 |
International Multi-Conference on Computing in the
Global Information Technology ICCGI 2006 August 1-3, 2006 - Bucharest, Romania |
| Tutorials |
Note: A tutorial takes place when a minimum of 6 attendees register.
T1: Mobile P2P (Half Day)
by Ouri Wolfson, University of Illinois at Chicago, USA
T2: Data Protection Techniques, Cryptographic Protocols and PKI Systems in Modern Computer Networks (Half day)
by Milan Markovic, Banca Intesa ad Beograd, Serbia and Montenegro
T3: Multi-Agent Systems: From Theory to Applications (Half day)
by Mihaela Oprea, University of Ploiesti, Romania
T4: Fixed Mobile Convergence: Architectures, Solutions, Services (Half day)
by Joseph Ghetie, TCOM & NET, USA
T5: Collaborative Knowledge Acquisition on the Semantic Web
by Vasant Honavar and Doina Caragea
T6: IPv6
by IPv6 Forum
SPEAKERS:
Ouri Wolfson
(to be completed)
Milan Markovic
Milan Marković received the B.S.E.E., M.S.E.E., and Ph.D. degrees in electrical engineering from Faculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia, in 1989, 1992, and 2001, respectively. He is a leading researcher at the Mathematical Institute SANU, Belgrade and is currently a lecturer on MilitaryTechnicalAcademy for the “Secure Computer Networks” course and Faculty of Business Informatics Belgrade. His research interests are in cryptographic algorithms, public key infrastructure, combined SW/HW security solutions, smart cards, robust speech analysis, coding and recognition, statistical pattern recognition, signal processing, multimedia communication, wireless communications and wearable computing. He has been included in very sophisticated security projects, such as: PKI systems for s National Bank of Serbia, PKI systems for commercial banks, PKI systems for Ministries of Internal and Foreign Affaires, as well as PKI systems for ongoing Serbian smart card ID project. He is currently in Banca Intesa ad Beograd, as a Project Manager for security and is included in project of developing security policies, as well as in PKI consolidation project in the bank and in project of EMV DDA MasterCards with PKI applications on them.
Mihaela Oprea
Mihaela Oprea is a Full Professor in the Department of Informatics, University of Ploiesti, Romania, where she is teaching courses in Artificial Intelligence since 1995. She
received her PhD degree in Computer Science from the University of Ploiesti in 1996, and her MSc degree in Computer Science from University Politehnica Bucharest in 1990. Her current research interests include multi-agent systems, machine learning, and expert systems. Dr. Oprea has published more than 50 scientific papers, and as a single author five books, all in the area of Artificial Intelligence. She is a member of the Romanian Society for Informatics and Control, the Slovenian Artificial Intelligence Society, the IASTED Technical Committee on Artificial Intelligence and Expert Systems, and the International Society of Machine Learning. Also, she is actively involved as a project manager in several national and international research projects, and she has visited as a research visitor some universities and Artificial Intelligence Research Institutes in UK, Spain, Austria, Greece, and Sweden. Dr. Oprea is the manager of the AgentLink III activity held at University of Ploiesti (node 052).
Recently, she taught three international tutorials in the area of Multi-agents systems.
- in 2002 at the 20th IASTED International Conference Applied Informatics, Innsbruck, Austria, the tutorial: Adaptability in Agent-Based E-Commerce Negotiation
- in 2004 at the 18th IFIP World Computer Congress, Toulouse, France, the tutorial: Applications of Multi-Agent Systems
- in 2006 at the 24th IASTED International Multi-Conference Applied Informatics, Software Engineering Conference, Innsbruck, Austria, the tutorial: Agent-Oriented Software Engineering.
Dr. Oprea has served as a member of the programme committee for several international conferences in the area of artificial intelligence.
Joseph Ghetie
Joseph Ghetie is a network and systems engineer consultant and instructor for Telcordia Technologies (Bell Communications Research). In his position, J. Ghetie was responsible for developing architectures, requirements, and solutions for network management integration, providing consulting, and supporting management standards development. Joseph Ghetie has also developed and taught numerous advanced technical courses in the areas of telecommunications, data communications and Internet network management. He owns his consulting and training business, TCOM & NET.
