A binary algorithm for solving n by n systems of integer equations

We discuss a “binary” algorithm for solving systems of linear equations with integer coefficients. So-called “binary” algorithms differ from ordinary ones in that there is no roundoff error, but only overflow, and the underlying analysis is p-adic analysis rather than conventional real analysis. The advantages of this algorithm are especially apparent when extremely large numbers are involved and no roundoff error can be tolerated.

VLSI implementation of this and other “binary” algorithms is very appealing because of the extreme regularity of the circuits involved.     

Retrieval of movie scenes by semantic matrix and automatic feature weight update

A new semantic-based video scene retrieval method is proposed in this paper. Twelve low-level features extracted from a video clip are represented in a genetic chromosome and target videos that user has in mind are retrieved by the interactive genetic algorithm through the feedback iteration. In this procedure, high-level semantic relevance between retrieved videos is accumulated with so-called semantic relevance matrix and semantic frequency matrix for each iteration, and they are combined with an automatic feature weight update scheme to retrieve more target videos at the next iteration. Experiments over 300 movie scene clips extracted from latest well-known movies, showed an user satisfaction of 0.71 at the fourth iteration for eight queries such as “gloominess”, “happiness”, “quietness”, “action”, “conversation”, “explosion”, “war”, and “car chase”.     

Some permutation routing algorithms for low-dimensional hypercubes

Oblivious permutation routing in binary d-cubes has been well studied in the literature. In a permutation routing, each node initially contains a packet with a destination such that all the 2^d destinations are distinct. Kaklamanis et al. (Math. Syst. Theory 24 (1991) 223–232) used the decomposability of hypercubes into Hamiltonian circuits to give an asymptotically optimal routing algorithm. The notion of “destination graph” was first introduced by Borodin and Hopcroft to derive lower bounds on routing algorithms. This idea was recently used by Grammatikakis et al. (Proceedings of the Advancement in Parallel Computing, Elsevier, Amsterdam, 1993) to construct many–one routing algorithms for the binary 2-cube and 3-cube. In the present paper, further theoretical development is made along this line. It is then applied to obtain algorithms for binary d-cubes with d up to 12, which compare favorably with the above-mentioned “Hamiltonian circuit” algorithm. Some results on t-nary cubes with t3 are also obtained.     

Overload effects and their prevention

The so-called “thrashing effect”, well known from virtual storage, but also reported from data-base systems and packet switching networks, has turned out to be a common phenomenon of large systems with concurrent processing. It simply means that beyond a saturation point an increase of the load (e.g. number of jobs) leads to a (sometimes sudden) decrease in performance (e.g. throughput). With growing size and complexity of computer systems and the general trend towards distribution, overload phenomena of different origin can interfere and superimpose mutually, resulting in a composite overload effect that can hardly be broken down into its constituents. Because the complexity of such systems defies detailed modeling, it is more appropriate to look at those systems in a more macroscopic, behavioral way, considering only the two externally measurable variables “load” and “throughput”. The resulting abstraction from internal details can smooth the way to a more general treatment and application. The article deals with such overload phenomena and their prevention in a general way using a control-theoretic approach. Special emphasis is placed on dynamic behavior, where load characteristics are changing with time, making feedback mechanisms necessary. The problem is approached as a dynamic optimum search problem for which different algorithms are presented and compared by simulation.     

Comparison of two inference methods for P-type fuzzy logic control through experimental investigation using a hydraulic manipulator

This paper presents a comparison of the two important inference schemes: “individual-rule-based inference” and “compositional rule of inference” as applied to fuzzy logic control, through experimental investigation. The techniques are implemented on a hydraulic manipulator of an industrial machine with P-type fuzzy control. The fuzzy logic controller is designed for automatic positioning of the cutter blade of an automated fish-cutting machine. The features of the machine, which uses hydraulic servo control for cutter positioning, are outlined. The performance of the machine under the two inference schemes is examined and contrasted. Some practical implementations of the results are indicated.     

A hybrid method for robust car plate character recognition

Image-based car plate recognition is an indispensable part of an intelligent traffic system. The quality of the images taken for car plates, especially for Chinese car plates, however, may sometimes be very poor, due to the operating conditions and distortion because of poor photographical environments. Furthermore, there exist some “similar” characters, such as “8” and “B”, “7” and “T” and so on. They are less distinguishable because of noises and/or distortions. To achieve robust and high recognition performance, in this paper, a two-stage hybrid recognition system combining statistical and structural recognition methods is proposed. Car plate images are skew corrected and normalized before recognition. In the first stage, four statistical sub-classifiers recognize the input character independently, and the recognition results are combined using the Bayes method. If the output of the first stage contains characters that belong to prescribed sets of similarity characters, structure recognition method is used to further classify these character images: they are preprocessed once more, structure features are obtained from them and these structure features are fed into a decision tree classifier. Finally, genetic algorithm is employed to achieve optimum system parameters. Experiments show that our recognition system is very efficient and robust. As part of an intelligent traffic system, the system has been in successful commercial use.     

