ERM4HCI '14 Pub Date : 2014-11-16 DOI: 10.1145/2668056.2668057

L. Chen, Su-Youn Yoon, C. W. Leong, Michelle P. Martín‐Raugh, Min Ma

引用次数: 16

A Design Platform for Emotion-Aware User Interfaces 情感感知用户界面的设计平台

ERM4HCI '14 Pub Date : 2014-11-16 DOI: 10.1145/2668056.2668058

Eunjung Lee, Gyu-Wan Kim, Byung-Soo Kim, Mi-Ae Kang

引用次数: 8

Detection of Emotional Events utilizing Support Vector Methods in an Active Learning HCI Scenario 在主动学习HCI场景中使用支持向量方法检测情绪事件

ERM4HCI '14 Pub Date : 2014-11-16 DOI: 10.1145/2668056.2668062

Patrick Thiam, S. Meudt, Markus Kächele, G. Palm, F. Schwenker

{"title":"Detection of Emotional Events utilizing Support Vector Methods in an Active Learning HCI Scenario","authors":"Patrick Thiam, S. Meudt, Markus Kächele, G. Palm, F. Schwenker","doi":"10.1145/2668056.2668062","DOIUrl":"https://doi.org/10.1145/2668056.2668062","url":null,"abstract":"In recent years the fields of affective computing and emotion recognition have experienced a steady increase in attention and especially the creation and analysis of multi-modal corpora has been the focus of intense research. Plausible annotation of this data, however is an enormous problem. In detail emotion annotation is very time consuming, cumbersome and sensitive with respect to the annotator. Furthermore emotional reactions are often very sparse in HCI scenarios resulting in a large annotation overhead to gather the interesting moments of a recording, which in turn are highly relevant for powerful features, classifiers and fusion architectures. Active learning techniques provide methods to improve the annotation processes since the annotator is asked to only label the relevant instances of a given dataset. In this work an unsupervised one-class Support Vector Machine is used to build a background model of non-emotional sequences on a novel HCI dataset. The human annotator is iteratively asked to label instances that are not well explained by the background model, which in turn renders them candidates for being interesting events such as emotional reactions that diverge from the norm. The outcome of the active learning procedure is a reduced dataset of only 14% the size of the original dataset that contains most of the significant information, in this case more than 75% of the emotional events.","PeriodicalId":408721,"journal":{"name":"ERM4HCI '14","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125262166","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: 13

A Model to Incorporate Emotional Sensitivity into Human Computer Interactions 一个将情感敏感性纳入人机交互的模型

ERM4HCI '14 Pub Date : 2014-11-16 DOI: 10.1145/2668056.2668059

S. Ramnani, R. Gorthi

{"title":"A Model to Incorporate Emotional Sensitivity into Human Computer Interactions","authors":"S. Ramnani, R. Gorthi","doi":"10.1145/2668056.2668059","DOIUrl":"https://doi.org/10.1145/2668056.2668059","url":null,"abstract":"Human machine interaction has started receiving much attention in recent years. The 'Link' which plays a major role in interfacing and facilitating human machine interaction, is getting more and more equipped with different modalities (such as natural language processing , intelligent reasoning etc.) so as to shift the cognitive load of understanding from human to machine. This makes machines not only be able to respond in more appropriate and relevant ways but also reduces the human efforts (in making the system understand and act) to the extent possible. This research work contributes to the design and implementation of the interface by presenting a unified model that takes human machine interaction to a advanced level of understanding with an intelligent interface that not only consists of incorporating emotional intelligence into machines but also identifying contextual information, intention recognition (for a given user's textual response) leading to strategic adaptive dialogue management. The motivation for the system proposed is to provide an inexpensive, unobtrusive means for human beings to interact with computer applications in a meaningful way that requires minimal effort. Research work is completely dedicated to provide a baseline mechanism for achieving stress free satisfying interaction between human and machines. Our system is tested against the existing IVR technology and corresponding results are captured.","PeriodicalId":408721,"journal":{"name":"ERM4HCI '14","volume":"30 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127362234","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

A Neural Network Based Approach to Social Touch Classification 基于神经网络的社交触觉分类方法

ERM4HCI '14 Pub Date : 2014-11-16 DOI: 10.1145/2668056.2668060

S. V. Wingerden, Tobias J. Uebbing, Merel M. Jung, M. Poel

引用次数: 15

Emotion Expression and Conversation Assessment in First Acquaintance Dialogues 初次相识对话中的情绪表达与对话评估

ERM4HCI '14 Pub Date : 2014-11-16 DOI: 10.1145/2668056.2668061

Patrizia Paggio

引用次数: 0

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