Induced ordered weighted averaging operators

Since the introduction of the ordered weighted averaging operator [18], the OWA has received great attention with applications in fields including decision making, recommender systems [8, 21], classification [10] and data mining [16] among others. The most important step in the calculation of the OWA is the permutation of the input vector according to the size of its arguments. In some applications, it makes sense that the inputs be reordered by values different to those used in calculation. For instance, if we have a number of mobile sensor readings, we may wish to allocate more importance to the reading taken from the sensor closest to us at a given point in time, rather than the largest reading.

Construction of strong equality index from implication operators

We present the concept of strong equality index, staring from the definition of strong inclusion given by Dubois and Prade in 1980, We also present a construction method based on the use of implication operators and two specific properties of the implications.

Image reduction operators based on non-monotonic averaging functions

Image reduction is a crucial task in image processing, underpinning many practical applications. This work proposes novel image reduction operators based on non-monotonic averaging aggregation functions. The technique of penalty function minimisation is used to derive a novel mode-like estimator capable of identifying the most appropriate pixel value for representing a subset of the original image. Performance of this aggregation function and several traditional robust estimators of location are objectively assessed by applying image reduction within a facial recognition task. The FERET evaluation protocol is applied to confirm that these non-monotonic functions are able to sustain task performance compared to recognition using nonreduced images, as well as significantly improve performance on query images corrupted by noise. These results extend the state of the art in image reduction based on aggregation functions and provide a basis for efficiency and accuracy improvements in practical computer vision applications.

Consistency and stability in aggregation operators : an application to missing data problems

In this work we analyze the key issue of the relationship that should hold between the operators in a family {An} of aggregation operators in order to understand they properly define a consistent whole. Here we extend some of the ideas about stability of a family of aggregation operators into a more general framework, formally defining the notions of i – L and j – R strict stability for families of aggregation operators. The notion of strict stability of order k is introduced as well. Finally, we also present an application of the strict stability conditions to deal with missing data problems in an information aggregation process. For this analysis, we have focused in the weighted mean family and the quasi-arithmetic weighted means families.

Use of artificial neural networks as explicit finite difference operators

Results of a numerical exercise, substituting a numerical operator by an artificial neural network (ANN) are presented in this paper. The numerical operator used is the explicit form of the finite difference (FD) scheme. The FD scheme was used to discretize the one-dimensional transport equation, which included both the advection and dispersion terms. Inputs to the ANN are the FD representation of the transport equation, and the concentration was designated as the output. Concentration values used for training the ANN were obtained from analytical solutions. The numerical operator was reconstructed from a back calculation of the weights of the ANN. Linear transfer functions were used for this purpose. The ANN was able to accurately recover the velocity used in the training data, but not the dispersion coefficient. This capability was improved when numerical dispersion was taken into account; however, it is limited to the condition: C/P<0.5 , where C is the Courant number and P , the Peclet number (i.e., the restriction imposed by the Neumann stability condition).

The role of general and specific stressors in the health and well-being of call centre operators

The call centre industry has developed a reputation for generating a highly stressful work environment with high absenteeism and turnover rates. Research has identified role ambiguity, role conflict, role overload, and work-family conflict as common stressors in other settings. Call centre research has additionally identified performance monitoring, job design and job opportunities as call centre specific stressors.OBJECTIVE AND METHODS: This study investigated the impact of the identified stressors on burnout, somatic symptomology, and turnover intent among 126 call centre representatives (CCRs) from 11 call centres in metropolitan Melbourne, Australia.

International perspectives on psychosocial working conditions, mental health, and stress of dairy farm operators

Dairy farm operators-farmers, workers, and family members-are faced with many demands and stressors in their daily work and these appear to be shared across countries and cultures. Dairy operators experience high psychosocial demands with respect to a hard work and production ethos, economic influences, and social and environmental responsibility. Furthermore, both traditional and industrial farms are highly dependent on external conditions, such as weather, fluctuating markets, and regulations from government authorities. Possible external stressors include disease outbreaks, taxes related to dairy production, and recent negative societal attitudes to farming in general. Dairy farm operators may have very few or no opportunities to influence and control these external conditions, demands, and expectations. High work demands and expectations coupled with low control and lack of social support can lead to a poor psychosocial work environment, with increased stress levels, ill mental health, depression, and, in the worst cases, suicide. Internationally, farmers with ill mental health have different health service options depending on their location. Regardless of location, it is initially the responsibility of the individual farmer and farm family to handle mental health and stress, which can be of short- or long-term duration. This paper reviews the literature on the topics of psychosocial working conditions, mental health, stress, depression, and suicide among dairy farm operators, farm workers, and farm family members in an international perspective.

Image reduction operators as aggregation functions: Fuzzy transform and undersampling

After studying several reduction algorithms that can be found in the literature, we notice that there is not an axiomatic definition of this concept. In this work we propose the definition of weak reduction operators and we propose the properties of the original image that reduced images must keep. From this definition, we study whether two methods of image reduction, undersampling and fuzzy transform, satisfy the conditions of weak reduction operators.

Novel approach to big data collaboration with network operators network function virtualisation (NFV)

The intersection of network function virtualisation (NFV) technologies and big data has the potential of revolutionising today's telecommunication networks from deployment to operations resulting in significant reductions in capital expenditure (CAPEX) and operational expenditure, as well as cloud vendor and additional revenue growths for the operators. One of the contributions of this article is the comparisons of the requirements for big data and network virtualisation and the formulation of the key performance indicators for the distributed big data NFVs at the operator's infrastructures. Big data and virtualisation are highly interdependent and their intersections and dependencies are analysed and the potential optimisation gains resulted from open interfaces between big data and carrier networks NFV functional blocks for an adaptive environment are then discussed. Another contribution of this article is a comprehensive discussion on open interface recommendations which enables global collaborative and scalable virtualised big data applications.