Access control for web data: models and policy languages

The web has made easily available an enormous amount of information in digital form and has facilitated the exchange of such information. In this environment, access control is a primary concern. The key issue is how to trade-off between maximizing the sharing of information and enforcing a controlled access to web data. In this paper we start by outlining which are the main access control requirements of web data. Then, we review researches carried on in the field, by mainly focusing on xml. Finally, we discuss policy languages for the semantic web, and outline which are the main research directions in this field.     

Analysis of spatial and temporal stability of airborne laser swath mapping data in feature space

Several features extracted from airborne laser swath mapping (ALSM) data are examined to determine their effectiveness in separating buildings from trees across geographically and temporally diverse landscapes. These two classes are often spatially mixed in urban and suburban areas and can be quite difficult to separate based solely on geometric information due to the discrete sampling of ALSM. New median-based distance measures are used to quantify the separability of the classes using the different features. Information-based measures are also applied to the same data. For each of the test cases, it is possible to identify a common feature space in which the distance between the two classes is large. This distance information is an indication of the separability between classes and is therefore indicative of the potential success likely when trying to classify ALSM data. This analysis provides new insights into the richness of simple two-return ALSM data and to the spatial and temporal stability of ALSM features when discriminating between classes.     

基于时空相似性的跨域目标关联分析方法

多业务域目标聚合是情报分析的核心问题之一,为解决跨域目标关联分析工程化实现,提出了一种时空相似计算的分析方法。首先,对接收到的目标时空数据进行处理,形成元数据;其次,按照预定义的数据模型,从元数据中抽取目标信息;再次,通过时空相似度计算,对目标间的时空信息进行分析,得到目标间的时空关系;最后,利用先验知识对目标时空关系进行约束,实现从时空关系到语义关系的转换。数值仿真验证了该方法能够简单有效地实现跨域目标关联分析。     

Analysis of fMRI data using improved self-organizing mapping and spatio-temporal metric hierarchical clustering

The self-organizing mapping (SOM) and hierarchical clustering (HC) methods are integrated to detect brain functional activation; functional magnetic resonance imaging (fMRI) data are first processed by SOM to obtain a primary merged neural nodes image, and then by HC to obtain further brain activation patterns. The conventional Euclidean distance metric was replaced by the correlation distance metric in SOM to improve clustering and merging of neural nodes. To improve the use of spatial and temporal information in fMRI data, a new spatial distance (node coordinates in the 2-D lattice) and temporal correlation (correlation degree of each time course in the exemplar matrix) are introduced in HC to merge the primary SOM results. Two simulation studies and two in vivo fMRI data that both contained block-design and event-related experiments revealed that brain functional activation can be effectively detected and that different response patterns can be distinguished using these methods. Our results demonstrate that the improved SOM and HC methods are clearly superior to the statistical parametric mapping (SPM), independent component analysis (ICA), and conventional SOM methods in the block-design, especially in the event-related experiment, as revealed by their performance measured by receiver operating characteristic (ROC) analysis. Our results also suggest that the proposed new integrated approach could be useful in detecting block-design and event-related fMRI data.      

Fusion of 3D-LIDAR and camera data for scene parsing

Fusion of information gathered from multiple sources is essential to build a comprehensive situation picture for autonomous ground vehicles. In this paper, an approach which performs scene parsing and data fusion for a 3D-LIDAR scanner (Velodyne HDL-64E) and a video camera is described. First of all, a geometry segmentation algorithm is proposed for detection of obstacles and ground areas from data collected by the Velodyne scanner. Then, corresponding image collected by the video camera is classified patch by patch into more detailed categories. After that, parsing result of each frame is obtained by fusing result of Velodyne data and that of image using the fuzzy logic inference framework. Finally, parsing results of consecutive frames are smoothed by the Markov random field based temporal fusion method. The proposed approach has been evaluated with datasets collected by our autonomous ground vehicle testbed in both rural and urban areas. The fused results are more reliable than that acquired via analysis of only images or Velodyne data.     

Hyperspectral image data analysis

The fundamental basis for space-based remote sensing is that information is potentially available from the electromagnetic energy field arising from the Earth's surface and, in particular, from the spatial, spectral, and temporal variations in that field. Rather than focusing on the spatial variations, which imagery perhaps best conveys, why not move on to look at how the spectral variations might be used. The idea was to enlarge the size of a pixel until it includes an area that is characteristic from a spectral response standpoint for the surface cover to be discriminated. The article includes an example of an image space representation, using three bands to simulate a color IR photograph of an airborne hyperspectral data set over the Washington, DC, mall     

A matched-filter-based reverse-time migration algorithm forground-penetrating radar data

