2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)最新文献_第5页

Visualization of social emotional appraisal process of an agent 行为人社会情感评价过程的可视化

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-09-28 DOI: 10.1109/aciiw52867.2021.9666329

Motoaki Sato, K. Terada, J. Gratch

引用次数: 0

Event Representation and Semantics Processing System for F-2 Companion Robot F-2同伴机器人事件表示与语义处理系统

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-09-28 DOI: 10.1109/aciiw52867.2021.9666303

A. Kotov, N. Arinkin, Alexander Filatov, L. Zaidelman, A. Zinina, Kirill Kivva

引用次数: 0

AffectGAN: Affect-Based Generative Art Driven by Semantics AffectGAN:由语义驱动的基于情感的生成艺术

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-09-28 DOI: 10.1109/aciiw52867.2021.9666317

Theodoros Galanos, Antonios Liapis, Georgios N. Yannakakis

引用次数: 9

A Therapeutic Dialogue Agent for Polish Language 波兰语治疗对话代理

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-09-28 DOI: 10.1109/aciiw52867.2021.9666281

Artur Zygadło

{"title":"A Therapeutic Dialogue Agent for Polish Language","authors":"Artur Zygadło","doi":"10.1109/aciiw52867.2021.9666281","DOIUrl":"https://doi.org/10.1109/aciiw52867.2021.9666281","url":null,"abstract":"Mental disorders affect large numbers of people worldwide. Computer-aided therapies have been designed and successfully applied in the context of mental health, including solutions based on artificial intelligence (AI). Application of AI in mental disorder therapies frequently has the form of dialogue systems. The long-term goal of this research is to develop a therapeutic chatbot capable of having a conversation in Polish. The initial work was focused on literature review and choosing the software framework for the dialogue system. As a next step, we have prepared a dataset of emotionally grounded dialogue turns in Polish, by applying machine translation to an existing resource for English. The resulting dataset was used for training an emotion recognition model which will be incorporated into the dialogue system. Next, the chatbot will be exposed to a group of test users, and its language understanding capabilities will be further developed. In future, the system will be equipped with a speech interface to also enable verbal communication. We plan to conduct experiments with the help of therapists and patients, followed by thorough analyses of their interactions with the chatbot.","PeriodicalId":105376,"journal":{"name":"2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134110731","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

Individual Differences and the Function of Emotions in Socio-Emotional and Cognitive Conflict: If an Agent Shames you, will you still be Bored? 个体差异和情绪在社会情绪和认知冲突中的作用:如果一个代理人羞辱你，你还会感到无聊吗?

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-09-28 DOI: 10.1109/aciiw52867.2021.9666343

Lara Chehayeb, Dimitra Tsovaltzi, Rhythm Arora, Patrick Gebhard

{"title":"Individual Differences and the Function of Emotions in Socio-Emotional and Cognitive Conflict: If an Agent Shames you, will you still be Bored?","authors":"Lara Chehayeb, Dimitra Tsovaltzi, Rhythm Arora, Patrick Gebhard","doi":"10.1109/aciiw52867.2021.9666343","DOIUrl":"https://doi.org/10.1109/aciiw52867.2021.9666343","url":null,"abstract":"Employers seek to support their employees to increase their well-being and long-term performance. Boredom is considered as a main unpleasant emotional state at the workplace, which could lead to stress, depression and decreased well-being. Boredom is related to the situation at hand and may lead to decreased performance. We frame the situation by connecting socio-emotional and cognitive conflict and individual differences to shed light onto the function of boredom. We look at the relation of boredom to theoretically connected emotions and their regulation. We conducted an online experiment in a virtual workplace setting (N = 57). We induced a socio-emotional and cognitive conflict situation by using a socially interactive agent which shames participants. Individual differences were operationalised through social identity and self-consciousness. We tested how social identity (predictor) and self-consciousness (moderator) influence cognitive appraisal of threat (mediator) and boredom (outcome). The results revealed that self-consciousness moderates the effect of social identity on cognitive appraisal of threat which then mediates the effect of social identity on boredom.","PeriodicalId":105376,"journal":{"name":"2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132467161","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

Socially-Aware Personality Adaptation 社会意识型人格适应

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-09-28 DOI: 10.1109/aciiw52867.2021.9666197

Thomas Kiderle, Hannes Ritschel, Kathrin Janowski, Silvan Mertes, F. Lingenfelser, E. André

引用次数: 3

An Affect as Interaction Approach for Stress Management Among Paramedics 护理人员压力管理的情感互动研究

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-09-28 DOI: 10.1109/aciiw52867.2021.9666402

Akiri Surely

引用次数: 0

Go-Blend Behavior and Affect 混合行为和影响

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-09-24 DOI: 10.1109/aciiw52867.2021.9666376

M. Barthet, Antonios Liapis, Georgios N. Yannakakis

引用次数: 7

AffRankNet+: Ranking Affect Using Privileged Information AffRankNet+:排名影响使用特权信息

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-08-12 DOI: 10.1109/aciiw52867.2021.9666328

Konstantinos Makantasis

{"title":"AffRankNet+: Ranking Affect Using Privileged Information","authors":"Konstantinos Makantasis","doi":"10.1109/aciiw52867.2021.9666328","DOIUrl":"https://doi.org/10.1109/aciiw52867.2021.9666328","url":null,"abstract":"Many of the affect modelling tasks present an asymmetric distribution of information between training and test time; additional information is given about the training data, which is not available at test time. Learning under this setting is called Learning Under Privileged Information (LUPI). At the same time, due to the ordinal nature of affect annotations, formulating affect modelling tasks as supervised learning ranking problems is gaining ground within the Affective Computing research community. Motivated by the two facts above, in this study, we introduce a ranking model that treats additional information about the training data as privileged information to accurately rank affect states. Our ranking model extends the well-known RankNet model to the LUPI paradigm, hence its name Af-fRankNet+. To the best of our knowledge, it is the first time that a ranking model based on neural networks exploits privileged information. We evaluate the performance of the proposed model on the public available Afew-VA dataset and compare it against the RankNet model, which does not use privileged information. Experimental evaluation indicates that the AffRankNet+ model can yield significantly better performance.","PeriodicalId":105376,"journal":{"name":"2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131914935","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: 4

Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition 基于可穿戴设备的深度多模态情感识别的跨模态连接

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2021-08-04 DOI: 10.1109/aciiw52867.2021.9666360

Anubha Bhatti, Behnam Behinaein, D. Rodenburg, P. Hungler, A. Etemad

{"title":"Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition","authors":"Anubha Bhatti, Behnam Behinaein, D. Rodenburg, P. Hungler, A. Etemad","doi":"10.1109/aciiw52867.2021.9666360","DOIUrl":"https://doi.org/10.1109/aciiw52867.2021.9666360","url":null,"abstract":"Classification of human emotions can play an essential role in the design and improvement of human-machine systems. While individual biological signals such as Electrocardiogram (ECG) and Electrodermal Activity (EDA) have been widely used for emotion recognition with machine learning methods, multimodal approaches generally fuse extracted features or final classification/regression results to boost performance. To enhance multimodal learning, we present a novel attentive cross-modal connection to share information between convolutional neural networks responsible for learning individual modalities. Specifically, these connections improve emotion classification by sharing intermediate representations among EDA and ECG and apply attention weights to the shared information, thus learning more effective multimodal embeddings. We perform experiments on the WESAD dataset to identify the best configuration of the proposed method for emotion classification. Our experiments show that the proposed approach is capable of learning strong multimodal representations and outperforms a number of baselines methods.","PeriodicalId":105376,"journal":{"name":"2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133441345","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