Discriminative power of the receptors activated by k-contiguous bits rule

The paper provides a brief introduction into a relatively new discipline: artificial immune systems (AIS). These are computer systems exploiting the natural immune system (or NIS for brevity) metaphor: protect an organism against invaders. Hence, a natural field of applications of AIS is computer security. But the notion of invader can be extended further: for instance a fault occurring in a system disturbs patterns of its regular functioning. Thus fault, or anomaly detection is another field of applications. It is convenient to represent the information about normal and abnormal functioning of a system in binary form (e.g. computer programs/viruses are binary files). Now the problem can be stated as follows: given a set of self patterns representing normal behaviour of a system under considerations find a set of detectors (i.e, antibodies, or more precisely, receptors) identifying all non self strings corresponding to abnormal states of the system. A new algorithm for generating antibody strings is presented. Its interesting property is that it allows to find in advance the number of of strings which cannot be detected by an "ideal" receptors repertoire.Facultad de Informátic

Discriminative power of the receptors activated by k-contiguous bits rule

The paper provides a brief introduction into a relatively new discipline: artificial immune systems (AIS). These are computer systems exploiting the natural immune system (or NIS for brevity) metaphor: protect an organism against invaders. Hence, a natural field of applications of AIS is computer security. But the notion of invader can be extended further: for instance a fault occurring in a system disturbs patterns of its regular functioning. Thus fault, or anomaly detection is another field of applications. It is convenient to represent the information about normal and abnormal functioning of a system in binary form (e.g. computer programs/viruses are binary files). Now the problem can be stated as follows: given a set of self patterns representing normal behaviour of a system under considerations find a set of detectors (i.e, antibodies, or more precisely, receptors) identifying all non self strings corresponding to abnormal states of the system. A new algorithm for generating antibody strings is presented. Its interesting property is that it allows to find in advance the number of of strings which cannot be detected by an "ideal" receptors repertoire.Facultad de Informátic

Personalization for the Semantic Web III

This report provides an overview of the achievements of working group A3 for bringing personalization functionality to the Semantic Web. It continues the work started in the deliverable A3-D1 and A3-D4. In the deliverable at hand, we report on a successfully held workshop on Semantic Web Personalization at the 3rd European Semantic Web Conference, and the research results on techniques and algorithms for enabling personalization in the Semantic Web, and achievements on developing suitable architectures for the personalized information systems in the Semantic Web.peer-reviewe

A sufficient condition for belief function construction from conditional belief functions

It is commonly acknowledged that we need to accept and handle uncertainty when reasoning with real world data. The most profoundly studied measure of uncertainty is the probability. However, the general feeling is that probability cannot express all types of uncertainty, including vagueness and incompleteness of knowledge. The Mathematical Theory of Evidence or the Dempster-Shafer Theory (DST) [1, 12] has been intensely investigated in the past as a means of expressing incomplete knowledge. The interesting property in this context is that DST formally fits into the framework of graphoidal structures [13] which implies possibilities of efficient reasoning by local computations in large multivariate belief distributions given a factorization of the belief distribution into low dimensional component conditional belief functions. But the concept of conditional belief functions is generally not usable because composition of conditional belief functions is not granted to yield joint multivariate belief distribution, as some values of the belief distribution may turn out to be negative [4, 13, 15]. To overcome this problem creation of an adequate frequency model is needed. In this paper we suggest that a Dempster-Shafer distribution results from ''clustering'' (merging) of objects sharing common features. Upon ''clustering'' two (or more) objects become indistinguishable (will be counted as one) but some attributes will behave as if they have more than one value at once. The next elements of the model needed are the concept of conditional independence and that of merger conditions. It is assumed that before merger the objects move closer in such a way that conditional distributions of features for the objects to merge are identical. The traditional conditional independence of feature variables is assumed before merger (thereafter only the DST conditional independence holds). Furthermore it is necessary that the objects get ''closer'' before the merger independly for each feature variable and only those areas merge where the conditional distributions get identical in each variable. The paper demonstrates that within this model, the graphoidal properties hold and a sufficient condition for non-negativity of the graphoidally represented belief function is presented and its validity demonstrated.V Workshop sobre Aspectos Teóricos de la Inteligencia Artificial (ATIA)Red de Universidades con Carreras en Informática (RedUNCI

