We investigate a problem of object-oriented (OO) software quality estimation from a multi-instance (MI) perspective. In detail, each set of classes that have an inheritance relation, named ‘class hierarchy’, is regarded as a bag, while each class in the set is regarded as an instance. The learning task in this study is to estimate the label of unseen bags, i.e., the fault-proneness of untested class hierarchies. A fault-prone class hierarchy contains at least one fault-prone (negative) class, while a non-fault-prone (positive) one has no negative class. Based on the modification records (MRs) of the previous project releases and OO software metrics, the fault-proneness of an untested class hierarchy can be predicted. Several selected MI learning algorithms were evaluated on five datasets collected from an industrial software project. Among the MI learning algorithms investigated in the experiments, the kernel method using a dedicated MI-kernel was better than the others in accurately and correctly predicting the fault-proneness of the class hierarchies. In addition, when compared to a supervised support vector machine (SVM) algorithm, the MI-kernel method still had a competitive performance with much less cost.

The widespread availability of portable computing power and inexpensive digital cameras are opening up new possibilities for retailers in some markets. One example is in optical shops, where a number of systems exist that facilitate eyeglasses selection. These systems are now more necessary as the market is saturated with an increasingly complex array of lenses, frames, coatings, tints, photochromic and polarizing treatments, etc. Research challenges encompass Computer Vision, Multimedia and Human-Computer Interaction. Cost factors are also of importance for widespread product acceptance. This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

We investigate the bandwidth allocation and power control schemes in orthogonal frequency division multiplexing (OFDM) based multi-hop cognitive radio networks, and the color-sensitive graph coloring (CSGC) model is viewed as an efficient solution to the spectrum assignment problem. We extend the model by taking into account the power control strategy to avoid interference among secondary users and adapt dynamic topology. We formulate the optimization problem encompassing the channel allocation, power control with the interference constrained below a tolerable limit. The optimization objective with two different optimization strategies focuses on the routes rather than the links as in traditional approaches. A heuristic solution to this nondeterministic polynomial (NP)-hard problem is presented, which performs iterative channel allocation according to the lowest transmission power that guarantees the link connection and makes channel reuse as much as possible, and then the transmission power of each link is maximized to improve the channel capacity by gradually adding power level from the lowest transmission power until all co-channel links cannot satisfy the interference constraints. Numerical results show that our proposed strategies outperform the existing spectrum assignment algorithms in the performance of both the total network bandwidth and minimum route bandwidth of all routes, meanwhile, saving the transmission power.

Carrier frequency offset (CFO) estimation is critical for orthogonal frequency-division multiplexing (OFDM) based transmissions. In this paper, we present a low-complexity, blind CFO estimator for OFDM systems with constant modulus (CM) signaling. Both single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems are considered. Based on the assumption that the channel keeps constant during estimation, we prove that the CFO can be estimated uniquely and exactly through minimizing the power difference of received data on the same subcarriers between two consecutive OFDM symbols; thus, the identifiability problem is assured. Inspired by the sinusoid-like cost function, curve fitting is utilized to simplify our algorithm. Performance analysis reveals that the proposed estimator is asymptotically unbiased and the mean square error (MSE) exhibits no error floor. We show that this blind scheme can also be applied to a MIMO system. Numerical simulations show that the proposed estimator provides excellent performance compared with existing blind methods.

The classical vector median filter (VMF) has been widely used to remove impulse noise from color images. However, since the VMF cannot identify thin lines during the denoising process, many thin lines may be removed out as noise. This serious problem can be solved by a newly proposed filter that uses a noise detector to find these thin lines and then keep them unchanged. In this new approach, the noise detection scheme applied on a current processed pixel is realized through counting the close pixels in its eight neighbor positions and the expanded window to see whether the current pixel is corrupted by impulse noise. Based on the previous outputs, our algorithm can increase the performance in detecting and canceling the impulse noise. Extensive experiments indicate that this approach can be used to remove the impulse noise from a color image without distorting the useful information.

Software reverse engineering techniques are applied most often to reconstruct the architecture of a program with respect to quality constraints, or non-functional requirements such as maintainability or reusability. In this paper, AOPR, a novel actor-oriented program reverse engineering approach, is proposed to reconstruct an object-oriented program architecture based on a high performance model such as an actor model. Reconstructing the program architecture based on this model results in the concurrent execution of the program invocations and consequently increases the overall performance of the program provided enough processors are available. The proposed reverse engineering approach applies a hill climbing clustering algorithm to find actors.

In this paper, we propose an image driven shape deformation approach for stylizing a 3D mesh using styles learned from existing 2D illustrations. Our approach models a 2D illustration as a planar mesh and represents the shape styles with four components: the object contour, the context curves, user-specified features and local shape details. After the correspondence between the input model and the 2D illustration is established, shape stylization is formulated as a style-constrained differential mesh editing problem. A distinguishing feature of our approach is that it allows users to directly transfer styles from hand-drawn 2D illustrations with individual perception and cognition, which are difficult to identify and create with 3D modeling and editing approaches. We present a sequence of challenging examples including unrealistic and exaggerated paintings to illustrate the effectiveness of our approach.

Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.

This paper presents a robust lossless data hiding scheme. The original cover image can be recovered without any distortion after data extraction if the stego-image remains intact, and conversely, the hidden data can still be extracted correctly if the stego-image goes through JPEG compression to some extent. A cover image is divided into a number of non-overlapping blocks, and the arithmetic difference of each block is calculated. By shifting the arithmetic difference value, we can embed bits into the blocks. The shift quantity and shifting rule are fixed for all blocks, and reversibility is achieved. Furthermore, because the bit-0- and bit-1-zones are separated and the particularity of the arithmetic differences, minor changes applied to the stego-image generated by non-malicious attacks such as JPEG compression will not cause the bit-0- and bit-1-zones to overlap, and robustness is achieved. The new embedding mechanism can enhance embedding capacity and the addition of a threshold can make the algorithm more robust. Experimental results showed that, compared with previous schemes, the performance of the proposed scheme is significantly improved.

Vector graphic , as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is {image meshing}, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

We propose a framework of hand articulation detection from a monocular depth image using curvature scale space (CSS) descriptors. We extract the hand contour from an input depth image, and obtain the fingertips and finger-valleys of the contour using the local extrema of a modified CSS map of the contour. Then we recover the undetected fingertips according to the local change of depths of points in the interior of the contour. Compared with traditional appearance-based approaches using either angle detectors or convex hull detectors, the modified CSS descriptor extracts the fingertips and finger-valleys more precisely since it is more robust to noisy or corrupted data; moreover, the local extrema of depths recover the fingertips of bending fingers well while traditional appearance-based approaches hardly work without matching models of hands. Experimental results show that our method captures the hand articulations more precisely compared with three state-of-the-art appearance-based approaches.

In this paper, a novel Bayesian-Gaussian neural network (BGNN) is proposed and applied to on-line modeling of a hydraulic turbine system (HTS). The new BGNN takes account of the complex nonlinear characteristics of HTS. Two redefined training procedures of the BGNN include the off-line training of the threshold matrix parameters, optimized by swarm optimization algorithms, and the on-line BGNN predictive application driven by the sliding window data method. The characteristics models of an HTS are identified using the new BGNN method and simulation results are presented which show the effectiveness of the BGNN in addressing modeling problems of HTS.