Computer Science | Grinnell College

On Friday, 3 October 2014, at CS Table, we will consider an intersection between computing and the arts, exploring the ways in which recommender systems can create experiences of serendipity. Alex Dodge, the College's Artist in Residence, will join us for the discussion.

Iaquinta, L., Gemmis, M. De, Lops, P., Semeraro, G., & Molino, P. (n.d.). Can a Recommender System induce serendipitous encounters?, 229–247. Read sections 1, 2, 3, and 4 (read further optionally). Available online at http://cdn.intechopen.com/pdfs-wm/10158.pdf.

Today recommenders are commonly used with various purposes, especially dealing with e- commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/searched/visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen.

Sun, T., & Mei, Q. (2012). Unexpected Relevance : An Empirical Study of Serendipity in Retweets. Read sections: Intro, Related Work, and Definition (read further optionally). Available online at http://www-personal.umich.edu/~qmei/pub/icwsm2013-sun.pdf.

Serendipity is a beneficial discovery that happens in an unexpected way. It has been found spectacularly valuable in various contexts, including scientific discoveries, acquisition of business, and recommender systems. Although never formally proved with large-scale behavioral analysis, it is believed by scientists and practitioners that serendipity is an important factor of positive user experience and increased user engagement. In this paper, we take the initiative to study the ubiquitous occurrence of serendipitious information diffusion and its effect in the context of microblogging communities. We refer to serendipity as unexpected relevance, then propose a principled statistical method to test the unexpectedness and the relevance of information received by a microblogging user, which identifies a serendipitous diffusion of information to the user. Our findings based on large-scale behavioral analysis reveal that there is a surprisingly strong presence of serendipitous information diffusion in retweeting, which accounts for more than 25% of retweets in both Twitter and Weibo. Upon the identification of serendipity, we are able to conduct observational analysis that reveals the benefit of serendipity to microblogging users. Results show that both the discovery and the provision of serendipity increase the level of user activities and social interactions, while the provision of serendipitous information also increases the influence of Twitter users.

The readings are available outside of Science 3821 or from Sam Rebelsky.

Computer science table is a weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science. CS Table meets Fridays from 12:10-12:50 in the Day PDR (JRC 224A). Contact Sam Rebelsky rebelsky@grinnell.edu for the weekly reading. Students on meal plans, faculty, and staff are expected to cover the cost of their meals. Students not on meal plans can charge their meals to the department.

On Friday, 26 September 2014, at CS Table, we will consider some recent ethical issues with the use of "Big Data" in social sciences research, including data from xMOOCs (Massive, Open, Online, Courses). Our reading will include a short article from Atlantic Monthly on the recent Facebook Controversy and a CACM article on uses of xMOOC data.

Sara M. Watson. Data Science: What the Facebook Controversy is Really About. The Atlantic. July 1, 2014. Available online at http://www.theatlantic.com/technology/archive/2014/07/data-science-what-the-facebook-controversy-is-really-about/373770/>.

Facebook has always “manipulated” the results shown in its users’ News Feeds by filtering and personalizing for relevance. But this weekend, the social giant seemed to cross a line, when it announced that it engineered emotional responses two years ago in an “emotional contagion” experiment, published in the Proceedings of the National Academy of Sciences (PNAS).

Since then, critics have examined many facets of the experiment, including itsdesign, methodology, approval process, and ethics. Each of these tacks tacitly accepts something important, though: the validity of Facebook’s science and scholarship. There is a more fundamental question in all this: What does it mean when we call proprietary data research data science?

As a society, we haven't fully established how we ought to think about data science in practice. It's time to start hashing that out.

Jon P. Daries, Justin Reich, Jim Waldo, Elise M. Young, Jonathan Whittinghill, Andrew Dean Ho, Daniel Thomas Seaton, and Isaac Chuang. 2014. Privacy, anonymity, and big data in the social sciences. Commun. ACM 57, 9 (September 2014), 56-63. DOI=10.1145/2643132 https://dl.acm.org/citation.cfm?doid=2663191.2643132.

