Monitoring the status of large computing systems is essential to identify
unexpected behavior and improve their performance and uptime. However, due to
the large-scale and distributed design of such computing systems as well as a
large number of monitoring parameters, automated monitoring methods should be
applied. Such automatic monitoring methods should also have the ability to
adapt themselves to the continuous changes in the computing system. In
addition, they should be able to identify behavioral anomalies in useful time,
to perform appropriate reactions. This work proposes a general lightweight and
unsupervised method for near real-time anomaly detection using operational data
measurement on large computing systems. The proposed model requires as little
as 4 hours of data and 50 epochs for each training process to accurately
resemble the behavioral pattern of computing systems.

该研究提出了一种轻量级、无监督的方法，通过对大型计算系统的操作数据进行测量，实时检测异常行为并能够在较短的时间内做出适当反应。

一种轻量级和无监督的方法，用于使用运营数据测量进行近实时行为分析

A Light-weight and Unsupervised Method for Near Real-time Behavioral  Analysis using Operational Data Measurement

With the rapid development of artificial intelligence (AI) community,
education in AI is receiving more and more attentions. There have been many AI
related courses in the respects of algorithms and applications, while not many
courses in system level are seriously taken into considerations. In order to
bridge the gap between AI and computing systems, we are trying to explore how
to conduct AI education from the perspective of computing systems. In this
paper, a course practice in intelligent computing architectures are provided to
demonstrate the system education in AI era. The motivation for this course
practice is first introduced as well as the learning orientations. The main
goal of this course aims to teach students for designing AI accelerators on
FPGA platforms. The elaborated course contents include lecture notes and
related technical materials. Especially several practical labs and projects are
detailed illustrated. Finally, some teaching experiences and effects are
discussed as well as some potential improvements in the future.

本文讨论了从计算系统的角度出发如何进行人工智能的教育，并提供了一门针对智能计算架构的课程实践，旨在通过讲授人工智能加速器在 FPGA 平台的设计，来弥合人工智能和计算系统之间的差距。

人工智能系统教育之路：智能计算架构课程实践

Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures

This volume contains the proceedings of the Thirteenth International Workshop
on the ACL2 Theorem Prover and Its Applications, ACL2 2015, a two-day workshop
held in Austin, Texas, USA, on October 1-2, 2015. ACL2 workshops occur at
approximately 18-month intervals and provide a major technical forum for
researchers to present and discuss improvements and extensions to the theorem
prover, comparisons of ACL2 with other systems, and applications of ACL2 in
formal verification.
ACL2 is a state-of-the-art automated reasoning system that has been
successfully applied in academia, government, and industry for specification
and verification of computing systems and in teaching computer science courses.
In 2005, Boyer, Kaufmann, and Moore were awarded the 2005 ACM Software System
Award for their work on ACL2 and the other theorem provers in the Boyer-Moore
family.

该论文介绍了一个自动化推理系统 ACL2 定理证明器，其应用包括计算系统规范和验证，形式验证等，Boyer-Moore 家族的成员在 ACL2 Theorem Prover 研究和发展中做出重要贡献。