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作者： Nguyen, Thuong Gupta, Sunil Venkatesh, Svetha Phung, Dinh Centre for Pattern Recognition and Data Analytics Deakin University Australia

The discovery of contexts is important for context-aware applications in pervasive computing. This is a challenging problem because of the stream nature of data, the complexity and changing nature of contexts. We propose a Bayesian nonparametric model for the detection of co-location contexts from Bluetooth signals. By using an Indian buffet process as the prior distribution, the model can discover the number of contexts automatically. We introduce a novel fixed-lag particle filter that processes data incrementally. This sampling scheme is especially suitable for pervasive computing as the computational requirements remain constant in spite of growing data. We examine our model on a synthetic dataset and two real world datasets. To verify the discovered contexts, we compare them to the communities detected by the Louvain method, showing a strong correlation between the results of the two methods. Fixed-lag particle filter is compared with Gibbs sampling in terms of the normalized factorization error that shows a close performance between the two inference methods. As fixed-lag particle filter processes a small chunk of data when it comes and does not need to be restarted, its execution time is significantly shorter than that of Gibbs sampling. © 2015 Elsevier B.V. All rights reserved.

关键词： Ubiquitous computing

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An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal

An EEMD-PCA approach to extract heart rate, respiratory rate...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Mohammod Abdul Motin Chandan Kumar Karmakar Marimuthu Palaniswami Department of Electrical & Electronic Engineering University of Melbourne Melbourne Australia Centre of Pattern Recognition and Data Analytics (PRaDA) Deakin University Geelong Australia

The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR, RR and RA simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 45 epochs of PPG, electrocardiogram (ECG) and respiratory signal extracted from the MIMIC database (Physionet ATM data bank). The ECG and capnograph based respiratory signal were used as the ground truth and several metrics such as magnitude squared coherence (MSC), correlation coefficients (CC) and root mean square (RMS) error were used to compare the performance of EEMD-PCA algorithm with most of the existing methods in the literature. Results of EEMD-PCA based extraction of HR, RR and RA from PPG signal showed that the median RMS error (quartiles) obtained for RR was 0 (0, 0.89) breaths/min, for HR was 0.62 (0.56, 0.66) beats/min and for RA the average value of MSC and CC was 0.95 and 0.89 respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR, RR and RA than other existing methods.

关键词： principal component analysis biological tissues blood cardiovascular system feature extraction mean square error methods medical signal processing oximetry photoplethysmography Mean square error Heart Rate Photoplethysmography medical signal processing Oximetry SRGN gene TISSUES (BIOLOGY) Cardiovascular System Feature extraction principal components analysis Right Ascension Vital Signs signals Root mean square Blood

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Bayesian nonparametric Multiple Instance Regression

Bayesian nonparametric Multiple Instance Regression

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International Conference on pattern recognition

作者： Saravanan Subramanian Santu Rana Sunil Gupta P. Bagavathi Sivakumar C. Shunmuga Velayutham Svetha Venkateshc Centre for Pattern Recognition and Data Analytics Deakin University Australia Dept of Computer Science and Engineering Amrita University Coimbatore India Deakin University Burwood VIC AU

ISBN: (纸本)9781509048489

Multiple Instance Regression jointly models a set of instances and its corresponding real-valued output. We present a novel multiple instance regression model that infers a subset of instances in each bag that best describes the bag label and uses them to learn a predictive model in a unified framework. We assume that instances in each bag are drawn from a mixture distribution and thus naturally form groups, and instances from one of this group explain the bag label. The largest cluster is assumed to be correlated with the label. We evaluate this model on the crop yield prediction and aerosol depth prediction problems. The predictive accuracy of our model is better than the state of the art MIR methods.

