Most past work on social network link fraud detection tries to separate
genuine users from fraudsters, implicitly assuming that there is only one type
of fraudulent behavior. But is this assumption true? And, in either case, what
are the characteristics of such fraudulent behaviors? In this work, we set up
honeypots ("dummy" social network accounts), and buy fake followers (after
careful IRB approval). We report the signs of such behaviors including oddities
in local network connectivity, account attributes, and similarities and
differences across fraud providers. Most valuably, we discover and characterize
several types of fraud behaviors. We discuss how to leverage our insights in
practice by engineering strongly performing entropy-based features and
demonstrating high classification accuracy. Our contributions are (a)
instrumentation: we detail our experimental setup and carefully engineered data
collection process to scrape Twitter data while respecting API rate-limits, (b)
observations on fraud multimodality: we analyze our honeypot fraudster
ecosystem and give surprising insights into the multifaceted behaviors of these
fraudster types, and (c) features: we propose novel features that give strong
(>0.95 precision/recall) discriminative power on ground-truth Twitter data.

本文探讨了社交网络链接欺诈检测中的多样性和特征，提供了强有效的基于熵的特征工程，对 Twitter 数据的地面真实性能表现达到了超过 0.95 的精确度和召回率。

链接欺诈的多种表现

The Many Faces of Link Fraud

Purpose: Basic surgical skills of suturing and knot tying are an essential
part of medical training. Having an automated system for surgical skills
assessment could help save experts time and improve training efficiency. There
have been some recent attempts at automated surgical skills assessment using
either video analysis or acceleration data. In this paper, we present a novel
approach for automated assessment of OSATS based surgical skills and provide an
analysis of different features on multi-modal data (video and accelerometer
data). Methods: We conduct the largest study, to the best of our knowledge, for
basic surgical skills assessment on a dataset that contained video and
accelerometer data for suturing and knot-tying tasks. We introduce "entropy
based" features - Approximate Entropy (ApEn) and Cross-Approximate Entropy
(XApEn), which quantify the amount of predictability and regularity of
fluctuations in time-series data. The proposed features are compared to
existing methods of Sequential Motion Texture (SMT), Discrete Cosine Transform
(DCT) and Discrete Fourier Transform (DFT), for surgical skills assessment.
Results: We report average performance of different features across all
applicable OSATS criteria for suturing and knot tying tasks. Our analysis shows
that the proposed entropy based features out-perform previous state-of-the-art
methods using video data. For accelerometer data, our method performs better
for suturing only. We also show that fusion of video and acceleration features
can improve overall performance with the proposed entropy features achieving
highest accuracy. Conclusions: Automated surgical skills assessment can be
achieved with high accuracy using the proposed entropy features. Such a system
can significantly improve the efficiency of surgical training in medical
schools and teaching hospitals.

本文介绍了一种基于熵的特征来自动评估 OSATS 基本手术技能的方法，并分析了多模数据（视频和加速度计数据）上不同的特征。结果显示，所提出的熵特征优于现有的视频数据分析的方法，而加速度计数据则仅在缝合中的恢复表现优异，融合视频和加速度数据可以提高总体性能。