We propose methods for extracting facemarks (emoticons) in text and classifying them into some emotional categories. In text-based communication, facemarks have gained popularity, since they help us understand what writers imply. However, there are two problems in text-based communication using facemarks; the first is the variety of facemarks and the second is lack of good comprehension in using facemarks. These problems are more serious in the areas where 2-byte characters are used, because the 2-byte characters can generate a quite large number of different facemarks. Therefore, we are going to propose methods for extraction and classification of facemarks. Regarding the extraction of facemarks as a chunking task, we automatically annotate a tag to each character in text. In the classification of the extracted facemarks, we apply the dynamic time alignment kernel (DTAK) and the string subsequence kernel (SSK) for scoring in the k-nearest neighbor (k-NN) method and for expanding usual Support Vector Machines (SVMs) to accept sequential data such as facemarks. We empirically show that our methods work well in classification and extraction of facemarks, with appropriate settings of parameters.
{"title":"Extraction and classification of facemarks","authors":"Yuki Tanaka, Hiroya Takamura, M. Okumura","doi":"10.1145/1040830.1040847","DOIUrl":"https://doi.org/10.1145/1040830.1040847","url":null,"abstract":"We propose methods for extracting facemarks (emoticons) in text and classifying them into some emotional categories. In text-based communication, facemarks have gained popularity, since they help us understand what writers imply. However, there are two problems in text-based communication using facemarks; the first is the variety of facemarks and the second is lack of good comprehension in using facemarks. These problems are more serious in the areas where 2-byte characters are used, because the 2-byte characters can generate a quite large number of different facemarks. Therefore, we are going to propose methods for extraction and classification of facemarks. Regarding the extraction of facemarks as a chunking task, we automatically annotate a tag to each character in text. In the classification of the extracted facemarks, we apply the dynamic time alignment kernel (DTAK) and the string subsequence kernel (SSK) for scoring in the k-nearest neighbor (k-NN) method and for expanding usual Support Vector Machines (SVMs) to accept sequential data such as facemarks. We empirically show that our methods work well in classification and extraction of facemarks, with appropriate settings of parameters.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134552304","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}
There has been an increasing interest in exploring how recognition of a user's affective state can be exploited in creating more effective human-computer interaction. It has been argued that IUIs may be able to improve interaction by including affective elements in their communication with the user (e.g. by showing empathy via adequate phrasing of feedback.) This workshop will address a variety of issues related to the development of what we will call the affective loop: detection/modeling of relevant user's states, selection of appropriate system responses (including responses that are designed to influence the user affective state but are not overtly affective), as well as synthesis of the appropriate affective expressions.
{"title":"Affective interactions: the computer in the affective loop","authors":"C. Conati, S. Marsella, Ana Paiva","doi":"10.1145/1040830.1040838","DOIUrl":"https://doi.org/10.1145/1040830.1040838","url":null,"abstract":"There has been an increasing interest in exploring how recognition of a user's affective state can be exploited in creating more effective human-computer interaction. It has been argued that IUIs may be able to improve interaction by including affective elements in their communication with the user (e.g. by showing empathy via adequate phrasing of feedback.) This workshop will address a variety of issues related to the development of what we will call the affective loop: detection/modeling of relevant user's states, selection of appropriate system responses (including responses that are designed to influence the user affective state but are not overtly affective), as well as synthesis of the appropriate affective expressions.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122081755","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}
Preference-based search, defined as finding the most preferred item in a large collection, is becoming an increasingly important subject in computer science with many applications: multi-attribute product search, constraint-based plan optimization, configuration design, and recommendation systems. Decision theory formalizes what the most preferred item is and how it can be identified. In recent years, decision theory has pointed out discrepancies between the normative models of how people should reason and empirical studies of how they in fact think and decide. However, many search tools are still based on the normative model, thus ignoring some of the fundamental cognitive aspects of human decision making. Consequently these search tools do not find accurate results for users. This tutorial starts by giving an overview of recent literature in decision theory, and explaining the differences between descriptive, and normative approaches. It then describes some of the principles derived from behavior decision theory and how they can be turned into principles for developing intelligent user interfaces to help users to make better choices while searching. It develops in particular the issues of how to model user preferences with a limited interaction effort, how to support tradeoff, and how to implement practical search tools using the principles.
