Modeling and navigation of social information networks in metric spaces

We are living in a world of various kinds of social information networks with small-world and scale-free characteristics. It is still an intriguing problem for researchers to explain how and why so many obviously different networks emerge and share common intrinsic characteristics such as short diameter, higher cluster and power-law degree distribution. Most previous works studied the topology formation and information navigation of complex networks in separated models. In this paper, we propose a metric based range intersection model to explore the topology evolution and information navigation in a synthetic way. We model the network as a set of nodes in a distance metric space where each node has an ID and a range of neighbor information around its ID in the metric space. The range of a node can be seen as the local knowledge or information that the node has around its position in the metric space. The topology is formed by setting up a link between two nodes that have intersected ranges. Information navigation over the network is modeled as a greedy routing process using neighbor links and the distance metric. Different from previous models, we do not assume that nodes join the network one by one and set up link according to the degree distribution of existing nodes or distances between nodes. Range of node is the key factor determining the topology and navigation properties of a network. Moreover, as the ranges of nodes grow, the network evolves from a set of totally isolated nodes to a connected network. Thus, we can easily model the network evolutions in terms of the network size and the individual node information range using the range intersection model. A set of experiments shows that networks constructed using the range intersection model have the scale-free degree distribution, high cluster, short diameter, and high navigability properties that are owned by the real networks.     

(2+1)维广义5阶KdV方程N-孤子解

利用Hirota双线性方法研究了(2+1)维广义5阶KdV方程,得到了单孤子解、双孤子解和三孤子解.通过进一步分析得到N-孤子解析解的表达式.借助计算机符号计算得出多孤子演化图形,展示了多孤子之间的相互作用.     

A taint based approach for automatic reverse engineering of gray-box file formats

File format vulnerabilities have been highlighted in recent years, and the performance of fuzzing tests relies heavily on the knowledge of target formats. In this paper, we present systematic algorithms and methods to automatically reverse engineer input file formats. The methodology employs dynamic taint analysis to reveal implicit relational information between input file and binary procedures, which is used for the measurement of correlations among data bytes, format segmentation and data type inference. We have implemented a prototype, and its general tests on 10 well-published binary formats yielded an average of over 85 % successful identification rate, while more detailed structural information was unveiled beyond coarse granular format analysis. Besides, a practical pseudo-fuzzing evaluation method is discussed in accordance with real-world demands on security analysis, and the evaluation results demonstrated the practical effectiveness of our system.     

Analysis and Implementation of an Open Programmable Router Based on Forwarding and Control Element Separation

A router architecture based upon ForCES (Forwarding and Control Element Separation), which is being standardized by IETF ForCES working group, gains its competitive advantage over traditional router architectures in flexibility, programmability, and cost-effectiveness. In this paper, design and implementation of a ForCES-based router (ForTER) is illustrated. Firstly, the implementation architecture of ForTER is discussed. Then, a layered software model, which well illustrates ForCES features, is proposed. Based on the model, design and implementation of Control Element (CE) and Forwarding Element (FE) in ForTER are introduced in detail. Moreover, security for ForTER is considered and an algorithm to prevent DoS attacks is presented. Lastly, experiments of ForTER are illustrated for routing and running routing protocols, network management, DoS attack prevention, etc. The experimental results show the feasibility of the ForTER design. Consequently, the ForTER implementation basically testifies the feasibility of ForCES architecture and some IETF ForCES specifications.     

Intensity standardization simplifies brain MR image segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.     

Review of Broadband Metamaterial Absorbers: From Principles,Design Strategies,and Tunable Properties to Functional Applications

Metamaterial absorbers have been widely studied and continuously concerned owing to their excellent resonance features of ultra-thin thickness, light-weight, and high absorbance. Their applications, however, are typically restricted by the intrinsic dispersion of materials and strong resonant features of patterned arrays (mainly referring to narrow absorption bandwidth). It is, therefore essential to reassert the principles of building broadband metamaterial absorbers (BMAs). Herein, the research progress of BMAs from principles, design strategies, tunable properties to functional applications are comprehensively and deeply summarized. Physical principles behind broadband absorption are briefly discussed, typical design strategies in realizing broadband absorption are further emphasized, such as top-down lithography, bottom-up self-assembly, and emerging 3D printing technology. Diversified active components choices, including optical response, temperature response, electrical response, magnetic response, mechanical response, and multi-parameter responses, are reviewed in achieving dynamically tuned broadband absorption. Following this, the achievements of various interdisciplinary applications for BMAs in energy-harvesting, photodetectors, radar-IR dual stealth, bolometers, noise absorbing, imaging, and fabric wearable are summarized. Finally, the challenges and perspectives for future development of BMAs are discussed.     

可见光与红外图像融合质量评价指标分析

目的 客观评价作为图像融合的重要研究领域,是评价融合算法性能的有力工具。目前,已有几十种不同类型的评价指标,但各应用领域包括可见光与红外图像融合,仍缺少统一的选择依据。为了方便比较不同融合算法性能,提出一种客观评价指标的通用分析方法并应用于可见光与红外图像融合。方法 将可见光与红外图像基准数据集中的客观评价指标分为两类,分别是基于融合图像的评价指标与基于源图像和融合图像的评价指标。采用Kendall相关系数分析融合指标间的相关性,聚类得到指标分组;采用Borda计数排序法统计算法的综合排序,分析单一指标排序和综合排序的相关性,得到一致性较高的指标集合;采用离散系数分析指标均值随不同算法的波动程度,选择充分体现不同算法间差异的指标;综合相关性分析、一致性分析及离散系数分析,总结具有代表性的建议指标集合。结果 在13对彩色可见光与红外和8对灰度可见光与红外两组图像源中,分别统计分析不同图像融合算法的客观评价数据,得到可见光与红外图像融合的建议指标集(标准差、边缘保持度),作为融合算法性能评估的重要参考。相较于现有方法,实验覆盖20种融合算法和13种客观评价指标,并且不依赖主观评价结果。结论...     

校园网网络事件源定位技术研究

为了解决在校园网内部分网络安全事件和故障事件中只能确认攻击和事故的发生,而无法确定攻击和事故的源头这一问题.文章提出一种三步骤的校园网内网网络事件源定位模型.该模型将神经网络和证据理论相结合应用于攻击源定位.提高了校园网内网攻击定位的效率和准确性.     

基于Bell多项式方法下广义浅水波方程的N-孤子解

利用Bell多项式和Hirota双线性方法研究了流体力学中广义浅水波方程,得到了广义浅水波方程的单孤子解、双孤子解和三孤子解,以及N-孤子解的解析表达形式.通过多孤子的演化图形,讨论了不同类型的孤子解的性质.     

变系数(2+1)维耦合可积广义Kaup型模型的N-孤子解析解及其应用

借助计算机符号运算和齐次平衡法,研究了流体力学和等离子体物理中因环境影响导致的高阶非线性耦合因素产生的变系数(2+1)维耦合可积广义Kaup型模型.通过齐次平衡法,得到了该模型变系数之间的约束条件和Bcklund变换,求出了该模型的单孤子解、双孤子解、三孤子解以及N-孤子解的解析表达式.最后给出了相关的单孤子、双孤子、三孤子变化图形及其相关性质的分析,解释了不同的外界环境因素会影响孤立波间的相互作用,由此更好地理解在流体力学和等离子体物理中一些孤子传播的物理现象.