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In this paper, we propose a novel graph kernel specifically to address a challenging problem in the field of cyber-security, namely, malware detection. Previous research has revealed the following: (1) Graph representations of programs are ideally suited for malware detection as they are robust against several attacks, (2) Besides capturing topological neighbourhoods (i.e., structural information)...
It is well-known that malware constantly evolves so as to evade detection and this causes the entire malware population to be non-stationary. Contrary to this fact, prior works on machine learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i.e., features) do not change over time. In this work, we address the problem of malware population...
In traffic video surveillance system, target-level tracking and feature-level tracking are two important areas for research. Therefore, the combination between them is an interesting question. Mean-shift is a traditional target-level tracking algorithm with no adaptation to vehicle scale and orientation change. In order to solve the problem, algorithm combine SURF (speed-up robust feature) feature...
Along with the inclusion of GPU cores within the same CPU die, the performance of Intel's processor-graphics has been significantly improved over earlier generation of integrated graphics. This paper presents a highly optimized SURF cascade based face detector which efficiently exploits both CPU and GPU computing power on the latest Sandy Bridge processor. The SURF cascade classifier procedure is...
We present an approach for recognizing the broken area of the traffic signs. The method is based on the Recognition System for Traffic Signs (RSTS). This paper describes an approach to using the location histogram matching for the broken traffic signs recognition, after the general process of the image detection and image categorization. The recognition proceeds by using the SIFT matching to adjust...
In the Brain-computer interface, classification and recognition technology plays an important role, especially the EEG classification and recognition for the movement imagery. In this paper, we use a new type of sensors to collect EEG signals. According to imagine the movement of left or right hand to identify two types of thinking, we proposed a new recognition method based on AR(auto-regressive)...
Fuzzy Fisher Criterion(FFC) based clustering method uses the fuzzy Fisher's linear discriminant(FLD) as its clustering objective function and is more robust to noises and outliers than fuzzy c-means clustering(FCM). But FFC can only be used in linear separable dataset. In this paper, a novel fuzzy clustering algorithm, called Kernelized Fuzzy Fisher Criterion(KFFC) based clustering algorithm, is proposed...
For the traditional surveillance system with the weakness of over-reliance on monitor environment, poor mobility and so on, this paper proposed a monitoring scheme based on android smart phone terminal. By collecting and processing data at server, sending data to smart phone terminal via socket, it reaches the purpose of monitoring the target site anywhere and anytime under the coverage of wireless...
Expert finding is the task of identifying persons with expertise on a given topic. Existing methods try to model the dependencies between candidates and terms with distance measure or sequential measure, which have been proven to be effective. However, to the best of our knowledge, no work has been conducted on the combination of the two dependencies. In this paper, we propose a language model based...
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