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Events for May 28, 2015
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Short Course: Lean Green BeltThu, May 28, 2015
DEN@Viterbi, Executive Education
Conferences, Lectures, & Seminars
Abstract: This three-day course provides an in-depth understanding of lean enterprise principles and how to apply them within your organization. Your lean journey begins with a series of interactive simulations that demonstrate how each lean concept is applied and its impact on the process. Mapping the process flow and identifying the activities that add value from the customer's perspective is the cornerstone of this class. The class is then given a scenario and the students simulate the conversion from traditional to lean in a practical hands-on environment. The course also provides a structure for how to manage a lean process for continuous improvement. Participants will learn how to structure their organizations to support and continuously improve a lean process. Participants will also fully understand how to implement 5S within their plants and how to begin reducing setup time using the SMED process.
More Info: http://gapp.usc.edu/professional-programs/short-courses/industrial-systems/lean-green-belt
Audiences: Registered Attendees
Contact: Viterbi Professional Programs
Event Link: http://gapp.usc.edu/professional-programs/short-courses/industrial-systems/lean-green-belt
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PhD Defense - Xun Fan
Thu, May 28, 2015 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Defense - Xun Fan
May 28, 2015
10am-12pm
SAL 213
Committee:
John Heidemann (chair)
Ramesh Govindan
Ethan Katz-Bassett
Konstantinos Psounis
ENABLING EFFICIENT SERVICE ENUMERATION THROUGH SMART
SELECTION OF MEASUREMENTS
The Internet is becoming more and more important in our daily lives. Both the government and industry invest in the growth of the Internet, bringing more users to the world of networks. As the Internet grows, researchers and operators need to track and understand the behavior of global Internet services to achieve smooth operation. Active measurements are often used to study behavior of large Internet service, and efficient service enumeration is required. For example, studies of Internet topology may need active probing to all visible network prefixes; monitoring large replicated service requires periodical enumeration of all service replicas. To achieve efficient service enumeration, it is important to select probing sources and destinations wisely. However, there are challenges for making smart selection of probing sources and destinations. Prior methods to select probing destinations are either inefficient or hard to maintain. Enumerating replicas of large Internet services often requires many widely distributed probing sources. Current measurement platforms don't have enough probing sources to approach complete
enumeration of large services.
This dissertation makes the thesis statement that smart selection of probing sources and destinations enables efficient enumeration of global Internet services to track
and understand their behavior. We present three studies to demonstrate this thesis statement. First, we propose new automated approach to generate a list of destination
IP addresses that enables efficient enumeration of Internet edge links. Second, we show that using large number of widely distributed open resolvers enables efficient enumeration of anycast nodes which helps study abnormal behavior of anycast DNS services. In our last study, we efficiently enumerate Front-End (FE) Clusters of Content Delivery Networks (CDNs) and use the efficient enumeration to track and understand the dynamics of user-to-FE Cluster mapping of large CDNs. We achieve the efficient enumeration of CDN FE Clusters by selecting probing sources from a large set of open resolvers.
Our selected probing sources have smaller number of open resolvers but provide same coverage on CDN FE Cluster as the larger set.
In addition to our direct results, our work has also been used by several published studies to track and understand the behavior of Internet and large network services.
These studies not only support our thesis as additional examples but also suggest this thesis can further benefit many other studies that need efficient service enumeration to
track and understand behavior of global Internet services.
Location: 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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CS Colloquium: Hyun Soo Park (University of Pennsylvania) - Computational Social Cognition
Thu, May 28, 2015 @ 12:00 PM - 01:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Hyun Soo Park, University of Pennsylvania
Talk Title: Computational Social Cognition
Series: CS Colloquium
Abstract: Humans interact with one another by sending visible social signals such as facial expressions, body gestures, and gaze directions.
Computational understanding of these social signals is becoming more important for artificial agents such as service robots because they are increasingly integrated in our social space.
In this talk, I will present a computational framework for social cognition - the ability to perceive, model, and predict social signals.
The main challenges of developing computational social cognition are that 1) social signals are too subtle to be detected by current computer vision solutions and 2) they cannot be understood by analyzing an individual signal in isolation as they are reliant upon each other. I will argue that first person cameras, e.g., head-mounted cameras, are an ideal sensor placement to capture such subtlety and will show that the relationship between the signals can be modeled by leveraging a 3D reconstruction of human body motion. In the first part of my talk, I will focus on joint attention that encodes the relationship between gaze directions and present its predictive model to recognize social interactions. This predictive model is applied various tasks, e.g., event video editing, social anomaly recognition, and region of interest detection. In the second part, I will introduce a large scale motion capture system (510 cameras) to recover subtle social signals. This system reconstructs dense 3D trajectories of body gestures at unprecedented level of high spatial resolution (~20,000 trajectories per body). Then, I will demonstrate applications of computational social cognition in behavioral analysis, sport analytics, and robotics.
Biography: Hyun Soo Park is a Postdoctoral Fellow in Computer and Information Science at the University of Pennsylvania working with Prof. Jianbo Shi. He earned Ph.D. degree from Carnegie Mellon University in 2014 under the supervision of Prof. Yaser Sheikh. His research aims to develop a computational representation of social behaviors. He has over 15 publications in top tier conferences and journals that include computer vision (IJCV, ICCV, CVPR, ECCV), graphics (SIGGRAPH), machine learning (NIPS), and robotics (IJRR, ICRA, IROS). He organized Workshop on Human Behavior Understanding (2014) in conjunction with ECCV 2014 and will give a tutorial on Group Behavioral Analysis and its Applications in conjunction with CVPR 2015 based on his Ph.D. thesis. His work has been covered by various major media including Discovery Channel, MSNBC, WIRED, NSF, and Slashdot. Prior to his Ph.D., he received his M.S. degree from Carnegie Mellon University and his B.S. degree from POSTECH.
Host: Hao Li
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
