Wiki articles are created and maintained by a crowd of editors, producing a
continuous stream of reviews. Reviews can take the form of additions, reverts,
or both. This crowdsourcing model is exposed to manipulation since neither
reviews nor editors are automatically screened and purged. To protect articles
against vandalism or damage, the stream of reviews can be mined to classify
reviews and profile editors in real-time. The goal of this work is to
anticipate and explain which reviews to revert. This way, editors are informed
why their edits will be reverted. The proposed method employs stream-based
processing, updating the profiling and classification models on each incoming
event. The profiling uses side and content-based features employing Natural
Language Processing, and editor profiles are incrementally updated based on
their reviews. Since the proposed method relies on self-explainable
classification algorithms, it is possible to understand why a review has been
classified as a revert or a non-revert. In addition, this work contributes an
algorithm for generating synthetic data for class balancing, making the final
classification fairer. The proposed online method was tested with a real data
set from Wikivoyage, which was balanced through the aforementioned synthetic
data generation. The results attained near-90 % values for all evaluation
metrics (accuracy, precision, recall, and F-measure).

通过挖掘维基百科中的评论流，使用自解释的分类算法对评论进行分类和编辑进行特征分析，以保护文章免受破坏，并提供了一种生成合成数据进行分类的算法，该方法在实验中取得了近 90% 的评估指标值。

可解释的 Wiki 评论流分类

Interpretable classification of wiki-review streams

Selecting the right resources for big data analytics jobs is hard because of
the wide variety of configuration options like machine type and cluster size.
As poor choices can have a significant impact on resource efficiency, cost, and
energy usage, automated approaches are gaining popularity. Most existing
methods rely on profiling recurring workloads to find near-optimal solutions
over time. Due to the cold-start problem, this often leads to lengthy and
costly profiling phases. However, big data analytics jobs across users can
share many common properties: they often operate on similar infrastructure,
using similar algorithms implemented in similar frameworks. The potential in
sharing aggregated profiling runs to collaboratively address the cold start
problem is largely unexplored.
We present Karasu, an approach to more efficient resource configuration
profiling that promotes data sharing among users working with similar
infrastructures, frameworks, algorithms, or datasets. Karasu trains lightweight
performance models using aggregated runtime information of collaborators and
combines them into an ensemble method to exploit inherent knowledge of the
configuration search space. Moreover, Karasu allows the optimization of
multiple objectives simultaneously. Our evaluation is based on performance data
from diverse workload executions in a public cloud environment. We show that
Karasu is able to significantly boost existing methods in terms of performance,
search time, and cost, even when few comparable profiling runs are available
that share only partial common characteristics with the target job.

Karasu 是一种更高效的资源配置剖析方法，通过促进类似基础设施、框架、算法或数据集的用户之间的数据共享，训练轻量级性能模型并将它们组合成一个集成方法，以开发配置搜索空间中固有的知识，同时优化多个目标，从而显著提升现有方法在性能、搜索时间和成本方面的表现。

Karasu：大数据分析高效集群配置的合作方法

Karasu: A Collaborative Approach to Efficient Cluster Configuration for  Big Data Analytics

The human mind is a powerful multifunctional knowledge storage and management
system that performs generalization, type inference, anomaly detection,
stereotyping, and other tasks. A dynamic KR system that appropriately profiles
over sparse inputs to provide complete expectations for unknown facets can help
with all these tasks. In this paper, we introduce the task of profiling,
inspired by theories and findings in social psychology about the potential of
profiles for reasoning and information processing. We describe two generic
state-of-the-art neural architectures that can be easily instantiated as
profiling machines to generate expectations and applied to any kind of
knowledge to fill gaps. We evaluate these methods against Wikidata and crowd
expectations, and compare the results to gain insight in the nature of
knowledge captured by various profiling methods. We make all code and data
available to facilitate future research.

介绍了基于社会心理学理论的 profile 在知识预测中的应用，提出了两种可实例化的神经架构，经过对维基数据和众包期望的评估和比较，揭示了各种 profiling 方法捕获知识的本质，同时将所有代码和数据公开，以促进未来的研究。