Simulated environments with animated agents: effects on visual attention,emotion, performance,and perception

This research assessed how emotive animated agents in a simulation‐based training affect the performance outcomes and perceptions of the individuals interacting in real time with the training application. A total of 56 participants consented to complete the study. The material for this investigation included a nursing simulation in which participants interacted with three animated agents. The results of this investigation indicated that both experienced and novice participants focused more visual attention time on the body of the animated agent than the other defined areas of interest in the simulated environment. The results also indicated that novice participants conveyed more neutral facial expressions during the interaction with the animated agents than experience participants. The results of the simulation performance scores indicated that novice participants achieved higher simulation performance scores on the simulation task than experienced participants. Lastly, the results of the agent persona instrument showed that experienced and novice participants perceived the animated agents as facilitators of learning, credible, human‐like and engaging.     

Uncertainties of facial emotion recognition technologies and the automation of emotional labour

ABSTRACT

Automated detection and recognition of faces have been implemented in a broad range of media environments. Following that development, what concerns us in this article is the analysis of emotions from facial expressions using computer-based systems, in relation to which we critically investigate the use of theories of basic emotions. We explore in depth the company Affectiva’s attempts to translate, represent and schematize human emotions, as they raise a variety of problems and issues of uncertainty. We analyse the uncertainties concerning the processing of the human face ‘as image’ due to issues concerning temporality and static images as well as polyphony and modulations of the spectrum of expressions. One of our key arguments concerns the temporal character of human emotions, and we address how algorithmically regulated protocols of discretization may be said to prompt specific patterns of emotional responses and expressions based on an ideal of eliminating uncertainty. Through discussions via art pieces by Lauren McCarthy and Kyle McDonald, we question what happens when the protocols of computer systems start to perform aspects of emotional labour for us, making judgments by predicting adequate emotional responses based on the use of the strict metrics criticized in the article.     

基于词向量模型的情感分析

随着移动互联网的发展,以商品评论等带有主观性的短文本信息急剧增加.海量的文本信息使得人工管理越来越困难.本文以商品评论为研究对象进行情感分析.针对商品评论为短文本的特点,本文在词向量的基础上提出了词向量叠加方法和加权词向量方法进行文本特征的提取,从而更深层次的提取短文本特征.在进行评论情感分析模型性能的比较中,说明了本文所提方法的有效性.基于情感分析技术可以解决人工难以胜任的海量商品评论的分类,方便用户快速获取有效信息.     

一种级联式微博情感分类器的构建方法

该文从统计学方法与机器学习的分类思想出发,对中文微博文本的情感类别进行研究。针对微博文本的特点,提出了一种级联式微博情感分类器模型,该模型首先构建基于情感词典和新浪表情符号词典的微博情感初级分类模型;然后根据基准词构建基于类别倾向相似度的二级分类模型,对初级模型未能确定情感类别的微博进行再次分类,并对初级模型的词典进行更新;最后采用朴素贝叶斯分类器构建三级分类模型,对以上还未确定情感类别的微博进行三级分类。通过与NLPCC2014微博情感分类评测参赛结果进行比较,说明了所提方法的有效性。     

汉语自动分词词典新机制—词值哈希机制

汉语词典查询是中文信息处理系统的重要基础部分, 对系统效率有重要的影响. 国内自80年代中后期就开展了中文分词词典机制的研究, 为了提高现有基于词典的分词机制的查询效率, 对于词长不超过4字的词提出了一种全新的分词词典机制——基于汉字串进制值的拉链式哈希机制即词值哈希机制. 对每个汉字的机内码从新编码, 利用进制原理, 计算出一个词语的词值, 建立一个拉链式词值哈希机制, 从而提高查询匹配速度.     

基于内容的网页特征提取

文章主要研究基于内容的中文网页的特征提取技术,具体介绍了分词词典的建造方法,网页正文、标记信息和超链信息的特征提取。通过对旅游类网页的实验结果显示,文中的方法和应用效果良好。     

一种中文文档的非受限无词典抽词方法

本文提出了一种非受限无词典抽词模型,该模型通过自增长算法获取中文文档中的汉字结合模式,并引入支持度、置信度等概念来筛选词条。实验表明:在无需词典支持和利用语料库学习的前提下,该算法能够快速、准确地抽取中文文档中的中、高频词条。适于对词条频度敏感,而又对计算速度要求很高的中文信息处理应用,例如实时文档自动分类系统。     

Efficient Algorithms for Image Template and Dictionary Matching

Given a large text image and a small template image, the Template Matching Problem is that of finding every location within the text which looks like the pattern. This problem, which has received attention for low-level image processing, has been formalized by defining a distance metric between arrays of pixels and finding all subarrays of the large image which are within some threshold distance of the template. These so-called metric methods tends to be too slow for many applications, since evaluating the distance function can take too much time. We present a method for quickly eliminating most positions of the text from consideration as possible matches. The remaining candidate positions are then evaluated one by one against the template for a match. We are still guaranteed to find all matching positions, and our method gives significant speed-ups. Finally, we consider the problem of matching a dictionary of templates against a text. We present methods which are much faster than matching the templates individually against the input image.     

基于统计方法的中文姓名识别

本文介绍一个中文姓名的自动识别系统,该系统使用从姓名样本库和真实文本语料库中得到的大量统计数据,以提高系统识别性能。我们从1994年人民日报中随机抽取100篇文章作为测试样本,实验结果表明,准确率和召回率可同时达到90%以上。     

Emotional agent based on rough set

To improve the ability of agent for handling emotions, a new architecture for emotion agent is presented, based on the traditional BDI agent model and the rough set theory. By a new emotional reasoning algorithm based on rough set, the architecture performs emotion reasoning and implements the emotion treatment. An emotional agent grid based on this architecture has been realized, experiment results show that it is efficient to transact simple emotions by emotional agents.