N3 − Technical Session: Network Reliability and · Web viewSession Chair Ben Tang...

N1 − Technical Session: Network Planning, Design, and Operation

Session Chair

Ben Tang

600-700 Mountain AvenueMurray Hill, NJ 07974

[email protected]

BIAOGRAPHY

Dr. Ben Tang is a distinguished member of technical staff in the Network Modeling and Optimization Group at Bell Laboratories. He joined AT&T Bell Labs in 1991, working on software development for 5ESS and advanced decision support system. Since 1996, he has been working on the planning, modeling and design of end-to-end network solutions.Currently, Dr. Tang’s work focuses on all aspects of data networking, including architecture, traffic modeling, network design and optimization, evolution planning and economic analysis, as well as modeling and end-to-end solution development for emerging topics in next generation networks, such as IMS, triple play broadband access, IPv6, content delivery and LTE. His work also includes the development of advanced IP/MPLS network design methods and tools. Dr. Tang has led the work on numerous global service provider projects to enhance their network efficiency and performance and reduce total cost of ownership.Dr. Tang has a B.S. degree from the National Taiwan University, M.S. from the University of Florida, and Ph.D. from Purdue University, all in electrical engineering. He was invited as a member of Telecommunication Advisory Board for the Ministry of Transportation and Communications, Republic of China, in 1999. He was the session chairman at several telecommunications conferences.


Additional Switching Nodes: Not a Panacea for Congested Wireless Networks

Amit Mukhopadhyay, John ZhaoBell Laboratories, Alcatel-Lucent

600-700 Mountain Avenue, Murray Hill, NJ [email protected]@alcatel-lucent.com

ABSTRACT:

Traditionally, service providers have been adding Mobile Switching Centers (MSCs) in their wireless networks whenever the switches run out of capacity. In a recent analysis of a large metropolitan network, we observed something seemingly counter-intuitive: additional MSCs do not necessarily add subscriber capacity and they can sometimes even cause decreased network capacity.

The apparent anomaly can be intuitively explained by noting that the subscriber capacity of a network is a function of both its call-processing capacity as well as mobility-handling capacity. A new MSC in a network will always add call-processing capacity but additional MSCs will also result in increased inter-MSC signaling for mobility handling. Exhaustion of signaling capacity may offset any benefit of the additional call-processing capacity of the new MSC.

In this paper, we establish a methodology for predicting capacity exhaust in an expanding network and also present practical suggestions for avoiding network congestion. Even though the analysis was done for a network with traditional monolithic switches, the methodology can be applied towards next generation distributed network elements as well.

BIAOGRAPHY:

Amit Mukhopadhyay is a Distinguished Member of Technical Staff in the Network Planning, Performance and Economic Analysis Center in Bell Labs, New Jersey. His current work focuses on next generation wireless technologies, including access and core networks for LTE/ePC and WiMAX. His networking interest spans other wireless technologies, e.g., GSM/GPRS/EDGE, UMTS/HSPA, CDMA2000-1x/EVDO/UMB, DVB-H/MediaFlo etc. He is also deeply involved in converged IMS networks with other broadband access technologies including DSL, HFC Cable and Fiber. He has received numerous internal awards and is a member of the Alcatel-Lucent Technical Academy.

He holds a B.Tech. in Naval Architecture from Indian Institute of Technology, Kharagpur, India and a Ph. D. in Operations Research from the University of Texas, Dallas. He is a Senior Member of the IEEE, serves as an officer in the AP/EMC/VT Chapter in IEEE NJ Coast Section and received the IEEE Region 1 award. He has numerous publications in refereed journals and has received one patent award.

John Zhao is a Member of Technical Staff in the Network Modeling & Optimization Group in Bell Labs, New Jersey. His current work focuses on design and deployment of scalable multi-service networks with MPLS/IP, metro Ethernet, broadband access, and their inter-working technologies. His recent patent (pending) describes the

mailto:[email protected]


optimized design of implementable resilient Ethernet. He is also interested in developing innovative planning and design methods and procedures to optimize both core and access network architectures in NGN deployment.

John Zhao received his B.Sc. degree in Automatic Control Engineering from Nanjing Institute of Technology in China and his Ph.D. degree in Electrical Engineering from Polytechnic University, Brooklyn, New York. He is an active member of IEEE Communication Society and a member of ACM/SIGCOMM.


Packet Optical Transport Network Architecture Impact on Carrier Migration Strategy

Mohcene Mezhoudi, Poudyal VijayaAlcatel-Lucent Technologies

[email protected]

ABSTRACT: Carriers are decreasing their dependence on SONET/SDH and ATM as they move from TDM and single wavelength transmission to packet and multi-wavelength transmission. Market research predicts that in 2009 there will be less spending on SONET/SDH than on WDM in North America. It is also forecasted that worldwide WDM spending will overtake SONET/SDH in 2010. A packet optical transport network carries packet traffic, typically on Ethernet interfaces, on SONET/SDH and/or WDM gear. Surveys show that the majority of Carriers have packet optical transport network now and almost 70% will have it by the end of 2009. It is the general industry belief that packet optical transport networks bring efficiencies and the majority of carriers expect operational expenditure (OPEX) savings from packet transport. The burning question that needs to be discussed is what is the correct migration strategy that will allow Carriers to realize these efficiencies? Many carriers are taking a dual approach: using Packet/SONET/SDH/WDM optical transports equipment in parts of their networks, and going straight to Packet/WDM/ROADM products in other parts. In addition, many carriers operate multiple backbone networks (SONET/SDH, WDM, IP/MPLS) separately which complicates the migration strategy further since there is a need to converge multiple networks infrastructures in order to achieve the anticipated CAPEX/OPEX savings. In this talk we identify various approaches to the architectural solutions to the packet optical transport (POT) network. A hierarchical architecture uses a converged routing/switching platform at the core, with flexible OTN platform (ROADM) at the Core and Edge. A different approach to packet optical transport architecture will be based on flexible OTN platforms to interconnect both Core and Edge nodes, and leaves the switching to the IP/MPLS layer. The POT architecture choice, together with the introduction of ultra-high speed transmission such as 40G/100G and next-generation multi-degree ROADM with high channel density (80 Channel or higher) will have a very strong impact on the Carrier migration strategy. It would involve the economical phase-out of existing platforms and a thorough business case study of each approach is required. It is recognizable that the migration phase of existing customers to the new network infrastructure will be costly in terms of operational expenditure in the short term but the overall payback period will depend on the existing network and selected migration plan and needs a case by case study.

BIOGRAPHY: Dr. M. Mezhoudi got his M.S.E.E. and Ph.D. From Stevens Institute of Technology, New Jersey. After teaching at Stevens as an assistant professor and running the Optical Communications laboratory at Stevens, he joined Bell Laboratories as a Member of Technical Staff in 1995. In 1999, he became a Distinguished Member of Technical Staff. He then was promoted to Consultant Member of Technical Staff.Dr. Mezhoudi has several publications in technical journals and conferences. He was awarded twice the Bell Lab President award. His current research involves optical network transport, packet/optical network switching and routing optimization techniques and reliability

Dr. V. Poudyal received the M.E. and Ph.D. degrees in electrical engineering from Stevens Institute of Technology, Hoboken, NJ. He was formerly a Senior Systems Engineer at Telcordia Technologies, Inc., Piscataway, NJ and a Lecturer of electrical engineering at the Institute of Engineering, Kathmandu, Nepal. He has advised local government agencies on various communications technology issues as a private consultant. He is currently a Senior Systems Engineer working for the Optical Networking Group of Alcatel-Lucent. For the past 10 years, he has been working on various aspects of optical networking, including SONET and DWDM network design, DWDM equipment specifications and standards, metropolitan and ultra long haul network design, and network design software tools specification. His current research is on multilayer and multi-period network optimization techniques and tools.



