درخت حوزه‌های تخصصی

The present research seeks to assess the relationship between the Individual value system and decision-making styles. The statistical population (190 people) consists of Pars Special Economic Energy Zone (PSEEZ) Organization managers who were assessed using the Census Method. The Myers and Myers cognitive demographic and value systems questionnaire and Scott and Bruce’s decision-making questionnaire were used with a response rate of 90%.. Data was analyzed with the SPSS and Smart Planning softwares. It was found that the dominant Individual value system is success-oriented, followed respectively by system-oriented, realistic, tribal, community-oriented, and autocentric.Also, the dominant decision-making style in the statistical population is rational, followed by intuitive, dependent, avoidant, and spontaneous decision-making styles respectively. It was also concluded that success-oriented and tribal value systems had the most significant relationship with decision-making styles and the community-oriented value system the least. Only the individual variables of race (ethnicity) and field of study had a significant relationship with the Individual value system. The other personal and organizational variables had no significant relationship with the Individual value system. Creating motivation and a sense of employee participation in the future of the organization is proposed as the most important way of improving the value system of the statistical population so that the tendency to make spontaneous decisions can lean more towards making rational decisions to the same extent. Investigating the relationship between decision-making styles is another effective topic in improving these in an organization and is thus recommended to other researchers in the field.

This experimental research centered on considering the effect of cognitive intervention instruction on the phonological awareness of Iranian dyslexic students at primary schools. For this purpose, 32 male and female students between 8 to 11 years of age with 90 to 110 IQs average, were purposefully selected from primary schools and randomly assigned to a control group and an experimental group. Wechsler Intelligence test (WIT), Karami and Noori (KNT) test, and a cognitive intervention package were used as tools in this research. Then, to check the participants’ phonological awareness, (KNT) measurement was applied to the students. The covariance analysis of the resultant data showed that this package was effective on the phonological awareness of the experimental group after receiving the cognitive intervention package in contrast to the control group. The independent sample t-test also showed that there was no statistically meaningful difference between dyslexic male and female students in the experimental group regarding phonological awareness. The findings recommend that instructors at primary schools should teach the students to use cognitive intervention programs in phonological awareness to accelerate and do their more effective homework and develop productive insights into their phonological awareness.

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.