Due to small screens, inaccuracy of input and other limitations of mobile devices, revisitation of Web pages in mobile browsers takes more time than that in desktop browsers. In this paper, we propose a novel approach to facilitate revisitation. We designed AutoWeb, a system that clusters opened Web pages into different topics based on their contents. Users can quickly find a desired opened Web page by narrowing down the searching scope to a group of Web pages that share the same topic. Clustering accuracy is evaluated to be 92.4% and computing resource consumption was proved to be acceptable. A user study was conducted to explore user experience and how much AutoWeb facilitates revisitation. Results showed that AutoWeb could save up a significant time for revisitation and participants rated the system highly.
{"title":"Clustering web pages to facilitate revisitation on mobile devices","authors":"Jie Liu, Chun Yu, Wenchang Xu, Yuanchun Shi","doi":"10.1145/2166966.2167010","DOIUrl":"https://doi.org/10.1145/2166966.2167010","url":null,"abstract":"Due to small screens, inaccuracy of input and other limitations of mobile devices, revisitation of Web pages in mobile browsers takes more time than that in desktop browsers. In this paper, we propose a novel approach to facilitate revisitation. We designed AutoWeb, a system that clusters opened Web pages into different topics based on their contents. Users can quickly find a desired opened Web page by narrowing down the searching scope to a group of Web pages that share the same topic. Clustering accuracy is evaluated to be 92.4% and computing resource consumption was proved to be acceptable. A user study was conducted to explore user experience and how much AutoWeb facilitates revisitation. Results showed that AutoWeb could save up a significant time for revisitation and participants rated the system highly.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90114646","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}
Multimodal interaction allows users to specify commands using combinations of inputs from multiple different modalities. For example, in a local search application, a user might say "gas stations" while simultaneously tracing a route on a touchscreen display. In this demonstration, we describe the extension of our cloud-based speech recognition architecture to a Multimodal Semantic Interpretation System (MSIS) that supports processing of multimodal inputs streamed over HTTP. We illustrate the capabilities of the framework using Speak4itSM, a deployed mobile local search application supporting combined speech and gesture input. We provide interactive demonstrations of Speak4it on the iPhone and iPad and explain the challenges of supporting true multimodal interaction in a deployed mobile service.
{"title":"Collecting multimodal data in the wild","authors":"Michael Johnston, Patrick Ehlen","doi":"10.1145/2166966.2167042","DOIUrl":"https://doi.org/10.1145/2166966.2167042","url":null,"abstract":"Multimodal interaction allows users to specify commands using combinations of inputs from multiple different modalities. For example, in a local search application, a user might say \"gas stations\" while simultaneously tracing a route on a touchscreen display. In this demonstration, we describe the extension of our cloud-based speech recognition architecture to a Multimodal Semantic Interpretation System (MSIS) that supports processing of multimodal inputs streamed over HTTP. We illustrate the capabilities of the framework using Speak4itSM, a deployed mobile local search application supporting combined speech and gesture input. We provide interactive demonstrations of Speak4it on the iPhone and iPad and explain the challenges of supporting true multimodal interaction in a deployed mobile service.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74181134","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}
Recent developments in machine listening present opportunities for innovative new paradigms for computer-human interaction. Voice recognition systems demonstrate a typical approach that conforms to event oriented control models. However, acoustic sound is continuous, and highly dimensional, presenting a rich medium for computer interaction. Unsupervised machine learning models present great potential for real-time machine listening and understanding of audio and sound data. We propose a method for harnessing unsupervised machine learning algorithms, Adaptive Resonance Theory specifically, in order to inform machine listening, build musical context information, and drive real-time interactive performance systems. We present the design and evaluation of this model leveraging the expertise of trained, improvising musicians.
