{"title":"I-SED: An Interactive Sound Event Detector","authors":"B. Kim, Bryan Pardo","doi":"10.1145/3025171.3025231","DOIUrl":"https://doi.org/10.1145/3025171.3025231","url":null,"abstract":"Tagging of sound events is essential in many research areas. However, finding sound events and labeling them within a long audio file is tedious and time-consuming. Building an automatic recognition system using machine learning techniques is often not feasible because it requires a large number of human-labeled training examples and fine tuning the model for a specific application. Fully automated labeling is also not reliable enough for all uses. We present I-SED, an interactive sound detection interface using a human-in-the-loop approach that lets a user reduce the time required to label audio that is tediously long (e.g. 20 hours) to do manually and has too few prior labeled examples (e.g. one) to train a state-of-the-art machine audio labeling system. We performed a human-subject study to validate its effectiveness and the results showed that our tool helped participants label all target sound events within a recording twice as fast as labeling them manually.","PeriodicalId":166632,"journal":{"name":"Proceedings of the 22nd International Conference on Intelligent User Interfaces","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114066042","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":"How to Recommend?: User Trust Factors in Movie Recommender Systems","authors":"S. Berkovsky, R. Taib, Dan Conway","doi":"10.1145/3025171.3025209","DOIUrl":"https://doi.org/10.1145/3025171.3025209","url":null,"abstract":"How much trust a user places in a recommender is crucial to the uptake of the recommendations. Although prior work established various factors that build and sustain user trust, their comparative impact has not been studied in depth. This paper presents the results of a crowdsourced study examining the impact of various recommendation interfaces and content selection strategies on user trust. It evaluates the subjective ranking of nine key factors of trust grouped into three dimensions and examines the differences observed with respect to users' personality traits.","PeriodicalId":166632,"journal":{"name":"Proceedings of the 22nd International Conference on Intelligent User Interfaces","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116640482","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":"A Data-Driven, Multidimensional Approach to Hint Design in Video Games","authors":"H. Wauck, W. Fu","doi":"10.1145/3025171.3025224","DOIUrl":"https://doi.org/10.1145/3025171.3025224","url":null,"abstract":"Hint systems are designed to adjust a video game's difficulty to suit the individual player, but too often they are designed without analyzing player behavior and lack intelligence and adaptability, resulting in hints that are at best ineffective and at worst hurt player experience. We present an alternative approach to hint design focusing on player experience rather than performance. We had 25 participants play a difficult spatial puzzle game and collected player behavior, demographics, and self-reported player experience measures. We found that more exploratory behavior improved player experience, so we designed three types of hints encouraging this behavior: adaptive, automatic, and on-demand. We found that certain players found hints more helpful regardless of whether the hints changed their behavior, and players seemed to prefer seeing fewer hints than the adaptive and automatic conditions gave them. Our findings contribute a deeper empirical understanding of hint design strategies and their effect on player behavior and experience, with practical recommendations for designers of interactive systems.","PeriodicalId":166632,"journal":{"name":"Proceedings of the 22nd International Conference on Intelligent User Interfaces","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116945040","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}
L. Micallef, Iiris Sundin, P. Marttinen, Muhammad Ammad-ud-din, Tomi Peltola, Marta Soare, Giulio Jacucci, Samuel Kaski
{"title":"Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets","authors":"L. Micallef, Iiris Sundin, P. Marttinen, Muhammad Ammad-ud-din, Tomi Peltola, Marta Soare, Giulio Jacucci, Samuel Kaski","doi":"10.1145/3025171.3025181","DOIUrl":"https://doi.org/10.1145/3025171.3025181","url":null,"abstract":"Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only available from domain experts. We present a novel approach that uses interactive visualization to elicit the tacit prior knowledge and uses it to improve the accuracy of prediction models. The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task. In particular, based on the expert's earlier input, the user model guides the selection of the features on which to elicit user's knowledge next. The results of a controlled user study show that the user model significantly improves prior knowledge elicitation and prediction accuracy, when predicting the relative citation counts of scientific documents in a specific domain.","PeriodicalId":166632,"journal":{"name":"Proceedings of the 22nd International Conference on Intelligent User Interfaces","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131257815","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}
Thanapong Intharah, Daniyar Turmukhambetov, G. Brostow
{"title":"Help, It Looks Confusing: GUI Task Automation Through Demonstration and Follow-up Questions","authors":"Thanapong Intharah, Daniyar Turmukhambetov, G. Brostow","doi":"10.1145/3025171.3025176","DOIUrl":"https://doi.org/10.1145/3025171.3025176","url":null,"abstract":"Non-programming users should be able to create their own customized scripts to perform computer-based tasks for them, just by demonstrating to the machine how it's done. To that end, we develop a system prototype which learns-by-demonstration called HILC (Help, It Looks Confusing). Users train HILC to synthesize a task script by demonstrating the task, which produces the needed screenshots and their corresponding mouse-keyboard signals. After the demonstration, the user answers follow-up questions. We propose a user-in-the-loop framework that learns to generate scripts of actions performed on visible elements of graphical applications. While pure programming-by-demonstration is still unrealistic, we use quantitative and qualitative experiments to show that non-programming users are willing and effective at answering follow-up queries posed by our system. Our models of events and appearance are surprisingly simple, but are combined effectively to cope with varying amounts of supervision. The best available baseline, Sikuli Slides, struggled with the majority of the tests in our user study experiments. The prototype with our proposed approach successfully helped users accomplish simple linear tasks, complicated tasks (monitoring, looping, and mixed), and tasks that span across multiple executables. Even when both systems could ultimately perform a task, ours was trained and refined by the user in less time.","PeriodicalId":166632,"journal":{"name":"Proceedings of the 22nd International Conference on Intelligent User Interfaces","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116233167","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":"Novelty Learning via Collaborative Proximity Filtering","authors":"Arun Kumar, Paul Schrater","doi":"10.1145/3025171.3025180","DOIUrl":"https://doi.org/10.1145/3025171.3025180","url":null,"abstract":"The vast majority of recommender systems model preferences as static or slowly changing due to observable user experience. However, spontaneous changes in user preferences are ubiquitous in many domains like media consumption and key factors that drive changes in preferences are not directly observable. These latent sources of preference change pose new challenges. When systems do not track and adapt to users' tastes, users lose confidence and trust, increasing the risk of user churn. We meet these challenges by developing a model of novelty preferences that learns and tracks latent user tastes. We combine three innovations: a new measure of item similarity based on patterns of consumption co-occurrence; model for spontaneous changes in preferences; and a learning agent that tracks each user's dynamic preferences and learns individualized policies for variety. The resulting framework adaptively provides users with novelty tailored to their preferences for change per se.","PeriodicalId":166632,"journal":{"name":"Proceedings of the 22nd International Conference on Intelligent User Interfaces","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130775715","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":"Proceedings of the 22nd International Conference on Intelligent User Interfaces","authors":"","doi":"10.1145/3025171","DOIUrl":"https://doi.org/10.1145/3025171","url":null,"abstract":"","PeriodicalId":166632,"journal":{"name":"Proceedings of the 22nd International Conference on Intelligent User Interfaces","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130053215","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}