About this community

Current research areas include bioinformatics, computer architecture, computer vision, distributed systems, graphics and human-computer interaction, logic and semantics, machine learning, natural language processing, networking and wireless communication, operating systems and virtualization, programming, security, and sustainable computing

The Computer Laboratory undertakes research in a broad range of subjects within the disciplines of Computer Science, Engineering, Technology, and Mathematics. Current research areas include bioinformatics, computer architecture, computer vision, distributed systems, graphics and human-computer interaction, logic and semantics, machine learning, natural language processing, networking and wireless communication, operating systems and virtualization, programming, security, and sustainable computing.

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Recent Submissions

  • SmileNet: Registration-Free Smiling Face Detection in the Wild 

    Jang, Y; Gunes, Hatice; Patras, I (IEEE, 2017-10-29)
    We present a novel smiling face detection framework called SmileNet for detecting faces and recognising smiles in the wild. SmileNet uses a Fully Convolutional Neural Network (FCNN) to detect multiple smiling faces in a ...
  • TCP in the Internet of Things: from ostracism to prominence 

    Carles Gomez, CG; Arcia Moret, Andres Emilio; Crowcroft, Jonathon Andrew
    TCP has traditionally been neglected as a transport-layer protocol for the Internet of Things (IoT). However, recent trends and industry needs are favouring TCP presence in IoT environments. In this paper, we first motivate ...
  • Verifying Spatial Properties of Array Computations 

    Orchard, D; Contrastin, M; Danish, M; Rice, Andrew Colin (ACM, 2017-10)
    Arrays computations are at the core of numerical modelling and computational science applications. However, low-level manipulation of array indices is a source of program error. Many practitioners are aware of the need to ...
  • An Error-Oriented Approach to Word Embedding Pre-Training 

    Farag, Y; Rei, Marek; Briscoe, T (Association for Computational Linguistics, 2017-09-08)
    We propose a novel word embedding pre-training approach that exploits writing errors in learners' scripts. We compare our method to previous models that tune the embeddings based on script scores and the discrimination ...

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