The Design and Analysis of Context-Aware, Secure Workflow Systems

Workflows are set of activities that implement and realise business goals. Modern business goals add extra requirements on workflow systems and their management. Workflows may cross many organisations and utilise services on a variety of devices and/or supported by different platforms. Current workflows are therefore inherently context-aware. Each context is governed and constrained by its own policies and rules to prevent unauthorised participants from executing sensitive tasks and also to prevent tasks from accessing unauthorised services and/or data. We present a sound and multi-layered design language for the design and analysis of secure and context aware workflows systems.

Context-Aware and Adaptable Architecture CA

In the paper some important notions and features of context-aware and adaptable service provision have been discussed. An approach has been described which can be used to develop architectures with the mentioned features. The abstract architecture AC3 and its application for implementing an eLearning environment have been described as well.

Agent-Oriented Middleware Supporting Delivery of Mobile Elearning Services

Within project Distributed eLearning Center (DeLC) we are developing a system for distance and eLearning, which offers fixed and mobile access to electronic content and services. Mobile access is based on InfoStation architecture, which provides Bluetooth and WiFi connectivity. On InfoStation network we are developing multi-agent middleware that provides context-aware, adaptive and personalized access to the mobile services to the users. For more convenient testing and optimization of the middleware a simulation environment, called CA3 SiEnv, is being created.

A Method of Context-sensitive Help Generation Using a Task Project

The article presents a new method to automatic generation of help in software. Help generation is realized in the framework of the tool for development and automatic generation of user interfaces based on ontologies. The principal features of the approach are: support for context-sensitive help, automatic generation of help using a task project and an expandable system of help generation.

Applications of the Sufficiency Principle in Acceleration of Neural Networks Trainig

One of the problems in AI tasks solving by neurocomputing methods is a considerable training time. This problem especially appears when it is needed to reach high quality in forecast reliability or pattern recognition. Some formalised ways for increasing of networks’ training speed without loosing of precision are proposed here. The offered approaches are based on the Sufficiency Principle, which is formal representation of the aim of a concrete task and conditions (limitations) of their solving [1]. This is development of the concept that includes the formal aims’ description to the context of such AI tasks as classification, pattern recognition, estimation etc.

Derivation of Context-free Stochastic L-Grammar Rules for Promoter Sequence Modeling Using Support Vector Machine

Formal grammars can used for describing complex repeatable structures such as DNA sequences. In this paper, we describe the structural composition of DNA sequences using a context-free stochastic L-grammar. L-grammars are a special class of parallel grammars that can model the growth of living organisms, e.g. plant development, and model the morphology of a variety of organisms. We believe that parallel grammars also can be used for modeling genetic mechanisms and sequences such as promoters. Promoters are short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. Promoters can be recognized by certain patterns that are conserved within a species, but there are many exceptions which makes the promoter recognition a complex problem. We replace the problem of promoter recognition by induction of context-free stochastic L-grammar rules, which are later used for the structural analysis of promoter sequences. L-grammar rules are derived automatically from the drosophila and vertebrate promoter datasets using a genetic programming technique and their fitness is evaluated using a Support Vector Machine (SVM) classifier. The artificial promoter sequences generated using the derived L- grammar rules are analyzed and compared with natural promoter sequences.

Reasoning by Structural Analogy Taking into Account the Context for Intelligent Decision Support Systems

Development of methods and tools for modeling human reasoning (common sense reasoning) by analogy in intelligent decision support systems is considered. Special attention is drawn to modeling reasoning by structural analogy taking the context into account. The possibility of estimating the obtained analogies taking into account the context is studied. This work was supported by RFBR.

Traceability Management Architectures Supporting Total Traceability in the Context of Software Engineering

In the area of Software Engineering, traceability is defined as the capability to track requirements, their evolution and transformation in different components related to engineering process, as well as the management of the relationships between those components. However the current state of the art in traceability does not keep in mind many of the elements that compose a product, specially those created before requirements arise, nor the appropriated use of traceability to manage the knowledge underlying in order to be handled by other organizational or engineering processes. In this work we describe the architecture of a reference model that establishes a set of definitions, processes and models which allow a proper management of traceability and further uses of it, in a wider context than the one related to software development.