Joseph Ghetie is the author of a published book on "Network and Systems Management Platforms Analysis", Kluwer Academics Publishers. Since 1993, he has taught over 30 tutorials at major network and service management related international conferences and symposia such as INMS (IN), NOMS, SICOM, EMS, APNOMS, LANOMS, SBRC, ITC, ISCC, Supercom, Globecom, ICNS, etc.
Vasant Honavar
Dr. Vasant Honavar received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 1990. He is currently a full professor of Computer Science at Iowa State University. He directs the Iowa State University Artificial Intelligence Research Laboratory, which he founded in 1990. Honavar also directs the Center for Computational Intelligence, Learning and Discovery (www.cild.iastate.edu), which he founded in 2005. Honavar is on the faculty of graduate programs in Computer Science, Bioinformatics and Computational Biology, Human-Computer Interaction, Neuroscience, and Information Assurance at Iowa State University. He has served as the associate chair (2001-2003) and chair (2003-2005) of the Bioinformatics and Computational Biology Graduate Program, which he helped establish at ISU with
support from an Integrative Graduate Education and Research Training (IGERT) award from the National Science Foundation. Professor Honavar has held visiting professorships at Carnegie Mellon University (School of Computer Science) and the University of Wisconsin at Madison (Department of Biomedical Informatics and Biological Statistics).
Professor Honavar's research and teaching interests include Artificial Intelligence, Machine Learning, Bioinformatics, Computational Molecular Biology, Intelligent Agents and Multi-agent systems, Collaborative Information Systems, Semantic Web, Environmental Informatics, Security Informatics, Social Informatics, Neural Computation, Systems Biology, Data Mining, Knowledge Discovery and Visualization. Honavar has published over 150 research articles in refereed journals, conferences and books, and has co-edited 6 books. He is currently working on a research monograph (with Doina Caragea) on Algorithms and Software for Collaborative Discovery from Autonomous, Distributed, Semantically Heterogeneous Information Sources. Honavar will serve as the chair of the 2006 AAAI Fall Symposium on this topic.
Honavar is a co-editor-in-chief of the Journal of Cognitive Systems Research and a member of the Editorial Board of the Machine Learning Journal, and the International Journal of Computer and Information Security, and the International Journal of Data Mining and Bioinformatics. Eight Ph.D. students and 20 M.S. students have graduated under his supervision. Honavar has served on the program committees of several major conferences in artificial intelligence, data mining, and bioinformatics including in particular, International Conference on Machine Learning (ICML), IEEE Conference on Data Mining (ICDM), IEEE Conference on Tools With Artificial Intelligence (ICTAI), the National Conference on Artificial Intelligence (AAAI), ACM/IEEE Conference on Intelligent Agent Technology (IAT), IEEE Conference on Bioinformatics and Bioengineering (BIBE), among others. Honavar has served as a principal or a co-principal investigator on research grants totaling approximately $15.2 million during 1990-2005 from various agencies, including the National Science Foundation and the National Institutes of Health. He currently serves as a principal or co-principal investigator on grants totaling approximately $6.2 million.
Honavar has extensive curriculum development and teaching experience in Artificial Intelligence, Machine Learning, Data Mining, Information Integration, and Bioinformatics and Computational Biology. Prof. Honavar is a member of the Association for Computing Machinery (ACM), American Association for
Artificial Intelligence (AAAI), International Society for Computational Biology (ISCB) and the New York Academy of Sciences, and a senior member of the Institute of Electrical and Electronic Engineers (IEEE).
Additional information about professor Honavar can be found at www.cs.iastate.edu/~honavar/. Honavar’s detailed curriculum vitae is available at www.cs.iastate.edu/~honavar/honavar-cv06.pdf
Doina Caragea
Dr. Doina Caragea received her Ph.D. in Computer Science, specializing in artificial intelligence, in 2004 from Iowa State University, where she worked with Professor Vasant Honavar. Her dissertation research focused on algorithms for knowledge acquisition from distributed, semantically heterogeneous information sources. She has published more than 10 refereed conference papers and journal articles based on her thesis research. She has received several awards, including the Iowa State University Research Excellence Award, which recognizes students whose research accomplishments place them among the top 10 percent of all graduate students at Iowa State University, and an IBM Research Fellowship during 2002- 2003 and 2003-2004.
