as abstract mathematical objects and the realizations of these networks and functions on finite memory devices, we introduce the categories of general and actual computabilities and show that there exist correspondences, i.e., bijections, between functions computable by trained feedforward artificial neural networks on finite memory automata and classes of primitive recursive functions.

In the investigation, we consider the problem of classification of audio samples resulting from the audio beehive monitoring. Audio beehive monitoring is a key component of electronic beehive monitoring (EBM) that can potentially automate the identification of various stressors for honeybee colonies. We propose to use convolutional neural networks (ConvNets) and compare developed ConvNets in classifying audio samples from electronic beehive monitors deployed in live beehives. As a result, samples are placed in one of the three non-overlapping categories: bee buzzing (B), cricket chirping (C), and ambient noise (N). We show that ConvNets trained to classify raw audio samples perform slightly better than ConvNets trained to classify spectrogram images of audio samples. We demonstrate that ConvNets can successfully operate in situ on low voltage devices such as the credit card size raspberry pi computer.

Female athletes are at an increased risk of developing the female athlete triad, which is associated with negative consequences to the performance and health of the athlete. An intervention for this population must be brief and easily incorporated into their daily schedule. Cell phones offer a viable option for connecting with female athletes who are at risk for the female athlete triad. The purpose of this study was to assess the utility of using text message reminders as a way to encourage female athletes at risk for the female athlete triad to increase their energy intake.

This article describes FAQ FINDER, a natural language question-answering system that uses files of frequently asked questions as its knowledge base. Unlike AI question-answering systems that focus on the generation of new answers, FAQ FINDER retrieves existing ones found in frequently asked question files. Unlike information-retrieval approaches that rely on a purely lexical metric of similarity between query and document, FAQ FINDER uses a semantic knowledge base (WORDNET) to improve its ability to match question and answer. We include results from an evaluation of the system's performance and show that a combination of semantic and statistical techniques works better than any single approach.