92
Fall 2010 1 Faculty Research Areas Labs/Centers Meetings
Fall 2010 1
Faculty Research AreasLabs/Centers
Meetings
Fall 2010 2
Areas
Artificial Intelligence Bio-Informatics Databases Graphics, Image Processing and Multimedia Networks Pervasive Computing Software Engineering Systems and Architecture Security
Fall 2010 3
Artificial Intelligence
Manfred HuberFarhad KamangarVassilis AthitsosGian Luca Mariottini
Fall 2010 4
Manfred Huber
Research Projects• Personal Service Robots• Hierarchical Skill Acquisition• CONNECT - Information
Technologies for the Disabled
Contact: [email protected] (GACB114)
Fall 2010 5
Farhad Kamangar
Research Projects• Computer Vision• Neural Networks• Robotics• CONNECT - Information
Technologies for the Disabled
Contact: [email protected] (GACB
112)
Fall 2010 6
Bio-Informatics
Dr. Nikola Stojanovic301 Nedderman Hall
Phone: (817) 272-7627E-mail: [email protected]: http://ranger.uta.edu/~nick
Dr. Jean Gao338 Nedderman Hall
Phone: (817) 272-3628E-mail: [email protected]: http://crystal.uta.edu/~gao
Dr. Fillia MakedonDr. Heng HuangDr. Chris Ding
Fall 2010 7http://www.washbac.org/images/farside.gif
Fall 2010 8
What is BIOINFORMATICS? Have you ever thought that a cure for cancers could be
developed by people working at their computers?
Fall 2010 9
What is BIOINFORMATICS? Have you ever thought that a cure for cancers could be
developed by people working at their computers?
it will probably happen exactly that way
Fall 2010 10
What is BIOINFORMATICS? Have you ever thought that a cure for cancers could be
developed by people working at their computers?
Modern high-throughput technologies are generating tremendous volume of data - somebody needs to store and manipulate the data, generate reports and share them with the scientific community.
it will probably happen exactly that way
Fall 2010 11
What is BIOINFORMATICS? Have you ever thought that a cure for cancers could be
developed by people working at their computers?
Modern high-throughput technologies are generating tremendous volume of data - somebody needs to store and manipulate the data, generate reports and share them with the scientific community.
it will probably happen exactly that way
Fall 2010 12
What is BIOINFORMATICS? Have you ever thought that a cure for cancers could be
developed by people working at their computers?
Modern high-throughput technologies are generating tremendous volume of data - somebody needs to store and manipulate the data, generate reports and share them with the scientific community.
Can we turn that data into information, and eventually knowledge?
it will probably happen exactly that way
Fall 2010 13
What is BIOINFORMATICS? Have you ever thought that a cure for cancers could be
developed by people working at their computers?
Modern high-throughput technologies are generating tremendous volume of data - somebody needs to store and manipulate the data, generate reports and share them with the scientific community.
Can we turn that data into information, and eventually knowledge?
it will probably happen exactly that way
Fall 2010 14http://bioinformatics.ubc.ca/about/what_is_bioinformatics/
Fall 2010 15http://bioinformatics.ubc.ca/about/what_is_bioinformatics/
Fall 2010 16http://bioinformatics.ubc.ca/about/what_is_bioinformatics/
Fall 2010 17
Fall 2010 18
Biotechnology and pharmaceutical industry
Biotechnology and pharmaceutical industry revenues are estimated at hundreds of billions of dollars annually.
The industry's claim is that they spend $800 million on research & development for every new drug which receives FDA approval.
Much of the R&D efforts are pursued computationally these days.
Fall 2010 19
Biotechnology and pharmaceutical industry
Biotechnology and pharmaceutical industry revenues are estimated at hundreds of billions of dollars annually.
The industry's claim is that they spend $800 million on research & development for every new drug which receives FDA approval.
Much of the R&D efforts are pursued computationally these days.
This is a large and growing industry - whether in R&D or just software support, you may see yourself working for one of these companies in a few years.
