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SUMMARY:Michaelmas Talklets: Sian and Siana - Sian Gooding (University of 
 Cambridge)\; Siana Zhekova (University of Cambridge)
DTSTART:20201119T130000Z
DTEND:20201119T140000Z
UID:TALK154159@talks.cam.ac.uk
CONTACT:Agnieszka Slowik
DESCRIPTION:Speaker 1: Sian Gooding\n\nTitle: Predicting Text Readability 
 and Reading Comprehension from Reading Interactions\n\nAbstract: Judging t
 he readability of text has many important applications\, for instance in t
 ext simplification and when sourcing reading material for language learner
 s. \nAdditionally\, being able to infer if a reader has understood text ca
 n be crucial when delivering critical information. \nIn this paper\, we pr
 esent a 600 participant study which investigates how the implicit user int
 eractions of readers relate to (1) the complexity of a text and (2) the us
 er's comprehension of the text. \nWe make our dataset publicly available a
 nd show that implicit measures of interaction not only correlate with text
  readability and reading comprehension but are also able to predict them.\
 n\nSpeaker 2: Siana Zhekova\n\nTitle: Project Silica: Encoding and Storing
  Data into Glass\n\nAbstract: Over the last decade\, the potential applica
 tions of the Cloud in the form of a long-term data storage medium have bee
 n evolving and significantly expanding into the zettabytes. Nonetheless\, 
 current technologies do not possess an efficient solution to a perennial d
 ata storage framework. In order for us to establish such a large-scale sys
 tem\, we would need to re-design its operational specifications as well as
  the underlying storage platforms.\n\nThe teams at Project Silica at Micro
 soft Research have been developing an innovative technology incorporating 
 recent applications of ultra-fast laser optics to encode data in quartz gl
 ass by using femtosecond lasers\, and decode it efficiently via imaging pr
 ocessed by machine learning algorithms.  \n\nIn the summer of 2020\, over 
 the course of 12 weeks I was virtually interning at Microsoft Research\, C
 ambridge\, where I was a part of the Cloud Computing team at Project Silic
 a.  My project revolved around developing a framework for detecting differ
 ent types of error patterns that could be attributed to errors occurring a
 t various stages of the decoding pipeline of optically encoded data\, rang
 ing from polarization state errors to inaccuracies occurring in the machin
 e learning algorithms that had been deployed.
LOCATION:Remote
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