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Software vulnerabilities are a crucial and serious concern in the software industry and computer security. A variety of methods have been proposed to detect vulnerabilities in real-world software. Recent methods based on deep learning approaches for automatic feature extraction have improved software vulnerability identification compared with machine learning approaches based on hand-crafted feature extraction. However, these methods can usually only detect software vulnerabilities at a function or program level, which is much less informative because, out of hundreds (thousands) of code statements in a program or function, only a few core statements contribute to a software vulnerability. This requires us to find a way to detect software vulnerabilities at a fine-grained level. In this paper, we propose a novel method based on the concept of mutual information that can help us to detect and isolate software vulnerabilities at a fine-grained level (i.e., several statements that are highly relevant to a software vulnerability that include the core vulnerable statements) in both unsupervised and semi-supervised contexts. We conduct comprehensive experiments on real-world software projects to demonstrate that our proposed method can detect vulnerabilities at a fine-grained level by identifying several statements that mostly contribute to the vulnerability detection decision.

Probabilistic inference is a common and important task in statistical machine learning. The recently proposed Stein variational gradient descent (SVGD) is a generic Bayesian inference method that has been shown to be successfully applied in a wide range of contexts, especially in dealing with large datasets, where existing probabilistic inference methods have been known to be ineffective. In a large-scale data setting, SVGD employs the mini-batch strategy but its mini-batch estimator has large variance, hence compromising its estimation quality in practice. To this end, we propose in this paper a generic SVGD-based inference method that can significantly reduce the variance of mini-batch estimator when working with large datasets. Our experiments on 14 datasets show that the proposed method enjoys substantial and consistent improvements compared with baseline methods in binary classification task and its pseudo-online learning setting, and regression task. Furthermore, our framework is generic and applicable to a wide range of probabilistic inference problems such as in Bayesian neural networks and Markov random fields.

Function identification is a preliminary step in binary analysis for many extensive applications from malware detection, common vulnerability detection and binary instrumentation to name a few. In this paper, we propose the Code Pointer Network that leverages the underlying idea of a pointer network to efficiently and effectively tackle function scope identification – the hardest and most crucial task in function identification. We establish extensive experiments to compare our proposed method with the deep learning based baseline. Experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art baseline in terms of both predictive performance and running time.

Due to the ubiquity of computer software, software vulnerability detection (SVD) has become crucial in the software industry and in the field of computer security. Two significant issues in SVD arise when using machine learning, namely: i) how to learn automatic features that can help improve the predictive performance of vulnerability detection and ii) how to overcome the scarcity of labeled vulnerabilities in projects that require the laborious labeling of code by software security experts. In this paper, we address these two crucial concerns by proposing a novel architecture which leverages deep domain adaptation with automatic feature learning for software vulnerability identification. Based on this architecture, we keep the principles and reapply the state-of-the-art deep domain adaptation methods to indicate that deep domain adaptation for SVD is plausible and promising. Moreover, we further propose a novel method named Semi-supervised Code Domain Adaptation Network (SCDAN) that can efficiently utilize and exploit information carried in unlabeled target data by considering them as the unlabeled portion in a semi-supervised learning context. The proposed SCDAN method enforces the clustering assumption, which is a key principle in semi-supervised learning. The experimental results using six real-world software project datasets show that our SCDAN method and the baselines using our architecture have better predictive performance by a wide margin compared with the Deep Code Network (VulDeePecker) method without domain adaptation. Also, the proposed SCDAN significantly outperforms the DIRT-T which to the best of our knowledge is currently the-state-of-the-art method in deep domain adaptation and other baselines.

Current existing multi-hyperplane machine approach deals with high-dimensional and complex datasets by approximating the input data region using a parametric mixture of hyperplanes. Consequently, this approach requires an excessively time-consuming parameter search to find the set of optimal hyper-parameters. Another serious drawback of this approach is that it is often suboptimal since the optimal choice for the hyper-parameter is likely to lie outside the searching space due to the space discretization step required in grid search. To address these challenges, we propose in this paper BAyesian Multi-hyperplane Machine (BAMM). Our approach departs from a Bayesian perspective, and aims to construct an alternative probabilistic view in such a way that its maximum-a-posteriori (MAP) estimation reduces exactly to the original optimization problem of a multi-hyperplane machine. This view allows us to endow prior distributions over hyper-parameters and augment auxiliary variables to efficiently infer model parameters and hyper-parameters via Markov chain Monte Carlo (MCMC) method. We then employ a Stochastic Gradient Descent (SGD) framework to scale our model up with ever-growing large datasets. Extensive experiments demonstrate the capability of our proposed method in learning the optimal model without using any parameter tuning, and in achieving comparable accuracies compared with the state-of-art baselines; in the meantime our model can seamlessly handle with large-scale datasets.

