A three-layer back-propagation neural network for spam detection using artificial immune concentration

In this paper, a three-layer back-propagation neural network (BPNN) is employed for spam detection by using a concentration based feature construction (CFC) approach. In the CFC approach, ‘self’ and ‘non-self’ concentrations are constructed through ‘self’ and ‘non-self’ gene libraries, respectively, to form a two-element concentration vector for expressing the e-mail efficiently. A three-layer BPNN with two-element input is then employed to classify e-mails automatically. Comprehensive experiments are conducted on two public benchmark corpora PU1 and Ling to demonstrate that the proposed CFC approach based BPNN classifier not only has a very much fast speed but also achieves 97 and 99% of classification accuracy on corpora PU1 and Ling by just using a two-element concentration feature vector.     

P2P的流媒体复制技术综述

流媒体将是未来通信中的杀手业务,P2P(Peer-to-Peer)网络是未来通信的重要组成形式之一,本文提出流媒体复制技术应该具有的特性,综述了基于P2P的流媒体的复制技术,包括复制对象的选择,需要副本的个数,副本放置的位置(副本的定位)和更新控制等问题的研究现状和解决方案,通过分析它们的优缺点,总结解决方案的特点,并指出了P2P流媒体复制技术的未来研究方向.     

Gabor滤波器的快速实现

本文提出了 Gabor 滤波器的两种快速实现方法. 这两种方法首先把 Gabor 滤波器分解为多个不同方向上有着不同参数的一维高斯滤波器的组合, 然后通过递归的方法分别实现这些高斯滤波器, 从而实现 Gabor 滤波器. 实验和分析结果表明, 本文提出的 Gabor 滤波器的快速实现方法, 不论是计算复杂度还是计算精度, 都比基于卷积的标准实现方法有着更好的性能和效果.     

Fast mining erasable itemsets using NC_sets

Mining erasable itemsets first introduced in 2009 is one of new emerging data mining tasks. In this paper, we present a new data representation called NC_set, which keeps track of the complete information used for mining erasable itemsets. Based on NC_set, we propose a new algorithm called MERIT for mining erasable itemsets efficiently. The efficiency of MERIT is achieved with three techniques as follows. First, the NC_set is a compact structure, which prunes irrelevant data automatically. Second, the computation of the gain of an itemset is transformed into the combination of NC_sets, which can be completed in linear time complexity by an ingenious strategy. Third, MERIT can directly find erasable itemsets without generating candidate itemsets in some cases. For evaluating MERIT, we have conducted extensive experiments on a lot of synthetic product databases. Our performance study shows that the MERIT is efficient and is on average about two orders of magnitude faster than the META, the first algorithm for mining erasable itemsets.     

Sparse representation for face recognition by discriminative low-rank matrix recovery

This paper proposes a discriminative low-rank representation (DLRR) method for face recognition in which both the training and test samples are corrupted owing to variations in occlusion and disguise. The proposed method extends the sparse representation-based classification algorithm by incorporating the low-rank structure of data representation. The DLRR algorithm recovers a clean dictionary with enhanced discrimination ability from the corrupted training samples for sparse representation. Simultaneously, it learns a low-rank projection matrix to correct corrupted test samples by projecting them onto their corresponding underlying subspaces. The dictionary elements from different classes are encouraged to be as independent as possible by regularizing the structural incoherence of the original training samples. This leads to a compact representation of a corrected test sample by a linear combination of more dictionary elements from the corrected class. The experimental results on benchmark databases show the effectiveness and robustness of our face recognition technique.     

分布式2D视频到3D视频转换系统

2D-to-3D video conversion is a feasible way to generate 3D programs for the current 3DTV industry. However, for large-scale 3D video production, current systems are no longer ade-quate in terms of the time and labor required for conversion. In this paper, we introduce a distributed 2D-to-3D video conversion system that includes a 2D-to-3D video conversion module, architecture of the parallel computation on the cloud, and 3D video cod-ing in the system. The system enables coope-ration among multiple users in the simultane-ous completion of their conversion tasks so that the conversion efficiency is greatly pro-moted. In the experiments, we evaluate the system based on criteria related to both time consumption and video coding performance.     

Robust minimization of lighting variation for real-time defect detection

In machine vision applications that involve comparing two images, it is necessary to match the capture conditions, which can affect their graylevels. Illumination and exposure are two important causes for lighting variation that we should compensate for in the resulting images. A standard technique for this purpose is to map one of the images to achieve the smallest mean square error (MSE) between the two. However, applications in defect detection for manufacturing processes are more challenging, because the existence of defects would affect the mapping significantly. In this paper, we present a robust method that is more tolerant to defects, and discuss its formulation as a linear programming to achieve fast implementations. This algorithm is also flexible and capable of incorporating further constraints, such as ensuring non-negativity of the pixel values.