Communications of ACM June 06 2016

8/16/2019 Communications of ACM June 06 2016

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Whitfield Diffie

Martin E. HellmanRecipients of ACM’sA.M. Turing Award

COMMUNICATIONSOF THE

ACMCACM.ACM.ORG 06/2016 VOL.59 NO.06

Association for

Computing Machinery

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Is Internet sofware so different rom “ordinary” sofware? Tis book practically answers this question

through the presentation o a sofware design method based on the State Chart XML W3C standardalong with Java. Web enterprise, Internet-o-Tings, and Android applications, in particular, areseamlessly specified and implemented rom “executable models.”

Internet sofware puts orward the idea o event-driven or reactive programming, as pointed out inBonér et al.’s “Reactive Maniesto”. It tells us that reactiveness is a must. However, beyond concepts,sofware engineers require effective means with which to put reactive programming into practice.Reactive Internet Programming outlines and explains such means.

Te lack o proessional examples in the literature that illustrate how reactive sofware shouldbe shaped can be quite rustrating. Tereore, this book helps to fill in that gap by providing in-

depth proessional case studies that contain comprehensive details and meaningul alternatives.Furthermore, these case studies can be downloaded or urther investigation.

Internet sofware requires higher adaptation, at run time in particular. Afer reading Reactive InternetProgramming , you will be ready to enter the orthcoming Internet era.

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this.splash 2016 Amsterdam Sun 30 October – Fri 4 November 2016

ACM SIGPLAN Conference on Systems, Programming, Languages and Applications:

Software for Humanity (SPLASH)

Novel research on software development and programming

Radical new ideas and visions related to programming and software

World class speakers on current topics in software, systems, and languages research

Researchers and educators share educational results, ideas, and challenges

Dynamic languages, implementations, and applications

Generative programming: concepts and experiences

Principles of software language engineering, language design, and evolution

Mövenpick

Amsterdam

SPLASH General Chair: Eelco Visser

OOPSLA Papers: Yannis Smaragdakis

OOPSLA Artifacts: Michael Bond, Michael Hind

Onward! Papers: Emerson Murphy-HillOnward! Essays: Crista Lopes

SPLASH-I: Eelco Visser, Tijs van der Storm

SPLASH-E: Matthias Hauswirth, Steve Blackburn

DLS: Roberto Ierusalimschy

Workshops: Jan Rellermeyer, Craig Anslow

SLE General Chair: Tijs van der Storm

SLE Papers: Emilie Balland, Daniel Varro

GPCE General Chair: Bernd Fischer

GPCE Papers: Ina SchaeferStudent Research Competition: Sam Guyer, Patrick Lam

Posters: Jeff Huang, Sebastian Erdweg

Publications: Alex Potanin

Publicity and Web: Tijs van der Storm, Ron Garcia

Student Volunteers: Daco Harkes

OOPSLA

Onward!

SPLASH-I

SPLASH-E

DLS

GPCE

SLE

@splashcon bit.ly/splashcon162016.splashcon.org

Biermann

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2 COMMUNICATIONS OF THE ACM | JUNE 2016 | VOL. 59 | NO. 6

COMMUNICATIONS OF THE ACM

I M A G E S B Y C R E A T I O N S , E V E R E T T C O L L E C T I O N / S H U T T E R S T O C K

News

12 Turing Profile

The Key to Privacy

40 years ago, Whitfield Diffie and

Martin Hellman introduced

the public key cryptography used to

secure today’s online transactions.

By Neil Savage

15 What Happens When

Big Data Blunders?

Big data is touted as a cure-all for

challenges in business, government,

and healthcare, but as disease

outbreak predictions show,

big data often fails.

By Logan Kugler

17 Reimagining Search

Search engine developers are moving

beyond the problem of document

analysis, toward the elusive goal of

figuring out what people really want.

By Alex Wright

20 What’s Next for Digital Humanities?

New computational tools spur

advances in an evolving field.

By Gregory Mone

Watch the Turingrecipients discuss theirwork in this exclusiveCommunications video.http://cacm.acm.org/videos/the-key-to-privacy

Viewpoints

22 Inside Risks

The Risks of Self-Auditing Systems

Unforeseen problems can

result from the absence of

impartial independent evaluations.

By Rebecca T. Mercuri

and Peter G. Neumann

26 Kode Vicious What Are You Trying to Pull?

A single cache miss is more

expensive than many instructions.

By George V. Neville-Neil

28 The Profession of IT

How to Produce Innovations

Making innovations happen

is surprisingly easy, satisfying,

and rewarding if you start

small and build up.

By Peter J. Denning

31 Interview

An Interview with Yale Patt

ACM Fellow Professor Yale Patt

reflects on his career.

By Derek Chiou

Watch Patt discusshis work in this exclusiveCommunications video.http://cacm.acm.org/videos/an-interview-with-yale-patt

For the full-length video,please visit https://vimeo.com/an-interview-with-

yale-patt

Departments

5 From the President

Moving Forward

By Alexander L. Wolf

7 Cerf’s Up

Celebrations!

By Vinton G. Cerf

8 Letters to the Editor

No Backdoor Required or Expected

10 BLOG@CACM

The Solution to AI, What Real

Researchers Do, and Expectations

for CS Classrooms

John Langford on AlphaGo, Bertrand

Meyer on Research as Research,

and Mark Guzdial on correlating

CS classes with laboratory results.

29 Calendar

Last Byte

112 Q&A

Finding New Directions

in Cryptography

Whitfield Diffie and Martin Hellman

on their meeting, their research, and

the results that billions use every day.

