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SUMMARY:women@CL talklet -- NetOS\, Security\, and NLP group - Desislava H
 ristova\, Sheharbano Khattak\, Menglin Xia
DTSTART:20160526T120000Z
DTEND:20160526T130000Z
UID:TALK66174@talks.cam.ac.uk
CONTACT:Helen Yannakoudakis
DESCRIPTION:--\n\n*Speaker*: Desislava Hristova (NetOS)\n\n*Title*: Measur
 ing Urban Social Diversity Using Interconnected Geo-Social Networks\n\n*Ab
 stract*: Large metropolitan cities bring together diverse individuals\, cr
 eating opportunities for cultural and intellectual exchanges\, which can u
 ltimately lead to social and economic enrichment.  I will present a novel 
 network perspective on the interconnected nature of people and places\, al
 lowing us to capture the social diversity\nof urban locations through the 
 social network and mobility patterns of their visitors.  Using a dataset o
 f approximately 37K users and 42K venues in London\, we build a network of
  Foursquare places and the parallel  Twitter social network of  visitors t
 hrough check-ins. I will describe four metrics of the social diversity of 
 places which relate to their social brokerage role\, their entropy\, the h
 omogeneity of their visitors and the amount of serendipitous encounters th
 ey are able to induce. This allows us to distinguish between places that b
 ring together strangers versus those which tend to bring together friends\
 , as well as places that attract diverse individuals as opposed to those w
 hich attract regulars.  We correlate these properties  with  wellbeing ind
 icators  for London neighbourhoods and discover signals of gentrification 
 in deprived areas with high entropy and brokerage\, where an influx of mor
 e affluent and diverse visitors points to an overall improvement of their 
 rank according to the UK Index of Multiple Deprivation for the area over t
 he five-year census period. \n\n--\n\n*Speaker*: Sheharbano Khattak (Secur
 ity)\n\n*Title*: Do You See What I See? Differential Treatment of Anonymou
 s Users\n\n\n*Abstract*: The utility of anonymous communication is undermi
 ned by a growing number of websites treating users of such services in a d
 egraded fashion. The second-class treatment of anonymous users ranges from
  outright rejection to limiting their access to a subset of the service’
 s functionality or imposing hurdles such as CAPTCHA-solving. To date\, the
  observation of such practices has relied upon anecdotal reports catalogue
 d by frustrated anonymity users. We present a study to methodically enumer
 ate and characterize\, in the context of Tor\, the treatment of anonymous 
 users as second-class Web citizens.\n\nWe focus on first-line blocking: at
  the transport layer\, through reset or dropped connections\; and at the a
 pplication layer\, through explicit blocks served from website home pages.
  Our study draws upon several data sources: comparisons of Internet-wide p
 ort scans from Tor exit nodes versus from control hosts\; scans of the hom
 e pages of top-1\,000 Alexa websites through every Tor exit\; and analysis
  of nearly a year of historic HTTP crawls from Tor network and control hos
 ts. We develop a methodology to distinguish censorship events from inciden
 tal failures such as those caused by packet loss or network outages\, and 
 incorporate consideration of the endemic churn in web-accessible services 
 over both time and geographic diversity. We find clear evidence of Tor blo
 cking on the Web\, including 3.5% of the top-1\,000 Alexa sites. Some bloc
 ks specifically target Tor\, while others result from fate-sharing when ab
 use-based automated blockers trigger due to misbehaving web sessions shari
 ng the same exit node.\n\n--\n\n*Speaker*:  Menglin Xia (NLP)\n\n\n*Title*
 : Text Readability Assessment for Second Language Learners\n\n*Abstract*: 
 Developing reading ability is an essential part of language acquisition.\n
 However\, finding proper reading materials for training language learners 
 at\na specific level of proficiency is a demanding and time-consuming task
  for\nEnglish instructors as well as the readers themselves. To automate t
 he\nprocess of reading material selection and the assessment of reading ab
 ility\nfor non-native learners\, a system that focuses on text readability
  analysis\nfor second language (L2) learners can be developed.\n\nOne of t
 he major challenges in the task of readability assessment for the\ntexts a
 imed at L2 learners is the lack of significantly sized\nlevel-annotated da
 ta. For the present work\, we collected a dataset of\nCEFR-graded texts ta
 ilored for learners of English as an L2 and investigated\ntext readability
  assessment for both native and L2 learners. We applied a\ngeneralization 
 method to adapt models trained on larger native corpora to\nestimate text 
 readability for learners\, and explored domain adaptation and\nself-learni
 ng techniques to make use of the native data to improve system\nperformanc
 e on the limited L2 data.\n\n--\n
LOCATION:Computer Laboratory\, William Gates Building\, Room SW01
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