Run-time malware detection based on positive selection

This paper presents a supervised methodology that detects malware based on positive selection. Malware detection is a challenging problem due to the rapid growth of the number of malware and increasing complexity. Run-time monitoring of program execution behavior is widely used to discriminate between benign and malicious executables due to its effectiveness and robustness. This paper proposes a novel classification algorithm based on the idea of positive selection, which is one of the important algorithms in Artificial Immune Systems (AIS), inspired by positive selection of T-cells. The proposed algorithm is applied to learn and classify program behavior based on I/O Request Packets (IRP). In our experiments, the proposed algorithm outperforms ANSC, Na? ve Bayes, Bayesian Networks, Support Vector Machine, and C4.5 Decision Tree. This algorithm can also be used in general purpose classification problems not just two-class but multi-class problems.     

超高频RFID零中频接收机噪声分析

为了降低成本和降低电路复杂程度,超高频RFID（Radio Frequency Identification,即射频识别技术）阅读器接收电路一般采用零中频解调方式。文章对采用收发天线共用方式的零中频RFID接收链路的噪声水平进行分析,为超高频RFID射频接收电路的详细设计提供依据。     

计算机网络入侵检测系统与技术措施分析

如今,计算机网络已经在人们的生产生活中得到了广泛的应用。由于网络的安全问题导致的信息泄露等情况时有发生。在计算机网络中采用入侵检测系统和技术,是增强计算机网络安全的根本手段。本文首先对计算机网络入侵检测系统和技术进行了概述,之后分析了检测技术的分类和存在的问题,并对其发展进行了展望。     

Curvature-direction measures for 3D feature detection

Using dark channel prior—a kind of statistics of the haze-free outdoor images—to remove haze from a single image input is simple and effective. However, due to the use of soft matting algorithm, the method suffers from massive consumption of both memory and time, which largely limits its scalability for large images. In this paper, we present a hierarchical approach to accelerate dark channel based image dehazing. The core of our approach is a novel, efficient scheme for solving the soft matting problem involved in image dehazing, using adaptively subdivided quadtrees built in image space. Acceleration is achieved by transforming the problem of solving a N-variable linear system required in soft matting, to a problem of solving a much smaller m-variable linear system, where N is the number of pixels and m is the number of the corners in the quadtree. Our approach significantly reduces both space and time cost while still maintains visual fidelity, and largely extends the practicability of dark channel based image dehazing to handle large images.     

A formal framework for positive and negative detection schemes.

In anomaly detection, the normal behavior of a process is characterized by a model, and deviations from the model are called anomalies. In behavior-based approaches to anomaly detection, the model of normal behavior is constructed from an observed sample of normally occurring patterns. Models of normal behavior can represent either the set of allowed patterns (positive detection) or the set of anomalous patterns (negative detection). A formal framework is given for analyzing the tradeoffs between positive and negative detection schemes in terms of the number of detectors needed to maximize coverage. For realistically sized problems, the universe of possible patterns is too large to represent exactly (in either the positive or negative scheme). Partial matching rules generalize the set of allowable (or unallowable) patterns, and the choice of matching rule affects the tradeoff between positive and negative detection. A new match rule is introduced, called r-chunks, and the generalizations induced by different partial matching rules are characterized in terms of the crossover closure. Permutations of the representation can be used to achieve more precise discrimination between normal and anomalous patterns. Quantitative results are given for the recognition ability of contiguous-bits matching together with permutations.     

干涉型光纤水听器零差解调虚拟仪器实现

对以Mach-Zehnder干涉仪为基础的光纤水听器零差解调技术进行研究.在干涉信号的输出端做了改进,采用了差动接收的方式,提出了基于虚拟仪器(以下简称VI)的软件解调方法,结合物理模型在LabVIEW平台下开发出零差解调的VI软件.对不同噪声环境下的单频信号和多频信号进行仿真测试,解调结果与原信号进行幅值对比和互相关分析,信号幅度衰减小于10%,相关系数达95%以上,基本保持了原信号的特征,表明干涉型光纤水听器零差解调VI软件具有较高的可靠性.     

Deep autoencoder for false positive reduction in handgun detection

Neural Computing and Applications - In an object detection system, the main objective during training is to maintain the detection and false positive rates under acceptable levels when the model is...     

图像质量参数分析及其在图像拼接检测中的应用

分析了常用的图像质量参数,采用方差分析对图像质量参数在拼接图像盲检测中的应用进行筛选.通过提取对拼接图像较为敏感的图像质量评价量再融合基于隐马尔科夫模型的特征向量来建立模型,以捕获原始图像和拼接图像之间的统计差异,选用支持向量机作为分类器进行训练和测试,对拼接图像的盲检测进行了研究.实验结果表明,该方法精确度高、应用面广,在图像拼接检测中有着广阔的前景.     

