Proceedings of the 24th International Conference on Intelligent User Interfaces最新文献_第3页

Peripheral vision: a new killer app for smart glasses 周边视觉:智能眼镜的新杀手级应用

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302263

Ishan Chaturvedi, Farshid Hassani Bijarbooneh, Tristan Braud, Pan Hui

引用次数: 32

Induction of an active attitude by short speech reaction time toward interaction for decision-making with multiple agents 短言语反应时间对多主体决策互动的积极态度诱导

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302330

Y. Ohmoto, So Kumano, T. Nishida

{"title":"Induction of an active attitude by short speech reaction time toward interaction for decision-making with multiple agents","authors":"Y. Ohmoto, So Kumano, T. Nishida","doi":"10.1145/3301275.3302330","DOIUrl":"https://doi.org/10.1145/3301275.3302330","url":null,"abstract":"An interactive decision-making is useful to put our ambiguous desires into concrete through the interaction with others. However, in human-agent interaction, the agents are often not regarded as well-experienced consultants but rather as human-centered interfaces that provide information. We aimed to induce an active human attitude toward decision-making interactions with agents by controlling the speech reaction time (SRT) of the agents in order to consider the agents as reliable consultants. We conducted an experiment to investigate whether the SRT could influence the human participant's attitude. We used two kinds of agents; one had no SRT (no-SRT) and the other had a SRT of two seconds (2s-SRT). As a result, we found that the no-SRT agents could keep the participants' speech reaction times short even during the decision-making task in which the participants need time for careful consideration. In addition, from the analysis of the number of proposed categories and participant's behavior, we suggest that the participants had an active attitude toward interaction with no-SRT agents.","PeriodicalId":153096,"journal":{"name":"Proceedings of the 24th International Conference on Intelligent User Interfaces","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129515990","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}

引用次数: 0

What data should I protect?: recommender and planning support for data security analysts 我应该保护哪些资料?:为数据安全分析师提供推荐和规划支持

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302294

Tianyi Li, G. Convertino, Ranjeet Kumar Tayi, Shima Kazerooni

{"title":"What data should I protect?: recommender and planning support for data security analysts","authors":"Tianyi Li, G. Convertino, Ranjeet Kumar Tayi, Shima Kazerooni","doi":"10.1145/3301275.3302294","DOIUrl":"https://doi.org/10.1145/3301275.3302294","url":null,"abstract":"Major breaches of sensitive company data, as for Facebook's 50 million user accounts in 2018 or Equifax's 143 million user accounts in 2017, are showing the limitations of reactive data security technologies. Companies and government organizations are turning to proactive data security technologies that secure sensitive data at source. However, data security analysts still face two fundamental challenges in data protection decisions: 1) the information overload from the growing number of data repositories and protection techniques to consider; 2) the optimization of protection plans given the current goals and available resources in the organization. In this work, we propose an intelligent user interface for security analysts that recommends what data to protect, visualizes simulated protection impact, and helps build protection plans. In a domain with limited access to expert users and practices, we elicited user requirements from security analysts in industry and modeled data risks based on architectural and conceptual attributes. Our preliminary evaluation suggests that the design improves the understanding and trust of the recommended protections and helps convert risk information in protection plans.","PeriodicalId":153096,"journal":{"name":"Proceedings of the 24th International Conference on Intelligent User Interfaces","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126125316","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}

引用次数: 14

Prediction of music pairwise preferences from facial expressions 通过面部表情预测音乐配对偏好

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302266

M. Tkalcic, Nima Maleki, Matevž Pesek, Mehdi Elahi, F. Ricci, M. Marolt

{"title":"Prediction of music pairwise preferences from facial expressions","authors":"M. Tkalcic, Nima Maleki, Matevž Pesek, Mehdi Elahi, F. Ricci, M. Marolt","doi":"10.1145/3301275.3302266","DOIUrl":"https://doi.org/10.1145/3301275.3302266","url":null,"abstract":"Users of a recommender system may be requested to express their preferences about items either with evaluations of items (e.g. a rating) or with comparisons of item pairs. In this work we focus on the acquisition of pairwise preferences in the music domain. Asking the user to explicitly compare music, i.e., which, among two listened tracks, is preferred, requires some user effort. We have therefore developed a novel approach for automatically extracting these preferences from the analysis of the facial expressions of the users while listening to the compared tracks. We have trained a predictor that infers user's pairwise preferences by using features extracted from these data. We show that the predictor performs better than a commonly used baseline, which leverages the user's listening duration of the tracks to infer pairwise preferences. Furthermore, we show that there are differences in the accuracy of the proposed method between users with different personalities and we have therefore adapted the trained model accordingly. Our work shows that by introducing a low user effort preference elicitation approach, which, however, requires to access information that may raise potential privacy issues (face expression), one can obtain good prediction accuracy of pairwise music preferences.","PeriodicalId":153096,"journal":{"name":"Proceedings of the 24th International Conference on Intelligent User Interfaces","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123775954","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}

