A PROPOSAL FOR A KNOWLEDGE MARKET BASED ON QUANTITY AND QUALITY OF KNOWLEDGE

Autonomous market environments have been proposed in the literature as the future of electronic markets. The ability to delegate complex negotiation processes and obtain similar or better results than their human counterparts has generated a great interest in agent-based markets. More recently, such a paradigm has been applied in the field of knowledge management and, more specifically, to knowledge sharing and exchange; however, most of the knowledge market proposals in the literature fail to give details on a key component of their models: knowledge quality. This article presents a new proposal for an agent-based market environment that aims at filling the previously mentioned gap in research. The main contribution of our research is the integration of formal mechanisms for knowledge quality and quantity measurement and the use of these values to set a price for knowledge and select the most suitable agent for negotiation.     

Efficiently solving quantified bit-vector formulas

In recent years, bit-precise reasoning has gained importance in hardware and software verification. Of renewed interest is the use of symbolic reasoning for synthesising loop invariants, ranking functions, or whole program fragments and hardware circuits. Solvers for the quantifier-free fragment of bit-vector logic exist and often rely on SAT solvers for efficiency. However, many techniques require quantifiers in bit-vector formulas to avoid an exponential blow-up during construction. Solvers for quantified formulas usually flatten the input to obtain a quantified Boolean formula, losing much of the word-level information in the formula. We present a new approach based on a set of effective word-level simplifications that are traditionally employed in automated theorem proving, heuristic quantifier instantiation methods used in SMT solvers, and model finding techniques based on skeletons/templates. Experimental results on two different types of benchmarks indicate that our method outperforms the traditional flattening approach by multiple orders of magnitude of runtime.     

Modified imperialist competitive algorithm based on attraction and repulsion concepts for reliability-redundancy optimization

System reliability analysis and optimization are important to efficiently utilize available resources and to develop an optimal system design architecture. System reliability optimization has been solved by using optimization techniques including meta-heuristics. Meanwhile, the development of meta-heuristics has been an active research field of the reliability optimization wherein the redundancy, the component reliability, or both are to be determined. In recent years, a broad class of stochastic meta-heuristics, such as simulated annealing, genetic algorithm, tabu search, ant colony, and particle swarm optimization paradigms, has been developed for reliability-redundancy optimization of systems. Recently, a new kind of evolutionary algorithm called Imperialist Competitive Algorithm (ICA) was proposed. The ICA is based on imperialistic competition where the populations are represented by countries, which are classified as imperialists or colonies. However, the trade-off between the exploration (i.e. the global search) and the exploitation (i.e. the local search) of the search space is critical to the success of the classical ICA approach. An improvement in the ICA by implementing an attraction and repulsion concept during the search for better solutions, the AR-ICA approach, is proposed in this paper. Simulations results demonstrates the AR-ICA is an efficient optimization technique, since it obtained promising solutions for the reliability redundancy allocation problem when compared with the previously best-known results of four different benchmarks for the reliability-redundancy allocation problem presented in the literature.     

Mining the impact of evolution categories on object-oriented metrics

Despite the relevance of the software evolution phase, there are few characterization studies on recurrent evolution growth patterns and on their impact on software properties, such as coupling and cohesion. In this paper, we report a study designed to investigate whether the software evolution categories proposed by Lanza can be used to explain not only the growth of a system in terms of lines of code (LOC), but also in terms of metrics from the Chidamber and Kemerer (CK) object-oriented metrics suite. Our results show that high levels of recall (ranging on average from 52 to 72 %) are achieved when using LOC to predict the evolution of coupling and size. For cohesion, we have achieved smaller recall rates (<27 % on average).     

DILIGENT: integrating digital library and Grid technologies for a new Earth observation research infrastructure

This paper introduces DILIGENT, a digital library infrastructure built by integrating digital library and Grid technologies and resources. This infrastructure allows different communities to dynamically build specialised digital libraries capable to support the entire e-Science knowledge production and consumption life-cycle by using shared computing, storage, content, and application resources. The paper presents some of the main software services that implement the DILIGENT system. Moreover, it exemplifies the provided features by presenting how the DILIGENT infrastructure is being exploited in supporting the activity of user communities working in the Earth Science Environmental sector. This work is partially funded by the European Commission in the context of the DILIGENT project, under the 2nd call of FP6 IST priority.     

On the necessity of explicit cross-layer data formats in near-data processing systems

Distributed and Parallel Databases - Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability....     

Nonlinear Versus Linear Learning Devices: A Procedural Perspective

We provide a discussion of bounded rationality learning behind traditional learning mechanisms, i.e., Recursive Ordinary Least Squares and Bayesian Learning . These mechanisms lack for many reasons a behavioral interpretation and, following the Simon criticism, they appear to be substantively rational. In this paper, analyzing the Cagan model, we explore two learning mechanisms which appear to be more plausible from a behavioral point of view and somehow procedurally rational: Least Mean Squares learning for linear models and Back Propagation for Artificial Neural Networks . The two algorithms look for a minimum of the variance of the error forecasting by means of a steepest descent gradient procedure. The analysis of the Cagan model shows an interesting result: non-convergence of learning to the Rational Expectations Equilibrium is not due to the restriction to linear learning devices; also Back Propagation learning for Artificial Neural Networks may fail to converge to the Rational Expectations Equilibrium of the model.