School of
Information Technology and Electrical Engineering

Bio-inspired Robotics (iRat):

Bio-inspired computational paradigms for artificial learning and intelligence are used as the basis for the modelling of the artificial thinking systems. Featuring elements with complex biological behaviours, these paradigms are used to empower autonomous robots with adaptive and reactive cognitive behaviours. In addition, the group has also developed a robot, the iRAT (intelligent Rat Animat Technology), specifically to embody the bio-inspired neural based cognition models.

This integrated approach leads to an increased understanding of the neural, behavioural and information processing bases of complex and intelligent systems. Insight from neurocognitive systems will be used to develop computational models, autonomous robots and intelligent software agents, which in turn will lead to a deeper understanding of the relationship between neurocognitive mechanisms and their behaviour in whole systems.

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A key aspect in investigating how information is understood in biological systems is to study the creation, evolution and application of languages. Combining with robotics, we examine the effects of language acquisition and learning among embodied agents, in particular the representation of spatial and temporal concepts. These concepts include where and when events, objects and agents are located in space and time.

Besides inventing their own languages to representing these concepts, the robots also converse with each other in order to better understand and establish concepts relating to their world, allowing for more effective communication between agents. Furthermore, they are able to demonstrate their understanding of these concepts by their ability to perform behavioural and navigational tasks.

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Discursis is a new communication analytics technology that allows a user to analyse text based communication data, in the form of conversations, web forums, training scenarios, and many more.

Discursis uses natural language processing algorithms to automatically processes transcribed text to highlight participant interactions around specific topics and over the time-course of the conversation.

Discursis can assist practitioners in understanding the structure, information content, and inter-speaker relationships that are present within input data. Discursis also provides quantitative measures of key metrics, such as topic introduction; topic consistency; and topic novelty.

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Relaxed correctness criteria for modern multi-core architectures

This project seeks to lay groundwork for fully exploiting the potential of multicore computers. Multicore computers have become ubiquitous over the last decade, now being standard in everything from laptops to mobile phones. Their benefits are clear – better performance leading to more sophisticated applications. Key to ensuring those benefits are complex, and often subtle, algorithms that exploit the parallelism that multicore computers offer. This project aims to lay foundations for extending those benefits to applications where high reliability is a concern. It plans to do so by developing theoretical results about the correctness of algorithms on standard multicore computers, and practical tools and techniques to help programmers of multicore computers to better understand the behaviour of their code.

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Machine Learning, Data analysis and Visualization

There is an abundance of data associated with many important problems in science, commerce, our environment and man-made systems. This creates a demand for techniques that can be used to model and understand large and complex datasets. Typical applications include prediction and classification, anomaly detection and support systems to assist in understanding the data. An example current area of application is in the analysis of healthcare systems data in collaboration with healthcare professionals.

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‘Harlie’ stands for Human and Robot Language Interaction Experiment. It is the name of an Android smartphone application that our research team has recently developed. Harlie can call you on your smartphone to have a chat with you and ask for voice samples.

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We envision Florence as a personal assistant for someone living with dementia and for their carers.Through this project, we aim to be a step change in communication for people living with dementia. We want to provide an example for others on the design of technology that truly empowers and enables the person to live an ordinary, everyday life.

To deliver this vision, we are creating a language bank for a person,creating an intelligent assistant, and establishing a platform for the creation of technology in this context.

We want to make sure that the technology we design and build is for people and we adopt a true vision of calm computing.

Find out more

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The OPAL project is a collaboration between engineering, computer science, psychology, interaction design, and robotics with the aim of building a social robot for studying child-robot interaction.

This project is inspired by the RUBI project at UCSD. Early prototypes have been given the name "Opie" and have been shown to evoke interesting social behaviours from children.

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Transcription Acceleration Project (TAP)

The Transcription Acceleration Project (TAP) is a Centre of Excellence for the Dynamics of Language (CoEDL) cross-disciplinary project aimed at identifying the workflows of linguists and language workers during transcription, and developing assistive tools to accelerate that process.

Tasks such as transcribing recorded audio can be very slow. Using contemporary software techniques such as speech recognition, we can improve the the transcription experience, resulting in practical and psychological benefits for people's work.

Technologies such as Google's Cloud Speech can transcribe 100+ of the world's languages, but these tools don't support any endangered languages. For languages with small quantities of data, or for research situations which prohibit the use of cloud technologies, TAP is developing Elpis, a tool to obtain a first-pass transcription on untranscribed audio. This best-guess is used as a canvas for the language worker to refine. Elpis brings cutting-edge speech recognition technology within reach of language workers and researchers who don’t have backgrounds in speech engineering. With Elpis, we hope to enable community members to make a significant impact on transcribing their own languages from new recordings, or breath life into archival, cultural heritage material.