Towards the Creation of Scalable Tools for automatic Quality of Experience Evaluation and a Multi-Purpose Dataset for Affective Computing

Juan Antonio De Rus Arance, M. Montagud, M. Cobos
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Abstract

Traditional tools used to evaluate the Quality of Experience (QoE) of users after browsing an ad, using a product, or performing any kind of task typically involves surveys, user testing, and analytics. However, these methods provide limited insights and have limitations due to the need of users’ active cooperation and sincerity, the long testing time, the high cost, and the limited scalability. On this work we present the tools we are developing to automatically evaluate QoE in different use cases such as dashboards that show on real time reactions to different events in the form of emotions and affections predicted by different models based on physiological data. To develop these tools, we require datasets on affective computing. We highlight some limitations of the available ones, the difficulties during the creation of such data, and our current work in the confection of a new one with automatic annotation of ground truth.
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面向创建可扩展的自动体验质量评估工具和情感计算的多用途数据集
用于评估用户在浏览广告、使用产品或执行任何任务后的体验质量(QoE)的传统工具通常包括调查、用户测试和分析。但是,由于需要用户的积极配合和诚意,测试时间长,成本高,可扩展性有限,这些方法提供的洞察力有限,并且存在局限性。在这项工作中,我们展示了我们正在开发的工具,用于在不同的用例中自动评估QoE,例如仪表板,它以基于生理数据的不同模型预测的情绪和情感的形式显示对不同事件的实时反应。为了开发这些工具,我们需要情感计算的数据集。我们强调了现有数据的一些局限性,在创建此类数据时的困难,以及我们目前在创建具有自动注释地面事实的新数据方面的工作。
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