What can we learn from the classics of Finnish literature by using
computational emotion analysis? This article tries to answer this question by
examining how computational methods of sentiment analysis can be used in the
study of literary works in conjunction with a qualitative or more 'traditional'
approach to literature and affect. We present and develop a simple but robust
computational approach of affect analysis that uses a carefully curated emotion
lexicon adapted to Finnish turn-of-the-century literary texts combined with
word embeddings to map out the semantic emotional spaces of seminal works of
Finnish literature. We focus our qualitative analysis on selected case studies:
four works by Juhani Aho, Minna Canth, Maria Jotuni, and F. E. Sillanp\"a\"a,
but provide emotion arcs for a total of 975 Finnish novels.
We argue that a computational analysis of a text's lexicon can be valuable in
evaluating the large distribution of the emotional valence in a text and
provide guidelines to help other researchers replicate our findings. We show
that computational approaches have a place in traditional studies on affect in
literature as a support tool for close-reading-based analyses, but also
allowing for large-scale comparison between, for example, genres or national
canons.

这篇文章通过使用情感分析的计算方法，研究了如何结合定性或更传统的文学研究方法，从芬兰文学经典中汲取知识。我们提出并发展了一种简单但强大的情感分析的计算方法，使用经过精心筛选的适应芬兰 20 世纪之交文学文本的情感词典与词嵌入相结合，来绘制芬兰文学重要作品的语义情感空间。我们的定性分析主要集中在几个案例研究中：Juhani Aho、Minna Canth、Maria Jotuni 和 F. E. Sillanp"a"a 的几部作品，但总共提供了 975 部芬兰小说的情感弧线。我们认为计算分析一个文本的词汇可以帮助评估文本中情感价值的大分布，并提供指导帮助其他研究人员重现我们的发现。我们展示了计算方法在传统情感研究中的地位，作为一种支持工具来进行基于仔细阅读的分析，同时也允许进行大规模的比较，例如跨流派或国家的经典。

结合定性和计算方法进行芬兰小说的文学分析

Combining Qualitative and Computational Approaches for Literary Analysis  of Finnish Novels

Emotion and a broader range of affective driver states can be a life decisive
factor on the road. While this aspect has been investigated repeatedly, the
advent of autonomous automobiles puts a new perspective on the role of
computer-based emotion recognition in the car -- the passenger's one. This
includes amongst others the monitoring of wellbeing during the commute such as
to adjust the driving style or to adapt the info- and entertainment. This
contribution aims to foresee according challenges and provide potential avenues
towards affect modelling in a multimodal "audiovisual plus x" on the road
context. From the technical end, this concerns holistic passenger modelling and
reliable diarisation of the individuals in a vehicle. In conclusion, automated
affect analysis has just matured to the point of applicability in autonomous
vehicles in first selected use-cases, which will be discussed towards the end.

该研究探讨了在自主驾驶汽车中进行情感分析、乘客建模和情感分析以及在特定案例中将自主驾驶汽车的情感分析应用实现的挑战和潜在途径。

音视频情感评估与自主汽车：应用

Audiovisual Affect Assessment and Autonomous Automobiles: Applications

Over the past few years many research efforts have been devoted to the field
of affect analysis. Various approaches have been proposed for: i) discrete
emotion recognition in terms of the primary facial expressions; ii) emotion
analysis in terms of facial Action Units (AUs), assuming a fixed expression
intensity; iii) dimensional emotion analysis, in terms of valence and arousal
(VA). These approaches can only be effective, if they are developed using
large, appropriately annotated databases, showing behaviors of people
in-the-wild, i.e., in uncontrolled environments. Aff-Wild has been the first,
large-scale, in-the-wild database (including around 1,200,000 frames of 300
videos), annotated in terms of VA. In the vast majority of existing emotion
databases, their annotation is limited to either primary expressions, or
valence-arousal, or action units. In this paper, we first annotate a part
(around $234,000$ frames) of the Aff-Wild database in terms of $8$ AUs and
another part (around $288,000$ frames) in terms of the $7$ basic emotion
categories, so that parts of this database are annotated in terms of VA, as
well as AUs, or primary expressions. Then, we set up and tackle multi-task
learning for emotion recognition, as well as for facial image generation.
Multi-task learning is performed using: i) a deep neural network with shared
hidden layers, which learns emotional attributes by exploiting their
inter-dependencies; ii) a discriminator of a generative adversarial network
(GAN). On the other hand, image generation is implemented through the generator
of the GAN. For these two tasks, we carefully design loss functions that fit
the examined set-up. Experiments are presented which illustrate the good
performance of the proposed approach when applied to the new annotated parts of
the Aff-Wild database.

本论文针对情感分析的各个领域进行了研究，提出了基于神经网络的多任务学习方法，该方法有效地应用于 Aff-Wild 情感数据库的新注释部分并实现了良好的性能。

一种多任务学习与生成框架：情感价值 - 唤起度、动作单位和基本表情

A Multi-Task Learning & Generation Framework: Valence-Arousal, Action  Units & Primary Expressions

Eye gaze is an important non-verbal cue for human affect analysis. Recent
gaze estimation work indicated that information from the full face region can
benefit performance. Pushing this idea further, we propose an appearance-based
method that, in contrast to a long-standing line of work in computer vision,
only takes the full face image as input. Our method encodes the face image
using a convolutional neural network with spatial weights applied on the
feature maps to flexibly suppress or enhance information in different facial
regions. Through extensive evaluation, we show that our full-face method
significantly outperforms the state of the art for both 2D and 3D gaze
estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on
EYEDIAP for person-independent 3D gaze estimation. We further show that this
improvement is consistent across different illumination conditions and gaze
directions and particularly pronounced for the most challenging extreme head
poses.

提出了一种全称人脸输入的外观识别方法，使用卷积神经网络对面部图像进行编码，应用于 2D 和 3D 注视估计中实现了重大的性能提升，对于极端头部姿势尤其明显。