Proceedings of the 2015 ACM on International Conference on Multimodal Interaction最新文献

{"title":"Connections: 2015 ICMI Sustained Accomplishment Award Lecture","authors":"E. Horvitz","doi":"10.1145/2818346.2835500","DOIUrl":"https://doi.org/10.1145/2818346.2835500","url":null,"abstract":"Our community has long pursued principles and methods for enabling fluid and effortless collaborations between people and computing systems. Forging deep connections between people and machines has come into focus over the last 25 years as a grand challenge at the intersection of artificial intelligence, human-computer interaction, and cognitive psychology. I will review experiences and directions with leveraging advances in perception, learning, and reasoning in pursuit of our shared dreams.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89114812","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":"Personality Trait Classification via Co-Occurrent Multiparty Multimodal Event Discovery","authors":"S. Okada, O. Aran, D. Gática-Pérez","doi":"10.1145/2818346.2820757","DOIUrl":"https://doi.org/10.1145/2818346.2820757","url":null,"abstract":"This paper proposes a novel feature extraction framework from mutli-party multimodal conversation for inference of personality traits and emergent leadership. The proposed framework represents multi modal features as the combination of each participant's nonverbal activity and group activity. This feature representation enables to compare the nonverbal patterns extracted from the participants of different groups in a metric space. It captures how the target member outputs nonverbal behavior observed in a group (e.g. the member speaks while all members move their body), and can be available for any kind of multiparty conversation task. Frequent co-occurrent events are discovered using graph clustering from multimodal sequences. The proposed framework is applied for the ELEA corpus which is an audio visual dataset collected from group meetings. We evaluate the framework for binary classification task of 10 personality traits. Experimental results show that the model trained with co-occurrence features obtained higher accuracy than previously related work in 8 out of 10 traits. In addition, the co-occurrence features improve the accuracy from 2 % up to 17 %.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87982317","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":"ECA Control using a Single Affective User Dimension","authors":"Fred Charles, Florian Pecune, Gabor Aranyi, C. Pelachaud, M. Cavazza","doi":"10.1145/2818346.2820730","DOIUrl":"https://doi.org/10.1145/2818346.2820730","url":null,"abstract":"User interaction with Embodied Conversational Agents (ECA) should involve a significant affective component to achieve realism in communication. This aspect has been studied through different frameworks describing the relationship between user and ECA, for instance alignment, rapport and empathy. We conducted an experiment to explore how an ECA's non-verbal expression can be controlled to respond to a single affective dimension generated by users as input. Our system is based on the mapping of a high-level affective dimension, approach/avoidance, onto a new ECA control mechanism in which Action Units (AU) are activated through a neural network. Since 'approach' has been associated to prefrontal cortex activation, we use a measure of prefrontal cortex left-asymmetry through fNIRS as a single input signal representing the user's attitude towards the ECA. We carried out the experiment with 10 subjects, who have been instructed to express a positive mental attitude towards the ECA. In return, the ECA facial expression would reflect the perceived attitude under a neurofeedback paradigm. Our results suggest that users are able to successfully interact with the ECA and perceive its response as consistent and realistic, both in terms of ECA responsiveness and in terms of relevance of facial expressions. From a system perspective, the empirical calibration of the network supports a progressive recruitment of various AUs, which provides a principled description of the ECA response and its intensity. Our findings suggest that complex ECA facial expressions can be successfully aligned with one high-level affective dimension. Furthermore, this use of a single dimension as input could support experiments in the fine-tuning of AU activation or their personalization to user preferred modalities.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87401564","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":"Image based Static Facial Expression Recognition with Multiple Deep Network Learning","authors":"Zhiding Yu, Cha Zhang","doi":"10.1145/2818346.2830595","DOIUrl":"https://doi.org/10.1145/2818346.2830595","url":null,"abstract":"We report our image based static facial expression recognition method for the Emotion Recognition in the Wild Challenge (EmotiW) 2015. We focus on the sub-challenge of the SFEW 2.0 dataset, where one seeks to automatically classify a set of static images into 7 basic emotions. The proposed method contains a face detection module based on the ensemble of three state-of-the-art face detectors, followed by a classification module with the ensemble of multiple deep convolutional neural networks (CNN). Each CNN model is initialized randomly and pre-trained on a larger dataset provided by the Facial Expression Recognition (FER) Challenge 2013. The pre-trained models are then fine-tuned on the training set of SFEW 2.0. To combine multiple CNN models, we present two schemes for learning the ensemble weights of the network responses: by minimizing the log likelihood loss, and by minimizing the hinge loss. Our proposed method generates state-of-the-art result on the FER dataset. It also achieves 55.96% and 61.29% respectively on the validation and test set of SFEW 2.0, surpassing the challenge baseline of 35.96% and 39.13% with significant gains.