Rock mass classification systems are crucial for assessing stability and risk
in underground construction globally and guiding support and excavation design.
However, systems developed primarily in the 1970s lack access to modern
high-resolution data and advanced statistical techniques, limiting their
effectiveness as decision-support systems. Initially, we outline the
limitations observed in this context and later describe how a data-driven
system, based on drilling data as detailed in this study, can overcome these
limitations. Using extracted statistical information from thousands of MWD-data
values in one-meter sections of a full tunnel profile, thus working as a
signature of the rock mass, we have demonstrated that it is possible to form
well-defined clusters that can act as a foundational basis for various rock
mass classification systems. We reduced the dimensionality of 48-value vectors
using nonlinear manifold learning techniques (UMAP) and linear principal
component analysis (PCA) to enhance clustering. Unsupervised machine learning
methods (HDBSCAN, Agglomerative Clustering, K-means) were employed to cluster
the data, with hyperparameters optimised through multi-objective Bayesian
optimisation for effective clustering. Using domain knowledge, we experienced
improved clustering and system tuning opportunities in adding extra features to
core clusters of MWD-data. We structured and correlated these clusters with
physical rock mass properties, including labels of rock type and rock quality,
and analysed cumulative distributions of key MWD-parameters for rock mass
assessment to determine if clusters meaningfully differentiate rock masses. The
ability of MWD data to form distinct rock mass clusters suggests substantial
potential for future classification systems grounded in this objective,
data-driven methodology, free from human bias.

基于钻探数据开发的数据驱动系统在岩体分类中能够形成明确的岩体簇群，为未来的分类系统提供了潜力。

基于钻井数据的岩体无监督机器学习分类：数据驱动系统如何处理现有岩体分类系统的局限性？

Unsupervised machine learning for data-driven classification of rock  mass using drilling data: How can a data-driven system handle limitations in  existing rock mass classification systems?

The Sustainable Development Goals (SDGs) were introduced by the United
Nations in order to encourage policies and activities that help guarantee human
prosperity and sustainability. SDG frameworks produced in the finance industry
are designed to provide scores that indicate how well a company aligns with
each of the 17 SDGs. This scoring enables a consistent assessment of
investments that have the potential of building an inclusive and sustainable
economy. As a result of the high quality and reliability required by such
frameworks, the process of creating and maintaining them is time-consuming and
requires extensive domain expertise. In this work, we describe a data-driven
system that seeks to automate the process of creating an SDG framework. First,
we propose a novel method for collecting and filtering a dataset of texts from
different web sources and a knowledge graph relevant to a set of companies. We
then implement and deploy classifiers trained with this data for predicting
scores of alignment with SDGs for a given company. Our results indicate that
our best performing model can accurately predict SDG scores with a micro
average F1 score of 0.89, demonstrating the effectiveness of the proposed
solution. We further describe how the integration of the models for its use by
humans can be facilitated by providing explanations in the form of data
relevant to a predicted score. We find that our proposed solution enables
access to a large amount of information that analysts would normally not be
able to process, resulting in an accurate prediction of SDG scores at a
fraction of the cost.

通过数据驱动系统，自动创建可持续发展目标框架，实现准确预测公司的目标得分，并提供与预测得分相关的数据解释，从而以较低成本提供大量信息。

利用网络和知识图谱进行自动化影响投资评分

Harnessing the Web and Knowledge Graphs for Automated Impact Investing  Scoring

As one of the most pervasive applications of machine learning, recommender
systems are playing an important role on assisting human decision making. The
satisfaction of users and the interests of platforms are closely related to the
quality of the generated recommendation results. However, as a highly
data-driven system, recommender system could be affected by data or algorithmic
bias and thus generate unfair results, which could weaken the reliance of the
systems. As a result, it is crucial to address the potential unfairness
problems in recommendation settings. Recently, there has been growing attention
on fairness considerations in recommender systems with more and more literature
on approaches to promote fairness in recommendation. However, the studies are
rather fragmented and lack a systematic organization, thus making it difficult
to penetrate for new researchers to the domain. This motivates us to provide a
systematic survey of existing works on fairness in recommendation. This survey
focuses on the foundations for fairness in recommendation literature. It first
presents a brief introduction about fairness in basic machine learning tasks
such as classification and ranking in order to provide a general overview of
fairness research, as well as introduce the more complex situations and
challenges that need to be considered when studying fairness in recommender
systems. After that, the survey will introduce fairness in recommendation with
a focus on the taxonomies of current fairness definitions, the typical
techniques for improving fairness, as well as the datasets for fairness studies
in recommendation. The survey also talks about the challenges and opportunities
in fairness research with the hope of promoting the fair recommendation
research area and beyond.

这篇论文系统地概述了推荐系统公平性的研究现状和挑战，介绍了现有的公平性定义分类、公平性提高技术和用于公平研究的数据集。