We address the problem of selecting and extracting key features by using singular value decomposition and latent semantic analysis. As a consequence, we are able to discover latent information which allows us to design signatures for forensics and in a dual approach for real-time intrusion detection systems. The validity of this method is shown by using several automated classification algorithms (Maxim, SYM, LGP). Using the original data set we classify 99.86% of the calls correctly. After feature extraction we classify 99.68% of the calls correctly, while with feature selection we classify 99.78% of the calls correctly, justifying the use of these techniques in forensics. The signatures obtained after feature selection and extraction using LSA allow us to class 95.69% of the calls correctly with features that can be computed in real time. We use Support Vector Decision Function and Linear Genetic Programming for feature selection on a real data set generated on a live performance network that consists of probe and denial of service attacks. We find that the results reinforce our feature selection method.

The filtering method considered in this paper is based on approximation of a spatial object in d-dimensional space by the minimal convex polyhedron that encloses the object and whose facets are normal to preselected axes. These axes are not necessarily the standard coordinate axes and, furthermore, their number is not determined by the dimension of the space. We optimize filtering by selecting optimal such axes based on a pre-processing analysis of stored objects or a sample thereof. The number of axes selected represents a trade-off between access time and storage overhead, as more axes usually lead to better filtering but require more overhead to store the associated access structures. We address the problem of minimizing the number of axes required to achieve a predefined quality of filtering and the reverse problem of optimizing the quality of filtering when the number of axes is fixed. In both cases we also show how to find an optimal collection of axes. In order to sol...

An axiomatic approach that accounts for examples that come up in logic programming, symbolic computation, affine geometry, and elsewhere is presented. It is shown that if disjunction behaves in an intuitionistic fashion, notions of canonical form for positive constraints can be systematically extended to include negative constraints. As a consequence, completeness theorems involving positive and negative constraints can be proven in a general setting for constraint propagation.>

The design of languages to tackle constraint satisfaction problems has a long history. Only more recently the reverse problem of introducing constraints as primitive constructs in programming languages has been addressed. A main task that the designers and implementers of such languages face is to use and adapt the concepts and algorithms from the extensive studies on constraints done in areas such as Mathematical Programming, Symbolic Computation, Artificial Intelligence, Program Verification and Computational Geometry. In this paper, we illustrate this task in a simple and yet important domain: linear arithmetic constraints. We show how one can design a querying system for sets of linear constraints by using basic concepts from logic programming and symbolic computation, as well as algorithms from linear programming and computational geometry. We conclude by reporting briefly on how notions of negation and canonical representation used in linear constraints can be generalized to account for cases in term algebras, symbolic computation, affine geometry, and elsewhere.

We address the problem of designing programming systems to reason with and about constraints. Taking a logic programming approach, we define a class of programming languages, the CLP languages, all of which share the same essential semantic properties. From a conceptual point of view, CLP programs are highly declarative and are soundly based within a unified framework of formal semantics. This framework not only subsumes that of logic programming, but satisfies the core properties of logic programs more naturally. From a user's point of view, CLP programs have great expressive power due to the constraints which they naturally manipulate. Intuition in the reasoning about programs is enhanced as a result of working directly in the intended domain of discourse. This contrasts with working in the Herbrand Universe wherein every semantic object has to be explicitly coded into a Herbrand term; this enforces reasoning at a primitive level. Finally, from an implementor's point of view, CLP systems can be efficient because of the exploitation of constraint solving techniques over specific domains.

Several examples of simple program schemes are used to study the influence of basic con structs on measures of Software Science. A min imal low level language is used in order to build examples which contain large numbers of the constructs under study. The measures are expressed as functions depending on the number of con ceptually unique input-output operands. They may therefore be evaluated and compared to their estimators analytically rather than statistically.

We introduce an elementary family of prefix codes, which is closely related to many other families of prefix codes.