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报告主题:Taming Social Bots: Detection, Exploration and Measurement

报告摘要:社交机器人已经存在了十多年了。 社交机器人可以摇摆政治见解,散布虚假信息并招募恐怖组织。 社交机器人通过采用情感,同情跟随,同步删除和轮廓转换来使用各种复杂的技术。 文献中提出了几种检测,探索和衡量社交机器人的方法。 我们将从数据挖掘和机器学习的角度对现有工作进行全面概述,讨论各种方法的相对优势和劣势,为研究人员和从业人员提供建议,并为驯服社交机器人的未来研究提出新的方向。 本教程还将讨论在社交机器人上收集和共享数据的陷阱。

邀请嘉宾:Nikan Chavoshi于2018年6月加入Oracle,担任高级技术人员。此前,她在新墨西哥大学获得了计算机科学博士学位。 她的研究兴趣是时间序列挖掘和时间活动分析。 她的研究生工作是分析Twitter中自动帐户的时态行为,为此,她于2018年获得了CS部门的杰出研究生奖。在博士学位期间,她与Abdullah Mueen博士合作并设计了一种近实时系统,名为DeBot,以检测Twitter中的自动帐户。 Chavoshi博士在包括WWW,ICDM,SocInfo,ASONAM和KAIS在内的顶级网络采矿场所发表了研究文章。

Amanda J. Minnich是劳伦斯·利弗莫尔国家实验室(LLNL)的机器学习研究科学家和分子数据驱动的建模团队负责人。在LLNL,她是多机构ATOM联盟的成员,在那里她将机器学习技术应用于生物学数据以进行药物发现。 Minnich博士获得了加州大学伯克利分校的综合生物学学士学位(2009年),并获得了新墨西哥大学的计算机科学硕士学位(2014年)和杰出博士学位(2017年)。她的论文主题是“垃圾邮件,欺诈和僵尸程序:提高在线社交媒体数据的完整性”。在UNM期间,她被任命为NSF研究生研究员,PiBBs研究员,Grace Hopper学者以及该领域的杰出研究生。 CS部门在2017年发表了她的作品。她曾在包括WWW,ASONAM,KDD,ICDM,SC,GTC和ICWE在内的顶级会议的程序委员会上发表论文并任职,并因此获得了论文研究专利。 Minnich博士还热衷于倡导技术女性。她是UNM第一个特许的女性参与计算小组的联合创始人并担任总裁,她经常在科技活动中为女性做志愿者,她将在Grace Hopper Celebration 2019担任人工智能专题的联合主席。

Abdullah Mueen,新墨西哥大学计算机科学的助理教授。在此之前,他曾是Microsoft Corporation云和信息科学实验室的科学家。 他的主要兴趣是时间数据挖掘,重点是两种独特的信号类型:社交网络和电子传感器。 他一直积极参与数据挖掘会议,包括KDD,ICDM和SDM,以及包括DMKD和KAIS在内的期刊。 他在2012年KDD博士论文竞赛中获得亚军。他在SIGKDD 2012上获得了最佳论文奖。他的研究由NSF,DARPA和AFRL资助。 此前,他在加利福尼亚大学河滨分校获得博士学位,并在孟加拉国工程技术大学获得理学学士学位。

代码链接:https://www.cs.unm.edu/~chavoshi/debot/

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We study time/memory tradeoffs of function inversion: an algorithm, i.e., an inverter, equipped with an s-bit advice on a randomly chosen function $f : [n] -> [n]$ and using $q$ oracle queries to $f$, tries to invert a randomly chosen output $y$ of $f$, i.e., to find $x\in f^{-1}(y)$. Much progress was done regarding adaptive function inversion - the inverter is allowed to make adaptive oracle queries. Hellman [IEEE transactions on Information Theory 80] presented an adaptive inverter that inverts with high probability a random $f$. Fiat and Naor [SICOMP 00] proved that for any $s$, $q$ with $s^3q = n$ (ignoring low-order terms), an $s$-advice, $q$-query variant of Hellmans algorithm inverts a constant fraction of the image points of any function. Yao [STOC 90] proved a lower bound of $sq \geq n$ for this problem. Closing the gap between the above lower and upper bounds is a long-standing open question. Very little is known for the non-adaptive variant of the question. The only known upper bounds, i.e., inverters, are the trivial ones (with $s+q = n$), and the only lower bound is the above bound of Yao. In a recent work, Corrigan-Gibbs and Kogan [TCC 19] partially justified the difficulty of finding lower bounds on non-adaptive inverters, showing that a lower bound on the time/memory tradeoff of non-adaptive inverters implies a lower bound on low-depth Boolean circuits. Bounds that, for a strong enough choice of parameters, are notoriously hard to prove. We make progress on the above intriguing question, both for the adaptive and the non-adaptive case, proving the following lower bounds on restricted families of inverters.

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We study time/memory tradeoffs of function inversion: an algorithm, i.e., an inverter, equipped with an s-bit advice on a randomly chosen function $f : [n] -> [n]$ and using $q$ oracle queries to $f$, tries to invert a randomly chosen output $y$ of $f$, i.e., to find $x\in f^{-1}(y)$. Much progress was done regarding adaptive function inversion - the inverter is allowed to make adaptive oracle queries. Hellman [IEEE transactions on Information Theory 80] presented an adaptive inverter that inverts with high probability a random $f$. Fiat and Naor [SICOMP 00] proved that for any $s$, $q$ with $s^3q = n$ (ignoring low-order terms), an $s$-advice, $q$-query variant of Hellmans algorithm inverts a constant fraction of the image points of any function. Yao [STOC 90] proved a lower bound of $sq \geq n$ for this problem. Closing the gap between the above lower and upper bounds is a long-standing open question. Very little is known for the non-adaptive variant of the question. The only known upper bounds, i.e., inverters, are the trivial ones (with $s+q = n$), and the only lower bound is the above bound of Yao. In a recent work, Corrigan-Gibbs and Kogan [TCC 19] partially justified the difficulty of finding lower bounds on non-adaptive inverters, showing that a lower bound on the time/memory tradeoff of non-adaptive inverters implies a lower bound on low-depth Boolean circuits. Bounds that, for a strong enough choice of parameters, are notoriously hard to prove. We make progress on the above intriguing question, both for the adaptive and the non-adaptive case, proving the following lower bounds on restricted families of inverters.

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