Identifying team style in soccer using formations learned from spatiotemporal tracking data

To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league.

On-line estimation of Allan variance parameters

A new online method is presented for estimation of the angular random walk and rate random walk coefficients of IMU (inertial measurement unit) gyros and accelerometers. The online method proposes a state space model and proposes parameter estimators for quantities previously measured from off-line data techniques such as the Allan variance graph. Allan variance graphs have large off-line computational effort and data storage requirements. The technique proposed here requires no data storage and computational effort of O(100) calculations per data sample.

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Dispatch strategy of PHEVs to mitigate selected patterns of seasonally varying outputs from renewable generation

Rapid development of plug-in hybrid electric vehicles (PHEVs) brings new challenges and opportunities to the power industry. A large number of idle PHEVs can potentially be employed to form a distributed energy storage system for supporting renewable generation. To reduce the negative effects of unsteady renewable generation outputs, a stochastic optimization-based dispatch model capable of handling uncertain outputs of PHEVs and renewable generation is formulated in this paper. The mathematical expectations, second-order original moments, and variances of wind and photovoltaic (PV) generation outputs are derived analytically. Incorporated all the derived uncertainties, a novel generation shifting objective is proposed. The cross-entropy (CE) method is employed to solve this optimal dispatch model. Multiple patterns of renewable generation depending on seasons and renewable market shares are investigated. The feasibility and efficiency of the developed optimal dispatch model, as well as the CE method, are demonstrated with a 33-node distribution system.

Comparative studies made simple in GPFlow

This work describes recent extensions to the GPFlow scientific workflow system in development at MQUTeR (www.mquter.qut.edu.au), which facilitate interactive experimentation, automatic lifting of computations from single-case to collection-oriented computation and automatic correlation and synthesis of collections. A GPFlow workflow presents as an acyclic data flow graph, yet provides powerful iteration and collection formation capabilities.

Short-data recursive HMM parameter estimation for rapid vision-based aircraft heading estimation

Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that place an emphasis on the early availability of reasonable estimates (e.g. for change detection) rather than the provision of longer-term asymptotic properties (such as convergence, convergence rate, and consistency). In the context of vision- based aircraft (image-plane) heading estimation, this paper suggests and evaluates the short-data estimation properties of 3 recursive HMM parameter estimation techniques (a recursive maximum likelihood estimator, an online EM HMM estimator, and a relative entropy based estimator). On both simulated and real data, our studies illustrate the feasibility of rapid recursive heading estimation, but also demonstrate the need for careful step-size design of HMM recursive estimation techniques when these techniques are intended for use in applications where short-data behaviour is paramount.

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.

An observation about circular shortest paths: Dealing with additional constraints using branch and bound

Circular shortest paths represent a powerful methodology for image segmentation. The circularity condition ensures that the contour found by the algorithm is closed, a natural requirement for regular objects. Several implementations have been proposed in the past that either promise closure with high probability or ensure closure strictly, but with a mild computational efficiency handicap. Circularity can be viewed as a priori information that helps recover the correct object contour. Our "observation" is that circularity is only one among many possible constraints that can be imposed on shortest paths to guide them to a desirable solution. In this contribution, we illustrate this opportunity under a volume constraint but the concept is generally applicable. We also describe several adornments to the circular shortest path algorithm that proved useful in applications. © 2011 IEEE.

Tubule detection in testis images using boundary weighting and circular shortest path

In studies of germ cell transplantation, measureing tubule diameters and counting cells from different populations using antibodies as markers are very important. Manual measurement of tubule sizes and cell counts is a tedious and sanity grinding work. In this paper, we propose a new boundary weighting based tubule detection method. We first enhance the linear features of the input image and detect the approximate centers of tubules. Next, a boundary weighting transform is applied to the polar transformed image of each tubule region and a circular shortest path is used for the boundary detection. Then, ellipse fitting is carried out for tubule selection and measurement. The algorithm has been tested on a dataset consisting of 20 images, each having about 20 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually. © 2013 IEEE.