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82912237","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":"Multimodal Interaction with a Bifocal View on Mobile Devices","authors":"S. Pelurson, L. Nigay","doi":"10.1145/2818346.2820731","DOIUrl":"https://doi.org/10.1145/2818346.2820731","url":null,"abstract":"On a mobile device, the intuitive Focus+Context layout of a detailed view (focus) and perspective/distorted panels on either side (context) is particularly suitable for maximizing the utilization of the limited available display area. Interacting with such a bifocal view requires both fast access to data in the context view and high precision interaction with data in the detailed focus view. We introduce combined modalities that solve this problem by combining the well-known flick-drag gesture-based precise modality with modalities for fast access to data in the context view. The modalities for fast access to data in the context view include direct touch in the context view as well as navigation based on drag gestures, on tilting the device, on side-pressure inputs or by spatially moving the device (dynamic peephole). Results of a comparison experiment of the combined modalities show that the performance can be analyzed according to a 3-phase model of the task: a focus-targeting phase, a transition phase (modality switch) and a cursor-pointing phase. Moreover modalities of the focus-targeting phase based on a discrete mode of navigation control (direct access, pressure sensors as discrete navigation controller) require a long transition phase: this is mainly due to disorientation induced by the loss of control in movements. This effect is significantly more pronounced than the articulatory time for changing the position of the fingers between the two modalities (\"homing\" time).","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"55 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83334731","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":"Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning","authors":"Hongwei Ng, Viet Dung Nguyen, Vassilios Vonikakis, Stefan Winkler","doi":"10.1145/2818346.2830593","DOIUrl":"https://doi.org/10.1145/2818346.2830593","url":null,"abstract":"This paper presents the techniques employed in our team's submissions to the 2015 Emotion Recognition in the Wild contest, for the sub-challenge of Static Facial Expression Recognition in the Wild. The objective of this sub-challenge is to classify the emotions expressed by the primary human subject in static images extracted from movies. We follow a transfer learning approach for deep Convolutional Neural Network (CNN) architectures. Starting from a network pre-trained on the generic ImageNet dataset, we perform supervised fine-tuning on the network in a two-stage process, first on datasets relevant to facial expressions, followed by the contest's dataset. Experimental results show that this cascading fine-tuning approach achieves better results, compared to a single stage fine-tuning with the combined datasets. Our best submission exhibited an overall accuracy of 48.5% in the validation set and 55.6% in the test set, which compares favorably to the respective 35.96% and 39.13% of the challenge baseline.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88925689","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":"Providing Real-time Feedback for Student Teachers in a Virtual Rehearsal Environment","authors":"R. Barmaki, C. Hughes","doi":"10.1145/2818346.2830604","DOIUrl":"https://doi.org/10.1145/2818346.2830604","url":null,"abstract":"Research in learning analytics and educational data mining has recently become prominent in the fields of computer science and education. Most scholars in the field emphasize student learning and student data analytics; however, it is also important to focus on teaching analytics and teacher preparation because of their key roles in student learning, especially in K-12 learning environments. Nonverbal communication strategies play an important role in successful interpersonal communication of teachers with their students. In order to assist novice or practicing teachers with exhibiting open and affirmative nonverbal cues in their classrooms, we have designed a multimodal teaching platform with provisions for online feedback. We used an interactive teaching rehearsal software, TeachLivE, as our basic research environment. TeachLivE employs a digital puppetry paradigm as its core technology. Individuals walk into this virtual environment and interact with virtual students displayed on a large screen. They can practice classroom management, pedagogy and content delivery skills with a teaching plan in the TeachLivE environment. We have designed an experiment to evaluate the impact of an online nonverbal feedback application. In this experiment, different types of multimodal data have been collected during two experimental settings. These data include talk-time and nonverbal behaviors of the virtual students, captured in log files; talk time and full body tracking data of the participant; and video recording of the virtual classroom with the participant. 