N3 − Technical Session: Network Reliability and Security
N1 − Technical Session: Network Planning, Design, and Operation
Session Chair
|Ben Tang |[pic] |
|600-700 Mountain Avenue | |
|Murray Hill, NJ 07974 | |
|btang@alcatel- | |
| |
|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. |
N1 − Technical Session: Network Planning, Design, and Operation
| | |
|Additional Switching Nodes: Not a Panacea for Congested Wireless Networks|[pic] [pic] |
| | |
|Amit Mukhopadhyay, John Zhao | |
|Bell Laboratories, Alcatel-Lucent | |
|600-700 Mountain Avenue, Murray Hill, NJ 07974 | |
|amitm@alcatel- | |
|zjzhao@alcatel- | |
| |
|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 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. |
N1 − Technical Session: Network Planning, Design, and Operation
| |[pic] [pic] |
|Packet Optical Transport Network Architecture Impact on Carrier Migration Strategy | |
| | |
|Mohcene Mezhoudi, Poudyal Vijaya | |
|Alcatel-Lucent Technologies | |
|mezhoudi@alcatel- | |
| |
|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. |
N1 − Technical Session: Network Planning, Design, and Operation
| | |
|An IPv6 Migration Economic Study |[pic] |
| | |
|Ben Tang | |
|Bell Laboratories, Alcatel-Lucent | |
|600-700 Mountain Avenue, Murray Hill, NJ 07974 | |
|btang@alcatel- | |
| | |
| |
|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. |
N1 − Technical Session: Network Planning, Design, and Operation
| | |
|Network Optimization Tools for Complex Networks | |
|Ming-Jye Sheng | |
|The MITRE Corporation, Eatontown, NJ, USA | |
|msheng@ | |
| | |
|Thomas Mak | |
|US 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 Road | |
|Laurel MD 20723-6099 | |
|z.bo.tang@jhuapl.edu | |
| |
|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. |
N2 − Technical Session: Network Solution and Performance Enhancement
| | |
|IP Fast Reroute for Shared Risk Link Group Failure Recovery |[pic] |
| | |
|Kang Xi | |
|Polytechnic Institute of New York University | |
|6 Metrotech Center, Brooklyn, NY 11201 | |
|kxi@poly.edu | |
| |
|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. |
N2 − Technical Session: Network Solution and Performance Enhancement
| | |
|View-Upload Decoupling: A Redesign of Multi-Channel P2P Video Systems |[pic] |
| | |
|Yong Liu | |
| | |
|Polytechnic Institute of NYU | |
|5 MetroTech Center, Brookly, NY, 11201 | |
|mailto:yongliu@poly.edu | |
| |
|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: |
| |
N2 − Technical Session: Network Solution and Performance Enhancement
| | |
|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. |
N2 − Technical Session: Network Solution and Performance Enhancement
| | |
|Crossing a Non-Jackson Network (With or Without a Map) |[pic] |
| | |
|Michael Tortorella | |
|Rutgers University | |
|Piscataway, NJ 08854 | |
|assurenet@ | |
| |
|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 Avenue |[pic] |
|Murray Hill, NJ 07974 | |
|btang@alcatel- | |
| |
|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. |
N3 − Technical Session: Network Reliability and Security
|Simple Security Using Flow Data |[pic] |
| | |
|Kenichi Futamura | |
|AT&T, Inc. | |
|200 S. Laurel Ave, Middletown, NJ 07748 | |
|futamura@ | |
| |
|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. |
N3 − Technical Session: Network Reliability and Security
| | |
|Detection of Spam Hosts and Spam Bots |[pic] |
|Using Network Flow Traffic Modeling | |
| | |
|Willa Ehrlich, Danielle Liu, David Hoeflin and Anestis Karasaridis | |
| | |
|AT&T Labs, 200 Laurel Avenue, NJ 07981 | |
|dliu@ | |
| |
|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. |
| |
N3 − Technical Session: Network Reliability and Security
| | |
|Network Vulnerability Identification via Network Interdiction Research |[pic] |
| | |
|Jose Emmanuel Ramirez-Marquez | |
|Stevens Institute of Technology | |
|School of Systems & Enterprises, Hoboken, NJ, 07030 | |
|jmarquez@stevens.edu | |
| |
|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.|
| |
N3 − Technical Session: Network Reliability and Security
| | |
|End-to-End Service Reliability Considerations for |[pic] |
|Converged Telecommunication Networks | |
| | |
|Xuemei Zhang and Carolyn R. Johnson | |
|AT&T Labs | |
|200 St. Laurel Ave., Middletown, NJ 07748 | |
|xuemei.zhang@; carolyn.johnson@ | |
| |
|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 |[pic] |
| | |
|AT&T Labs Research | |
|Florham Park, New Jersey, USA | |
|junlan@research. | |
| |
|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 |[pic] |
| | |
|Junlan Feng | |
|AT&T Labs Research | |
|Florham Park, New Jersey, USA | |
|junlan@research. | |
| |
|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. |
M1 – New Trends in Multimedia Technologies
| | |
|Perceptual Quality Evaluation of Transmitted Videos |[pic] |
| | |
|Tao Liu | |
|Polytechnic Institute of New York University | |
|LC 220, 5 MetroTech Ctr, Brooklyn, NY 11201 | |
|taoliu_bit@ | |
| |
|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. |
M1 – New Trends in Multimedia Technologies
