Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.     

Principles of adjustable autonomy: a framework for resilient human–machine cooperation

Unmanned ground vehicles tend to be more and more autonomous, but both complete teleoperation and full autonomy are not efficient enough to deal with all possible situations. To be efficient, the human–robot system must be able to anticipate, react and recover from errors of different kinds, i.e., to be resilient. From this observation, this paper proposes a survey on the resilience of a human–machine system and the means to control the resilience. The resilience of a system can be defined as the ability to maintain or recover a stable state when subject to disturbance. Adjustable autonomy and human–machine cooperation are considered as means of resilience for the system. This paper then proposes three indicators to assess different meanings of resilience of the system: foresight and avoidance of events, reaction to events and recovery from occurrence of events. The third of these metrics takes into consideration the concept of affordances that allows a common representation for the opportunities of action between the automated system and its environment.     

Bond graph based control of a three-phase inverter with LC filter – Connection to passive and active loads

This paper proposes a model of a three phase electrical inverter with a LC output filter in delta connection used in a renewable energy supply system. The concept of inverse bond graph via bicausality is used for the control law design. The control law robustness is tested by connecting passive and active (induction machine) loads.     

Randomized constraint solvers: a comparative study

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compared the effectiveness of a symbolic solver (CVC3), a random solver, two heuristic search solvers, and seven hybrid solvers (i.e. mix of random, symbolic, and heuristic solvers). We evaluated the solvers on a benchmark generated with a concolic execution of 9 subjects. The performance of each solver was measured by its precision, which is the fraction of constraints that the solver can find solution out of the total number of constraints that some solver can find solution. As expected, symbolic solving subsumes the other approaches for the 4 subjects that only generate decidable constraints. For the remaining 5 subjects, which contain undecidable constraints, the hybrid solvers achieved the highest precision (fraction of constraints that a solver can find a solution out of the total number of satisfiable constraints). We also observed that the solvers were complementary, which suggests that one should alternate their use in iterations of a concolic execution driver.     

Combining preprocessor slicing with C/C++ language slicing

Of the very few practical implementations of program slicing algorithms, the majority deal with C/C++ programs. Yet, preprocessor-related issues have been marginally addressed by these slicers, despite the fact that ignoring (or only partially handling) these constructs may lead to serious inaccuracies in the slicing results and hence in the program analysis task being performed. Recently, an accurate slicing method for preprocessor-related constructs has been proposed, which-when combined with existing C/C++ language slicers-can provide more complete slices and hence a more successful analysis of programs written in one of these languages. In this paper, we present our approach which combines the two slicing methods and, via practical experiments, describe its benefits in terms of the completeness of the resulting slices.     

Improved Interface Conditions for 2<Emphasis Type="Italic">D</Emphasis> Domain Decomposition with Corners: Numerical Applications

This article deals with a local improvement of domain decomposition methods for 2-dimensional elliptic problems for which either the geometry or the domain decomposition presents conical singularities. After explaining the main results of the theoretical analysis carried out in Chniti et al. (Calcolo 45, 2008), the numerical experiments presented in this article confirm the optimality properties of the new interface conditions.     

Symbolic hierarchical analysis in currency markets: An application to contagion in currency crises

In this paper we introduce a new method to describe dynamical patterns of the real exchange rate co-movements time series and to analyze contagion in currency crisis. The method combines the tools of symbolic time series analysis with the nearest neighbor single linkage clustering algorithm. Data symbolization allows us obtaining a metric distance between two different time series that is used to construct an ultrametric distance. By analyzing the data of various countries, we derive a hierarchical organization, constructing minimal-spanning and hierarchical trees. From these trees we detect different clusters of countries according to their proximity. We show that this methodology permits us to construct a structural and dynamic topology that is useful to study interdependence and contagion effects among financial time series.     

Aerodynamic optimization via multi-objective micro-genetic algorithm with range adaptation,knowledge-based reinitialization,crowding and ε-dominance

In this study, an optimization of the airfoil of a sailplane is carried out by a recently developed multi-objective genetic algorithm based on microevolution, containing crowding, range adaptation, knowledge-based reinitialization and ε-dominance. Its efficiency was tested on a set of test problems. The results are encouraging, suggesting that very small populations can be used effectively to solve real-world multi-objective optimization problems in many cases of interest.