Evangelia Spiliopoulou, S. Rugaber, Ashok K. Goel, Lianghao Chen, Bryan Wiltgen, A. K. Jagannathan
In Biologically Inspired Design (BID), engineers use biology as a source of ideas for solving engineering problems. However, locating relevant literature is difficult due to vocabulary differences and lack of domain knowledge. IBID is an intelligent search mechanism that uses a functional taxonomy to direct search and a formal modeling notation for annotating relevant search targets.
{"title":"Intelligent Search for Biologically Inspired Design","authors":"Evangelia Spiliopoulou, S. Rugaber, Ashok K. Goel, Lianghao Chen, Bryan Wiltgen, A. K. Jagannathan","doi":"10.1145/2732158.2732182","DOIUrl":"https://doi.org/10.1145/2732158.2732182","url":null,"abstract":"In Biologically Inspired Design (BID), engineers use biology as a source of ideas for solving engineering problems. However, locating relevant literature is difficult due to vocabulary differences and lack of domain knowledge. IBID is an intelligent search mechanism that uses a functional taxonomy to direct search and a formal modeling notation for annotating relevant search targets.","PeriodicalId":177570,"journal":{"name":"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134437811","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}
G. V. D. L. Cruz, Bei Peng, Walter S. Lasecki, Matthew E. Taylor
Reinforcement learning is a powerful machine learning paradigm that allows agents to autonomously learn to maximize a scalar reward. However, it often suffers from poor initial performance and long learning times. This paper discusses how collecting on-line human feedback, both in real time and post hoc, can potentially improve the performance of such learning systems. We use the game Pac-Man to simulate a navigation setting and show that workers are able to accurately identify both when a sub-optimal action is executed, and what action should have been performed instead. Demonstrating that the crowd is capable of generating this input, and discussing the types of errors that occur, serves as a critical first step in designing systems that use this real-time feedback to improve systems' learning performance on-the-fly.
{"title":"Towards Integrating Real-Time Crowd Advice with Reinforcement Learning","authors":"G. V. D. L. Cruz, Bei Peng, Walter S. Lasecki, Matthew E. Taylor","doi":"10.1145/2732158.2732180","DOIUrl":"https://doi.org/10.1145/2732158.2732180","url":null,"abstract":"Reinforcement learning is a powerful machine learning paradigm that allows agents to autonomously learn to maximize a scalar reward. However, it often suffers from poor initial performance and long learning times. This paper discusses how collecting on-line human feedback, both in real time and post hoc, can potentially improve the performance of such learning systems. We use the game Pac-Man to simulate a navigation setting and show that workers are able to accurately identify both when a sub-optimal action is executed, and what action should have been performed instead. Demonstrating that the crowd is capable of generating this input, and discussing the types of errors that occur, serves as a critical first step in designing systems that use this real-time feedback to improve systems' learning performance on-the-fly.","PeriodicalId":177570,"journal":{"name":"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134570941","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 propose WallSHOP, a novel interactive shopping experience that extends content sharing between publicly available digital signage and mobile devices. Multiple users can freely access and browse the content of public digital signage through a public network. Furthermore, users can interact with this content using a personalized cursor that can be controlled with a touch-screen mobile device. WallSHOP also supports pulling content from the digital signage to a user's device, allowing users to browse through available products and privately perform checkouts by utilizing the advantages of both public and private displays. WallSHOP focuses on feasibility and scalability; therefore, it is implemented using only web-based components and does not require the installation of additional software.
{"title":"WallSHOP: Multiuser Interaction with Public Digital Signage using Mobile Devices for Personalized Shopping","authors":"Soh Masuko, Masafumi Muta, Keiji Shinzato, Adiyan Mujibiya","doi":"10.1145/2732158.2732179","DOIUrl":"https://doi.org/10.1145/2732158.2732179","url":null,"abstract":"We propose WallSHOP, a novel interactive shopping experience that extends content sharing between publicly available digital signage and mobile devices. Multiple users can freely access and browse the content of public digital signage through a public network. Furthermore, users can interact with this content using a personalized cursor that can be controlled with a touch-screen mobile device. WallSHOP also supports pulling content from the digital signage to a user's device, allowing users to browse through available products and privately perform checkouts by utilizing the advantages of both public and private displays. WallSHOP focuses on feasibility and scalability; therefore, it is implemented using only web-based components and does not require the installation of additional software.","PeriodicalId":177570,"journal":{"name":"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122230683","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}
Identifying and using the information from distributed and heterogeneous information sources is a challenging task in many application fields. Even with services that offer well-defined structured content, such as digital libraries, it becomes increasingly difficult for a user to find the desired information. To cope with an overloaded information space, we propose a novel approach - VizRec - combining recommender systems (RS) and visualizations. VizRec suggests personalized visual representations for recommended data. One important aspect of our contribution and a prerequisite for VizRec are user preferences that build a personalization model. We present a crowd based evaluation and show how such a model of preferences can be elicited.
{"title":"VizRec: A Two-Stage Recommender System for Personalized Visualizations","authors":"Belgin Mutlu, Eduardo Veas, C. Trattner, V. Sabol","doi":"10.1145/2732158.2732190","DOIUrl":"https://doi.org/10.1145/2732158.2732190","url":null,"abstract":"Identifying and using the information from distributed and heterogeneous information sources is a challenging task in many application fields. Even with services that offer well-defined structured content, such as digital libraries, it becomes increasingly difficult for a user to find the desired information. To cope with an overloaded information space, we propose a novel approach - VizRec - combining recommender systems (RS) and visualizations. VizRec suggests personalized visual representations for recommended data. One important aspect of our contribution and a prerequisite for VizRec are user preferences that build a personalization model. We present a crowd based evaluation and show how such a model of preferences can be elicited.","PeriodicalId":177570,"journal":{"name":"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131474339","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}
Although collaborative activities are paramount in science, little attention has been devoted to supporting on-line scientific collaborations. Our work focuses on scientific collaborations that revolve around complex science questions that require significant coordination to synthesize multi-disciplinary findings, enticing contributors to remain engaged for extended periods of time, and continuous growth to accommodate new contributors as needed as the work evolves over time. This paper presents the interface of the Organic Data Science Wiki to address these challenges. Our solution is based on the Semantic MediaWiki and extends it with new features for scientific collaboration. We present preliminary results from the usage of the interface in a pilot research project.
{"title":"A Task-Centered Interface for On-Line Collaboration in Science","authors":"Felix Michel, Y. Gil, V. Ratnakar, M. Hauder","doi":"10.1145/2732158.2732181","DOIUrl":"https://doi.org/10.1145/2732158.2732181","url":null,"abstract":"Although collaborative activities are paramount in science, little attention has been devoted to supporting on-line scientific collaborations. Our work focuses on scientific collaborations that revolve around complex science questions that require significant coordination to synthesize multi-disciplinary findings, enticing contributors to remain engaged for extended periods of time, and continuous growth to accommodate new contributors as needed as the work evolves over time. This paper presents the interface of the Organic Data Science Wiki to address these challenges. Our solution is based on the Semantic MediaWiki and extends it with new features for scientific collaboration. We present preliminary results from the usage of the interface in a pilot research project.","PeriodicalId":177570,"journal":{"name":"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129603641","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 20th International Conference on Intelligent User Interfaces Companion","authors":"","doi":"10.1145/2732158","DOIUrl":"https://doi.org/10.1145/2732158","url":null,"abstract":"","PeriodicalId":177570,"journal":{"name":"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion","volume":"37 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":"115372984","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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