The rise in internet usage has led to the generation of massive amounts of
data, resulting in the adoption of various supervised and semi-supervised
machine learning algorithms, which can effectively utilize the colossal amount
of data to train models. However, before deploying these models in the real
world, these must be strictly evaluated on performance measures like worst-case
recall and satisfy constraints such as fairness. We find that current
state-of-the-art empirical techniques offer sub-optimal performance on these
practical, non-decomposable performance objectives. On the other hand, the
theoretical techniques necessitate training a new model from scratch for each
performance objective. To bridge the gap, we propose SelMix, a selective
mixup-based inexpensive fine-tuning technique for pre-trained models, to
optimize for the desired objective. The core idea of our framework is to
determine a sampling distribution to perform a mixup of features between
samples from particular classes such that it optimizes the given objective. We
comprehensively evaluate our technique against the existing empirical and
theoretically principled methods on standard benchmark datasets for imbalanced
classification. We find that proposed SelMix fine-tuning significantly improves
the performance for various practical non-decomposable objectives across
benchmarks.

通过互联网使用量的增加产生了大量的数据，从而导致了各种监督和半监督机器学习算法的采用，这些算法可以有效利用庞大的数据量来训练模型。然而，在将这些模型部署到实际环境之前，必须严格评估性能指标（如最坏情况召回率）并满足公平性等约束。我们发现目前最先进的经验技术在这些实际的不可分解性能目标上提供了次优的性能。另一方面，理论技术需要针对每个性能目标从头开始训练新模型。为了弥合这一差距，我们提出了 SelMix 技术，这是一种选择性混合的低成本微调技术，用于已预训练模型以优化所需的目标。我们的核心思想是确定一个采样分布，以在特定类别之间的样本之间进行特征混合，从而优化给定的目标。我们在标准基准数据集上对我们的技术进行了全面评估，与现有的经验和理论基础方法进行了比较，用于不平衡分类。我们发现所提出的 SelMix 微调显著提高了在各种实际的不可分解目标上的性能。

选择性混合微调以优化不可分解的目标

Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives

The growth in Internet usage has contributed to a large volume of
continuously available data, and has created the need for automatic and
efficient organization of the data. In this context, text clustering techniques
are significant because they aim to organize documents according to their
characteristics. More specifically, hierarchical and incremental clustering
techniques can organize dynamic data in a hierarchical form, thus guaranteeing
that this organization is updated and its exploration is facilitated. Based on
the relevance and contemporary nature of the field, this study aims to analyze
various hierarchical and incremental clustering techniques; the main
contribution of this research is the organization and comparison of the
techniques used by studies published between 2010 and 2018 that aimed to texts
documents clustering. We describe the principal concepts related to the
challenge and the different characteristics of these published works in order
to provide a better understanding of the research in this field.

这篇论文旨在分析 2010 年至 2018 年期间发表的各种层次和增量聚类技术，以进一步组织和比较文本聚类的相关研究，从而提供对该领域研究的更好理解。

增量层次文本聚类方法：综述

Incremental hierarchical text clustering methods: a review

After a pandemic that caused internet usage to grow by 70%, there has been an
increased number of people all across the world using social media.
Applications like Twitter, Meta Threads, YouTube, and Reddit have become
increasingly pervasive, leaving almost no digital space where public opinion is
not expressed. This paper investigates sentiment and semantic relationships
among comments across various social media platforms, as well as discusses the
importance of shared opinions across these different media platforms, using
word embeddings to analyze components in sentences and documents. It allows
researchers, politicians, and business representatives to trace a path of
shared sentiment among users across the world. This research paper presents
multiple approaches that measure the relatedness of text extracted from user
comments on these popular online platforms. By leveraging embeddings, which
capture semantic relationships between words and help analyze sentiments across
the web, we can uncover connections regarding public opinion as a whole. The
study utilizes pre-existing datasets from YouTube, Reddit, Twitter, and more.
We made use of popular natural language processing models like Bidirectional
Encoder Representations from Transformers (BERT) to analyze sentiments and
explore relationships between comment embeddings. Additionally, we aim to
utilize clustering and Kl-divergence to find semantic relationships within
these comment embeddings across various social media platforms. Our analysis
will enable a deeper understanding of the interconnectedness of online comments
and will investigate the notion of the internet functioning as a large
interconnected brain.

由于一次导致互联网使用增长 70% 的大流行病，全球范围内使用社交媒体的人数大幅增加。本研究通过使用词嵌入来分析评论中的组成部分和文档，调查了不同社交媒体平台上的情感和语义关系，论述了共享意见在这些不同媒体平台之间的重要性，为研究人员、政治家和商业代表提供跟踪全球用户共享情绪的路径。本研究呈现了多种测量从这些热门在线平台上提取的文本相关性的方法，通过利用捕捉语义关系的词嵌入来分析网络上的情感，我们可以揭示公众舆论作为一个整体的联系。该研究利用了来自 YouTube、Reddit、Twitter 等的现有数据集，并利用了像双向编码器表征来自变形器（BERT）等热门自然语言处理模型来分析情感和探索评论嵌入之间的关系。此外，我们还将利用聚类和 Kl 散度来发现不同社交媒体平台上这些评论嵌入之间的语义关系。我们的分析将使人们更加深入地了解在线评论之间的相互关联，并探讨互联网作为一个庞大的相互连接的大脑的概念。