Fall 2010 20
http://bioinformatics.uta.edu
Fall 2010 21
Bioinformatics lab projects Motif discovery in DNA sequences. Identification and characterization of mobile elements in
DNA. Studying structure and conservation patterns in genomic
sequences. Characterization of chromosomal recombination patterns. Studying human genetic variation and its relation to disease
susceptibility.
Fall 2010 22
Bioinformatics lab projects Motif discovery in DNA sequences. Identification and characterization of mobile elements in
DNA. Studying structure and conservation patterns in genomic
sequences. Characterization of chromosomal recombination patterns. Studying human genetic variation and its relation to disease
susceptibility.
Research funded by the National Institutes of Health, and preformed in collaboration with UTA Biology Department and the University of Texas Southwestern Medical Center in Dallas.
Fall 2010 23
UT Arlington
http://www.biotconf.org
Fall 2010 24
Databases
Sharma ChakravarthyRamez ElmasriLeonidas FegarasGautham DasChengkai Li
Fall 2010 25
Information Technology LaboratoryProf. Sharma ChakravarthyEmail: [email protected], URL: http://itlab.uta.edu/sharmaFunding Sources: NSF, Spawar, Rome Lab, ONR, DARPA, TI, MCC
Select Projects
InfoMosaic (information integration from heterogeneous sources)
MavEStream: (Event and Stream Processing)
Active Technology (Push Paradigm, pub/sub, event-driven architectures)
WebVigiL: (General Purpose Change Monitoring for the web)
Mining: Graph, Text, Assoc Rules
Prediction of Event Patterns
Information Search, Filtering, and classification
Information Security
Mobile Caching
Select Publications
1. 1. R. Adaikkalavan and S. Chakravarthy, Event Specification and Processing for Advanced Applications: Generalization and Formalization, DEXA Sep 2007
2. A. Telang, R. Mishra, and S. Chakravarthy, Ranking Issues for Information Integration, DBrank workshop (ICDE 2007), Turkey, 2007.
3. S. Savla and S. Chakravarthy, Efficient Main Memory Algorithms for Significant Episode Discovery, To appear in the Int’l Journal of Data warehousing and Mining, 2006.
4. R. Balachandran, S. Padmanabhan, S. Chakravarthy Enhanced DB-Subdue: Supporting Subtle Aspects of Graph Mining Using a Relational approach in PAKDD, 2006
5. A. Srinivasan, D. Bhatia, and S. Chakravarthy, Discovery of Interesting episodes in Sequence Data, in 21st ACM SAC, Data Mining Track, 2006.
6. M. Aery, S. Chakravarthy: eMailSift: Email Classification Based on Structure and Content in IEEE ICDM 2005
7. H. Kona, S. Chakravarthy, and A. Arora, SQL-Based Approach to Incremental Association Rule Mining, in ADBIS Workshop on DMKD, 2005.
8. Q. Jiang, R. Adaikkalavan and S. Chakravarthy, NFMi: An Inter-domain Network Fault Management System. IEEE ICDE, 2005.
9. R. Adaikkalavan, and S. Chakravarthy: Active Authorization Rules for Enforcing Role-Based Access Control and its Extensions, PDM Workshop, IEEE ICDE, 2005.
10. L. Elkhalifa, R. Adaikkalavan, and S. Chakravarthy, InfoFilter: A System for Expressive Pattern Specification and Detection Over Text Streams, ACM SAC, 2005.….
