Proceedings of the 2020 International Conference on Multimodal Interaction最新文献_第2页

Multimodal Groups' Analysis for Automated Cohesion Estimation 自动衔接估计中的多模态群分析

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3421153

Lucien Maman

引用次数: 0

Advanced Multi-Instance Learning Method with Multi-features Engineering and Conservative Optimization for Engagement Intensity Prediction 基于多特征工程和保守优化的交战强度预测的高级多实例学习方法

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3417959

Jianming Wu, Bo Yang, Yanan Wang, Gen Hattori

{"title":"Advanced Multi-Instance Learning Method with Multi-features Engineering and Conservative Optimization for Engagement Intensity Prediction","authors":"Jianming Wu, Bo Yang, Yanan Wang, Gen Hattori","doi":"10.1145/3382507.3417959","DOIUrl":"https://doi.org/10.1145/3382507.3417959","url":null,"abstract":"This paper proposes an advanced multi-instance learning method with multi-features engineering and conservative optimization for engagement intensity prediction. It was applied to the EmotiW Challenge 2020 and the results demonstrated the proposed method's good performance. The task is to predict the engagement level when a subject-student is watching an educational video under a range of conditions and in various environments. As engagement intensity has a strong correlation with facial movements, upper-body posture movements and overall environmental movements in a given time interval, we extract and incorporate these motion features into a deep regression model consisting of layers with a combination of long short-term memory(LSTM), gated recurrent unit (GRU) and a fully connected layer. In order to precisely and robustly predict the engagement level in a long video with various situations such as darkness and complex backgrounds, a multi-features engineering function is used to extract synchronized multi-model features in a given period of time by considering both short-term and long-term dependencies. Based on these well-processed engineered multi-features, in the 1st training stage, we train and generate the best models covering all the model configurations to maximize validation accuracy. Furthermore, in the 2nd training stage, to avoid the overfitting problem attributable to the extremely small engagement dataset, we conduct conservative optimization by applying a single Bi-LSTM layer with only 16 units to minimize the overfitting, and split the engagement dataset (train + validation) with 5-fold cross validation (stratified k-fold) to train a conservative model. The proposed method, by using decision-level ensemble for the two training stages' models, finally win the second place in the challenge (MSE: 0.061110 on the testing set).","PeriodicalId":402394,"journal":{"name":"Proceedings of the 2020 International Conference on Multimodal Interaction","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131039134","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: 22

Detecting Depression in Less Than 10 Seconds: Impact of Speaking Time on Depression Detection Sensitivity 在10秒内检测抑郁症:说话时间对抑郁症检测灵敏度的影响

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418875

Nujud Aloshban, A. Esposito, A. Vinciarelli

引用次数: 4

Gesture Enhanced Comprehension of Ambiguous Human-to-Robot Instructions 手势增强对模糊人机指令的理解

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418863

Dulanga Weerakoon, Vigneshwaran Subbaraju, Nipuni Karumpulli, Tuan Tran, Qianli Xu, U-Xuan Tan, Joo-Hwee Lim, Archan Misra

引用次数: 6

Personalised Human Device Interaction through Context aware Augmented Reality 通过上下文感知增强现实的个性化人类设备交互

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3421157

Madhawa Perera

引用次数: 1

SmellControl: The Study of Sense of Agency in Smell 嗅觉控制:嗅觉代理感的研究

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418810

Patricia Ivette Cornelio Martinez, E. Maggioni, Giada Brianza, S. Subramanian, Marianna Obrist

{"title":"SmellControl: The Study of Sense of Agency in Smell","authors":"Patricia Ivette Cornelio Martinez, E. Maggioni, Giada Brianza, S. Subramanian, Marianna Obrist","doi":"10.1145/3382507.3418810","DOIUrl":"https://doi.org/10.1145/3382507.3418810","url":null,"abstract":"The Sense of Agency (SoA) is crucial in interaction with technology, it refers to the feeling of 'I did that' as opposed to 'the system did that' supporting a feeling of being in control. Research in human-computer interaction has recently studied agency in visual, auditory and haptic interfaces, however the role of smell on agency remains unknown. Our sense of smell is quite powerful to elicit emotions, memories and awareness of the environment, which has been exploited to enhance user experiences (e.g., in VR and driving scenarios). In light of increased interest in designing multimodal interfaces including smell and its close link with emotions, we investigated, for the first time, the effect of smell-induced emotions on the SoA. We conducted a study using the Intentional Binding (IB) paradigm used to measure SoA while participants were exposed to three scents with different valence (pleasant, unpleasant, neutral). Our results show that participants? SoA increased with a pleasant scent compared to neutral and unpleasant scents. We discuss how our results can inform the design of multimodal and future olfactory interfaces.","PeriodicalId":402394,"journal":{"name":"Proceedings of the 2020 International Conference on Multimodal Interaction","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132848893","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: 7