Ground-penetrating radar (GPR) is a remote sensing technique used to obtain information on subsurface features from data collected over the surface. The process of collecting data may be viewed as mapping from the object space to an image space. Since most GPRs use broad beam width antennas, the energy reflected from a buried structure is recorded over a large lateral aperture in the image spare, migration algorithms are used to reconstruct an accurate scattering map by refocusing the recorded scattering events to their true spatial locations through a backpropagation process. The goal of this paper is to present a pair of finite-difference time-domain (FDTD) reverse-time migration algorithms for GPR data processing. Linear inverse scattering theory is used to develop a matched-filter response for the GPR problem. The reverse-time migration algorithms, developed for both bistatic and monostatic antenna configurations, are implemented via FDTD in the object space. Several examples are presented     

一种基于自举原理的语义模式自动获取方法

语义模式的自动获取是解决信息抽取系统可移植性问题的关键之一。文章提出了一种基于自举原理的语义模式自动获取方法。该方法是一种新的双循环自举机制，由内自举和外自举两部分组成。内自举借助通用的《同义词词林》扩张模式的语义范围；外自举利用模式之间的交叉项作为链接点自动寻找新模式。用于从未标注的文本中自动发现语义模式，启动时只须用户提供少量的抽取示例作为种子，运行中即可增量式地发现新语义模式。测试表明．该方法产生的语义模式在信息抽取中具有较好的准确率和召回率。     

Semantic privacy-preserving framework for electronic health record linkage

The combination of digitized health information and web-based technologies offers many possibilities for data analysis and business intelligence. In the healthcare and biomedical research domain, applications depending on electronic health records (EHRs) identify privacy preservation as a major concern. Existing solutions cannot always satisfy the evolving research demands such as linking patient records across organizational boundaries due to the potential for patient re-identification. In this work, we show how semantic methods can be applied to support the formulation and enforcement of access control policy whilst ensuring that privacy leakage can be detected and prevented. The work is illustrated through a case study associated with the Australasian Diabetes Data Network (ADDN – www.addn.org.au), the national paediatric type-1 diabetes data registry, and the Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) platform that supports Australia-wide access to urban and built environment data sets. We demonstrate that through extending the eXtensible Access Control Markup Language (XACML) with semantic capabilities, finer-grained access control encompassing data risk disclosure mechanisms can be supported. We discuss the contributions that can be made using this approach to socio-economic development and political management within business systems, and especially those situations where secure data access and data linkage is required.     

LSPEnv: location-based service provider for environmental data

The state of our environment becomes a very important issue and especially people with health problems need more information and support in their daily life. This article presents an approach for forecasting values of several environmental-state variables as a basis for location-based services. We propose a system for making predictions for several spatial temporal variables using the Bayesian Network method as a machine learning technique. The system is based on a three-tier architecture, which assists the environmental data acquisition, processing and dissemination of predictions. To handle the missing values of data we use the structural expectation maximisation algorithm. The system's evaluation case study is based on real environmental data acquired from the Swiss national network. The data represents several environmental-state variables at different types of location, e.g. rural, urban, and at different times in a time span of a year.     

轨迹大数据：数据、应用与技术现状

移动互联技术的飞速发展催生了大量的移动对象轨迹数据。这些数据刻画了个体和群体的时空动态性,蕴含着人类、车辆、动物的行为信息,对交通导航、城市规划、车辆监控等应用具有重要的价值。为了实现有效的轨迹数据价值提取,近年来学术界和工业界针对轨迹管理问题开展了大量研究工作,包括轨迹数据预处理,以解决数据冗余高、精度差、不一致等问题;轨迹数据库技术,以支持有效的数据组织和高效的查询处理;轨迹数据仓库,支持大规模轨迹的统计、理解和分析;最后是知识提取,从数据中挖掘有价值的模式与规律。因此,综述轨迹大数据分析,从企业数据、企业应用、前沿技术这3个角度揭示该领域的现状。     

Digital audio data compression

Conventional digital audio, as used for instance on compact discs, requires a large bandwidth for transmission and enormous amounts of storage space. Developments in high-speed digital signal processing chips have made it practical to reduce these requirements by employing sophisticated data compression techniques which reduce redundant and irrelevant information in the source signal. The paper reviews the types of digital audio data compression coders that are now available, their applications and the principles involved     

SAKURA-viewer: intelligent order history viewer based on two-viewpoint architecture.

We propose a new intelligent order history viewer applied to consolidating and visualizing data. SAKURA-viewer is a highly effective tool, as: 1) it visualizes both the semantic viewpoint and the temporal viewpoint of patient records simultaneously; 2) it promotes awareness of contextual information among the daily data; and 3) it implements patient-centric data entry methods. This viewer contributes to decrease the user's workload in an order entry system. This viewer is now incorporated into an order entry system being run on an experimental basis. We describe the evaluation of this system using results of a user satisfaction survey, analysis of information consolidation within the database, and analysis of the frequency of use of data entry methods.     