One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under geometric and optimization views, hence termed geometric-based online GP (GoGP). We developed theory to guarantee that with a good convergence rate our proposed algorithm always produces a (sparse) solution which is close to the true optima to any arbitrary level of approximation accuracy specified a priori. Furthermore, our method is proven to scale seamlessly not only with large-scale datasets, but also to adapt accurately with streaming data. We extensively evaluated our proposed model against state-of-the-art baselines using several large-scale datasets for online regression task. The experimental results show that our GoGP delivered comparable, or slightly better, predictive performance while achieving a magnitude of computational speedup compared with its rivals under online setting. More importantly, its convergence behavior is guaranteed through our theoretical analysis, which is rapid and stable while achieving lower errors.

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.

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.

Support vector machines (SVMs) are widely-used for classification in machine learning and data mining tasks. However, they traditionally have been applied to small to medium datasets. Recent need to scale up with data size has attracted research attention to develop new methods and implementation for SVM to perform tasks at scale. Distributed SVMs are relatively new and studied recently, but the distributed implementation for SVM with data augmentation has not been developed. This paper introduces a distributed data augmentation implementation for SVM on Apache Spark, a recent advanced and popular platform for distributed computing that has been employed widely in research as well as in industry. We term our implementation sparkling vector machine (SkVM) which supports both classification and regression tasks by scanning through the data exactly once. In addition, we further develop a framework to handle the data with new classes arriving under an online classification setting where new data points can have labels that have not previously seen - a problem we term label-drift classification. We demonstrate the scalability of our proposed method on large-scale datasets with more than one hundred million data points. The experimental results show that the predictive performances of our method are comparable or better than those of baselines whilst the execution time is much faster at an order of magnitude.

Logistic regression (LR) for classification is the workhorse in industry, where a set of predefined classes is required. The model, however, fails to work in the case where the class labels are not known in advance, a problem we term label-drift classification. Label-drift classification problem naturally occurs in many applications, especially in the context of streaming settings where the incoming data may contain samples categorized with new classes that have not been previously seen. Additionally, in the wave of big data, traditional LR methods may fail due to their expense of running time. In this paper, we introduce a novel variant of LR, namely one-pass logistic regression (OLR) to offer a principled treatment for label-drift and large-scale classifications. To handle largescale classification for big data, we further extend our OLR to a distributed setting for parallelization, termed sparkling OLR (Spark-OLR). We demonstrate the scalability of our proposed methods on large-scale datasets with more than one hundred million data points. The experimental results show that the predictive performances of our methods are comparable orbetter than those of state-of-the-art baselines whilst the executiontime is much faster at an order of magnitude. In addition, the OLR and Spark-OLR are invariant to data shuffling and have no hyperparameter to tune that significantly benefits data practitioners and overcomes the curse of big data cross-validationto select optimal hyperparameters.

Bayesian nonparametric (BNP) models have recently become popular due to their flexibility in identifying the unknown number of clusters. However, they have difficulties handling heterogeneous data from multiple sources. Existing BNP methods either treat each of these sources independently - hence do not get benefits from the correlating information between them, or require to explicitly specify data sources as primary and context channels. In this paper, we present a BNP framework, termed MCNC, which has the ability to (1) discover co-patterns from multiple sources; (2) explore multi-channel data simultaneously and treat them equally; (3) automatically identify a suitable number of patterns from data; and (4) handle missing data. The key idea is to utilize a richer base measure of a BNP model being a product-space. We demonstrate our framework on synthetic and real-world datasets to discover the identity-location-time (a.k.a who-where-when) patterns. The experimental results highlight the effectiveness of our MCNC framework in both cases of complete and missing data.

