Using pattern analysis methods to supplement attention directing analytical procedures

How might the application of analytical procedures be improved given the inherent shortcomings of traditional analytic techniques and the apparent difficulties auditors have in combining all critical cues when evaluating the results of the analytical procedures? This research attempts to improve analytical methods by applying a new technology, Artificial Neural Networks (ANNs), to perform pattern recognition of the investigation signals generated by analytical procedures. ANNs, a type of artificial intelligence technology, are able to recognize patterns in data even when the data is noisy, ambiguous, distorted or variable. Four years of audited financial data from a medium-sized distributor were used to calculate five commonly applied financial ratios. The performance of these ratios, applied independently and in combinations, was evaluated using a presumed lack of actual errors and certain seeded material errors. The ANN method evaluated the information content of the combinations of financial ratios using an entropy cost function derived from information theory. This exploratory study suggests that the use of an ANN to analyze patterns of related fluctuations across numerous financial ratios provides a more reliable indication of the presence of material errors than either traditional analytic procedures or pattern analysis, as well as providing insight to the plausible causes of the error. Preliminary results suggest that the use of pattern analysis methods as a supplement to traditional analytical procedures will offer improved performance in recognizing material misstatements within the financial accounts.     

A maximum-likelihood interpretation for slow feature analysis

The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal of theoretical neuroscience is to work out how it does so. One proposed feature extraction strategy is motivated by the observation that the meaning of sensory data, such as the identity of a moving visual object, is often more persistent than the activation of any single sensory receptor. This notion is embodied in the slow feature analysis (SFA) algorithm, which uses "slowness" as a heuristic by which to extract semantic information from multidimensional time series. Here, we develop a probabilistic interpretation of this algorithm, showing that inference and learning in the limiting case of a suitable probabilistic model yield exactly the results of SFA. Similar equivalences have proved useful in interpreting and extending comparable algorithms such as independent component analysis. For SFA, we use the equivalent probabilistic model as a conceptual springboard with which to motivate several novel extensions to the algorithm.     

A maximum-likelihood surface estimator for dense range data

Describes how to estimate 3D surface models from dense sets of noisy range data taken from different points of view, i.e., multiple range maps. The proposed method uses a sensor model to develop an expression for the likelihood of a 3D surface, conditional on a set of noisy range measurements. Optimizing this likelihood with respect to the model parameters provides an unbiased and efficient estimator. The proposed numerical algorithms make this estimation computationally practical for a wide variety of circumstances. The results from this method compare favorably with state-of-the-art approaches that rely on the closest-point or perpendicular distance metric, a convenient heuristic that produces biased solutions and fails completely when surfaces are not sufficiently smooth, as in the case of complex scenes or noisy range measurements. Empirical results on both simulated and real ladar data demonstrate the effectiveness of the proposed method for several different types of problems. Furthermore, the proposed method offers a general framework that can accommodate extensions to include surface priors, more sophisticated noise models, and other sensing modalities, such as sonar or synthetic aperture radar.     

A maximum-likelihood Kalman filter for switching discrete-time linear systems

State estimation is addressed for a class of discrete-time systems that may switch among different modes taken from a finite set. The system and measurement equations of each mode are assumed to be linear and perfectly known, but the current mode of the system is unknown. Moreover, additive, independent, normally distributed noises are assumed to affect the dynamics and the measurements. First, relying on a well-established notion of mode observability developed “ad hoc” for switching systems, an approach to system mode estimation based on a maximum-likelihood criterion is proposed. Second, such a mode estimator is embedded in a Kalman filtering framework to estimate the continuous state. Under the unique assumption of mode observability, stability properties in terms of boundedness of the mean square estimation error are proved for the resulting filter. Simulation results showing the effectiveness of the proposed filter are reported.     

A computationally-efficient maximum-likelihood classifier employing prior probabilities for remotely-sensed data

A time-efficient method for evaluating the maximum-likelihood classifier for LANDSAT MSS data is described and its extension to the case of unequal prior probabilities is summarized, following Shlien (1975) and Strahler (1980). The use of unequal prior probabilities is demonstrated by example and it is shown that, where classes are well-separated, then the effect of including prior probability estimates is negligible, but where classes are closely-related, then the choice of prior probability estimate can have a considerable effect.     

A new maximum-likelihood phase estimation method for X-ray pulsar signals

X-ray pulsar navigation （XPNAV） is an attractive method for autonomous navigation of deep space in the future. Currently, techniques for estimating the phase of X-ray pulsar radiation involve the maximization of the general non-convex object functions based on the average profile fxom the epoch folding method. This results in the suppression of useful information and highly complex computation. In this paper, a new maximum likelihood （ML） phase estimation method that directly utilizes the measured time of arrivals （TOAs） is presented. The X-ray pulsar radiation will be treated as a cyclo-stationary process and the TOAs of the photons in a period will be redefined as a new process, whose probability distribution function is the normalized standard profile of the pulsar. We demonstrate that the new process is equivalent to the generally used Poisson model. Then, the phase estimation problem is recast as a cyclic shift parameter estimation under the ML estimation, and we also put forward a parallel ML estimation method to improve the ML solution. Numerical simulation results show that the estimator described here presents a higher precision and reduces the computational complexity compared with currently used estimators.     

