一种新的人机界面—PUIs

长期以来 ,图形用户界面 (GUIs)一直是人机交互 (HCI)的主流平台。GUI风格的交互让用户使用计算机时更为方便 ,尤其对于办公自动化应用。然而 ,随着人们使用计算机方式的变化以及计算任务种类和数量的大量增加 ,GUIs已经很难实现满足用户需求所必需的所有交互形式。为了适应更多更广的情况 ,人们需要一种更自然、更直观、能自适应、更易为人们接受的界面形式。于是 ,感知用户界面 (PUIs—PerceptualUserInterfaces)成为人机界面中的新热点 ,其主要目的是使人机交互更像人与人之间的交互以及人与世界的交互。本文介绍了蓬勃发展的PUI领域 ,然后简要介绍了三个基于视觉技术的实际系统     

用VC设计实现可多次扩展的对话框

人机界面(Human-Computer Interface)是计算机与人(用户)进行通信和对话的接口,是计算机系统的重要组成部分。人机界面经历了手工操作、命令行界面和图形用户界面三个阶段。图形用户界面是当前被广泛注重的人机界面,Windows操作系统使图形用户界面蔚然成风。基于Windows操作系统开发软件时,设计实现方便实用的对话框,实现人机交互、信息通信成为软件开发中经常遇到的问题。     

维吾尔族人名解义电子词典的设计与实现

计算机技术的迅速发展,为人民带来了极大的便利。电子词典的开发与使用,使人们不用再翻阅厚厚的词典去学习或查询信息了。现代信息技术已进入维吾尔族民间社会并改变着维吾尔族人的生活方式。笔者开发的维吾尔族人名解义词典主要功能是为用户提供人名、意义、来源等信息,未来将会重点开发维吾尔族人名维汉对比翻译查询系统以满足社会的需求。本系统主要采用了C#Windows Form来设计,从窗口设计效果来看,人机交互十分简单。     

向着智能化发展的人机交互技术

从计算机问世以来就伴随着出现的人机交互技术现日益引起人们的兴趣与重视，但人机交互技术发展滞后于应用功能软件，上前尚有许多问题与困难有待研究解决。人机交互的智能化是人机界面发展的必然趋势。人机交互与人工智能的结合将使人机界面趄着具有知识，智能和能更好适应不同用户和不同应用任务的方向发展。     

智能用户界面的发展及趋势

智能用户界面是致力于改善人机交互的高效率、有效性和自然性的人机界面。它通过表达、推理，并按照用户模型、领域模型、任务模型、谈话模型和媒体模型来实现人机交互。本文首先介绍了人机交互的基本内容，然后从用户和用户需求的多样化以及技术的变化为线索介绍了智能用户界面的发展，给出了智能用户界面的体系结构，总结了当前智能用户界面的主要研究课题。最后，简要介绍了智能用户界面当前的研究热点。未来的用户界面不仅能够通过认知因素来了解用户，也能够从非认知因素方面感知和理解用户。随着新的交互方式的出现和技术的更进一步发展，人们将很难区分当前进行的交互是人-机交互，还是人-人交互。     

交互技术及用户接口

人机交互是利用交互技术,借助一些交互设备,比如键盘、鼠标等,实现人机交流。用户接口又是人机交互的主要部分,用户接口的设计涉及很多方面,只有充分地考虑到,才能实现人机交互的和谐。本论文首先对人机交互、交互技术、交互设备等进行简单介绍,其次对用户接口的设计进行详细分析,包括表现方式、工作方式、实现方法等,最后对人机交互的重要性、局限性和展望进行阐述。     

面向虚实融合的人机交互

面向虚实融合的人机交互涉及计算机科学、认知心理学、人机工程学、多媒体技术和虚拟现实等领域，旨在提高人机交互的效率，同时响应人类认知与情感的需求，在办公教育、机器人和虚拟/增强现实设备中都有广泛应用。本文从人机交互涉及感知计算、人与机器人交互及协同、个性化人机对话和数据可视化等4个维度系统阐述面向虚实融合人机交互的发展现状。对国内外研究现状进行对比，展望未来的发展趋势。本文认为兼具可迁移与个性化的感知计算、具备用户行为深度理解的人机协同、用户自适应的对话系统等是本领域的重要研究方向。     