The stability matters in clinical prediction models because it makes the model to be interpretable and generalizable. It is paramount for high dimensional data, which employ sparse models with feature selection ability. We propose a new method to stabilize sparse support vector machines using intrinsic graph structure of the electronic medical records. The graph structure is exploited using the Jaccard similarity among features. Our method employs a convex function to penalize the pairwise l ∞ -norm of connected feature coefficients in the graph. We apply the alternating direction method of multipliers to solve the proposed formulation. Our experiments are conducted on a synthetic and three real-world hospital datasets. We show that our proposed method is more stable than the state-of-the-art feature selection and classification techniques in terms of three stability measures namely, Jaccard similarity measure, Spearman's rank correlation coefficient and Kuncheva index. We further show that our method has resulted in better classification performance compared to the baselines.

Mortality prediction of rare cancer types with a small number of high-dimensional samples is a challenging task. We propose a transfer learning model where both classes in rare cancers (target task) are modeled in a joint framework by transferring knowledge from the source task. The knowledge transfer is at the data level where only “related” data points are chosen to train the target task. Moreover, both positive and negative class in training enhances the discrimination power of the proposed framework. Overall, this approach boosts the generalization performance of target task with a small number of data points. The formulation of the proposed framework is convex and expressed as a primal problem. We convert this to a dual problem and efficiently solve by alternating direction multipliers method. Our experiments with both synthetic and three real-world datasets show that our framework outperforms state-of-the-art single-task, multi-task, and transfer learning baselines.

Social capital is linked to mental illness. It has been proposed that higher social capital is associated with better mental well-being in both individuals and groups in offline setting. However, in online settings, the association between online social capital and mental health conditions has not yet been explored. Social media offer us a rich opportunity to determine the link between social capital and aspects of mental wellbeing. In this paper, we examine social capital based on levels of social connectivity of bloggers can be connected to aspects of depression in individuals and online depression community. We explore apparent properties of textual contents, including expressed emotions, language styles and latent topics, of a large corpus of blog posts, to analyze the aspect of social capital in the community. Using data collected from online Livejoumal depression community, we apply both statistical tests and machine learning approaches to examine how predictive factors vary between low and high social capital groups. Significant differences are found between low and high social capital groups when characterized by a set of latent topics, language features derived from blog posts, suggesting discriminative features, proved to be useful in the classification task. This shows that linguistic styles are better predictors than latent topics as features. The findings indicate the potential of using social media as a sensor for monitoring mental well-being in online settings.

Inferring abstract contexts and activities from heterogeneous data is vital to context-aware ubiquitous applications but still remains one of the most challenging problems. Recent advances in Bayesian nonparametric machine learning, in particular the theory of topic models based on Hierarchical Dirichlet Process (HDP), has provided an elegant solution towards these challenges. However, limited existing methods have addressed the problem of inferring latent multifaceted activities and contexts from heterogeneous data sources such as those collected from mobile devices. In this paper, we extend the original HDP to model heterogeneous data using a richer structure of the base measure being a product-space. The proposed model, called product-space HDP (PS-HDP), naturally handles the heterogeneous data from multiple sources and identify the unknown number of latent structures in a principle way. Although this framework is generic, our current work primarily focuses on inferring (latent) threefold activities of who-when-where simultaneously, which corresponds to inducing activities from data collected for identity, location and time. We demonstrate our model on synthetic data as well as on a real-world dataset – the StudentLife dataset. We report results and provide analysis on the discovered activities and patterns to demonstrate the merit of the model. We also quantitatively evaluate the performance of PS-HDP model using standard metrics including F1-score, NMI, RI, purity, and compare them with well-known existing baseline methods.

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.

Modelling patient flow is crucial in understanding resource demand and prioritization. To date, there has been limited work in predicting ward-level discharges. Our study investigates forecasting total next-day discharges from an open ward. In the absence of real-time clinical data, we propose to construct a feature set from patient demographics, ward data and discharge time series to derive a random forest model for forecasting daily discharge. Using data from a general ward of a large regional Australian hospital, we compared our random forest model with a classical auto-regressive integrated moving average (ARIMA) for 12,141 patient visits over 1826 days. Forecasting quality was measured using Mean Forecast Error, Mean Absolute Error, symmetric Mean Absolute Percentage Error and Root Mean Square Error. When compared to the baseline model, next day discharge forecasts using random forests achieved 17.4 % improvement in Mean Absolute Error, for all days in the year 2014.

