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Title: Faculty Research Areas Labs/Centers Meetings


1
Faculty Research AreasLabs/CentersMeetings
2
Areas
  • Artificial Intelligence
  • Bio-Informatics
  • Databases
  • Graphics, Image Processing and Multimedia
  • Networks
  • Pervasive Computing
  • Software Engineering
  • Systems and Architecture
  • Security

3
Artificial Intelligence
  • Manfred Huber
  • Farhad Kamangar

4
Manfred Huber
  • Research Projects
  • Personal Service Robots
  • Hierarchical Skill Acquisition
  • CONNECT - Information Technologies
  • for the Disabled
  • Contact
  • huber_at_uta.edu (GACB114)

5
Farhad Kamangar
  • Research Projects
  • Computer Vision
  • Neural Networks
  • Robotics
  • CONNECT - Information Technologies
  • for the Disabled
  • Contact
  • kamangar_at_uta.edu (GACB 112)

6
Bio-Informatics
Dr. Jean Gao 338 Nedderman Hall Phone (817)
272-3628 E-mail gao_at_cse.uta.edu URL
http//crystal.uta.edu/gao
  • Dr. Nikola Stojanovic
  • 301 Nedderman Hall
  • Phone (817) 272-7627
  • E-mail nick_at_cse.uta.edu
  • URL http//ranger.uta.edu/nick

7
http//www.washbac.org/images/farside.gif
8
What is BIOINFORMATICS?
  • Have you ever thought that a cure for cancers
    could be developed by people working at their
    computers?

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
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
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
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
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
14
http//bioinformatics.ubc.ca/about/what_is_bioinfo
rmatics/
15
http//bioinformatics.ubc.ca/about/what_is_bioinfo
rmatics/
16
http//bioinformatics.ubc.ca/about/what_is_bioinfo
rmatics/
17
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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 RD efforts are pursued
    computationally these days.

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 RD efforts are pursued
    computationally these days.
  • This is a large and growing industry - whether in
    RD or just software support, you may see
    yourself working for one of these companies in a
    few years.

20
http//bioinformatics.uta.edu
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.

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.
23
UT Arlington
http//www.biotconf.org
24
Databases
  • Sharma Chakravarthy
  • Ramez Elmasri
  • Leonidas Fegaras
  • Gautham Das
  • Chengkai Li

25
Information Technology LaboratoryProf. Sharma
ChakravarthyEmail sharma_at_cse.uta.edu, URL
http//itlab.uta.edu/sharma
Funding 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. R. Adaikkalavan and S. Chakravarthy, Event
    Specification and Processing for Advanced
    Applications Generalization and Formalization,
    DEXA Sep 2007
  • A. Telang, R. Mishra, and S. Chakravarthy,
    Ranking Issues for Information Integration,
    DBrank workshop (ICDE 2007), Turkey, 2007.
  • S. Savla and S. Chakravarthy, Efficient Main
    Memory Algorithms for Significant Episode
    Discovery, To appear in the Intl Journal of Data
    warehousing and Mining, 2006.
  • R. Balachandran, S. Padmanabhan, S. Chakravarthy
    Enhanced DB-Subdue Supporting Subtle Aspects of
    Graph Mining Using a Relational approach in
    PAKDD, 2006
  • A. Srinivasan, D. Bhatia, and S. Chakravarthy,
    Discovery of Interesting episodes in Sequence
    Data, in 21st ACM SAC, Data Mining Track, 2006.
  • M. Aery, S. Chakravarthy eMailSift Email
    Classification Based on Structure and Content in
    IEEE ICDM 2005
  • H. Kona, S. Chakravarthy, and A. Arora, SQL-Based
    Approach to Incremental Association Rule Mining,
    in ADBIS Workshop on DMKD, 2005.
  • Q. Jiang, R. Adaikkalavan and S. Chakravarthy,
    NFMi An Inter-domain Network Fault Management
    System. IEEE ICDE, 2005.
  • R. Adaikkalavan, and S. Chakravarthy Active
    Authorization Rules for Enforcing Role-Based
    Access Control and its Extensions, PDM Workshop,
    IEEE ICDE, 2005.
  • 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 Motgi Mr. Supreet Chakravarthy Mr. Aamir
Syed Group Meeting 1 Pm to 2 Pm on
Fridays in NH 232
26
A Distributed Middleware-Based Architecture for
Fault-Tolerant Computing Over Distributed
repositories
uav6
  • Semi-joins
  • Compression
  • Replication
  • Smart Routing

