典型的隐私保护技术模型研究

随着信息技术、网络技术的飞速发展,市民一通卡、物联网等新兴技术的兴起都带给了人们便捷的生活,知识发现和数据挖掘更是在一些深层次的应用中发挥着积极的作用,但与此同时产生了一个重要问题那就是信息的泄露.因此,如何在有效的在数据挖掘中保护隐私数据已经成为一个重要问题.本文结合数据挖掘技术来研究典型的隐私保护技术及模型,并对其进行分析介绍.     

数字签名技术及其在网络安全中的应用

在电子商务的虚拟世界中,能够在电子文件中识别双方交易人的真实身份,保证交易的安全性、真实性及不可抵懒性的电子技术手段,称之为电子签名.本文介绍了数字签名的概念和常用算法,分析了数字签名的技术原理,具体探讨了数字签名技术在网络安全中的应用.     

计算机及网络技术与现代继续临床医学教育

计算机网络技术是现代信息技术的核心内容,其运用于现代临床医学教育前景广阔,不但适用范围广,教学效率也相当的高。计算机多媒体是现代临床教学的重要手段,而网络技术的使用特使教学内容噩方式发生根本性变革。根据目前计算机技术已深入到医学诊断、治疗及科研领域且发展迅速的现状,提出在继续医学教育中开展计算机知识学习的问题,并针对不同年龄段卫生工作者的具体情况进行了探讨并提出了相应的对策,这对于提高卫生工作者的综合素质也有着重要的意义。     

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar clustering solution to the one obtained by an ensemble learning algorithm. In particular, a clustering solution is firstly achieved by a clustering ensembles method, then the population based incremental learning algorithm is adopted to find the feature subset that best fits the obtained clustering solution. One advantage of the proposed unsupervised feature selection algorithm is that it is dimensionality-unbiased. In addition, the proposed unsupervised feature selection algorithm leverages the consensus across multiple clustering solutions. Experimental results on several real data sets demonstrate that the proposed unsupervised feature selection algorithm is often able to obtain a better feature subset when compared with other existing unsupervised feature selection algorithms.     

An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.     

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.     

Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation

This paper describes a multi-agent coordination mechanism applied to intersection simulation situations. In a goal of urban traffic simulation, we must consider the dynamic interactions between autonomous vehicles. The field of multi-agent systems provides us some studies for such systems, in particular on the coordination mechanisms. Conflicts between vehicles (i.e. agents) are very frequent in such applications, and they may cause deadlocks, particularly at intersections such as crossroads. Our approach is based on the solving of two player games/decision matrices which characterize three basic situations. An aggregation method generalizes to n-player games for complex crossroads. The objective of this approach consists in searching basic two-player matrices for solving n-agent problems. To explain the principle, we describe our approach for a particular case of crossroad with three agents. Finally, the obtained results have been examined via a tool of road traffic simulation, ARCHISIM. We assume also that the global traffic replicates the behavior of agents in different situations.     

Zur personellen Verankerung der IT-Funktion im Vorstand börsennotierter Unternehmen: Ergebnisse einer inhaltsanalytischen Betrachtung

On the Rate of Board Members with IT Responsibilities in Firms Listed on the Stock Exchange: Results of a Content Analysis: This article reports on a content analysis that investigates the rate of board members with IT responsibilities in firms listed on the stock exchange in the German speaking countries. Furthermore, the study ascertains the correlation of six factors – which become evident through content analysis – with the rate of board members with IT responsibilities. A total of 679 annual reports of German, Austrian, and Swiss corporations have been analyzed. The results show that in Germany and Switzerland 26 percent of the firms investigated have a board member with IT responsibilities, in Austria the rate is 23 percent. Additionally, the results show that turnover and number of employees are highly positively correlated with the rate of board members with IT responsibilities. Furthermore, the data show a correlation between industry and the rate of board members with IT responsibilities.     

设计模式在基于SystemC的指令译码模块设计中的应用

SoC上的晶体管集成度在不断增长,系统功能越来越复杂,传统的设计方法已经不再适应现在的SoC设计.为了解决这种不适应性带来的"硬件危机",人们初步探索将软件中的面向对象技术引入到SoC设计领域中.设计模式是面向对象技术的核心,可以给同类问题提供比较合理的解决方案.在对软件中的设计模式进行分析的基础上,将用于SoC中的CPU指令译码模块进行基于模式的设计,并给出了部分实现.     

一种基于小波变换的脉搏波速度测量方法

动脉脉搏波速度变化是动脉血管病变的指示剂,因此脉搏波速度的检测对血管疾病的预防和治疗有很大的帮助作用.脉搏波中夹杂的各种噪声干扰会影响测量结果精度.利用小波变换可以去除脉搏波中的各种干扰,实现波形的平滑重构,为提高测量精度打下基础.脉搏波波速的测量精度在很大程度上取决于波速参考点的提取,首次采用二次差分峰值点作为波速参考点,提高了测量精度.临床实验证明,新方法能够有效地分析出脉搏波速度,有助于动脉血管弹性程度的辅助诊断.