未来发生的一些事件对于公司，政府甚至我们的个人生活可能很重要。在建立之前对这些事件的预测有助于有效的决策。我们称此类事件新兴实体。它们尚未发生，在KB中没有有关它们的信息。但是，有些线索存在于不同领域，尤其是在社交媒体上。因此，检索这些类型的实体是可能的。本文提出了一种早期发现新兴实体的方法。我们使用短消息的语义聚类。为了评估提案的绩效，我们设计和利用了绩效评估指标。结果表明，我们提出的方法发现了Twitter趋势并非总是能够有能力的那些新兴实体。

Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. A number of models exist that base charisma on various dimensions, often following the idea that charisma is given if someone could and would help others. Examples include influence (could help) and affability (would help) in scientific studies or power (could help), presence, and warmth (both would help) as a popular concept. Modelling high levels in these dimensions for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we, thereforem present a blueprint for building machines that can appear charismatic, but also analyse the charisma of others. To this end, we first provide the psychological perspective including different models of charisma and behavioural cues of it. We then switch to conversational charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behaviour by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.

Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modelling emotional trajectories in stories has thus attracted considerable scholarly interest. However, as most existing works have been limited to unsupervised dictionary-based approaches, there is no labelled benchmark for this task. We address this gap by introducing continuous valence and arousal annotations for an existing dataset of children's stories annotated with discrete emotion categories. We collect additional annotations for this data and map the originally categorical labels to the valence and arousal space. Leveraging recent advances in Natural Language Processing, we propose a set of novel Transformer-based methods for predicting valence and arousal signals over the course of written stories. We explore several strategies for fine-tuning a pretrained ELECTRA model and study the benefits of considering a sentence's context when inferring its emotionality. Moreover, we experiment with additional LSTM and Transformer layers. The best configuration achieves a Concordance Correlation Coefficient (CCC) of .7338 for valence and .6302 for arousal on the test set, demonstrating the suitability of our proposed approach. Our code and additional annotations are made available at https://github.com/lc0197/emotion_modelling_stories.

Automatic video captioning aims for a holistic visual scene understanding. It requires a mechanism for capturing temporal context in video frames and the ability to comprehend the actions and associations of objects in a given timeframe. Such a system should additionally learn to abstract video sequences into sensible representations as well as to generate natural written language. While the majority of captioning models focus solely on the visual inputs, little attention has been paid to the audiovisual modality. To tackle this issue, we propose a novel two-fold approach. First, we implement a reward-guided KL Divergence to train a video captioning model which is resilient towards token permutations. Second, we utilise a Bi-Modal Hierarchical Reinforcement Learning (BMHRL) Transformer architecture to capture long-term temporal dependencies of the input data as a foundation for our hierarchical captioning module. Using our BMHRL, we show the suitability of the HRL agent in the generation of content-complete and grammatically sound sentences by achieving $4.91$, $2.23$, and $10.80$ in BLEU3, BLEU4, and METEOR scores, respectively on the ActivityNet Captions dataset. Finally, we make our BMHRL framework and trained models publicly available for users and developers at https://github.com/d-rothen/bmhrl.

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks (ANNs). Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2.5. Given that traditional, highly sophisticated air quality monitors are expensive and are not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built on physical measurement data collected from sensors, they may not be suitable for predicting public health effects experienced from pollution exposure. This study aims to develop and validate models to nowcast the observed pollution levels using Web search data, which is publicly available in near real-time from major search engines. We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level, by using generally available meteorological data and aggregate Web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting three critical air pollutants (ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5)), across ten major U.S. metropolitan statistical areas (MSAs) in 2017 and 2018.