人机交互的若干关键技术

人机交互(Human-ComputerInteraction)是研究人、计算机以及它们相互影响的技术。人机结合以人为主,将是未来计算机系统的特点,实现人机高效合作将是新一代人机界面的主要目的。多通道用户界面、计算机支持的协同工作、三维人机交互等是实现高效自然的人机交互的关键技术。     

触摸屏技术的应用现状与发展前景

触摸屏技术(Touch Screen Technology)是随着计算机技术的发展,为了满足非专业计算机用户操作计算机的需要而逐渐成熟与完善起来的.触摸屏技术是计算机的一种输入技术,由用户通过显示屏输入信息以实现对计算机的控制.从计算机与人组成的人机系统来讲,触摸屏是一种交互输入设备.计算机的交互输入设备非常多,例如键盘 鼠标器、跟踪球(Trackerball)、操纵杆(Joystick)、数字化输入板(Digitizing Tablet)等,但它们都是为专业人员或具有一定计算机基础的用户而提供的.触摸屏技术则是面向非计算机     

连续语音识别系统声学单元选择与模型训练的研究

1引言 随着计算机技术的飞速发展,计算机设备逐步向小型化、便携式、智能化方向的发展,人们迫切要求各种设备具备与人交流的能力,语言作为人类最有效的表达思想工具,怎样利用语言实现人机交互一直是当今世界研究领域的热点之一.     

在线草图识别中的用户适应性研究

提出一种在线草图识别用户适应性解决方法，该方法分别采用支撑向量机主动式增量学习和动态用户建模技术进行笔划和复杂图形的识别．支撑向量机主动式增量学习方法通过主动“分析”用户增量数据，并根据用户反馈从中选择重要数据作为训练样本，可有效地鉴别用户手绘笔划特征，快速地识别用户输入笔划．动态用户建模技术则采用增量决策树记录草图的笔划构成及其手绘过程，有效捕捉用户的复杂图形手绘习惯，进而利用模糊匹配在草图绘制过程中预测和识别复杂图形．实验表明：该方法具有很好的效果，为解决在线草图识别及其用户适应性问题提供参考．     

在线草图识别中用户手绘习惯建模方法

手绘草图是概念设计和思路外化的一种高效的表达方式。用户绘制草图时存在的多种形式，及其随意性和模糊性使得用户适应性问题逐渐成为草图识别的核心课题。本文提出了一种在线草图识别的用户建模方法来捕捉绘制草图时的用户习惯，主要包括两个方面的内容：一是基于SVM的主动式增量学习方法，二是基于动态用户建模的手绘复杂图形的识别方法。前者与传统的增量式学习方法相比，在识别精度相同的情况下所需的训练时间和训练数据集要少得多。后者则是基于笔划信息以及笔划间的顺序和空间关系信息，采用增量式决策树捕捉用户的输入习惯和过程信息。实验证明了本文方法在在线草图识别中的有效性和高效性。     

The more you learn,the less you store: Memory-controlled incremental SVM for visual place recognition

The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs [43] with a method reducing the number of support vectors needed to build the decision function without any loss in performance [15] introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is able to achieve the same recognition results as the original incremental method while reducing the memory growth. Our method is especially suited to work for autonomous systems in realistic settings. We present experiments on two common scenarios in this domain: adaptation in presence of dynamic changes and transfer of knowledge between two different autonomous agents, focusing in both cases on the problem of visual place recognition applied to mobile robot topological localization. Experiments in both scenarios clearly show the power of our approach.     

MUSIPER: a system for modeling music similarity perception based on objective feature subset selection

We explore the use of objective audio signal features to model the individualized (subjective) perception of similarity between music files. We present MUSIPER, a content-based music retrieval system which constructs music similarity perception models of its users by associating different music similarity measures to different users. Specifically, a user-supplied relevance feedback procedure and related neural network-based incremental learning allows the system to determine which subset of a set of objective features approximates more accurately the subjective music similarity perception of a specific user. Our implementation and evaluation of MUSIPER verifies the relation between subsets of objective features and individualized music similarity perception and exhibits significant improvement in individualized perceived similarity in subsequent music retrievals.     

