摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsR X编辑在瑞典隆德的新闻报道，这项研究指出：“本文介绍了一个民族志案例研究，一个公司-学术团体为各种机器学习和计算机视觉任务构建了一个日常活动的基准数据集。”本文将数据集概念化为一个知识对象，由实际标准（用于日常活动、数据挖掘、注释和基准）和调整工作-即努力包括达成协议，使这些标准在实践中有效。"本研究的财政支持者包括欧洲研究理事会(ERC)，瑞典的STS环境。我们的新闻记者从隆德大学的研究中获得了一句话。“在关注协调工作的同时，本文强调了非正式的、沟通和支持的努力是标准成功和行为者与因素之间紧张关系缓和的基础。强调这些努力在几个方面构成了贡献。本文的民族志分析模式挑战并补充了数据集的量化指标。它通过对新基准数据集的开发进行详细的实证检验，推进了数据集分析领域。通过展示协调工作的重要性及其与标准的密切联系及其局限性，它增加了我们对机器学习数据集如何构建的理解。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Lund, Sweden, by NewsR x editors, the research stated, “This article presents an ethnographic case stud y of a corporate-academic group constructing a benchmark dataset of daily activi ties for a variety of machine learning and computer vision tasks. Using a socio- technical perspective, the article conceptualizes the dataset as a knowledge obj ect that is stabilized by both practical standards (for daily activities, datafi cation, annotation and benchmarks) and alignment work - that is, efforts includi ng forging agreements to make these standards effective in practice.”Financial supporters for this research include European Research Council (ERC), STS environment in Sweden. Our news journalists obtained a quote from the research from Lund University, “B y attending to alignment work, the article highlights the informal, communicativ e and supportive efforts that underlie the success of standards and the smoothin g of tensions between actors and factors. Emphasizing these efforts constitutes a contribution in several ways. This article’s ethnographic mode of analysis cha llenges and supplements quantitative metrics on datasets. It advances the field of dataset analysis by offering a detailed empirical examination of the developm ent of a new benchmark dataset as a collective accomplishment. By showing the im portance of alignment efforts and their close ties to standards and their limita tions, it adds to our understanding of how machine learning datasets are built.”
NETL
NSTL