People
PhD Students –
Mr. Aditya Telang (Adi)Ms. Roochi Mishra
Masters Students –
Mr. Mayur MotgiMr. Supreet ChakravarthyMr. Aamir Syed
Group Meeting:
1 Pm to 2 Pm on Fridays in NH 232
Fall 2010 26
…Ground controller 1 Ground controller 2 Ground controller n
uav1
uav2 uav3
uav4
uav5
uav6
A Distributed Middleware-Based Architecture for Fault-Tolerant Computing Over Distributed repositories
Semi-joins Compression Replication Smart Routing
Fall 2010 27
Network of computing nodes:Unmanned vehicles, Sensors, Robots, PCs ,
Servers, Ground Controlling devices
Fault Tolerance Services
Fault Tolerance Services
Context/ Knowledge
Base
Context/ Knowledge
Base
Local fusion/Materiali
zation
Local fusion/Materiali
zation
Publish Subscribe Capability
Publish Subscribe Capability
Query Capability
Query Capability Raw Data / fused
data /data from other nodes
Queries, Tasks, Requests, Continuous Queries Publish/Subscribe
SOA Distributed MiddlewareTask planning Join computationComposition pub/subContext-aware NotificationResource Management Data management
Limited ResourcesMobilityHeterogeneityDisconnections
Fall 2010 28
Ramez ElmasriProfessor
DatabasesDistributed XML Querying and Caching
Object-Oriented DatabasesKeyword-based XML Query Processing
Sensor NetworksEnergy-Efficient Querying of Sensor
NetworksCombining RFID and Sensor Networks
Indexing of Sensor Networks Data
BioinformaticsModelling Complex Bioinformatics and
Biomedical DataMediators for Accessing Heterogeneous Data
Sources
Fall 2010 29
Leonidas FegarasAssociate Professor(PhD: UMass 1993)
Areas of interest: Databases
Web Databases and XML Object-Oriented Databases Query Processing and Optimization Data Management on Peer-to-Peer Systems
Programming Languages Functional Programming Program Optimization
Fall 2010 30
Research Review Gautam Das
Database Exploration Web/Information Retrieval searching techniques in
databases OLAP, Data Warehouse, Approximate Query Processing
Data Mining Clustering, Classification, Similarity models, Time-
Series Analysis Algorithms
Graph Algorithms, Computational Geometry
More information available athttp://ranger.uta.edu/~gdas/website/research.htm
Chengkai LiAssistant Professor http://ranger.uta.edu/~cli [email protected] The Innovative Database and Information Systems Research (IDIR) Lab
http://idir.uta.edu , GeoScience 237Jared Ashman, Avinash Bharadwaj, Ebrahim Cutlerywala, Sunny Hasan, Naeemul Hassan, Angus Helm, Nandish Jayaram, Pat Jangyodsuk, Xiaonan Li, Vikramark Singh, Ning Yan
Research AreasDatabases, Web Data Management, Information Retrieval, Data Mining
Specific Topics Data Retrieval and Exploration, Ranking and Top-k Queries; Web
Search/Mining/Integration, Web Databases, Query Processing and Optimization, OLAP and Data Warehousing, Cloud Computing, Database Testing, XML
Projects: Search the Database and Query the Web Computational Journalism DBTest: Database Application Testing Entity-Centric Enterprise Information Management BestCloud: Query Optimization for Cloud Computing RankSQL: Ranking and Top-k Queries, Database Exploration SetQuery: Set-Oriented OLAP Queries WebEQ: Querying and Exploring Structured Information on the Web
31
Two Demos from WebEQ project
Facetedpediahttp://idir.uta.edu/facetedpedia/
Entity-Relationship Querieshttp://idir.uta.edu/erq/
32
Fall 2010 33
Graphics Image Proc., Multimedia
Ishfaq AhmadMultimedia Authoring, Compression, CommunicationVideo Processing, Next Generation TVNetwork SecurityParallel Algorithms
Dr. Gutemberg Guerra-Filho
Computer Vision, Animation, and Humanoid Robotics
Fall 2010 34
Dr. Ahmad works closely with federal agencies, Arlington police and multimedia industry.
Several projects in power-aware video compression, multimedia systems, next generation TV are being pursued in his lab.
