-------- Original Message -------- Subject: [Tccc] CFP: Special Issue IEEE TRANSACTION ON NEURAL NETWORKS Date: Thu, 24 Apr 2003 19:58:07 -0400 (EDT) From: Chuanyi Ji jic@ece.gatech.edu To: tccc@cs.columbia.edu CC: jic@ece.gatech.edu
Call for Papers IEEE TRANSACTIONS ON NEURAL NETWORKS Special Issue on Adaptive Learning Systems in Communication Networks
The recent years have seen an explosive growth in the progress and adoption of communication networks for data and telecommunication applications. In particular, the emergence of the Internet as a new medium for business transactions, government services, information acquisition, computing and communication has created a vast array of problems unforeseen just a few years ago. As the capabilities of the available networking infrastructure improve many foresee convergence of data, voice and video transport over what is currently known as the Internet or its derivatives. Communication networks and internetworks, and in particular the Internet, have been characterized as the ultimate data-rich environments, dynamically evolving and expanding practically without any centralized control. Such data-rich, unstructured environments present a particular challenge for traditional methods of analysis and design. Adaptive learning methods, in general, including adaptive signal processing, neural networks, fuzzy logic and other data-driven methods and algorithms are in the unique position to offer credible alternatives. Such approaches have the potential for solving and improving the available solutions for some of the toughest problems faced in this newly emerging set of interrelated information technologies. The goal of the proposed special issue is two-fold: " to highlight the on-going research in the field of adaptive learning systems, and in particular adaptive signal processing and neural networks, as it is applicable to computer and communication networks, and, " to present to the neural networks community and to others interested in adaptive learning systems, in general, a variety of new and challenging problems and their proposed solutions, originating from the rapidly expanding universe of computer and communication networks. As the use of these technologies spreads, numerous modeling, estimation, control, classification, clustering and signal processing problems are emerging. Many of these problems currently have no satisfactory solutions and some have been addressed with ad-hoc solutions with much room remaining for improvements. A common underlying theme of these problems is that they are " very data-rich, " represent a dynamically changing environment where the lack of valid mathematical models is predominant, and, " representative of systems with minimal or no centralized control. These problems appear amenable to data-driven methods and algorithms, such as adaptive learning methods, including neural networks and other non-parametric or semi-parametric approaches. This special issue will welcome contributions with proposed approaches to existing problems, either with currently known or unknown solutions, and to new problems in the subject areas of computer and communication networks. The focus of the proposed solutions will be on data-driven or the so-called measurement-based methods and algorithms, rooted in the general areas of adaptive learning methods. The Special Issue papers will cover topics of interest that include a broad range of underlying communication network infrastructure technologies. Papers are solicited from, but not limited to, the following topics:
Network Management Topics " Methods and algorithms for network traffic analysis, modeling and characterization " Network performance measurement and analysis techniques " Network fault monitoring and diagnosis methods " Network security and privacy, including intrusion detection methods " Approaches and methods for Quality of Service in IP networks " Scalable routing algorithms " Decentralized congestion control algorithms " Novel admission control algorithms " Control algorithms for high-speed network access technologies " Application of "new approaches" in adaptive learning systems to data-intensive tasks in complex networks Content Management Topics " Approaches for scalable Web caching and related optimization methods " Novel solutions to operational problems in content delivery and distribution networks " Web data mining and knowledge discovery - scalability and comparison of methods " Web personalization methods " Information hiding techniques and digital rights management " Novel solutions to information access and retrieval for dynamic Web content " Efficient compression algorithms and coding for continuous digital media - multimedia content " Architectures for Quality of Service guarantees in real-time distributed applications " Uncertainty management in real-time distributed applications " Concepts in real-time distributed applications enabled by new communication network technologies
Guest Editors: Alexander G. Parlos, Texas A&M University, College Station, Texas, USA (Coordinator) Chuanyi Ji, Georgia Institute of Technology, Atlanta, Georgia, USA K. Claffy, San Diego Supercomputer Center, University of California, San Diego, California, USA Thomas Parisini, University of Trieste, Trieste, Italy Marco Baglietto, University of Genoa, Genoa, Italy
Manuscripts will be screened for topical relevance, and those that pass the screening process will undergo the standard review process of the IEEE Transactions on Neural Networks (see the instructions for authors in the IEEE Transactions on Neural Networks). Paper submission deadline is November 1, 2003. Prospective authors are encouraged to submit an abstract by September 1, 2003. This will help in the planning and review process. The final Special Issue will be published in the Fall of 2004. Electronic manuscript submission is mandatory and only papers in pdf format will be considered for review. All manuscripts should be sent to the Coordinator of the guest editorial team at a-parlos@tamu.edu. Researchers interested in reviewing manuscripts for the Special Issue should contact the Guest Editors via e-mail and provide a brief description of expertise.