deception

Aim: The Aim of study was to investigate the effect of deception for intensity of exercise on fatigue in female students.Methods: Sixteen subjects were selected randomly among 30 students who volunteered to participate in the study and they performed maximal exercise test (Bruce) in two states. In the first state, the increase in exercise intensity during steps of Bruce test was correctly announced to the subjects, but in the second state, they were dictated that the increase in exercise intensity was less than in the first state. Dependent variables were measured at the end of the test and the data were compared with paired t-test using SPSS 23.Findings: The results showed that there was a significant difference for median frequency of rectus femoris muscle (p=0.043), maximium heart rate of the subjects at the end of Bruce test (p=0.001) and time to reach exhaustion (p=0.001) between the first state in which the actual increase in exercise intensity during steps of Bruce test was announced to the subjects and the second state in which they were dictated that the increase in exercise intensity, was less than the first state. However, no significant difference was observed for median frequency of vastus medialis (p=0.736) and vastus lateralis (p=0.762) muscles, as well as for rate of perceived exertion (p=1.000). Conclusion: According to the results, it seems that by deception for intensity of exercise during maximal exercise test, the amount of fatigue can be reduced and the time to reach fatigue can be increased.

Online reviews are crucial in influencing consumer decisions and business practices. However, some individuals exploit this system by posting fake reviews, known as spam opinions, to manipulate perceptions. Spam detection systems face significant challenges in robustness due to their primary focus on identifying spam attacks without accounting for adversaries that target the detection mechanisms. This oversight enables spammers to exploit vulnerabilities in traditional algorithms with complex deceptive strategies, ultimately undermining their effectiveness. This paper proposes a novel multi-layer graph-based method that represents reviews, reviewers, and products as interconnected nodes. This approach captures the complex relationships among them and addresses adversarial attempts to manipulate the detection process. Our approach utilizes three key nodes—opinion, reviewer, and product—to assess the honesty, trust, and reliability of reviews, reviewers, and products in the context of potential deception. Furthermore, we develop a simulation tool capable of generating diverse attack scenarios, including those targeting the detection system itself, enabling a comprehensive evaluation of robustness. We compared the performance of our method with other graph-based techniques through simulations and case studies, demonstrating that our method is a competitive solution among existing alternatives.