Prof. Ishfaq Ahmad
Fall 2010 35
High-Performance
Ishfaq Ahmad Resources Management in Parallel and Distributed SystemsPower Management in Data Center and Distributed Systems
Fall 2010 36
http://www.iris.uta.edu/
Institute for Research in Security (IRIS)
Ishfaq AhmadA Multi-disciplinary center focusing on infrastructure, people, and environmental security
Fall 2010 37
Networks
Sajal DasMohan KumarGergley ZarubaHao CheYonghe Liu
Fall 2010 38
Sajal K. DasCenter for Research in Wireless Mobility
and Networking (CReWMaN)
Sajal K. Das, Mohan Kumar Yonghe Liu, Hao Che
URL: http://crewman.uta.eduWoolf Hall 411,413,
Tel: 2-7409[Networking, Mobile Computing and Parallel Computing Research Group]
Fall 2010 39
Pervasive Computing Middleware Service creation, composition and deployment Prototype development Sensor networks and smart environments Information Fusion in pervasive/sensor environments
Uniform Information Access in Distributed, mobile and pervasive systems Caching, prefetching, and broadcasting Data management
Peer-to-Peer (P2P) Systems Information and service sharing Efficient communication and collaboration Security and privacy
Active and Overlay Networking Novel protocols Role in mobile, pervasive and P2P computing
Mohan KumarPervasive and Mobile ComputingSensor Systems
Recommended courses before
starting thesis work:
CSE5311, CSE5346,CSE5306 and CSE5347/5355
Directed Study
Fall 2010 40
Gergely Zaruba
Research Projects
Personal Area Networks
Heterogeneous Wireless NetworksArchitecture, Admission Control and Handoff
Optical NetworksOptical Burst Switching, Routing, QoS Provisioning
Traffic Modelling
Contact: [email protected] (GACB 112)
Fall 2010 41
Hao Che Embedded hardware/software design for NG
network processors Traffic engineering
Implementation issues and software development
MPLS path protection and fast rerouting Routing redundancy Traffic modeling for wireless networks
Contact: http://crystal.uta.edu/~hche/ [email protected]
Fall 2010 42
Yonghe Liu Sensor network and security
Prototyping and experimental study Theoretic design and analysis
Cross layer optimization Channel dependent performance
Software security Design and analysis
In need of Strong mathematic skill (probability/signal processing/number
theory/etc), or Strong programming skill (hardware/software)
Contact: http://ranger.uta.edu/~yonghe/
Fall 2010 43
Software Engineering
David KungYu LeiDr. Christoph CsallnerDavid Levine
Fall 2010 44
David Kung
Agent-Oriented Software Engineering Testing Object-Oriented Software Expert System for Design Patterns Formal Methods for Quality Assurance Fault Tolerance and Automatic Recovery
Using Dynamic Class Diversity
Contact: http://ranger.uta.edu/~kung/kung.html
Fall 2010 45
Yu Lei Concurrent and real-time software
systems Race analysis, Deterministic Execution
Environment, Reachability Testing, State Exploration-Based Verification
Automated software testing Object-Oriented Testing, Component-Based
Testing, Combinatorial Testing
Contact: http://ranger.uta.edu/~ylei
Fall 2010 46
David Levine, CSE@UTAProjects: (Computers applied to:) High Energy Physics, Bioinformatics, Medical Informatics, People with Disabilities, Streaming Processing, other..
David Levine High Throughput Computational Science: Clusters and Grids::
Fall 2010 47
Software EngineeringResearch Center
Faculty members:Dr. Christoph Csallner
Dr. Dave KungDr. Jeff Lei
Check out the lab: NH 246
Fall 2010 48
Fall 2010 49
Software Engineering
Software has become pervasive in modern society Directly contributes to quality of life Malfunctions cost billions of dollars every
year, and have severe consequences in a safe-critical environment
All about building quality software, especially for large-scale development Requirements, design, coding, testing,
maintenance, configuration, documentation, deployment, and etc.
Fall 2010 50
THE Best Job in America
What is the 2nd best job?
Go for a PhD in Software Engineering!!
