HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks

On electronic game platforms, different payment transactions have different levels of risk. Risk is generally higher for digital goods in e-commerce. However, it differs based on product and its popularity, the offer type (packaged game, virtual currency to a game or subscription service), storefront and geography. Existing fraud policies and models make decisions independently for each transaction based on transaction attributes, payment velocities, user characteristics, and other relevant information. However, suspicious transactions may still evade detection and hence we propose a broad learning approach leveraging a graph based perspective to uncover relationships among suspicious transactions, i.e., inter-transaction dependency. Our focus is to detect suspicious transactions by capturing common fraudulent behaviors that would not be considered suspicious when being considered in isolation. In this paper, we present HitFraud that leverages heterogeneous information networks for collective fraud detection by exploring correlated and fast evolving fraudulent behaviors. First, a heterogeneous information network is designed to link entities of interest in the transaction database via different semantics. Then, graph based features are efficiently discovered from the network exploiting the concept of meta-paths, and decisions on frauds are made collectively on test instances. Experiments on real-world payment transaction data from Electronic Arts demonstrate that the prediction performance is effectively boosted by HitFraud with fast convergence where the computation of meta-path based features is largely optimized. Notably, recall can be improved up to 7.93% and F-score 4.62% compared to baselines.Comment: ICDM 201

Unsupervised Learning of Complex Articulated Kinematic Structures combining Motion and Skeleton Information

In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs state-of-the-art methods both quantitatively and qualitatively

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Algorithms for Large Scale Problems in Eigenvalue and Svd Computations and in Big Data Applications

As ”big data” has increasing influence on our daily life and research activities, it poses significant challenges on various research areas. Some applications often demand a fast solution of large, sparse eigenvalue and singular value problems; In other applications, extracting knowledge from large-scale data requires many techniques such as statistical calculations, data mining, and high performance computing. In this dissertation, we develop efficient and robust iterative methods and software for the computation of eigenvalue and singular values. We also develop practical numerical and data mining techniques to estimate the trace of a function of a large, sparse matrix and to detect in real-time blob-filaments in fusion plasma on extremely large parallel computers. In the first work, we propose a hybrid two stage SVD method for efficiently and accurately computing a few extreme singular triplets, especially the ones corresponding to the smallest singular values. The first stage achieves fast convergence while the second achieves the final accuracy. Furthermore, we develop a high-performance preconditioned SVD software based on the proposed method on top of the state-of-the-art eigensolver PRIMME. The method can be used with or without preconditioning, on parallel computers, and is superior to other state-of-the-art SVD methods in both efficiency and robustness. In the second study, we provide insights and develop practical algorithms to accomplish efficient and accurate computation of interior eigenpairs using refined projection techniques in non-Krylov iterative methods. By analyzing different implementations of the refined projection, we propose a new hybrid method to efficiently find interior eigenpairs without compromising accuracy. Our numerical experiments illustrate the efficiency and robustness of the proposed method. In the third work, we present a novel method to estimate the trace of matrix inverse that exploits the pattern correlation between the diagonal of the inverse of the matrix and that of some approximate inverse. We leverage various sampling and fitting techniques to fit the diagonal of the approximation to that of the inverse. Our method may serve as a standalone kernel for providing a fast trace estimate or as a variance reduction method for Monte Carlo in some cases. An extensive set of experiments demonstrate the potential of our method. In the fourth study, we provide first results on applying outlier detection techniques to effectively tackle the fusion blob detection problem on extremely large parallel machines. We present a real-time region outlier detection algorithm to efficiently find and track blobs in fusion experiments and simulations. Our experiments demonstrated we can achieve linear time speedup up to 1024 MPI processes and complete blob detection in two or three milliseconds