A data mining based method for discovery of web services and their compositions

Due to the availability of huge number of web services, finding an appropriate Web service according to the requirements of a service consumer is still a challenge. Moreover, sometimes a single web service is unable to fully satisfy the requirements of the service consumer. In such cases, combinations of multiple inter-related web services can be utilised. This paper proposes a method that first utilises a semantic kernel model to find related services and then models these related Web services as nodes of a graph. An all-pair shortest-path algorithm is applied to find the best compositions of Web services that are semantically related to the service consumer requirement. The recommendation of individual and composite Web services composition for a service request is finally made. Empirical evaluation confirms that the proposed method significantly improves the accuracy of service discovery in comparison to traditional keyword-based discovery methods.

A conceptual approach to track design changes within a multi-disciplinary BIM environment

Successful management of design changes is critical for the efficient delivery of construction projects. Building Information Modeling (BIM) is envisioned to play an important role in integrating design, construction and facility management processes through coordinated changes throughout the project life-cycle. BIM currently provides significant benefits in coordinating changes across different views in a single model, and identifying conflicts between different discipline-specific models. However, current BIM tools provide limited support in managing changes across several discipline-specific models. This paper describes an approach to represent, coordinate, and track changes within a collaborative multi-disciplinary BIM environment. This approach was informed by a detailed case study of a large, complex, fast-tracked BIM project where we investigated numerous design changes, analyzed change management processes, and evaluated existing BIM tools. Our approach characterises design changes in an ontology to represent changed component attributes, dependencies between components, and change impacts. It explores different types of dependencies amongst different design changes and describes how a graph based approach and dependency matrix could assist with automating the propagation and impact of changes in a BIM-based project delivery process.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Histogram matching and global initialization for laser-only SLAM in large unstructured environments

This paper presents an enhanced algorithm for matching laser scan maps using histogram correlations. The histogram representation effectively summarizes a map's salient features such that pairs of maps can be matched efficiently without any prior guess as to their alignment. The histogram matching algorithm has been enhanced in order to work well in outdoor unstructured environments by using entropy metrics, weighted histograms and proper thresholding of quality metrics. Thus our large-scale scan-matching SLAM implementation has a vastly improved ability to close large loops in real-time even when odometry is not available. Our experimental results have demonstrated a successful mapping of the largest area ever mapped to date using only a single laser scanner. We also demonstrate our ability to solve the lost robot problem by localizing a robot to a previously built map without any prior initialization.

Edge-weighting of gene expression graphs

In recent years, considerable research efforts have been directed to micro-array technologies and their role in providing simultaneous information on expression profiles for thousands of genes. These data, when subjected to clustering and classification procedures, can assist in identifying patterns and providing insight on biological processes. To understand the properties of complex gene expression datasets, graphical representations can be used. Intuitively, the data can be represented in terms of a bipartite graph, with weighted edges corresponding to gene-sample node couples in the dataset. Biologically meaningful subgraphs can be sought, but performance can be influenced both by the search algorithm, and, by the graph-weighting scheme and both merit rigorous investigation. In this paper, we focus on edge-weighting schemes for bipartite graphical representation of gene expression. Two novel methods are presented: the first is based on empirical evidence; the second on a geometric distribution. The schemes are compared for several real datasets, assessing efficiency of performance based on four essential properties: robustness to noise and missing values, discrimination, parameter influence on scheme efficiency and reusability. Recommendations and limitations are briefly discussed. Keywords: Edge-weighting; weighted graphs; gene expression; bi-clustering

A Mammalian Circadian Clock Model Incorporating Daytime Expression Elements

Models of the mammalian clock have traditionally been based around two feedback loops-the self-repression of Per/Cry by interfering with activation by BMAL/CLOCK, and the repression of Bmal/Clock by the REV-ERB proteins. Recent experimental evidence suggests that the D-box, a transcription factor binding site associated with daytime expression, plays a larger role in clock function than has previously been understood. We present a simplified clock model that highlights the role of the D-box and illustrate an approach for finding maximum-entropy ensembles of model parameters, given experimentally imposed constraints. Parameter variability can be mitigated using prior probability distributions derived from genome-wide studies of cellular kinetics. Our model reproduces predictions concerning the dual regulation of Cry1 by the D-box and Rev-ErbA/ROR response element (RRE) promoter elements and allows for ensemble-based predictions of phase response curves (PRCs). Nonphotic signals such as Neuropeptide Y (NPY) may act by promoting Cry1 expression, whereas photic signals likely act by stimulating expression from the E/E' box. Ensemble generation with parameter probability restraints reveals more about a model's behavior than a single optimal parameter set.