Papers We Love began as a repository of academic computer science papers. It quickly morphed into an international series of monthly meetups. It flowered into a diverse community of computer enthusiasts interested in research as a hobby or applied to their daily work.

Papers We Love Conference is a one day event to cross-pollinate ideas between academic research and industrial practice. We are very excited to be back in St. Louis as part of the Strangeloop Pre-Conf program for the fourth year in a row.

Papers We Love Conf

Papers are like poems. Some are dazzling, some are pedestrian, some are insightful, and some reward long periods of quiet contemplation. They stir up an emotional reaction that goes beyond the strictly rational, and can often be deeply personal.

In graduate school, during a period of identity crisis, I came across Matthias Felleisen's “On the Expressive Power of Programming Languages”. At a time when the world was ruled by C++, I had immersed myself in Scheme, so I always looked skeptically at mainstream linguistic claims. However, the language wars seemed beyond rational discourse. So the idea that someone could take a concept as nebulous as “expressiveness&rdquo and formalize it was already a revelation. But the beauty of this paper goes well beyond that: it also lies in the cleanliness of the approach, the correspondence of the formalism to intuition, and the tautness of its execution.

It was the most stunning paper I had ever read, and remains so. It's like the poem that never leaves your soul.

Unfortunately, this paper may not be easy to read for the uninitiated: it depends on a certain amount of “cultural knowledge” of programming language theory. I hope to peel off some of those layers and help you, too, understand the paper — hopefully while preserving the joy and beauty I experienced.

References
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On the expressive power of programming languages
Felleisen M.
(1991)  Science of Computer Programming,  17  (1-3) , pp. 35-75.
https://www.sciencedirect.com/science/article/pii/016764239190036W

On the Expressive Power of Programming Languages

Recently, researchers have demonstrated deanonymization attacks that exploit weaknesses in the Bitcoin network's peer-to-peer (P2P) networking protocols. In particular, the P2P network currently forwards content in a structured way that allows observers to link users' Bitcoin addresses to their IP addresses. This is a substantial privacy vulnerability that extends to several other cryptocurrencies as well. In this talk, I will discuss how these attacks work, and how effective they are at deanonymizing users. I will also talk about countermeasures, including proposed modifications to the networking stack. 

References
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An analysis of anonymity in Bitcoin using P2P traffic by Philip Koshy, Diana Koshy, and Patrick McDaniel; FC 2014 (https://www.ifca.ai/fc14/papers/fc14_submission_71.pdf)

Deanonymisation of clients in Bitcoin P2P network by Alex Biryukov, Dmitry Khovratovich, and Ivan Pustogarov; CCS 2014 (https://arxiv.org/pdf/1405.7418)

Dandelion: Redesigning the bitcoin network for anonymity by Shaileshh Bojja Venkatakrishnan, Giulia Fanti, and Pramod Viswanath; Sigmetrics 2017 (https://arxiv.org/pdf/1701.04439)

Anonymity properties of the Bitcoin P2P network by Giulia Fanti and Pramod Viswanath; NeurIPS 2017 (https://arxiv.org/pdf/1703.08761.pdf)

Anonymity in the Bitcoin Peer-to-Peer Network

Program analysis is the art of reasoning about the run-time behavior of a program without necessarily executing it. This information is useful for various real-life applications such as supporting software developers (e.g., bug-finding tools, code refactoring tools, and code recommenders) and compiler optimizations. Program analysis is also used to ensure complex software adheres to standards and regulations (e.g., medical devices, car industry, and aviation industry).

In this talk, I will discuss the three main properties that enable program analyses to be useful in practice: scalability, precision, and usability. I will relate that to various papers that have been published in the field of program analysis, as well as some of the work that my group has done. I will conclude with where I see program analysis research going and the challenges that we aim to solve in the field. 

References
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Strictly declarative specification of sophisticated points-to analyses by Martin Bravenboer and Yannis Smaragdakis (https://people.cs.umass.edu/~yannis/doop-oopsla09prelim.pdf)

Dimensions of Precision in Reference Analysis of Object-Oriented Programming Languages by Barbara G. Ryder (http://prolangs.cs.vt.edu/refs/docs/cc03.pdf)

Averroes: Whole-Program Analysis without the Whole Program by Karim Ali and Ondřej Lhoták (https://karimali.ca/resources/papers/averroes.pdf)

FlowDroid: Precise Context, Flow, Field, Object-Sensitive and Lifecycle-Aware Taint Analysis for Android Apps by Steven Arzt, Siegfried Rasthofer, Christian Fritz, Eric Bodden, Alexandre Bartel, Jacques Klein, Yves Le Traon, Damien Octeau, and Patrick McDaniel (https://orbilu.uni.lu/bitstream/1099)