Discovering dynamics of emotion and mood changes for individuals has the potential to enhance the diagnosis and treatment of mental disorders. In this paper we study affective transitions and dynamics among individuals in online mental health communities. Using social media as form of ‘sensor’, we crawl a large dataset of blogs posted by online communities whose descriptions declared to be associated with affective disorder conditions such as depression, anxiety, or autism. We then apply nonnegative matrix factorization model to extract the common and individual factors of affective transitions across groups of individuals in different levels of affective disorders. We examine the latent patterns of emotional transitions and investigate the effects of emotional transitions across the cohorts. Our framework is novel as it utilizes social media as an online sensing platform of mood and emotional dynamics. Hence, our work has implication in constructing systems to screen individuals and communities at high risks of mental health problems in online settings.

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.

In many real-world computer vision applications, such as multi-camera surveillance, the objects of interest are captured by visual sensors concurrently, resulting in multi-view data. These views usually provide complementary information to each other. One recent and powerful computer vision method for clustering is sparse subspace clustering (SSC); however, it was not designed for multi-view data, which break down its linear separability assumption. To integrate complementary information between views, multi-view clustering algorithms are required to improve the clustering performance. In this paper, we propose a novel multi-view subspace clustering by searching for an unified latent structure as a global affinity matrix in subspace clustering. Due to the integration of affinity matrices for each view, this global affinity matrix can best represent the relationship between clusters. This could help us achieve better performance on face clustering. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other alternatives based on state-of-the-arts on challenging multi-view face datasets.

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.

Feature selection is an important step in building predictive models for most real-world problems. One of the popular methods in feature selection is Lasso. However, it shows instability in selecting features when dealing with correlated features. In this work, we propose a new method that aims to increase the stability of Lasso by encouraging similarities between features based on their relatedness, which is captured via a feature covariance matrix. Besides modeling positive feature correlations, our method can also identify negative correlations between features. We propose a convex formulation for our model along with an alternating optimization algorithm that can learn the weights of the features as well as the relationship between them. Using both synthetic and real-world data, we show that the proposed method is more stable than Lasso and many state-of-the-art shrinkage and feature selection methods. Also, its predictive performance is comparable to other methods.

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.

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. Refereed/Peer-reviewed

The circadian system regulates 24 hour rhythms in biological creatures. It impacts mood regulation. The disruptions of circadian rhythms cause destabilization in individuals with affective disorders, such as depression and bipolar disorders. Previous work has examined the role of the circadian system on effects of light interactions on mood-related systems, the effects of light manipulation on brain, the impact of chronic stress on rhythms. However, such studies have been conducted in small, preselected populations. The deluge of data is now changing the landscape of research practice. The unprecedented growth of social media data allows one to study individual behavior across large and diverse populations. In particular, individuals with affective disorders from online communities have not been examined rigorously. In this paper, we aim to use social media as a sensor to identify circadian patterns for individuals with affective disorders in online communities.We use a large scale study cohort of data collecting from online affective disorder communities. We analyze changes in hourly, daily, weekly and seasonal affect of these clinical groups in contrast with control groups of general communities. By comparing the behaviors between the clinical groups and the control groups, our findings show that individuals with affective disorders show a significant distinction in their circadian rhythms across the online activity. The results shed light on the potential of using social media for identifying diurnal individual variation in affective state, providing key indicators and risk factors for noninvasive wellbeing monitoring and prediction.

