{"title":"Adaptive game for reducing aggressive behavior","authors":"J. F. Mancilla-Caceres, Eyal Amir, D. Espelage","doi":"10.1145/2451176.2451183","DOIUrl":"https://doi.org/10.1145/2451176.2451183","url":null,"abstract":"Peer influence in social networks has long been recognized as one of the key factors in many of the social health issues that affect young people. In order to study peer networks, scientists have relied on the use of self-report surveys that impose limitations on the types of issues than can be studied. On the other hand, the ever increasing use of computers for communication has given rise to new ways of studying group dynamics and, even more importantly, it has enabled a new way to affect those dynamics as they are detected. Our work is focused on designing and analyzing computer social games that can be used as data collection tools for social interactions, and that can also react and change accordingly in order to promote prosocial, rather than aggressive, behavior.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115235152","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}
{"title":"PhotoAct: act on photo taking","authors":"Shuguang Wu, Jun Xiao, Ken Reily","doi":"10.1145/2451176.2451221","DOIUrl":"https://doi.org/10.1145/2451176.2451221","url":null,"abstract":"In many commercial environments understanding the user's intention can lead to more engaging and intelligent user interactions. We looked at theme park photo kiosks where many people use their camera phones to capture their ride photos on preview displays. We believe that by identifying people with photo-taking intention and engaging them through intelligent UI can help reduce the instances of people opting for low quality but free screen capture. We built a prototype system called PhotoAct, using depth camera to recognize human postures and in real time infer people's photo-taking intentions. In this paper, we describe the system components, the detection algorithm, and present preliminary lab study results.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122920257","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}
{"title":"Interactive design of planar curves based on spatial augmented reality","authors":"Ahyun Lee, J. Suh, Joo-Haeng Lee","doi":"10.1145/2451176.2451195","DOIUrl":"https://doi.org/10.1145/2451176.2451195","url":null,"abstract":"In this paper, we introduce an interactive application for planar curve design in a real world based on spatial augmented reality (SAR). The key component is a projector-camera unit that recognizes physical control objects (i.e., key points of an intended curve) using a camera and displays a design result (i.e., a B-spline curve) directly on the real world surface using a projector. Usually, geometric design is performed with the aid of CAD software and traditional user interfaces of a computer system. The main contribution of this paper is application of spatial augmented reality techniques in the domain of computer-aided geometric design (CAGD) for more tangible and intuitive interaction in a real world. We describe the feature of the prototype system and demonstrate the working application with examples.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"854 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128289725","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}
{"title":"Teaching agents with human feedback: a demonstration of the TAMER framework","authors":"W. B. Knox, P. Stone, C. Breazeal","doi":"10.1145/2451176.2451201","DOIUrl":"https://doi.org/10.1145/2451176.2451201","url":null,"abstract":"Incorporating human interaction into agent learning yields two crucial benefits. First, human knowledge can greatly improve the speed and final result of learning compared to pure trial-and-error approaches like reinforcement learning. And second, human users are empowered to designate \"correct\" behavior. In this abstract, we present research on a system for learning from human interaction - the TAMER framework - then point to extensions to TAMER, and finally describe a demonstration of these systems.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133097315","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}
S. Amershi, M. Cakmak, W. B. Knox, Todd Kulesza, T. Lau
{"title":"IUI workshop on interactive machine learning","authors":"S. Amershi, M. Cakmak, W. B. Knox, Todd Kulesza, T. Lau","doi":"10.1145/2451176.2451230","DOIUrl":"https://doi.org/10.1145/2451176.2451230","url":null,"abstract":"Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, traditional ML systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans.\u0000 However, a growing community of researchers at the intersection of ML and human-computer interaction are making interaction with humans a central part of developing ML systems. These efforts include applying interaction design principles to ML systems, using human-subject testing to evaluate ML systems and inspire new methods, and changing the input and output channels of ML systems to better leverage human capabilities. With this Interactive Machine Learning (IML) workshop at IUI 2013 we aim to bring this community together to share ideas, get up-to-date on recent advances, progress towards a common framework and terminology for the field, and discuss the open questions and challenges of IML.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115374132","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}
