Posts Tagged

UKCI2018

A Study on CNN Transfer Learning for Image Classification

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Authors – Mahbub Hussain, Jordan J. Bird, and Diego R. Faria
Aston Lab for Intelligent Collectives Engineering (ALICE)
School of Engineering and Applied Science, Aston University, UK.

Abstract – Many image classification models have been introduced to help tackle the foremost issue of recognition accuracy. Image classification is one of the core problems in Computer Vision field with a large variety of practical applications. Examples include object recognition for robotic manipulation, pedestrian or obstacle detection for autonomous vehicles, among others. A lot of attention has been associated with Machine Learning, specifically neural networks such as the Convolutional Neural Network (CNN) winning image classification competitions. This work proposes the study and investigation of such a CNN architecture model (i.e. Inception-v3) to establish whether it would work best in terms of accuracy and efficiency with new image datasets via Transfer Learning. The retrained model is evaluated, and the results are compared to some state-of-the-art approaches.

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Learning from Interaction: An Intelligent Networked-based Human-bot and Bot-bot Chatbot System

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This idea for a chatbot that actively learnt through interaction was developed for a final year project whilst studying for a Bachelor’s Degree in Computer Science from Aston University (Birmingham, UK). The work proved effective and as such a paper was written for UKCI2018 (18th Annual UK Workshop on Computational Intelligence). The work will also be published in Springer’s Advances in Intelligent Systems and Computing.

Authors – Jordan J. Bird, Diego R. Faria, Anikó Ekárt
Aston Lab for Intelligent Collectives Engineering (ALICE)
School of Engineering and Applied Science, Aston University, UK.

Abstract – In this paper we propose an approach to a chatbot software that is able to learn from interaction via text messaging between human-bot and bot-bot. The bot listens to a user and decides whether or not it knows how to reply to the message accurately based on current knowledge, otherwise it will set about to learn a meaningful response to the message through pattern matching based on its previous experience. Similar methods are used to detect offensive messages, and are proved to be effective at overcoming the issues that other chatbots have experienced in the open domain. A philosophy of giving preference to too much censorship rather than too little is employed given the failure of Microsoft Tay. In this work, a layered approach is devised to conduct each process, and leave the architecture open to improvement with more advanced methods in the future. Preliminary results show an improvement over time in which the bot learns more responses. A novel approach of message simplification is added to the bot’s architecture, the results suggest that the algorithm has a substantial improvement on the bot’s conversational performance at a factor of three.

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