Inspired by the human cognitive system, attention is a mechanism that
imitates the human cognitive awareness about specific information, amplifying
critical details to focus more on the essential aspects of data. Deep learning
has employed attention to boost performance for many applications.
Interestingly, the same attention design can suit processing different data
modalities and can easily be incorporated into large networks. Furthermore,
multiple complementary attention mechanisms can be incorporated in one network.
Hence, attention techniques have become extremely attractive. However, the
literature lacks a comprehensive survey specific to attention techniques to
guide researchers in employing attention in their deep models. Note that,
besides being demanding in terms of training data and computational resources,
transformers only cover a single category in self-attention out of the many
categories available. We fill this gap and provide an in-depth survey of 50
attention techniques categorizing them by their most prominent features. We
initiate our discussion by introducing the fundamental concepts behind the
success of attention mechanism. Next, we furnish some essentials such as the
strengths and limitations of each attention category, describe their
fundamental building blocks, basic formulations with primary usage, and
applications specifically for computer vision. We also discuss the challenges
and open questions related to attention mechanism in general. Finally, we
recommend possible future research directions for deep attention.

本文阐述了注意力机制在深度学习的应用，提出缺乏对于注意力技术的综合研究和分类的问题，描述了 50 种注意力技术分类的细节，并建议未来深度学习对注意力机制的研究方向。

深度学习中的视觉注意方法：深入调查

Visual Attention Methods in Deep Learning: An In-Depth Survey

We present the results and the main findings of SemEval-2019 Task 6 on
Identifying and Categorizing Offensive Language in Social Media (OffensEval).
The task was based on a new dataset, the Offensive Language Identification
Dataset (OLID), which contains over 14,000 English tweets. It featured three
sub-tasks. In sub-task A, the goal was to discriminate between offensive and
non-offensive posts. In sub-task B, the focus was on the type of offensive
content in the post. Finally, in sub-task C, systems had to detect the target
of the offensive posts. OffensEval attracted a large number of participants and
it was one of the most popular tasks in SemEval-2019. In total, about 800 teams
signed up to participate in the task, and 115 of them submitted results, which
we present and analyze in this report.

本文介绍了 SemEval-2019 任务 6（OffensEval）的结果和主要发现，该任务基于一个包含超过 14,000 个英文推文的新数据集 ——Offensive Language Identification Dataset（OLID），它有三个子任务，目标是在社交媒体上识别和分类攻击性语言。

SemEval-2019 任务 6：社交媒体中的攻击性语言识别和分类（OffensEval）

SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in  Social Media (OffensEval)

Predictive process monitoring has recently gained traction in academia and is
maturing also in companies. However, with the growing body of research, it
might be daunting for companies to navigate in this domain in order to find,
provided certain data, what can be predicted and what methods to use. The main
objective of this paper is developing a value-driven framework for classifying
existing work on predictive process monitoring. This objective is achieved by
systematically identifying, categorizing, and analyzing existing approaches for
predictive process monitoring. The review is then used to develop a
value-driven framework that can support organizations to navigate in the
predictive process monitoring field and help them to find value and exploit the
opportunities enabled by these analysis techniques.

本文旨在开发一个价值驱动的框架来分类现有的预测过程监控工作，通过系统地识别、分类和分析现有的预测过程监控方法，开发一个价值驱动的框架，以帮助组织在预测过程监控领域中找到价值并利用这些分析技术的机会。