By Leah Hoffmann

Association for Computing Machinery

Advancing Computing as a Science & Profession

15 26

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JUNE 2016 | VOL. 59 | NO. 6 | COMMUNICATIONS OF THE ACM 3

06/2016VOL. 59 NO. 06

I M A G E S B Y B E N I S A R A P O V I C / D O T S H O C K , F O R A N C E

Contributed Articles

62 Improving API Usability

Human-centered design can make

application programming interfaces

easier for developers to use.

By Brad A. Myers and Jeffrey Stylos

70 Physical Key Extraction

Attacks on PCs

Computers broadcast their secrets viainadvertent physical emanations that

are easily measured and exploited.

By Daniel Genkin, Lev Pachmanov,

Itamar Pipman, Adi Shamir,

and Eran Tromer

Viewpoints (cont’d.)

37 Viewpoint

Computer Science

Should Stay Young

Seeking to improve computer science

publication culture while retaining

the best aspects of the conference

and journal publication processes.

By Boaz Barak

39 Viewpoint

Privacy Is Dead, Long Live Privacy

Protecting social norms

as confidentiality wanes.

By Jean-Pierre Hubaux and Ari Juels

42 Viewpoint

A Byte Is All We Need

A teenager explores ways

to attract girls into the magical world

of computer science.

By Ankita Mitra

Practice

45 Nine Things I Didn’t Know I Would

Learn Being an Engineer Manager

Many of the skills

aren’t technical at all.

By Kate Matsudaira

48 The Flame Graph

This visualization of software execution

is a new necessity for performance

profiling and debugging.

By Brendan Gregg

58 Standing on DistributedShoulders of Giants

Farsighted physicists of yore

were danged smart!

By Pat Helland

Articles’ development led by

queue.acm.org

Review Articles

80 RandNLA: Randomized

Numerical Linear Algebra

Randomization offers new benefits

for large-scale linear computations.

By Petros Drineas and

Michael W. Mahoney

Research Highlights

92 Technical Perspective

Veritesting Tackles

Path-Explosion Problem

By Koushik Sen

93 Enhancing Symbolic

Execution with Veritesting

By Thanassis Avgerinos,

Alexandre Rebert, Sang Kil Cha,

and David Brumley

101 Technical PerspectiveComputing with the Crowd

By Siddharth Suri

102 AutoMan: A Platform for

Integrating Human-Based

and Digital Computation

By Daniel W. Barowy, Charlie Curtsinger,

Emery D. Berger, and Andrew McGregor

About the Cover: Whitfield Diffie (left) andMartin E. Hellman,cryptography pioneersand recipients ofthe 2015 ACM A.M. TuringAward, photographed atStanford University’sHuang Center in March.Photographed by

Richard Morgenstein,http://www.morgenstein.com/

8062

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COMMUNICATIONS OF THE ACMTrusted insights for computing’s leading professionals.

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from the president

AS M Y T E N U R E as ACMpresident ends, I find my-self reflecting on the pasttwo years and so I lookedback at my 2014 election

position statement. [W]e must confront the reality that

what ACM contributed to the computing

profession for more than 65 years mightnot sustain it in the future …

ACM was formed in 1947 by a smallgroup of scientists and engineers whohad helped usher in the computer ageduring World War II. They saw ACMas a means for professionals, primar-ily mathematicians and electrical en-gineers, to exchange and curate tech-nical information about “computingmachinery.” The fact that ACM is nowhome to a much broader community ofinterests, with members literally span-

ning the globe, was likely well beyondtheir imagination.

Conferences and publications re-main the primary means by which ourorganization sustains itself. I worriedin 2014 that revenue would eventuallyfall, and that we needed to prepare. Ipointed out in a 2015 Communications letter that conference surpluses go di-rectly back to the SIGs, while publica-tion surpluses are used to subsidizethe entire enterprise: allowing studentmembers everywhere, and reduced-

rate professional members in devel-oping regions, to receive full memberbenefits; contributing an additional$3M per year to the SIGs; and support-ing in entirety our volunteer-driven ef-forts in education, inclusion, and pub-lic policy. The specter of open accessundercutting the library subscriptionbusiness created many uncertainties,some of which remain to this day.

Two years on, some things are com-ing into better focus, giving hope thatconferences and publications will re-main viable revenue sources.

As it turns out, the popularity ofour conferences continues to rise withoverall conference attendance steadilyincreasing. I attribute this to the grow-ing importance and influence of com-puting, and the broadening of ACM’sconstituency and audience.

We have empowered authors and

conference organizers with new openaccess options. Yet the uptake of Gold(“author pays”) open access is sur-prisingly slow and the growth of thesubscription business is surprisinglyrobust. Perhaps most profound is therealization that the marketable valueof ACM’s Digital Library derives notso much from access to individual ar-ticles, as from access to the collection and the services that leverage and en-hance the collection. In other words,

ACM sells subscriptions to a collection,

so in a sense open access to articles isnot the immediate threat. Moreover,there is a potential future business tobe built around government mandatesfor open data, reproducible computa-tion, and digital preservation generallythat takes us far beyond today’s simplePDF artifact and collection index.

We must recognize that the natureof community, community identity, and

“belonging” is evolving rapidly … What is the value of being formally

associated with ACM? This seemingly

simple and fundamental questioncomes up so often that the answershould be obvious and immediate.Twenty years ago, perhaps it was. To-day, although I personally feel the value,I struggle to articulate an answer that Iam confident will convince someonenew to the community already engaged

with others through means falling out-side the traditional ACM circle.

What I do know is that remarkablyfew people are aware of the importantand impactful volunteer activities be-

yond conferences and publications

that are supported by ACM. This seemsto be the case whether the person isone of our more than 100,000 dues-paying members or one of the millionsof non-dues-paying participants andbeneficiaries in ACM activities.

