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Conferences, Lectures, & Seminars
Events for October
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CS Colloquium - Matthias Buechler: Security Testing with Fault-Models and Properties
Wed, Oct 09, 2013 @ 06:15 PM - 08:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Matthias Buechler, Technical University Munich (Technische Universität München)
Talk Title: Security Testing with Fault-Models and Properties
Series: CS Colloquium
Abstract: Web applications are complex and face a massive amount of sophisticated attacks. Since manually testing web applications for security issues is hard and time consuming, automated testing is preferable. In model-based testing, test cases are often generated using structural criteria. Since such test cases do not directly target security properties, my Ph.D thesis proposes to use a fault model for generating tests for web applications. Faults are represented as known source code vulnerabilities that, by using respective mutation operators at the model level, are injected into models of a System Under Validation to generate “interesting” test cases. To achieve this, advantages of penetration testing are combined with model-checkers dedicated to security analysis. To find attacks on real systems the gap between an abstract attack trace output by a model-checker and a penetration test needs to be addressed. My Ph.D thesis contributes with a semi-automatic methodology to turn abstract attack traces operational.
Host: William GJ Halfond
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium
Thu, Oct 10, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: CS Colloquium, CS Colloquium
Talk Title: NO EVENT - CS Colloquium
Series: CS Colloquium
Abstract: There will be no colloquium today due to Game Day.
Host:
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Department Advisory Board Meeting
Fri, Oct 11, 2013 @ 09:00 AM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: ,
Talk Title: Department Advisory Board Meeting
Host: Gaurav Sukhatme
Location: Seaver Science Library (SSL) - 332
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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CS Colloquium - Ashutosh Saxena: How should a robot perceive the world?
Tue, Oct 15, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ashutosh Saxena, Cornell University
Talk Title: How should a robot perceive the world?
Series: CS Colloquium
Abstract: In order to perform assistive tasks, a robot should learn a functional understanding of the environment. This comprises learning how the objects in the environment could be used (i.e., their affordances). In this talk, I will present methods to represent and learn these affordances using data-driven machine learning algorithms. Our learning algorithm will be Infinite Latent CRFs (ILCRFs) that allow modeling the data with different plausible graph structures. Unlike CRFs where the graph structure is fixed, our ILCRFs learn distributions over possible graph structures in an unsupervised manner.
We then show that our idea of modeling environments using object affordances and (hidden) humans is not only useful for robot manipulation tasks such as arranging a disorganized house, unloading items from a dishwasher, but also in significantly improving standard robotic tasks such as scene segmentation, 3D object detection, human activity detection and anticipation, and task and path planning.
Biography: Ashutosh Saxena is an assistant professor in computer science department at Cornell University. His research interests include machine learning and robotics perception, especially in the domain of robotics in human environments. He received his MS in 2006 and Ph.D. in 2009 from Stanford University, and his B.Tech. in 2004 from Indian Institute of Technology (IIT) Kanpur. He is a recipient of National Talent Scholar award in India, Google Faculty award, Alfred P. Sloan Fellowship, Microsoft Faculty Fellowship, and NSF Career award.
In the past, Ashutosh developed Make3D (http://make3d.cs.cornell.edu), an algorithm that converts a single photograph into a 3D model. Tens of thousands of users used this technology to convert their pictures to 3D. He has also developed algorithms that enable robots (such as STAIR, POLAR, see http://pr.cs.cornell.edu) to perform household chores such as unload items from a dishwasher, place items in a fridge, etc. His work has received substantial amount of attention in popular press, including the front-page of New York Times, BBC, ABC, New Scientist, Discovery Science, and Wired Magazine. He has won best paper awards in 3DRR, IEEE ACE and RSS, and was named a co-chair of the IEEE technical committee on robot learning.
Host: Fei Sha
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium - Richard Snodgrass: Database Ergalics: Examining Suboptimality
Wed, Oct 16, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Richard Snodgrass, University of Arizona
Talk Title: Database Ergalics: Examining Suboptimality
Series: CS Colloquium
Abstract: In this talk I apply the methodology of ergalics (the science of
computing) to database management systems, by articulating a model of database suboptimality: when a DBMS picks the wrong query execution plan. Along the way, I develop a protocol for accurately measuring query time, a surprisingly difficult task. The goal is to make the case that computer science integrates three equally ascendant perspectives: mathematics, science, and engineering. I'll look at how these three perspectives interact and the sources of endurance of ergalic theories.