{"title":"Machine listening: acoustic interface with ART","authors":"Benjamin D. Smith, Guy E. Garnett","doi":"10.1145/2166966.2167021","DOIUrl":"https://doi.org/10.1145/2166966.2167021","url":null,"abstract":"Recent developments in machine listening present opportunities for innovative new paradigms for computer-human interaction. Voice recognition systems demonstrate a typical approach that conforms to event oriented control models. However, acoustic sound is continuous, and highly dimensional, presenting a rich medium for computer interaction. Unsupervised machine learning models present great potential for real-time machine listening and understanding of audio and sound data. We propose a method for harnessing unsupervised machine learning algorithms, Adaptive Resonance Theory specifically, in order to inform machine listening, build musical context information, and drive real-time interactive performance systems. We present the design and evaluation of this model leveraging the expertise of trained, improvising musicians.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84537987","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}
Theater is a collaborative art form that involves production team members with different specialties. Because theater involves various technical elements, such as stage design and lighting, the production team must work in cooperation among various departments to design a theatrical production. When planning a theatrical production, it is difficult to visualize the stage as a whole and to incorporate the ideas of production team members from various departments. In this paper, we propose a system for reproducing the theatrical stage by means of a virtual stage linked to a physical miniature stage. The miniature stage is presented on a tabletop interface, and the virtual stage is created by computer graphics to reflect the actions on the miniature stage in real time. By actually presenting theatrical production ideas in two spaces, users can more easily collaborate and gain a comprehensive view of the stage.
{"title":"Virtual stage linked with a physical miniature stage to support multiple users in planning theatrical productions","authors":"Yosuke Horiuchi, T. Inoue, Ken-ichi Okada","doi":"10.1145/2166966.2166989","DOIUrl":"https://doi.org/10.1145/2166966.2166989","url":null,"abstract":"Theater is a collaborative art form that involves production team members with different specialties. Because theater involves various technical elements, such as stage design and lighting, the production team must work in cooperation among various departments to design a theatrical production. When planning a theatrical production, it is difficult to visualize the stage as a whole and to incorporate the ideas of production team members from various departments. In this paper, we propose a system for reproducing the theatrical stage by means of a virtual stage linked to a physical miniature stage. The miniature stage is presented on a tabletop interface, and the virtual stage is created by computer graphics to reflect the actions on the miniature stage in real time. By actually presenting theatrical production ideas in two spaces, users can more easily collaborate and gain a comprehensive view of the stage.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85418373","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}
We present a system for in-vehicle driver recognition based on biometric information extracted from electrocardiographic (ECG) signals collected at the hands. We recur to non-intrusive techniques, that are easy to integrate into components with which the driver naturally interacts with, such as the steering wheel. This system is applicable to the automatic customization of vehicle settings according to the perceived driver, being also prone to expand the security features of the vehicle through the detection of hands-off steering wheel events in a continuous or near-continuous manner. We have performed randomized tests for performance evaluation of the system, in a subject identification scenario, using closed sets of up to 5 subjects, showing promising results for the intended application.
{"title":"In-vehicle driver recognition based on hand ECG signals","authors":"H. Silva, A. Lourenço, A. Fred","doi":"10.1145/2166966.2166971","DOIUrl":"https://doi.org/10.1145/2166966.2166971","url":null,"abstract":"We present a system for in-vehicle driver recognition based on biometric information extracted from electrocardiographic (ECG) signals collected at the hands. We recur to non-intrusive techniques, that are easy to integrate into components with which the driver naturally interacts with, such as the steering wheel. This system is applicable to the automatic customization of vehicle settings according to the perceived driver, being also prone to expand the security features of the vehicle through the detection of hands-off steering wheel events in a continuous or near-continuous manner. We have performed randomized tests for performance evaluation of the system, in a subject identification scenario, using closed sets of up to 5 subjects, showing promising results for the intended application.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82195095","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}
In this paper we present a new bimanual markerless gesture interface for 3D full-body motion tracking sensors, such as the Kinect. Our interface uses a probabilistic algorithm to incrementally predict users' intended one-handed and twohanded gestures while they are still being articulated. It supports scale and translation invariant recognition of arbitrarily defined gesture templates in real-time. The interface supports two ways of gesturing commands in thin air to displays at a distance. First, users can use one-handed and two-handed gestures to directly issue commands. Second, users can use their non-dominant hand to modulate single-hand gestures. Our evaluation shows that the system recognizes one-handed and two-handed gestures with an accuracy of 92.7%--96.2%.