27
Limited Resources Mobility Heterogeneity Disconnec
tions
Network of computing nodes Unmanned vehicles,
Sensors, Robots, PCs , Servers, Ground
Controlling devices
Queries, Tasks, Requests, Continuous Queries
Publish/Subscribe

SOA Distributed Middleware Task planning Join
computation Composition pub/sub Context-aware N
otification Resource Management Data management
Context/ Knowledge Base
Fault Tolerance Services
Local fusion/Materialization
Publish Subscribe Capability
Query Capability
Raw Data / fused data /data from other nodes
28
Ramez Elmasri
  • Professor
  • Databases
  • Distributed XML Querying and Caching
  • Object-Oriented Databases
  • Keyword-based XML Query Processing
  • Sensor Networks
  • Energy-Efficient Querying of Sensor Networks
  • Combining RFID and Sensor Networks
  • Indexing of Sensor Networks Data
  • Bioinformatics
  • Modelling Complex Bioinformatics and Biomedical
    Data
  • Mediators for Accessing Heterogeneous Data Sources

29
Leonidas Fegaras
  • Associate 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

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 at
  • http//ranger.uta.edu/gdas/website/research.htm

31
Chengkai Li
  • Assistant Professor http//ranger.uta.edu/cli
    cli_at_uta.edu
  • The Innovative Database and Information Systems
    Research (IDIR) Lab
  • http//idir.uta.edu , GeoScience 237
  • Jared Ashman, Sunny Hasan, Xiaonan Li, Aditya
    Mone, Rakesh Ramegowda, Aakash Tuli, Ning Yan
  • Research Areas
  • Databases, Web Data Management, Data Mining,
    Information Retrieval
  • Specific Topics
  • Data Retrieval and Exploration, Ranking and Top-k
    Queries
  • Web Search/Mining/Integration, Deep Web, XML
  • Query Processing and Optimization
  • OLAP and Data Warehousing
  • Projects Search the Database and Query the Web
  • RankSQL Ranking and Top-k Queries, Database
    Exploration
  • WebEQ Querying and Exploring Structured
    Information on the Web
  • SetQuery Set-Oriented OLAP Queries

31
32
Two Demos from IDIR Lab
  • Please give it a try.
  • Facetedpedia
  • http//idir.uta.edu/facetedpedia/
  • Shallow Semantic Queries
  • http//idir.uta.edu/ssq/

Fall 2009
32
33
Graphics Image Proc., Multimedia
  • Ishfaq Ahmad
  • Multimedia Authoring, Compression, Communication
  • Video Processing,
  • Next Generation TV
  • Network Security
  • Parallel Algorithms
  • Dr. Gutemberg Guerra-Filho
  • Computer Vision, Animation, and Humanoid Robotics

34
Prof. Ishfaq Ahmad
  • 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.

35
High-Performance
  • Ishfaq Ahmad
  • Resources Management in Parallel and Distributed
    Systems
  • Power Management in Data Center and Distributed
    Systems

36
http//www.iris.uta.edu/
Institute for Research in Security (IRIS)
Ishfaq Ahmad
A Multi-disciplinary center focusing on
infrastructure, people, and environmental security
37
Networks
  • Sajal Das
  • Mohan Kumar
  • Gergley Zaruba
  • Hao Che
  • Yonghe Liu

38
Sajal K. Das
Center for Research in Wireless Mobility and
Networking (CReWMaN) Sajal K. Das, Mohan
Kumar Yonghe Liu, Hao Che das_at_cse.uta.edu URL
http//crewman.uta.edu Woolf Hall 411,413, Tel
2-7409 Networking, Mobile Computing and Parallel
Computing Research Group
39
Mohan Kumar Pervasive and Mobile
Computing Sensor Systems
  • 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

Recommended courses before starting thesis work
CSE5311, CSE5346,CSE5306 and CSE5347/5355 Directe
d Study
40
Gergely Zaruba
  • Research Projects
  • Personal Area Networks
  • Heterogeneous Wireless Networks
  • Architecture, Admission Control and Handoff
  • Optical Networks
  • Optical Burst Switching, Routing, QoS
    Provisioning
  • Traffic Modelling
  • Contact Zaruba_at_uta.edu (GACB 112)

