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University Calendar
Events for March
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PhD Defense - Abdulmajeed Alameer
Tue, Mar 19, 2019 @ 11:00 AM - 01:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate:
Abdulmajeed Alameer
Committee:
William G.J. Halfond (Chair)
Nenad Medvidovic
Sandeep Gupta
Chao Wang
Jyotirmoy V. Deshmukh
Dissertation Title:
Detection, Localization, and Repair of Internationalization Presentation Failures in Web Applications
Time and Location:
3/19 from 11am to 1:30pm - Room PHE 223.
Abstract:
Web applications can be easily made available to an international audience by leveraging frameworks
and tools for automatic translation and localization. However, these automated changes
can introduce Internationalization Presentation Failures (IPFs) - an undesired distortion of the
web page's intended appearance that occurs as HTML elements expand, contract, or move in
order to handle the translated text. It is challenging for developers to design websites that can
inherently adapt to the expansion and contraction of text after it is translated to different languages.
Existing web testing techniques do not support developers in debugging these types of
problems and manually testing every page in every language can be a labor intensive and error
prone task.
In my dissertation work, I designed and evaluated two techniques to help developers in debugging
web pages that have been distorted due to internationalization efforts. In the first part of
my dissertation, I designed an automated approach for detecting IPFs and identifying the HTML
elements responsible for the observed problem. In evaluation, my approach was able to detect
IPFs in a set of 70 web applications with high precision and recall and was able to accurately
identify the underlying elements in the web pages that led to the observed IPFs. In the second
part of my dissertation, I designed an approach that can automatically repair web pages that
have been distorted due to internationalization efforts. My approach models the correct layout
of a web page as a system of constraints. The solution to the system represents the new and
correct layout of the web page that resolves its IPFs. The evaluation of this approach showed
that it could more quickly produce repaired web pages that were rated as more attractive and
more readable than those produced by a prior state-of-the-art technique. Overall, these results
are positive and indicate that both my detection and repair techniques can assist developers in
debugging IPFs in web applications with high effectiveness and efficiency.
Time and Location:
3/19 from 11am to 1:30pm - Room PHE 223.
Location: Charles Lee Powell Hall (PHE) - 223
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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RASC seminar - How to Make, Sense, and Make Sense of Contact in Robotic Manipulation
Thu, Mar 21, 2019 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
University Calendar
"How to Make, Sense, and Make Sense of Contact in Robotic Manipulation"
Dexterous manipulation is still one of the key open problems for many new robotic applications, owing in great measure to the difficulty of dealing with transient contact. From an analytical standpoint, intermittent frictional contact (the essence of manipulation) is difficult to model, as it gives rise to non-convex problems with no known efficient solvers. Contact is also difficult to sense, particularly with sensors integrated in a mechanical package that must also be compact, highly articulated and appropriately actuated (i.e. a robot hand). Articulation and actuation present their own challenges: a dexterous hand comes with a high-dimensional posture space, difficult to design, actuate, and control. In this talk, I will present our work trying to address these challenges: analytical models of grasp stability (with realistic energy dissipation constraints), design and use of sensors (tactile and proprioceptive) for manipulation, and hand posture subspaces (for design optimization and teleoperation). These are stepping stones towards achieving versatile robotic manipulation, needed by applications as diverse as logistics, manufacturing, disaster response and space robots.
Matei Ciocarlie is an Associate Professor of Mechanical Engineering at Columbia University. His current work focuses on robot motor control, mechanism and sensor design, planning and learning, all aiming to demonstrate complex motor skills such as dexterous manipulation. Matei completed his Ph.D. at Columbia University in New York; before joining the faculty at Columbia, he was a Research Scientist and Group Manager at Willow Garage, Inc., a privately funded Silicon Valley robotics research lab, and then a Senior Research Scientist at Google, Inc. In recognition of his work, Matei has been awarded the Early Career Award by the IEEE Robotics and Automation Society, a Young Investigator Award by the Office of Naval Research, a CAREER Award by the National Science Foundation, and a Sloan Research Fellowship by the Alfred P. Sloan Foundation.
Hosted by: Gaurav Sukhatme
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Yuan Shi
Thu, Mar 21, 2019 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
University Calendar
Time and Location: 3/21 10 am - 11:30 am - PHE 223
PhD Candidate: Yuan Shi
Committee:
Craig Knoblock (Chair)
Yan Liu
T. K. Satish Kumar
Daniel Edmund O'Leary (external member)
Title: Learning to Adapt to Sensor Changes and Failures
Abstract:
Many software systems run on long-lifespan platforms that operate in diverse and dynamic environments. As a result, significant time and effort are spent manually adapting software to operate effectively when hardware, resources and external devices change. If software systems could automatically adapt to these changes, it would significantly reduce the maintenance cost and enable more rapid upgrade. As an important step towards building such long-lived, survivable software systems, we study the problem of how to automatically adapt to changes and failures in sensors.
We address several adaptation scenarios, including adaptation to individual sensor failure, compound sensor failure, individual sensor change, and compound sensor change. We develop two levels of adaptation approaches: sensor-level adaptation that reconstructs original sensor values, and model-level adaptation that directly adapts machine learning models built on sensor data. Sensor-level adaptation is based on preserving sensor relationships after adaptation, while model-level adaptation maps sensor data into a discriminative feature space that is invariant with respect to changes.
Compared to existing work, our adaptation approaches have the following novel capabilities: 1) adaptation to new sensors even when there is no overlapping period between new and old sensors; 2) efficient adaptation by leveraging sensor-specific transformations derived from sensor data; 3) scaling to a large number of sensors; 4) learning robust adaptation functions by leveraging spatial and temporal information of sensors; and 5) estimating the quality of adaptation.
Additionally, we present a constraint-based learning framework that performs joint sensor failure detection and adaptation by leveraging sensor relationships. Our framework learns sensor relationships from historical data and expresses them as a set of constraints. These constraints then provide a joint view for detection and adaptation: detection checks which constraints are violated, and adaptation reconstructs failed sensor values. Our framework is capable of handling multi-sensor failures which are challenging for existing methods.
To validate our approaches, we conduct empirical studies on sensor data from the weather and UUV (Unmanned Underwater Vehicle) domains. The results show that our approaches can automatically detect and adapt to sensor changes and failures with higher accuracy and robustness compared to other alternative approaches.
Location: Charles Lee Powell Hall (PHE) - 223
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