{"title":"Intelligent interfaces for preference-based search","authors":"P. Pu, B. Faltings","doi":"10.1145/1040830.1040842","DOIUrl":"https://doi.org/10.1145/1040830.1040842","url":null,"abstract":"Preference-based search, defined as finding the most preferred item in a large collection, is becoming an increasingly important subject in computer science with many applications: multi-attribute product search, constraint-based plan optimization, configuration design, and recommendation systems. Decision theory formalizes what the most preferred item is and how it can be identified. In recent years, decision theory has pointed out discrepancies between the normative models of how people should reason and empirical studies of how they in fact think and decide. However, many search tools are still based on the normative model, thus ignoring some of the fundamental cognitive aspects of human decision making. Consequently these search tools do not find accurate results for users. This tutorial starts by giving an overview of recent literature in decision theory, and explaining the differences between descriptive, and normative approaches. It then describes some of the principles derived from behavior decision theory and how they can be turned into principles for developing intelligent user interfaces to help users to make better choices while searching. It develops in particular the issues of how to model user preferences with a limited interaction effort, how to support tradeoff, and how to implement practical search tools using the principles.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129406838","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}
Interactive mobile robots are an active area of research. This paper presents a framework for designing a real-time vision based hand-body gesture user interface for such robots. The said framework works in real world lighting conditions, with complex background, and can handle intermittent motion of the camera. The input signal is captured by using a singular monocular color camera. Vision is the only feedback sensor being used. It is assumed that the gesturer is wearing clothes that are slightly different from the background. We have tested this framework on a gesture database consisting of 11 hand-body gestures and have recorded recognition accuracy up to 90%.
{"title":"Vision based GUI for interactive mobile robots","authors":"Randeep Singh, B. Seth, U. Desai","doi":"10.1145/1040830.1040887","DOIUrl":"https://doi.org/10.1145/1040830.1040887","url":null,"abstract":"Interactive mobile robots are an active area of research. This paper presents a framework for designing a real-time vision based hand-body gesture user interface for such robots. The said framework works in real world lighting conditions, with complex background, and can handle intermittent motion of the camera. The input signal is captured by using a singular monocular color camera. Vision is the only feedback sensor being used. It is assumed that the gesturer is wearing clothes that are slightly different from the background. We have tested this framework on a gesture database consisting of 11 hand-body gestures and have recorded recognition accuracy up to 90%.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130148360","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}
Shimei Pan, Siwei Shen, Michelle X. Zhou, K. Houck
Multimodal conversation systems allow users to interact with computers effectively using multiple modalities, such as natural language and gesture. However, these systems have not been widely used in practical applications mainly due to their limited input understanding capability. As a result, conversation systems often fail to understand user requests and leave users frustrated. To address this issue, most existing approaches focus on improving a system's interpretation capability. Nonetheless, such improvements may still be limited, since they would never cover the entire range of input expressions. Alternatively, we present a two-way adaptation framework that allows both users and systems to dynamically adapt to each other's capability and needs during the course of interaction. Compared to existing methods, our approach offers two unique contributions. First, it improves the usability and robustness of a conversation system by helping users to dynamically learn the system's capabilities in context. Second, our approach enhances the overall interpretation capability of a conversation system by learning new user expressions on the fly. Our preliminary evaluation shows the promise of this approach.