Vague集相似度量及其在模糊数据检测中的应用

分析现有一些Vague集相似度量方法,并指出其不足。考虑在实际应用中,未知度对相似度量的影响,从动态的角度出发,挖掘未知度中包含的赞成与反对信息,提出了一种基于未知度的Vague集相似度量新方法,并将该相似度量方法应用于模糊数据检测中,通过实际应用说明该方法更加有效。     

基于CORDIC的交流相位跟踪零差补偿方法及其实现

为了消除温度、振动等环境因素在光纤干涉测量中的影响,发展了一种基于交流相位跟踪零差补偿(PTAC)的干涉条纹相位稳定技术。利用马赫-泽德光纤干涉仪结构和杨氏双孔干涉原理实现高密度的余弦分布干涉条纹投射。光纤端面菲涅尔反射与光纤耦合器构成迈克尔逊干涉结构,光电探测器检测迈克尔逊干涉仪的输出信号。相位稳定子系统采用相位生成载波(PGC)方法提取环境因素引起的光纤干涉臂相位差,结合坐标旋转数字式计算机(CORDIC)求解相位差,并生成补偿信号反馈给压电陶瓷(PZT)驱动器,实现条纹相位的稳定。该系统相位解调精度达2.75 mrad,环境因素对干涉条纹相位的影响低于53.43 mrad。实验结果验证了该相位稳定方法的可行性。     

基于正反馈的多通道高精度温度检测系统

为了检测微热管的传热特性,采用Pt100温度传感器搭建了8路温度检测系统。该系统以MSP430单片机为下位机控制核心,利用集成的AD模块和UART模块完成A/D转换与通信等功能。上位机用VC++开发应用程序对多路温度信号进行数据拟合,将数据实时显示,并绘制8路温度曲线。通过加入正反馈电路,显著改善了输出线性度。系统测温范围为0～150℃,具有测温精度高,可靠性好,实时性强等优点。     

视觉感知正反馈的显著性检测

目的 人类视觉系统性能远超当前机器视觉,模拟人类视觉机制改进当前算法是有效研究途径,为此提出一种视觉感知正反馈模型,通过循环迭代、重复叠加视觉刺激生成更符合人类感知的视觉显著性图。方法 首先用多种常规方法检测图像显著度,模拟人类视觉多通道特性,再组合这些显著图为综合显著图;利用显著度大的像素构建初始注视区。其次借助集成RVFL（随机向量功能网络）模拟人脑神经网络产生视觉刺激,对注视与非注视区内像素在线“随机采样—学习建模”,图像像素经模型分类获得新注视区。对新注视区与非注视区,可重复迭代进行“随机采样—学习建模—像素分类”;迭代中若注视区连续相同,则表明感知饱和,迭代终止。若将每次像素分类结果看做是一种视觉刺激,则多次视觉刺激输出叠加,可生成新的图像显著性图。最终的像素分类结果就是图像分割目标。结果 将本文算法与现有方法在标准图像数据库上进行对比评测,包括通过对6种算法在ECSSD、SED2和MSRA10K 3个图像数据库上的P-R曲线,F-measure值和平均绝对误差（MAE）值上进行定量分析,对6种模型生成的显著性图作定性比较。数据表明,本文算法在SED2和MSRA10K图象数据库中性能最好,在ECSSD图象数据库中稍低于BL（bootstrap learning）和RBD（robust background detection）算法。本文算法的显著图与人类视觉感知更接近。且算法的正反馈迭代过程一般可迅速饱和,并未显著增加算法负担。实验结果表明,本文方法可作为一种有效的后处理手段,显著提升常规显著性检测算法的性能。结论 提出了一种模拟人类视觉机制的数据驱动显著性检测算法,无需图像先验知识和事先的标记样本。面对多目标,背景复杂等情况,本文方法具有相对好的鲁棒性和适用性,并且能够较好解决现实环境中图像处理算法的通用性、可靠性和准确性问题。     

基于改进电压/频率正反馈的孤岛检测方法研究

孤岛检测是分布式发电系统并网运行时的一个重要问题。在电压相位突变检测方法、电压-有功正反馈检测方法和基于无功-频率下垂特性的正反馈无功扰动检测方法的基础上,结合三者的优势,提出了一种基于改进电压/频率正反馈的孤岛检测方法。该方法克服了采用单一电压相位突变检测方法在负载近似呈阻性和功率相匹配时失效的问题,不存在检测盲区,对系统电能质量影响较小,并通过Matlab软件仿真验证了该方法的正确性。     