关键词： Predictive models Agriculture Prediction algorithms Bayes methods data models Computational modeling Clustering algorithms

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Multi-task transfer learning for in-hospital-death prediction of ICU patients

Multi-task transfer learning for in-hospital-death predictio...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Chandan Karmakar Budhaditya Saha Marimuthu Palaniswami Svetha Venkatesh Centre of Pattern Recognition and Data Analytics (PRaDA) Deakin University Gee-long VIC Australia Department of Electrical & Electronic Engineering The University of Melbourne Melbourne VIC Australia

Multi-Task Transfer Learning (MTTL) is an efficient approach for learning from inter-related tasks with small sample size and imbalanced class distribution. Since the intensive care unit (ICU) data set (publicly available in Physionet) has subjects from four different ICU types, we hypothesize that there is an underlying relatedness amongst various ICU types. Therefore, this study aims to explore MTTL model for in-hospital mortality prediction of ICU patients. We used single-task learning (STL) approach on the augmented data as well as individual ICU data and compared the performance with the proposed MTTL model. As a performance measurement metrics, we used sensitivity (Sens), positive predictivity (+Pred), and Score. MTTL with class balancing showed the best performance with score of 0.78, 0.73, o.52 and 0.63 for ICU type 1 (Coronary care unit), 2 (Cardiac surgery unit), 3 (Medical ICU) and 4 (Surgical ICU) respectively. In contrast the maximum score obtained using STL approach was 0.40 for ICU type 1 & 2. These results indicates that the performance of in-hospital mortality can be improved using ICU type information and by balancing the `non-survivor' class. The findings of the study may be useful for quantifying the quality of ICU care, managing ICU resources and selecting appropriate interventions.

关键词： data models Feature extraction Predictive models Surgery Measurement Computational modeling Covariance matrices

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Analysing the History of Autism Spectrum Disorder Using Topic Models

Analysing the History of Autism Spectrum Disorder Using Topi...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Adham Beykikhoshk Dinh Phung Ognjen Arandjelović Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia School of Computer Science University of St Andrews Fife Scotland United Kingdom University of Saint Andrews Saint Andrews Fife GB

ISBN: (纸本)9781509052073

We describe a novel framework for the discovery of underlying topics of a longitudinal collection of scholarly data, and the tracking of their lifetime and popularity over time. Unlike the social media or news data where the underlying topics evolve over time, the topic nuances in science result in new scientific directions to emerge. Therefore, we model the longitudinal literature data with a new approach that uses topics which remain identifiable over the course of time. Current studies either disregard the time dimension or treat it as an exchangeable covariate when they fix the topics over time or do not share the topics over epochs when they model the time naturally. We address these issues by adopting a non-parametric Bayesian approach. We assume the data is partially exchangeable and divide it into consecutive epochs. Then, by fixing the topics in a recurrent Chinese restaurant franchise, we impose a static topical structure on the corpus such that the topics are shared across epochs and the documents within epochs. We demonstrate the effectiveness of the proposed framework on a collection of medical literature related to autism spectrum disorder. We collect a large corpus of publications and carefully examine two important research issues of the domain as case studies. Moreover, we make the results of our experiment and the source code of the model, freely available to the public. This aids other researchers to analyse our results or apply the model to their data collections.

关键词： data models Autism Computational modeling Analytical models Atomic measurements Probability distribution Probabilistic logic

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Influence of embedding dimension on distribution entropy in analyzing heart rate variability

Influence of embedding dimension on distribution entropy in ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Radhagayathri K. Udhayakumar Chandan Karmakar Peng Li Marimuthu Palaniswami Department of Electrical & Electronic Engineering The University of Melbourne Melbourne VIC Australia Centre of Pattern Recognition and Data Analytics (PRaDA) Deakin University Geelong VIC Australia The School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9781457702198

Distribution entropy (DistEn) is a recent measure of complexity that is used to analyze Heart Rate Variability (HRV) data. DistEn which is a function of data length N, number of bins M and embedding dimension m is known to be stable and consistent with respect to parameters N and M respectively. Also, (N, M) are known to have a combined effect in deciding performance of DistEn as a classification feature. But, all such analysis have mostly ignored the influence of the third parameter m on DistEn properties. Though a random fixed choice of m value has so far succeeded in portraying the effect of other parameters on DistEn, it is considered equally important to reveal the influence of a varying m on DistEn and its characteristics. This study examines the impact of m on the stability, consistency and performance of DistEn when the latter is used to analyze HRV data belonging to (i) healthy subjects discerned by age and (ii) subjects discerned by their heart's physiologic condition. Here, data length N of each signal is varied from 50 to 1000, while the number of bins M used varies from 100 to 2000. Information pertaining to m variations is obtained by carrying out experiments at four different values of embedding dimension; m = 2, 3,4 and 5. The study shows that the stability, consistency and classification performance of DistEn is not much influenced by changes in m.

