Utilizing linguistically enhanced keystroke dynamics to predict typist cognition and demographics

Entering information on a computer keyboard is a ubiquitous mode of expression and communication. We investigate whether typing behavior is connected to two factors: the cognitive demands of a given task and the demographic features of the typist. We utilize features based on keystroke dynamics, stylometry, and “language production”, which are novel hybrid features that capture the dynamics of a typists linguistic choices. Our study takes advantage of a large data set (~350 subjects) made up of relatively short samples (~450 characters) of free text. Experiments show that these features can recognize the cognitive demands of task that an unseen typist is engaged in, and can classify his or her demographics with better than chance accuracy. We correctly distinguish High vs. Low cognitively demanding tasks with accuracy up to 72.39%. Detection of non-native speakers of English is achieved with F₁=0.462 over a baseline of 0.166, while detection of female typists reaches F₁=0.524 over a baseline of 0.442. Recognition of left-handed typists achieves F₁=0.223 over a baseline of 0.100. Further analyses reveal that novel relationships exist between language production as manifested through typing behavior, and both cognitive and demographic factors.     

基于内容分析的网络新闻中社会网络自动抽取

本文针对网络新闻报道,提出了一种基于文本内容分析的社会网络自动抽取方法。此方法在对输入文章进行分词标注、共指消解等预处理之后,通过名词合并及主动词识别,得到存在关系的命名实体之间的关系指向和关系描述,最后通过有向图把存在关系的命名实体进行连接,形成由命名实体、实体间关系指向、实体间关系描述构成的关系网络。试验结果表明该方法对新闻中的命名实体关系抽取比较有效。     

长江口澄通河段床沙粒度特征分析

为了进一步研究长江口水土资源和岸线开发利用工作对该段河床演变和水动力变化的影响,根据2012年及2013年枯水期长江口澄通河段两次大范围床沙取样监测成果,分析了各主支汊床沙的物质组成,粒度分布及丰枯水年粒度特征。结果表明：澄通河段主槽砂粒粗,浅滩砂粒细;主汊砂粒粗,支汊砂粒细;总体上,从上游向下游床沙粒度逐渐变细;主槽及主汊区域床沙类型主要为细砂及粉砂质砂,支汊及浅滩区域床沙类型主要为砂质粉砂及黏土质粉砂;2012年,全河段床沙的粒度组成表现为,砂含量大于粉砂含量,粉砂含量大于黏土含量。     

A new method for expiration code detection and recognition using gabor features based collaborative representation

Text in images and video contains important information for visual content understanding, indexing, and recognizing. Extraction of this information involves preprocessing, localization and extraction of the text from a given image. In this paper, we propose a novel expiration code detection and recognition algorithm by using Gabor features and collaborative representation based classification. The proposed system consists of four steps: expiration code location, character isolation, Gabor features extraction and characters recognition. For expiration code detection, the Gabor energy (GE) and the maximum energy difference (MED) are extracted. The performance of the recognition algorithm is tested over three Gabor features: GE, magnitude response (MR) and imaginary response (IR). The Gabor features are classified based on collaborative representation based classifier (GCRC). To encompass all frequencies and orientations, downsampling and principal component analysis (PCA) are applied in order to reduce the features space dimensionality. The effectiveness of the proposed localization algorithm is highlighted and compared with other existing methods. Extensive testing shows that the suggested detection scheme outperforms existing methods in terms of detection rate for large image database. Also, GCRC show very competitive results compared with Gabor feature sparse representation based classification (GSRC). Also, the proposed system outperforms the nearest neighbor (NN) classifier and the collaborative representation based classification (CRC).     

基于Map-Reduce并行构架和AE的大数据指纹监控识别算法

传统的指纹识别监控方法在应用到以海量、在线和增量为特征的大数据环境中时,不能自动高效地进行识别;由此设计了一种基于Map-Reduce并行框架和AE的大数据智能监控指纹识别算法;首先,设计了基于AE和BP神经网络的识别模型,采用AE进行指纹图像自动特征提取,然后提出了一种基于比较差异算法对AE进行参数初始化的训练算法,采用BP神经网络进行具体识别;最后,将识别过程分解为Map函数和Reduce函数;在Map-Reduce并行框架下对FVC2004中的指纹数据库进行实验,实验结果表明了文中方法能自动、高效地进行指纹识别。     

基于OPENCV的手势识别系统的设计与实现

人与计算机的交互技术是一种新型的计算机技术,且逐渐演变为一种主流技术和计算机领域的技术热点;为了能够更好地识别手势和跟踪手势的运动轨迹,提出了基于OPENCV的手势识别系统,系统引入了OPENCV计算机视觉库,OPENCV作为优秀的计算机视觉库,为设计的实现提供了便捷的代码,利用OPENCV技术中的图像处理算法,首现通过摄像头采集数据图像,并对采集到的图像进行一系列的缩放,去噪以及锐化等处理,然后对人体手势建立肤色模型,然后经过灰度阈值化来转换成二值图像,得到手轮廓的数据图像后,采用轮廓匹配方法识别出手型;最后通过10种基本的手势模型对比验证了本系统具有一定的实时性,并且识别率可以达到95％以上.     

超薄精密件对接接头图像采集和信息识别

针对0.1 mm不锈钢超薄板的超薄和微小尺寸等特点,研究其对接接头图像采集方案和信息识别方法;对比分析了曝光时间、亮度对图像采集的影响,采用Levenberg-Marquardt算法和全局优化法得出曝光时间、亮度和图像平均灰度值三者之间的匹配关系式;为了消除对接接头边缘毛刺、划痕和颗粒噪声对接头图像信息识别的影响,运用阈值分割、腐蚀和膨胀等形态学处理方法进行“去噪”处理,精确地得出了超薄板对接接头的信息.     

基于STM32的车牌识别模块在烟花溯源系统中的应用

烟花溯源需要对运送烟花的车辆进行监管以便后续追踪定位,所以论文设计了基于STM32的车牌识别模块,结合无线射频模块,利用先进的电子通讯技术对烟花出入库进行严格监管,以STM32F103处理器为控制中心,通过OV7670摄像头采集烟花出库过程中车辆的图像信息,通过RFID模块触发摄像头拍照,并将采集到的图像进行车牌识别,定位出车牌号码,并以文本形式无线上传至上位机,同时将图片信息存入SD卡.该系统硬件体积小,成本低,灵活性好,安装方便,适合出入车辆较少、背景较简单的厂区、仓库的车辆自动监管.     

基于BP人工神经网络的手写体数字识别

手写体数字识别是模式识别研究领域多年来的热点,BP人工神经网络是目前应用最广泛的神经网络模型之一。将两者融合并结合Matlab软件,提出了一种简单的基于BP神经网络数字识别的方法,仿真实验结果表明,该方法识别效果良好,准确率高,有一定实用性。