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The end-user has frequently been identified as the weakest link; however, motivated by the fact that different users react differently to the same stimuli, identifying the reasons behind variations in security behavior and why certain users could be “at risk” more than others is a step toward protecting and defending users against security attacks. This paper aims to explore the effect of personality trait variations (through the Big Five Inventory [BFI]) on users’ risk level of their intended security behaviors. In addition, age, gender, service usage and information technology (IT) proficiency are analyzed to identify what role and impact they have on behavior.

The authors developed a quantitative-oriented survey that was implemented online. The bi-variate Pearson two-tailed correlation was used to analyze survey responses.

The results obtained by analyzing 538 survey responses suggest that personality traits do play a significant role in affecting users’ security behavior risk levels. Furthermore, the results suggest that BFI score of a trait has a significant effect as users’ online personality is linked to their offline personality, especially in the conscientiousness personality trait. Additionally, this effect was stronger when personality was correlated with the factors of IT proficiency, gender, age and online activity.

The contributions of this paper are two-fold. First, with the aid of a large population sample, end-users’ security practice is assessed from multiple domains, and relationships were found between end-users’ risk-taking behavior and nine user-centric factors. Second, based upon these findings, the predictive ability for these user-centric factors were evaluated to determine the level of risk a user is subject to from an individual behavior perspective. Of 28 behaviors, 11 were found to have a 60 per cent or greater predictive ability, with the highest classification of 92 per cent for several behaviors. This provides a basis for organizations to use behavioral intent alongside personality traits and demographics to understand and, therefore, manage the human aspects of risk.

The purpose of this study was to investigate the difference between South Africa (SA) and the United Kingdom (UK) in terms of data protection compliance with the aim to establish if a country that has had data protection in place for a longer period of time has a higher level of compliance with data protection requirements in comparison with a country that is preparing for compliance.

An insurance industry multi-case study within the online insurance services environment was conducted. Personal information of four newly created consumer profiles was deposited to 10 random insurance organisation websites in each country to evaluate a number of data privacy requirements of the Data Protection Act and Protection of Personal Information Act.

The results demonstrate that not all the insurance organisations honored the selected opt-out preference for receiving direct marketing material. This was evident in direct marketing material that was sent from the insurance organisations in the sample to both the SA and UK consumer profiles who opted out for it. A total of 42 unsolicited third-party contacts were received by the SA consumer profiles, whereas the UK consumer profiles did not receive any third-party direct marketing. It was also found that the minimality principle is not always met by both SA and UK organisations.

As a jurisdiction with a heavy stance towards privacy implementation and regulation, it was found that the UK is more compliant than SA in terms of implementation of the evaluated data protection requirements included in the scope of this study, however not fully compliant.

Based upon the results obtained from this research, it suggests that the SA insurance organisations should ensure that the non-compliance aspects relating to direct marketing and sharing data with third parties are addressed. SA insurance companies should learn from the manner in which the UK insurance organisations implement these privacy requirements. Furthermore, the UK insurance organisations should focus on improved compliance for direct marking and the minimality principle. The study indicates the positive role that data protection legislation plays in a county like the UK, with a more mature stance toward compliance with data protection legislation.

The positioning and grasping of large-size objects have always had problems of low positioning accuracy, slow grasping speed and high application cost compared with ordinary small parts tasks. This paper aims to propose and implement a binocular vision-guided grasping system for large-size object with industrial robot.

To guide the industrial robot to grasp the object with high position and pose accuracy, this study measures the pose of the object by extracting and reconstructing three non-collinear feature points on it. To improve the precision and the robustness of the pose measuring, a coarse-to-fine positioning strategy is proposed. First, a coarse but stable feature is chosen to locate the object in the image and provide initial regions for the fine features. Second, three circular holes are chosen to be the fine features whose centers are extracted with a robust ellipse fitting strategy and thus determine the precise pose and position of the object.

Experimental results show that the proposed system has achieved high robustness and high positioning accuracy of −1 mm and pose accuracy of −0.5 degree.

It is a high accuracy method that can be used for industrial robot vision-guided and grasp location.

This paper aims to study six degrees-of-freedom (6DOF) pose measurement of reflective metal casts by machine vision, analyze the problems existing in the positioning of metal casts by stereo vision sensor in unstructured environment and put forward the visual positioning and grasping strategy that can be used in industrial robot cell.

A multikeypoints detection network Binocular Attention Hourglass Net is constructed, which can complete the two-dimensional positioning of the left and right cameras of the stereo vision system at the same time and provide reconstruction information for three-dimensional pose measurement. Generate adversarial networks is introduced to enhance the image of local feature area of object surface, and the three-dimensional pose measurement of object is completed by combining RANSAC ellipse fitting algorithm and triangulation method.

The proposed method realizes the high-precision 6DOF positioning and grasping of reflective metal casts by industrial robots; it has been applied in many fields and solves the problem of difficult visual measurement of reflective casts. The experimental results show that the system exhibits superior recognition performance, which meets the requirements of the grasping task.

Because of the chosen research approach, the research results may lack generalizability. The proposed method is more suitable for objects with plane positioning features.

This paper realizes the 6DOF pose measurement of reflective casts by vision system, and solves the problem of positioning and grasping such objects by industrial robot.