Similarity has been applied to a wide range of security applications,
typically used in machine learning models. We examine the problem posed by
masquerading samples; that is samples crafted by bad actors to be similar or
near identical to legitimate samples. We find that these samples potentially
create significant problems for machine learning solutions. The primary problem
being that bad actors can circumvent machine learning solutions by using
masquerading samples.
We then examine the interplay between digital signatures and machine learning
solutions. In particular, we focus on executable files and code signing. We
offer a taxonomy for masquerading files. We use a combination of similarity and
clustering to find masquerading files. We use the insights gathered in this
process to offer improvements to similarity based and machine learning security
solutions.

相似性在安全应用领域广泛应用，我们研究了仿冒样本的问题，发现这些样本可能对机器学习解决方案造成重大问题。我们进一步研究了数字签名与机器学习解决方案之间的相互作用，特别关注可执行文件和代码签名，并提出了一种仿冒文件的分类方法。我们利用相似性和聚类的结合来寻找仿冒文件，并根据这一过程得出的见解来改进基于相似性和机器学习的安全解决方案。

探讨相似性在检测伪装文件中的作用

On the Role of Similarity in Detecting Masquerading Files

Electronic health record (EHR) is more and more popular, and it comes with
applying machine learning solutions to resolve various problems in the domain.
This growing research area also raises the need for EHRs accessibility. Medical
Information Mart for Intensive Care (MIMIC) dataset is a popular, public, and
free EHR dataset in a raw format that has been used in numerous studies.
However, despite of its popularity, it is lacking benchmarking work, especially
with recent state of the art works in the field of deep learning with
time-series tabular data. The aim of this work is to fill this lack by
providing a benchmark for latest version of MIMIC dataset, MIMIC-IV. We also
give a detailed literature survey about studies that has been already done for
MIIMIC-III.

电子健康记录（EHR）与应用机器学习解决领域内的各种问题越来越受欢迎，而这一增长的研究领域也引发了对 EHR 可访问性的需求。本研究的目标是填补 MIMIC-IV 最新版本的 MIMIC 数据集的基准测试工作的不足，并提供一个详细的文献调查，对已经完成的 MIIMIC-III 研究进行评估。

使用 MIMIC-IV 进行基准测试：一种不规则、稀疏的临床时间序列数据集

Benchmarking with MIMIC-IV, an irregular, spare clinical time series  dataset

Foundation models are redefining how AI systems are built. Practitioners now
follow a standard procedure to build their machine learning solutions: from a
pre-trained foundation model, they fine-tune the weights on the target task of
interest. So, the Internet is swarmed by a handful of foundation models
fine-tuned on many diverse tasks: these individual fine-tunings exist in
isolation without benefiting from each other. In our opinion, this is a missed
opportunity, as these specialized models contain rich and diverse features. In
this paper, we thus propose model ratatouille, a new strategy to recycle the
multiple fine-tunings of the same foundation model on diverse auxiliary tasks.
Specifically, we repurpose these auxiliary weights as initializations for
multiple parallel fine-tunings on the target task; then, we average all
fine-tuned weights to obtain the final model. This recycling strategy aims at
maximizing the diversity in weights by leveraging the diversity in auxiliary
tasks. Empirically, it improves the state of the art on the reference DomainBed
benchmark for out-of-distribution generalization. Looking forward, this work
contributes to the emerging paradigm of updatable machine learning where, akin
to open-source software development, the community collaborates to reliably
update machine learning models.

本文提出一种称为 Model Ratatouille 的新技术，通过在多个辅助任务上重新使用同一基础模型的多个微调，利用微调之间的差异提高结果多样性以优化目标任务结果。通过该技术在参考 DomainBed 基准上显着改善了结果，并将贡献到可更新机器学习的新兴范例和开源软件开发类似的社区合作。