| | |
|Multimedia Concept Detection: Cross Concept and Cross Modality |[pic] |
| | |
|Wei Jiang | |
|Columbia University | |
|1300 S.W.Mudd, 500 West 120th Street, New York, NY 10027 | |
|Wj2122@columbia.edu | |
| |
|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. |
M1 – New Trends in Multimedia Technologies
| | |
|Modeling Rate and Perceptual Quality of Scalable Video and Its Application in Scalable Video |[pic] |
|Adaptation | |
| | |
|Zhan Ma | |
|Polytechnic Institute of New York University | |
|LC 220, 5 MetroTech Ctr, Brooklyn, NY 11201 | |
|zhan.ma@ | |
| |
|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 |[pic] |
| | |
|AT&T Labs Research | |
|Florham Park, New Jersey, USA | |
|rongduan@ | |
| |
|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 |[pic] |
|Wei Wang | |
|Dept. of Electrical and Computer Engineering | |
|Stevens Institute of Technology | |
|Hoboken, NJ 07030 | |
|wwang3@stevens.edu | |
| |
|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. |
M2 – Intelligent Multimedia Processing
|Adaptive Mean Shift for Target Tracking in FLIR Imagery | |
| | |
|Yafeng Yin | |
|ECE Department | |
|Stevens Institute of Technology | |
|Castle Point on Hudson, Hoboken, NJ 07030 | |
|yyin1@stevens.edu | |
| |
|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. |
M2 – Intelligent Multimedia Processing
| | |
|Fast Rerouting for IP Multicast in Managed IPTV Networks |[pic] |
| | |
|Dongmei Wang | |
|AT&T Labs Research | |
|Florham Park, New Jersey, USA | |
|mei@research. | |
| |
|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. |
M2 – Intelligent Multimedia Processing
| | |
|Learning Visual Features via the Neighbor-Constrained Hierarchical Network |[pic] |
| | |
|Yuhua Zheng | |
|Embedded Systems and Robotics Laboratory | |
|ECE, Stevens Institute of Technology, Hoboken, NJ, 07030 | |
|yzheng1@stevens.edu | |
| |
|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 |[pic] |
| | |
| | |
|Siemens | |
|51 Valley Stream Parkway, Malvern, PA 19355 | |
|Xiang.zhou@ | |
| |
|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." |
M3 – Novel Multimedia Applications
| | |
|Player Highlighting and Team Classification in Broadcast Soccer Videos for the Next Generation|[pic] |
|TV | |
| | |
|Yu Huang | |
|Multimedia Content Networking, Huawei Technologies (USA) | |
|400 Somerset Corporate Blvd, Suite 602, Bridgewater, NJ 08807 | |
|e-mail: yhuang@ | |
| |
|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. |
M3 – Novel Multimedia Applications
| | |
|Computer Graphics Classification Based on Markov Process Model | |
| |[pic][pic] |
|Patchara Sutthiwan*, Xiao Cai*, Yun Q. Shi*, Hong Zhang+ |[pic][pic] |
| | |
|*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, GA | |
|hong@drake.armstrong.edu | |
| |
|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. |
M3 – Novel Multimedia Applications
| | |
|An Adaptive Bottom Up Clustering Approach for Web News Extraction |[pic] |
| | |
|Jinlin Chen | |
|Queens College, City Univ. of New York | |
|65-30 Kissena Blvd., Flushing, NY, 11367 | |
|jchen@cs.qc.cuny.edu | |
| |
|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. |
M3 – Novel Multimedia Applications
| | |
|Computer Aided Detection of Anatomical Primitives in Medical Images and Its Applications |[pic] |
| | |
|Xiang Zhou | |
|Siemens | |
|51 Valley Stream Parkway, Malvern, PA 19355 | |
|Xiang.zhou@ | |
| |
|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 |[pic] |
| | |
|Machine Learning Department, NEC Lab America, Inc | |
|4 Independence Way, Suite 200, Princeton, NJ 08536 | |
|yanjun@nec- | |
| |
|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. |
M4 – Machine Learning for Information Management & Analysis
| | |
|Metric-based Automatic Taxonomy Induction | |
| | |
|Hui Yang | |
|Language Technologies Institute | |
|School of Computer Science | |
|Carnegie Mellon University | |
|5000 Forbes Ave, LTI, CMU, Pittsburgh, PA, 15213 | |
|huiyang@cs.cmu.edu | |
| |
|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. |
M4 – Machine Learning for Information Management & Analysis
| | |
|Non-rigid Face Tracking with Enforced Convexity and Local Appearance Consistency Constraint |[pic] |
| | |
|Yang Wang | |
|Siemens Corporate Research | |
|755 College Road East, Princeton, NJ 08540 | |
|wangy@cs.cmu.edu | |
| |
|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. |
M4 – Machine Learning for Information Management & Analysis
| | |
|Supervised Semantic Indexing |[pic] |
| | |
|Bing Bai | |
|NEC labs America, Inc. | |
|4 Independence Way, Princeton, NJ 08540 | |
|bbai@nec- | |
| |
|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. |
M4 – Machine Learning for Information Management & Analysis
| | |
|Probabilistic Knowledge Model for Document Retrieval |[pic] |
| | |
|Shuguang Wang | |
|University of Pittsburgh | |
|210 S. Bouquet St., Sennott Square 5406, Pittsburgh, PA 15260 | |
|swang@cs.pitt.edu | |
| |
|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. |
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