Search

Searching the Internet for some object characterized by its attributes in the form of data, such as a hotel in a certain city whose price is lower than some amount, is one of our most common activities when we access the web. We discuss this problem in a general setting, and compute the average amount of time and energy it takes to find an object in an infinitely large search space. We consider the use of N search agents that act concurrently in both the case where the search agent knows which way it needs to go to find the object, and the case where the search agent is completely ignorant and may even head away from the object being sought. We show that under mild conditions regarding the randomness of the search and the use of a time-out, the search agent will always find the object in spite of the fact that the search space is infinite. We obtain a formula for the average search time and the average energy expended by N search agents acting concurrently and independent of each other. We see that the time-out itself can be used to minimize the search time and the amount of energy that is consumed to find an object. An approximate formula is derived for the number of search agents that can help us guarantee that an object is found in a given time, and we discuss how the competition between search agents and other agents that try to hide the data object can be used by opposing parties to guarantee their own success. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Introduces a learning algorithm which applied both to recurrent and feedforward multiple signal class random neural networks (MCRNN). Popularity of artificial neural networks; Mathematical model of the MCRNN; Formula for the learning algorithm.

Provides information on a study which considered two area-based methods for reducing false alarms of mine detection and unexploded ordnance. Effect of declaration on false alarms and receiver operation characteristic; Conclusions.

Provides information on a study that examined the function approximation properties of the random neural-network model (GNN). Introduction of the GNN; Related models of GNN; Conclusions.

We consider the effect on system performance of the distribution of a data base in the form of multiple copies at distinct sites. The purpose of our analysis is to determine the gain in READ throughput that can be obtained in the presence of consistency preserving algorithms that have to be implemented when UPDATE operations are carried out on each copy. We show that READ throughput diminishes if the number or copies exceeds an optimal value. The theoretical model we develop is applied to a system in which consistency is preserved through the use of Ellis' ring algorithm. [ABSTRACT FROM AUTHOR]

We propose a new method for the control of a multiprogrammed virtual memory computer system. A mathematical model solved by decomposition permits us to justify that the method avoids thrashing. Simulation experiments are used to test the robustness of the predictions of the mathematical model when certain simplifying assumptions are relaxed and when a slightly simpler control technique based on the same principle is used. Comparisons are given with the case where an "optimal" control is used and with that with no control. We also provide a simulation evaluating the estimators used in an implementation of the control, as well as the responsiveness of the controlled system to transients in the workload. [ABSTRACT FROM AUTHOR]

This guest editorial introduces a special issue of the periodical "IEEE Transactions on Software Engineering" which focuses on neural network software and systems. It outlines the history of artificial neural networks and their role in the origin of the theory of computing. The special issue addresses aspects of neural computation that are closely related to software engineering. The main problem at hand is the development of paradigms to deal effectively with the issues related to neural network programming. The papers in the issue highlight increasingly important issues of artificial neural network programming tools and systems, and suggest applications of neural networks to the diverse and difficult problems of software engineering.

The October 1991 issue of the periodical "IEEE Transactions on Software Engineering," is devoted to research accomplishments in the area of parallel systems and architectures. It demonstrates the commitment of the periodical to the timely dissemination of advanced information on the performance of parallel systems. The papers featured cover important issues which have to be addressed to make parallel and distributed high-performance computing successful. It includes load balancing, performance evaluation methodology, predicting of response times from the detailed and realistic representation of task structures and digital signal applications.

This paper presents an integrated research and teaching model that has resulted from an NSF-funded effort to introduce results of current Machine Learning research into the engineering and computer science curriculum at the University of Central Florida (UCF). While in-depth exposure to current topics in Machine Learning has traditionally occurred at the graduate level, the model developed affords an innovative and feasible approach to expanding the depth of coverage in research topics to undergraduate students. The model has been self-sustaining as evidenced by its continued operation during the years after the NSF grant's expiration, and is transferable to other institutions due to its use of modular and faculty-specific technical content. This model offers a tightly coupled teaching and research approach to introducing current topics in Machine Learning research to undergraduates, while also involving them in the research process itself. The approach has provided new mechanisms to increase faculty participation in undergraduate research, has exposed approximately 15 undergraduates annually to research at UCF, and has effectively prepared a number of these students for graduate study through active involvement in the research process and coauthoring of publications. [ABSTRACT FROM AUTHOR]