情感计算即服务(ACaaS)

W. Murphy, Eoghan Furey, Juanita Blue
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引用次数: 1

摘要

情感计算旨在为系统引入更高水平的计算智能,从而能够模拟人类的情感和情绪。如今,在IT解决方案中很少能找到这些增强的计算能力。本文回顾了情感计算和云计算,以软件即服务解决方案的形式展示了通过公共云基础设施托管的组合结果。提出了一种用于情感计算即服务(ACaaS)解决方案的框架,其独特之处是它使用了先前创建的公共云处理服务。然后将该框架转换为包含PHP前端和Python后端的工作实现。该系统能够处理文本、图像和语音输入文件,并从中提取情感信息。然后对结果进行展示和评估,证明在大多数用例中,多模态输入将促进情感计算即服务解决方案,该解决方案将为情感计算目标提供必要的信息。探索可用的云计算技术和情感计算目标的结合,消除了研究人员建立自己模型的需要,从而支持了该领域的研究。该解决方案利用了大型提供商提供的最佳可用尖端技术。因此,训练新模型的需求和相关的开销都大大减少了。
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Affective Computing as a Service (ACaaS)
Affective Computing aims to introduce a higher level of computational intelligence to systems, which enables emulation of human affects and emotions. Today those enhanced computing capabilities are seldom found in IT solutions. This paper reviews both Affective Computing and Cloud Computing, presenting the combined outcome in the form of a Software-as-a-Service solution hosted via a Public Cloud Infrastructure. A framework is proposed for the Affective Computing as a Service (ACaaS) solution with the unique consideration that it uses previously created Public Cloud processing services. The framework is then transformed into a working implementation comprising a PHP front-end and a Python back-end. The system is capable of processing text, image, and voice input files and extracting emotional information from them. The results are then presented and evaluated, demonstrating that in most use cases, the multi-modal inputs will facilitate an Affective Computing as a Service solution which will deliver the necessary information for Affective Computing goals. Exploration of the combination of available cloud computing technologies and Affective Computing goals supports research in the area by removing the need for researchers to build their own models. This solution leverages the best available cutting-edge technologies available from large providers. Thereby, the requirement to train new models and the associated overheads are greatly reduced.
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