Autism Spectrum Disorder (ASD) is growing at a staggering rate, but, little is known about the cause of this condition. Inferring learning patterns from therapeutic performance data, and subsequently clustering ASD children into subgroups, is important to understand this domain, and more importantly to inform evidence-based intervention. However, this data-driven task was difficult in the past due to insufficiency of data to perform reliable analysis. For the first time, using data from a recent application for early intervention in autism (TOBY Play pad), whose download count is now exceeding 4500, we present in this paper the automatic discovery of learning patterns across 32 skills in sensory, imitation and language. We use unsupervised learning methods for this task, but a notorious problem with existing methods is the correct specification of number of patterns in advance, which in our case is even more difficult due to complexity of the data. To this end, we appeal to recent Bayesian nonparametric methods, in particular the use of Bayesian Nonparametric Factor Analysis. This model uses Indian Buffet Process (IBP) as prior on a binary matrix of infinite columns to allocate groups of intervention skills to children. The optimal number of learning patterns as well as subgroup assignments are inferred automatically from data. Our experimental results follow an exploratory approach, present different newly discovered learning patterns. To provide quantitative results, we also report the clustering evaluation against K-means and Nonnegative matrix factorization (NMF). In addition to the novelty of this new problem, we were able to demonstrate the suitability of Bayesian nonparametric models over parametric rivals.

We propose a novel hierarchical Bayesian framework, word-distance-dependent Chinese restaurant franchise (wd-dCRF) for topic discovery from a document corpus regularized by side information in the form of word-to-word relations, with an application on Electronic Medical Records (EMRs). Typically, a EMRs dataset consists of several patients (documents) and each patient contains many diagnosis codes (words). We exploit the side information available in the form of a semantic tree structure among the diagnosis codes for semantically-coherent disease topic discovery. We introduce novel functions to compute word-to-word distances when side information is available in the form of tree structures. We derive an efficient inference method for the wddCRF using MCMC technique. We evaluate on a real world medical dataset consisting of about 1000 patients with PolyVascular disease. Compared with the popular topic analysis tool, hierarchical Dirichlet process (HDP), our model discovers topics which are superior in terms of both qualitative and quantitative measures.

Monitoring daily physical activity of human plays an important role in preventing diseases as well as improving health. In this paper, we demonstrate a framework for monitoring the physical activity levels in daily life. We collect the data using accelerometer sensors in a realistic setting without any supervision. The ground truth of activities is provided by the participants themselves using an experience sampling application running on mobile phones. The original data is discretized by the hierarchical Dirichlet process (HDP) into different activity levels and the number of levels is inferred automatically. We validate the accuracy of the extracted patterns by using them for the multi-label classification of activities and demonstrate the high performances in various standard evaluation metrics. We further show that the extracted patterns are highly correlated to the daily routine of users.

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. Refereed/Peer-reviewed

Exploiting context from stream data in pervasive environments remains a challenge. We aim to extract proximal context from Bluetooth stream data, using an incremental, Bayesian nonparametric framework that estimates the number of contexts automatically. Unlike current approaches that can only provide final proximal grouping, our method provides proximal grouping and membership of users over time. Additionally, it provides an efficient online inference. We construct co-location matrix over time using Bluetooth data. A Poisson-exponential model is used to factorize this matrix into a factor matrix, interpreted as proximal groups, and a coefficient matrix that indicates factor usage. The coefficient matrix follows the Indian Buffet Process prior, which estimates the number of factors automatically. The non-negativity and sparsity of factors are enforced by using the exponential distribution to generate the factors. We propose a fixed-lag particle filter algorithm to process data incrementally. We compare the incremental inference (particle filter) with full batch inference (Gibbs sampling) in terms of normalized factorization error and execution time. The normalized error obtained through our incremental inference is comparable to that of full batch inference, whilst the execution time is more than 100 times faster. The discovered factors have similar meaning to the results of the popular Louvain method for community detection.

The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG.

Current growth of individuals on the autism spectrum disorder (ASD) requires continuous support and care. With the popularity of social media, online communities of people affected by ASD emerge. This paper presents an analysis of these online communities through understanding aspects that differentiate such communities. In this paper, the aspects given are not expressed in terms of friendship, exchange of information, social support or recreation, but rather with regard to the topics and linguistic styles that people express in their on-line writing. Using data collected unobtrusively from LiveJournal, we analyze posts made by ten autism communities in conjunction with those made by a control group of standard communities. Significant differences have been found between autism and control communities when characterized by latent topics of discussion and psycholinguistic features. Latent topics are found to have greater predictive power than linguistic features when classifying blog posts as either autism or control community. This study suggests that data mining of online blogs has the potential to detect clinically meaningful data. It opens the door to possibilities including sentinel risk surveillance and harnessing the power in diverse large datasets.

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.