34 student teachers participated in this 30-minute experiment. In each of the settings, the participants were provided with teaching plans from which they taught. All the participants took part in both of the experimental settings. In order to have a balanced experiment design, half of the participants received nonverbal online feedback in their first session and the other half received this feedback in the second session. A visual indication was used for feedback each time the participant exhibited a closed, defensive posture. Based on recorded full-body tracking data, we observed that only those who received feedback in their first session demonstrated a significant number of open postures in the session containing no feedback. However, the post-questionnaire information indicated that all participants were more mindful of their body postures while teaching after they had participated in the study.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76959158","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":"Multimodal Selfies: Designing a Multimodal Recording Device for Students in Traditional Classrooms","authors":"Federico Domínguez, K. Chiluiza, Vanessa Echeverría, X. Ochoa","doi":"10.1145/2818346.2830606","DOIUrl":"https://doi.org/10.1145/2818346.2830606","url":null,"abstract":"The traditional recording of student interaction in classrooms has raised privacy concerns in both students and academics. However, the same students are happy to share their daily lives through social media. Perception of data ownership is the key factor in this paradox. This article proposes the design of a personal Multimodal Recording Device (MRD) that could capture the actions of its owner during lectures. The MRD would be able to capture close-range video, audio, writing, and other environmental signals. Differently from traditional centralized recording systems, students would have control over their own recorded data. They could decide to share their information in exchange of access to the recordings of the instructor, notes form their classmates, and analysis of, for example, their attention performance. By sharing their data, students participate in the co-creation of enhanced and synchronized course notes that will benefit all the participating students. This work presents details about how such a device could be build from available components. This work also discusses and evaluates the design of such device, including its foreseeable costs, scalability, flexibility, intrusiveness and recording quality.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75843857","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 Computational Model of Culture-Specific Emotion Detection for Artificial Agents in the Learning Domain","authors":"Ganapreeta Renunathan Naidu","doi":"10.1145/2818346.2823307","DOIUrl":"https://doi.org/10.1145/2818346.2823307","url":null,"abstract":"Nowadays, intelligent agents are expected to be affect-sensitive as agents are becoming essential entities that supports computer-mediated tasks, especially in teaching and training. These agents use common natural modalities-such as facial expressions, gestures and eye gaze in order to recognize a user's affective state and respond accordingly. However, these nonverbal cues may not be universal as emotion recognition and expression differ from culture to culture. It is important that intelligent interfaces are equipped with the abilities to meet the challenge of cultural diversity to facilitate human-machine interaction particularly in Asia. Asians are known to be more passive and possess certain traits such as indirectness and non-confrontationalism, which lead to emotions such as (culture-specific form of) shyness and timidity. Therefore, a model based on other culture may not be applicable in an Asian setting, out-rulling a one-size-fits-all approach. This study is initiated to identify the discriminative markers of culture-specific emotions based on the multimodal interactions.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75694221","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 Multimodal System for Real-Time Action Instruction in Motor Skill Learning","authors":"I. D. Kok, J. Hough, Felix Hülsmann, M. Botsch, David Schlangen, S. Kopp","doi":"10.1145/2818346.2820746","DOIUrl":"https://doi.org/10.1145/2818346.2820746","url":null,"abstract":"We present a multimodal coaching system that supports online motor skill learning. In this domain, closed-loop interaction between the movements of the user and the action instructions by the system is an essential requirement. To achieve this, the actions of the user need to be measured and evaluated and the system must be able to give corrective instructions on the ongoing performance. Timely delivery of these instructions, particularly during execution of the motor skill by the user, is thus of the highest importance. Based on the results of an empirical study on motor skill coaching, we analyze the requirements for an interactive coaching system and present an architecture that combines motion analysis, dialogue management, and virtual human animation in a motion tracking and 3D virtual reality hardware setup. In a preliminary study we demonstrate that the current system is capable of delivering the closed-loop interaction that is required in the motor skill learning domain.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90107278","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}