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A sufficient condition for belief function construction from conditional belief functions

It is commonly acknowledged that we need to accept and handle uncertainty when reasoning with real world data. The most profoundly studied measure of uncertainty is the probability. However, the general feeling is that probability cannot express all types of uncertainty, including vagueness and incompleteness of knowledge. The Mathematical Theory of Evidence or the Dempster-Shafer Theory (DST) [1, 12] has been intensely investigated in the past as a means of expressing incomplete knowledge. The interesting property in this context is that DST formally fits into the framework of graphoidal structures [13] which implies possibilities of efficient reasoning by local computations in large multivariate belief distributions given a factorization of the belief distribution into low dimensional component conditional belief functions. But the concept of conditional belief functions is generally not usable because composition of conditional belief functions is not granted to yield joint multivariate belief distribution, as some values of the belief distribution may turn out to be negative [4, 13, 15]. To overcome this problem creation of an adequate frequency model is needed. In this paper we suggest that a Dempster-Shafer distribution results from ''clustering'' (merging) of objects sharing common features. Upon ''clustering'' two (or more) objects become indistinguishable (will be counted as one) but some attributes will behave as if they have more than one value at once. The next elements of the model needed are the concept of conditional independence and that of merger conditions. It is assumed that before merger the objects move closer in such a way that conditional distributions of features for the objects to merge are identical. The traditional conditional independence of feature variables is assumed before merger (thereafter only the DST conditional independence holds). Furthermore it is necessary that the objects get ''closer'' before the merger independly for each feature variable and only those areas merge where the conditional distributions get identical in each variable. The paper demonstrates that within this model, the graphoidal properties hold and a sufficient condition for non-negativity of the graphoidally represented belief function is presented and its validity demonstrated.V Workshop sobre Aspectos Teóricos de la Inteligencia Artificial (ATIA)Red de Universidades con Carreras en Informática (RedUNCI

An Interpretation for the Conditional Belief Function in The Theory of Evidence

. The paper provides a frequency-based interpretation for conditional belief functions that overcomes the well-formedness problem of DST belief networks by identifying a class of conditional belief functions for which well-formedness is granted. Key words: Knowledge Representation and Integration, Soft Computing, evidence theory, graphoidal structures, conditional belief functions, well-formedness 1 Introduction It is commonly acknowledged that we need to accept and handle uncertainty when reasoning with real world data, including vagueness and incompleteness of knowledge. The Mathematical Theory of Evidence or the Dempster-Shafer Theory (DST) [2, 14] has been intensely investigated in the past as a means of expressing incomplete knowledge. A number of implementations in various fields apparently confirm the usefulness of this model of representation and processing of uncertainty (e.g. reliability in real-time X-ray radioscopy and ultrasounds [3], multisensor image segmentation [1], ..

Reasoning under Uncertainty with Bayesian Belief Networks Enhanced with Rough Sets

The objective of this paper is to present a new approach to reasoning under uncertainty, based on the use of Bayesian belief networks (BBN’s) enhanced with rough sets. The role of rough sets is to provide additional reasoning to assist a BBN in the inference process, in cases of missing data or difficulties with assessing the values of related probabilities. The basic concepts of both theories, BBN’s and rough sets, are briefly introduced, with examples showing how they have been traditionally used to reason under uncertainty. Two case studies from the authors’ own research are discussed: one based on the evaluation of software tool quality for use in real-time safety-critical applications, and another based on assisting the decision maker in taking the right course of action, in real time, in the naval military exercise. The use of corresponding public domain software packages based on BBN’s and rough sets is outlined, and their application for real-time reasoning in processes under uncertainty is presented