That is why I sought to “change theconversation” around ACM, from merelyserving as computing’s premier confer-ence sponsor and publisher to also beinga potent and prominent force for good

in the community. My goal was to raiseawareness that ACM, as a professional society, offers a uniquely authoritative,respected voice, one that can amplifythe efforts of individuals in a way thatan ad hoc social network cannot. That

ACM and its assets are at the disposalof its members and volunteer leaders todrive its agenda forward. And that beinga member of this organization is a state-ment in support of that agenda. Gettingthis message out is largely about how

ACM presents itself to the world through

its communication channels, which arein the process of a long-overdue refresh.

ACM’s services and programs are

founded on three vital pillars: energeticvolunteers, dedicated HQ staff, and a

sufficient and reliable revenue stream …The most rewarding experiences I

had as president were visits with themany communities within the commu-nity that is ACM: SIGs, chapters, boards,and committees. Each different, yetbound by a commitment to excellencethat is our organization’s hallmark. En-

abling those communities is a profes-sional staff as passionate about ACM asits members. They deserve our thanksand respect.

As I end my term, I wish the nextpresident well in continuing to movethe organization forward. You havegreat people to work with and an im-portant legacy to continue.

Alexander L. Wolf is president of ACM and a professorin the Department of Computing at Imperial CollegeLondon, UK.

Copyright held by author.

Moving ForwardDOI:10.1145/2933245 Alexander L. Wolf

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ARL Distinguished Postdoctoral Fellowships

The Army Research Laboratory ( ARL) is the nation’s premier laboratory forland forces. The civilians working at ARL and i ts predecessors have hadmany successes in basic and applied research. Currently, ARL scientists andengineers are pioneering research in such areas as neuroscience, energetic

materials and propulsion, electronics technologies, network sciences, virtualinterfaces and synthetic environments and autonomous systems. They areleaders in modeling and simulation and have high performance computingresources on-site. They are expanding into frontier areas, including fields suchas quantum information and quantum networks.

We invite outstanding young researchers to participate in this excitementas ARL Distinguished Postdoctoral Fellows. These Fellows will displayextraordinary ability in scientific research and show clear promise ofbecoming outstanding future leaders. Candidates are expected to havealready successfully tackled a major scientific or engineering problem or tohave provided a new approach or insight evidenced by a recognized impactin their field. ARL offers these named Fellowships in honor of distinguishedresearchers and work that has been performed at Army labs.

The ARL Distinguished Postdoctoral Fellowships are three-year appointments.The annual stipend is $100,000, and the fellowship includes benefits and

potential additional funding for selected proposals. Applicants must hold aPh.D., awarded within the past three years, at the time of application. Forcomplete application instructions and more information, visit:http://sites.nationalacademies.org/PGA/Fellowships/ARL.

Applications must be received by July 1, 2016.

The National Academies of

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cerf’s up

There is a rhythm in the affairs of the Association for Computing Machinery and June marks our annual celebration of awardrecipients and the biennial election of new

officers. I will end my final year as

past president, Alex Wolf will beginhis first year in that role, and a newpresident and other officers will taketheir places in the leadership. Junealso marks Bobby Schnabel’s firstappearance at our annual awardsevent in his role as CEO of ACM. I amespecially pleased that two formerStanford colleagues, Martin Hellmanand Whitfield Diffie, are receivingthe ACM A.M. Turing Award this year.Nearly four decades have passed sincetheir seminal description of what has

become known as public key cryptog-raphy and in that time the technologyhas evolved and suffused into muchof our online and offline lives.

In another notable celebration, Alphabet, the holding company thatincludes Google, saw its AlphaGo sys-tem from DeepMind win four of fiveGO games in Seoul against a worldclass human player. The complexityof the state space of GO far exceedsthat of chess and many of us weresurprised to see how far neural net-

works have evolved in what seemssuch a short period of time. Interest-ingly, the system tries to keep trackof its own confidence level as it usesthe state of the board to guide itschoices of next possible moves. Weare reminded once again how com-plexity arises from what seems to bethe simplest of rules.

While we are celebrating advancesin artificial intelligence, other voicesare forecasting a dark fate for human-ity. Intelligent machines, once theycan match a human capacity, will go

on to exceed it, they say. Indeed, our

supercomputers and cloud-basedsystems can do things that no humancan do, particularly with regard to“big data.” Some of us, however, seethe evolution of computing capabil-ity in terms of partnership. When youdo a search on the World Wide Webor use Google to translate from onelanguage to another, you are makinguse of powerful statistical methods,parsing, and semantic graphs to ap-proximate what an accomplishedmultilingual speaker might do. These

translations are not perfect but theyhave been improving over time. Thisdoes not mean, however, that theprograms understand in the deepestcognitive sense what the words andsentences mean. In large measure,such translation rests on strong cor-relation and grammar. This is notto minimize the utility of such pro-grams—they enhance our ability tocommunicate across language barri-ers. They can also create confusion

when misinterpretation of colloqui-

alisms or other nuances interfere with precision.

One has to appreciate, however,the role of robotics in manufactur-ing in today’s world. The Tesla fac-tory in Fremont, CA, is a marvel ofautomationa and there are manyother examples, including the proc-ess of computer chip productionthat figures so strongly in the work of

ACM’s members. Automation can beconsidered an aspect of artificial in-

a https://www.youtube.com/watch?v=TuC8drQmXjg

telligence if by this we mean the au-

tonomous manipulation of the real world. Of course, one can also argue,as I have in the past, that stock mar-ket trading programs are robotic inthe sense they receive inputs, per-form analysis, and take actions thataffect the real world (for example, ourbank accounts). Increasingly, we seesoftware carrying out tasks in largelyautonomous ways, including the dra-matic progress made in self-drivingcars. Apart from what we usually callartificial intelligence, it seems impor-

tant to think about software that goesabout its operation with little or nohuman intervention. I must confess, Iam still leery of the software that runsthe massage chairs at Google—think-ing that a bug might cause the chair tofold up while I am sitting in it!