{"title":"Two-way adaptation for robust input interpretation in practical multimodal conversation systems","authors":"Shimei Pan, Siwei Shen, Michelle X. Zhou, K. Houck","doi":"10.1145/1040830.1040849","DOIUrl":"https://doi.org/10.1145/1040830.1040849","url":null,"abstract":"Multimodal conversation systems allow users to interact with computers effectively using multiple modalities, such as natural language and gesture. However, these systems have not been widely used in practical applications mainly due to their limited input understanding capability. As a result, conversation systems often fail to understand user requests and leave users frustrated. To address this issue, most existing approaches focus on improving a system's interpretation capability. Nonetheless, such improvements may still be limited, since they would never cover the entire range of input expressions. Alternatively, we present a two-way adaptation framework that allows both users and systems to dynamically adapt to each other's capability and needs during the course of interaction. Compared to existing methods, our approach offers two unique contributions. First, it improves the usability and robustness of a conversation system by helping users to dynamically learn the system's capabilities in context. Second, our approach enhances the overall interpretation capability of a conversation system by learning new user expressions on the fly. Our preliminary evaluation shows the promise of this approach.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125963192","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}
Pedagogical agent image is a key feature for animated interface agents. Experimental research indicates that agent interface images should be carefully designed, considering both the relevant outcomes (learning or motivational) together with student characteristics. This paper summarizes empirically-derived design guidelines for pedagogical agent image.
{"title":"Preliminary design guidelines for pedagogical agent interface image","authors":"A. L. Baylor","doi":"10.1145/1040830.1040884","DOIUrl":"https://doi.org/10.1145/1040830.1040884","url":null,"abstract":"Pedagogical agent image is a key feature for animated interface agents. Experimental research indicates that agent interface images should be carefully designed, considering both the relevant outcomes (learning or motivational) together with student characteristics. This paper summarizes empirically-derived design guidelines for pedagogical agent image.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125485718","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 order for intelligent systems to be applicable in a wide range of situations, end users must be able to modify their task descriptions. We introduce Tailor, a system that allows users to modify task information through instruction. In this approach, the user enters a short sentence to describe the desired change. The system maps the sentence into valid, plausible modifications and checks for unexpected side-effects they may have, working interactively with the user throughout the process. We conducted preliminary tests in which subjects used Tailor to make modifications to domains drawn from the eHow website, applying modifications posted by readers as 'tips'. In this way the subjects acted as interpreters between Tailor and the human-generated descriptions of modifications. Almost all the subjects were able to make all modifications to the process descriptions with Tailor, indicating that the interpreter role is quite natural for users.
{"title":"Task learning by instruction in tailor","authors":"J. Blythe","doi":"10.1145/1040830.1040874","DOIUrl":"https://doi.org/10.1145/1040830.1040874","url":null,"abstract":"In order for intelligent systems to be applicable in a wide range of situations, end users must be able to modify their task descriptions. We introduce Tailor, a system that allows users to modify task information through instruction. In this approach, the user enters a short sentence to describe the desired change. The system maps the sentence into valid, plausible modifications and checks for unexpected side-effects they may have, working interactively with the user throughout the process. We conducted preliminary tests in which subjects used Tailor to make modifications to domains drawn from the eHow website, applying modifications posted by readers as 'tips'. In this way the subjects acted as interpreters between Tailor and the human-generated descriptions of modifications. Almost all the subjects were able to make all modifications to the process descriptions with Tailor, indicating that the interpreter role is quite natural for users.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124027287","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}
Our goal is to automatically recognize and enroll new vocabulary in a multimodal interface. To accomplish this our technique aims to leverage the mutually disambiguating aspects of co-referenced, co-temporal handwriting and speech. The co-referenced semantics are spatially and temporally determined by our multimodal interface for schedule chart creation. This paper motivates and describes our technique for recognizing out-of-vocabulary (OOV) terms and enrolling them dynamically in the system. We report results for the detection and segmentation of OOV words within a small multimodal test set. On the same test set we also report utterance, word and pronunciation level error rates both over individual input modes and multimodally. We show that combining information from handwriting and speech yields significantly better results than achievable by either mode alone.