Runge-Kutta-Nystrom triples

The embedded Runge-Kutta-Nystrom process for the numerical solution of the special second-order initial-value problem can be extended by the addition of “dense” formulae for both y and y′ which yield solutions at points within the normal step intervals. As for Runge-Kutta methods two modes of implementation are possible and it is shown that these are equivalent under certain conditions. The cases of “dense” formulae applied to RKN4(3) and RKN6(4) families are considered and test results are presented.     

Integration of neural networks and genetic algorithms for an intelligent manufacturing controller

This paper addresses the development and implementation of a “controller” for a single manufacturing machine. This prototype will serve as an important tool to study the integration of several functions and the utilization of status data to evaluate scheduling and control decision alternatives. The emphasis is on creating a prediction capability to aid in assessing the long-term system performance impact resulting from decisions made and environmental changes. This prediction capability is implemented by using neural networks, simulation, and genetic algorithms. Neural networks predict the behavior of different sequencing policies available in the system. The contribution of the genetic algorithms to the decision-making process is the development of a “new” scheduling rule based on a “building blocks” procedure initiated by the neural networks     

Applying abstraction and formal specification in numerical software design

Numerical software development tends to struggle with an increasing complexity. This is, on the one hand, due to the integration of numerical models, and on the other hand, due to change of hardware. Parallel computers seem to fulfill the need for more and more computer resources, but they are more complex to program.

The article shows how abstraction is used to combat complexity. It motivates that separating a specification, “what,” its realisation, “how,” and its implementation, “when, where,” is of vital importance in software development. The main point is that development steps and levels of abstraction are identified, such that the obtained software has a clear and natural structure.

Development steps can be cast into a formal, i.e., mathematical framework, which leads to rigourous software development. This way of development leads to accurate and unambiguous recording of development steps, which simplifies maintenance, extension and porting of software. Portability is especially important in the field of parallel computing where no universal parallel computer model exists.     

Distributed discrete-event simulation using null message algorithms on hypercube architectures

This paper explores several variants of the Chandy-Misra Null Message algorithm for distributed simulation. The Chandy-Misra algorithm is one of a class of “conservative” algorithms that maintains the correct order of simulation throughout the execution of the model by means of constraints on simulation time advance. The algorithms developed in this paper incorporate an “event-oriented” view of the physical process and message-passing. The effects of the computational workload to compute each event is related to speedup attained over an equivalent sequential simulation. The effects of network topology are investigated, and performance is evaluated for the variants on transmission of null messages. The performance analysis is supported with empirical results based on an implementation of the algorithm on an Intel iPSC 32-node hypercube multiprocessor. Results show that speedups over sequential simulation of greater than N, using N processors, can be achieved in some circumstances.     

A distributed approach for fault detection and diagnosis based on Time Petri Nets

This paper proposes an algorithm for the model based design of a distributed protocol for fault detection and diagnosis for very large systems. The overall process is modeled as different Time Petri Net (TPN) models (each one modeling a local process) that interact with each other via guarded transitions that becomes enabled only when certain conditions (expressed as predicates over the marking of some places) are satisfied (the guard is true). In order to use this broad class of time DES models for fault detection and diagnosis we derive in this paper the timing analysis of the TPN models with guarded transitions. In this paper we also extend the modeling capability of the faults calling some transitions faulty when operations they represent take more or less time than a prescribed time interval corresponding to their normal execution. We consider here that different local agents receive local observation as well as messages from neighboring agents. Each agent estimates the state of the part of the overall process for which it has model and from which it observes events by reconciling observations with model based predictions. We design algorithms that use limited information exchange between agents and that can quickly decide “questions” about “whether and where a fault occurred?” and “whether or not some components of the local processes have operated correctly?”. The algorithms we derive allow each local agent to generate a preliminary diagnosis prior to any communication and we show that after communicating the agents we design recover the global diagnosis that a centralized agent would have derived. The algorithms are component oriented leading to efficiency in computation.     

