In 2021, the pioneering work on TypeNet showed that keystroke dynamics
verification could scale to hundreds of thousands of users with minimal
performance degradation. Recently, the KVC-onGoing competition has provided an
open and robust experimental protocol for evaluating keystroke dynamics
verification systems of such scale, including considerations of algorithmic
fairness. This article describes Type2Branch, the model and techniques that
achieved the lowest error rates at the KVC-onGoing, in both desktop and mobile
scenarios. The novelty aspects of the proposed Type2Branch include: i)
synthesized timing features emphasizing user behavior deviation from the
general population, ii) a dual-branch architecture combining recurrent and
convolutional paths with various attention mechanisms, iii) a new loss function
named Set2set that captures the global structure of the embedding space, and
iv) a training curriculum of increasing difficulty. Considering five enrollment
samples per subject of approximately 50 characters typed, the proposed
Type2Branch achieves state-of-the-art performance with mean per-subject EERs of
0.77% and 1.03% on evaluation sets of respectively 15,000 and 5,000 subjects
for desktop and mobile scenarios. With a uniform global threshold for all
subjects, the EERs are 3.25% for desktop and 3.61% for mobile, outperforming
previous approaches by a significant margin.

Type2Branch 是一个成功的模型和技术，使用合成时间特征、双分支架构、全局结构的嵌入空间、以及难度逐步递增的训练课程，在大规模的键入动力学验证系统中取得了最低错误率。

基于双分支体系结构的按键生物特征识别技术：包含注意机制和 Set2set 损失

Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture  with Attention Mechanisms and Set2set Loss

We consider the challenging problem of entity typing over an extremely fine
grained set of types, wherein a single mention or entity can have many
simultaneous and often hierarchically-structured types. Despite the importance
of the problem, there is a relative lack of resources in the form of
fine-grained, deep type hierarchies aligned to existing knowledge bases. In
response, we introduce TypeNet, a dataset of entity types consisting of over
1941 types organized in a hierarchy, obtained by manually annotating a mapping
from 1081 Freebase types to WordNet. We also experiment with several models
comparable to state-of-the-art systems and explore techniques to incorporate a
structure loss on the hierarchy with the standard mention typing loss, as a
first step towards future research on this dataset.

介绍了 TypeNet 数据集，该数据集由 1941 个类型组成，通过人工注释从 1081 个 Freebase 类型映射到 WordNet。本文还探讨了几种与先进系统可比的模型，以及在标准提及类型损失的基础上结合层次结构损失的技术，是未来研究的第一步。