While we celebrate the advancesmade in artificial intelligence and au-tonomous systems, we also have anobligation to think deeply about po-tential malfunctions and their conse-quences. This certainly persuades me

to keep in mind safety and reliabilityto say nothing of security, privacy, andusability, as we imbue more and moreappliances and devices with program-mable features and the ability to com-municate through the Internet.

Vinton G. Cerf is vice president and Chief Internet Evangelistat Google. He served as ACM president from 2012–2014.

Copyright held by author.

Celebrations!DOI:10.1145/2933148 Vinton G. Cerf

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etters to the editor

I WAS DISAPPOI NTED by EugeneH. Spafford’s column “TheStrength of Encryption” (Mar.2016) in which Spafford con-flated law enforcement re-

quests for access to the contents ofspecific smartphones with the pros-pect of the government requiringbackdoors through which any devicecould be penetrated. These are sepa-rate issues. Even if the methods theFBI ultimately used to unlock a par-

ticular Apple iPhone 5C earlier this year are too elaborate for the hun-dreds of encrypted or code-protectedphones now in police custody, theprinciple—that it is a moral if not le-gal responsibility for those with thecompetence to open the phones doso—would still be relevant.

Unlocking an individual phone would not legally compel a backdoorinto all Apple devices. Rather, Apple

would have to create and downloadinto a particular target phone only a

version of iOS that does two things—return to requesting password entryafter a failed attempt, without in-

voking the standard iOS delay-and-attempt-count code and allow pass-

word attempts at guessing the correctpassword be submitted electronicallyrather than through physical taps onthe phone’s keypad. The first is clear-ly trivial, and the second is, I expect,easily achieved.

The FBI would then observe, at an Apple facility, the modified iOS being

downloaded and be able to run mul-tiple brute-force password attemptsagainst it. When the phone is even-tually unlocked, the FBI would havethe former user’s correct password.

Apple could then reload the originaliOS, and the FBI could take away thephone and the password and accessthe phone’s contents without further

Apple involvement.No backdoor would have been re-

leased. No existing encryption security would have been compromised. Otherlaw-enforcement agencies, armed with

judicial orders, would likewise expectcompliance—and should receive it.

The secondary argument—thatshould Apple comply and authoritar-ian regimes worldwide would demandthe same sort of compliance from

Apple, as well as from other manufac-turers—is a straw man. Since Appleand other manufacturers, as well asresearchers, have acknowledged theyare able to gain access to the contentsof encrypted phones, other regimes are

already able to make such demands,independent of the outcome of anyspecific case.

R. Gary Marquart, Austin, TX

Author Responds:

My column was written and published

before the FBI vs. Apple lawsuit occurred

and was on the general issue of encryption

strength and backdoors. Nowhere in it did

I mention either Apple or the FBI. I also

made no mention of “unlocking” cellphones,

iOS, or passwords. I am thus unableto provide any reasonable response to

Marquart’s objections as to items not in it.

Eugene H. Spafford, West Lafayette, IN

The What in the GNU/Linux Name

George V. Neville-Neil’s Kode Viciouscolumn “GNL Is Not Linux” (Apr. 2016)

would have been better if it had ended with the opening paragraph. InsteadNeville-Neil recapped yet again thehistory of Unix and Linux, then went

off the rails, hinting, darkly, at ulteriormotives behind GPL, particularly thatit is anti-commercial. Red Hat’s bil-lions in revenue ($1.79 billion in 2015)should put such an assertion to rest.The Free Software Foundation appar-ently has no problem with individu-als or companies making money fromfree software.

We do not call houses by the tools we use to build them, as in, say, “… aCraftsman/House, a Makita/House, ora Home Depot/House …” in Neville-Neil’s example. But we do call a house

No BackdoorRequired or Expected

DOI:10.1145/2931085

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letters to the editor

materials to as many teachers andlearners as possible. Other institutions

will be challenged to match our num-bers, particularly percentage of womenengaged in CS. It is an exciting time.

References1. Hulette, D. ‘Computer Science for All’ (Really).

Princeton University, Princeton, NJ, Mar. 1, 2016;

https://www.cs.princeton.edu/news/‘computer-science-all’-really2. Sedgewick, R. A 21st Century Model for Disseminating

Knowledge. Princeton University, Princeton, NJ; http://www.cs.princeton.edu/~rs/talks/Model.pdf

Robert Sedgewick and Kevin Wayne,

Princeton, NJ

Communications welcomes your opinion. To submit aLetter to the Editor, please limit yourself to 500 words orless, and send to [email protected].

© 2016 ACM 0001-0782/16/06 $15.00

versity in computer science educationand Lawrence M. Fisher’s news storyon President Barack Obama’s “Com-puter Science for All” initiative madeus think Communications readersmight be interested in our experienceat Princeton University over the pastdecade dramatically increasing both

CS enrollments in general and the per-centage of women in CS courses. Asof the 2015–2016 academic year, ourintroductory CS class was the highest-enrolled class at Princeton and includ-ed over 40% women, with the numberand percentage of women CS majorsapproaching similar levels.

Our approach is to teach a CS coursefor everyone, focusing outwardly onapplications in other disciplines,from biology and physics to art and

music.

1

We begin with a substantiveprogramming component, with eachconcept introduced in the contextof an engaging application, rangingfrom simulating the vibration of aguitar string to generate sound to im-plementing Markov language modelsto computing DNA sequence align-ments. This foundation allows us toconsider the great intellectual con-tributions of Turing, Shannon, vonNeumann, and others in a scientificcontext. We have also had success

embracing technology, moving toactive learning with online lectures.2

We feel CS is something every collegestudent can and must learn, no matter

what their intended major, and thereis much more to it than programmingalone. Weaving CS into the fabric ofmodern life and a broad educationalexperience in this way is valuable toall students, particularly women andunderrepresented minorities. Otherinstitutions adopting a similar ap-proach have had similar success.