On the dimensionality of multiparameter microwave image data fromthin sea ice in the Labrador Sea

Thin ice is typically defined as comprising the World Meteorological Organization's young and new ice categories, referring to sea ice that is less than 0.3 m thick. This ice type is an extremely important factor in both the thermodynamic and dynamic properties of both polar and marginal ice covers. In the LIMEX'89 experiment, spatially and temporally registered multipolarization data from 5.3 and 9.25 GHz SARs and from 37 and 90 GHz imaging radiometers were acquired over a region containing a wide range of new ice growth stages in the Labrador ice pack. The temperatures at the data acquisition time were -10 and correspond to ice growth conditions. In this paper, the dimensionality of the multifrequency, multipolarization active and passive data set is examined to determine the complementarity of the sensor parameters and sensor types for thin ice measurements. Principal component analysis is used to provide estimates of the information content of individual measurement channels and their combinations. Various measurement subspaces are examined. Criteria for channel redundance are proposed and tested and the classification potential of the multidimensional measurement set is tested for thin ice growth stages that are known to present classification difficulties for microwave sensors. Given six nominally independent SAR measurement channels, the information space dimension of this subspace is shown to be greater than five. The four radiometer channels are shown to have information space dimension two and are redundant in frequency. The combined, ten element, SAR/radiometer measurement space was shown to have information space dimension eight under the criteria used when the scattering polarization ratios are included     

一种基于潜在语义分析和直推式谱图算法的文本分类方法LSASGT

 本文针对训练数据较少以及在基于图的分类算法中的文本表示问题,提出了一种基于潜在语义分析技术和直推式谱图算法的文本分类方法LSASGT,该方法将潜在语义分析技术和直推式谱图算法这两种基于谱分析理论的技术有机地结合在一起,对所有训练数据和测试数据进行统一建模,挖掘数据中潜在的多种结构信息.LSASGT引入潜在语义分析技术用于构造文本图表示模型,在能够反映人的分类标准的潜在语义特征空间中,描述文本之间的语义相关性;基于这样的文本表示,利用半监督的直推式谱图算法进行文本分类.在基准英文文本分类数据集Reuters21578和中文文本分类数据集Tan-Corp上的实验结果表明,本文给出的LSASGT文本分类方法获得了较好的分类结果.     

Broadband DOA estimation using “Spatial-Only” modelingof array data

Most of the existing techniques for DOA estimation of broadband sources use both spatial and temporal modeling. This may lead to increased complexity besides a large algorithmic delay. We propose a technique that employs only spatial information in the form of a single spatial array covariance matrix. Assuming the source to have an ideal bandpass power spectral density, we formulate two subspace-based search functions for the estimation of the DOAs of broadband sources. One of these employs a multidimensional search in the parameter space, whereas the other requires a MUSIC like one-dimensional (1-D) search. The multidimensional cost function is shown to be consistent, yields performance close to the Cramer-Rao (CR) bound, and is insensitive to correlation between sources. Both the proposed methods are shown to be robust to deviations from the assumption of ideal bandpass power spectral density used in their formulation     

PolInSAR analysis of X-band data over vegetated and urban areas

This paper investigates the polarimetric and polarimetric interferometric synthetic aperture radar (PolInSAR) information contained in the high-resolution X-band data acquired by the RAMSES airborne SAR system over an area around Avignon, France containing bare surfaces, vegetation, and urban areas. The interferometric coherences are computed over natural and urban areas for all possible baseline copolar polarizations. In the complex plane, the obtained regions of coherence corresponding to most vegetation areas display small angular extents, meaning that if penetration occurs in the foliage, it is shallower than the system height accuracy. To quantify the PolInSAR information, an analysis of the interferometric height accuracy is first performed, and the results are compared with those associated with a theoretical and an empirical model. Concerning vegetation, a 6-m height difference is measured between the different polarimetric phase centers over a sparse pine forest, probably due to the presence of holes in the canopy. Crop study reveals also that wheat-type fields present oriented media properties at X-band due to their vertical structure. Over urban areas, in most cases, building height can be accurately obtained by using Pauli polarimetric phase center information.     

A natural language information retrievel system

The problem of natural language information retrieval is difficult because of vocabulary size, semantics, and syntax. For some specialized disciplines, in particular tissue pathology, vocabularies are limited, semantic problems largely disappear, and syntactic structure can be replaced by a much simpler structure. Experience with the language of surgical pathology is given, also an outline of an automatic data processing system applied to the problem. A considerably expanded system, for dealing with larger data bases and larger varieties of queries, is being designed.     

利用状态数据的无人平台测控协议逆向分析方法

针对无人平台测控数据通信安全问题,提出了一种测控协议逆向分析模型.模型采用数据挖掘方法对通信报文中的协议格式和语义信息进行分析,主要采用改进BF(Brute-Force)算法和AP(Affinity-Propagation)算法进行模式串匹配和关联规则提取,以提取协议初步格式;采用序列比对技术中改进SW(Smith-Waterman)算法,结合监测状态数据对格式和语义信息作进一步分析.通过仿真实验模拟协议逆向分析了所需要的无人平台与基站的通信数据和雷达监测状态数据;根据协议逆向模型仿真试验结果可得到100%的协议格式识别率和90.9%的语义识别率,结果证明了提出的逆向分析模型的有效性.