The recent advancements in generative AI models, which can create realistic
and human-like content, are significantly transforming how people communicate,
create, and work. While the appropriate use of generative AI models can benefit
the society, their misuse poses significant threats to data reliability and
authentication. However, due to a lack of aligned multimodal datasets,
effective and robust methods for detecting machine-generated content are still
in the early stages of development. In this paper, we introduce RU-AI, a new
large-scale multimodal dataset designed for the robust and efficient detection
of machine-generated content in text, image, and voice. Our dataset is
constructed from three large publicly available datasets: Flickr8K, COCO, and
Places205, by combining the original datasets and their corresponding
machine-generated pairs. Additionally, experimental results show that our
proposed unified model, which incorporates a multimodal embedding module with a
multilayer perceptron network, can effectively determine the origin of the data
(i.e., original data samples or machine-generated ones) from RU-AI. However,
future work is still required to address the remaining challenges posed by
RU-AI. The source code and dataset are available at
this https URL

介绍了 RU-AI 数据集，该数据集是一个大规模的多模态数据集，旨在鲁棒且高效地检测文本、图像和语音中的机器生成内容；提出了一个统一模型，该模型融合了多模态嵌入模块和多层感知器网络，能够有效确定数据的来源（即原始数据样本还是机器生成的数据）。

RU-AI：一个用于机器生成内容检测的大型多模态数据集

RU-AI: A Large Multimodal Dataset for Machine Generated Content  Detection

In barter exchanges, participants directly trade their endowed goods in a
constrained economic setting without money. Transactions in barter exchanges
are often facilitated via a central clearinghouse that must match participants
even in the face of uncertainty---over participants, existence and quality of
potential trades, and so on. Leveraging robust combinatorial optimization
techniques, we address uncertainty in kidney exchange, a real-world barter
market where patients swap (in)compatible paired donors. We provide two
scalable robust methods to handle two distinct types of uncertainty in kidney
exchange---over the quality and the existence of a potential match. The latter
case directly addresses a weakness in all stochastic-optimization-based methods
to the kidney exchange clearing problem, which all necessarily require explicit
estimates of the probability of a transaction existing---a still-unsolved
problem in this nascent market. We also propose a novel, scalable kidney
exchange formulation that eliminates the need for an exponential-time
constraint generation process in competing formulations, maintains provable
optimality, and serves as a subsolver for our robust approach. For each type of
uncertainty we demonstrate the benefits of robustness on real data from a
large, fielded kidney exchange in the United States. We conclude by drawing
parallels between robustness and notions of fairness in the kidney exchange
setting.

本文旨在提供两种可扩展的健壮方法来处理肾脏交换中的不确定性，并在现实数据中展示健壮性的好处，从而使得在鲜为人知的市场上实现公正。