手工卫生对于预防病毒和感染是至关重要的。由于Covid-19的普遍爆发，戴着面具和手工卫生似乎是公众遏制这些病毒的传播最有效的方式。世界卫生组织（世卫组织）建议在八个步骤中推荐一支基于酒精的手摩擦的指导，以确保所有手表的手都完全干净。由于这些步骤涉及复杂的手势，对它们的人为评估缺乏足够的准确性。然而，深度神经网络（DNN）和机器视觉使得能够为培训和反馈的目的准确地评估手摩擦质量。本文介绍了一种具有实时反馈的自动化深度学习的手RUB评估系统。该系统使用在从志愿者收集的视频数据集上培训的DNN架构来评估符合8步指南的遵守情况，并在手动摩擦指南之后的各种肤色和手部特征。测试了各种DNN架构，并且成立型号的模型导致了97％的测试精度的最佳效果。在建议的系统中，NVIDIA Jetson Agx Xavier嵌入板运行软件。在各种用户使用的具体情况下评估系统的功效，并确定具有挑战性的步骤。在这个实验中，志愿者中手摩擦步骤的平均时间是27.2秒，符合世卫组织指导方针。

如今，推荐系统和搜索引擎在时尚电子商务中发挥积分作用。尽管如此，许多挑战谎言，这项研究试图解决一些问题。本文首先介绍了一种基于内容的时尚推荐系统，它使用并行神经网络作为输入，通过列出商店中可用的类似物品来获取单个时尚项目商店映像。接下来，增强相同的结构以基于用户偏好来个性化结果。然后，这项工作引入了一个背景增强技术，使系统更强大地对域外查询，使其仅使用培训的目录商店图像进行街道到商店建议。此外，本文的最后贡献是推荐任务的新评估度量，称为客观引导的人为评分。该方法是一个完全可定制的框架，可以产生来自人类评分术的主观评估的可解释，可比的分数。

时尚界正在濒临前所未有的变化。在时尚应用中实施机器学习，计算机愿景和人工智能（AI）正在为这一行业开辟很多新的机会。本文对此事提供了全面的调查，将超过580篇相关文章分类为22个与定义的时尚相关的任务。这种基于结构的基于任务的时尚研究文章的多标签分类为研究人员提供了明确的研究方向，并促进了他们对相关研究的访问，同时提高了研究的可见性。对于每项任务，提供了一个时间图来分析多年来的进展。此外，我们提供了86个公共时装数据集的列表，并附上建议的应用程序列表和每个数据项。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.

In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content. In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data. More specifically, we employed several transformers both individually and in combination, so as to differentiate between relevant and non-relevant Twitter posts, achieving the highest F1-score of 0.87.

Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Among these data sources, the scientific articles published in the biomedical literature usually make a principled pipeline to study disease. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.

Neural models that do not rely on pre-training have excelled in the keyphrase generation task with large annotated datasets. Meanwhile, new approaches have incorporated pre-trained language models (PLMs) for their data efficiency. However, there lacks a systematic study of how the two types of approaches compare and how different design choices can affect the performance of PLM-based models. To fill in this knowledge gap and facilitate a more informed use of PLMs for keyphrase extraction and keyphrase generation, we present an in-depth empirical study. Formulating keyphrase extraction as sequence labeling and keyphrase generation as sequence-to-sequence generation, we perform extensive experiments in three domains. After showing that PLMs have competitive high-resource performance and state-of-the-art low-resource performance, we investigate important design choices including in-domain PLMs, PLMs with different pre-training objectives, using PLMs with a parameter budget, and different formulations for present keyphrases. Further results show that (1) in-domain BERT-like PLMs can be used to build strong and data-efficient keyphrase generation models; (2) with a fixed parameter budget, prioritizing model depth over width and allocating more layers in the encoder leads to better encoder-decoder models; and (3) introducing four in-domain PLMs, we achieve a competitive performance in the news domain and the state-of-the-art performance in the scientific domain.

Privacy policies provide individuals with information about their rights and how their personal information is handled. Natural language understanding (NLU) technologies can support individuals and practitioners to understand better privacy practices described in lengthy and complex documents. However, existing efforts that use NLU technologies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices. To this end, we introduce the Privacy Policy Language Understanding Evaluation (PLUE) benchmark, a multi-task benchmark for evaluating the privacy policy language understanding across various tasks. We also collect a large corpus of privacy policies to enable privacy policy domain-specific language model pre-training. We demonstrate that domain-specific pre-training offers performance improvements across all tasks. We release the benchmark to encourage future research in this domain.

While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i.e., in-file context, but ignore the rich semantics in other files within the same project, i.e., cross-file context, a critical source of information that is especially useful in modern modular software development. Such overlooking constrains code language models' capacity in code completion, leading to unexpected behaviors such as generating hallucinated class member functions or function calls with unexpected arguments. In this work, we develop a cross-file context finder tool, CCFINDER, that effectively locates and retrieves the most relevant cross-file context. We propose CoCoMIC, a framework that incorporates cross-file context to learn the in-file and cross-file context jointly on top of pretrained code LMs. CoCoMIC successfully improves the existing code LM with a 19.30% relative increase in exact match and a 15.41% relative increase in identifier matching for code completion when the cross-file context is provided.