Abstract
In agent mediated electronic commerce the diversity of the involved actors can lead to diff erent conceptualizations of their needs and capabilities giving rise to semantic incompatibilities that might hamper negotiations and the fulfilling of satisfactory transactions. In order to provide help in the conversation among diff erent agents, these systems should provide ontology services, more specifically, ontology matching services. However, given the natural ambiguity of the ontology matching process, raising the possibility of multiple alignments between the same pair of ontologies, it is necessary to choose the one that best meets the interests of both agents. On the other hand, agents may possess diff erent interests, therefore the ontology alignment may also become the object of further negotiation. In this context, the application and exploitation of relationships captured in social networks can result in the establishment of more accurate adequacy relations of ontology alignments to agents, as well as the improvement of the negotiations' efficiency and, consequently, the users' satisfaction with the electronic commerce system. In this paper we present the AEMOS system which follows an ontology-based information integration approach, exploiting the ontology matching paradigm, improved by the application and exploitation of the relationships captured in the social networks. Copyright © 2014, Australian Computer Society Inc.

Abstract
This paper presents a survey on multi-robot search inspired by swarm intelligence by further classifying and discussing the theoretical advantages and disadvantages of the existing studies. Subsequently, the most attractive techniques are evaluated and compared by highlighting their most relevant features. This is motivated by the gradual growth of swarm robotics solutions in situations where conventional search cannot find a satisfactory solution. For instance, exhaustive multi-robot search techniques, such as sweeping the environment, allow for a better avoidance of local solutions but require too much time to find the optimal one. Moreover, such techniques tend to fail in finding targets within dynamic and unstructured environments. This paper presents experiments conducted to benchmark five state-of-the-art algorithms for cooperative exploration tasks. The simulated experimental results show the superiority of the previously presented Robotic Darwinian Particle Swarm Optimization (RDPSO), evidencing that sociobiological inspiration is useful to meet the challenges of robotic applications that can be described as optimization problems (e.g., search and rescue). Moreover, the RDPSO is further compared with the best performing algorithms within a population of 14 e-pucks. It is observed that the RDPSO algorithm converges to the optimal solution faster and more accurately than the other approaches without significantly increasing the computational demand, memory and communication complexity.

Abstract
Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology.

Abstract

Abstract
In a climate change context, changes in extreme sea-levels rather than changes in the mean are of particular interest from the coastal protection point of view. In this work, extreme sea-levels in the Baltic Sea are investigated based on daily tide gauge records for the period 1916-2005 using the annual block maxima approach. Extreme events are analysed based on the generalised extreme value distribution considering both stationary and time-varying models. The likelihood ratio test is applied to select between stationary and non-stationary models for the maxima and return values are estimated from the final model. As an independent and complementary approach, quantile regression is applied for comparison with the results from the extreme value approach. The rates of change in the uppermost quantiles are in general consistent and most pronounced for the northernmost stations.

Abstract
Service experience is the core of service offering. However, research on the role of the service environment when creating experiences is still underdeveloped, and even less empirical evidence is available that relates servicescape with customers' perceptions of value. Our purpose is to focus on the role of servicescape as a driver of customer experience by analyzing its impact on customer value creation and behavior. We have chosen an experience-centric service organization, a football club, to develop our analysis. A regression analysis was performed to determine the main drivers of value among ‘sportscape’ components and how it influenced customers' attitude and behavior. A total of 349 questionnaires were collected during a match of the Portuguese Football League at Dragon Stadium. Our analysis showed that servicescape influences consumer perceived value, attitudes, and behavior when creating service experiences: value-in-context generates customers' satisfaction, which in turn has a positive effect on their desire to repeat the experience. © 2014, Taylor & Francis.