Pir\'a is a reading comprehension dataset focused on the ocean, the Brazilian
coast, and climate change, built from a collection of scientific abstracts and
reports on these topics. This dataset represents a versatile language resource,
particularly useful for testing the ability of current machine learning models
to acquire expert scientific knowledge. Despite its potential, a detailed set
of baselines has not yet been developed for Pir\'a. By creating these
baselines, researchers can more easily utilize Pir\'a as a resource for testing
machine learning models across a wide range of question answering tasks. In
this paper, we define six benchmarks over the Pir\'a dataset, covering closed
generative question answering, machine reading comprehension, information
retrieval, open question answering, answer triggering, and multiple choice
question answering. As part of this effort, we have also produced a curated
version of the original dataset, where we fixed a number of grammar issues,
repetitions, and other shortcomings. Furthermore, the dataset has been extended
in several new directions, so as to face the aforementioned benchmarks:
translation of supporting texts from English into Portuguese, classification
labels for answerability, automatic paraphrases of questions and answers, and
multiple choice candidates. The results described in this paper provide several
points of reference for researchers interested in exploring the challenges
provided by the Pir\'a dataset.

Pir'a 是一个以海洋、巴西海岸和气候变化为重点的阅读理解数据集，从科学摘要和报告集合构建而成。本文定义了六个基准，对 Pir'a 数据集进行了测试，涵盖封闭生成问题回答、机器阅读理解、信息检索、开放性问题回答、答案触发和多项选择问题回答。结果为对 Pir'a 数据集的挑战提供了几个参考点。

Pirá 2.0 基准：关于海洋、巴西海岸和气候变化的阅读理解数据集

Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the  Ocean, the Brazilian Coast, and Climate Change

You Only Look Once (YOLO) algorithm is a representative target detection
algorithm emerging in 2016, which is known for its balance of computing speed
and accuracy, and now plays an important role in various fields of human
production and life. However, there are still many limitations in the
application of YOLO algorithm in underwater environments due to problems such
as dim light and turbid water. With limited land area resources, the ocean must
have great potential for future human development. In this paper, starting from
the actual needs of marine engineering applications, taking underwater
structural health monitoring (SHM) and underwater biological detection as
examples, we propose improved methods for the application of underwater YOLO
algorithms, and point out the problems that still exist.

YOLO 算法在水下环境中的应用存在限制，本文从海洋工程应用的实际需求出发，以水下结构健康监测和水下生物检测为例，提出了改进的水下 YOLO 算法应用方法，并指出了仍然存在的问题。