An IPv6 Migration Economic Study

Ben TangBell Laboratories, Alcatel-Lucent

600-700 Mountain Avenue, Murray Hill, NJ [email protected]

ABSTRACT: The global pool of public IPv4 addresses is quickly running out. Service providers, driven by the exhaustion of IPv4 addresses along with growing IP endpoints (in wireless and sensor network, for example) and the need for a public IP address for emerging applications such as those based on peer-to-peer, are faced with the challenge of migrating to IPv6. This study compares two migration scenarios for a fixed residential broadband access service provider - one scenario which continues to use private IPv4 addresses after the exhaustion of public IPv4 addresses and deploy Service Provider NAT (SP-NAT) for connection to the global Internet, and another scenario which introduces IPv6 immediately after the address exhaustion through the deployment of Dual Stack (DS) residential gateways. The study examined the consequent capital investment and operations expense incurred over a multi-year period in the two migration scenarios based on possible penetrations of DS residential gateways, DS hosts, global IPv6 endpoints and IPv6 supporting applications. The impact of an industry formed IPv6 Consortium on the costs of the migration scenarios was also addressed. Sensitivity analysis was performed to identify most influential factors affecting the comparison of the two migration scenarios. The results of the study help answer key questions for service providers, such as which migration scenario is more cost effective, what is the best timing to introduce IPv6, how does the IPv6 Consortium affect the economics of IPv6.

BIAOGRAPHY: Dr. Ben Tang is a distinguished member of technical staff in the Network Modeling and Optimization Group at Bell Laboratories. He joined AT&T Bell Labs in 1991, working on software development for 5ESS and advanced decision support system. Since 1996, he has been working on the planning, modeling and design of end-to-end network solutions.Currently, Dr. Tang’s work focuses on all aspects of data networking, including architecture, traffic modeling, network design and optimization, evolution planning and economic analysis, as well as modeling and end-to-end solution development for emerging topics in next generation networks, such as IMS, triple play broadband access, IPv6, content delivery and LTE. His work also includes the development of advanced IP/MPLS network design methods and tools. Dr. Tang has led the work on numerous global service provider projects to enhance their network efficiency and performance and reduce total cost of ownership.Dr. Tang has a B.S. degree from the National Taiwan University, M.S. from the University of Florida, and Ph.D. from Purdue University, all in electrical engineering. He was invited as a member of Telecommunication Advisory Board for the Ministry of Transportation and Communications, Republic of China, in 1999. He was the session chairman at several telecommunications conferences.



Network Optimization Tools for Complex Networks

Ming-Jye ShengThe MITRE Corporation, Eatontown, NJ, USA

[email protected]

Thomas MakUS Army, PM WIN-T, Fort Monmouth, NJ, USA

ABSTRACT:

The algorithms/models proposed in the literature for TCP/IP networks, Mobile Ad-hoc Networks (MANETs), and Sensor networks are quite complex. Currently much of the evaluation of these network mechanisms has been done using high fidelity discrete-event simulation tools like OPNET Modeler. Development of models for these complex protocols is time consuming and expensive. An analytical approach to the simulation results could provide a better test coverage for network plan and design.

The method is based on the construction of a mathematical model of the algorithm/protocol of interest by representing all the possible states of the algorithm/protocol, and the probabilities of the transitions that can occur between these states. We develop formal techniques and tools to systematically and accurately assist with the design, testing and performance analysis of MANETs by specifying states and transition probabilities of models and evaluating the satisfaction probabilities over the state space; furthermore, approximate algorithms and statistical methods are developed to overcome state explosion problems caused by larger and more complex models of the networks of interest. This approach will correlate these models with simulation results and real world performance results to benefit network deployment.

BIAOGRAPHY:

Ming-Jye Sheng, is currently a member of MITRE’s network, communication, and sensors programs. He was the founder of SysAir from 2002 to 2006, and developed first PC-based WCDMA baseband solution. From 2000 to 2002, He was the director of software development for Wiscom Technologies, a venture-capital funded start-up, designing WCDMA baseband chip for cellular phone. From 1996 to 2000, He was a distinguished Member of Technical Staff with Lucent Bell Labs and contributed to the NTT DoCoMo deliverables for commercial and multi-millions R&D contracts. In early nineties, He worked at AT&T Bell Labs, and co-founded internet startup. He received Ph.D. in Computer Science from the Ohio State University and undergraduate degree in Electrical Engineering.

Thomas Mak, is currently a project manager of Technical Management Division of WIN-T, US Army, and serves as a technical authority to provide directions to a group of senior researchers and system engineers from FFRDCs and DoD contractors. He is the point of contact of US Army Terminal Program Office for TSAT/HC3/WIN-T system integration. He represents Army/WIN-T for interfacing WIN-T routing, QoS, and tactical radios performance requirements within GIG.

Prior to WIN-T, he worked for US Army CECOM and CERDEC Space and Terrestrial Communication Directorate. He has over 20 years of program acquisition experience leading to contract awards for major military satellite and commercial programs.

He has 10 years commercial broadband network deployment experience with AT&T and Lucent, served as a senior project engineer for successfully deployed AirTel, Global Crossing, and Qwest networks. He received master degrees of electrical engineering and mechanical engineering.


N2 − Technical Session: Network Solution and Performance Enhancement

Session Chair

Zhuangbo (Bo) Tang

11100 Johns Hopkins RoadLaurel MD [email protected]

BIAOGRAPHY

Zhuangbo Tang is currently a senior professional staff member at Applied Physics Laboratories of Johns Hopkins University. Before that he worked at AT&T laboratories and Tellium (a start up company). His academic experiences include a Post-doc position at Harvard University and a faculty position at Hong Kong University of Science and Technology. He has been conducting research in the areas of communication networks (IP, optical, wireless, Satcom), sensor networks, control systems, optimization, and stochastic systems modeling/analysis/simulation.


IP Fast Reroute for Shared Risk Link Group Failure Recovery

Kang XiPolytechnic Institute of New York University

6 Metrotech Center, Brooklyn, NY [email protected]

ABSTRACT: Failure recovery in IP networks is critical to high-quality service provisioning. In IP over wavelength division multiplexing (WDM) networks, a fiber carries multiple IP logical links. When a fiber fails, all the logical links it carries are disconnected simultaneously. This is called a shared risk link group (SRLG) failure. Recovery from SRLG failures using route recalculation could lead to long service disruption. In this paper, we present a scheme called multi-section shortest path first (MSSPF) that achieves ultra fast recovery from SRLG failures. MSSPF performs all the recovery related calculations in advance. On the detection of an SRLG failure, the affected IP packets are detoured to their destinations through pre-calculated paths to avoid failed links. We prove that MSSPF guarantees 100% recovery from SRLG failures and causes no permanent loops. In particular, the scheme has low complexity and can be implemented in today's networks running link-state routing protocols, e.g., open shortest path first (OSPF). The performance of our scheme is validated with a variety of practical and randomly generated topologies.

BIAOGRAPHY: Dr. Kang Xi got his BSEE, MSEE and Ph.D. in Electronic Engineering from Tsinghua University (Beijing, China) in 1998, 2000 and 2003, respectively. From 2004 to 2005 he worked as research associate as Osaka University (Osaka, Japan). Since 2005 he has been faculty of Electrical and Computer Engineering at Polytechnic University. His research interests including high speed networks, network resilience, traffic engineering, and topology design.