Fall 2010 51
Great Impact
Fall 2010 52
Quotes from Dr. Parnas
Extracted from his ACM Fellow Profilehttp://www.sigsoft.org/SEN/parnas.html
Fall 2010 53
Current Research Projects
Object-Oriented Software Analysis and Testing (Dr. Kung)
Software Security Analysis and Testing (with Drs. Kung and Liu)
Pervasive Context-Aware Computing (with Dr. Kumar)
Formal Testing and Verification of Concurrent Software Systems (with GMU)
Automated Combinatorial Testing for Software (with National Institute of Standards and Technology)
Interaction Testing of Web Applications (with UMBC)
Fall 2010 54
Current Research Projects
Hybrid static-dynamic program analyses Automatic test case generators
JCrasher, Check ‘n’ Crash, DSD-Crasher New: Testing of database-centric applications
OrmCheck with ToDo: Support complex languages like UML
New: Dynamic symbolic invariant detector Pex/DySy with ToDo: Scale analysis to large applications ToDo: Add static knowledge to dynamic inference
Fall 2010 55
Fall 2010 56
If you want to improve..
..come talk to us
Fall 2010 57
Embedded Systems :: Roger Walker
Embedded Systems for Transportation Applications: Real-time Multi-core Systems for Embedded
Applications Stochastic Modeling From Sensor
Measurements Development of Special Measurement
Systems for Transportation Related Applications
Contact: http://ranger.uta.edu/~walker/
Design and Development of a Mobile Bridge
Monitoring/Measurement System
Integrate Data
Profiler
Gyroscope
ScanningLaser
Video
Video
VideoSurface
Data
SurfaceData
StructureData
Design and Development of a Mobile Bridge Monitoring/Measurement
System
Design and Development of Portable Real-Time Embedded Measure and Control Systems
Current Research Projects supported by Texas Department of Transportation, Federal Highway Administration, & Intel
Fall 2007 60
Donggang LiuMatt Wright
Information Security
Fall 2007 61
Jobs in Infosec
Fall 2007 62
One aspect of security
Operational Security Classified material can be
leaked based on how it’s used or through side effects
Domino’s Pizza Anyone?Last Wednesday, he adds, "we got a lot of orders, starting around midnight. We figured something was up." This time the news arrived quickly: Iraq's surprise invasion of Kuwait. "And Bomb the Anchovies", Time, p. 13,
8/13/90
Fall 2007 63
Border Security with WSNs
Website: http://isec.uta.edu/borde
r/
PIs: Donggang Liu, Sajal K. Das, Matthew WrightPost-Doc: Jun-Won HoStudents: Andy Fox, Na Li, Nabila Rahman, Mayank Raj, Kartik SiddhabathulaFunded in part by the National Science Foundation
Goal Intruder tracking
Intruders Corrupt many
sensors Jam wireless
channels Destroy key
infrastructure Seek gaps in the
sensing coverage
Fall 2007 64
Wireless and System Security :: Donggang LiuSecurity in wireless sensor
networks key management, security of services
such as localization, routing, clustering etc.
