An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The outstanding performance of machine learning models across various fields enables them to be used in sensitive and high-risk activities such as healthcare, automated driving, and security services. However, the explainability of their outputs and the safety of their operation are major concerns. Although Explainable AI (XAI) can enhance the interpretability of AI models, further research is necessary to evaluate its effectiveness in explaining adversarial attacks. The use of XAI techniques and the rise of adversarial attacks are important issues related to the explainability and security of deep learning, respectively. Furthermore, the relation between the explainability and safety of deep learning, such as the vulnerability of the XAI explanation to an adversarial attack, is key to unlocking these concerns. In this paper, we use the Saliency Map as an XAI technique to explain the behavior of the Fast Gradient Sign Method (FGSM) adversarial attack on the ResNet model, and show the vulnerability related to such an explanation with respect to the attack. Extensive experiments show that as the severity of FGSM attack on the ResNet model increases, the Saliency Map gradually fails, exposing its potential vulnerability.
This work presents the fabrication and investigation of thermoelectric cells based on composite of carbon nanotubes (CNT) and silicone adhesive. The composite contains CNT and silicon adhesive 1∶1 by weight. The current-voltage characteristics and dependences of voltage, current and Seebeck coefficient on the temperature gradient of cell were studied. It was observed that with increase in temperature gradient the open circuit voltage, short circuit current and the Seebeck coefficient of the cells increase. Approximately 7 times increase in temperature gradient increases the open circuit voltage and short circuit current up to 40 and 5 times, respectively. The simulation of experimental results is also carried out; the simulated results are well matched with experimental results.