View-Upload Decoupling: A Redesign of Multi-Channel P2P Video Systems

Yong Liu

Polytechnic Institute of NYU5 MetroTech Center, Brookly, NY, 11201


ABSTRACT: P2P video streaming is becoming an alternative IPTV solution with low server infrastructure cost. In current multi-channel live P2P video systems, there are several fundamental performance problems including exceedingly-large channel switching delays, long playback lags, and poor performance for less popular channels. These performance problems primarily stem from two intrinsic characteristics of multi-channel P2P video systems: channel churn and channel-resource imbalance. In this paper, we propose a radically different cross-channel P2P streaming framework, called View-Upload Decoupling (VUD). VUD strictly decouples peer downloading from uploading, bringing stability to multichannel systems and enabling cross-channel resource sharing. We propose a set of peer assignment and bandwidth allocation algorithms to properly provision bandwidth among channels, and introduce substream swarming to reduce the bandwidth overhead. We evaluate the performance of VUD via extensive simulations as well with a PlanetLab implementation. Our simulation and PlanetLab results show that VUD is resilient to channel churn, and achieves lower switching delay and better streaming quality. In particular, the streaming quality of small channels is greatly improved.

BIAOGRAPHY: Dr. Yong Liu received his bachelor and master degree from the University of Science and Technology of China, in 1994 and 1997 respectively. He graduated with Ph.D degree from ECE Dept. at University of Massachusetts, Amherst in 2002. From February 2002 to February 2005, he worked as a Postdoc in Computer Networks Research Group at UMass. In March 2005, he joined the ECE department of Polytechnic University as an assistant professor. His current research interest includes: P2P systems, overlay networks, network measurement and robust network design. More information about his research and teaching is available at:http://eeweb.poly.edu/faculty/yongliu/

http://eeweb.poly.edu/faculty/yongliu/



Admission Control for VoIP Calls with Heterogeneous Codecs

Xiaowen Mang, Yonatan Levy, Carolyn Johnson

and David Hoeflin AT&T Labs Research

200 Laurel Ave. Middletown NJ 07748, USA

ABSTRACT: As VoIP services proliferate due to rapid advances in technology and market demands, service providers are aggressively looking for better ways to offer VoIP services to customers including support of different codec technologies via a single network access point. This service flexibility introduces major traffic engineering challenges, especially when supporting a mix of data and voice services. In order to guarantee individual source’s call blocking probability, intelligence must be added to the call admission process. To tackle the issue, we propose an intelligent call blocking algorithm that is guided by a pre-defined blocking targets.

BIAOGRAPHY: Xiaowen Mang received her BS in Telecommunication Engineering from Beijing University of Post and Telecommunications, Beijing, China; DEA (Diplome d'Etudes Approfondies) in Computer Methods for Industrial Systems from University of Paris VI, Paris, France; and her Ph.D. in electrical and computer engineering from Duke University, North Carolina, USA. She was awarded France Telecom Fellowship for her studies in France and IBM Research Fellowship for her Ph.D. studies at Duke University. She is currently with AT&T Labs-Research. During her tenure with AT&T she has worked on modeling and designing algorithms for a variety of AT&T networks and systems. Dr. Mang holds eight US patents.

Yonatan Levy is executive director of the Network Design and Performance Analysis Division at AT&T Labs - Research. Yoni has a vast experience in performance modeling and analysis and was responsible for efforts that led to significant improvements in performance as well as to effective and reliable operation of network products and services. Yoni holds several patents on dynamic network call distribution, packet network control and QoS, has more than 20 publications, and in 2000 organized an ITC specialist seminar - the first international workshop dedicated to IP traffic. Yoni has a Ph.D. in Mathematical Sciences from The Johns Hopkins University.

Carolyn Johnson is a Director of Quantitative Analysis at AT&T Labs Research in Middletown, NJ. Dr. Johnson has extensive experience in telecommunications performance and reliability analysis. She has modeled voice services performance, evaluated new network technologies, and designed congestion control algorithms. Her recent work includes VoIP technologies and services, SIP overload control design, and IP network survivability analysis. Dr. Johnson holds patents in overload controls, reliable element design, and priority mechanisms. Dr. Johnson joined Bell Laboratories after receiving her Ph.D. in Mathematics from the University of Florida in 1980, and has been with AT&T Labs since 1996.

David Hoeflin is a Technical Manager in the Network Design and Performance Analysis department of AT&T Labs – Research. After receiving a Ph.D. in Mathematics (with a Minor in Statistics) from Iowa State University in 1984, David join AT&T Bell Labs to do performance and reliability analysis and remained in AT&T Labs to do more of the same. More recently, Dave and his group have been doing reliability/performance in the area of IP networks, services and products.


Crossing a Non-Jackson Network (With or Without a Map)

Michael TortorellaRutgers University

Piscataway, NJ [email protected]

ABSTRACT: This presentation considers the problem of computing cross-network values of performance parameters whose values on single links are known in stochastic flow networks that have Markovian routing but need not otherwise satisfy the Jackson network conditions. The method relies on generalizing the traffic equation to path-additive functions of the flow in the network. A matrix-based approach provides computationally convenient expressions for the results. These expressions involve the inverse of the matrix I R where R is the routing matrix of the network. Because routing in IP networks is address-based, not Markovian, we provide three models for approximating address-based routing in a network model with Markovian routing.

BIOGRAPHY: Dr. Tortorella is a leading communications industry expert in reliability management, engineering, modeling, and life data analysis. During a 26-year career at Bell Laboratories he was responsible for research and implementations in fundamental system, network, and service reliability engineering methodologies as well as for management of reliability in such critical projects as the SL-280 undersea cable system, the world's first application of fiber-optic technology in an intercontinental, undersea system. He played a major role in many AT&T and Lucent product reliability studies, culminating in the creation of CADRE, a reliability modeling system for circuit packs that encompasses circuit simulation, thermal analysis, and uncertainty modeling in a single package that is fully integrated with computer-aided design systems used for circuit pack creation.

Formerly technical manager and a Distinguished Member of Technical Staff in the Design for Reliability Processes and Technologies Group and Next Generation Networks Reliability Group in Bell Laboratories, Dr. Tortorella is now a research professor of industrial and systems engineering at Rutgers University. In addition to teaching courses in industrial engineering and statistics, he maintains a robust research program that includes investigations into how the stochastic flows in an IP network determine the performance and reliability of services carried on those networks, developing modeling frameworks for control of IP networks under stressed conditions, and foundational issues in queueing theory. Additional current research interests include stochastic flows, network performance, management, and control, stochastic processes and their applications to reliability, life data analysis, and next-generation networks, as well as design for reliability methods and technologies. Dr. Tortorella has published extensively in these areas. At Bell Labs, his responsibilities included systems and reliability engineering for next-generation networks (voice, wireless, and data). He received the Ph. D. degree in mathematics from Purdue University in 1973. He is Advisory Editor for Quality Technology and Quantitative Management, where he has worked to increase the number of publications pertaining to the communications industry. He was formerly Area Editor for Reliability Modeling and Optimization for the IIE Transactions on Reliability and Quality Engineering and was Guest Editor of a recent issue on Reliability Economics. He also served an Associate Editor of Naval Research Logistics and was Guest Editor for a recent issue on Computations in Networks.


N3 − Technical Session: Network Reliability and Security

Session Chair

Mohcene Mezhoudi

600-700 Mountain AvenueMurray Hill, NJ 07974

[email protected]

BIAOGRAPHY

Dr. M. Mezhoudi got his M.S.E.E. and Ph.D. From Stevens Institute of Technology, New Jersey. After teaching at Stevens as an assistant professor and running the Optical Communications laboratory at Stevens, he joined Bell Laboratories as a Member of Technical Staff in 1995. In 1999, he became a Distinguished Member of Technical Staff. He then was promoted to Consultant Member of Technical Staff.Dr. Mezhoudi has several publications in technical journals and conferences. He was awarded twice the Bell Lab President award. His current research involves optical network transport, packet/optical network switching and routing optimization techniques and reliability.


Simple Security Using Flow Data

Kenichi FutamuraAT&T, Inc.