关键词： Gold databases Entropy Time series analysis Heart rate variability Stability analysis Physiology

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Differentiating sub-groups of online depression-related communities using textual cues 1

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16th International Conference on Web Information Systems Engineering, WISE 2015

作者： Nguyen, Thin O’Dea, Bridianne Larsen, Mark Phung, Dinh Venkatesh, Svetha Christensen, Helen Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Black Dog Institute University of New South Wales Sydney Australia

ISBN: (数字)9783319261874

ISBN: (纸本)9783319261867

Depression is a highly prevalent mental illness and is a comorbidity of other mental and behavioural disorders. The Internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities;however, the characteristics of these online conversations and the language styles of those interested in depression have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A random sample of 5,000 blog posts was crawled. Five groupings were identified: depression, bipolar, self-harm, grief, and suicide. Independent variables included psycholinguistic processes and content topics extracted from the posts. Machine learning techniques were used to discriminate messages posted in the depression sub-group from the *** predictive validity in depression classification using topics and psycholinguistic clues as features was found. Clear discrimination between writing styles and content, with good predictive power is an important step in understanding social media and its use in mental health. © Springer International Publishing Switzerland 2015.

关键词： Social networking (online)

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Topic Model Kernel Classification With Probabilistically Reduced Features

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Journal of data Science 2015年第2期13卷 323-340页

作者： Vu Nguyen tvnguye@deakin.edu.au Dinh Phung Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics (PRaDA) Deakin University Melbourne Australia

Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics interpretation, but could also be informative for classification tasks. In this paper, we describe the Topic Model Kernel (TMK), a topicbased kernel for Support Vector Machine classification on data being processed by probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks with real world datasets. TMK outperforms existing kernels on the distributional features and give comparative results on nonprobabilistic data types. [ABSTRACT FROM AUTHOR]

关键词： Bayesian Nonparametric Classification Dimensionality Reduction Kernel Method Support Vector Machine Topic Models

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Nonparametric discovery of online mental health-related communities

Nonparametric discovery of online mental health-related comm...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Bo Dao Thin Nguyen Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University Australia

People are increasingly using social media, especially online communities, to discuss mental health issues and seek supports. Understanding topics, interaction, sentiment and clustering structures of these communities informs important aspects of mental health. It can potentially add knowledge to the underlying cognitive dynamics, mood swings patterns, shared interests, and interaction. There has been growing research interest in analyzing online mental health communities; however sentiment analysis of these communities has been largely under-explored. This study presents an analysis of online Live Journal communities with and without mental health-related conditions including depression and autism. Latent topics for mood tags, affective words, and generic words in the content of the posts made in these communities were learned using nonparametric topic modelling. These representations were then input into a nonparametric clustering to discover meta-groups among the communities. The best performance results can be achieved on clustering communities with latent mood-based representation for such communities. The study also found significant differences in usage latent topics for mood tags and affective features between online communities with and without affective disorders. The findings reveal useful insights into hyper-group detection of online mental health-related communities.

关键词： Media Mood Blogs Autism Sentiment analysis Variable speed drives

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Differentially Private Random Forest with High Utility

Differentially Private Random Forest with High Utility

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IEEE International Conference on data Mining (ICDM)

作者： Santu Rana Sunil Kumar Gupta Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467395052

Privacy-preserving data mining has become an active focus of the research community in the domains where data are sensitive and personal in nature. For example, highly sensitive digital repositories of medical or financial records offer enormous values for risk prediction and decision making. However, prediction models derived from such repositories should maintain strict privacy of individuals. We propose a novel random forest algorithm under the framework of differential privacy. Unlike previous works that strictly follow differential privacy and keep the complete data distribution approximately invariant to change in one data instance, we only keep the necessary statistics (e.g. variance of the estimate) invariant. This relaxation results in significantly higher utility. To realize our approach, we propose a novel differentially private decision tree induction algorithm and use them to create an ensemble of decision trees. We also propose feasible adversary models to infer about the attribute and class label of unknown data in presence of the knowledge of all other data. Under these adversary models, we derive bounds on the maximum number of trees that are allowed in the ensemble while maintaining privacy. We focus on binary classification problem and demonstrate our approach on four real-world datasets. Compared to the existing privacy preserving approaches we achieve significantly higher utility.

关键词： Decision trees Privacy data privacy Vegetation Sensitivity Standards

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