Precise Interprocedural Dataflow Analysis with Applications to Constant Propagation by Shmuel Sagiv, Thomas W. Reps, and Susan Horwitz (https://link.springer.com/content/pdf/10.1007/3-540-59293-8_226.pdf)

Context-, Flow-, and Field-Sensitive Data-Flow Analysis using Synchronized Pushdown Systems by Johannes Späth, Karim Ali, and Eric Bodden (https://karimali.ca/resources/papers/spds.pdf)

Debugging Static Analysis by Lisa Nguyen Quang Do, Stefan Krüger, Patrick Hill, Karim Ali, and Eric Bodden (https://karimali.ca/resources/papers/visuflow.pdf)

Towards Better Inlining Decisions Using Inlining Trials by Jeffrey Dean and Craig Chamber (https://courses.cs.washington.edu/courses/cse501/06wi/reading/dean-lfp94.pdf)

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks by Guy Katz, Clark W. Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer (https://theory.stanford.edu/~barrett/pubs/KBD+17.pdf)

Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification by Pengfei Yang, Jiangchao Liu, Jianlin Li, Liqian Chen, and Xiaowei Huang (https://arxiv.org/abs/1902.09866)

Soot (https://github.com/Sable/soot)

IBM. T.J. Watson Libraries for Analysis WALA (http://wala.sourceforge.net/)

Is Program Analysis the Silver Bullet Against Software Bugs?

30 years ago Colin Runciman asked What About the Natural Numbers? Now, in 2019, we find ourselves in need of a successor to carry Runciman's banner. Despite major advances in type systems and the growing adoption of the slogan 'make illegal states unrepresentable', we often rely on Integers in cases where negative values have no meaning. Runciman's paper reminds us of a fact that we all know: the choice in types can change the nature of an API. Integers are often the default in many systems and APIs, often for no reason beyond programmer familiarity. In this talk we will argue two main points: that for many cases Natural numbers retain all of the positive aspects of the Integers with none of the negatives, and that when designing a system or an API we should constantly be asking ourselves "What about X?" 

References
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What About the Natural Numbers by Colin Runciman (https://www.sciencedirect.com/science/article/pii/0096055189900040?via%3Dihub)

What About the Natural Numbers?

 Distributed consensus, the ability to reach agreement in the face of failures and asynchrony, is a fundamental and powerful primitive for constructing reliable distributed systems from unreliable components. For over two decades, the Paxos algorithm has been synonymous with distributed consensus. Paxos is widely deployed in production systems, yet it is poorly understood and it proves to be heavyweight, unscalable and unreliable in practice.

In this talk, we re-examine the foundations of how Paxos solves distributed consensus. Our hypothesis is that these limitations are not inherent to the problem of consensus but instead specific to the approach of Paxos. The surprising result of our analysis is a substantial weakening to the requirements of this widely studied algorithm. Building on this insight, we are able to prove an extensive generalisation over the Paxos algorithm. 

References
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Distributed consensus revised by Heidi Howard (https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-935.pdf)

The part-time parliament by Leslie Lamport (https://lamport.azurewebsites.net/pubs/lamport-paxos.pdf)

Paxos Made Moderately Complex by Robbert Van Renesse and Deniz Altinbuken (http://www.cs.cornell.edu/courses/cs7412/2011sp/paxos.pdf)

Flexible Paxos: Quorum intersection revisited by Heidi Howard, Dahlia Malkhi, Alexander Spiegelman (https://arxiv.org/abs/1608.06696)

A Generalised Solution to Distributed Consensus by Heidi Howard, Richard Mortier (https://arxiv.org/abs/1902.06776)

the morning paper: Distributed consensus revised - Part I (https://blog.acolyer.org/2019/05/07/distributed-consensus-revised-part-i/)

the morning paper: Distributed consensus revised - Part II (https://blog.acolyer.org/2019/05/08/distributed-consensus-revised-part-ii/)

the morning paper: Distributed consensus revised - Part III (https://blog.acolyer.org/2019/05/10/distributed-consensus-revised-part-iii/)

Distributed Consensus Revised

References
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Matching In-Car Voice with Driver State: Impact on Attitude and Driving Performance by Ing-Marie Jonsson, Clifford Nass, Helen Harris, and Leila Takayama, 2005. (http://www.leilatakayama.org/downloads/Takayama.DriverEmotion_2005_prepress.pdf)

Papers We Love Conf 2019

Sep 12 | Missouri, USA