过场动物是许多视频游戏不可或缺的一部分，但是它们的创作既昂贵又耗时，并且需要许多游戏开发人员缺乏的技能。尽管AI已被利用为半自动过场动画的生产，但结果通常缺乏专业人类董事特征的样式的内部一致性和统一性。我们用Cine-AI克服了这一缺点，Cine-AI是一种开源程序性摄影工具集，能够以杰出的人类导演的风格生成游戏中过场动画。 Cine-AI在流行的游戏引擎团结中实现，具有新颖的时间轴和情节板界面，用于设计时间操纵，并结合运行时摄影自动化。通过两项使用定量和定性措施的用户研究，我们证明了Cine-AI产生过过过场动物，这些过场动物与目标主管正确关联，同时提供高于平均水平的可用性。我们的导演模仿数据集可公开使用，可以由用户和电影爱好者扩展。

We present a novel neural model for modern poetry generation in French. The model consists of two pretrained neural models that are fine-tuned for the poem generation task. The encoder of the model is a RoBERTa based one while the decoder is based on GPT-2. This way the model can benefit from the superior natural language understanding performance of RoBERTa and the good natural language generation performance of GPT-2. Our evaluation shows that the model can create French poetry successfully. On a 5 point scale, the lowest score of 3.57 was given by human judges to typicality and emotionality of the output poetry while the best score of 3.79 was given to understandability.

We present a DialGPT based model for generating creative dialog responses that are conditioned based on one of the following emotions: anger, disgust, fear, happiness, pain, sadness and surprise. Our model is capable of producing a contextually apt response given an input sentence and a desired emotion label. Our model is capable of expressing the desired emotion with an accuracy of 0.6. The best performing emotions are neutral, fear and disgust. When measuring the strength of the expressed emotion, we find that anger, fear and disgust are expressed in the most strong fashion by the model.

We present a novel approach to generating news headlines in Finnish for a given news story. We model this as a summarization task where a model is given a news article, and its task is to produce a concise headline describing the main topic of the article. Because there are no openly available GPT-2 models for Finnish, we will first build such a model using several corpora. The model is then fine-tuned for the headline generation task using a massive news corpus. The system is evaluated by 3 expert journalists working in a Finnish media house. The results showcase the usability of the presented approach as a headline suggestion tool to facilitate the news production process.

We present a method for extracting a multilingual sentiment annotated dialog data set from Fallout New Vegas. The game developers have preannotated every line of dialog in the game in one of the 8 different sentiments: \textit{anger, disgust, fear, happy, neutral, pained, sad } and \textit{surprised}. The game has been translated into English, Spanish, German, French and Italian. We conduct experiments on multilingual, multilabel sentiment analysis on the extracted data set using multilingual BERT, XLMRoBERTa and language specific BERT models. In our experiments, multilingual BERT outperformed XLMRoBERTa for most of the languages, also language specific models were slightly better than multilingual BERT for most of the languages. The best overall accuracy was 54\% and it was achieved by using multilingual BERT on Spanish data. The extracted data set presents a challenging task for sentiment analysis. We have released the data, including the testing and training splits, openly on Zenodo. The data set has been shuffled for copyright reasons.

Probabilistic user modeling is essential for building collaborative AI systems within probabilistic frameworks. However, modern advanced user models, often designed as cognitive behavior simulators, are computationally prohibitive for interactive use in cooperative AI assistants. In this extended abstract, we address this problem by introducing widely-applicable differentiable surrogates for bypassing this computational bottleneck; the surrogates enable using modern behavioral models with online computational cost which is independent of their original computational cost. We show experimentally that modeling capabilities comparable to likelihood-free inference methods are achievable, with over eight orders of magnitude reduction in computational time. Finally, we demonstrate how AI-assistants can computationally feasibly use cognitive models in a previously studied menu-search task.

数据驱动的优化和基于机器学习的无线电访问网络的性能诊断不仅需要源于基本数据源的性质，而且还归因于复杂的时空关系以及由于用户移动性和不同流量模式而引起的单元格之间的相互依赖性。我们讨论如何使用多元分析来研究这些配置和性能管理数据集以及在关键性能指标方面识别细胞之间的关系。为此，我们利用了基于规范相关分析（CCA）的新框架，这不仅是降低维度的高效方法，而且还用于分析跨不同多元数据集的关系。作为一个案例研究，我们讨论了基于商业蜂窝网络中细胞关闭的节能用例，在该案例中，我们将CCA应用于分析容量细胞关闭对同一部门覆盖电池KPI的影响。来自LTE网络的数据用于分析示例案例。我们得出的结论是，CCA是一种可行的方法，用于识别网络计划和配置数据之间的关键关系，还可以动态绩效数据，为诸如降低维度降低，绩效分析和性能诊断的根本原因分析等努力铺平道路。

角色扮演游戏（RPG）在视频游戏对话中具有相当多的文本。游戏开发人员经常将此文本半通知。在本文中，我们从几个RPG中提取了有说服力对话的多语言数据集。我们使用称为BERT的自然语言处理（NLP）模型来显示该数据在构建说服检测系统中的生存能力。我们认为，作为各种NLP任务的数据源，视频游戏具有许多未使用的潜力。本文中描述的代码和数据可在Zenodo上找到。

该研究形成了由芬兰民族学家和语言学家，Matthias Alexander Castr \'en（1813-1852）收集和出版的材料进行的各种任务的技术报告。 Finno-Ugrian社会正在将Castr \'en的稿件作为新的关键和数字版本出版，同时不同的研究团体也关注这些材料。我们讨论了所用的工作流程和技术基础设施，并考虑如何创建有利于不同计算任务的数据集以进一步提高这些材料的可用性，并帮助进一步处理类似的归档集合。我们专注于以一种方式处理的集合的部分，这些集合可以在更提高其在更多技术应用中的可用性，补充较早的这些材料的文化和语言方面的工作。大多数这些数据集在Zenodo公开使用。该研究指出需要进一步研究的特定区域，并为文本识别任务提供基准。

测量不同文本的语义相似性在数字人文研究中具有许多重要应用，例如信息检索，文档聚类和文本摘要。不同方法的性能取决于文本，域和语言的长度。本研究侧重于试验一些目前的芬兰方法，这是一种形态学丰富的语言。与此同时，我们提出了一种简单的方法TFW2V，它在处理长文本文档和有限的数据时显示出高效率。此外，我们设计了一种客观评估方法，可以用作基准标记文本相似性方法的框架。