Dr. Caragea is currently a postdoctoral research associate in the Iowa State University Center for Computational Intelligence, Learning, and Discovery. Her research interests include artificial intelligence, machine learning, data mining and knowledge discovery, statistical query answering, visual data mining, ontologies, information integration, semantic web, computational biology and bioinformatics, and collaborative information systems. She has published several papers in refereed conferences and journals on these topics.
Dr. Caragea has served on the program committee of several conferences and workshops in artificial intelligence, including a recent ICDM workshop that she co-chaired.
Additional information about Dr. Caragea, including representative publications related to the topic of the proposed course, can be found at www.cs.iastate.edu/~dcaragea. Her detailed curriculum vitae is available at www.cs.iastate.edu/~dcaragea/resume.pdf.
CONTENT
Abstract T1
(to be completed)
Outline T1
(to be completed)
Abstract T2
Outline T2
1. Introduction
2. Information System Security - Key Questions of Security
3. Trends in Computer Network Security
4. Three lists of main security mistakes - end users
5. Three lists of main security mistakes - corporate management
6. Three lists of main security mistakes - profesionnal informaticions
7. Potential attacks on computer networks of Intranet/Internet type
8. Possible ways of protecting from the considered attacks
9. Information eavesdropping
10. Faking identities
11. Destroying of valid messages or their replaying
12. Non-authorized modification of message contents
13. Repudiation
14. Security technologies
15. Cryptography and algorithm types
16. Types of cryptographic systems
17. Absolutely secret cryptographic system
18. Conditions of absolute secrecy - Shannon theorem
19. Symmetrical cryptographic systems
20. Stream cipher cryptographic systems
21. An example: RC4
22. Block ciphers
23. Features of block cipher algorithms
24. Working modes of the block cipher algorithms
25. Examples: DES, 3DES
26. An example: IDEA
27. An example: AES
28. Asymmetrical cryptographic systems
29. Diffie-Hellman system
30. An example: RSA algorythm
31. An example: DSA algorythm
32. An example: ECDSA algorythm
33. Hash functions
34. Examples: MD5 and SHA-1 algorithms
35. Digital signature technology
36. Digital envelope technology
37. PKCS standards
38. Multilayer architecture of the secure modern computer networks
39. Application layer security
40. S/MIME cryptographic protocol
41. Cryptographic API
42. PKCS#7 standard format of cryptographic messages
43. An example: FileSecure system
44. Transport layer security
45. SSL protocol
46. An example: SWT system
47. An example: WebWatch system
48. An example: WTLS protocol
49. Network layer security
50. IPSec cryptographic protocol
51. Firewalls
52. Cryptographic proxy security servers
53. Multilevel firewall configuration
54. Software and hardware security solutions
55. Smart cards
56. Cryptographic coprocessor modules
57. An example: NST 2000 crypto card
58. PKI systems
59. Component of PKI sistems
60. Basic documents of PKI systems
61. Certification Authority (CA)
62. CA - security aspects
63. X.509 digital certificates
64. Digital certificate extensions
65. Certificate life cycle management
66. Certificate distribution systems
67. Registration Authority
68. PKI applications
69. An example: NetCert PKI system
70. E-government systems
71. Open problems in e-government systems applying
72. Digital signature law
73. European experiences in applying digital signature laws
74. Qualified electronic signatures and criteria for its creation
75. Secure Signature Creation Devices and conforming criteria
76. Criteria for certification authorities issuing qualified certificates
77. PKI systems - some experiences and open questions
78. PKI systems - some Serbian experiences
79. Conclusions
Abstract T3
Multi-agent systems (MAS) provide a key technology for the development of complex systems composed by autonomous agents that are able to interact between them. The agents share a common environment, and they may have a global goal and/or their own goals to pursue. The main benefit of MAS technology is the ability to cope with dynamics.
Outline T3
The tutorial will make a tour from MAS theory to MAS applications in different domains. The following questions will be answered:
• What is a MAS?
• What are the basic MAS architectures?
• What are the properties of a MAS (functional and non-functional)?
• How a MAS can be designed?
