A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

Random breath testing in Queensland and Western Australia : Examination of how the random breath testing rate influences alcohol related traffic crash rates

In this paper we explore the relationship between monthly random breath testing (RBT) rates (per 1000 licensed drivers) and alcohol-related traffic crash (ARTC) rates over time, across two Australian states: Queensland and Western Australia. We analyse the RBT, ARTC and licensed driver rates across 12 years; however, due to administrative restrictions, we model ARTC rates against RBT rates for the period July 2004 to June 2009. The Queensland data reveals that the monthly ARTC rate is almost flat over the five year period. Based on the results of the analysis, an average of 5.5 ARTCs per 100,000 licensed drivers are observed across the study period. For the same period, the monthly rate of RBTs per 1000 licensed drivers is observed to be decreasing across the study with the results of the analysis revealing no significant variations in the data. The comparison between Western Australia and Queensland shows that Queensland's ARTC monthly percent change (MPC) is 0.014 compared to the MPC of 0.47 for Western Australia. While Queensland maintains a relatively flat ARTC rate, the ARTC rate in Western Australia is increasing. Our analysis reveals an inverse relationship between ARTC RBT rates, that for every 10% increase in the percentage of RBTs to licensed driver there is a 0.15 decrease in the rate of ARTCs per 100,000 licenced drivers. Moreover, in Western Australia, if the 2011 ratio of 1:2 (RBTs to annual number of licensed drivers) were to double to a ratio of 1:1, we estimate the number of monthly ARTCs would reduce by approximately 15. Based on these findings we believe that as the number of RBTs conducted increases the number of drivers willing to risk being detected for drinking driving decreases, because the perceived risk of being detected is considered greater. This is turn results in the number of ARTCs diminishing. The results of this study provide an important evidence base for policy decisions for RBT operations.

Order tracking for discrete-random separation in variable speed conditions

The transmission path from the excitation to the measured vibration on the surface of a mechanical system introduces a distortion both in amplitude and in phase. Moreover, in variable speed conditions, the amplification/attenuation and the phase shift, due to the transfer function of the mechanical system, varies in time. This phenomenon reduces the effectiveness of the traditionally tachometer based order tracking, compromising the results of a discrete-random separation performed by a synchronous averaging. In this paper, for the first time, the extent of the distortion is identified both in the time domain and in the order spectrum of the signal, highlighting the consequences for the diagnostics of rotating machinery. A particular focus is given to gears, providing some indications on how to take advantage of the quantification of the disturbance to better tune the techniques developed for the compensation of the distortion. The full theoretical analysis is presented and the results are applied to an experimental case.

Optimising the length of Random Breath Tests : results from the Queensland Community Engagement Trial

Research suggests that the length and quality of police-citizen encounters affect policing outcomes. The Koper Curve, for example, shows that the optimal length for police presence in hot spots is between 14 and 15 minutes, with diminishing returns observed thereafter. Our study, using data from the Queensland Community Engagement Trial (QCET), examines the impact of encounter length on citizen perceptions of police performance. QCET involved a randomised field trial, where 60 random breath test (RBT) traffic stop operations were randomly allocated to an experimental condition involving a procedurally just encounter or a business-as-usual control condition. Our results show that the optimal length of time for procedurally just encounters during RBT traffic stops is just less than 2 minutes. We show, therefore, that it is important to encourage and facilitate positive police–citizen encounters during RBTat traffic stops, while ensuring that the length of these interactions does not pass a point of diminishing returns.

Efficient selective identity-based encryption without random oracles

We construct two efficient Identity-Based Encryption (IBE) systems that admit selective-identity security reductions without random oracles in groups equipped with a bilinear map. Selective-identity secure IBE is a slightly weaker security model than the standard security model for IBE. In this model the adversary must commit ahead of time to the identity that it intends to attack, whereas in an adaptive-identity attack the adversary is allowed to choose this identity adaptively. Our first system—BB1—is based on the well studied decisional bilinear Diffie–Hellman assumption, and extends naturally to systems with hierarchical identities, or HIBE. Our second system—BB2—is based on a stronger assumption which we call the Bilinear Diffie–Hellman Inversion assumption and provides another approach to building IBE systems. Our first system, BB1, is very versatile and well suited for practical applications: the basic hierarchical construction can be efficiently secured against chosen-ciphertext attacks, and further extended to support efficient non-interactive threshold decryption, among others, all without using random oracles. Both systems, BB1 and BB2, can be modified generically to provide “full” IBE security (i.e., against adaptive-identity attacks), either using random oracles, or in the standard model at the expense of a non-polynomial but easy-to-compensate security reduction.