200 S. Laurel Ave, Middletown, NJ [email protected]

ABSTRACT: Malware attacks have caused hundreds of billions of dollars in economic damage worldwide yearly, and attackers are becoming smarter. We examine techniques for detecting security attacks in a network using flow-level data. While worm exploits may be difficult to detect due to the wide range of payloads, the propagation phase of a worm is generally much easier to recognize. We examine this step and present one simple method for detecting network worms with no previously known signatures.

BIOGRAPHY: Kenichi Futamura received M.S. degrees in Mathematics (1994) and Statistics (1994) and a Ph.D. in Operations Research (1996) at Stanford University. Since joining AT&T Labs, he has investigated various areas including credit risk management, performance analysis, network grooming, access optimization, and internet security. His recent security efforts include developing various intrusion detection tools for the AT&T Internet Protect platform, including WARD, a worm detection tool. He has various publications in technical journals and conferences as well as patents in network security and other areas. Currently, he is a Principal Technical Staff Member, working on anomaly detection, intrusion correlation, and capacity planning.



Detection of Spam Hosts and Spam Bots Using Network Flow Traffic Modeling

Willa Ehrlich, Danielle Liu, David Hoeflin and Anestis Karasaridis

AT&T Labs, 200 Laurel Avenue, NJ [email protected]

ABSTRACT: In this paper, we present an approach for detecting email spammers and spam bots based on SMTP network flow statistics. Our approach consists of establishing SMTP traffic models of legitimate vs. spammer SMTP clients and then classifying an "unknown" SMTP client with respect to his/her current SMTP traffic distance from these models. We illustrate this approach based on a case study with SMTP flow data collected from our backbone network. We demonstrate that a periodicity effect exists for SMTP traffic initiated by legitimate SMTP clients and that we can adjust the traffic model parameter values for this periodicity using Exponentially Weighted Moving Average (EWMA) smoothing. Given adjusted model parameter values, we demonstrate the accuracy of this approach in classifying known blacklisted and whitelisted SMTP clients. Finally, we present an application of our email spammer classification algorithm for detecting spammers that belong to botnets (also known as spam bots) and the interactions utilized by these spam bots for command-and-control.

BIAOGRAPHY: Danielle Liu received her Ph.D. in Industrial Engineering under the guidance of Professor Marcel Neuts at University of Arizona in 1993. She was a visiting professor at Department of Electrical Engineering at Case Western Reserve University for one year before joining Bell labs in 1994. Danielle has worked on various projects in AT&T including Internet traffic characterization, IP QoS, WiMAX and IP security. She is currently working on email SPAM detection and network capacity planning.

Dr. Liu is the author of over 20 papers on in the fields of Queuing Analysis of Telecommunications Systems, Internet Traffic Characterization and IP security. She also served as an editor for the journal of Queueing Systems: Theory and Applications.



Network Vulnerability Identification via Network Interdiction Research

Jose Emmanuel Ramirez-MarquezStevens Institute of Technology

School of Systems & Enterprises, Hoboken, NJ, [email protected]

ABSTRACT: In many illegal or terrorist activities, networks are set up by the perpetrators to conduct their operations, such as smuggling goods, contraband, and people across borders or ports, spreading CBRNE in an area, etc. In order to potentially interdict these networks in a successful and cost effective way, multi‐objective evolutionary algorithms are being developed to overcome issues of current techniques (e.g., the assumption that a link interdiction will always disrupt network flow or a single criterion is used for optimization of a network). The applications of this research go well beyond illegal activities and may be applied to any service system (i.e. electric distribution systems, communication systems). This presentation will discuss how network interdiction can be used to describe metrics such as vulnerability, resiliency and restoration response.

BIAOGRAPHY: Dr. Jose Emmanuel Ramirez-Marquez is an Assistant Professor of the School of Systems & Enterprises at Stevens Institute of Technology. A former Fulbright Scholar, he holds degrees from Rutgers University in Industrial Engineering (Ph.D. and M.Sc.) and Statistics (M.Sc.) and from Universidad Nacional Autonoma de Mexico in Actuarial Science. His research efforts are currently focused on the reliability analysis and optimization of complex systems, the development of mathematical models for sensor network operational effectiveness and the development of evolutionary optimization algorithms. In these areas, Dr. Ramirez-Marquez has conducted funded research for both private industry and government. Also, he has published more than 50 refereed manuscripts related to these areas in technical journals, book chapters, conference proceedings and industry reports. Dr. Ramirez-Marquez has presented his research findings both nationally and internationally in conferences such as INFORMS, IERC, ARSym and ESREL. He is an Associate Editor for the International Journal of Performability Engineering and currently serves as director of the QCRE division board of the IIE and is a member of the Technical Committee on System Reliability for ESRA.



End-to-End Service Reliability Considerations for Converged Telecommunication Networks

Xuemei Zhang and Carolyn R. JohnsonAT&T Labs

200 St. Laurel Ave., Middletown, NJ [email protected]; [email protected]

ABSTRACT: Reliability metrics and modeling techniques have been successfully used to analyze the reliability and availability of a system, which typically consists of some hardware platform and software running on top of the hardware platform. In the telecommunications applications, system level reliability evaluation and improvement activities are better-understood. However, measurements for assessing reliability of complex networks still need to be better defined and understood. This becomes particularly essential for the mordent telecommunications networks are getting more complicated with diversified technologies, multimedia services and evolving infrastructure. Besides the traditional voice application, today’s telecommunication networks support multimedia applications that blended text, voice and video services. These solutions deal with different technologies (e.g., wireless and wireline) and involve network elements from different vendors. Moreover, different applications (e.g., games, gambling, bank transactions, etc.) can have very different reliability requirements and characteristics. Techniques to analyze the end-to-end service reliability of networks of such complexity and size are not well established. This paper discusses reliability considerations in estimating and analyzing the end-to-end reliability of complicated telecommunications network solutions.

BIAOGRAPHY:

XUEMEI ZHANG received her Ph.D. in Industrial Engineering and her Master of Science degree in Statistics from Rutgers University, New Brunswick, New Jersey. Currently she is a principle member of technical staff in the Network Design and Performance Analysis Department in AT&T Labs. Prior to joining AT&T Labs, she has worked in the Performance Analysis Department and the Reliability Department in Bell Labs in Lucent Technologies (and later Alcatel-Lucent), in Holmdel, New Jersey. She has been working on reliability and performance analysis of wire line and wireless communications systems and networks. Her major work and research areas are system and architectural reliability and performance, product and solution reliability and performance modeling, and software reliability. She has published more than 30 journal and conference papers. She has 6 awarded and pending patent applications in the areas of system redundancy design, software reliability, radio network redundancy, and end-to-end solution key performance and reliability evaluation. Dr. Zhang is the recipient of a number of awards and scholarships, including the Bell Labs President's Gold Awards in 2002 and 2004, Bell Labs President's Silver Award in 2005, and Best Contribution Award 3G WCDMA in 2000 and 2001.

CAROLYN JOHNSON is a Director of Quantitative Analysis at AT&T Labs Research in Middletown, NJ. Dr. Johnson has extensive experience in telecommunications performance and reliability analysis. She has modeled voice services performance, evaluated new network technologies, and designed congestion control algorithms. Her recent work includes VoIP technologies and services, SIP overload control design, and IP network survivability analysis. Dr. Johnson holds patents in overload controls, reliable element design, and priority mechanisms. Dr. Johnson joined Bell Laboratories after receiving her Ph.D. in Mathematics from the University of Florida in 1980, and has been with AT&T Labs since 1996.


M1 − Technical Session: New Trends in Multimedia Technologies

Session Chair

Junlan Feng

AT&T Labs ResearchFlorham Park, New Jersey, USA

[email protected]

BIOGRAPHY

Dr. Junlan Feng is a principal research member at AT&T LABS RESEARCH. She received her Ph.D in Acoustics from Chinese Academy of Sciences in 2001 and joined AT&T in the same year. Dr. Feng's research interest lies in several technical areas including web mining, question answering, natural language understanding, machine learning, information extraction, information retrieval, speech recognition, and spoken dialog management. Dr. Feng holds dozens of issued or pending U.S. patents and has authored scores of publications.