Open data has tremendous potential for science, but, in human subjects research, there is a tension between privacy and releasing high-quality open data. Federal law governing student privacy and the release of student records suggests that anonymizing student data protects student privacy. Guided by this standard, we de-identified and released a data set from 16 MOOCs (massive open online courses) from MITx and HarvardX on the edX platform. In this article, we show that these and other de-identification procedures necessitate changes to data sets that threaten replication and extension of baseline analyses. To balance student privacy and the benefits of open data, we suggest focusing on protecting privacy without anonymizing data by instead expanding policies that compel researchers to uphold the privacy of the subjects in open data sets. If we want to have high-quality social science research and also protect the privacy of human subjects, we must eventually have trust in researchers. Otherwise, we'll always have the strict tradeoff between anonymity and science illustrated here.

Printed copies of the readings are available next to Science 3821.

Computer science table is a weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science. CS Table meets Fridays from 12:10-12:50 in the Day PDR (JRC 224A). Contact Sam Rebelsky rebelsky@grinnell.edu for the weekly reading. Students on meal plans, faculty, and staff are expected to cover the cost of their meals. Students not on meal plans can charge their meals to the department.

This Friday in CS Table, we will consider recent trends in browser tracking. That is, we will explore the ways in which people who want to know what you are doing on the Web can keep track of you. We have one popular CS article and one research paper.

Nikiforakis, Nick & Güner Acar (2014). Browser Fingerprinting and the Online Tracking Arms Race. IEEE Spectrum, August 2014. Also available at http://spectrum.ieee.org/computing/software/browser-fingerprinting-and-the-onlinetracking-arms-race.

In July 1993, The New Yorker published a cartoon by Peter Steiner that depicted a Labrador retriever sitting on a chair in front of a computer, paw on the keyboard, as he turns to his beagle companion and says, “On the Internet, nobody knows you’re a dog.” Two decades later, interested parties not only know you’re a dog, they also have a pretty good idea of the color of your fur, how often you visit the vet, and what your favorite doggy treat is.

How do they get all that information? In a nutshell: Online advertisers collaborate with websites to gather your browsing data, eventually building up a detailed profile of your interests and activities. These browsing profiles can be so specific that they allow advertisers to target populations as narrow as mothers with teenage children or people who require allergy-relief products. When this tracking of our browsing habits is combined with our self-revelations on social media, merchants’ records of our off-line purchases, and logs of our physical whereabouts derived from our mobile phones, the information that commercial organizations, much less government snoops, can compile about us becomes shockingly revealing.

Here we examine the history of such tracking on the Web, paying particular attention to a recent phenomenon called fingerprinting, which enables companies to spy on people even when they configure their browsers to avoid being tracked.

Gunes Acar, Christian Eubank, Steven Englehardt, Marc Juarez, Arvind Narayana, Claudia Diaz. The Web Never Forgets: Persistent Tracking Mechanisms in the Wild. Preprint available at https://securehomes.esat.kuleuven.be/~gacar/persistent/the_web_never_forgets.pdf.

We present the first large-scale studies of three advanced web tracking mechanisms — canvas fingerprinting, evercookies and use of “cookie syncing” in conjunction with evercookies. Canvas fingerprinting, a recently developed form of browser fingerprinting, has not previously been reported in the wild; our results show that over 5% of the top 100,000 websites employ it. We then present the first automated study of evercookies and respawning and the discovery of a new evercookie vector, IndexedDB. Turning to cookie syncing, we present novel techniques for detection and analysing ID flows and we quantify the amplification of privacy-intrusive tracking practices due to cookie syncing.

Our evaluation of the defensive techniques used by privacy-aware users finds that there exist subtle pitfalls — such as failing to clear state on multiple browsers at once — in which a single lapse in judgment can shatter privacy defenses. This suggests that even sophisticated users face great difficulties in evading tracking techniques.

Computer science table is a weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science. CS Table meets Fridays at noon in the Day PDR (JRC 224A). Contact Sam Rebelsky rebelsky@grinnell.edu for the weekly reading. Students on meal plans, faculty, and staff are expected to cover the cost of their meals. Students not on meal plans can charge their meals to the department.

In CS table on Friday, 12 September 2014, we will consider some recent approaches to using social robots to help autistic children develop social, emotional, and communication skills.

We will start with a recent news article.

USC Viterbi School of Engineering, "Socially-Assistive Robots Help Children with Autism Learn Imitative Behavior by Providing Personalized Encouragement". Press Release, University of Southern California School of Engineering. Online document at http://viterbi.usc.edu/news/news/2014/august-28-2014.htm.

We will continue with a broader survey of such approaches. The survey is long, so we will understand if people skim.

