Proceedings of the 2015 ACM on International Conference on Multimodal Interaction最新文献_第10页

Spoken Interruptions Signal Productive Problem Solving and Domain Expertise in Mathematics 口头中断信号生产问题解决和领域专业知识的数学

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2820743

S. Oviatt, Kevin Hang, Jianlong Zhou, Fang Chen

引用次数: 14

Gaze+Gesture: Expressive, Precise and Targeted Free-Space Interactions 凝视+手势:富有表现力的、精确的、有针对性的自由空间互动

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2820752

Ishan Chatterjee, R. Xiao, Chris Harrison

引用次数: 113

Interactive Web-based Image Sonification for the Blind 盲人交互式网络图像超声

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2823298

T. Wörtwein, Boris Schauerte, Karin Müller, R. Stiefelhagen

引用次数: 6

A Visual Analytics Approach to Finding Factors Improving Automatic Speaker Identifications 寻找提高自动说话人识别因素的可视化分析方法

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2820769

P. Bruneau, M. Stefas, H. Bredin, Johann Poignant, T. Tamisier, C. Barras

引用次数: 1

Presentation Trainer, your Public Speaking Multimodal Coach 演讲培训师，你的公共演讲多模式教练

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2830603

J. Schneider, D. Börner, P. V. Rosmalen, M. Specht

引用次数: 73

Exploring Behavior Representation for Learning Analytics 探索学习分析的行为表示

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2820737

M. Worsley, Stefan Scherer, Louis-Philippe Morency, Paulo Blikstein

{"title":"Exploring Behavior Representation for Learning Analytics","authors":"M. Worsley, Stefan Scherer, Louis-Philippe Morency, Paulo Blikstein","doi":"10.1145/2818346.2820737","DOIUrl":"https://doi.org/10.1145/2818346.2820737","url":null,"abstract":"Multimodal analysis has long been an integral part of studying learning. Historically multimodal analyses of learning have been extremely laborious and time intensive. However, researchers have recently been exploring ways to use multimodal computational analysis in the service of studying how people learn in complex learning environments. In an effort to advance this research agenda, we present a comparative analysis of four different data segmentation techniques. In particular, we propose affect- and pose-based data segmentation, as alternatives to human-based segmentation, and fixed-window segmentation. In a study of ten dyads working on an open-ended engineering design task, we find that affect- and pose-based segmentation are more effective, than traditional approaches, for drawing correlations between learning-relevant constructs, and multimodal behaviors. We also find that pose-based segmentation outperforms the two more traditional segmentation strategies for predicting student success on the hands-on task. In this paper we discuss the algorithms used, our results, and the implications that this work may have in non-education-related contexts.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88539425","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: 16

Adjacent Vehicle Collision Warning System using Image Sensor and Inertial Measurement Unit 基于图像传感器和惯性测量单元的相邻车辆碰撞预警系统

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2820741

Asif Iqbal, C. Busso, N. Gans

{"title":"Adjacent Vehicle Collision Warning System using Image Sensor and Inertial Measurement Unit","authors":"Asif Iqbal, C. Busso, N. Gans","doi":"10.1145/2818346.2820741","DOIUrl":"https://doi.org/10.1145/2818346.2820741","url":null,"abstract":"Advanced driver assistance systems are the newest addition to vehicular technology. Such systems use a wide array of sensors to provide a superior driving experience. Vehicle safety and driver alert are important parts of these system. This paper proposes a driver alert system to prevent and mitigate adjacent vehicle collisions by proving warning information of on-road vehicles and possible collisions. A dynamic Bayesian network (DBN) is utilized to fuse multiple sensors to provide driver awareness. It detects oncoming adjacent vehicles and gathers ego vehicle motion characteristics using an on-board camera and inertial measurement unit (IMU). A histogram of oriented gradient feature based classifier is used to detect any adjacent vehicles. Vehicles front-rear end and side faces were considered in training the classifier. Ego vehicles heading, speed and acceleration are captured from the IMU and feed into the DBN. The network parameters were learned from data via expectation maximization(EM) algorithm. The DBN is designed to provide two type of warning to the driver, a cautionary warning and a brake alert for possible collision with other vehicles. Experiments were completed on multiple public databases, demonstrating successful warnings and brake alerts in most situations.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91189600","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: 5

Digital Flavor: Towards Digitally Simulating Virtual Flavors 数字风味:迈向数字模拟虚拟风味

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2820761

Nimesha Ranasinghe, Gajan Suthokumar, Kuan-Yi Lee, E. Do

引用次数: 26

Combining Two Perspectives on Classifying Multimodal Data for Recognizing Speaker Traits 基于多模态数据分类的说话人特征识别

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2820747

Moitreya Chatterjee, Sunghyun Park, Louis-Philippe Morency, Stefan Scherer

{"title":"Combining Two Perspectives on Classifying Multimodal Data for Recognizing Speaker Traits","authors":"Moitreya Chatterjee, Sunghyun Park, Louis-Philippe Morency, Stefan Scherer","doi":"10.1145/2818346.2820747","DOIUrl":"https://doi.org/10.1145/2818346.2820747","url":null,"abstract":"Human communication involves conveying messages both through verbal and non-verbal channels (facial expression, gestures, prosody, etc.). Nonetheless, the task of learning these patterns for a computer by combining cues from multiple modalities is challenging because it requires effective representation of the signals and also taking into consideration the complex interactions between them. From the machine learning perspective this presents a two-fold challenge: a) Modeling the intermodal variations and dependencies; b) Representing the data using an apt number of features, such that the necessary patterns are captured but at the same time allaying concerns such as over-fitting. In this work we attempt to address these aspects of multimodal recognition, in the context of recognizing two essential speaker traits, namely passion and credibility of online movie reviewers. We propose a novel ensemble classification approach that combines two different perspectives on classifying multimodal data. Each of these perspectives attempts to independently address the two-fold challenge. In the first, we combine the features from multiple modalities but assume inter-modality conditional independence. In the other one, we explicitly capture the correlation between the modalities but in a space of few dimensions and explore a novel clustering based kernel similarity approach for recognition. Additionally, this work investigates a recent technique for encoding text data that captures semantic similarity of verbal content and preserves word-ordering. The experimental results on a recent public dataset shows significant improvement of our approach over multiple baselines. Finally, we also analyze the most discriminative elements of a speaker's non-verbal behavior that contribute to his/her perceived credibility/passionateness.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74390640","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: 12

Model of Personality-Based, Nonverbal Behavior in Affective Virtual Humanoid Character 情感虚拟类人角色中基于人格的非语言行为模型

Proceedings of the 2015 ACM on International Conference on Multimodal Interaction Pub Date : 2015-11-09 DOI: 10.1145/2818346.2823296

M. Saberi, Ulysses Bernardet, S. DiPaola

引用次数: 6