M1 – New Trends in Multimedia Technologies

Natural Language Understanding on Mobile Voice Search

Junlan FengAT&T Labs Research

Florham Park, New Jersey, [email protected]

ABSTRACT: Mobile voice-enabled search is emerging as one of the most popular applications abetted by the exponential growth in the number of mobile devices. The automatic speech recognition (ASR) output of the voice query is parsed into several fields and search is performed on a text or a database. In order to improve the robustness of the query parser to noise in the ASR output, we extend our query parser powered by natural language techniques to exploit multiple hypotheses from ASR, in the form of word confusion networks, in order to achieve tighter coupling between ASR and query parsing. We observed improved accuracy of the query parser. We further investigate the results of this improvement on search accuracy. Word confusion-network based query parsing outperforms ASR 1-best based query-parsing by 2.7% absolute and the search performance improves by 1.8% absolute on one of our data sets.

BIOGRAPHY: Dr. Junlan Feng is a principal research member at AT&T LABS RESEARCH. She received her Ph.D in Acoustics from Chinese Academy of Sciences in 2001 and joined AT&T in the same year. Dr. Feng's research interest lies in several technical areas including web mining, question answering, natural language understanding, machine learning, information extraction, information retrieval, speech recognition, and spoken dialog management. Dr. Feng holds dozens of issued or pending U.S. patents and has authored scores of publications.



Perceptual Quality Evaluation of Transmitted Videos

Tao LiuPolytechnic Institute of New York University

LC 220, 5 MetroTech Ctr, Brooklyn, NY [email protected]

ABSTRACT: Due to the rapid development of communication technologies nowadays, there is an increasing demand for multimedia contents, such as videos, to be encoded with various codecs and transmitted over various networks. Since human eyes are the very end users, the perceptual quality plays a key role in designing image/video storage and transmission systems. Although subjective evaluation may be the only way to obtain the quality assessment closest to the “true” value, it is extremely expensive to perform, and even not feasible in some circumstances. Therefore, effective objective quality evaluation is of significant importance.

However, the evaluation of such videos is a highly challenging and complicated problem. Generally speaking, the qualities of received videos are degraded at different levels, depending on both the choice of compression methods and channel conditions. Additionally, the contents of transmitted videos, such as motion and saliency, also greatly impact on the perceived quality too.

In order to solve this problem, we investigate each of the aforementioned quality-affecting elements individually by designing and performing a series of subjective tests. And by taking advantage of several attributes of human visual system, we finally propose our perceptual quality metrics which are shown that, from our subjective test data, they can automatically and accurately predict the quality of the addressed videos.

BIOGRAPHY: Tao Liu is a Ph.D student in Electrical & Computer Engineering Department, Polytechnic Institute of New York University, Brooklyn, NY, where he also received his Master degree in Electrical Engineering in 2007. He earned his Bachelor degree in Electrical Engineering from Beijing Institute of Technology, Beijing, China, in 2004.

He joined in the Image Processing Lab at ECE department in Poly in 2004. In the summer of 2007, he worked as an intern at Thomson Corporate Research, Princeton, NJ. From 2008, he participated in the collaborative work between Poly and AT&T Labs- research, Florham Park, NJ. His current research interests include image analysis and processing, pattern recognition, and perceptual video quality evaluation and enhancement.



Multimedia Concept Detection: Cross Concept and Cross Modality

Wei JiangColumbia University

1300 S.W.Mudd, 500 West 120th Street, New York, NY [email protected]

ABSTRACT: Semantic indexing of images and videos becomes increasingly important. Traditional efforts classify each concept individually, based on visual features such as color, texture, shape, etc.. Besides visual appearances, other modalities, e.g., audio signals, provide useful information to help semantic classification, and how to utilize information from multiple modalities is a very interesting issue. On the other hand, semantic concepts usually do not occur in isolation and inter-conceptual relationship can help detect individual concepts. My talk focuses on effective multimedia concept classification via cross-modality learning and cross-concept learning. The covered topics include: joint semi-supervised learning of feature subspace and SVM classifier, which falls into the Early Fusion of audio and visual feature representations by processing individual concepts separately; context-based concept fusion that falls into the Late Fusion of audio-based and visual-based concept detectors with the setting of cross-concept learning. Experiments over the Kodak’s consumer benchmark data set demonstrate significant performance improvements..

BIOGRAPHY: Wei Jiang is a Ph.D student in the Electrical Engineering Department of Columbia University. She graduated from Tsinghua University in China with a B.S. in June 2002 and a M.S. in June 2005, both in Department of Automation. She had worked for IBM T.J. Watson and Eastman Kodak Company as a summer intern in 2008 and 2007, respectively. She also had been a visiting student in Microsoft Research Asia from 2003 to 2004. Her main research interests are content-based image and video classification, indexing, and search, image analysis, and machine learning. She is currently working with Professor Shih-Fu Chang studying semantic concept classification with cross-modality learning, cross-concept learning, and cross-domain learning.



Modeling Rate and Perceptual Quality of Scalable Video and Its Application in Scalable Video Adaptation

Zhan MaPolytechnic Institute of New York University

LC 220, 5 MetroTech Ctr, Brooklyn, NY [email protected]

ABSTRACT: Our work investigates the impact of frame rate and quantization on the bit rate and perceptual quality of a scalable video with temporal and quality scalability. We propose a rate model and a quality model, both in terms of the quantization stepsize and frame rate. Both models are developed based on the key observation from experimental data that the relative reduction of either rate and quality when the frame rate decreases is quite independent of the quantization stepsize. This observation enables us to express both rate and quality as the product of separate functions of quantization stepsize and frame rate, respectively. The proposed rate and quality models are analytically tractable, each requiring only two content-dependent parameters. Both models fit the measured data very accurately, with high Pearson correlation. We further apply these models for rate-constrained bitstream adaptation, where the problem is to determine the optimal combination of quality and temporal layers that provides the highest perceptual quality for a given bandwidth constraint.

BIOGRAPHY: Zhan Ma was born in China on September 20, 1982. He received the B.S. and M.S. degrees in Electrical Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2004 and 2006 respectively. During the period of pursuing the M.S. degree, he had joined national digital audio and video standardization (AVS) workgroup to participate into standardizing the video coding standard in China. Since September 2006, he has been a Ph.D. candidate at the Dept. of Electrical and Computer Engineering in Polytechnic Institute of New York University, Brooklyn, NY, under the guidance of Professor Yao Wang. From May 2008 to May 2009, he was an intern in Corporate Research, Thomson Inc., NJ. He mainly focused on the power, rate, and perceptual quality modeling of the scalable video, and applications to the scalable video adaptation. He was the recipient of the 2006 Special Contribution Award of the national digital audio and video standardization workgroup, China for his contribution in standardizing the AVS Part 7 for mobile application.


M2 –Intelligent Multimedia Processing

Session Chair

Rong Duan

AT&T Labs ResearchFlorham Park, New Jersey, USA

[email protected]

BIOGRAPHY

Rong Duan received her B.S and M.S in Computer Science and expects to receive her Ph.D. in Computer Engineering in May, 2007. She joined AT&T Labs in 1998 and is currently a member of Applied Data Mining Group. Her research interests include data mining with applications in image/video analysis, business intelligence, and marketing. In particular, her research dissertation investigates supervised and semi-supervised learning methods in pattern recognition problems. She is a member of INFORMS and IEEE and currently serves as the secretary and treasurer of the INFORMS Data mining section.


