G-networks with resets

Gelenbe networks (G-networks) are product form queuing networks which, in addition to ordinary customers, contain unusual entities such as “negative customers” which eliminate normal customers and “triggers” which move other customers from some queue to another. These models have generated much interest in the literature since the early 1990s. The present paper discusses a novel model for a reliable system composed of N unreliable systems, which can hinder or enhance each other’s reliability. Each of the N systems also tests other systems at random; it is able to reset another system if it is itself in working condition and discovers that the other system has failed, so that the global reliability of the system is enhanced. This paper shows how an extension of G-networks that includes novel “reset” customers can be used to model this behavior. We then show that a general G-network model with resets has product form, and prove existence and uniqueness of its solution.  

Forecast Combination by Using Artificial Neural Networks

One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial neural networks and fuzzy time series approaches have proved their success in the field of forecasting. In this study, a new forecast combination approach based on artificial neural networks is proposed. The forecasts obtain from different fuzzy time series models are combined by utilizing artificial neural networks. The proposed method is applied to index of Istanbul stock exchange (IMKB) time series and the results are compared to other forecast combination methods available in the literature. As a result of the implementation, it is seen that the proposed forecast combination approach produces better forecasts than those produced by other methods.  

Dealing with software viruses: A biological paradigm

We introduce a probability model for populations of cells and viruses that interact in the presence of an anti-viral agent. Cells can be infected by viruses, and their longevity and ability to avoid infection are modified if they survive successive attacks by viruses. Viruses that survive the effect of the anti-viral agent may find that their ability to survive a future encounter with molecules of the anti-viral agent is modified, as is their ability to infect a healthy cell. Additionally, we assume that the anti-viral agents can be a cocktail with different proportions of agents that target different strains of the virus. In this paper, we give the state equations for the model and derive its analytical solution in steady state. The solution then provides insight into the appropriate mix or “cocktail” of anti-viral agents that can be designed to deal with the virus' ability to mutate. In particular, the analysis shows that the concentration of anti-viral agent by itself does not suffice to ultimately control the infection, and that it is important to dose a mix of anti-viral agents so as to target each strain of virus in a specific manner, taking into account the ability of each virus strain to survive in the presence of the anti-viral agents. Models of this kind may eventually lead to the computer aided design of therapeutic protocols or drug design.  

Genetic algorithms for route discovery.

Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making.  

Shape-based retrieval of video objects

The increasing availability of object-based video content requires new technologies for automatically extracting and matching of the low level features of arbitrarily shaped video. This paper proposes methods for shape retrieval of arbitrarily shaped video objects. Our methods take into account not only the still shape features but also the shape deformations that may occur in an object's lifespan. We compute the shape similarity of video objects by comparing the similarity of their representative temporal instances. We also describe motion of a video object via describing the deformations in an object's shape. Experimental results show that our proposed methods offer very good retrieval performance and match closely with the human ranking.  

Towards a human–robot symbiotic system

Partnership between a person and a robot could be simplified if the robot were intelligent enough to understand human intentions and perform accordingly. During the last decade, we have been developing such an intelligent robot called ISAC. Originally, ISAC was designed to assist the physically disabled, but gradually became a test bed for more robust human–robot teaming (see http://eecs.vanderbilt.edu/CIS/). In this paper, we will describe a framework for human–robot interaction, a multi-agent based robot control architecture, and short- and long-term memory structures for the robot brain. Two applications will illustrate how ISAC interacts with the human.  

Measurement and performance of a cognitive packet network

As the size of the Internet grows by orders of magnitude both in terms of users, number of IP addresses, and number of routers, and as the links we use (be they wired, optical or wireless) continuously evolve and provide varying reliability and quality of service, the IP based network architecture that we know so well will have to evolve and change. Both scalability and QoS have become key issues. We are currently conducting a research project that revisits the IP routing architecture issues and proposes new designs for routers. As part of this effort, this paper discusses a packet network architecture called a cognitive packet network (CPN), in which intelligent capabilities for routing and flow control are moved towards the packets, rather than being concentrated in the nodes. In this paper we outline the design of the CPN architecture, and discuss the quality-of-service based routing algorithm that we have designed and implemented. We then present our test-bed and report on extensive measurement experiments that we have conducted.  

Automatic key video object plane selection using the shapeinformation in the MPEG-4 compressed domain

Object-based video representation, such as the one suggested by the MPEG-4 standard, offers a framework that is better suited for object-based video indexing and retrieval. In such a framework, the concept of a “key frame” is replaced by that of a “key video object plane”. In this paper, we propose a method for key video object plane selection using the shape information in the MPEG-4 compressed domain. The shape of the video object (VO) is approximated using the shape coding modes of I, P, and B video object planes (VOPs) without decoding the shape information in the MPEG-4 bit stream. Two popular shape distance measures, the Hamming and Hausdorff distance measures, are modified to measure the similarities between the approximated shapes of the video objects. Although they feature different computational and implementation complexity tradeoffs, the corresponding algorithms achieve essentially the same performance levels in selecting key video object planes that represent efficiently the salient content of the video objects  

A concealment method for shape information in MPEG-4 coded videosequences

We propose a new method for error concealment of shape information in MPEG-4 video bit streams that are transmitted over error prone channels. The proposed method employs a MAP estimator with a Markov random field (MRF) as the image a priori model. The MRF is designed for binary shape information and its parameters are adapted based on the information of neighboring blocks. Our experimental results show that the proposed concealment method restores missing shape blocks with high accuracy. Compared to the median filtering method, our method restores 20% more missing shape data, with a much greater subjective improvement. The proposed algorithm requires a relatively small number of integer multiplications and additions and simple logic operations, making it suitable for real-time implementations  

Web based metacomputing

Programming tools that are simultaneously sustainable, highly functional, robust and easy to use have been hard to come by in the HPCC arena. This is partially due to the difficulty in developing sophisticated customized systems for what is a relatively small part of the worldwide computing enterprise. Thus, we have developed a new strategy – termed High Performance Commodity Computing (HPCC) [G. Fox, W. Furmanski, HPCC as high performance commodity computing, in: I. Foster, C. Kesselman (Eds.), Building National Grid, http://www.npac.syr.edu/users/gcf/HPcc/HPcc.html] – which builds HPCC programming tools on top of the remarkable new software infrastructure being built for the commercial web and distributed object areas. We add high performance to commodity systems using multi-tier architecture with Globus metacomputing toolkit as the backend of a middle-tier of commodity web and object servers. We have demonstrated the fully functional prototype of WebFlow during Alliance’98 meeting.