Automatic boundary detection based on entropy measures for text-independent syllable segmentation

In this paper, we study the boundary detection in syllable segmentation field. We describe an algorithm proposed for text-independent syllable segmentation. This algorithm provides a performance comparison between the entropies of Shannon, Tsallis and Renyi in an effective detection of beginning-ending points of syllable in a speech signal. The Shannon generalizations (Tsallis and Renyi) quantify the degree of signal organization and offer the relevant information such as the voicing degree on the first syllable segment that we obtained from the temporal dynamics of singularity exponents. The method we propose is focused on an aggregation measure based on entropies to enhance the syllable boundaries detection. It has been also demonstrated in this paper that the best suited entropy for efficient boundary detection is Renyi entropy. Once evaluated, our algorithm produced better performance with efficient results on two languages, i.e., the Fongbe (an African tonal language spoken especially in Benin, Togo, and Nigeria) and an American English. The overall accuracy of syllable boundaries was obtained on Fongbe dataset and validated subsequently on TIMIT dataset with a margin of error < 5m s.     

Defect detection in multi-crystal solar cells using clustering with uniformity measures

Solar cells that convert sunlight into electrical energy are the main component of a solar power system. Quality inspection of solar cells ensures high energy conversion efficiency of the product. The surface of a multi-crystal solar wafer shows multiple crystal grains of random shapes and sizes. It creates an inhomogeneous texture in the surface, and makes the defect inspection task extremely difficult. This paper proposes an automatic defect detection scheme based on Haar-like feature extraction and a new clustering technique. Only defect-free images are used as training samples. In the training process, a binary-tree clustering method is proposed to partition defect-free samples that involve tens of groups. A uniformity measure based on principal component analysis is evaluated for each cluster. In each partition level, the current cluster with the worst uniformity of inter-sample distances is separated into two new clusters using the Fuzzy C-means. In the inspection process, the distance from a test data point to each individual cluster centroid is computed to measure the evidence of a defect. Experimental results have shown that the proposed method is effective and efficient to detect various defects in solar cells. It has shown a very good detection rate, and the computation time is only 0.1 s for a 550 × 550 image.     

结合互信息和主题模型的微博话题发现方法

为了解决短文本信息流的特征稀疏性对热点话题发现带来的挑战,提出了结合词语互信息和概率主题模型的微博热点话题发现方法。通过建立词共现矩阵并应用对称非负矩阵分解算法获取词项-主题矩阵,再利用概率潜在语义分析模型进行主题发现,最终通过定义微博热度分析和排序,有效地支持微博热点话题发现。实验表明,此方法能有效地进行话题聚类并检测出热点话题。     

3D measures exploitation for a monocular semi-supervised fall detection system

Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. Thus, the development of robust home surveillance systems is of great importance. In this article, such a system is presented, which tries to address the fall detection problem through visual cues. The proposed methodology utilizes a fast, real-time background subtraction algorithm, based on motion information in the scene and pixels intensity, capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object. At the same time, it exploits 3D space’s measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning approach. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.     

Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures

Statistical dependency analysis is the basis of all empirical science. A commonly occurring problem is to find the most significant dependency rules, which describe either positive or negative dependencies between categorical attributes. In medical science, for example, one is interested in genetic factors, which can either predispose or prevent diseases. The requirement of statistical significance is essential, because the discoveries should hold also in future data. Typically, the significance is estimated either by Fisher??s exact test or the ?? ²-measure. The problem is computationally very difficult, because the number of all possible dependency rules increases exponentially with the number of attributes. As a solution, different kinds of restrictions and heuristics have been applied, but a general, scalable search method has been missing. In this paper, we introduce an efficient algorithm, called Kingfisher, for searching for the best non-redundant dependency rules with statistical significance measures. The rules can express either positive or negative dependencies between a set of positive attributes and a single consequent attribute. The algorithm itself is independent from the used goodness measure, but we concentrate on Fisher??s exact test and the ?? ²-measure. The algorithm is based on an application of the branch-and-bound search strategy, supplemented by several pruning properties. Especially, we prove a new lower bound for Fisher??s p and introduce a new effective pruning principle. According to our experiments on classical benchmark data, the algorithm is well scalable and can efficiently handle even dense and high-dimensional data sets. An interesting observation was that Fisher??s exact test did not only produce more reliable rules than the ?? ²-measure, but it also performed the search much faster.