M2 – Intelligent Multimedia Processing

Attack Estimation in Multimedia Contents Sharing Systems

Wei WangDept. of Electrical and Computer Engineering

Stevens Institute of TechnologyHoboken, NJ 07030

[email protected]

ABSTRACT: Nowadays, peer-to-peer (P2P) systems make multimedia sharing applications dominant the Internet traffic. As a consequence, multimedia contents pollution became a serious security problem on the global Internet. It is generally impractical to exhaustedly search for the pollution sources due to the huge amount of users in the P2P network. At the same time, because the infrastructure of a P2P network is flat, there are no centered servers that could employ the access control over contents published or shared by every live user. In this paper, we first review most pervasive attacks in current P2P systems, such as poisoning and content spam pollution. Then we propose a passive scheme by deploying a very few agent nodes to detect malicious content alternation users. Simulation results show that after applying our detection system, we can roughly estimate the proportion of malicious users who apply content alternation attacks in a P2P network.

BIOGRAPHY: Wei Wang got her B.S. and M.S. from Huazhong University of Science and Technology in 2001 and 2004 separately. From 2006 to now, she is a Ph. D. student in ECE department of Stevens Institute of Technology. Her research interests mainly concentrate on network security, social networks and overlay networks.


Adaptive Mean Shift for Target Tracking in FLIR Imagery

Yafeng YinECE Department

Stevens Institute of TechnologyCastle Point on Hudson, Hoboken, NJ 07030

[email protected]

ABSTRACT: Reliable tracking of targets in the Forward-Looking Infrared (FLIR) imagery is a challenging work in the computer vision, since IR images usually have extremely low contrast and inconspicuous difference between targets and background. In this paper, we present a novel adaptive mean shift tracker for tracking moving targets in the FLIR imagery, captured from an airborne moving platform. First, each target’s initial position is manually marked to initialize the adaptive mean-shift based tracker. For each target, multiple different features are extracted from both the targets and background during tracking, and an on-line feature ranking method is deployed to adaptively select the most discriminative feature for the mean-shift iteration. In addition, to compensate the motion of the moving platform, a block matching method is applied to compute the motion vector, which will be used in the RANSAC algorithm to estimate the affine model for global motion. We test our method on the AMCOM FLIR data set, the result indicate that our Adaptive mean-shift tracker can track each target accurately and robustly.

BIOGRAPHY: Yafeng Yin received his bachelor degree in the department of Automatic Control from Beijing Institute of Technology in 2005. He is Currently a PhD candidate at the ECE department of Stevens Institute of Technology. His research interests mainly involve image analysis and machine learning, with application on video-base people tracking and human behavior analysis.


Fast Rerouting for IP Multicast in Managed IPTV Networks

Dongmei WangAT&T Labs Research

Florham Park, New Jersey, USA [email protected]

ABSTRACT: Recent deployment of IP based multimedia distribution, especially broadcast TV distribution has increased the importance of simple and fast restoration during IP network failures for service providers. The restoration mechanisms currently adopted in IP networks use either IGP re-convergence (which could be too slow for multimedia content distribution) or IP/MPLS fast reroute. Both would increase router configuration and network operation complexity as well as human-errors. Also the service provider IP/MPLS networks are mainly tuned to support unicast traffic, and some of the multicast functions are not fully supported yet. In this paper, we propose and evaluate a simple but efficient method for fast rerouting of IP multicast traffic during link failures in managed IPTV networks. More specifically, we devise an algorithm for tuning IP link weights so that the multicast routing path and the unicast routing path between any two routers are failure disjoint, allowing us to use unicast IP encapsulation for undelivered multicast packets during link failures. We demonstrate that, our method can be realized with minor modification to the current multicast routing protocol (PIM-SM). We run our prototype implementation in Emulab which shows our method yields to good performance.

BIOGRAPHY: Dr. Dongmei Wang received her M.S from Beijing Normal University in 1995, and PhD from the college of William and Mary in 2000. Since then, she joined AT&T research lab and has been working on network related research topics, from optical layer to application, architectures to protocols, algorithms to simulation, provisioning to restoration. Most recently, she has been focusing on IPTV related problems. She has been the author of more than 30 research papers and filed 15 patents.



Learning Visual Features via the Neighbor-Constrained Hierarchical Network

Yuhua ZhengEmbedded Systems and Robotics Laboratory

ECE, Stevens Institute of Technology, Hoboken, NJ, [email protected]

ABSTRACT: Learning and recognition of visual objects has been a central research topic in computer vision for several decades. Many different models and approaches have been proposed to represent and learn the visual features, among which the hierarchical network, like convolutional neural network, HMAX model and deep Boltzmann machine, has demonstrated the ability of representing the multiple-level patterns of objects. In this paper, a multi-layer neighbor-constrained network model (NCHN) is proposed to represent hierarchical features for visual object representation. The connections in this network are constrained by the neighborhoods of nodes, which reflect the topologies and dependencies of different parts of the object. Compared with the fully-connected network, the number of connections is reduced and the spatial relationships are kept. By applying a learning algorithm of minimizing contrastive divergence, this model is able to learn complex feature structures from unlabelled data. More specifically, this model can provide hierarchical feature structures of the object of interest. The lower layer expresses more detailed appearance features while the feature represented by the higher layer is more compact and abstract. The experimental results demonstrate the efficiency of the learning capability of the proposed model and the feature hierarchies from the model for reconstruction.

BIOGRAPHY: Yuhua Zheng is now a PhD student of the department of Electronic and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ. He got both bachelor and master degree of electronic engineering from Huazhong University of Scienc and Technology in 2001 and 2004 respectively. Then since 2004, he worked for Alcatel Shanghai-Bell as an engineer on multimedia broadcast projects.

Yuhua Zheng’s research interests include computer vision, pattern recognition and machine learning, especially on visual object recognition and tracking, complex pattern representation and clustering, and hierarchical network evolving with bio-inspired algorithms.


M3 – Novel Multimedia Applications

Session Chair

Xiang Zhou

Siemens51 Valley Stream Parkway, Malvern, PA 19355

[email protected]

BIOGRAPHY

Xiang "Sean" Zhou conducted his PhD study (1998-2002) at Beckman Institute for Advanced Science and Technology at University of Illinois at Urbana Champaign (UIUC). He worked as a researcher (2002) and later project manager (2004) at Siemens Corporate Research in Princeton, New Jersey. Since June 2005, he has been working for Siemens Medical Solutions, as a senior staff scientist, a program manager, and now a senior manager at the Computer Aided Diagnosis and Knowledge Solutions Group in Malvern, Pennsylvania.

He publishes in IEEE Transactions on Medical Imaging, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, Optical Engineering, ACM Multimedia Systems Journal, etc., and leading international conferences. He is the principle author of the book "Exploration of Visual Data", published by Kluwer Academic Publishers in 2003. He was the recipient of several top scholarships and awards from Tsinghua University. While studying in UIUC, he was awarded the 2001 M. E. Van Valkenburg Fellowship, an award given to one or two PhD students in the ECE department of UIUC each year "for demonstrated excellence in research in the areas of circuits, systems, or computers."


Player Highlighting and Team Classification in Broadcast Soccer Videos for the Next Generation TV

Yu HuangMultimedia Content Networking, Huawei Technologies (USA)

400 Somerset Corporate Blvd, Suite 602, Bridgewater, NJ 08807e-mail: [email protected]

ABSTRACT: The coming next generation TV is making the viewing experience more interactive and personalized, for example, the popular IPTV. In this paper, we discuss a scenario about a rich media interactive TV application for IPTV, mainly interaction with objects of interests in the sports programs. We propose a framework for player segmentation, tracking, highlighting and team classification in soccer game videos. In player segmentation, playfield modeling is realized by a semi-supervised method, combining the Gaussian distribution model with the dominant color detection. In player tracking, a modified mean shift-based method is proposed, which takes into account the soft constraints from the foreground map, to handle the fast moving players. In the tracking process, scale change and drifting artifacts are two of critical issues. In our tracking module, a discriminant similarity metric de-weighted by the surrounding background distribution is applied to handle the “shrinkage” problem in scale adaptation; meanwhile, a conservative way to handle the object’s appearance variation is proposed, which updates the target model by aligning with its initial. In our experiment demonstration, two use cases are presented: one is player highlighting based on segmentation and tracking; and the other is team classification with a bi-histogram matching scheme.

