Cognitive diagnosis has been developed for decades as an effective
measurement tool to evaluate human cognitive status such as ability level and
knowledge mastery. It has been applied to a wide range of fields including
education, sport, psychological diagnosis, etc. By providing better awareness
of cognitive status, it can serve as the basis for personalized services such
as well-designed medical treatment, teaching strategy and vocational training.
This paper aims to provide a survey of current models for cognitive diagnosis,
with more attention on new developments using machine learning-based methods.
By comparing the model structures, parameter estimation algorithms, model
evaluation methods and applications, we provide a relatively comprehensive
review of the recent trends in cognitive diagnosis models. Further, we discuss
future directions that are worthy of exploration. In addition, we release two
Python libraries: EduData for easy access to some relevant public datasets we
have collected, and EduCDM that implements popular CDMs to facilitate both
applications and research purposes.

本文旨在对认知诊断模型进行回顾，并比较模型结构、参数估计算法、模型评估方法和应用，同时讨论未来研究方向。此外，我们还发布了两个 Python 库：EduData 用于访问相关公共数据集，EduCDM 实现了常见的 CDMs 以方便应用和研究。

认知诊断模型综述：新的发展与未来方向

A Survey of Models for Cognitive Diagnosis: New Developments and Future  Directions

Hyper-relational knowledge graphs (KGs) contain additional key-value pairs,
providing more information about the relations. In many scenarios, the same
relation can have distinct key-value pairs, making the original triple fact
more recognizable and specific. Prior studies on hyper-relational KGs have
established a solid standard method for hyper-relational graph encoding. In
this work, we propose a message-passing-based graph encoder with global
relation structure awareness ability, which we call ReSaE. Compared to the
prior state-of-the-art approach, ReSaE emphasizes the interaction of relations
during message passing process and optimizes the readout structure for link
prediction tasks. Overall, ReSaE gives a encoding solution for hyper-relational
KGs and ensures stronger performance on downstream link prediction tasks. Our
experiments demonstrate that ReSaE achieves state-of-the-art performance on
multiple link prediction benchmarks. Furthermore, we also analyze the influence
of different model structures on model performance.

本文提出了一种基于消息传递的具有全局关系结构感知能力的图编码器 ReSaE，用于超关系知识图谱的编码，并能够在下游链接预测任务中提供更强的性能。实验证明，ReSaE 在多个链接预测基准上实现了最先进的性能，并分析了不同模型结构对模型性能的影响。

一种关系交互式的超关系知识图中信息传递方法

A Relation-Interactive Approach for Message Passing in Hyper-relational  Knowledge Graphs

The field of urban spatial-temporal prediction is advancing rapidly with the
development of deep learning techniques and the availability of large-scale
datasets. However, challenges persist in accessing and utilizing diverse urban
spatial-temporal datasets from different sources and stored in different
formats, as well as determining effective model structures and components with
the proliferation of deep learning models. This work addresses these challenges
and provides three significant contributions. Firstly, we introduce "atomic
files", a unified storage format designed for urban spatial-temporal big data,
and validate its effectiveness on 40 diverse datasets, simplifying data
management. Secondly, we present a comprehensive overview of technological
advances in urban spatial-temporal prediction models, guiding the development
of robust models. Thirdly, we conduct extensive experiments using diverse
models and datasets, establishing a performance leaderboard and identifying
promising research directions. Overall, this work effectively manages urban
spatial-temporal data, guides future efforts, and facilitates the development
of accurate and efficient urban spatial-temporal prediction models. It can
potentially make long-term contributions to urban spatial-temporal data
management and prediction, ultimately leading to improved urban living
standards.

该研究介绍了对城市空间时间数据进行有效管理和预测的工作，包括引入了一种统一的存储格式，对多样化的数据集进行了验证，提供了城市空间时间预测模型的技术进展综述，进行了广泛的实验以建立性能排行，并指出了有前景的研究方向。这项工作有效地管理了城市空间时间数据，指导未来的努力，并促进了准确高效的城市空间时间预测模型的发展，可能会对城市空间时间数据管理和预测做出长期贡献，最终提高城市居民的生活水平。