{"title":"Multimodal new vocabulary recognition through speech and handwriting in a whiteboard scheduling application","authors":"E. Kaiser","doi":"10.1145/1040830.1040851","DOIUrl":"https://doi.org/10.1145/1040830.1040851","url":null,"abstract":"Our goal is to automatically recognize and enroll new vocabulary in a multimodal interface. To accomplish this our technique aims to leverage the mutually disambiguating aspects of co-referenced, co-temporal handwriting and speech. The co-referenced semantics are spatially and temporally determined by our multimodal interface for schedule chart creation. This paper motivates and describes our technique for recognizing out-of-vocabulary (OOV) terms and enrolling them dynamically in the system. We report results for the detection and segmentation of OOV words within a small multimodal test set. On the same test set we also report utterance, word and pronunciation level error rates both over individual input modes and multimodally. We show that combining information from handwriting and speech yields significantly better results than achievable by either mode alone.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124045698","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}
Ana Gabriela Maguitman, David B. Leake, T. Reichherzer
Much intelligent user interfaces research addresses the problem of providing information relevant to a current user topic. However, little work addresses the complementary question of helping the user identify potential topics to explore next. In knowledge acquisition, this question is crucial to deciding how to extend previously-captured knowledge. This paper examines requirements for effective topic suggestion and presents a domain-independent topic-generation algorithm designed to generate candidate topics that are novel but related to the current context. The algorithm iteratively performs a cycle of topic formation, Web search for connected material, and context-based filtering. An experimental study shows that this approach significantly outperforms a baseline at developing new topics similar to those chosen by an expert for a hand-coded knowledge model.
{"title":"Suggesting novel but related topics: towards context-based support for knowledge model extension","authors":"Ana Gabriela Maguitman, David B. Leake, T. Reichherzer","doi":"10.1145/1040830.1040876","DOIUrl":"https://doi.org/10.1145/1040830.1040876","url":null,"abstract":"Much intelligent user interfaces research addresses the problem of providing information relevant to a current user topic. However, little work addresses the complementary question of helping the user identify potential topics to explore next. In knowledge acquisition, this question is crucial to deciding how to extend previously-captured knowledge. This paper examines requirements for effective topic suggestion and presents a domain-independent topic-generation algorithm designed to generate candidate topics that are novel but related to the current context. The algorithm iteratively performs a cycle of topic formation, Web search for connected material, and context-based filtering. An experimental study shows that this approach significantly outperforms a baseline at developing new topics similar to those chosen by an expert for a hand-coded knowledge model.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126492605","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}
Every program tells a story. Programming, then, is the art of constructing a story about the objects in the program and what they do in various situations. So-called programming languages, while easy for the computer to accurately convert into code, are, unfortunately, difficult for people to write and understand.We explore the idea of using descriptions in a natural language as a representation for programs. While we cannot yet convert arbitrary English to fully specified code, we can use a reasonably expressive subset of English as a visualization tool. Simple descriptions of program objects and their behavior generate scaffolding (underspecified) code fragments, that can be used as feedback for the designer. Roughly speaking, noun phrases can be interpreted as program objects; verbs can be functions, adjectives can be properties. A surprising amount of what we call programmatic semantics can be inferred from linguistic structure. We present a program editor, Metafor, that dynamically converts a user's stories into program code, and in a user study, participants found it useful as a brainstorming tool.
{"title":"Metafor: visualizing stories as code","authors":"Hugo Liu, H. Lieberman","doi":"10.1145/1040830.1040908","DOIUrl":"https://doi.org/10.1145/1040830.1040908","url":null,"abstract":"Every program tells a story. Programming, then, is the art of constructing a story about the objects in the program and what they do in various situations. So-called programming languages, while easy for the computer to accurately convert into code, are, unfortunately, difficult for people to write and understand.We explore the idea of using descriptions in a natural language as a representation for programs. While we cannot yet convert arbitrary English to fully specified code, we can use a reasonably expressive subset of English as a visualization tool. Simple descriptions of program objects and their behavior generate scaffolding (underspecified) code fragments, that can be used as feedback for the designer. Roughly speaking, noun phrases can be interpreted as program objects; verbs can be functions, adjectives can be properties. A surprising amount of what we call programmatic semantics can be inferred from linguistic structure. We present a program editor, Metafor, that dynamically converts a user's stories into program code, and in a user study, participants found it useful as a brainstorming tool.","PeriodicalId":376409,"journal":{"name":"Proceedings of the 10th international conference on Intelligent user interfaces","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132767747","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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