IUI. International Conference on Intelligent User Interfaces最新文献_第4页

Optimizing temporal topic segmentation for intelligent text visualization 面向智能文本可视化的时间主题分割优化

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449441

Shimei Pan, Michelle X. Zhou, Yangqiu Song, Weihong Qian, Fei Wang, Shixia Liu

引用次数: 16

Directing exploratory search: reinforcement learning from user interactions with keywords 指导探索性搜索:从用户与关键字的交互中强化学习

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449413

D. Glowacka, Tuukka Ruotsalo, Ksenia Konyushkova, Kumaripaba Athukorala, Samuel Kaski, Giulio Jacucci

{"title":"Directing exploratory search: reinforcement learning from user interactions with keywords","authors":"D. Glowacka, Tuukka Ruotsalo, Ksenia Konyushkova, Kumaripaba Athukorala, Samuel Kaski, Giulio Jacucci","doi":"10.1145/2449396.2449413","DOIUrl":"https://doi.org/10.1145/2449396.2449413","url":null,"abstract":"Techniques for both exploratory and known item search tend to direct only to more specific subtopics or individual documents, as opposed to allowing directing the exploration of the information space. We present an interactive information retrieval system that combines Reinforcement Learning techniques along with a novel user interface design to allow active engagement of users in directing the search. Users can directly manipulate document features (keywords) to indicate their interests and Reinforcement Learning is used to model the user by allowing the system to trade off between exploration and exploitation. This gives users the opportunity to more effectively direct their search nearer, further and following a direction. A task-based user study conducted with 20 participants comparing our system to a traditional query-based baseline indicates that our system significantly improves the effectiveness of information retrieval by providing access to more relevant and novel information without having to spend more time acquiring the information.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78334273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 116

SmartDCap: semi-automatic capture of higher quality document images from a smartphone SmartDCap:从智能手机半自动捕获更高质量的文档图像

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449433

Francine Chen, S. Carter, Laurent Denoue, J. Kumar

{"title":"SmartDCap: semi-automatic capture of higher quality document images from a smartphone","authors":"Francine Chen, S. Carter, Laurent Denoue, J. Kumar","doi":"10.1145/2449396.2449433","DOIUrl":"https://doi.org/10.1145/2449396.2449433","url":null,"abstract":"People frequently capture photos with their smartphones, and some are starting to capture images of documents. However, the quality of captured document images is often lower than expected, even when an application that performs post-processing to improve the image is used. To improve the quality of captured images before post-processing, we developed the Smart Document Capture (SmartDCap) application that provides real-time feedback to users about the likely quality of a captured image. The quality measures capture the sharpness and framing of a page or regions on a page, such as a set of one or more columns, a part of a column, a figure, or a table. Using our approach, while users adjust the camera position, the application automatically determines when to take a picture of a document to produce a good quality result. We performed a subjective evaluation comparing SmartDCap and the Android Ice Cream Sandwich (ICS) camera application; we also used raters to evaluate the quality of the captured images. Our results indicate that users find SmartDCap to be as easy to use as the standard ICS camera application. Also, images captured using SmartDCap are sharper and better framed on average than images using the ICS camera application.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74655565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

LinkedVis: exploring social and semantic career recommendations LinkedVis:探索社交和语义职业推荐

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449412

Svetlin Bostandjiev, J. O'Donovan, Tobias Höllerer

{"title":"LinkedVis: exploring social and semantic career recommendations","authors":"Svetlin Bostandjiev, J. O'Donovan, Tobias Höllerer","doi":"10.1145/2449396.2449412","DOIUrl":"https://doi.org/10.1145/2449396.2449412","url":null,"abstract":"This paper presents LinkedVis, an interactive visual recommender system that combines social and semantic knowledge to produce career recommendations based on the LinkedIn API. A collaborative (social) approach is employed to identify professionals with similar career paths and produce personalized recommendations of both companies and roles. To unify semantically identical but lexically distinct entities and arrive at better user models, we employ lightweight natural language processing and entity resolution using semantic information from a variety of end-points on the web. Elements from the underlying recommendation algorithm are exposed through an interactive interface that allows users to manipulate different aspects of the algorithm and the data it operates on, allowing users to explore a variety of \"what-if\" scenarios around their current profile. We evaluate LinkedVis through leave-one-out accuracy and diversity experiments on a data corpus collected from 47 users and their LinkedIn connections, as well as through a supervised study of 27 users exploring their own profile and recommendations interactively. Results show that our approach outperforms a benchmark recommendation algorithm without semantic resolution in terms of accuracy and diversity, and that the ability to tweak recommendations interactively by adjusting profile item and social connection weights further improves predictive accuracy. Questionnaires on the user experience with the explanatory and interactive aspects of the application reveal very high user acceptance and satisfaction.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83629073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 38

