Modeling and predicting the performance of students in collaborative learning
paradigms is an important task. Most of the research presented in literature
regarding collaborative learning focuses on the discussion forums and social
learning networks. There are only a few works that investigate how students
interact with each other in team projects and how such interactions affect
their academic performance. In order to bridge this gap, we choose a software
engineering course as the study subject. The students who participate in a
software engineering course are required to team up and complete a software
project together. In this work, we construct an interaction graph based on the
activities of students grouped in various teams. Based on this student
interaction graph, we present an extended graph transformer framework for
collaborative learning (CLGT) for evaluating and predicting the performance of
students. Moreover, the proposed CLGT contains an interpretation module that
explains the prediction results and visualizes the student interaction
patterns. The experimental results confirm that the proposed CLGT outperforms
the baseline models in terms of performing predictions based on the real-world
datasets. Moreover, the proposed CLGT differentiates the students with poor
performance in the collaborative learning paradigm and gives teachers early
warnings, so that appropriate assistance can be provided.

基于学生活动构建交互图的协作学习图形转换器框架（CLGT）用于评估和预测学生的表现，该框架超越基准模型，区分协作学习中表现差的学生并提供及时的教师干预。

CLGT: 协作学习中学生表现预测的图转换器

CLGT: A Graph Transformer for Student Performance Prediction in  Collaborative Learning

Knowledge graph completion (KGC) can predict missing links and is crucial for
real-world knowledge graphs, which widely suffer from incompleteness. KGC
methods assume a knowledge graph is static, but that may lead to inaccurate
prediction results because many facts in the knowledge graphs change over time.
Recently, emerging methods have shown improved predictive results by further
incorporating the timestamps of facts; namely, temporal knowledge graph
completion (TKGC). With this temporal information, TKGC methods can learn the
dynamic evolution of the knowledge graph that KGC methods fail to capture. In
this paper, for the first time, we summarize the recent advances in TKGC
research. First, we detail the background of TKGC, including the problem
definition, benchmark datasets, and evaluation metrics. Then, we summarize
existing TKGC methods based on how timestamps of facts are used to capture the
temporal dynamics. Finally, we conclude the paper and present future research
directions of TKGC.

本研究首次总结了时间知识图补全方法的研究进展，包括背景、基准数据集、评估指标以及现有方法，旨在解决静态知识图补全方法的不准确问题。未来的研究方向也在文章中提出。

时间知识图谱补全：综述

Temporal Knowledge Graph Completion: A Survey

Test data measured by medical instruments often carry imprecise ranges that
include the true values. The latter are not obtainable in virtually all cases.
Most learning algorithms, however, carry out arithmetical calculations that are
subject to uncertain influence in both the learning process to obtain models
and applications of the learned models in, e.g. prediction. In this paper, we
initiate a study on the impact of imprecision on prediction results in a
healthcare application where a pre-trained model is used to predict future
state of hyperthyroidism for patients. We formulate a model for data
imprecisions. Using parameters to control the degree of imprecision, imprecise
samples for comparison experiments can be generated using this model. Further,
a group of measures are defined to evaluate the different impacts
quantitatively. More specifically, the statistics to measure the inconsistent
prediction for individual patients are defined. We perform experimental
evaluations to compare prediction results based on the data from the original
dataset and the corresponding ones generated from the proposed precision model
using the long-short-term memories (LSTM) network. The results against a real
world hyperthyroidism dataset provide insights into how small imprecisions can
cause large ranges of predicted results, which could cause mis-labeling and
inappropriate actions (treatments or no treatments) for individual patients.

本文研究了不精确数据对医疗保健中预测结果的影响，以预测患者未来甲状腺功能亢进的状态为例，利用一种模型生成不精确样本进行对比实验并给出了一组定量评估指标，结果表明即使对于小范围的不精确数据，预测结果也会有较大的差异，这可能会导致个体患者的错标或不当操作。

医学数据不准确性对学习结果的影响

Impact of Medical Data Imprecision on Learning Results

Predicting the future direction of community evolution is a problem with high
theoretical and practical significance. It allows to determine which
characteristics describing communities have importance from the point of view
of their future behaviour. Knowledge about the probable future career of the
community aids in the decision concerning investing in contact with members of
a given community and carrying out actions to achieve a key position in it. It
also allows to determine effective ways of forming opinions or to protect group
participants against such activities. In the paper, a new approach to group
identification and prediction of future events is presented together with the
comparison to existing method. Performed experiments prove a high quality of
prediction results. Comparison to previous studies shows that using many
measures to describe the group profile, and in consequence as a classifier
input, can improve predictions.

本文介绍了一种新的小组识别和未来事件预测方法，并与现有方法进行了比较。所进行的实验证明了预测结果的高质量，并表明使用多种指标来描述群体轮廓可以提高预测的效果。