A maximum-likelihood and moment-matching density estimator for crowd-sourcing label prediction

We deal with the parameter estimation problem for probability density models with latent variables. For this problem traditionally the expectation maximization (EM) algorithm has been broadly used. However, it suffers from bad local maxima, and the quality of the estimator is sensitive to the initial model choice. Recently, an alternative density estimator has been proposed that is based on matching the moments between sample averaged and model averaged. This moment matching estimator is typically used as the initial iterate for the EM algorithm for further refinement. However, there is actually no guarantee that the EM-refined estimator still yields the moments close enough to the sample-averaged one. Motivated by this issue, in this paper we propose a novel estimator that takes merits of both worlds: we do likelihood maximization, but the moment discrepancy score is used as a regularizer that prevents the model-averaged moments from straying away from those estimated from data. On some crowd-sourcing label prediction problems, we demonstrate that the proposed approach yields more accurate density estimates than the existing estimators.     

A note on convergence requirements for nonlinear maximum-likelihood estimation of parameters from mixture models

Modifications are offered for some algorithms previously established by the authors for the maximum-likelihood estimation of the parameters of mixed exponential and Weibull distributions. The prior work assumed that the likelihood function was “well behaved” and that good starting points were available. We now provide enhancements of the methods to permit the handling of any alternative possibility.     

Algorithms for maximum-likelihood bandwidth selection in kernel density estimators

In machine learning and statistics, kernel density estimators are rarely used on multivariate data due to the difficulty of finding an appropriate kernel bandwidth to overcome overfitting. However, the recent advances on information-theoretic learning have revived the interest on these models. With this motivation, in this paper we revisit the classical statistical problem of data-driven bandwidth selection by cross-validation maximum likelihood for Gaussian kernels. We find a solution to the optimization problem under both the spherical and the general case where a full covariance matrix is considered for the kernel. The fixed-point algorithms proposed in this paper obtain the maximum likelihood bandwidth in few iterations, without performing an exhaustive bandwidth search, which is unfeasible in the multivariate case. The convergence of the methods proposed is proved. A set of classification experiments are performed to prove the usefulness of the obtained models in pattern recognition.     

A part-based attention network for person re-identification

Multimedia Tools and Applications - Person re-identification (re-id) is the task of recognizing images of the same pedestrian captured by different cameras with non-overlapping views. Person re-id...     

A testability strategy for microprocessor architecture

The authors present a method for fully testing chips designed using synthesis and silicon compilation. The method is targeted for a multiprocessor architecture that implements low-speed to medium-speed signal-processing algorithms. By taking advantage of the specific properties of the architecture, the method allows a chip to be partitioned into several functional units. The authors use the C-test concept instead of the traditional automatic test-pattern generation to derive a compact set of test vectors. The fault model covers both the stuck-at class and part of the transistor stuck-open and stuck-closed cases. For large units with embedded memory, the authors adopt a self-test approach     

A quality-time-cost-oriented strategy for product conceptualization

In general, product development companies aim to deliver products of optimal quality while incurring minimal cost in the shortest time possible. In this work, a quality-time-cost-oriented strategy (QTCOS) is proposed to facilitate product concept generation and selection. Firstly, general sorting is employed to elicit an initial product platform. The platform, constructed with a design space framework (DSF), serves as a base for generating a preliminary range of design options. Using the repertory grids elicitation method, designers contribute importance ratings with respect to a set of time and cost criteria for the range of design options. To account for trade-offs between cost and time related concerns, these ratings are employed to reduce the number of the derived design options, and thereby used as input features to a restricted coulomb energy (RCE) neural network. The RCE network function is applied to classify the set of design options into different patterns, i.e. cost-time-pairs. The classification results can subsequently serve as bases for the selection of preferred design options. A case study on wood golf club design is conducted to illustrate the proposed QTCOS.     

A control-relevant identification strategy for GPC

The question of a suitable control-relevant identification strategy for a class of long-range predictive controllers is addressed. It is shown that under certain conditions the best process model for predictive control is that which is estimated using an identification objective function that is a dual of the control objective function. The resulting nonlinear least squares calculation is asymptotically equal to a standard recursive least squares with an appropriate (model and controller-dependent) FIR data prefilter. Experimental results demonstrate the validity and practicality of the proposed estimation law     

Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method

The satellite image deconvolution problem is ill-posed and must be regularized. Herein, we use an edge-preserving regularization model using a ? function, involving two hyperparameters. Our goal is to estimate the optimal parameters in order to automatically reconstruct images. We propose to use the maximum-likelihood estimator (MLE), applied to the observed image. We need sampling from prior and posterior distributions. Since the convolution prevents use of standard samplers, we have developed a modified Geman-Yang algorithm, using an auxiliary variable and a cosine transform. We present a Markov chain Monte Carlo maximum-likelihood (MCMCML) technique which is able to simultaneously achieve the estimation and the reconstruction.     

A three-module strategy for edge detection

The first module is a parallel process computing local edge strength and direction, while the last module is sequential process following edges. The originality of the overall method resides in the intermediate module, which is seen as a generalization of the nonmaximum-deletion algorithm. The role of this module is twofold: It enables one to postpone some deletion to the last module where contextual information is available, and it transmits the local edge direction in order to guide the contour following. A postprocessing method called learning edges is proposed as a refinement of the method. The binary edge images extracted from various gray-level images illustrate the power of the strategy     

混合差分变异策略

为了改善差分进化算法的求解性能,提出一种新的混合差分变异策略．该策略将种群中的每一个个体视作带电粒子,利用粒子所带的电荷量以及粒子之间的吸引排斥机制确定个体移动方向和位移大小．该策略会使个体在其他3个个体施加于它的力的方向上自适应地移动．数值实验表明基于该策略的差分进化算法求解精度高、评估次数少．