Particle Swarm Optimization with Chaos-based Initialization for Numerical Optimization

Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been deeply studied and widely applied to a variety of problems. However, it is easily trapped into the local optima and premature convergence appears when solving complex multimodal problems. To address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other hand, Gaussian mutation as well as the local re-initialization strategy based on the maximal focus distance is exploited to help the algorithm escape from the local optima and make the particles proceed with searching in other regions of the solution space. In addition, an auxiliary velocity-position update strategy is exclusively used for the global best particle, which can effectively guarantee the convergence of the proposed particle swarm optimization. Extensive experiments on eight well-known benchmark functions with different dimensions demonstrate that the proposed PSO is superior or highly competitive to several state-of-the-art PSO variants in dealing with complex multimodal problems.     

Learning multiple experiences useful visual features for active maps localization in crowded environments

Crowded urban environments are composed of different types of dynamic and static elements. Learning and classification of features is a major task in solving the localization problem in such environments. This work presents a gradual learning methodology to learn the useful features using multiple experiences. The usefulness of an observed element is evaluated by a scoring mechanism which uses two scores – reliability and distinctiveness. The visual features thus learned are used to partition the visual map into smaller regions. The robot is efficiently localized in such a partitioned environment using two-level localization. The concept of active map (AM) is proposed here, which is a map that represents one partition of the environment in which there is a high probability of the robot existing. High-level localization is used to track the mode of the AMs using discrete Bayes filter. Low-level localization uses a bag-of-words model to retrieve images and accurately localize the robot. The pose of the robot is the one retrieved from the AM that has maximum a posteriori. Experiments have been conducted on a unique highly crowded data-set collected from Indian roads. The results support the proposed method due to speed and localization accuracy.     

基于阴影图的阴影生成算法研究现状

阴影效果在计算机图形的真实感显示中非常重要,而阴影图算法又是阴影算法的主流.文中对阴影图算法展开了系统的研究,特别是对该领域近几年的发展进行综述,从阴影图数量、参数化和内容三个方面总结归纳了各类阴影图算法的基本思想、特点及其应用环境.综合考虑以上三个方面后对特定的场景选择合适的阴影图算法,除了选择场景外,还包括光源类型、内存消耗、实时性要求、硬件支持及特殊阴影要求等.最后结合当前的技术发展和应用需求展望了阴影图领域未来的研究方向.     

基于深度自动编码器的脑网络状态观测矩阵降维方法

针对人脑网络状态观测矩阵高维无特征的特点,给出了一种基于深度自动编码器(DAE)的降维算法.利用深度学习网络,将高维的人脑网络空间表达映射到低维的本质特征空间中,为进一步提炼脑网络的动态性能提供了基础.实验结果证明:应用该方法可以达到有效的降维效果,且降维后脑网络状态通过自组织特征映射聚类具有一定的规律性,从而为脑网络的动态特性研究提供了基础.     

一种基于EPID的安全OA系统

文章对OA系统中存在的安全问题进行了讨论,引人了一种新的基于远程匿名身份认证EPID模式,它能通过保存设备拥有者私密信息的硬件设施进行身份鉴定。一个嵌入EPID私密的硬件设备可以向远程机构证实它的有效性,而不必暴露自己的身份和其他私密信息。可以撤消一个签名的EPID私钥,即使不知道密钥的身份。同时,把EPID和其它的一些方法进行了对比。     

概念图在高职学生协作学习中的应用研究

协作能力是高职学生应必备的一种重要的实践能力.本研究利用《大学生信息素养》课程,尝试应用概念图开展协作学习,培养学生的协作能力.通过问卷调查和量表测试,分析概念图在大学生协作学习中的应用效果,初步证实概念图在高职学生协作学习中应用具有一定的效果,并认为基于概念图的协作学习活动应包括协作学习准备、协作学习实施、学习成果汇报三个阶段.     

基于Guided Filter的地形图中线要素提取算法

针对彩色扫描地形图中线要素与背景要素难以分离,现有线要素提取算法提取的结果中存在边界不准确的问题,提出基于Guided Filter的地形图中线要素提取算法。利用基于能量密度和Shear变换相结合的线要素提取算法对线要素进行粗提取;引入Guided Filter,以源地形图图像作为Guided Filter的引导图像,以粗提取的线要素图像作为Guided Filter的输入图像,经过滤波处理获得的线要素信息更为显著;利用OTSU算法得到最终的线要素信息。实验结果表明：相对于现有的线要素提取算法,提出的算法能够更为准确地提取出地形图中的线要素,并具有更好的噪声抑制能力。     

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.     

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。     

基于多线性映射的环签密广播公钥方案

提出了基于多线性映射的环签密广播方案,环成员代表环群体匿名签密并广播给多个接收者。其具有两个用户群之间环签密通信的功能。该方案满足环签密广播的安全要求,能够保证签密所传输消息的保密性、签密的不可伪造性、签密者的匿名性。在随机预言模型下,把方案的安全性归约到分级Diffie-Hellman判定问题(GDDH)进行求解。