Force9: Force-assisted Miniature Keyboard on Smart Wearables Force9:智能可穿戴设备上的力辅助微型键盘

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418827

Lik-Hang Lee, Ngo Yan Yeung, Tristan Braud, Tong Li, Xiang Su, Pan Hui

{"title":"Force9: Force-assisted Miniature Keyboard on Smart Wearables","authors":"Lik-Hang Lee, Ngo Yan Yeung, Tristan Braud, Tong Li, Xiang Su, Pan Hui","doi":"10.1145/3382507.3418827","DOIUrl":"https://doi.org/10.1145/3382507.3418827","url":null,"abstract":"Smartwatches and other wearables are characterized by small-scale touchscreens that complicate the interaction with content. In this paper, we present Force9, the first optimized miniature keyboard leveraging force-sensitive touchscreens on wrist-worn computers. Force9 enables character selection in an ambiguous layout by analyzing the trade-off between interaction space and the easiness of force-assisted interaction. We argue that dividing the screen's pressure range into three contiguous force levels is sufficient to differentiate characters for fast and accurate text input. Our pilot study captures and calibrates the ability of users to perform force-assisted touches on miniature-sized keys on touchscreen devices. We then optimize the keyboard layout considering the goodness of character pairs (with regards to the selected English corpus) under the force-based configuration and the users? familiarity with the QWERTY layout. We finally evaluate the performance of the trimetric optimized Force9 layout, and achieve an average of 10.18 WPM by the end of the final session. Compared to the other state-of-the-art approaches, Force9 allows for single-gesture character selection without addendum sensors.","PeriodicalId":402394,"journal":{"name":"Proceedings of the 2020 International Conference on Multimodal Interaction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126212775","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: 3

Multisensory Approaches to Human-Food Interaction 人与食物相互作用的多感官方法

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3419749

Carlos Velasco, A. Nijholt, C. Spence, Takuji Narumi, Kosuke Motoki, Gijs Huisman, Marianna Obrist

引用次数: 3

Recognizing Emotion in the Wild using Multimodal Data 使用多模态数据识别野外情绪

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3417970

Shivam Srivastava, Saandeep Aathreya Sidhapur Lakshminarayan, Saurabh Hinduja, Sk Rahatul Jannat, Hamza Elhamdadi, Shaun J. Canavan

引用次数: 5

Preserving Privacy in Image-based Emotion Recognition through User Anonymization 通过用户匿名保护基于图像的情感识别中的隐私

Proceedings of the 2020 International Conference on Multimodal Interaction Pub Date : 2020-10-21 DOI: 10.1145/3382507.3418833

Vansh Narula, Kexin Feng, Theodora Chaspari

{"title":"Preserving Privacy in Image-based Emotion Recognition through User Anonymization","authors":"Vansh Narula, Kexin Feng, Theodora Chaspari","doi":"10.1145/3382507.3418833","DOIUrl":"https://doi.org/10.1145/3382507.3418833","url":null,"abstract":"The large amount of data captured by ambulatory sensing devices can afford us insights into longitudinal behavioral patterns, which can be linked to emotional, psychological, and cognitive outcomes. Yet, the sensitivity of behavioral data, which regularly involve speech signals and facial images, can cause strong privacy concerns, such as the leaking of the user identity. We examine the interplay between emotion-specific and user identity-specific information in image-based emotion recognition systems. We further study a user anonymization approach that preserves emotion-specific information, but eliminates user-dependent information from the convolutional kernel of convolutional neural networks (CNN), therefore reducing user re-identification risks. We formulate an adversarial learning problem implemented with a multitask CNN, that minimizes emotion classification and maximizes user identification loss. The proposed system is evaluated on three datasets achieving moderate to high emotion recognition and poor user identity recognition performance. The resulting image transformation obtained by the convolutional layer is visually inspected, attesting to the efficacy of the proposed system in preserving emotion-specific information. Implications from this study can inform the design of privacy-aware emotion recognition systems that preserve facets of human behavior, while concealing the identity of the user, and can be used in ambulatory monitoring applications related to health, well-being, and education.","PeriodicalId":402394,"journal":{"name":"Proceedings of the 2020 International Conference on Multimodal Interaction","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121666569","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