• What methodologies are available for the MAS development?
• What is coordination and how it can be achieved?
• Which coordination techniques are appropriate for a MAS?
• What is negotiation and how it can be achieved?
• What negotiation models can be used in a MAS?
• How learning can improve the performance of a MAS?
• What applications are appropriate for a MAS implementation?
• What software products are available for MAS implementation?
• What are the most known and successful MAS implementations?
• What are the lessons learned from the developed MAS applications?
• What are the future research directions in the area of MAS development?
Abstract T4
The "Fixed Mobile Convergence: Architectures, Solutions, Services" tutorial covers the integration aspects of fixed and mobile wireless networks with focus on voice and data communication. The wireless networking coverage includes fixed Wireless Local Area Networks (WLAN), Wireless Access (WiMAX), Wireless Personal Area Networks (WPAN) and GSM/CDMA mobile cellular radio networks. Current and emerging networking solutions are evaluated for their approach, functionality and management abilities. 802.11a/b/g WLANs, 802.16 WiMAX, GSM/GPRS cellular radio, and the standardization effort in IEEE, Wi-Fi Alliance, 3GGP Release 6, Unlicensed Mobile Access (UMA), IP Multimedia Subsystem (IMS), and IETF Session Initiation Protocol (SIP) are analyzed. The tutorial also evaluates the Quality of Services of various solutions targeting the use of a common handsets and unique telephone numbers across wireless networks.
Outline T4
Introduction
1. Wireless LAN-based Networking Solutions
2. Voice over Wireless LAN Management and Services
3. Quality of Services in WLAN and VoWLAN
4. Mobile Cellular Radio Networking Solutions
5 Cellular Mobile Networks Management and Services
6. Quality of Services in Mobile Networks
7. Fixed LAN/PAN/MAN and Mobile Cellular Radio Networking Convergence
8. UMA-based Fixed Mobile Convergence Solutions, Products, and Services
9. IMS-based Fixed Mobile Convergence Solutions, Products, and Services
10. Quality of Services in Convergent Wi-Fi and Mobile Radio Networks
11. Issues in Fixed-Mobile ConvergenceAbstract T5
Developments of high throughput data acquisition technologies, together with advances in computing and communications, have resulted in an explosive growth in the number, size, and diversity of potentially useful information sources. Furthermore, with the advent of the Semantic Web, there is increased availability of meta data (ontologies) that make explicit the semantic commitments associated with the data sources. This has created unprecedented opportunities for data-driven knowledge acquisition and decisionmaking in a number of emerging increasingly data-rich application domains, such as bioinformatics, environmental informatics, medical informatics, enterprise informatics, security informatics (among others). However, the massive size, semantic heterogeneity, autonomy, and distributed nature of the data repositories present significant hurdles in acquiring useful knowledge from the available data. Against this background, there is an urgent need for software systems for collaborative knowledge acquisition from autonomous, semantically heterogeneous, distributed information sources.
Outline T5
This tutorial will:
- Introduce some of the specific challenges in the design of software systems for collaborative knowledge acquisition from autonomous, semantically heterogeneous, distributed information sources.
- Present a sufficient statistics based general framework for learning from such sources.
- Describe how this framework can be used to transform standard learning algorithms into algorithms for knowledge acquisition from distributed data and show that the resulting algorithms offer rigorous performance guarantees (relative to their centralized, single agent, or batch counterparts that assume centralized access to the entire data set).
- Introduce ontology-extended data sources (OEDS) to facilitate collaborative analysis of semantically heterogeneous information sources. OEDS make explicit the structure (schema) and semantics (content) of the data sources, as well as the query answering capabilities of these sources.
- Introduce a framework for specifying semantic correspondences that reconcile the semantic differences between a user view and the individual information sources in some important special cases (e.g., partial order ontologies) that are commonly encountered in practice.
- Describe how the sufficient statistics based framework for learning from distributed data can be extended to yield theoretically well-founded algorithms for learning from semantically heterogeneous, autonomous information sources.
- Point out some statistical problems that arise when learning from data in this setting, e.g., problems caused by the differences in the levels of abstraction used by autonomous information sources to describe the objects of interest.
- Conclude with some open problems and promising avenues for further research.
