在许多多机构设置中，参与者可以组建团队以实现可能超过其个人能力的集体成果。衡量代理商的相对贡献并分配促进持续合作的奖励份额是艰巨的任务。合作游戏理论提供了识别分配方案（例如沙普利价值）的解决方案概念，这些概念公平地反映了个人对团队或核心表现的贡献，从而减少了代理人放弃团队的动机。此类方法的应用包括识别有影响力的特征并分享合资企业或团队成立的成本。不幸的是，即使在受限设置中，使用这些解决方案也需要解决计算障碍，因为它们很难计算。在这项工作中，我们展示了如何通过训练神经网络提出公平和稳定的回报分配来将合作游戏理论解决方案蒸馏成学习的模型。我们表明，我们的方法创建的模型可以推广到远离训练分布的游戏，并且可以预测比训练期间观察到的更多玩家的解决方案。我们框架的一个重要应用是可以解释的AI：我们的方法可用于加快在许多情况下的Shapley价值计算。

As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

Recent developments in natural language generation (NLG) using neural language models have brought us closer than ever to the goal of building AI-powered creative writing tools. However, most prior work on human-AI collaboration in the creative writing domain has evaluated new systems with amateur writers, typically in contrived user studies of limited scope. In this work, we commissioned 13 professional, published writers from a diverse set of creative writing backgrounds to craft stories using Wordcraft, a text editor with built-in AI-powered writing assistance tools. Using interviews and participant journals, we discuss the potential of NLG to have significant impact in the creative writing domain--especially with respect to brainstorming, generation of story details, world-building, and research assistance. Experienced writers, more so than amateurs, typically have well-developed systems and methodologies for writing, as well as distinctive voices and target audiences. Our work highlights the challenges in building for these writers; NLG technologies struggle to preserve style and authorial voice, and they lack deep understanding of story contents. In order for AI-powered writing assistants to realize their full potential, it is essential that they take into account the diverse goals and expertise of human writers.

National Health and Nutritional Status Survey (NHANSS) is conducted annually by the Ministry of Health in Negara Brunei Darussalam to assess the population health and nutritional patterns and characteristics. The main aim of this study was to discover meaningful patterns (groups) from the obese sample of NHANSS data by applying data reduction and interpretation techniques. The mixed nature of the variables (qualitative and quantitative) in the data set added novelty to the study. Accordingly, the Categorical Principal Component (CATPCA) technique was chosen to interpret the meaningful results. The relationships between obesity and the lifestyle factors like demography, socioeconomic status, physical activity, dietary behavior, history of blood pressure, diabetes, etc., were determined based on the principal components generated by CATPCA. The results were validated with the help of the split method technique to counter verify the authenticity of the generated groups. Based on the analysis and results, two subgroups were found in the data set, and the salient features of these subgroups have been reported. These results can be proposed for the betterment of the healthcare industry.

家庭中的移动操纵器可以为患有严重运动障碍的人提供越来越多的自治权，他们在没有照料者的帮助下通常无法完成日常生活（ADL）的活动。辅助移动操纵器的远距离运行可以使患有运动障碍的人能够独立执行自我保健和家庭任务，但是有限的运动功能会阻碍人们与机器人接触的能力。在这项工作中，我们介绍了一个独特的基于惯性的可穿戴辅助界面，该辅助界面嵌入了熟悉的头饰服装中，适用于具有严重运动障碍的人，可以通过移动操纵器进行远程处理和执行身体任务。我们评估了这种可穿戴的界面（n = 16）和有运动障碍的个体（n = 2），用于执行ADL和日常家庭任务。我们的结果表明，可穿戴界面使参与者能够完成错误率，高度可感知的易用性和低工作负载度量的身体任务。总体而言，这种基于惯性的可穿戴设备是一种新的辅助接口选项，可控制家庭中移动操纵器。