Negative correlation in incremental learning

Negative Correlation Learning (NCL) has been successfully applied to construct neural network ensembles. It encourages the neural networks that compose the ensemble to be different from each other and, at the same time, accurate. The difference among the neural networks that compose an ensemble is a desirable feature to perform incremental learning, for some of the neural networks can be able to adapt faster and better to new data than the others. So, NCL is a potentially powerful approach to incremental learning. With this in mind, this paper presents an analysis of NCL, aiming at determining its weak and strong points to incremental learning. The analysis shows that it is possible to use NCL to overcome catastrophic forgetting, an important problem related to incremental learning. However, when catastrophic forgetting is very low, no advantage of using more than one neural network of the ensemble to learn new data is taken and the test error is high. When all the neural networks are used to learn new data, some of them can indeed adapt better than the others, but a higher catastrophic forgetting is obtained. In this way, it is important to find a trade-off between overcoming catastrophic forgetting and using an entire ensemble to learn new data. The NCL results are comparable with other approaches which were specifically designed to incremental learning. Thus, the study presented in this work reveals encouraging results with negative correlation in incremental learning, showing that NCL is a promising approach to incremental learning.

Discovering user communities on the Internet using unsupervised machine learning techniques

Interest in the analysis of user behaviour on the Internet has been increasing rapidly, especially since the advent of electronic commerce. In this context, we argue here for the usefulness of constructing communities of users with common behaviour, making use of machine learning techniques. In particular, we assume that the users of any service on the Internet constitute a large community and we aim to construct smaller communities of users with common characteristics. The paper presents the results of three case studies for three different types of Internet service: a digital library, an information broker and a Web site. Particular attention is paid on the different types of information access involved in the three case studies: query-based information retrieval, profile-based information filtering and Web-site navigation. Each type of access imposes different constraints on the representation of the learning task. Two different unsupervised learning methods are evaluated: conceptual clustering and cluster mining. One of our main concerns is the construction of meaningful communities that can be used for improving information access on the Internet. Analysis of the results in the three case studies brings to surface some of the important properties of the task, suggesting the feasibility of a common methodology for the three different types of information access on the Internet.     

基于SVM 的中文文本分类反馈学习技术的研究

基于相关反馈技术的基本原理，以SVM分类方法为基础，研究了基于SVM的中文文本分类反馈学习技术，分析了分类处理中反馈学习的主要模式，给出了基于SVM文本分类反馈学习的具体实现方法．并进行了相应的实验验证．实验结果表明，反馈学习具有明显提高SVM分类性能的能力。     

基于用户行为及语义相关实时更新用户兴趣的推荐系统

在现有的推荐系统中,其用户兴趣模型都能够有效地表达出用户的兴趣,但在用户兴趣发生变化时却不能很好地调整用户兴趣模型,不能及时适应用户兴趣的动态变化。本文提出一种基于语义相关实时更新用户兴趣模型的推荐系统。该系统能够及时响应用户兴趣变化,从而改善了以往推荐系统对用户兴趣更新不及时所导致的推荐结果不够全面、准确的问题。实验表明该系统能够准确表达用户兴趣,特别是在用户兴趣发生变化时比以往系统具有更高的准确性。     

Exploring Versus Exploiting when Learning User Models for Text Recommendation

The text recommendation task involves delivering sets of documents to users on the basis of user models. These models are improved over time, given feedback on the delivered documents. When selecting documents to recommend, a system faces an instance of the exploration/exploitation tradeoff: whether to deliver documents about which there is little certainty, or those which are known to match the user model learned so far. In this paper, a simulation is constructed to investigate the effects of this tradeoff on the rate of learning user models, and the resulting compositions of the sets of recommended documents, in particular World-Wide Web pages. Document selection strategies are developed which correspond to different points along the tradeoff. Using an exploitative strategy, our results show that simple preference functions can successfully be learned using a vector-space representation of a user model in conjunction with a gradient descent algorithm, but that increasingly complex preference functions lead to a slowing down of the learning process. Exploratory strategies are shown to increase the rate of user model acquisition at the expense of presenting users with suboptimal recommendations; in addition they adapt to user preference changes more rapidly than exploitative strategies. These simulated tests suggest an implementation for a simple control that is exposed to users, allowing them to vary a system's document selection behavior depending on individual circumstances.     

基于支持向量机的垃圾标签检测模型

为解决Folksonomy存在垃圾标签的问题,提出垃圾标签检测模型。利用向量空间模型表征用户特征,再用支持向量机将Folksonomy用户二分类。通过检测出隐藏在正常用户群体中的垃圾投放人,以此减少垃圾标签数量。实验结果表明,基于支持向量机的垃圾标签检测模型具有更高的分类精度,优于其他检测方法。