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In this paper a new vulnerability detecting method is proposed to detect buffer boundary violations. The main idea is to use the metric of array index manipulation rather than using any heuristic method. We employ a SVM-based classifier to classify the vulnerable functions and innocent functions. Then the vulnerable functions are fed to function call graph guided symbolic execution to precisely determine...
This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral images (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of ideal kernel according to a regularization kernel learning framework,...
To address the multi-classification problems of hyperspectral dataset, a new method with weighted kernel function based on Chernoff distance is proposed. Chernoff distance utilizes the information between categories and strengthens the separability of original dataset. The adjustable parameter in Chernoff distance can fit the hyperspectral dataset well compared with other least upper bounds. Pairwise...
In this paper, we propose a l2,1-norm based discriminative robust transfer learning (DKTL) method for domain adaptation tasks. The key idea is to simultaneously learn discriminative subspaces by using the proposed domain-class-consistency (DCC) metric, and the representation based robust transfer model between source domain and target domain via l21-norm minimization. The DCC metric includes two parts:...
Class imbalance is a major problem in machine learning. It occurs when the number of instances in the majority class is significantly more than the number of instances in the minority class. This is a common problem which is recurring in most datasets, including the one used in this paper (i.e. direct marketing dataset). In direct marketing, businesses are interested in identifying potential buyers,...
Although security starts to be taken into account during software development, the tendency for source code to contain vulnerabilities persists. Open source static analysis tools provide a sensible approach to mitigate this problem. However, these tools are programmed to detect a specific set of vulnerabilities and they are often difficult to extend to detect new ones. WAP is a recent popular open...
This paper addresses the need to use the knowledge about the human perceived quality, adding machine learning models to the objective quality estimation. A new technique is proposed based on the division of images into several cells where the mean of the SSIM metric is computed. A sliding window over a grid of cells that divide the image will define a set of image descriptors that are aggregated using...
The structural connectome classification is a challenging task due to a small sample size and high dimensionality of feature space. In this paper, we propose a new data prepossessing method that combines geometric and topological connectome normalization and significantly improves classification results. We validate this approach by performing classification between autism spectrum disorder and normal...
In recent years, text classification have been widely used. Dimension of text data has increased more and more. Working of almost all classification algorithms is directly related to dimension. In high dimension data set, working of classification algorithms both takes time and occurs over fitting problem. So feature selection is crucial for machine learning techniques. In this study, frequently used...
Data science is becoming more important for software engineering problems. Software defect prediction is a critical area which can help the development team allocate test resource efficiently and better understand the root cause of defects. Furthermore, it can help find the reason why a component or even a project is failure-prone. This paper deals with binary classification in predicting if a software...
Content Based Image Retrieval (CBIR) is a developing trend in Digital Image Processing for searching and retrieving the query image from wide range of databases. Conventional content-based image retrieval (CBIR) schemes have following limitations: 1. It is slow 2. difficult to label negative examples; 3. Accuracy is poor in a single step; 4. users may introduce some noisy examples into the query....
Prediction of faults in a proposed software is helpful in deciding the amount of effort to be given for software development. We observed that, a good number of authors hypothesized that the performance of fault prediction model depends on the source code metrics which are used as input of the model. Feature selection technique is a process of selecting suitable set of source code metrics which may...
After the subprime crisis in 2008, an efficient Financial Distress Prediction (FDP) model has become necessary. Many research works have attempted to provide a model using statistical or intelligent methods. In this respect, this paper adopts a two-stage hybrid model that integrates Deep Learning and Support Vector Machine as a FDP modeling method. Local receptive fields is a technique used in order...
Automatic security classification is a new researcharea about to emerge. It utilizes machine learning to assisthumans in their manual classification. In this paper, weinvestigate the importance of the training time of the machinelearner. To the best of our knowledge, this has not beenanalyzed in previous works. We compare various machinelearning methods, including SVM, LASSO and the ensemblemethods...
Sentiment analysis in its simplest form is the classification of a piece of text into positive or negative class based on the polarity of the text. Horoscopes consist of future predictions for each of the twelve zodiac signs and are very popular in India. All major TV channels and newspapers publish their horoscope expert's predictions on a daily basis. These daily horoscopes are well suited for the...
This paper presents an approach to build a data classifier based on a simple and inexpensive evaluation function aimed to reduce the computational costs when processing new incoming instances. The classifier agent employs in its training concepts of Self-Organized Maps and Multiple Instance Learning. The motivation for this proposal was the need of a classifier for the processing of signals from partial...
The main emphasis of this paper is to develop an approach able to detect and assess blindly the perceptual blur degradation in images. The idea deals with a statistical modelling of perceptual blur degradation in the frequency domain using the discrete cosine transform (DCT) and the Just Noticeable Blur (JNB) concept. A machine learning system is then trained using the considered statistical features...
Recently, there is an increasing motivation to develop telemonitoring systems that enable cost-effective screening of Parkinson's Disease (PD) patients. These systems are generally based on measuring the motor system disorders seen in PD patients by the help of non-invasive data collection tools. Vocal impairments one of the most commonly seen PD symptoms in the early stages of the disease, and building...
Computational methods such as clustering, classification and regression methods can be applied in immunoin-formatics to construct predictive models to reveal relationships between antibody features and their functional outcomes. This paper studies the effect of antibody features and the functional outcome obtained on RV144 vaccine recipients. The RV144 vaccine data set contains 100 data samples in...
There are evidences that temporal lobe epilepsy can cause some lateralized atrophy and deformation on hippocampus and other substructures of the brain. Multi scale 3D shape representation and analysis of hippocampus is useful for diagnosis temporal lobe epilepsy in magnetic resonance imaging. Spherical harmonics (SPHARM) is a powerful tool for representation and analysis of 3D closed shape surfaces...
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