基于细胞因子网络协同进化的Web服务合成方法

借鉴生物神经内分泌免疫系统的协同进化机制,基于细胞因子网络平台提出了Web服务的合成方法.在细胞因子网络调控下的生物实体代理Web服务,构成为一个带有条件的米兰机单元,Web服务的合成可以转换为米兰机进化过程.生物实体通过消息匹配和条件约束形成细胞因子网络,Web服务的合成通过生物实体细胞因子网络支持.在服务协同进化过程中可以动态调整其合成的服务,完成服务的动态自组织合成和管理等工作.仿真结果表明该方法在环境动态变化时具有适应性.     

Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation

This paper focuses on hierarchical classification problems where the classes to be predicted are organized in the form of a tree. The standard top-down divide and conquer approach for hierarchical classification consists of building a hierarchy of classifiers where a classifier is built for each internal (non-leaf) node in the class tree. Each classifier discriminates only between its child classes. After the tree of classifiers is built, the system uses them to classify test examples one class level at a time, so that when the example is assigned a class at a given level, only the child classes need to be considered at the next level. This approach has the drawback that, if a test example is misclassified at a certain class level, it will be misclassified at deeper levels too. In this paper we propose hierarchical classification methods to mitigate this drawback. More precisely, we propose a method called hierarchical ensemble of hierarchical rule sets (HEHRS), where different ensembles are built at different levels in the class tree and each ensemble consists of different rule sets built from training examples at different levels of the class tree. We also use a particle swarm optimisation (PSO) algorithm to optimise the rule weights used by HEHRS to combine the predictions of different rules into a class to be assigned to a given test example. In addition, we propose a variant of a method to mitigate the aforementioned drawback of top-down classification. These three types of methods are compared against the standard top-down hierarchical classification method in six challenging bioinformatics datasets, involving the prediction of protein function. Overall HEHRS with the rule weights optimised by the PSO algorithm obtains the best predictive accuracy out of the four types of hierarchical classification method.     

基于模板法的网页英语试卷自动抽取技术的研究

为解决在线考试系统中建立海量数据库的问题,采用基于模板法的Web信息抽取方法,提取相似网页中的正文内容。并根据包含英文试卷的网页特点,制定正文抽取规则,最终可获得完整的英语试卷及其答案。实验结果表明,该方法具有较高的准确率和提取速度。     

一个基于Web日志的用户访问数据分析系统

通过结合网站设计初期的网站结构和典型用例的预先定义,构建一个基于Web日志数据的用户访问数据分析系统。该系统（AS-UAB,Analysis System of User Access Behaviors）通过对网站结构和典型用例的预先植入,利用该领域最新的研究方法和结论,为网站相关人员提供可视化的用户访问信息统计及趋势预测,网站结构设计及交互设计上存在的问题及改进建议等通用功能和个性化定制功能。     

Web Services技术在气象产品发布中的应用

根据气象产品发布现状,结合统一的Web Service对象封装技术,提出了一种跨平台、无缝集成的方案;采用Web Service技术发布气象产品,实现了跨平台和不同语言的应用集成,并能够使气象产品的发布速度更快、效率更高。     

基于SOA的空间信息资源整合与服务模式探讨

提高空间信息资源的整合与服务水平,在迈向信息和服务型社会的过程中具有十分重要的意义。文章根据作者近年来在该领域的相关应用研究,对空间信息资源整合与服务模式进行了探讨,总结了基于SOA实现空间信息资源整合与服务的模式,并通过实例阐述了空间信息资源整合与服务的组织、实施和应用。     

网络资源的多层监测方法与实现

设计了针对网络资源不同层次进行监测的实现方法,这些层次包括：网络传输层对象（网络服务器性能、TCP、UDP实现过程）、应用层对象（主要是业务数据库）、表示层对象（页面及相关的脚本行为）、用户（浏览器）以及Web服务（HTTP协议实现过程）。对于不同层次的数据,采用了不同的采集方法,并实现了对不同监测资源的重新组合分析。在分层获取、分析、存储数据基础上进行了统一身份认证、统一展示标准和指标集成展示,从而实现了分布式监测系统的集成。     

一种改进的应用于噪声数据中的KNN算法

基于实例的KNN算法不可避免地要依赖于数据的质量,但原始数据含有噪声,因而KNN算法的结果势必会因为数据中的噪声而受到严重的影响。事实上,大多噪声都服从一定的模型,而且模型一般是已知的。充分利用数据中的噪声模型,以减小噪声对KNN算法结果的影响。通过实验结果表明该方法是有效的。     

地铁站场景下的可行驶区域检测

在室内环境下的机器人视觉导航任务中,可行驶区域检测是不可或缺的一部分,这是保证自动驾驶任务实现的基础.目前较多的解决方法是对数据集中出现过的障碍物进行识别来检测可行驶区域,缺乏灵活性,因此本文提出了一种针对地铁站等室内平坦地面的可行驶区域检测方法,提高实用性.本文采用经典的MobileNetV3网络对采集到的前方图像进行分类,判断是否为地面区域.由于室内地面的地标、箭头等贴纸的影响,因此需要对非地面区域进一步判断,与常规的立体障碍物进行区分.本文利用连续帧之间的特征点匹配获得相机移动距离,并利用直线拟合计算斜率的方法达到区分立体障碍物与平面地标的目的.实验表明,本文提出的方法能较好地检测机器人前方可行驶区域,具有较高的实用价值.     

Transfer Learning on Deep Neural Networks to Detect Pornography

While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.