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How Climate Change Was Won 如何赢得气候变化
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3623643
Ross Koningstein
From the intersection of computational science and technological speculation, with boundaries limited only by our ability to imagine what could be.
从计算科学和技术推测的交叉点开始,边界只被我们想象可能发生的事情的能力所限制。
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引用次数: 0
The Gamification of Academia 学术界的游戏化
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3625253
Sean Flaherty, Gregg Gordon
Gaming the system.
玩弄体制。
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引用次数: 0
The Infrapolitics of Algorithmic Resistance 算法抵抗的基础政治
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3573008
Ricardo Fabrino Mendonça, Fernando Filgueiras, Virgilio Almeida
Exploring the complex interactions between humans and machines governed by algorithms.
探索人类和由算法控制的机器之间复杂的相互作用。
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引用次数: 0
Accessibility and Inclusion through Technology 通过技术实现无障碍和包容
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3623642
Keith Kirkpatrick
Helping the sensory-impaired overcome their impediments.
帮助感觉障碍者克服障碍。
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引用次数: 0
What's Old Is New Again 旧的又是新的
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3624009
Logan Kugler
Analog computing offers one possible solution to the downsides of digital computing.
模拟计算为数字计算的缺点提供了一个可能的解决方案。
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引用次数: 0
Is TinyML Sustainable? TinyML可持续发展吗?
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3608473
Shvetank Prakash, Matthew Stewart, Colby Banbury, Mark Mazumder, Pete Warden, Brian Plancher, Vijay Janapa Reddi
Assessing the environmental impacts of machine learning on microcontrollers.
评估机器学习对微控制器的环境影响。
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引用次数: 2
Privacy in the Age of AI 人工智能时代的隐私
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3625254
Sauvik Das, Hao-Ping (Hank) Lee, Jodi Forlizzi
What has changed and what should we do about it?
发生了什么变化,我们应该做些什么?
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引用次数: 0
DevEX: What Actually Drives Productivity? DevEX:到底是什么推动了生产力?
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3610285
Abi Noda, Margaret-Anne Storey, Nicole Forsgren, Michaela Greiler
The developer-centric approach to measuring and improving productivity.
以开发人员为中心的度量和改进生产力的方法。
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引用次数: 0
Boosting Fuzzer Efficiency: An Information Theoretic Perspective 从信息论的角度提高模糊器的效率
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3611019
Marcel Böhme, Valentin J. M. Manès, Sang Kil Cha
In this paper, we take the fundamental perspective of fuzzing as a learning process. Suppose before fuzzing, we know nothing about the behaviors of a program P : What does it do? Executing the first test input, we learn how P behaves for this input. Executing the next input, we either observe the same or discover a new behavior. As such, each execution reveals "some amount" of information about P 's behaviors. A classic measure of information is Shannon's entropy. Measuring entropy allows us to quantify how much is learned from each generated test input about the behaviors of the program. Within a probabilistic model of fuzzing, we show how entropy also measures fuzzer efficiency. Specifically, it measures the general rate at which the fuzzer discovers new behaviors. Intuitively, efficient fuzzers maximize information. From this information theoretic perspective, we develop ENTROPIC, an entropy-based power schedule for greybox fuzzing that assigns more energy to seeds that maximize information. We implemented ENTROPIC into the popular greybox fuzzer LIBFUZZER. Our experiments with more than 250 open-source programs (60 million LoC) demonstrate a substantially improved efficiency and confirm our hypothesis that an efficient fuzzer maximizes information. ENTROPIC has been independently evaluated and integrated into the main-line LIBFUZZER as the default power schedule. ENTROPIC now runs on more than 25,000 machines fuzzing hundreds of security-critical software systems simultaneously and continuously.
在本文中,我们将模糊的基本观点看作是一个学习过程。假设在模糊测试之前,我们对程序的行为一无所知P:它做什么?执行第一个测试输入,我们了解P对这个输入的行为。执行下一个输入,我们要么观察到相同的行为,要么发现一个新的行为。因此,每次执行都揭示了关于P行为的“一些”信息。一个经典的信息度量是香农熵。度量熵允许我们量化从每个生成的关于程序行为的测试输入中学习到多少。在模糊的概率模型中,我们展示了熵是如何衡量模糊器效率的。具体来说,它测量模糊器发现新行为的一般速率。直觉上,高效的模糊者会最大化信息。从信息理论的角度来看,我们开发了ENTROPIC,这是一种基于熵的灰盒模糊调度,它将更多的能量分配给最大化信息的种子。我们在流行的灰盒模糊器LIBFUZZER中实现了ENTROPIC。我们对250多个开源程序(6000万LoC)的实验证明了效率的大幅提高,并证实了我们的假设,即高效的fuzzer可以最大化信息。ENTROPIC已被独立评估并集成到主线LIBFUZZER中,作为默认的电力计划。ENTROPIC现在在超过25,000台机器上运行,同时连续地对数百个安全关键软件系统进行模糊测试。
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引用次数: 3
Uncloneable Cryptography 不可克隆加密技术
3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2023-10-20 DOI: 10.1145/3576897
Or Sattath
In memory of Stephen Wiesner, 1942--2021.
纪念斯蒂芬·威斯纳,1942年-2021年。
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引用次数: 1
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