标准模型下基于身份的签密密钥封装

针对随机预言模型下的签密密钥封装机制依赖现实世界无法实现的随机预言假设的问题,提出在标准模型下可证明安全的基于身份签密密钥封装机制(IBSC-KEM)。新提出的IBSC-KEM方案基于一种受到广泛研究的身份加密机制,在标准模型下被规约为求解q-ABDHE问题和判定性q-ABDHE问题,具有机密性和不可伪造性。新方案主要计算开销为5次群G上的指数运算、3次群GT上的指数运算和3次双线性对运算,与类似的签密方案比较,计算开销较低。此外,新方案还具有公开可验证性,适用于构建安全的端到端传输。     

基于腾讯地图的北斗/GPS/GSM三定位车辆监控系统

基于腾讯地图和北斗定位系统设计了一套三定位车辆监控系统,车辆将北斗卫星导航系统作为主要定位系统,GPS导航系统和基站定位作为辅助定位系统,保证了定位信号的可靠性.监控端可通过手机客户端实时查询车辆位置信息,并可通过腾讯地图查看车辆所处的确切位置.系统具有车辆防盗功能和安全驾驶功能,防止车辆意外丢失,可提醒驾驶员已疲劳驾驶、超速驾驶等行驶安全隐患.     

基于Wi—Fi的区域无线定位系统建设GIS的设计与实现

本文介绍了基于Wi—Fi的区域无线定位系统建设GIS的设计思想与实现方法,对智能地图的创建进行了详细阐述,并对具体系统功能进行了讨论。     

Shadow geometry maps for alias-free shadows

Shadow maps sample scene visibility in the light source space and offer an efficient solution to generate hard shadows.However,they suffer from aliasing artifacts because of discretization errors,inadequate resolution and projection distortion.In this paper,we propose the shadow geometry map method,where a shadow depth map is augmented by storing geometry information about scenes.This leads to a new shadowrendering algorithm that combines a supersampling filter,a geometry-aware reconstruction kernel and an irregular sampling filter.Our method produces high quality alias-free and subpixel supersampling shadow rendering and retains the simplicity and the efficiency of shadow maps.We show that the algorithm pipeline is efficiently parallelized using current programmable graphics hardware and that our method is capable of generating high quality hard shadows.     

实时自适应导航手机地图服务系统的研究和实现①

通过分析传统手机地图服务系统,设计与实现了导航手机地图服务系统。系统对地图数据进行有效组织,并通过记录与分析用户的交互操作对用户感兴趣对象进行归类,从而提供实时更新和自适应的手机地图。     

一种基于混合核函数的SOM网络流量分类方法

由于传统的自组织映射SOM方法对高维、非线性的网络流量数据的分类性能效果不佳,本文引入核方法,提出一种基于混合核函数的SOM(MIX-KSOM)网络流量分类方法。该方法结合了全局性和局部性核函数的优点,采用径向基函数和多项式函数线性组合构成的混合核函数代替内积作为距离度量,使输入空间中复杂的流量样本在特征空间得以简化。实验结果表明,采用MIX-KSOM方法能较好地对网络流量进行分类,较传统的SOM、采用单一核函数的SOM(KSOM)分类方法性能更好,分类准确率也高于NB方法。     

暂态混沌神经网络在蛋白质关联图预测中应用研究

蛋白质结构预测是生物信息学的一个主要研究方向,而蛋白质关联图预测是其中的一个重要内容.针对蛋白质关联图预测问题,提出一种暂态混沌神经网络实现方法,所提出的方法具有暂态混沌特性和平稳收敛特性,能有效避免传统Hopfield 神经网络极易陷入局部极值的缺陷.它通过短暂的倒分叉过程,能很快进入稳定收敛状态.仿真结果表明:暂态混沌神经网络解决蛋白质关联图预测问题时,总能收敛到全局最优或几乎接近全局最优,同时具有更高的搜索效率.这种方法预测精度达到0.27,比随机预测器高9倍.     

主题图融合技术研究综述

主题图在信息资源上层构建了一个结构化的语义网,提供了一个良好的语义模型,可以弥补web 2.0在应用上存在的语义缺陷,而主题图融合作为主题图的重要研究内容,是将分布式环境下同一领域内分散的局部主题图合并为一个全局主题图,实现Web信息的有效组织和管理以及信息的集成与共享.归纳总结了主题图融合的处理过程,分析评价了主题图融合中的难点--主题图的相似性算法,并对融合的原则和算法进行了分析总结,明确了高相似度主题融合的过程.此外,在分析主题图融合冲突的基础上,提出了主题图融合冲突检测与消除的整体设计方案,并将主题图的动态更新进行了阶段划分,最后指出了主题图融合未来的研究方向.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.