Recent Submissions

  • Exploring the Deployment and Utilisation of Web Infrastructure in Africa 

    Fanou, R; TYSON, G; LEAO FERNANDES, E; Francois, P; Sathiaseelan, A (Association for Computing Machinery, 2018-11)
    It is well known that internet infrastructure deployment is progressing at a rapid pace in the African continent. A flurry of recent research has quantified this, highlighting the expansion of its underlying connectivity ...
  • Benchmarking the SHL Recognition Challenge with classical and deep-learning pipelines 

    WANG, L; Gjoreski, H; Ciliberto, M; Mekki, S; Valentin, S; Roggen, D; HASCA Workshop of Ubicomp 2018 (Association for Computing Machinery, 2018-10)
    In this paper we, as part of the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organizing team, present reference recognition performance obtained by applying various classical and deep-learning ...
  • A case study for human gestures recognition from poorly annotated data 

    Ciliberto, M; WANG, L; Roggen, D; Zillmer, R; HASCA Workshop of Ubicomp 2018 (Association for Computing Machinery, 2018-10)
    In this paper we present a case study on drinking gesture recognition from a dataset annotated by Experience Sampling (ES). The dataset contains 8825 "sensor events" and users reported 1808 "drink events" through experience ...
  • Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge 

    WANG, L; Gjoreski, H; Murao, K; Okita, T; Roggen, D; HASCA Workshop of Ubicomp 2018 (Association for Computing Machinery, 2018-10)
    In this paper we summarize the contributions of participants to the Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp 2018. The SHL challenge is a machine learning ...
  • Analysing replay spoofing countermeasure performance under varied conditions 

    CHETTRI, B; STURM, BLT; BENETOS, E; IEEE International Workshop on Machine Learning for Signal Processing (IEEE, 2018-09)
    In this paper, we aim to understand what makes replay spoofing detection difficult in the context of the ASVspoof 2017 corpus. We use FFT spectra, mel frequency cepstral coefficients (MFCC) and inverted MFCC (IMFCC) frontends ...
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