BIAOGRAPHY: Dr. Huang got his B.S. from Xi’an Jiao Tong University, M.S. from Xidian University (formerly Xi’an Institute of Telecommunications and Engineering) and Ph. D. from Beijing Jiao Tong University. After being the researcher and lecturer at Tsinghua University for two years, he was awarded the Alexander von Humboldt Research Fellowship in 1999, hosted at Institute of Pattern Recognition, University of Erlangen-Nuremberg (Bayern, Germany). During 2000-2003 he was a Postdoctoral Research Associate of Prof. Thomas S. Huang, Beckman Institute of Advanced Science & Technology, University of Illinois at Urbana-Champaign. After working as a R&D algorithm engineer at Rapiscan Systems Inc. (a subsidiary of OSI systems Inc.) for more than two years, he became a Senior Member of Technical Staff at Corporate Research of Thomson Multimedia Inc. in 2005-2008. Since April 2008, he has been a Senior Researcher of Multimedia Content Networking at Core Network Research Dept., Huawei Technologies (USA).

Dr. Huang has published more than 30 academic papers in international conferences and journals, and he has filed 8 US patents. His experience consists of signal/image processing, video analysis and mining, machine learning, image-based rendering, data visualization, computer vision and human-computer interaction etc.

Dr. Huang is member of IEEE and ACM.



Computer Graphics Classification Based on Markov Process Model

Patchara Sutthiwan*, Xiao Cai*, Yun Q. Shi*, Hong Zhang+

*Dept. of Electrical and Computer Engineering, New Jersey Institute of Technology,

Newark, NJ{ps249, xc27, shi}@njit.edu

+Dept. of Computer Science, Armstrong Atlantic State University, Savannah, [email protected]

ABSTRACT: In this work, a novel technique is proposed to identify computer graphics, employing second-order statistics to capture the significant statistical difference between computer graphics and photographic images. Due to the wide availability of JPEG images, a JPEG 2-D array formed from the magnitudes of quantized block DCT coefficients is deemed a feasible input, but a difference JPEG 2-D array tells a better story about image statistics with less influence from image content. Characterized by transition probability matrix (TPM), Markov process, widely used in digital image processing, is applied to model the difference JPEG 2-D arrays along horizontal and vertical directions. We resort to a thresholding technique to reduce the dimensionality of feature vectors formed from TPM. YCbCr color system is selected because of its demonstrated better performance in computer graphics classification than RGB color system. Furthermore, only Y and Cb components are utilized for feature generation because of the high correlation found in the features derived from Cb and Cr components. The effectiveness of the image feature vector is then evaluated by the classifier in the machine learning (ML) framework, such as Support Vector Machines (SVM) classifier. Experimental works have shown that the proposed method outperforms the prior arts by a distinct margin.

BIOGRAPHY:

Patchara Sutthiwan is a Ph.D. student of the Electrical and Computer Engineering Department at New Jersey Institute of Technology. His research interests include visual signal processing, multimedia security and digital image forensics. He received the B.Eng degree from Chulalongkorn University, Bangkok, Thailand and M.S. degree from New Jersey Institute of Technology, both in Electrical Engineering, in 2001 and 2006, respectively.

Xiao Cai earned his Bachelor of Science in Information Engineering from Tianjin University, Tianjin, P.R. China in 2003 and Master of Science in Electrical Engineering from New Jersey Institute of Technology in January 2009. He is currently an intern at Vitro Imaging Systems department at Abbott Point of Care Inc, Princeton. His research is about image processing and machine learning.

Dr. Yun Qing Shi has joined the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology since 1987, and is currently a professor there. He obtained his B.S.degree and M.S.degree from the Shanghai Jiao Tong University, Shanghai, China; his M.S. and Ph.D. degrees from the University of Pittsburgh. His research interests include visual signal processing and communications, multimedia data hiding and security, applications of digital image processing, computer vision and pattern recognition to industrial automation and biomedical engineering, theory of multidimensional systems and signal processing.

Dr. Hong Zhang is currently a professor of Computer Science at Armstrong Atlantic State University. He received his MS.EE in Electrical Engineering and Ph.D. in Mathematics from University of Pittsburgh. Dr. Zhang has extensive experiences in both academia and industries. His research interests include graph theory, algorithms, control theory, machine learning, computer graphics, image processing and biomedical applications.


An Adaptive Bottom Up Clustering Approach for Web News Extraction

Jinlin ChenQueens College, City Univ. of New York

65-30 Kissena Blvd., Flushing, NY, [email protected]

ABSTRACT: An adaptive bottom up Web news extraction approach based on human perception is presented in this paper. The approach simulates how a human perceives and identifies Web news information by using an adaptive bottom up clustering strategy to detect possible news areas. It first detects news areas based on content function, space continuity, and formatting continuity of news information. It further identifies detailed news content based on the position, format, and semantic of detected news areas. Experiment results show that our approach achieves much better performance (in average more than 99% in terms of F1 Value) compared to previous approaches such as Tree Edit Distance and Visual Wrapper based approaches. Furthermore, our approach does not assume the existence of Web templates in the tested Web pages as required by Tree Edit Distance based approach, nor does it need training sets as required in Visual Wrapper based approach. The success of our approach demonstrates the strength of the perception based Web information extraction methodology and represents a promising approach for automatic information extraction from sources with presentation design for humans.

BIOGRAPHY: Dr. Chen got his B.S. and Ph.D. from Tsinghua University. After working at Microsoft Research Asia for two years from 1999 to 2001, he joined Univ. of Pittsburgh as a Visiting Professor in 2002. In 2003, he joined Queens College, City Univ. of New York and has been a faculty member at Computer Science Department since then.

Dr. Chen’s research interests include data mining, information retrieval/extraction, Web information modeling and processing. He received a highlight paper award in WWW2001 and best paper award in IEEE International Conference on Digital Information Management 2006. He also holds four US patents.



Computer Aided Detection of Anatomical Primitives in Medical Images and Its Applications

Xiang ZhouSiemens

51 Valley Stream Parkway, Malvern, PA [email protected]

ABSTRACT: Medical image retrieval applications pose unique challenges but at the same time offer many new opportunities. On one hand, while one can easily understand news or sports videos, a medical image is often completely incomprehensible to untrained eyes. On the other hand, semantics in the medical domain is much better defined and there is a vast accumulation of formal knowledge representations that could be exploited to support semantic search for any specialty areas in medicine.

In this talk, however, we will not dwell on any one particular specialty area, but rather address the question of how to support scalable semantic search across the whole of medical imaging field: what are the advantages to take and gaps to fill, what are the key enabling technologies, and the critical success factor from an industrial point of view.

In terms of enabling technologies, we discuss three aspects: 1. scalable image analysis and anatomical tagging algorithms; 2. anatomical, disease, and contextual semantics, and their representations using ontologies; and 3. ontological reasoning and its role in guiding and improving image analysis and retrieval.

More specifically, for scalable image analysis we present a learning- based anatomy detection and segmentation framework using distribution-free priors. It is easily adaptable to different anatomies and different imaging modalities. Examples of intelligent algorithms for medical imaging equipments (such as CT, MRI, and Ultrasound machines) will be presented. For ontological representation of medical imaging semantics, we discuss the potential use of FMA, RadLex, ICD, and AIM.