Meanwhile, we have finally (after25 years of development) completedour CS textbook Computer Science, An Interdisciplinary Approach (Addison- Wesley, 2016), which we feel can standalongside standard textbooks in biol-ogy, physics, economics, and otherdisciplines. It will be available along

with studio-produced lectures and as-sociated Web content (http://introcs.cs.princeton.edu) that attract morethan one million visitors per year.

Over the next few years, we will seekopportunities to disseminate these

made of bricks a brick house in a no-menclature that causes no confusion.

Why then would it be confusing tocall a system with a Linux kernel and auser space largely from the GNU proj-ect a “GNU/Linux system”? Including“GNU” in the name seems to be a prob-lem only for people with an anti-GNU

bias or misunderstanding of GPL, bothof which Neville-Neil exhibited throughhis “supposedly” slight (in paragraph10) intended to cast aspersions on theHurd operating system project andthe dig (as I read it) at GPLv3 for beingmore restrictive than GPLv2. However,in fairness, GPLv3 is more restrictiveand explicit about not allowing patentsto circumvent the freedoms inherentin a license otherwise granted by copy-right. As Neville-Neil appeared disdain-

ful of the GPLv2 methods of securingusers’ freedoms, it is not surprising he would take a negative view of GPLv3.

Neville-Neil also suggested the“GNU/Linux” name is inappropriate,as it reflects the tools used to build thekernel. But as Richard Stallman ex-plained in his 2008 article “Linux andthe GNU System” (http://www.gnu.org/gnu/linux-and-gnu.html) to which Nev-ille-Neil linked in his column, a typicalLinux distribution includes more codefrom the GNU project than from the

Linux kernel project. Perhaps Neville-Neil should pour himself a less-“strongbeverage” and read Stallman’s articleagain. He may find himself much lessconfused by the “GNU/Linux” name.

Todd M. Lewis, Sanford, NC

Author Responds:

Lewis hints at my anti-GPL bias, though

I have been quite direct in my opposition

to any open source license that restricts

the freedoms of those using the code, as

is done explicitly by the GPLv2 licenses.Open source means just that—open, free to

everyone, without strings, caveats, codicils,

or clawbacks. As for a strong drink and a re-

read of anything from Richard Stallman it

would have to be a very strong drink indeed

to induce me to do it again.

George V. Neville-Neil, Brooklyn, NY

Diversity and ‘CS for All’

Vinton G. Cerf’s Cerf’s Up column “En-rollments Explode! But diversity stu-dents are leaving …” (Apr. 2016) on di-

The Rise of Social Bots

Statistics for Engineers

On the Growth

of Polyominoes

Turing’s Red Flag

Should You Upload

or Ship Big Data

to the Cloud?

Inverse Privacy

Formula-Based

Software Debugging

The Motivation for

a Monolithic Codebase

Mesa: Geo-Replicated

Online Data

Warehouse for Google’s

Advertising System

Plus the latest news aboutsolving graph isomorphism,AI and the LHC, and appsthat fight parking tickets.

C o m i n g N e x t M o n t h i n C O M M U

N I C A T I O N S

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Follow us on Twitter at http://twitter.com/blogCACM

The Communications Web site, http://cacm.acm.org,

features more than a dozen bloggers in the BLOG@CACM

community. In each issue of Communications, we’ll publishselected posts or excerpts.

DOI:10.1145/2911969 http://cacm.acm.org/blogs/blog-cacm

where MCTS does not work so well.Maybe? It will be interesting to see.

Delving into existing computergames, the Atari results (http://bit.ly/1YbLBgl, Figure 3) are very fun but ob-

viously unimpressive on about a quarterof the games. My hypothesis for why theirsolution does only local (epsilon-greedystyle) exploration rather than globalexploration so they can only learn poli-cies addressing either very short creditassignment problems or with greedily

accessible polices. Global explorationstrategies are known to result in expo-nentially more efficient strategies ingeneral for deterministic decision proc-ess (1993, http://bit.ly/1YbLKjQ), Mar-kov Decision Processes (1998, http://bit.ly/1RXTRCk), and for MDPs withoutmodeling (2006, http://bit.ly/226J1tc).

The reason these strategies are notused is because they are based on tabu-lar learning rather than function fitting.That is why I shifted to Contextual Ban-dit research (http://bit.ly/1S4iiHT) afterthe 2006 paper. We have learned quite a

bit there, enough to start tackling a Con-

textual Deterministic Decision Process(http://arxiv.org/abs/1602.02722), butthat solution is still far from practical.

Addressing global exploration effectivelyis only one of the significant challengesbetween what is well known now and

what needs to be addressed for what I would consider a real AI.

This is generally understood by peo-ple working on these techniques butseems to be getting lost in translationto public news reports. That is danger-ous because it leads to disappointment

(http://bit.ly/1ql1dDW). The field willbe better off without an overpromise/bust cycle, so I would encourage peopleto keep and inform a balanced view ofsuccesses and their extent. MasteringGo is a great accomplishment, but it isquite far from everything.

See further discussion athttp://bit.ly/20106Ff.

Bertrand Meyer

What’s Your Research?

http://bit.ly/1QRo9Q9

March 3, 2016

One of the pleasures ofhaving a research activity

is that you get to visit research institutionsand ask people what they do. Typically, theanswer is “I work in X” or “I work in the ap-plication of X to Y,” as in (made-up exampleamong countless ones, there are manyXs and many Ys): I work in model checking for distributed systems. Notice the “in.”

This is, in my experience, the domi-nant style of answers to such a question.I find it disturbing. It is about researchas a job, not research as research.