Integrity of wireless embedded devices Code integrity, tamper-resistant
techniques
Software and system security Security testing, detection of malicious
code
Contact: http://ranger.uta.edu/~dliu
Fall 2007 65
Matthew Wright
Internet Privacy
Robust P2P
Distributed Twitter
Sensors/Mobile/Social/Ubicomp …
Contact: http://isec.uta.edu/mwright
Fall 2010 66
Computer Science and Engineering DepartmentThe University of Texas at Arlington
Assist Laboratory
F. Kamangar, M. Huber, D. Levine, G. Zaruba
Fall 2010 67
Information Technologies for Persons with Disabilities and Health Care
• Assistance for Persons with Disabilities
• Communication devices and technologies• Intelligent assistive devices• IT for improved care
• Information Technologies for Healthcare and Aging
• Automatic health monitoring• Intelligent environments• IT to improve uniform communication needs
Fall 2010 68
Connect - Intelligent Communication Technologies for Disability & Health Care
ClientsHuman Service Providers
Technical support
Servers, Databases, Web pages
Wireless CommunicationProvider
Internet
ClientsHuman Service ProvidersHuman Service Providers
Technical supportTechnical support
Servers, Databases, Web pagesServers, Databases, Web pages
Wireless CommunicationProvider
Internet
• Intelligent communication services connect individuals with care providers and with important information • Seamlessly connected devices• Adaptive interfaces• Universal underlying
software architecture• Intelligent information
analysis and interpretation• Seamless, omnipresent
access to information
Fall 2010 69
Assistive Technologies
• Computer Technologies Can Enhance Assistive Devices• Ayuda – Intelligent wheelchair
• Autonomous navigation capabilities
• Environment sensing• Integration of computer control
and user instructions • Force feedback technologies to
enhance interaction capabilities for persons with physical disabilities
Fall 2010 70
Health Monitoring and Intelligent Environments for Aging in Place
• Wirelessly Connected Sensors Provide Health Information and can Improve Quality of Life• Health sensors can monitor conditions
and detect problems• Wireless communications permit
continuous monitoring• Prediction and modeling technologies
facilitate automatic analysis of the data• Communication technologies allow
connectivity to physician
• Sensors in the environment allow automation of important functions and assistance
• Monitoring and assistance for Aging in Place
Fall 2010 71
Computer Science and Engineering DepartmentThe University of Texas at Arlington
AI and Robotics Laboratory
M. Huber, F. Kamangar
Fall 2010 72
Adaptation and Learning in Robots and Computer Systems
• Personal Service Robots• Service robots have to interact with people• Programmability by unskilled users• Robustness in real world situations
• Variable Autonomy• Robots have to be easy to program • Robots should understand any kind of user command
• Cognitive Development• Computer systems have to learn how
to act and reason in the world
Fall 2010 73
Robot Imitation – Programming by Demonstration
• Learning to Sense• Imitating robots have to be able to interpret their observations
• Learning to Relate Human Demonstrations to Robot Actions
• Learning to extract the important aspects of human actions • Translating human actions into corresponding robot controls
• Learning to Interpret Task Requirements
• Robots have to be able to learn to ignore dangerous commands
Fall 2010 74
Hierarchical Skill Learning / Cognitive Development
• Learning Behavioral Strategies• Adaptation to unknown
conditions• Automatic extraction of
subtasks
• Hierarchical Learning• Learning with abstract actions• Learning using state
abstractions• Facilitation of incrementally
more complex behavior
Fall 2010 75
Robot Activities and Platforms
• Robot Soccer (RoboCup)• Autonomous robotic soccer with
robot dogs • Student team
• Computer Game Trials• UCT – Urban Combat Testbed
Fall 2010 76
The HERACLEIA Human Centered Computing Lab
HERACLEIA was a thriving outpost of Hellenic culture south of the Black Sea. Symbolizes a world where technologies are placed at the service of humans, esp. those needing special help, and bringing out the human side of technology.
Vicon Motion Capture System
Bioloid Robot
Vicon Camera
Peoplebot
SunSPOT Wireless
Sensor Node
Fall 2010 77
The HeracleiansFillia Makedon (Director)Professor Chair of Computer Science and Engineering Current work: Computational Multimedia Applications, Multimedia Authoring and Retrieval, Analysis of fMRI Brain Activations, and Electronic Commerce
Zhengyi Le (Assistant Director) Research Assistant ProfessorCurrent work: Security, Privacy, and Collaboration System
Kyungseo Park Academic Interests: Data Mining in Wireless Sensor Networks
Fall 2010 78
Some of our Security Work
Mobile Device Protection against Loss and Capture (PETRA09) Our forward secure two-party signature scheme provides stronger
device authentication to make it work against impersonation Privacy-Enhanced Opportunistic Networks (PSPAE09)
group mobile nodes together to randomly detour the traffic to protect from timing traffic analysis (which leads to privacy leakage)
Providing Location Privacy (PETRA08) use dynamic zone to mix some location records of some moving objects
to protect against tracking Source Location Privacy (SecureCom08)
hide event messages into maintenance messages so that an attacker can not track where an event is happening (if source location information is sensitive)
Preventing Unofficial Information Propagation (ICICS07) use short-lived certificates with forward secure signatures to make the information on a
certificate not verifiable shortly after usage Challenges
how to apply expensive (resource consuming) cryptosystems in mobile, portable, assistive devices (computationally limited)
faster encryption methods that a light mobile device can afford. anti-data-mining mechanisms and privacy preserving
technologies to address the increasing public concerns on privacy information leakage.
