This study aims to explore the mechanisms by which training influences employees’ information security policy compliance (ISPC) through the mediating roles of compliance knowledge and social pressure. An empirical model is conceptualized on the theoretical basis of the Elaboration Likelihood Model (ELM). Based on a survey conducted in China, a sample of 304 participants was used to test the impact of training on ISPC as well as the mediating effects of compliance knowlgedge (as a central route) and social pressure (as a peripheral route). The results revealed that training had a positive effect on ISPC, and the mediating effects of compliance knowledge and social pressure were both positive and complementary. The results also indicated that the mediating effect of compliance knowledge was stronger than that of social pressure, which was consistent with the judgment of ELM.

The formation of information security behavioural intention (ISBI) can be complex and dynamic in different contexts. This paper aims to examine and compare different users’ ISBI formalisation mechanisms when dealing with their personal affairs (non-work users) and organisational affairs (work users). Drawing on two principles of Conservation of Resources (COR) theory (i.e. resource loss principle, and resource gain principle), we developed two models to examine how situational support affects ISBI formation. The results of a study of 432 non-worker users and 261 work users indicate a curvilinear relationship between situational support and ISBI through subjective norms and risk perception for non-worker users, whilst a linear relationship via subjective norms is found for worker users. This is the first time that COR has been applied to explain the formation of ISBI. The findings broaden the research scope of individuals’ ISBI by revealing how situational support affects the formalisation mechanism for different users in cross-contexts. The theoretical and practical implications of the findings and the future study are discussed.

Reliable welding quality monitoring (WQM) is a long-standing challenge for climbing gas tungsten arc welding (GTAW) due to the inherent instability and complexity of the weld pool during upward welding, especially for the fabrication of large-scale structural components with medium-thick and thick aluminum plates. This article presents a novel WQM approach based on multigranularity spatiotemporal attentive representation learning, aiming to accurately characterize molten pool state and detect welding defects in real time. A passive vision sensing system is constructed to monitor the climbing GTAW process. A long-term dynamic information-enhanced multigranularity spatiotemporal attentive representation learning network is proposed. The network adopts a feature-level image fusion strategy and multigranularity attention mechanism to simultaneously aggregate discriminative information at different semantic levels on the temporal and spatial dimensions from a global view, while utilizing a bilateral branch structure to alleviate class imbalance in the data. Moreover, long-term dynamic information is mined from the molten pool time series images through motion edge history images. Experimental results show that the proposed approach has a remarkable classification performance and robustness compared with the typical comparison models even with class imbalance and noisy training data. This approach offers a promising new solution for WQM and is expected to be utilized to provide real-time feedback in a closed-loop quality control system.

Ultrathin sheets edge welding is a critical technology for aircraft, aerospace, or much high-end equipment manufacturing industrial applications. However, its online quality monitoring is still challenging due to the complicated and multicoupled transport phenomena in the mesoscale molten pool. This article presents an intelligent methodology for real-time monitoring of edge welds forming quality using microvision sensing and support vector machine based on swarm optimized and computationally inexpensive floating selection (SOCIFS-SVM). The clear images of the mesoscale molten pool are continuously acquired by using the passive microvision sensor. Based on the analysis for the dynamic behavior of the mesoscale molten pool and the morphology of the formed edge welds, a robust image processing procedure is developed to extract the single-frame spatial features and interframe correlation features from the microvision image sequence of the molten pool. A Kalman filter is applied to suppress the noise caused by the pulse current, etc. Then, the SOCIFS-SVM-based approach is established to predict three typical types of welding states. Experimental results with multiple groups of different welding parameters show that the proposed method can effectively and robustly identify the lack of fusion, humping, and sound weld with the highest accuracy of 96.09%. In addition, the established model can still achieve mean accuracy of 94.98% for additional untrained data, indicating that the model has strong generalization ability. The proposed real-time quality monitoring method makes online defect diagnosis of ultrathin sheets edge welding possible and provides a basis for online quality closed-loop control.

Psychological and behavioral characteristics are among the most important factors that instigate information security incidents. Although many previous studies have discussed the influencing factors of information security policy compliance behavior in an organization, few have considered the influence of organizational structures. In this study, the mechanism by which information security policy compliance behavioral intention is formed was studied by integrating the theory of planned behavior (TPB) and perceived organizational formalization. Data analysis was performed using the structural equation modeling (SEM) with data obtained from a survey of 261 company employees. The empirical results reveal that perceived organizational formalization significant affected cognitive processes theorized by TPB, behavioral habits, and deterrent certainty. This study suggests that formalized rules, procedures, and communications should be designed to improve employee information security policy compliance behavioral habits and intentions.

BACKGROUND: Prioritizing disease genes is trying to identify potential disease causing genes for a given phenotype, which can be applied to reveal the inherited basis of human diseases and facilitate drug development. Our motivation is inspired by label propagation algorithm and the false positive protein-protein interactions that exist in the dataset. To the best of our knowledge, the false positive protein-protein interactions have not been considered before in disease gene prioritization. Label propagation has been successfully applied to prioritize disease causing genes in previous network-based methods. These network-based methods use basic label propagation, i.e. random walk, on networks to prioritize disease genes in different ways. However, all these methods can not deal with the situation in which plenty false positive protein-protein interactions exist in the dataset, because the PPI network is used as a fixed input in previous methods. This important characteristic of data source may cause a large deviation in results. RESULTS: A novel network-based framework IDLP is proposed to prioritize candidate disease genes. IDLP effectively propagates labels throughout the PPI network and the phenotype similarity network. It avoids the method falling when few disease genes are known. Meanwhile, IDLP models the bias caused by false positive protein interactions and other potential factors by treating the PPI network matrix and the phenotype similarity matrix as the matrices to be learnt. By amending the noises in training matrices, it improves the performance results significantly. We conduct extensive experiments over OMIM datasets, and IDLP has demonstrated its effectiveness compared with eight state-of-the-art approaches. The robustness of IDLP is also validated by doing experiments with disturbed PPI network. Furthermore, We search the literatures to verify the predicted new genes got by IDLP are associated with the given diseases, the high prediction accuracy shows IDLP can be a powerful tool to help biologists discover new disease genes. CONCLUSIONS: IDLP model is an effective method for disease gene prioritization, particularly for querying phenotypes without known associated genes, which would be greatly helpful for identifying disease genes for less studied phenotypes. AVAILABILITY: https://github.com/nkiip/IDLP.