User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities 用户自适应信息可视化:利用眼睛注视数据推断可视化任务和用户认知能力

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449439

B. Steichen, G. Carenini, C. Conati

引用次数: 150

Automatic and continuous user task analysis via eye activity 通过眼活动自动和连续的用户任务分析

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449406

Siyuan Chen, J. Epps, Fang Chen

{"title":"Automatic and continuous user task analysis via eye activity","authors":"Siyuan Chen, J. Epps, Fang Chen","doi":"10.1145/2449396.2449406","DOIUrl":"https://doi.org/10.1145/2449396.2449406","url":null,"abstract":"A day in the life of a user can be segmented into a series of tasks: a user begins a task, becomes loaded perceptually and cognitively to some extent by the objects and mental challenge that comprise that task, then at some point switches or is distracted to a new task, and so on. Understanding the contextual task characteristics and user behavior in interaction can benefit the development of intelligent systems to aid user task management. Applications that aid the user in one way or another have proliferated as computing devices become more and more of a constant companion. However, direct and continuous observations of individual tasks in a naturalistic context and subsequent task analysis, for example the diary method, have traditionally been a manual process. We propose a method for automatic task analysis system, which monitors the user's current task and analyzes it in terms of the task transition, and perceptual and cognitive load imposed by the task. An experiment was conducted in which participants were required to work continuously on groups of three sequential tasks of different types. Three classes of eye activity, namely pupillary response, blink and eye movement, were analyzed to detect the task transition and non-transition states, and to estimate three levels of perceptual load and three levels of cognitive load every second to infer task characteristics. This paper reports statistically significant classification accuracies in all cases and demonstrates the feasibility of this approach for task monitoring and analysis.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76876068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Helping users with information disclosure decisions: potential for adaptation 帮助用户做出信息披露决策:适应的潜力

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449448

Bart P. Knijnenburg, A. Kobsa

引用次数: 39

Team reactions to voiced agent instructions in a pervasive game 在一个普遍的游戏中，团队对语音代理指令的反应

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449445

Stuart Moran, Nadia Pantidi, K. Bachour, J. Fischer, Martin Flintham, T. Rodden, Simon Evans, Simon Johnson

引用次数: 24

Towards cooperative brain-computer interfaces for space navigation 面向空间导航的协同脑机接口

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449417

R. Poli, C. Cinel, A. Matran-Fernandez, F. Sepulveda, A. Stoica

引用次数: 50

Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits 通过击键分析、任务评估和稳定特征来检测写作过程中的无聊和投入

IUI. International Conference on Intelligent User Interfaces Pub Date : 2013-03-19 DOI: 10.1145/2449396.2449426

R. Bixler, S. D’Mello

{"title":"Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits","authors":"R. Bixler, S. D’Mello","doi":"10.1145/2449396.2449426","DOIUrl":"https://doi.org/10.1145/2449396.2449426","url":null,"abstract":"It is hypothesized that the ability for a system to automatically detect and respond to users' affective states can greatly enhance the human-computer interaction experience. Although there are currently many options for affect detection, keystroke analysis offers several attractive advantages to traditional methods. In this paper, we consider the possibility of automatically discriminating between natural occurrences of boredom, engagement, and neutral by analyzing keystrokes, task appraisals, and stable traits of 44 individuals engaged in a writing task. The analyses explored several different arrangements of the data: using downsampled and/or standardized data; distinguishing between three different affect states or groups of two; and using keystroke/timing features in isolation or coupled with stable traits and/or task appraisals. The results indicated that the use of raw data and the feature set that combined keystroke/timing features with task appraisals and stable traits, yielded accuracies that were 11% to 38% above random guessing and generalized to new individuals. Applications of our affect detector for intelligent interfaces that provide engagement support during writing are discussed.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90405146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 98