本文主要探究了一种现象:在游戏中,即使平日温和的人也可能变得易怒和恶劣,甚至对他人进行辱骂.作者利用心理学理论作为分析工具,并结合具体实验,把焦点放在了玩家背后的心理运作上.他指出了游戏中的"去个体化"的现象,即在游戏环境中,玩家的个人责任感会降低,对自身冲动行为的反思也会减少,这使他们更易受到环境和他人影响.此外,通过具体的实例分析,作者揭示了游戏公司如何利用心理学原理来改善游戏环境,从而为颇具争议的游戏暴力话题提供了 一个全新的视角.
在2001年在巴黎展出的名为"集中营回忆录"(Mémoire des camps:Photographies des camps de concentration et d'exdermination nazis 1933-1999)的展览中,展出了四幅由行刑队(Sonderkommando)队员拍摄的照片,乔治·迪迪-于贝尔曼认为这些图像是"in spite of all"(漠视一切)的,这一看法招致了伊丽莎白·帕努克斯、杰拉德·瓦克曼与克劳德·朗兹曼等人的质疑与批评.在《档案图像或表象图像》中,于贝尔曼围绕档案图像的真实性与图像是否具有想象力,以四张照片与朗兹曼的电影《浩劫》(Shoah,1985)为例,为自己的观点展开了辩护,探讨了摄影影像作为历史见证的合法性.
Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giving precise model predictions. An automatic machine learning framework is needed to ease the manual efforts in fine-tuning the models so that the experts can focus more on other problems that really need humans' engagement such as problem definition, deployment, and business services. However, there are three main challenges for building automatic solutions for temporal relational data: 1) how to effectively and automatically mining useful information from the multiple tables and the relations from them? 2) how to be self-adjustable to control the time and memory consumption within a certain budget? and 3) how to give generic solutions to a wide range of tasks? In this work, we propose our solution that successfully addresses the above issues in an end-to-end automatic way. The proposed framework, AutoSmart, is the winning solution to the KDD Cup 2019 of the AutoML Track, which is one of the largest AutoML competition to date (860 teams with around 4,955 submissions). The framework includes automatic data processing, table merging, feature engineering, and model tuning, with a time and memory controller for efficiently and automatically formulating the models. The proposed framework outperforms the baseline solution significantly on several datasets in various domains.