One of the major challenges in acoustic modelling of child speech is the rapid changes that occur in the children's articulators as they grow up, their differing growth rates and the subsequent high variability in the same age group. These high acoustic variations along with the scarcity of child speech corpora have impeded the development of a reliable speech recognition system for children. In this paper, a speaker- and age-invariant training approach based on adversarial multi-task learning is proposed. The system consists of one generator shared network that learns to generate speaker- and age-invariant features connected to three discrimination networks, for phoneme, age, and speaker. The generator network is trained to minimize the phoneme-discrimination loss and maximize the speaker- and age-discrimination losses in an adversarial multi-task learning fashion. The generator network is a Time Delay Neural Network (TDNN) architecture while the three discriminators are feed-forward networks. The system was applied to the OGI speech corpora and achieved a 13% reduction in the WER of the ASR.

幽默是人类情感和认知的重要因素。它的自动理解可以促进更自然的人类设备互动和人工智能的人性化。当前的幽默检测方法仅基于分阶段数据，使其不适用于“现实世界”应用程序。我们通过引入新颖的Passau自发足球教练幽默（Passau-SFCH）数据集来解决这种缺陷，包括大约11个小时的录音。在马丁的幽默风格问卷中提出的幽默及其尺寸（情感和方向）的存在，请注释Passau-SFCH数据集。我们进行了一系列实验，采用了经过预定的变压器，卷积神经网络和专家设计的功能。分析了每种模式（文本，音频，视频）的表现，以进行自发幽默识别，并研究了它们的互补性。我们的发现表明，对于对幽默及其情感的自动分析，面部表情是最有希望的，而幽默方向可以通过基于文本的功能进行建模。结果揭示了各种主题之间的差异，突出了幽默用法和风格的个性。此外，我们观察到决策级融合会产生最佳认可结果。最后，我们在https://www.github.com/eihw/passau-sfch上公开代码。可以根据要求获得Passau-SFCH数据集。

鉴于大量具有相似属性但域不同的标记数据的可用性，域的适应性是一种有吸引力的方法。在图像分类任务中，获得足够的标签数据具有挑战性。我们提出了一种名为Selda的新方法，用于通过扩展三种域适应方法来堆叠合奏学习，以有效解决现实世界中的问题。主要假设是，当将基本域适应模型组合起来时，我们可以通过利用每个基本模型的能力来获得更准确，更健壮的模型。我们扩展最大平均差异（MMD），低级别编码和相关比对（珊瑚），以计算三个基本模型中的适应损失。同样，我们利用一个两双连接的层网络作为元模型来堆叠这三个表现良好的域适应模型的输出预测，以获得眼科图像分类任务的高精度。使用与年龄相关的眼病研究（AREDS）基准眼科数据集的实验结果证明了该模型的有效性。

我们考虑开放的联合学习（FL）系统，客户可以在FL过程中加入和/或离开系统。鉴于当前客户端数量的差异，在开放系统中不能保证与固定模型的收敛性。取而代之的是，我们求助于一个新的性能指标，该指标称我们的开放式FL系统的稳定性为量，该指标量化了开放系统中学习模型的幅度。在假设本地客户端的功能强烈凸出和平滑的假设下，我们从理论上量化了两种FL算法的稳定性半径，即本地SGD和本地ADAM。我们观察到此半径依赖于几个关键参数，包括功能条件号以及随机梯度的方差。通过对合成和现实世界基准数据集的数值模拟，我们的理论结果得到了进一步验证。

我们介绍了本地重新考虑的任务，该任务通过打开和关闭图像中可见的光源来改变场景的照片。这项新任务与传统的图像重新确定问题不同，因为它引入了检测光源并推断出从它们中散发出的光模式的挑战。我们提出了一种用于本地重新考虑的方法，该方法通过使用另一个模型的合成生成的图像对来训练模型，而无需监督任何新型图像数据集。具体而言，我们从样式空间操纵的gan中收集了配对的训练图像；然后，我们使用这些图像来训练有条件的图像到图像模型。为了基于本地重新测试，我们介绍了Lonoff，这是一个在室内空间中拍摄的306张精确对齐图像的集合，其中灯的不同组合打开了。我们表明，我们的方法显着优于基于GAN倒置的基线方法。最后，我们演示了分别控制不同光源的方法的扩展。我们邀请社区解决这项新的当地重新任务。