[cs-talks] Upcoming CS Seminars: NRG (Mon) + IVC (Tues) BUSec (Wed) + Student Sem. (Thurs)
fgreen1 at bu.edu
Mon Nov 2 10:46:09 EST 2015
Filter Bubble: a Dynamic Evaluation over Recommendation Systems
November 2, 2015 at 11am in MCS 148
ABSTRACT: The term filter bubble refers to a narrowed access of information caused by personalization. In this work we seek to understand the circumstances under which a filter bubble can arise, and its effect on users.
In particular, we look at whether the use of recommender system can affect user experience and user opinions in a systematic way. We consider the user and the user's personalization system as a closed loop, and ask what the effects are on both user and the recommender system over time. We define and analyze two metrics - simplification and intensity - to understand those effects over recommendation systems using matrix factorization. The presentation will cover some results and findings of this on-going research.
Large-scale Learning in Affective Computing
Daniel McDuff, MIT
Tuesday, November 3, 2015 at 2pm in CAS 200- Dean’s Conference Room
ABSTRACT: Emotions play a huge role in our everyday lives. They influence memory, decision-making and well-being. I will present my research on quantifying emotional responses on a large scale using webcams and wearable devices in everyday environments. I will present state-of-the-art work on unobtrusive measurement of facial expressions and physiology and insights from analysis from the world’s largest dataset of naturalistic emotional responses.
I will present state-of-the-art automated recognition of facial expressions and show how these can be tuned to work in mobile and offline settings - balancing accuracy and computational cost.
Bio: Daniel McDuff is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. He is currently Director of Research at Affectiva and a post-doctoral research affiliate at the MIT Media Lab. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotion responses. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR, the Center for Integrated Medicine and Innovative Technology (CIMIT) and several IEEE conferences. His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel has been named a 2015 WIRED Innovation Fellow. Two of his papers were recently recognized within the list of the most influential articles to appear in the Transactions on Affective Computing.
Silas Richelson, MIT
Wednesday, November 4, 2015 at 10am in MCS 148
Abstract: Secure Multi-party Computation (MPC) is one of the foundational achievements of modern cryptography, allowing multiple, distrusting, parties to jointly compute a function of their inputs, while revealing nothing but the output of the function. Following the seminal works of Yao and Goldreich, Micali and Wigderson and Ben-Or, Goldwasser and Wigderson, the study of MPC has expanded to consider a wide variety of questions, including variants in the attack model, underlying assumptions, complexity and composability of the resulting protocols.
One question that appears to have received very little attention, however, is that of MPC over an underlying communication network whose structure is, in itself, sensitive information. This question, in addition to being of pure theoretical interest, arises naturally in many contexts: designing privacy-preserving social-networks, private peer-to-peer computations, vehicle-to-vehicle networks and the ``internet of things'' are some of the examples.
In this paper, we initiate the study of ``topology-hiding computation'' in the computational setting. We give formal definitions in both simulation-based and indistinguishability-based flavors. We show that, even for fail-stop adversaries, there are some strong impossibility results. Despite this, we show that protocols for topology-hiding computation can be constructed in the semi-honest and fail-stop models, if we somewhat restrict the set of nodes the adversary may corrupt.
Joint work with Tal Moran and Ilan Orlov
The Spanish Town That Runs on Twitter
Martin Saveski, MIT Media Lab
Thursday, November 5, 2015 at 12pm in MCS 148
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