Learning and motivational couplings promote smarter behaviors of an animat in an unknown world

This paper reports on statistical results of an animat behavior in an unknown environment using a cognitive map. We study the coupling effect between the motivation for eating and the one for drinking. Some smart behaviors are not caused by a sophisticated “intelligent” algorithm, but only through coupling of the motivations. Adding a learning rule on the links of the cognitive map allows to reinforce particular paths, and to forget others. This also leads to reinforce the same “smart” behaviors.     

Spring algorithms and symmetry

Spring algorithms are regarded as effective tools for visualizing undirected graphs. One major feature of applying spring algorithms is to display symmetric properties of graphs. This feature has been confirmed by numerous experiments. In this paper, firstly we formalize the concepts of graph symmetries in terms of “reflectional” and “rotational” automorphisms; and characterize the types of symmetries, which can be displayed simultaneously by a graph layout, in terms of “geometric” automorphism groups. We show that our formalization is complete. Secondly, we provide general theoretical evidence of why many spring algorithms can display graph symmetry. Finally, the strength of our general theorem is demonstrated from its application to several existing spring algorithms.     

A clustering method to distribute a database on a grid

Clusters and grids of workstations provide available resources for data mining processes. To exploit these resources, new distributed algorithms are necessary, particularly concerning the way to distribute data and to use this partition. We present a clustering algorithm dubbed Progressive Clustering that provides an “intelligent” distribution of data on grids. The usefulness of this algorithm is shown for several distributed datamining tasks.     

Color enhancement of highly correlated images. I. Decorrelation and HSI contrast stretches

Conventional enhancements for the color display of multispectral images are based on independent contrast modifications or “stretches” of three input images. This approach is not effective if the image channels are highly correlated or if the image histograms are strongly bimodal or more complex. Any of several procedures that tend to “stretch” color saturation while leaving hue unchanged may better utilize the full range of colors for the display of image information. Two conceptually different enhancements are discussed: the “decorrelation stretch”, based on principal-component (PC) analysis, and the “stretch” of “hue”-“saturation”-intensity (HSI) transformed data. The PC transformation is scene-dependent, but the HSI transformation is invariant. Examples of images enhanced by conventional linear stretches, decorrelation stretch, and by stretches of HSI transformed data are compared. Schematic variation diagrams or two- and three-dimensional histograms are used to illustrate the “decorrelation stretch” method and the effect of the different enhancements.     

Collaborative recommender systems: Combining effectiveness and efficiency

Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the “big picture”, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms.     

Personalization of internet telephony services for presence with SIP and extended CPL

This paper discusses issues of personalization of presence services in the context of Internet Telephony. Such services take into consideration the willingness and ability of a user to communicate in a network, as well as possibly other factors such as time, address, etc. Via a three-layer service architecture for communications in the session initiation protocol (SIP) standard, presence system basic services and personalized services (personal policies) are clearly separated and discussed. To enrich presence related services, presence information is illustratively extended from the well known “online” and “offline” indicators to a much broader meaning that includes “location”, “lineStatus”, “role”, “availability”, etc. Based on this, the call processing language (CPL) is extended in order to describe presence related personalized services for both call processing systems and presence systems using information such as a person’s presence status, time, address, language, or any of their combinations. A web-based system is designed and implemented to simulate these advanced services. In the implementation, personal policies are programmed by end users via a graphic user interface (GUI) and are automatically translated into extended CPL. The simulation system clearly displays when, where and what CPL policies should be used for the provision of personalized presence services and call processing services. Policy conflicts are also addressed by setting policy priorities in the system.     

Limits of economic and strategic rationality for agents and MA systems

AI adoption of the game-theoretic paradigm although motivated and productive, suffers from basic limits for modelling autonomous agents and MA systems. First, we briefly restate game-theory's role for DAI and MAS: the introduction of formal prototypical social situations (“games”); the use of formal and sound notions, a self-interested view of autonomous agents, etc. Then, a number of criticisms, that have an impact on modelling intelligent social/individual action, are examined: the economicist interpretation of rationality; its instrumentalist conception, which leaves implicit the ends of agents' choices; the consequent multiple equilibria allowed by the theory; the context-unboundedness of rationality. Some contributions for a more heterarchic, context-bounded, architecture of rational agent are given, and a goal-based strategy, as distinct from a strictly utilitarian principle of decision-making, is proposed. Troubles of game-theory with multi-agent systems and in particular with modelling “cooperation” are outlined. Finally, some limits inherent in the notion of “incentive engineering” are pointed out.