基于多媒体应用的安全研究

网络的迅猛发展,让多媒体的应用更加广泛,多媒体的安全问题受到了越来越多的关注。本文首先对安全的相关技术进行了详细分析,描述了PKI/PMI技术及XML安全技术;其次对多媒体应用的安全框架进行了详细的分析和论述,将其分为三个层次模块,对每个模块进行初步的介绍;最后对部分模块的实现进行了分析。本文对于研究IT安全和多媒体工作人员有着积极的作用。     

A grid-based clustering algorithm for wild bird distribution

Advanced satellite tracking technologies provide biologists with long-term location sequence data to understand movement of wild birds then to find explicit correlation between dynamics of migratory birds and the spread of avian influenza. In this paper, we propose a hierarchical clustering algorithm based on a recursive grid partition and kernel density estimation (KDE) to hierarchically identify wild bird habitats with different densities. We hierarchically cluster the GPS data by taking into account the following observations: 1) the habitat variation on a variety of geospatial scales; 2) the spatial variation of the activity patterns of birds in different stages of the migration cycle. In addition, we measure the site fidelity of wild birds based on clustering. To assess effectiveness, we have evaluated our system using a large-scale GPS dataset collected from 59 birds over three years. As a result, our approach can identify the hierarchical habitats and distribution of wild birds more efficiently than several commonly used algorithms such as DBSCAN and DENCLUE.     

高校网络教育学院远程教学质量保证体系探讨

现阶段我国高校网络教育学院发展速度和规模都在不断的加速、扩大.2013年教育部公示我国共有68所网络教育院校具有招生资格.面对着高校扩招的步伐逐步扩大,各网络教育学院为了得到进一步的发展也在不断的进行着各种的招生宣传, “211重点院校”、“在家上名牌大学”等成为一些高校网络远程教育学院高呼的口号.但是招生宣的各种招式对于网络教育学院的长久发展来说只是临渊慕鱼.保证教学质量才是高校网络教育学院的长久发展之道.本文从高校网络教育学院远程教学的质量保证体系方面进行阐述,旨在探讨保证高校网络教育学院长久、健康发展的方法.     

普适计算环境下的网络管理

本文首先介绍了普适计算的基本概念;然后对当前经典的SNMP、CMIP等几种网络管理模型的基本原理和特点进行分析。在此基础上总结普适环境下网络管理的特点,建立了基于SOA、支持管理服务组合的普适网络管理参考模型。最后介绍了当前国内外有关普适计算的研究热点及未来的发展方向。     

Design and evaluation of visualization support to facilitate association rules modeling

Association rules mining is a popular data mining modeling tool. It discovers interesting associations or correlation relationships among a large set of data items, showing attribute values that occur frequently together in a given dataset. Despite their great potential benefit, current association rules modeling tools are far from optimal. This article studies how visualization techniques can be applied to facilitate the association rules modeling process, particularly what visualization elements should be incorporated and how they can be displayed. Original designs for visualization of rules, integration of data and rule visualizations, and visualization of rule derivation process for supporting interactive visual association rules modeling are proposed in this research. Experimental results indicated that, compared to an automatic association rules modeling process, the proposed interactive visual association rules modeling can significantly improve the effectiveness of modeling, enhance understanding of the applied algorithm, and bring users greater satisfaction with the task. The proposed integration of data and rule visualizations can significantly facilitate understanding rules compared to their nonintegrated counterpart.     

Detecting Driver Normal and Emergency Lane-Changing Intentions With Queuing Network-Based Driver Models

Driver intention detection is an important component in human-centric driver assistance systems. This article proposes a novel method for detecting driver normal and emergency left- or right-lane-changing intentions by using driver models based on the queuing network cognitive architecture. Driver lane-changing and lane-keeping models are developed and used to simulate driver behavior data associated with 5 kinds of intentions (i.e., normal and emergency left- or right-lane-changing and lane-keeping intentions). The differences between 5 sets of simulated behavior data and the collected actual behavior data are computed, and the intention associated with the smallest difference is determined as the detection outcome. The experimental results from 14 drivers in a driving simulator show that the method can detect normal and emergency lane-changing intentions within 0.325 s and 0.268 s of the steering maneuver onset, respectively, with high accuracy (98.27% for normal lane changes and 90.98% for emergency lane changes) and low false alarm rate (0.294%).     

Testing Aesthetic and Function Design Preference for Touch Screen and Non–Touch Screen Mobile Phones Using Interactive Genetic Algorithms

Rapid mobile phone market growth has drastically shortened design cycle times forcing designers to identify user preferences quickly. Two studies test a novel method to quickly determine design preferences for touch screen and non–touch screen mobile phones. Interactive Genetic Algorithms (IGAs) are tested as an option for preference identification. IGAs efficiently explore design spaces, finding user preferences via an iterative system mimicking evolution. Each study tested six independent variables: horizontal and vertical button spacing; horizontal and vertical screen dimensions; corner radius; and user’s goal between aesthetically pleasing, functional, and both aesthetic and functional (first experiment), and aesthetic preference differences between touch screen and non–touch screen mobile phones for dialing phone numbers only (second experiment). Results showed IGAs to be a viable method; participant preference varied by goal (horizontal button spacing and screen width were most effected). Designers can employ IGAs to quickly determine aesthetic and other user preferences.     

Kernel generalized fuzzy c-means clustering with spatial information for image segmentation

The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a novel modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, it is found that GFCM is sensitive to noise in gray images. In order to overcome GFCM?s sensitivity to noise in the image, a kernel version of GFCM with spatial information is proposed. In this method, first a term about the spatial constraints derived from the image is introduced into the objective function of GFCM, and then the kernel induced distance is adopted to substitute the Euclidean distance in the new objective function. Experimental results show that the proposed method behaves well in segmentation performance and convergence speed for gray images corrupted by noise.     

Biologically inspired task oriented gist model for scene classification

Capturing the scene gist is account for rapid and accurate scene classification in human visual system. This paper presents a biologically inspired task oriented gist model (BT-Gist) that attempts to emulate two important attributes of biological gist: holistic scene centered spatial layout representation and task oriented resolution determination. For the first attribute, we enrich the model of Oliva and Torralba by refining the low-level features in several biological plausible ways, extending the spatial layout to multiple resolution and followed by perceptually meaningful manifold analysis for a set of multi-resolution biologically inspired intrinsic manifold spatial layouts (BMSLs). Since the optimal resolution that best represents the spatial layout varies from task to task, we embody the second attribute as learning the combination of BMSLs of multiple resolution with respect to their optimal discriminative invariance trade-off for the task at hand, and then cast it in the SVM based localized multiple kernel learning (LMKL) framework, by which the kernel of each scene gist is approximated as a local combination of kernels associated to multi-resolution BMSLs. By exploring the task specific category distribution pattern over BMSL, we define the local model as a category distribution sensitive (CDS) kernel, which can accommodate both the diverse individuality of specific BMSL and the universality shared within the whole category space. Via CDS-LMKL, both the optimal resolution for spatial layouts and the final classifier can be efficiently obtained in a joint manner. We evaluate BT-Gist on four natural scene databases and one cluttered indoor scene database with a range of comparison: From different MKL methods, to various biologically inspired models and BoF based computer vision models. CDS-LMKL leads to better results compared to several existing MKL algorithms. Given the two biological attributes that the framework has to follow, BT-Gist, despite its holistic nature, outperforms existing biologically inspired models and BoF based computer vision models in natural scene classification, and competes with the object segmentation based ROI-Gist in cluttered indoor scene classification.