John Langford

AlphaGo Is Not

the Solution to AIhttp://bit.ly/1QSqgHWMarch 14, 2016

Congratulations are inorder for the folks at Google Deepmind(https://deepmind.com) who havemastered Go (https://deepmind.com/alpha-go.html).

However, some of the discussionaround this seems like giddy over-

statement. Wired says, “machineshave conquered the last games”(http://bit.ly/200O5zG ) and Slashdot says, “we know now that we don’t needany big new breakthroughs to get to true

AI” (http://bit.ly/1q0Pcmg). The truth isnowhere close.

For Go itself, it has been well knownfor a decade that Monte Carlo treesearch (MCTS, http://bit.ly/1YbLm4M; that is, valuation by assuming random-ized playout) is unusually effective inGo. Given this, it is unclear the AlphaGoalgorithm extends to other board games

The Solution to AI,What Real ResearchersDo, and Expectationsfor CS Classrooms John Langford on AlphaGo, Bertrand Meyer on Research as Research,and Mark Guzdial on correlating CS classes with laboratory results.

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Research is indeed, for most research-ers, a job. It was not always like that: up tothe time when research took on its mod-ern form, in the 18th and early 19th centu-ries, researchers were people employed atsomething else, or fortunate enough notto need employment, who spent some oftheir time looking into open problems of

science. Now research is something thatalmost all its practitioners do for a living.

But a real researcher does not justfollow the flow, working “in” a certainfashionable area or at the confluence oftwo fashionable areas. A real research-er attempts to solve open problems.

This is the kind of answer I wouldexpect: I am trying to find a way to do A,

which no one has been able to do yet; orto find a better way to do B, because thecurrent ways are deficient; or to solve the

C conjecture as posed by M; or to find out why phenomenon D is happening; or tobuild a tool that will address need E.

A researcher does not work “in” anarea but “on” a question.

This observation also defines whatit means for research to be successful.If you are just working “in” an area, theonly criteria are bureaucratic: paperaccepted, grant obtained. They coverthe means, not the end. If you viewresearch as problem solving, successis clearly and objectively testable: you

solved the problem you set out to solve,or not. Maybe that is the reason we areuneasy with this view: it prevents usfrom taking cover behind artificial anddeceptive proxies for success.

Research is about solving problems;at least about trying to solve a problem,or—more realistically and modestly—bringing your own little incrementalcontribution to the ongoing quest fora solution. We know our limits, but if

you are a researcher and do not natu-rally describe your work in terms of the

open problems you are trying to close, you might wonder whether you aretough enough on yourself.

Mark Guzdial

CS Classes Have

Different Results

than Laboratory

Experiments—

Not in a Good Way

http://bit.ly/1UUrOUuMarch 29, 2016

I have collaborated with Lauren Mar-gulieux on a series of experiments and

papers around using subgoal labelingto improve programming education.She has just successfully defended herdissertation. I describe her disserta-tion work, and summarize some ofher earlier findings, in the blog post athttp://bit.ly/23bxRWd.

She had a paragraph in her disserta-

tion’s methods section that I just flewby when I first read it:

Demographic information was col-lected for participants’ age, gender, aca-demic field of study, high school GPA,college GPA, year in school, computer sci-ence experience, comfort with computers,and expected difficulty of learning App Inventor because they are possible pre-dictors of performance (Rountree, Roun-tree, Robins, & Hannah, 2004; see Table1). These demographic characteristics

were not found to correlate with problemsolving performance (see Table 1).Then I realized her lack of result was

a pretty significant result.I asked her about it at the defense.

She collected all these potential pre-dictors of programming performancein all the experiments. Were theyever a predictor of the experimentoutcome? She said she once, out ofeight experiments, found a weak cor-relation between high school GPAand performance. In all other cases,

“these demographic characteris-tics were not found to correlate withproblem solving performance” (toquote her dissertation).

There has been a lot of research into what predicts success in programmingclasses. One of the more controversialclaims is that a mathematics back-ground is a prerequisite for learningprogramming. Nathan Ensmengersuggests the studies show a correlationbetween mathematics backgroundand success in programming classes,

but not in programming performance.He suggests overemphasizing mathe-matics has been a factor in the declinein diversity in computing (see http://bit.ly/1ql27jD about this point).

These predictors are particularly im-portant today. With our burgeoning un-dergraduate enrollments, programs arelooking to cap enrollment using factorslike GPA to decide who gets to stay in CS(see Eric Roberts’ history of enrollmentcaps in CS at http://bit.ly/2368RmV).Margulieux’s results suggest choosing

who gets into CS based on GPA might

be a bad idea. GPA may not be an im-portant predictor of success.

I asked Margulieux how she might ex-plain the difference between her experi-mental results and the classroom-basedresults. One possibility is that thereare effects of these demographic vari-ables, but they are too small to be seen

in short-term experimental settings. Aclass experience is the sum of many ex-periment-size learning situations.

There is another possibility Margu-lieux agrees could explain the differ-ence between classrooms and labora-tory experiments: we may teach betterin experimental settings than we doin classes. Lauren has almost no onedropping out of her experiments, andshe has measurable learning. Every-body learns in her experiments, but

some learn more than others. The dif-ferences cannot be explained by any ofthese demographic variables.

Maybe characteristics like “partici-pants’ age, gender, academic field ofstudy, high school GPA, college GPA,

year in school, computer science expe-rience, comfort with computers, andexpected difficulty of learning” pro-gramming are predictors of success inprogramming classes because of how

we teach programming classes. Maybeif we taught differently, more of these

students would succeed. The predictor variables may say more about our teach-ing of programming than about thechallenge of learning programming.