Short signature without random oracles and the SDH assumption in bilinear groups

We describe a short signature scheme that is strongly existentially unforgeable under an adaptive chosen message attack in the standard security model. Our construction works in groups equipped with an efficient bilinear map, or, more generally, an algorithm for the Decision Diffie-Hellman problem. The security of our scheme depends on a new intractability assumption we call Strong Diffie-Hellman (SDH), by analogy to the Strong RSA assumption with which it shares many properties. Signature generation in our system is fast and the resulting signatures are as short as DSA signatures for comparable security. We give a tight reduction proving that our scheme is secure in any group in which the SDH assumption holds, without relying on the random oracle model.

Decomposition construction for secret sharing schemes with graph access structures in polynomial time

The purpose of this paper is to describe a new decomposition construction for perfect secret sharing schemes with graph access structures. The previous decomposition construction proposed by Stinson is a recursive method that uses small secret sharing schemes as building blocks in the construction of larger schemes. When the Stinson method is applied to the graph access structures, the number of such “small” schemes is typically exponential in the number of the participants, resulting in an exponential algorithm. Our method has the same flavor as the Stinson decomposition construction; however, the linear programming problem involved in the construction is formulated in such a way that the number of “small” schemes is polynomial in the size of the participants, which in turn gives rise to a polynomial time construction. We also show that if we apply the Stinson construction to the “small” schemes arising from our new construction, both have the same information rate.

Flexible process graph : a prologue

Businesses document their operational processes as process models. The common practice is to represent process models as directed graphs. The nodes of a process graph represent activities and directed edges constitute activity ordering constraints. A flexible process graph modeling approach proposes to generalize process graph structure to a hypergraph. Obtained process structure aims at formalization of ad-hoc process control flow. In this paper we discuss aspects relevant to concurrent execution of process activities in a collaborative manner organized as a flexible process graph. We provide a real world flexible process scenario to illustrate the approach.

Random Gabor based templates for facial expression recognition in images with facial occlusion

Robust facial expression recognition (FER) under occluded face conditions is challenging. It requires robust algorithms of feature extraction and investigations into the effects of different types of occlusion on the recognition performance to gain insight. Previous FER studies in this area have been limited. They have spanned recovery strategies for loss of local texture information and testing limited to only a few types of occlusion and predominantly a matched train-test strategy. This paper proposes a robust approach that employs a Monte Carlo algorithm to extract a set of Gabor based part-face templates from gallery images and converts these templates into template match distance features. The resulting feature vectors are robust to occlusion because occluded parts are covered by some but not all of the random templates. The method is evaluated using facial images with occluded regions around the eyes and the mouth, randomly placed occlusion patches of different sizes, and near-realistic occlusion of eyes with clear and solid glasses. Both matched and mis-matched train and test strategies are adopted to analyze the effects of such occlusion. Overall recognition performance and the performance for each facial expression are investigated. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the high robustness and fast processing speed of our approach, and provide useful insight into the effects of occlusion on FER. The results on the parameter sensitivity demonstrate a certain level of robustness of the approach to changes in the orientation and scale of Gabor filters, the size of templates, and occlusions ratios. Performance comparisons with previous approaches show that the proposed method is more robust to occlusion with lower reductions in accuracy from occlusion of eyes or mouth.

Random projections on manifolds of symmetric positive definite matrices for image classification

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.

Joint 2D and 3D cues for image segmentation towards robotic applications

This thesis investigates the fusion of 3D visual information with 2D image cues to provide 3D semantic maps of large-scale environments in which a robot traverses for robotic applications. A major theme of this thesis was to exploit the availability of 3D information acquired from robot sensors to improve upon 2D object classification alone. The proposed methods have been evaluated on several indoor and outdoor datasets collected from mobile robotic platforms including a quadcopter and ground vehicle covering several kilometres of urban roads.

Crossword puzzle attack on NLS

NLS is one of the stream ciphers submitted to the eSTREAM project. We present a distinguishing attack on NLS by Crossword Puzzle (CP) attack method which is introduced in this paper. We build the distinguisher by using linear approximations of both the non-linear feedback shift register (NFSR) and the nonlinear filter function (NLF). Since the bias of the distinguisher depends on the Konst value, which is a key-dependent word, we present the graph showing how the bias of distinguisher vary with Konst. In result, we estimate the bias of the distinguisher to be around O(2^−30). Therefore, we claim that NLS is distinguishable from truly random cipher after observing O(2^60) keystream words. The experiments also show that our distinguishing attack is successful on 90.3% of Konst among 2^32 possible values. We extend the CP attack to NLSv2 which is a tweaked version of NLS. In result, we build a distinguisher which has the bias of around 2− 48. Even though this attack is below the eSTREAM criteria (2^−40), the security margin of NLSv2 seems to be too low.