May 2, 3 and 4 Lectures at LIACS
Posted: 28 April 2017 08:34 PM
Total Posts:  142
Joined  2013-12-02

You are cordially invited to attend the following three lectures.

2 May: 10:00-10:30 by Dr. Erik van der Kouwe (Free University of Amsterdam) in room 407/409
Title: DangSan: Scalable Use-after-free Detection

Use-after-free vulnerabilities due to dangling pointers are an important and growing threat to systems security. While various solutions exist to address this problem, none of them is sufficiently practical for real-world adoption. Some can be bypassed by attackers, others cannot support complex multithreaded applications prone to dangling pointers, and the remainder have prohibitively high overhead. One major source of overhead is the need to synchronize threads on every pointer write due to pointer tracking.

DangSan is a use-after-free detection system that scales efficiently to large numbers of pointer writes as well as to many concurrent threads. To significantly reduce the overhead of existing solutions, DangSan considers the fact that pointer tracking is write-intensive but requires very few reads. Moreover, there is no need for strong consistency guarantees as inconsistencies can be reconciled at read (i.e., object deallocation) time. Building on these intuitions, DangSan’s design mimics that of log-structured file systems, which are ideally suited for similar workloads. The results show that DangSan can run heavily multithreaded applications, while introducing
less than half the overhead of previous multithreaded use-after-free detectors.

3 May: 11:00-11:30 by Dr. Mitra Baratchi (University of Twente) in room 407/409
Title: Mining spatio-temporal data

Daily activities of each and everyone of us generates streams of spatio-temproal data. Such data is collected from our phones, cars, or even check-in records in social networks. Analysis of spatio-temporal data has a variety of urban-level applications ranging from intelligent transportation to urban planning. In this lecture, I will briefly introduce the topic of spatio-temporal data mining, the special characteristics of such data, and give examples of how data mining algorithms are designed to perform on them.


4 May: 09:30-10:00 by Dr. Arianna Bisazza (University of Amsterdam) in room 407/409

Title: Statistical machine translation at a turning point

Over the last three decades, statistical machine translation (MT) has developed to become a pervasive and reliable technology. In just the last three years, a completely new approach based on neural networks has challenged and almost surpassed the existing technology.
In this short lecture I will explain how modern machine translation systems work, starting from the established phrase-based framework and including the still experimental, but impressively effective, neural encoder-decoder framework.