Reader’s comment:

Back in the 1970s when I was looking

for my first software development job,

companies were using all sorts of tests

and “metrics” to determine who would be

a good programmer. I’m not sure any of

them had any validity. I don’t know that

we have any better predictors today. In my

classes these days, I see lots of lower-GPAstudents who do very well in computer

science classes. Maybe it is how I teach.

Maybe it is something else (interest?), but

all I really know is that I want to learn

better how to teach.

—Alfred Thompson

John Langford is a Principal Researcher at MicrosoftResearch New York. Bertrand Meyer is a professor atETH Zurich. Mark Guzdial is a professor at the GeorgiaInstitute of Technology.

© 2016 ACM 0001-0782/16/06 $15.00

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Nnews

P H O T O G R A P H

B Y R I C H A R D M O R G E N S T E I N

Profile | DOI:10.1145/2911979 Neil Savage

have been named the 2015 recipientsof the ACM A.M. Turing Award.

Public key cryptography arose as thesolution to two problems, says Diffie,former vice president and chief securityofficer at Sun Microsystems. One wasthe problem of sharing cryptographickeys. It was possible to encrypt a mes-

sage, but for the recipient to decrypt it,he would need the secret key with whichit was encrypted. The sender couldphysically deliver the secret key by cou-rier or registered mail, but with millionsof messages, that quickly becomes un-

wieldy. Another possibility would be tohave a central repository of keys and dis-tribute them as needed. That is still dif-ficult, and not entirely satisfactory, Dif-fie says. “I was so countercultural thatI didn’t regard a call as secure if somethird party knew the key.”

Meanwhile, Diffie’s former boss, John McCarthy, a pioneer in the fieldof artificial intelligence, had writtenabout future computer systems in

which people could use home termi-nals to buy and sell things; that wouldrequire digital signatures that couldnot be copied, in order to authenticatethe transactions.

Both problems were solved with theidea of a public key. It is possible togenerate a pair of complementary cryp-tographic keys. A person who wants toreceive a message generates the pair

IT WAS UNUSUAL for Martin Hell-man, a professor of electricalengineering at Stanford Uni-

versity, to present two paperson cryptography at the Inter-

national Symposium on InformationTheory in October 1977. Under normalcircumstances, Steve Pohlig or Ralph

Merkle, the doctoral students who alsohad worked on the papers, would havegiven the talks, but on the advice ofStanford’s general counsel, it was Hell-man who spoke.

The reason for the caution wasthat an employee of the U.S. Nation-al Security Agency, J.A. Meyer, hadclaimed publicly discussing theirnew approach to encryption would

violate U.S. law prohibiting the ex-port of weapons to other countries.Stanford’s lawyer did not agree with

that interpretation of the law, buttold Hellman it would be easier forhim to defend a Stanford employeethan it would be to defend graduatestudents, so he recommended Hell-man give the talk instead.

Whitfield Diffie, another studentof Hellman’s who says he was a hip-pie with “much more anti-societal

views then,” had not been scheduledto present a paper at the conference,but came up with one specifically tothumb his nose at the government’sclaims. “This was just absolute non-

sense, that you could have laws thatcould affect free speech,” Diffie says.“It was very important to defy them.”

In the end, no one was charged withbreaking any laws, though as Hellman,now professor emeritus, recalls, “there

was a time there when it was pretty dic-ey.” Instead, the researchers’ work start-

ed to move the field of cryptography intoacademia and the commercial world,

where the cutting edge had belongedalmost exclusively to government re-searchers doing classified work.

Diffie and Hellman wrote a paperin 1976, “New Directions in Cryptogra-phy,” introducing public key cryptogra-phy that still prevails in secure onlinetransactions today. As a result, they

The Key to Privacy 40 years ago, Whitfield Diffie and Martin E. Hellman introducedthe public key cryptography used to secure today’s online transactions.

NThe researchers’work started tomove the field ofcryptography intothe realm ofacademia and thecommercial world.

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P H O T O G R A P H

B Y R I C H A R D M O R G E N S T E I N

and makes one public; then a sendercan use that public key to encrypt amessage, but only the person with theprivate key, which does not have to besent anywhere, can decrypt it.

Hellman compares it to a box withtwo combination locks, one to lockthe box and the other to unlock it. Al-

ice, the sender, and Bob, the receiver,each generate a pair of keys and makeone public. Alice puts a message in the“box,” then locks it with her secret key,guaranteeing its authenticity sinceonly she knows how to do that. Shethen places that locked box inside alarger one, which she locks with Bob’spublic key. When Bob gets the box, heuses his private key to get past the out-er box, and Alice’s public key to openthe inner box and see the message.

Hellman and Diffie, building onan approach developed by Merkle,later came up with a variation on thescheme now called the Diffie-Hell-man Key Exchange (though Hellmanargues Merkle’s name should be onit as well). In this version, the boxhas a hasp big enough for two locks.

Alice places her mess age in the boxand locks it with a lock to which onlyshe knows the combination, then

sends it to Bob. Bob cannot openit, nor can anyone who intercepts iten route, but he adds his own lockand sends it back. Alice then takes

off her lock and sends the box backto Bob with only his lock securingit. On arrival, he can open it. In theInternet world, that translates to acommutative one-way function thatallows Alice and Bob to create a com-mon key in a fraction of a second.

While an eavesdropper, in theory,could compute the same key from

what he hears, that would take mil-lions of years.

Ron Rivest, an Institute Professorin the Massachusetts Institute of Tech-nology’s Computer Science and Artifi-cial Intelligence Laboratory, calls theduo’s impact on the field revolutionary.“Cryptography has really blossomedsince the publication of their paper,”he says. “It’s become a key tool of the

information age.” Rivest, with his col-leagues Adi Shamir and Leonard Adle-man, developed the first practical im-plementation of public key encryption,stimulated, Rivest says, by Diffie andHellman’s paper. Rivest, Shamir, and

Adleman were awarded the ACM A.M.Turing Award for that work in 2002.

