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IEEE International Conference on Multimedia and Expo (ICME)

作者： Cheng Li Dinh Phung Santu Rana Svetha Venkatesh Centre for Pattern Recognition and Data Analytics (PRaDA) Deakin University Geelong Australia

ISBN: (纸本)9781479900145

Multimedia contents often possess weakly annotated data such as tags, links and interactions. The weakly annotated data is called side information. It is the auxiliary information of data and provides hints for exploring the link structure of data. Most clustering algorithms utilize pure data for clustering. A model that combines pure data and side information, such as images and tags, documents and keywords, can perform better at understanding the underlying structure of data. We demonstrate how to incorporate different types of side information into a recently proposed Bayesian nonparametric model, the distance dependent Chinese restaurant process (DD-CRP). Our algorithm embeds the affinity of this information into the decay function of the DD-CRP when side information is in the form of subsets of discrete labels. It is flexible to measure distance based on arbitrary side information instead of only the spatial layout or time stamp of observations. At the same time, for noisy and incomplete side information, we set the decay function so that the DD-CRP reduces to the traditional Chinese restaurant process, thus not inducing side effects of noisy and incomplete side information. Experimental evaluations on two real-world datasets NUS WIDE and 20 Newsgroups show exploiting side information in DD-CRP significantly improves the clustering performance.

关键词： Clustering algorithms Noise measurement Mutual information Bayes methods Multimedia communication Time measurement Layout

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Individualized arrhythmia detection with ECG signals from wearable devices

Individualized arrhythmia detection with ECG signals from we...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Thanh-Binh Nguyen Wei Lou Terry Caelli Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University National ICT Australia

Low cost pervasive electrocardiogram (ECG) monitors is changing how sinus arrhythmia are diagnosed among patients with mild symptoms. With the large amount of data generated from long-term monitoring, come new data science and analytical challenges. Although traditional rule-based detection algorithms still work on relatively short clinical quality ECG, they are not optimal for pervasive signals collected from wearable devices-they don't adapt to individual difference and assume accurate identification of ECG fiducial points. To overcome these short-comings of the rule-based methods, this paper introduces an arrhythmia detection approach for low quality pervasive ECG signals. To achieve the robustness needed, two techniques were applied. First, a set of ECG features with minimal reliance on fiducial point identification were selected. Next, the features were normalized using robust statistics to factors out baseline individual differences and clinically irrelevant temporal drift that is common in pervasive ECG. The proposed method was evaluated using pervasive ECG signals we collected, in combination with clinician validated ECG signals from Physiobank. Empirical evaluation confirms accuracy improvements of the proposed approach over the traditional clinical rules.

关键词： Electrocardiography Heart beat Monitoring Biomedical monitoring Robustness Accuracy

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Overcoming data scarcity of Twitter: Using tweets as bootstrap with application to autism-related topic content analysis

Overcoming data scarcity of Twitter: Using tweets as bootstr...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Adham Beykikhoshk Ognjen Arandjelović Dinh Phung Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Geelong VIC Australia

ISBN: (纸本)9781509020942

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.

关键词： Twitter data mining Web pages Media Autism Tagging Uniform resource locators

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Forward-Backward Smoothing for Hidden Markov Models of Point pattern data

Forward-Backward Smoothing for Hidden Markov Models of Point...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Nhan Dam Dinh Phung Ba-Ngu Vo Viet Huynh Deakin University Centre for Pattern Recognition and Data Analytics Australia Curtin University Australia

This paper considers a discrete-time sequential latent model for point pattern data, specifically a hidden Markov model (HMM) where each observation is an instantiation of a random finite set (RFS). This so-called RFS-HMM is worthy of investigation since point pattern data are ubiquitous in artificial intelligence and data science. We address the three basic problems typically encountered in such a sequential latent model, namely likelihood computation, hidden state inference, and parameter estimation. Moreover, we develop algorithms for solving these problems including forward-backward smoothing for likelihood computation and hidden state inference, and expectation-maximisation for parameter estimation. Simulation studies are used to demonstrate key properties of RFS-HMM, whilst real data in the domain of human dynamics are used to demonstrate its applicability.

关键词： Hidden Markov models data models Radio frequency Maximum likelihood estimation Computational modeling Finite element analysis Smoothing methods

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data-mining twitter and the autism spectrum disorder: A Pilot study

Data-mining twitter and the autism spectrum disorder: A Pilo...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Adham Beykikhoshk Ognjen Arandjelović Dinh Phung Svetha Venkatesh Terry Caelli Centre for Pattern Recognition and Data Analytics Deakin Univeristy Australia National ICT Australia (NICTA) Melbourne Australia

The autism spectrum disorder (ASD) is increasingly being recognized as a major public health issue which affects approximately 0.5-0.6% of the population. Promoting the general awareness of the disorder, increasing the engagement with the affected individuals and their carers, and understanding the success of penetration of the current clinical recommendations in the target communities, is crucial in driving research as well as policy. The aim of the present work is to investigate if Twitter, as a highly popular platform for information exchange, can be used as a data-mining source which could aid in the aforementioned challenges. Specifically, using a large data set of harvested tweets, we present a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.

