Abstract: Ultrasonic vocalizations (USVs) are one of the evolutionarily oldest forms of animal communication. In order to study the communication architecture in an aversive social situation, we used a behavioral model in which one animal, the observer, is witnessing as his cagemate, the demonstrator, is experiencing a series of mild electrical foot-shocks (aversive stimuli). We studied the effect of foot-shocks experience in the observer and the influence of a warning sound (emit-ted shortly before the shock is applied) on USVs communication. These experiments revealed that such a warning seems to increase the arousal level, which differentiates the responses depending on previous experience. It can be identified by the emission of characteristic, short 22-kHz calls, of a duration below 100 ms. Furthermore, by analyzing temporally overlapping USVs, we found that in ‘Warned’ pairs with a naive observer, 22-kHz were mixed with 50-kHz calls. This fact, combined with a high fraction of very high-pitched 50-kHz calls (over 75-kHz), suggests the presence of the phenomenon of social buffering. On the other hand, in ‘Warned’ pairs with an experienced observer, pure 22-kHz overlaps were mostly found, signifying possible fear contagion with dis-tress sharing. Hence the importance of differentiating 22-kHz calls to long and short.

Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach are better than those obtained by state of the art methods. On the REPLAY-ATTACK database, we were able to attain a Half Total Error Rate (HTER) of 0.62% and an Equal Error Rate (EER) of 0.58%. It was possible to attain an EER of 0% on both the CASIA-FASD and the MSU FAS databases.

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, whereas it is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 300 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.