John-John Cabibihan, Hifza Javed, Marcelo Ang Jr and Sharifah Mariam Aljunied, “Why Robots? A Survey on the Roles and Benefits of Social Robots for the Therapy of Children with Autism” International Journal of Social Robotics, 2013, 5(4), 593-618, doi 10.1007/s12369-013-0202-2. Available online at http://arxiv.org/pdf/1311.0352.pdf.

Students will lead this week's discussion.

Computer science table is a weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science. CS Table meets Fridays at noon in the Day PDR (JRC 224A).

On Friday, 29 August 2014 at 12:10 in JRC 224A, we have the first CS table of the year. As is the norm, our opening meeting will be a chance for people to chat about what they did over the summer and for us to make plans about readings for the year. If you have suggestions, please bring them with you or send them to rebelsky@grinnell.edu in advance. As is usual, we will try to create an appropriate mix of classic papers and new articles.

Computer science table is a weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science. CS Table meets Fridays at noon in the Day PDR. Contact Sam Rebelsky for the weekly reading. Students on meal plans, faculty, and staff are expected to cover the cost of their meals. Students not on meal plans can charge their meals to the department.

At this week's Computer Science Table (at noon on Friday, April 18, in Rosenfield 224A), we will discuss privacy in the age of big data and analytics, and specifically the issues are raised in two videos (one recent, one classic):

“Demo: Big data and analytics at work in banking”
IBM Big Data and Analytics, YouTube, September 7, 2013
http://www.youtube.com/watch?v=1RYKgj-QK4I

“Scary pizza”
American Civil Liberties Union, YouTube, January 15, 2009
https://www.youtube.com/watch?v=33CIVjvYyEk

For more extensive discussions of some of these issues, you might want to read:

“Big data and the future of privacy”
John Podesta, whitehouse.gov, March 3, 2014
http://www.whitehouse.gov/blog/2014/01/23/big-data-and-future-privacy

“Comments of the Electronic Privacy Information Center to the Office of Science and Technology Policy: Request for information: Big data and the future of privacy”
Electronic Privacy Information Center, April 4, 2014
https://epic.org/privacy/big-data/EPIC-OSTP-Big-Data.pdf

Computer Science Table is an open weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science.

As you may have heard, one of the new features of Java 8 is the introduction of anonymous functions (which everyone calls “lambda expressions,” even though there's no lambda in the syntax). This Friday at CS Table, we will explore Java 8's anonymous functions. Here are a few readings you might read or skim in advance of the discussion:

Horstmann, Cay S. “Lambda expressions in Java 8.” Dr Dobb's journal, March 25, 2014.

Weiss, Tal. “The dark side of lambda expressions in Java 8.” The Takipi blog, March 25, 2014.

You may also want to explore the formal tutorial on lambdas:

Oracle. “Lambda expressions.” The Java tutorials, 2014.

Computer Science Table is an open weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science.

This Friday at CS Table, we will consider skip lists, an interesting data structure that, like lists, makes it easy to add and remove elements, and like arrays, lets you do something like binary search to quickly find elements.

Pugh, W. “Skip lists: A probabilistic alternative to balanced trees.” Communications of the ACM 33 (1990), no. 6, p. 668.

Computer Science Table is an open weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science.

This Friday at CS Table, we will consider the classic “P vs. NP” problem.

Fortnow, Lance. “The status of the P versus NP problem.” Communications of the ACM 52 (2009), no. 9, pp. 78–86.

In this article I look at how people have tried to solve the P versus NP problem as well as how this question has shaped so much of the research in computer science and beyond. I will look at how to handle NP-complete problems and the theory that has developed from those approaches. I show how a new type of “interactive proof systems” led to limitations of approximation algorithms and consider whether quantum computing can solve NP-complete problems (short answer: not likely). And I close by describing a new long-term project that will try to separate P from NP using algebraic-geometric techniques.

This article does not try to be totally accurate or complete either technically or historically, but rather informally describes the P versus NP problem and the major directions in computer science inspired by this question over the past several decades.

Computer Science Table is an open weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science.

This Friday at CS Table, we will discuss the ACM Code of Ethics. Along the way, we will consider the purpose and roles of professional codes of ethics and what it means to think of yourself as a “professional.”

Computer Science Table is an open weekly meeting of Grinnell College community members (students, faculty, staff, etc.) interested in discussing topics related to computing and computer science.