关键词： Variable speed drives Twitter Autism Communities data mining Pragmatics Conferences

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SECC: Simultaneous extraction of context and community from pervasive signals

SECC: Simultaneous extraction of context and community from ...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Thuong Nguyen Vu Nguyen Flora D. Salim Dinh Phung Computer Science and Information Technology RMIT University Australia Centre for Pattern Recognition and Data Analytics Deakin University Australia

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture highorder and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.

关键词： Context data mining Feature extraction Context modeling data models Mixture models Bluetooth

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Multi-modal abnormality detection in video with unknown data segmentation

Multi-modal abnormality detection in video with unknown data...

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International Conference on pattern recognition

作者： Tien Vu Nguyen Dinh Phung Santu Rana Duc Son Pham Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University VIC Australia Department of Computing Curtin University WA Australia

ISBN: (纸本)9781467322164

This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras; and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days.

关键词： Hidden Markov models Vectors data models Computational modeling Cameras Detectors Surveillance

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Animal recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring

Animal Recognition and Identification with Deep Convolutiona...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Hung Nguyen Sarah J. Maclagan Tu Dinh Nguyen Thin Nguyen Paul Flemons Kylie Andrews Euan G. Ritchie Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University Geelong VIC Australia Centre for Integrative Ecology Deakin University Burwood VIC Australia Centre for Pattern Recognition & Data Analytics Deakin University Burwood VIC Australia Australian Museum Research Institute Sydney NSW Australia ABC Radio National Australia

Efficient and reliable monitoring of wild animals in their natural habitats is essential to inform conservation and management decisions. Automatic covert cameras or "camera traps" are being an increasingly popular tool for wildlife monitoring due to their effectiveness and reliability in collecting data of wildlife unobtrusively, continuously and in large volume. However, processing such a large volume of images and videos captured from camera traps manually is extremely expensive, time-consuming and also monotonous. This presents a major obstacle to scientists and ecologists to monitor wildlife in an open environment. Leveraging on recent advances in deep learning techniques in computer vision, we propose in this paper a framework to build automated animal recognition in the wild, aiming at an automated wildlife monitoring system. In particular, we use a single-labeled dataset from Wildlife Spotter project, done by citizen scientists, and the state-of-the-art deep convolutional neural network architectures, to train a computational system capable of filtering animal images and identifying species automatically. Our experimental results achieved an accuracy at 96.6% for the task of detecting images containing animal, and 90.4% for identifying the three most common species among the set of images of wild animals taken in South-central Victoria, Australia, demonstrating the feasibility of building fully automated wildlife observation. This, in turn, can therefore speed up research findings, construct more efficient citizen sciencebased monitoring systems and subsequent management decisions, having the potential to make significant impacts to the world of ecology and trap camera images analysis.

关键词： Wildlife Cameras Monitoring Automation Australia

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Software Abstraction for Casual Games Using Temporal Model: An Alloy-Based Approach

Software Abstraction for Casual Games Using Temporal Model: ...

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International Conference on Advanced Computing and Applications (ACOMP)

作者： Long-Phuoc Tôn Hoang-Anh Pham Bo Dao Faculty of Computer Science & Engineering HCMC University of Technology Vietnam Centre for Pattern Recognition and Data Analytics Deakin University Australia

Software abstraction plays an important role in both requirements engineering and software design. It facilitates the principle of separate of concerns while opening the door for software automation. This paper revisits the notion of software abstraction made for casual games where a single player accumulates her points by performing game activities under a set of rules. We propose to make software abstraction in Alloy - a lightweight, declarative language that is semantically based on first-order logic and syntactically being object-oriented. We show that casual games can be conceptually abstracted and semantically represented in Alloy. More specifically, game concepts (e.g., the main player, gold mine) are represented under Alloy signatures while game rules (e.g., game over, score upped/downed) as Alloy fact. To precisely specify game rules and make them simulatable, we need a mechanism for querying and updating the states of game concepts at different moments during the course of the game. To this end, we employ temporal modeling in making software abstraction for casual games.

关键词： Games Metals Tools Software design Object oriented modeling Requirements engineering

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Graph classification via deep learning with virtual nodes

arXiv

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arXiv 2017年

作者： Pham, Trang Tran, Truyen Dam, Hoa Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia School of Computing and Information Technology University of Wollongong Australia

Learning representation for graph classification turns a variable-size graph into a fixed-size vector (or matrix). Such a representation works nicely with algebraic manipulations. Here we introduce a simple method to augment an attributed graph with a virtual node that is bidirectionally connected to all existing nodes. The virtual node represents the latent aspects of the graph, which are not immediately available from the attributes and local connectivity structures. The expanded graph is then put through any node representation method. The representation of the virtual node is then the representation of the entire graph. In this paper, we use the recently introduced Column Network for the expanded graph, resulting in a new end-to-end graph classification model dubbed Virtual Column Network (VCN). The model is validated on two tasks: (i) predicting bioactivity of chemical compounds, and (ii) finding software vulnerability from source code. Results demonstrate that VCN is competitive against wellestablished rivals. Copyright © 2017, The Authors. All rights reserved.

关键词： Graph theory

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