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.

Investigating Facebook groups through a random graph model

Facebook disseminates messages for billions of users everyday. Though there are log files stored on central servers, law enforcement agencies outside of the U.S. cannot easily acquire server log files from Facebook. This work models Facebook user groups by using a random graph model. Our aim is to facilitate detectives quickly estimating the size of a Facebook group with which a suspect is involved. We estimate this group size according to the number of immediate friends and the number of extended friends which are usually accessible by the public. We plot and examine UML diagrams to describe Facebook functions. Our experimental results show that asymmetric Facebook friendship fulfills the assumption of applying random graph models.

Preserving utility in social network graph anonymization

To protect from privacy disclosure, the social network graph is modified in order to hide the information that potentially be used to disclose person's identity. However, when the social network graph is changed, it is a great challenge to balance between the privacy gained and the loss of data utility. In this paper, we address this problem. We propose a new graph topological-based metric to improve utility preservation in social network graph anonymization. We compare the proposed approach with the amount-of-edge-change metric that popularly used in most of previous works. Experimental evaluation shows that our approach generates anonymized social network with improved utility preservation.

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).

Efficient mining of top-k breaker emerging subgraph patterns from graph datasets

This paper introduces a new type of discriminative subgraph pattern called breaker emerging subgraph pattern by introducing three constraints and two new concepts: base and breaker. A breaker emerging sub-graph pattern consists of three subpatterns: a con-strained emerging subgraph pattern, a set of bases and a set of breakers. An efficient approach is pro-posed for the discovery of top-k breaker emerging sub-graph patterns from graph datasets. Experimental re-sults show that the approach is capable of efficiently discovering top-k breaker emerging subgraph patterns from given datasets, is more efficient than two previ-ous methods for mining discriminative subgraph pat-terns. The discovered top-k breaker emerging sub-graph patterns are more informative, more discrim-inative, more accurate and more compact than the minimal distinguishing subgraph patterns. The top-k breaker emerging patterns are more useful for sub-structure analysis, such as molecular fragment analy-sis. © 2009, Australian Computer Society, Inc.

On the definition of an kinematic hardening effect graph for sheet metal forming process simulations

The objective of this work is to develop a kinematic hardening effect graph (KHEG) which can be used to evaluate the effect of kinematic hardening on the model accuracy of numerical sheet metal forming simulations and this without the need of complex material characterisation. The virtual manufacturing process design and optimisation depends on the accuracy of the constitutive models used to represent material behaviour. Under reverse strain paths the Bauschinger effect phenomenon is modelled using kinematic hardening models. However, due to the complexity of the experimental testing required to characterise this phenomenon in this work the KHEG is presented as an indicator to evaluate the potential benefit of carrying out these tests. The tool is validated with the classic three point bending process and the U-channel width drawbead process. In the same way, the capability of the KHEG to identify effects in forming processes that do not include forming strain reversals is identified.

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences.

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.

Propagation model of smartphone worms based on semi-Markov process and social relationship graph

Smartphone applications are getting more and more popular and pervasive in our daily life, and are also attractive to malware writers due to their limited computing source and vulnerabilities. At the same time, we possess limited understanding of our opponents in cyberspace. In this paper, we investigate the propagation model of SMS/MMS-based worms through integrating semi-Markov process and social relationship graph. In our modeling, we use semi-Markov process to characterize state transition among mobile nodes, and hire social network theory, a missing element in many previous works, to enhance the proposed mobile malware propagation model. In order to evaluate the proposed models, we have developed a specific software, and collected a large scale real-world data for this purpose. The extensive experiments indicate that the proposed models and algorithms are effective and practical. © 2014 Elsevier Ltd. All rights reserved.