Biography: Richard T. Snodgrass joined the University of Arizona in 1989, where he is a Professor of Computer Science. He holds a B.A. degree in Physics from Carleton College and M.S. and Ph.D. degrees in Computer Science from Carnegie Mellon University. He is an ACM Fellow.
Rick's research foci are ergalics, compliant databases, and temporal databases.
Rick was Editor-in-Chief of the ACM Transactions on Database Systems from 2001 to 2007, was ACM SIGMOD Chair from 1997 to 2001, and has chaired the ACM Publications Board, the ACM History Committee, the ACM SIG Governing Board Portal Committee, the ACM Outstanding Award Committee, and program committees for SIGMOD and VLDB. His web page is at http://www.cs.arizona.edu/people/rts
Host: Shahram Ghandeharizadeh
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium - Jieping Ye: Large-Scale Sparse Learning for Biomedical Data
Thu, Oct 17, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jieping Ye, Arizona State University
Talk Title: Large-Scale Sparse Learning for Biomedical Data
Series: CS Colloquium
Abstract: Sparse methods have been applied extensively to analyze biomedical data. In this talk, we consider sparse methods for (1) variable selection where the structure over the features can be represented as an undirected graph or a collection of disjoint groups, (2) multi-source data fusion with a "blockwise" data missing pattern, and (3) network construction. We address the computational challenge by designing novel screening strategies which scale sparse methods to large-size problems.
Biography: Jieping Ye is an Associate Professor of Computer Science and Engineering at the Arizona State University. He is a core faculty member of the Bio-design Institute at ASU. He received his Ph.D. degree in Computer Science from University of Minnesota, Twin Cities in 2005. His research interests include machine learning, data mining, and biomedical informatics. He has served as Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, ICML, KDD, IJCAI, ICDM, SDM, ACML, and PAKDD. He serves as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the SCI Young Investigator of the Year Award at ASU in 2007, the SCI Researcher of the Year Award at ASU in 2009, and the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at the International Conference on Machine Learning in 2004, the KDD best research paper honorable mention in 2010, the KDD best research paper nomination in 2011 and 2012, the SDM best research paper runner up in 2013, and the KDD best research paper runner up in 2013.
Host: Fei Sha
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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PhD Student Colloquium: Megha Gupta (Robotics Research Lab) & Hien To (Information Laboratory)
Tue, Oct 22, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Megha Gupta & Hien To , Robotics Research Lab & Information Laboratory
Talk Title: Megha Gupta: Interactive Environment Exploration in Clutter; Hien To: Entropy-based Histograms for Selectivity Estimation
Series: CS Colloquium
Abstract: Presenter: Megha Gupta (Robotics Research Lab)
Title: Interactive Environment Exploration in Clutter
Robotic environment exploration in cluttered environments is a challenging problem. The number and variety of objects present not only make perception very difficult but also introduce many constraints for robot navigation and manipulation. In this talk, we investigate the idea of a robot exploring a small, bounded environment (eg. the shelf of a home refrigerator) by physically interacting with the objects in the environment. The presence of multiple objects results in partial and occluded views of the scene. This inherent uncertainty in the scene's state forces the robot to adopt an observe-plan-act strategy and interleave planning (which object to move, where to move) with execution (rearrangement of the objects). Objects occupying the space and potentially occluding other hidden objects are rearranged to reveal more of the unseen area. The environment is considered explored when the state (free or occupied) of every voxel in the volume is known. The presented algorithm can be easily adapted to real world problems like object search, taking inventory, and mapping. We evaluate our planner using various metrics, then present an implementation on the PR2 robot and use it for object search in clutter.
Presenter: Hien To (Information Laboratory)
Title: Entropy-based Histograms for Selectivity Estimation
Selectivity estimation is the task of estimating the size of the result set of a relational algebra operator. For a particular query, multiple execution plans can be generated with different ordering of operators. Thus, selectivity estimation of intermediate temporary relations significantly influences the choice of a query plan chosen by a query optimizer. Accurate estimations are crucial to generate optimal execution plans while bad estimations often lead to large overhead in performance.
Histograms have been extensively used for selectivity estimation by academics and have successfully been adopted by database industry. However, the estimation error is usually large for skewed distributions and biased attributes, which are typical in real-world data. Therefore, we propose effective models to quantitatively measure bias and selectivity based on information entropy. These models together with the principles of maximum entropy are then used to develop a class of entropy-based histograms that achieves near-optimal quality in linear runtime. We conducted an extensive set of experiments to compare the accuracy and efficiency of our proposed techniques with many other histogram-based techniques, showing the superiority of the entropy-based approaches for both equality and range queries.
Host: PhD Committee
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
