Marcel Cremene

Service Composition Based on Natural Language Requests

The easiest way for a user to express his needs regarding a desired service is to use natural language. The main issues come from the fact that the natural language is incomplete and ambiguous, while the service composition process should lead to valid services. In this paper we propose a natural language service assemblage method based on composition templates (patterns). The use of templates assures that the composition result is always valid. The proposed system, called NLSC (Natural Language Service Composer), was implemented on the top of a service-oriented middleware called WComp and tested in an intelligent home environment.

Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition

Graphical abstractDisplay Omitted HighlightsMost existing approaches reduce the multi-objective optimization problem of QoS-aware web service composition to a single-objective problem using scalarization.No comparative study with several multi-objective algorithms has been found in the literature for this specific problem.Several EMOAs are tested: NSGA-II, SPEA2, POSDE, GDE3, and MOEA/D.Differential evolution (DE) algorithms - in particular GDE3 - yields the best results on this specific problem for several scenarios, also having the lowest time complexity. Web service composition combines available services to provide new functionality. The various available services have different quality-of-service (QoS) attributes. Building a QoS-optimal web service composition is a multi-criteria NP-hard problem. Most of the existing approaches reduce this problem to a single-criterion problem by aggregating different criteria into a unique global score (scalarization). However, scalarization has some significant drawbacks: the end user is supposed to have a complete a priori knowledge of its preferences/constraints about the desired solutions and there is no guarantee that the aggregated results match it. Moreover, non-convex parts of the Pareto set cannot be reached by optimizing a convex weighted sum. An alternative is to use Pareto-based approaches that enable a more accurate selection of the end-user solution. However, so far, only few solutions based on these approaches have been proposed and there exists no comparative study published to date. This motivated us to perform an analysis of several state-of-the-art multi-objective evolutionary algorithms. Multiple scenarios with different complexities are considered. Performance metrics are used to compare several evolutionary algorithms. Results indicate that GDE3 algorithm yields the best performances on this problem, also with the lowest time complexity.

Adaptive MOEA/D for QoS-based web service composition

QoS aware service composition is one of the main research problem related to Service Oriented Computing (SOC). A certain functionality may be offered by several services having different Quality of Service (QoS) attributes. Although the QoS optimization problem is multiobjective by its nature, most approaches are based on single-objective optimization. Compared to single-objective algorithms, multiobjective evolutionary algorithms have the main advantage that the user has the possibility to select a posteriori one of the Pareto optimal solutions. A major challenge that arises is the dynamic nature of the problem of composing web services. The algorithms performance is highly influenced by the parameter settings. Manual tuning of these parameters is not feasible. An evolutionary multiobjective algorithm based on decomposition for solving this problem is proposed. To address the dynamic nature of this problem we consider the hybridization between an adaptive heuristics and the multiobjective algorithm. The proposed approach outperforms state of the art algorithms.

Adaptation platform for autonomic context-aware services

Most context-aware services are not autonomic because of two main reasons. The first one is that a context-aware service reacts only to context states that are entirely predicted by the developer. The second reason is that the adaptation control is based on predefined, application and context-specific policies. In this paper we propose a solution based on an application-context description, which allows the machine to autonomously discover the context structure and the adaptation strategies. We have tested our model using a simple scenario where a forum service is adapted to the user language.

An Adaptive Combiner-Equalizer for Multiple-Input Receivers

Where multiple-input receivers are concerned diversity combining is one of the most efficient techniques against fading effects. In this paper we propose a multipleinput adaptive combiner-equalizer. The novelty of the solution lies in the unified combining-equalization approach - the two classical operations being performed simultaneously and not sequentially. Our simulations show significant performance in terms of outage probability for both indoor and outdoor conditions, while complexity is lower than for a classical MRC implementation. The concept of unifying certain operations along the transmission chain is feasible today more than ever as it can be easily implemented on SDR (Software Defined Radio) platforms.

On-demand service composition based on natural language requests

The widespread and diversity of Web services in the ubiquitous computing era and the impossibility to predict a priori all possible user demands, generates the need of a system capable of dynamically composing new services, based on unrestricted natural language requests.

Service oriented architecture for medical image processing

ObjectiveOne of the problem existent today in the medical domain is that we have an important number of legacy applications that cannot be reused, shared, composed. The objective of this paper is to propose a solution that make possible to overcome these limitations and makes possible the creation of new composite services that may be used in common by several institutions.MethodsA generic model for a service, based on a loosely coupled, message-based communication model is proposed. The service model takes into account the possibility to integrate legacy applications. Specialized image processing services can be dynamically discovered and integrated into client applications or other services. A new model and software product based on Service Oriented Architecture (SOA) is proposed. SOA improves the reusability and maintainability of distributed systems. In service oriented architectures, the most important element is the service, a resource provided to remote clients via a service contract.ResultsA basic integration example is presented in the paper and the possibility to integrate dedicated scenario.ConclusionThe aim of this paper is to present a services based architecture for medical image processing in assisted diagnosis. A service can be discovered at runtime, can be replaced if it is unavailable or can be incorporated in a new service.

Equitable solutions in QoS-aware service optimization

Web services QoS optimization problem using the concept of Lorenz dominance is addressed. Lorenz solutions are equitable and well balanced. Such an approach could simplify the Decision Makers choice. Evolutionary detection of Lorenz solution seem to be an appealing one. Some state-of-the art MOEAs are slightly modified for addressing the QoS optimization problem. Lorenz solutions drastically reduce the number of solutions in the Pareto set, and thus the decision costs.

Analyzing human gaze path during an interactive optimization task

Interactive Evolutionary Computation (IEC) community aims at reducing users fatigue during an optimization task involving subjective criteria: a set of graphic potential solutions are simultaneously shown to a user which task is to identify most interesting solutions to the problem he had to solve. Evolutionary operators are applied to user choices expecting to produce better solutions. As traditional IEC ask the user to give a mark to each solution or to explicitly choose bests solutions with a mouse, we propose a new framework that uses in real time gaze information to predict which parts of a screen is more significant for a user. We can therefore avoid the user to explicitly choose which solutions are interesting for him. In this paper, we mainly focus on automatically ordering solutions shown on a screen given a gaze path obtained by an eye-tracker. We applied several supervised learning methods (SVM, neural networks...) on two different experiments. We obtain a formula that predict with 85% user choices. We demonstrate that decisive criterion is time spent on one solution and we show the independency between this formula and the experiment.

Multifaceted based language for pervasive services with deterministic and fully defined behavior

One important feature of pervasive services is their awareness to the surrounding context. The behavior of context-aware pervasive services should be fully defined and deterministic. These services may be implemented using context-aware languages. So far, these languages either have dealt with only one of these features (i.e. fully defined or deterministic behavior) or have not taken into account any of the two features. In this paper, we propose a programming paradigm which facilitates the development of the context-aware pervasive services with fully defined and deterministic behavior. The multifaceted programming entity is the core concept of our proposal. In order to demonstrate the practical validity of the proposed paradigm we extended C# language with multifaceted programming entity.