Fall 2010 79
Data Sharing: Open CollaborationSupport: Group, Role, File Sharing, Recommendation
Groups, Roles, Files Recommendations Group Operations
Files andaccesspolicies
Top 10 recommendations
Group name, Description andExpiration date
Roles andRequiredattributes
Fall 2010 80
Behavioral Markers: Making Genotype-Phenotype Correlations
Certain genetic anomalies lead to certain diseases/disabilities (phenotype is any demonstration of the conditions, such as a scan).
Understanding Genotype-Phenotype correlations may help create more effective treatments.
Challenges: How to correlate certain medical conditions with
observable behaviors or physiological conditions. How to use correlations to enhance decision making. How to analyze the effects of medical
treatments and adapt to patient
condition.
Deletion 9q34.3 syndrome 80
Fall 2010 81
@Home Apartment
Fall 2010 82
Active Service Robots
Approach: • robot investigates and prompts human to respond by keyboard, touch screen, or voice. • Human cancels/confirms alarm or no action. • Then robot makes a decision based on the available streams of sensor and human information using partial order Markov decision processes. Challenges: • Setting up the hierarchy of decision making to determine what level of action is appropriate by funneling the events of four different data streams into the partial order Markov decision process.• Able to access additional sensors to confirm the status of the human• Evaluating and testing the correctness of the decisions.
Yong Lin, Eric Becker, Kyungseo Park, Zhengyi Le, Fillia Makedon Decision Making in Assistive Environments using Multimodal Observations Proceedings of the 2nd International Conference on Pervasive Technologies Related to Assistive Environments (PETRA'09), Corfu, Greece, June 9-13, 2010.
Problem: When abnormal event occurs, how can a robot decide what to do?
Fall 2010 83
Conference Proceedings: ACM will be the publisher of the proceedings of the PETRA conference Selected papers will be in invited to the International Journal of Functional Informatics and Personalized Medicine, eJeta, and Journal of Personal and Ubiquitous Computing
WWW.PETRAE.ORGPETRA 2010
Fall 2010 84
Research at the Vision-Learning-Mining Lab
Vassilis Athitsos
University of Texas at Arlington
Fall 2010 85
American Sign Language
0.5-2 million users in the US. Complete and independent language.
Not a signed version of English.
Fall 2010 86
Looking Up a Sign
It is easy to go from an English word to ASL.
Fall 2010 87
Looking Up a Sign
It is easy to go from an English word to ASL.
It is hard to look up the meaning of a sign.
Fall 2010 88
Looking Up a Sign Our goal: automated
sign lookup. Input: video of a sign.
The user performs the sign in front of a camera.
Output: best matches in a database of 3000 signs.
Fall 2010 89
Research Directions
Challenging problems in vision, learning, database indexing. Large-scale motion-based video retrieval.
Need for developing novel atabase indexing methods
Efficient large-scale multiclass recognition.How can a computer learn to recognize 3000 signs?
Learning complex patterns from few examples.
Fall 2010 90
Object Detection
Fall 2010 91
Object Detection
Fall 2010 92
Parsing Satellite Images
Research goals: Accuracy. Efficiency.