The Turing Award carries a $1 mil-lion prize, which Diffie and Hellmann

will split. Diffie says he plans to use hishalf of the award to pursue research on

the history of cryptography. Hellmanand his wife, Dorothie, will use themoney, and the attendant publicity,to bring attention to their forthcom-ing book about how they transformedan almost-failed marriage into onein which they have reclaimed the lovethey felt when they first met, and howthat same approach can be used to res-cue the world from the risk posed bynuclear weapons.

If young people want to go into thefield of cryptography, there are three

great problems for them to tackle,Diffie says: cryptography resistantto quantum computing; proof of thecomputational complexity of crypto-systems; and homomorphic encryp-tion that would allow computations tobe carried out on encrypted data.

Hellman encourages people totake risks and not wait to know every-thing they think they should know be-fore launching a project. “When I firststarted working in cryptography, mycolleagues all told me I was crazy,” he

says. “My advice, is don’t worry aboutdoing something foolish.”

Neil Savage is a science anvd technology writer based inLowell, MA.

© 2016 ACM 0001-0782/16/06 $15.00

Watch the Turing recipientsdiscuss their work in thisexclusive Communications

video. http://cacm.acm.org/videos/the-key-to-privacyMartin E. Hellman (left) and Whitfield Diffie.

“Cryptography hasreally blossomedsince the publicationof their paper.

It’s becomethe key tool ofthe information age.”

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I M A G E B Y C R E A T I O N S

Technology | DOI:10.1145/2911975 Logan Kugler

What Happens WhenBig Data Blunders? Big data is touted as a cure-all for challenges in business, government, andhealthcare, but as disease outbreak predictions show, big data often fails.

The Hubris of Humans

Google Flu Trends failed for two rea-sons, say Lazer and Kennedy: big datahubris, and algorithmic dynamics.

Big data hubris means Google re-searchers placed too much faith in bigdata, rather than partnering big data

with traditional data collection and

analysis. Google Flu Trends was built tomap not only influenza-related trends,but also seasonal ones. Early on, engi-neers found themselves weeding outfalse hits concerned with seasonal, butnot influenza-related, terms—such asthose related to high school basketballseason. This, say Lazer and Kennedy,should have raised red flags aboutthe data’s reliability. Instead, it wasthought the terms could simply be re-moved until the results looked sound.

As Lazer and Kennedy say in their

article in Science: “Elsewhere, we haveasserted that there are enormous scien-tific possibilities in big data. However,quantity of data does not mean that onecan ignore foundational issues of mea-surement and construct validity and reli-ability and dependencies among data.”

In addition, Google itself turned outto be a major problem.

The second failure condition wasone of algorithmic dynamics, or theidea that Google Flu Trends predictions

were based on a commercial search al-

gorithm that frequently changes basedon Google’s business goals.

Google’s search algorithms changeoften; in fact, say Lazer and Kennedy,in June and July 2012 alone, Google’salgorithms changed 86 times as thefirm tweaked how it returned searchresults in line with its business andgrowth goals. This sort of dynamism

was not accounted for in Google FluTrends models.

“Google’s core business is improvingsearch and driving ad revenue,” Kenne-dy told Communications. “To do this, it is

YOU CANNOT BROWSE tech-nology news or dive into anindustry report without typi-cally seeing a reference to“big data,” a term used to

describe the massive amounts of infor-mation companies, government organi-zations, and academic institutions can

use to do, well, anything. The problemis, the term “big data” is so amorphousthat it hardly has a tangible definition.

While it is not clearly defined, wecan define it for our purposes as: theuse of large datasets to improve howcompanies and organizations work.

While often heralded as The NextBig Thing That Will Cure All Ills, bigdata can, and often does, lead to bigblunders. Nowhere is that more evi-dent than its use in forecasting out-breaks and spread of diseases.

An influenza forecasting service pio-neered by Google employed big data—and failed spectacularly to predict the2013 flu outbreak. Data used to prog-nosticate Ebola’s spread in 2014 andearly 2015 yielded wildly inaccurateresults. Similarly, efforts to predict thespread of avian flu have run into prob-lems with data sources and interpreta-tions of those sources.

These initiatives failed due to a com-bination of big data inconsistencies andhuman errors in interpreting that data.

Together, those factors lay bare how bigdata might not be the solution to everyproblem—at least, not on its own.

Big Data Gets the Flu

Google Flu Trends was an initiativethe Internet search giant began in2008. The program aimed to betterpredict flu outbreaks using Googlesearch data and information from theU.S. Centers for Disease Control andPrevention (CDC).

The big data from online searches,combined with the CDC’s cache of dis-

ease-specific information, representeda huge opportunity. Many people willsearch online the moment they feel abug coming on; they look for informa-tion on symptoms, stages, and reme-dies. Combined with the CDC’s insightsinto how diseases spread, the knowl-edge of the numbers and locations of

people seeking such information couldtheoretically help Google predict whereand how severely the flu would strikenext—before even the CDC could. Infact, Google theorized it could beat CDCpredictions by up to two weeks.

The success of Google Flu Trends would have big implications. In the lastthree decades, thousands have diedfrom influenza-related causes, says theCDC, while survivors can face severehealth issues because of the disease.

Also, many laid up by the flu consume

the time, energy, and resources ofhealthcare organizations. Any improve-ment in forecasting outbreaks couldsave lives and dollars.

However, over the years, Google FluTrends consistently failed to predict flucases more accurately than the CDC.

After the program failed to predict the2013 flu outbreak, Google quietly shut-tered the program.

David Lazer and Rya

Communications of ACM June 06 2016

Documents

Transcript of Communications of ACM June 06 2016