Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Teaching a digital performing agent: artificial neural network and hidden Markov Model for recognising and performing dance movement

For a Digital Performing Agent to be able to perform live with a human dancer, it would be useful for the agent to be able to contextualize the movement the dancer is performing and to have a suitable movement vocabulary with which to contribute to the performance. In this paper we will discuss our research into the use of Artificial Neural Networks (ANN) as a means of allowing a software agent to learn a shared vocabulary of movement from a dancer. The agent is able to use the learnt movements to form an internal representation of what the dancer is performing, allowing it to follow the dancer, generate movement sequences based on the dancer's current movement and dance independently of the dancer using a shared movement vocabulary. By combining the ANN with a Hidden Markov Model (HMM) the agent is able to recognize short full body movement phrases and respond when the dancer performs these phrases. We consider the relationship between the dancer and agent as a means of supporting the agent's learning and performance, rather than developing the agent's capability in a self-contained fashion.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Noise level classification for EEG using Hidden Markov Models

EEG signal is one of the most important signals for diagnosing some diseases. EEG is always recorded with an amount of noise, the more noise is recorded the less quality is the EEG signal. The included noise can represent the quality of the recorded EEG signal, this paper proposes a signal quality assessment method for EEG signal. The method generates an automated measure to detect the noise level of the recorded EEG signal. Mel-Frequency Cepstrum Coefficient is used to represent the signals. Hidden Markov Models were used to build a classification model that classifies the EEG signals based on the noise level associated with the signal. This EEG quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information. Moreover, our model was applied on an uncontrolled environment and on controlled environment and a result comparison was applied.

Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions

Protein-protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%-80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

Negotiating the hidden curriculum: power and affect in negotiated classrooms

Garth Boomer's ideas in Negotiating the Curriculum (1992a) resonate with discussions of shifting teacher and student roles and relationships in the 'student voice' movement. Boomer (1988) critiqued his earlier conception of power in Negotiating the Curriculum, asserting that he would 'now like to write a book on Negotiating the Hidden Curriculum', in which he would conduct an ethnographic 'micro-analysis' of the 'moment-by-moment dance' between teachers and students and the fluctuations in the 'flows and ebbs of affect and primal resistance in teachers and taught' (p. 171). This article takes up this provocation, considering a 2013 meeting of a cross-age student voice group where students, teachers and researchers collectively discussed the meanings and manifestations of the hidden curriculum through exploring Pink Floyd's Another Brick in the Wall (Waters, 1979), other film representations of school, and their own school. Four students and I analysed a transcript from this meeting, considering the dynamics of power and affect in negotiated classrooms.

Post: 9/11: hidden pedagogy: the positional forces of pedagogy in Victoria, Australia

This qualitative study charts the lived narratives of twelve participants, six teachers and six students from urban and rural Victoria, Australia. The study examines in detail the question ‘How do teachers teach, post 9/11?’. 9/11 has become accepted shorthand for September 11th 2001, in which terrorist attacks took place in the United States of America. The attacks heralded a ‘post- 9/11 world, [in which] threats are defined more by the fault lines within societies than by the territorial boundaries between them’ (National Commission on Terrorist Attacks, 2011, p. 361). The study is embedded in the values that have come to the fore in the wake of the 9/11 attacks and the ideological shifts that have occurred globally. These values and ideologies are reflected via issues of culture and consumption. In education this is particularly visible through pedagogy. The research employs a multimethodological (Esteban-Guitart, 2012) form of inquiry through the use of bricolage (Kincheloe & Berry, 2004) which is comprised at the intersectional points of critical pedagogy (Kincheloe, 2008b), public pedagogy (Sandlin, Schultz, & Burdick, 2010b) and cultural studies (Hall, Hobson, Lowe, & Willis, 1992). This study adopts a critical ontological perspective, and is grounded in qualitative research approaches (Lather & St. Pierre, 2013). The methods of photo elicitation, artefact analysis, video observation and semi-structured interviews are used to critically examine the ways in which teacher and student identities are shaped by the pedagogies of contemporary schooling, and how they form common sense understandings of the world and themselves, charting possibilities between accepted common sense beliefs and 21st century neoliberal capitalism. The research is presented through a prototypical form of literary journalism and intertextuality which examines the interrelationship between teaching and social worlds exposing the hidden influence of enculturation and addressing the question ‘How do teachers teach, post 9/11?’