Is emotion relevant to requirements engineering?

This viewpoint argues that the introduction of most computer-based system to an organization transforms the organization and changes the work patterns of the system’s users in the organization. These changes interact with the users’ values and beliefs and trigger emotional responses which are sometimes directed against the software system and its proponents. A requirements engineer must be aware of these emotions.

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emotion3D和安森美合作推出创新的驾乘监控系统参考设计

<正>emotion3D和领先于智能电源和智能感知技术的安森美半导体发布一个用于驾乘监控系统 (DOMS)的联合参考设计。这独特的设计将驾驶员和乘员监控结合在一个摄像头中,赋能多个安全功能和更进一步的用户体验。这使汽车OEM厂商能够部署高性能、低成本的下一代座舱成像方案,使驾驶更安全、更愉悦。这款新的 DOMS 方案基于 emotion3D 的 CABIN EYE AI 软件栈和安森美获奖的 830万像素图像传感器AR0820AT,     

Glances,glares, and glowering: how should a virtual human express emotion through gaze?

Gaze is an extremely powerful expressive signal that is used for many purposes, from expressing emotion to regulating human interaction. The use of gaze as a signal has been exploited to strong effect in hand-animated characters, greatly enhancing the believability of the character’s simulated life. However, virtual humans animated in real-time have been less successful at using expressive gaze. One reason for this is that we lack a model of expressive gaze in virtual humans. A gaze shift towards any specific target can be performed in many different ways, using many different expressive manners of gaze, each of which can potentially imply a different emotional or cognitive internal state. However, there is currently no mapping that describes how a user will attribute these internal states to a virtual character performing a gaze shift in a particular manner. In this paper, we begin to address this by providing the results of an empirical study that explores the mapping between an observer’s attribution of emotional state to gaze. The purpose of this mapping is to allow for an interactive virtual human to generate believable gaze shifts that a user will attribute a desired emotional state to. We have generated a set of animations by composing low-level gaze attributes culled from the nonverbal behavior literature. Then, subjects judged the animations displaying these attributes. While the results do not provide a complete mapping between gaze and emotion, they do provide a basis for a generative model of expressive gaze.     

Optimizing emotion–cause pair extraction task by using mutual assistance single-task model,clause position information and semantic features

The traditional emotion–cause extraction task needs to give the exact emotion annotation contained in the document before extracting the cause. Different from this, the emotion–cause pair extraction (ECPE) task, which aims to extract emotion–cause pairs with causal relationships directly from the document, is a task proposed in the natural language processing field recently. At present, the task of ECPE is divided into two steps: emotion annotations and cause clause extraction, emotion–cause clause pair combining and filtering. In this article, we optimize these two steps. On the one hand, in the first step of ECPE, a mutual assistance single-task model proposed by us is used to replace the original multi-task model. On the other hand, the position information of the clause is added as an additional feature in the second step of ECPE. Furthermore, based on different levels of semantic features, we design three filtering models and explore their performance on ECPE tasks. The experimental results on the benchmark corpus show that our approach can make the ECPE task achieve better performance. Compared with the referenced method, F1-score is increased by 5.3%. Moreover, these optimization strategies improve the subtasks contained in ECPE to varying degrees.

     

Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments

The current study examined the relationships between learners’ (\(N = 123\)) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Overall, significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for four agent-directed emotions (enjoyment, pride, boredom, and neutral) though the relationships differed between pedagogical agents. These results demonstrate that some trait emotions and personality traits can be used to predict learners’ emotions directed toward specific pedagogical agents (with different roles). Results provide suggestions for adapting pedagogical agents to support learners’ (with certain characteristics; e.g., high in neuroticism or agreeableness) experience of adaptive emotions (e.g., enjoyment) and minimize their experience on non-adaptive emotions (e.g., boredom). Such an approach presents a scalable and easily implementable method for creating emotionally-adaptive, agent-based learning environments, and improving learner-pedagogical agent interactions in order to support learning.