Reading fluency assessment is a critical component of literacy programmes,
serving to guide and monitor early education interventions. Given the resource
intensive nature of the exercise when conducted by teachers, the development of
automatic tools that can operate on audio recordings of oral reading is
attractive as an objective and highly scalable solution. Multiple complex
aspects such as accuracy, rate and expressiveness underlie human judgements of
reading fluency. In this work, we investigate end-to-end modeling on a training
dataset of children's audio recordings of story texts labeled by human experts.
The pre-trained wav2vec2.0 model is adopted due its potential to alleviate the
challenges from the limited amount of labeled data. We report the performance
of a number of system variations on the relevant measures, and also probe the
learned embeddings for lexical and acoustic-prosodic features known to be
important to the perception of reading fluency.

评估阅读流畅性的自动化工具在教育干预中起着重要作用，本研究采用 pre-trained wav2vec2.0 模型对儿童故事文本的音频录音进行端到端建模，并对阅读流畅性知觉相关的词汇和声学 - 韵律特征进行了分析。

口语阅读流利度评估的深度学习

Deep Learning for Assessment of Oral Reading Fluency

Social media have the potential to provide timely information about emergency
situations and sudden events. However, finding relevant information among
millions of posts being posted every day can be difficult, and developing a
data analysis project usually requires time and technical skills. This study
presents an approach that provides flexible support for analyzing social media,
particularly during emergencies. Different use cases in which social media
analysis can be adopted are introduced, and the challenges of retrieving
information from large sets of posts are discussed.
The focus is on analyzing images and text contained in social media posts and
a set of automatic data processing tools for filtering, classification, and
geolocation of content with a human-in-the-loop approach to support the data
analyst. Such support includes both feedback and suggestions to configure
automated tools, and crowdsourcing to gather inputs from citizens. The results
are validated by discussing three case studies developed within the Crowd4SDG
H2020 European project.

本研究提出了一种方法，对社交媒体进行灵活的支持，特别是在紧急情况下的社交媒体分析，基于自动化数据处理的工具可用于筛选、分类和地理位置标记内容，同时杂交方法支持人工数据分析师的反馈和建议，从民众中收集输入。通过 Euroopean 项目中的三个案例研究进行结果验证。

在 Crowd4SDG 中使用众包分析社交媒体

Analyzing social media with crowdsourcing in Crowd4SDG

Recent studies have shown that the environment where people eat can affect
their nutritional behaviour. In this work, we provide automatic tools for a
personalised analysis of a person's health habits by the examination of daily
recorded egocentric photo-streams. Specifically, we propose a new automatic
approach for the classification of food-related environments, that is able to
classify up to 15 such scenes. In this way, people can monitor the context
around their food intake in order to get an objective insight into their daily
eating routine. We propose a model that classifies food-related scenes
organized in a semantic hierarchy. Additionally, we present and make available
a new egocentric dataset composed of more than 33000 images recorded by a
wearable camera, over which our proposed model has been tested. Our approach
obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly
outperforming the baseline methods.

该研究提供了自动工具，可通过每日拍摄的照片流来个性化分析人们的健康习惯，并提出了一种新的自动分类方法来分类与食物相关的环境，同时构建了一个具有语义层次结构的模型，并介绍了一个超过 33000 张图像的数据集，其分类准确度和 F 分数分别为 56％和 65％，明显优于基线方法。