{"title":"Continuous recognition of one-handed and two-handed gestures using 3D full-body motion tracking sensors","authors":"P. Kristensson, Thomas Nicholson, A. Quigley","doi":"10.1145/2166966.2166983","DOIUrl":"https://doi.org/10.1145/2166966.2166983","url":null,"abstract":"In this paper we present a new bimanual markerless gesture interface for 3D full-body motion tracking sensors, such as the Kinect. Our interface uses a probabilistic algorithm to incrementally predict users' intended one-handed and twohanded gestures while they are still being articulated. It supports scale and translation invariant recognition of arbitrarily defined gesture templates in real-time. The interface supports two ways of gesturing commands in thin air to displays at a distance. First, users can use one-handed and two-handed gestures to directly issue commands. Second, users can use their non-dominant hand to modulate single-hand gestures. Our evaluation shows that the system recognizes one-handed and two-handed gestures with an accuracy of 92.7%--96.2%.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84707548","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}
Moushumi Sharmin, L. Bergman, Jie Lu, Ravi B. Konuru
Reuse of existing presentation materials is prevalent among knowledge workers. However, finding the most appropriate material for reuse is challenging. Existing information management and search tools provide inadequate support for reuse due to their dependence on users' ability to effectively categorize, recall, and recognize existing materials. Based on our findings from an online survey and contextual interviews, we designed and implemented a slide-based contextual recommender, ConReP, for supporting reuse of presentation materials. ConReP utilizes a user-selected slide as a search-key, recommends materials based on similarity to the selected slide, and provides a local-context-based visual representation of the recommendations. Users input provides new insight into presentation reuse and reveals that slide-based search is more effective than keyword-based search, local-context-based visual representation helps in better recall and recognition, and shows the promise of this general approach of exploiting individual slides and local-context for better presentation reuse.
{"title":"On slide-based contextual cues for presentation reuse","authors":"Moushumi Sharmin, L. Bergman, Jie Lu, Ravi B. Konuru","doi":"10.1145/2166966.2166992","DOIUrl":"https://doi.org/10.1145/2166966.2166992","url":null,"abstract":"Reuse of existing presentation materials is prevalent among knowledge workers. However, finding the most appropriate material for reuse is challenging. Existing information management and search tools provide inadequate support for reuse due to their dependence on users' ability to effectively categorize, recall, and recognize existing materials. Based on our findings from an online survey and contextual interviews, we designed and implemented a slide-based contextual recommender, ConReP, for supporting reuse of presentation materials. ConReP utilizes a user-selected slide as a search-key, recommends materials based on similarity to the selected slide, and provides a local-context-based visual representation of the recommendations. Users input provides new insight into presentation reuse and reveals that slide-based search is more effective than keyword-based search, local-context-based visual representation helps in better recall and recognition, and shows the promise of this general approach of exploiting individual slides and local-context for better presentation reuse.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77832084","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}
Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. People from all walks of life share data about social events, express opinions, discuss their interests, publicize businesses, recommend products, and, explicitly or implicitly, reveal personal information. This workshop will focus on the use of social media data for creating models of individual users from the content that they publish. Deeper understanding of user behavior and associated attributes can benefit a wide range of intelligent applications, such as social recommender systems and expert finders, as well as provide the foundation in support of novel user interfaces (e.g., actively engaging the crowd in mixed-initiative question-answering systems). These applications and interfaces may offer significant benefits to users across a wide variety of domains, such as retail, government, healthcare and education. User modeling from public social media data may also reveal information that users would prefer to keep private. Such concerns are particularly important because individuals do not have complete control over the information they share about themselves. For example, friends of a user may inadvertently divulge private information about that user in their own posts. In this workshop we will also discuss possible mechanisms that users might employ to monitor what information has been revealed about themselves on social media and obfuscate any sensitive information that has been accidentally revealed.