BIOGRAPHY: Xiang "Sean" Zhou conducted his PhD study (1998-2002) at Beckman Institute for Advanced Science and Technology at University of Illinois at Urbana Champaign (UIUC). He worked as a researcher (2002) and later project manager (2004) at Siemens Corporate Research in Princeton, New Jersey. Since June 2005, he has been working for Siemens Medical Solutions, as a senior staff scientist, a program manager, and now a senior manager at the Computer Aided Diagnosis and Knowledge Solutions Group in Malvern, Pennsylvania.

He publishes in IEEE Transactions on Medical Imaging, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, Optical Engineering, ACM Multimedia Systems Journal, etc., and leading international conferences. He is the principle author of the book "Exploration of Visual Data", published by Kluwer Academic Publishers in 2003. He was the recipient of several top scholarships and awards from Tsinghua University. While studying in UIUC, he was awarded the 2001 M. E. Van Valkenburg Fellowship, an award given to one or two PhD students in the ECE department of UIUC each year "for demonstrated excellence in research in the areas of circuits, systems, or computers."

M4 – Machine Learning for Information Management & Analysis

Session Chair

Yanjun Qi

Machine Learning Department, NEC Lab America, Inc4 Independence Way, Suite 200, Princeton, NJ 08536

[email protected]

BIOGRAPHY: Dr. Qi obtained her Ph.D. degree from School of Computer Science at Carnegie Mellon University in 2008. She received her Bachelor with high honors (and also M.E. in the accelerated program) from Computer Science Department at Tsinghua University in 2001. Currently, Dr. Qi is a postdoctoral scientist in the Machine Learning Department at NEC Lab America, Princeton, NJ. Dr. Qi specializes in machine learning applications for biological networks, video analysis and text mining. She is a member of ACM and IEEE.


Metric-based Automatic Taxonomy Induction

Hui YangLanguage Technologies Institute

School of Computer ScienceCarnegie Mellon University

5000 Forbes Ave, LTI, CMU, Pittsburgh, PA, [email protected]

ABSTRACT: This talk presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clusters terms based on ontology metric, a score indicating semantic distance; and transforms the task into a multi-criteria optimization based on minimization of taxonomy structures and modeling of term abstractness. It combines the strengths of both lexico-syntactic patterns and clustering through incorporating heterogeneous features. The flexible design of the framework allows a further study on which features are the best for the task under various conditions. The experiments not only show that our system achieves higher F1-measure than other state-of-the-art systems, but also re-veal the interaction between features and various types of relations, as well as the interaction between features and term abstractness.

BIOGRAPHY: Hui Yang is a Ph.D. candidate in the Language Technologies Institute, School of Computer Science, Carnegie Mellon University. She received her Master of Computer Science from School of Computer Science, Carnegie Mellon University; and Bachelor of Computer Science from School of Computing, National University of Singapore.

Hui Yang was one of the top 200 students in China in 1996, and hence received the Singapore Ministry of Education Scholarship for her undergraduate study in National University of Singapore. After the undergraduate study, she was offered the position as a junior instructor by National University of Singapore. During her academic career in Singapore, she taught Artificial Intelligence, Multimedia Processing, and Software Engineering, as well as actively conducted research in Question Answering and Multimedia Information Retrieval. Her work on Question Answering participated in TREC 2002 and TREC 2003, and was the 2nd best system for both years among systems from all over the world.

Hui Yang’s research interests focus on text mining, information retrieval and machine learning. Her current research includes automatic/semi-automatic ontology generation, human-guided machine learning, text analysis and organization. Her earlier work includes near duplicate detection in large text corpora and the Web, question answering, multimedia information retrieval, and opinion and sentiment detection. She has published more than 20 research papers in various conferences, including the top conferences, such as SIGIR, WWW, ACM Multimedia and CIKM.

Hui Yang actively conducts professional service in her research field. She is the chair of student research paper for the Digital Government (DG.O) conference in 2009. She is also the PC member for SIGIR 2008, DG.O 2009, and reviewers for SIGIR 2004, SIGIR 2008. She organizes the Information Retrieval Seminar in Carnegie Mellon University since 2006.



Non-rigid Face Tracking with Enforced Convexity and Local Appearance Consistency Constraint

Yang WangSiemens Corporate Research

755 College Road East, Princeton, NJ [email protected]

ABSTRACT: Accurate and consistent tracking of non-rigid object motion is essential in many computer graphics and multimedia applications, especially dynamic facial expression analysis, such as facial expression recognition, classification, detection of emotional states, etc. In this paper we present a new discriminative approach, based on the constrained local model (CLM), to achieve consistent and efficient tracking of non-rigid object motion, such as facial expressions. By utilizing both spatial and temporal appearance coherence at the patch level, the proposed approach can reduce ambiguity and increase accuracy. More importantly, we show that the global warp update can be optimized jointly in an efficient manner using convex quadratic fitting. Finally, we demonstrate that our approach receives improved performance for the task of non-rigid facial motion tracking on the videos of clinical patients.

BIOGRAPHY: Dr. Wang received the Bachelor and Master degrees from Tsinghua University in 1998 and 2000, respectively. He spent 2 years as a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University from 2006 to 2008, after he obtained the Ph.D. degree from the Department of Computer Science at Stony Brook University. Currently, Dr. Wang is a research scientist in the Integrated Data Systems Department at Siemens Corporate Research, Princeton, NJ.

Dr. Wang specializes in non-rigid motion tracking in 2D videos and 3D medical images, facial expression analysis and synthesis, and illumination modeling. He has published more than 20 papers in the top journals and conferences in computer vision and graphics. He is a member of ACM and IEEE.



Supervised Semantic Indexing

Bing BaiNEC labs America, Inc.

4 Independence Way, Princeton, NJ [email protected]

ABSTRACT: We present a class of models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI),our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained with a supervised signal directly on the task of interest, which we argue is the reason for our superior results.As the query and target texts are modeled separately, our approach is easily generalized to other retrieval tasks such as cross-language retrieval as well. We provide an empirical study on retrieval tasks based on Wikipedia documents, where we obtain state-of-the-art performance using our method.

BIOGRAPHY: Dr. Bai got his M.S. and Ph.D. from Rutgers University. He is currently a postdoctoral scientist in the machine learning department of NEC labs America, INC.



Probabilistic Knowledge Model for Document Retrieval

Shuguang WangUniversity of Pittsburgh

210 S. Bouquet St., Sennott Square 5406, Pittsburgh, PA [email protected]

ABSTRACT: The objective of our research is to find ways of enhancing information retrieval methods with the help of domain knowledge, which can come from different sources. The basis of our knowledge model is a network of associations among concepts defining various domain entities. This network can be extracted from the literature corpus. The probabilistic model used to support inferences is built from this knowledge network with the help of the link analysis methods. We propose to build a probabilistic knowledge model of relations among domain concepts (defining entities) from literature corpus and exploit the model to improve the document retrieval. We test our approach on biomedical documents retrieval problem and show that the new approach outperforms Lucene.

BIOGRAPHY: Mr. Wang graduated with B.Sc. and M.Sc. from National University of Singapore. He is currently pursuing PhD in Intelligent Systems Program University of Pittsburgh. Before joining University of Pittsburgh, he was a software developer in a Tropical Marine Science Institute, Singapore and BBN Technologies Boston.

Mr. Wang has received multiple scholarships from Ministry of Education Singapore during his undergraduate study, and he also received fellowship from University of Pittsburgh.

Mr. Wang has participated in several projects in Question Answering, Machine Translation, Speech Recognition, Database Indexing and Machine Learning. He currently studies the problem of learning probabilistic knowledge model using link analysis from research literature and knowledge bases. This work has been applied into document retrieval task and shows positive results.

Mr. Wang has published several papers in technical journals and conferences on database indexing, question answering and document retrieval. He also served as a PC member in DG.O 2009 student research track.


N3 − Technical Session: Network Reliability and · Web viewSession Chair Ben Tang...

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