{"title":"Magnetic marionette: magnetically driven elastic controller on mobile device","authors":"Sungjae Hwang, Myungwook Ahn, K. Wohn","doi":"10.1145/2451176.2451207","DOIUrl":"https://doi.org/10.1145/2451176.2451207","url":null,"abstract":"In this paper, we present the Magnetic Marionette, a magnetically driven elastic controller that enables tangible interaction on mobile devices. This technique can determine eight different gestures in excess of 99% accuracy by sensing and tracking the magnets embedded on the controller. The advantage of this technique is that it is lightweight, battery-free, and inexpensive because it uses a magnetometer, which is already embedded in smart phones today. This simple and noble technique allows users to achieve richer tactile feedback, expand their interaction area, and enhance expressiveness without the need for hardware modification.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127301635","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}
{"title":"Finding the local angle in national news","authors":"S. O’Banion, L. Birnbaum, Scott Bradley","doi":"10.1145/2451176.2451214","DOIUrl":"https://doi.org/10.1145/2451176.2451214","url":null,"abstract":"Journalists often localize news stories that are not explicitly about the community they serve by investigating and describing how those stories affect that community. This is, in essence, a form of personalization based not on a reader's personal interests, but rather on their ties to a geographic location. In this paper we present The Local Angle, an approach for automating the process of finding national and international news stories that are locally relevant. The Local Angle associates the people, companies, and organizations mentioned in news stories with geographic locations using semantic analysis tools and online knowledge bases. We describe the design and implementation of our prototype system that helps content curators and consumers discover articles that are of local interest even if they do not originate locally.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122354736","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}
Steven Bourke, Michael P. O'Mahony, Rachael Rafter, Barry Smyth
{"title":"Ranking in information streams","authors":"Steven Bourke, Michael P. O'Mahony, Rachael Rafter, Barry Smyth","doi":"10.1145/2451176.2451219","DOIUrl":"https://doi.org/10.1145/2451176.2451219","url":null,"abstract":"Information streams allow social network users to receive and interact with the latest messages from friends and followers. But as our social graphs grow and mature it becomes increasingly difficult to deal with the information overload that these realtime streams introduce. Some social networks, like Facebook, use proprietary interestingness metrics to rank messages in an effort to improve stream relevance and drive engagement. In this paper we evaluate learning to rank approaches to rank content based on a variety of features taken from live-user data.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128327552","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}
{"title":"User interface adaptation based on user feedback and machine learning","authors":"Nesrine Mezhoudi","doi":"10.1145/2451176.2451184","DOIUrl":"https://doi.org/10.1145/2451176.2451184","url":null,"abstract":"With the growing need for intelligent software, exploring the potential of Machine Learning (ML) algorithms for User Interface (UI) adaptation becomes an ultimate requirement. The work reported in this paper aims at enhancing the UI interaction by using a Rule Management Engine (RME) in order to handle a training phase for personalization. This phase is intended to teach to the system novel adaptation strategies based on the end-user feedback concerning his interaction (history, preferences...). The goal is also to ensure an adaptation learning by capitalizing on the user feedbacks via a promoting/demoting technique, and then to employ it later in different levels of the UI development.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127670171","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}
{"title":"Improving rich internet application development using patterns","authors":"J. Mahmud","doi":"10.1145/2451176.2451204","DOIUrl":"https://doi.org/10.1145/2451176.2451204","url":null,"abstract":"With changes of customer requirements, web development, especially developing Rich Internet Applications (RIA) with complex widgets and data-driven behavior can be a time-consuming task. In our previous work [3], we have presented a test-driven web development approach using ClearScript test cases as requirements to automatically generate widgets, and thus reduce the barrier of web development and testing. We extend on this work, and develop a machine learning based algorithm to identify RIA patterns [1] from requirements specified as test cases, and automatically instantiate them using simple rules. We also present performance of our algorithm and a user study which demonstrates the viability of our approach.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116202432","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}