{"title":"1st international workshop on user modeling from social media","authors":"J. Mahmud, Jeffrey Nichols, Michelle X. Zhou","doi":"10.1145/2166966.2167058","DOIUrl":"https://doi.org/10.1145/2166966.2167058","url":null,"abstract":"Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. People from all walks of life share data about social events, express opinions, discuss their interests, publicize businesses, recommend products, and, explicitly or implicitly, reveal personal information. This workshop will focus on the use of social media data for creating models of individual users from the content that they publish. Deeper understanding of user behavior and associated attributes can benefit a wide range of intelligent applications, such as social recommender systems and expert finders, as well as provide the foundation in support of novel user interfaces (e.g., actively engaging the crowd in mixed-initiative question-answering systems). These applications and interfaces may offer significant benefits to users across a wide variety of domains, such as retail, government, healthcare and education. User modeling from public social media data may also reveal information that users would prefer to keep private. Such concerns are particularly important because individuals do not have complete control over the information they share about themselves. For example, friends of a user may inadvertently divulge private information about that user in their own posts. In this workshop we will also discuss possible mechanisms that users might employ to monitor what information has been revealed about themselves on social media and obfuscate any sensitive information that has been accidentally revealed.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80903008","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}
Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, or eye trackers, and therefore these data often need to be compressed for classification, storage, and/or retrieval purposes. This paper introduces a simple, accurate, and extremely fast technique inspired by the well-known K-means algorithm to properly cluster sequential data. We illustrate the feasibility of our algorithm on a web-based prototype that works with trajectories derived from mouse and touch input. As can be observed, our proposal outperforms the classical K-means algorithm in terms of accuracy (better, well-formed segmentations) and performance (less computation time).
{"title":"Simple, fast, and accurate clustering of data sequences","authors":"Luis A. Leiva, E. Vidal","doi":"10.1145/2166966.2167027","DOIUrl":"https://doi.org/10.1145/2166966.2167027","url":null,"abstract":"Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, or eye trackers, and therefore these data often need to be compressed for classification, storage, and/or retrieval purposes. This paper introduces a simple, accurate, and extremely fast technique inspired by the well-known K-means algorithm to properly cluster sequential data. We illustrate the feasibility of our algorithm on a web-based prototype that works with trajectories derived from mouse and touch input. As can be observed, our proposal outperforms the classical K-means algorithm in terms of accuracy (better, well-formed segmentations) and performance (less computation time).","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86694780","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}
Numerous interaction techniques have been developed that make "virtual" pointing at targets in graphical user interfaces easier than analogous physical pointing tasks by invoking target-based interface modifications. These pointing facilitation techniques crucially depend on methods for estimating the relevance of potential targets. Unfortunately, many of the simple methods employed to date are inaccurate in common settings with many selectable targets in close proximity. In this paper, we bring recent advances in statistical machine learning to bear on this underlying target relevance estimation problem. By framing past target-driven pointing trajectories as approximate solutions to well-studied control problems, we learn the probabilistic dynamics of pointing trajectories that enable more accurate predictions of intended targets.
{"title":"Probabilistic pointing target prediction via inverse optimal control","authors":"Brian D. Ziebart, Anind Dey, J. Andrew Bagnell","doi":"10.1145/2166966.2166968","DOIUrl":"https://doi.org/10.1145/2166966.2166968","url":null,"abstract":"Numerous interaction techniques have been developed that make \"virtual\" pointing at targets in graphical user interfaces easier than analogous physical pointing tasks by invoking target-based interface modifications. These pointing facilitation techniques crucially depend on methods for estimating the relevance of potential targets. Unfortunately, many of the simple methods employed to date are inaccurate in common settings with many selectable targets in close proximity. In this paper, we bring recent advances in statistical machine learning to bear on this underlying target relevance estimation problem. By framing past target-driven pointing trajectories as approximate solutions to well-studied control problems, we learn the probabilistic dynamics of pointing trajectories that enable more accurate predictions of intended targets.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86377746","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}
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