درخت حوزه‌های تخصصی

INTRODUCTION The phenomenon of organizational insentience has been one of the main challenges in recent years and refers to a situation in which the employees of an organization have become indifferent to their organizational environment. The current research was conducted to investigate the causes and contexts of the formation of organizational insentience in the Red Crescent Society of Tehran province, Iran. METHODS: This applied study was conducted based on the descriptive-correlation method of data collection. The statistical population of this research included all the managers of Red Crescent Society in Tehran province in 2021. The samples (n=90) were selected using Cochran's statistical formula and stratified random sampling. The required data were collected using a researcher made organizational insentience questionnaire. The validity and reliability of the questionnaire were checked and confirmed. The collected data were analyzed in Smart PLS software using structural equation modeling with partial least squares approach. FINDINGS: The results of the research showed that causal conditions had a direct, positive, and significant effect on the central category with a standard beta coefficient of 0.725, t=17.625, and P=0.001 at the level of one percent error. Moreover, contextual conditions with a beta coefficient of 0.410, t=3.107, and P=0.002 had a direct, positive, and significant effect on the category of strategies at the level of one percent error. Intervening conditions had a direct, positive, and significant effect on strategies at the level of one percent error (standard beta coefficient=0.221, t=3.008, and P=0.003). The central category had a direct, positive, and significant effect on the strategy category with a beta coefficient of 0.334, t=2.282, and P=0.023 at the five percent error level. Based on the results, at one percent error level, the strategies presented in the research had a direct, positive, and significant effect on the outcomes (standard beta coefficient=0.347, t=3.769, and P=0.000). In total, the results showed the existence of organizational insentience in the Red Crescent Society of Tehran province, which can be reduced by applying appropriate methods. CONCLUSION: According to the findings, by reducing the sense of meaninglessness, double standards, eliminating organizational discrimination, applying appropriate procedures in strategies and their implementation, paying attention to the structural dimensions of the Red Crescent Society in Tehran province, and implementing meritocracy management, it would be possible to increase organizational social capital, the level of interest, commitment, and responsibility of employees towards the Society and work, organizational independence, organizational justice, the atmosphere of trust, and supportive behaviors from the Society and employees. By adopting these measures, a step would be taken toward preventing the formation of organizational insentience in the Red Crescent Society of Tehran province.

Numerous face recognition systems employ deep learning techniques to identify individuals in public areas such as shopping malls, airports, and other high-security zones. However, adversarial attacks are susceptible to deep learning-based systems. The adversarial attacks are intentionally generated by the attacker to mislead the systems. These attacks are imperceptible to the human eye. In this paper, we proposed a feature-based masking iterative method (F-MIM) to generate the adversarial images. In this method, we utilize the features of the face to misclassify the models. The proposed approach is based on a black-box attack technique where the attacker does not have the information related to target models. In this black box attack strategy, the face landmark points are modified using the binary masking technique. In the proposed method, we have used the momentum iterative method to increase the transferability of existing attacks. The proposed method is generated using the ArcFace face recognition model that is trained on the Labeled Face in the Wild (LFW) dataset and evaluated the performance of different face recognition models namely ArcFace, MobileFace, MobileNet, CosFace and SphereFace under the dodging and impersonate attack. The F-MIM attack is outperformed in comparison to the existing attacks based on Attack Success Rate evaluation metrics and further improves the transferability.