引用次数: 15

I can do better than your AI: expertise and explanations 我可以比你的人工智能做得更好:专业知识和解释

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302308

J. Schaffer, J. O'Donovan, James R. Michaelis, A. Raglin, Tobias Höllerer

{"title":"I can do better than your AI: expertise and explanations","authors":"J. Schaffer, J. O'Donovan, James R. Michaelis, A. Raglin, Tobias Höllerer","doi":"10.1145/3301275.3302308","DOIUrl":"https://doi.org/10.1145/3301275.3302308","url":null,"abstract":"Intelligent assistants, such as navigation, recommender, and expert systems, are most helpful in situations where users lack domain knowledge. Despite this, recent research in cognitive psychology has revealed that lower-skilled individuals may maintain a sense of illusory superiority, which might suggest that users with the highest need for advice may be the least likely to defer judgment. Explanation interfaces - a method for persuading users to take a system's advice - are thought by many to be the solution for instilling trust, but do their effects hold for self-assured users? To address this knowledge gap, we conducted a quantitative study (N=529) wherein participants played a binary decision-making game with help from an intelligent assistant. Participants were profiled in terms of both actual (measured) expertise and reported familiarity with the task concept. The presence of explanations, level of automation, and number of errors made by the intelligent assistant were manipulated while observing changes in user acceptance of advice. An analysis of cognitive metrics lead to three findings for research in intelligent assistants: 1) higher reported familiarity with the task simultaneously predicted more reported trust but less adherence, 2) explanations only swayed people who reported very low task familiarity, and 3) showing explanations to people who reported more task familiarity led to automation bias.","PeriodicalId":153096,"journal":{"name":"Proceedings of the 24th International Conference on Intelligent User Interfaces","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130077581","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}

引用次数: 75

Assisting group activity analysis through hand detection and identification in multiple egocentric videos 在多个以自我为中心的视频中，通过手部检测和识别来协助团队活动分析

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302297

Nathawan Charoenkulvanich, Rie Kamikubo, Ryo Yonetani, Yoichi Sato

引用次数: 2

Progressive disclosure: empirically motivated approaches to designing effective transparency 渐进式披露:设计有效透明度的经验激励方法

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302322

Aaron Springer, S. Whittaker

引用次数: 62

The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images 解释和算法精度对艺术图像视觉推荐系统的影响

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302274

Vicente Dominguez, Pablo Messina, Ivania Donoso-Guzmán, Denis Parra

{"title":"The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images","authors":"Vicente Dominguez, Pablo Messina, Ivania Donoso-Guzmán, Denis Parra","doi":"10.1145/3301275.3302274","DOIUrl":"https://doi.org/10.1145/3301275.3302274","url":null,"abstract":"There are very few works about explaining content-based recommendations of images in the artistic domain. Current works do not provide a perspective of the many variables involved in the user perception of several aspects of the system such as domain knowledge, relevance, explainability, and trust. In this paper, we aim to fill this gap by studying three interfaces, with different levels of explainability, for artistic image recommendation. Our experiments with N=121 users confirm that explanations of recommendations in the image domain are useful and increase user satisfaction, perception of explainability and relevance. Furthermore, our results show that the observed effects are also dependent on the underlying recommendation algorithm used. We tested two algorithms: Deep Neural Networks (DNN), which has high accuracy, and Attractiveness Visual Features (AVF) with high transparency but lower accuracy. Our results indicate that algorithms should not be studied in isolation, but rather in conjunction with interfaces, since both play a significant role in the perception of explainability and trust for image recommendation. Finally, using the framework by Knijnenburg et al., we provide a comprehensive model which synthesizes the effects between different variables involved in the user experience with explainable visual recommender systems of artistic images.","PeriodicalId":153096,"journal":{"name":"Proceedings of the 24th International Conference on Intelligent User Interfaces","volume":"54 4-5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114059059","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}

引用次数: 37

Do I trust my machine teammate?: an investigation from perception to decision 我信任我的机器队友吗?从感知到决定的调查

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302277

Kun Yu, S. Berkovsky, R. Taib, Jianlong Zhou, Fang Chen

引用次数: 49

Popup 弹出

Proceedings of the 24th International Conference on Intelligent User Interfaces Pub Date : 2019-03-17 DOI: 10.1145/3301275.3302305

Jean Y. Song, Stephan J. Lemmer, M. Liu, Shiyan Yan, Juho Kim, Jason J. Corso, Walter S. Lasecki

{"title":"Popup","authors":"Jean Y. Song, Stephan J. Lemmer, M. Liu, Shiyan Yan, Juho Kim, Jason J. Corso, Walter S. Lasecki","doi":"10.1145/3301275.3302305","DOIUrl":"https://doi.org/10.1145/3301275.3302305","url":null,"abstract":"Collecting a sufficient amount of 3D training data for autonomous vehicles to handle rare, but critical, traffic events (e.g., collisions) may take decades of deployment. Abundant video data of such events from municipal traffic cameras and video sharing sites (e.g., YouTube) could provide a potential alternative, but generating realistic training data in the form of 3D video reconstructions is a challenging task beyond the current capabilities of computer vision. Crowdsourcing the annotation of necessary information could bridge this gap, but the level of accuracy required to obtain usable reconstructions makes this task nearly impossible for non-experts. In this paper, we propose a novel hybrid intelligence method that combines annotations from workers viewing different instances (video frames) of the same target (3D object), and uses particle filtering to aggregate responses. Our approach can leveraging temporal dependencies between video frames, enabling higher quality through more aggressive filtering. The proposed method results in a 33% reduction in the relative error of position estimation compared to a state-of-the-art baseline. Moreover, our method enables skipping (self-filtering) challenging annotations, reducing the total annotation time for hard-to-annotate frames by 16%. Our approach provides a generalizable means of aggregating more accurate crowd responses in settings where annotation is especially challenging or error-prone.","PeriodicalId":153096,"journal":{"name":"Proceedings of the 24th International Conference on Intelligent User Interfaces","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121485513","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}

引用次数: 18