Existing anomaly detection methods in video surveillance exhibit lack of congruence between rare events detected by algorithms and what is considered anomalous by users. This paper introduces a novel browsing model to address this issue, allowing users to interactively examine rare events in an intuitive manner. Introducing a novel way to compute rare motion patterns, we estimate latent factors of foreground motion patterns through Bayesian Nonparametric Factor analysis. Each factor corresponds to a typical motion pattern. A rarity score for each factor is computed, and ordered in decreasing order of rarity, permitting users to browse events using any proportion of rare factors. Rare events correspond to frames that contain the rare factors chosen. We present the user with an interface to inspect events that incorporate these rarest factors in a spatial-temporal manner. We demonstrate the system on a public video data set, showing key aspects of the browsing paradigm.

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An \ell_{1} - norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to be part of a sub-group that shares a smaller set of similar vocabulary, thus allowing for cleaner clusters. Extensive experimental evaluations on two real-world datasets from Reuters-21578 and 20Newsgroup corpora show that our proposed method consistently outperforms state-of-the-art algorithms. Significantly, the performance improvement over other methods is prominent for this datasets.

We introduce a new method for face recognition using a versatile probabilistic model known as Restricted Boltzmann Machine (RBM). In particular, we propose to regularise the standard data likelihood learning with an information-theoretic distance metric defined on intra-personal images. This results in an effective face representation which captures the regularities in the face space and minimises the intra-personal variations. In addition, our method allows easy incorporation of multiple feature sets with controllable level of sparsity. Our experiments on a high variation dataset show that the proposed method is competitive against other metric learning rivals. We also investigated the RBM method under a variety of settings, including fusing facial parts and utilising localised feature detectors under varying resolutions. In particular, the accuracy is boosted from 71.8% with the standard whole-face pixels to 99.2% with combination of facial parts, localised feature extractors and appropriate resolutions.

We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso regression and suggest numerical algorithms so solve the proposed formulation. The experiments on the benchmark Johns Hopkins 155 dataset demonstrate that exploiting spatial constraints significantly improves motion segmentation.

We identify and formulate a novel problem: cross channel anomaly detection from multiple data channels. Cross channel anomalies are common amongst the individual channel anomalies, and are often portent of significant events. Using spectral approaches, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single channel anomalies. Our mathematical analysis shows that our method is likely to reduce the false alarm rate. We demonstrate our method in two applications: document understanding with multiple text corpora, and detection of repeated anomalies in video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large scale data stream analysis.

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing a state-of-the-art probabilistic clustering technique, the Latent Dirichlet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.

To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.

The ability to learn and recognize human activities of daily living (ADLs) is important in building pervasive and smart environments. In this paper, we tackle this problem using the hidden semi-Markov model. We discuss the stateof- the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. Inference and learning are efficiently addressed by providing a graphical representation for the model in terms of a dynamic Bayesian network (DBN). We investigate both discrete and continuous distributions from the exponential family (Poisson and Inverse Gaussian respectively) for the problem of learning and recognizing ADLs. A full comparison between the exponential family duration models and other existing models including the traditional multinomial and the new Coxian are also presented. Our work thus completes a thorough investigation into the aspect of duration modeling and its application to human activities recognition in a real-world smart home surveillance scenario.

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the switching hidden semi-markov model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations.

In this paper, we exploit the discrete Coxian distribution and propose a novel form of stochastic model, termed as the Coxian hidden semi-Makov model (Cox-HSMM), and apply it to the task of recognising activities of daily living (ADLs) in a smart house environment. The use of the Coxian has several advantages over traditional parameterization (e.g. multinomial or continuous distributions) including the low number of free parameters needed, its computational efficiency, and the existing of closed-form solution. To further enrich the model in real-world applications, we also address the problem of handling missing observation for the proposed Cox-HSMM. In the domain of ADLs, we emphasize the importance of the duration information and model it via the Cox-HSMM. Our experimental results have shown the superiority of the Cox-HSMM in all cases when compared with the standard HMM. Our results have further shown that outstanding recognition accuracy can be achieved with relatively low number of phases required in the Coxian, thus making the Cox-HSMM particularly suitable in recognizing ADLs whose movement trajectories are typically very long in nature.