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/
  • hche_at_uta.edu

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/

43
Software Engineering
  • David Kung
  • Yu Lei
  • Dr. Christoph Csallner
  • Arthur Reyes
  • David Levine

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
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
46
Arthur Alexander Reyes, Ph.D.
  • Autonomous Vehicles Laboratory
  • Faculty Advisor, along with MAE, IE faculty
  • AUVSI Student UAV Competition
  • 2004 team didnt place
  • 2005 team won 1st Place Overall
  • 2006 team won 3rd Place Overall
  • Teaches
  • CSE 4310/5323 Software Eng. Processes
  • CSE 4321 Software Testing
  • CSE 4392 Game Development (new)
  • http//ranger.uta.edu/reyes/

47
David Levine High Throughput Computational
Science Clusters and Grids
  • David Levine, CSE_at_UTA
  • Projects (Computers applied to)
  • High Energy Physics, Bioinformatics,
  • Medical Informatics, People with
  • Disabilities, Streaming Processing, other..

48
Software EngineeringResearch Center
Check out the lab NH 246
  • Faculty members
  • Dr. Christoph Csallner
  • Dr. Dave Kung
  • Dr. Jeff Lei

49
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50
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.

51
THE Best Job in America
What is the 2nd best job?
Go for a PhD in Software Engineering!!
52
Great Impact
53
Quotes from Dr. Parnas
Extracted from his ACM Fellow Profile http//www.s
igsoft.org/SEN/parnas.html
54
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)

55
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

56
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57
If you want to improve..
..come talk to us
58
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/
59
Information Security
Donggang Liu Matt Wright
60
One aspect of security
  • Operational Security
  • Classified material can be leaked based on how
    its used or through side effects
  • Dominos 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

61
Wireless and System Security Donggang Liu
  • Security 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

62
Network Security and Privacy Matthew Wright
  • Anonymous Communications
  • timing analysis, performance, new defenses
  • Stepping-Stone Detection
  • Interplay between attack and defense
  • Incentives in Security and Privacy
  • Trust in complex, ad-hoc environments
  • Contact http//ranger.uta.edu/mwright

63
Computer Science and Engineering DepartmentThe
University of Texas at Arlington
Assist Laboratory
F. Kamangar, M. Huber, D. Levine, G. Zaruba
64
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

65
Connect - Intelligent Communication Technologies
for Disability Health Care
  • 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

66
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

67
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

68
Computer Science and Engineering DepartmentThe
University of Texas at Arlington
AI and Robotics Laboratory
M. Huber, F. Kamangar
69
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

70
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

71
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

72
Robot Activities and Platforms
  • Robot Soccer (RoboCup)
  • Autonomous robotic soccer with robot dogs
  • Student team
  • Computer Game Trials
  • UCT Urban Combat Testbed

73
The HERACLEIA Human Centered Computing Lab
Vicon Camera
Vicon Motion Capture System
Bioloid Robot
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.
SunSPOT Wireless Sensor Node
Peoplebot
74
The Heracleians
Fillia 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 Professor Current work Security,
Privacy, and Collaboration System
Kyungseo Park    Academic Interests Data Mining
in Wireless Sensor Networks
75
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.

76
Data Sharing Open CollaborationSupport Group,
Role, File Sharing, Recommendation
Group name, Description and Expiration date
Files and access policies
Roles and Required attributes
Top 10 recommendations
Groups, Roles, Files
Recommendations
Group Operations
77
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
77
78
_at_Home Apartment
79
Active Service Robots
Problem When abnormal event occurs, how can a
robot decide what to do?
  • 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, 2009.
80
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.ORG PETRA 2010
81
Research at the Vision-Learning-Mining Lab
  • Vassilis Athitsos
  • University of Texas at Arlington

82
American Sign Language
  • 0.5-2 million users in the US.
  • Complete and independent language.
  • Not a signed version of English.

83
Looking Up a Sign
  • It is easy to go from an English word to ASL.

84
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.

85
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.

86
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.

87
Object Detection
88
Object Detection
89
Parsing Satellite Images
  • Research goals
  • Accuracy.
  • Efficiency.
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