利用基于文本的节点属性的节点分类具有许多真实的应用程序，从学术引用图中的纸张主题到社交媒体网络中用户特征的分类范围。最新的节点分类框架（例如Giant）使用两阶段管道：首先嵌入图节点的文本属性，然后将所得嵌入的嵌入到节点分类模型中。在本文中，我们消除了这两个阶段，而是开发了建立在巨人基于端到端巨型（E2EG）的端到端节点分类模型。在我们的方法中，主体和辅助分类目标的串联利用导致了更强大的模型，从而使BERT主链可以切换为蒸馏编码器，其参数数量减少了25％-40％。此外，模型的端到端性质提高了易用性，因为它避免了链接多个模型进行节点分类的需求。与OGBN-ARXIV和OGBN产品数据集的巨型+MLP基线相比，我们的模型能够在换电环境（+0.5％）中获得稍好的精度，同时将模型培训时间最多减少40％。我们的模型也适用于电感设置，优于巨型 +MLP高达 +2.23％。

时间序列形状是最近发现对时间序列聚类有效（TSC）有效的歧视子序列。形状方便地解释簇。因此，TSC的主要挑战是发现高质量的可变长度形状以区分不同的簇。在本文中，我们提出了一种新型的自动编码器窗帘方法（AutoShape），这是第一次利用自动编码器和塑形器以不受欢迎的方式确定形状的研究。自动编码器的专门设计用于学习高质量的形状。更具体地说，为了指导潜在的表示学习，我们采用了最新的自我监督损失来学习不同变量的可变长度塑形塑形（时间序列子序列）的统一嵌入，并提出多样性损失，以选择歧视嵌入的嵌入方式统一空间。我们介绍了重建损失，以在原始时间序列空间中恢复形状，以进行聚类。最后，我们采用Davies Bouldin指数（DBI），将学习过程中的聚类性能告知AutoShape。我们介绍了有关自动赛的广泛实验。为了评估单变量时间序列（UTS）的聚类性能，我们将AutoShape与使用UCR存档数据集的15种代表性方法进行比较。为了研究多元时间序列（MTS）的性能，我们使用5种竞争方法评估了30个UEA档案数据集的AutoShape。结果证明了AutoShape是所有比较的方法中最好的。我们用形状来解释簇，并可以在三个UTS案例研究和一个MTS案例研究中获得有关簇的有趣直觉。

机器学习模型表现出两个看似矛盾的现象：训练数据记忆和各种遗忘形式。在记忆中，模型过于适合特定的培训示例，并容易受到隐私攻击的影响。在忘记时，最终忘记了在培训初期出现的例子。在这项工作中，我们将这些现象联系起来。我们提出了一种技术，以衡量训练示例的细节在多大程度上``忘记''，从而不易受到他们最近未曾见过的示例的隐私攻击的影响。我们表明，尽管非凸性可以防止在最坏的情况下忘记发生，但标准图像和语音模型在经验上确实会随着时间的流逝而忘记示例。我们将非确定性识别为潜在的解释，表明经过确定性训练的模型不会忘记。我们的结果表明，当使用极大的数据集培训（例如用于预训练模型的示例）时，早期看到的例子可能会观察到隐私益处，而牺牲了后来看到的示例。

歧义是一种自然语言现象，发生在不同级别的语法，语义和语用学水平。它经过广泛的研究；例如，在心理语言学中，我们对人类的歧义过程进行了各种竞争性研究。这些研究是经验性的，并且基于眼影测量。在这里，我们迈出了对这些过程进行正式化的语义歧义的第一步，在该过程中我们确定了两个特征的存在：（1）不同可能解释的联合合理性度，（2）因果结构，根据某些单词在过程中在过程中起着更为重要的作用。 Gogioso和Pinzani在QPL 2021中开发的确定因果关系的新型横扫理论模型提供了建模和理由的工具。我们将该理论应用于从心理语言学文献中提取的模棱两可短语的数据集，以及我们使用亚马逊机械Turk发动机收集的人类合理性判断。我们测量了短语中不同歧义顺序的因果分数，并发现了两个突出的顺序：从主语动词中从主语到动词，从对象到动词对象短语中的动词。我们还发现了延迟歧义多义与同义动词的证据，再次与心理语言发现兼容。