Biblio

In this paper, the layer choices of the image style transfer method using the VGG-19 neural network are studied. The VGG-19 network is used to extract the feature maps which have their implicit meaning as a learning basis. If the layers for stylistic learning are not suitably chosen, the quality of style transferred image may not look good. After making experiments, it can be observed that the color information is concentrated on lower layers from conv1-1 to conv2-2, and texture information is concentrated on the middle layers from conv3-1 to conv4-4. As to the higher layers from conv5-1 to conv5-4, they seem to be able to depict image content well. Based on these observations, the methods of color transfer, texture transfer and style transfer are presented and make comparisons with conventional methods.

Embry-Riddle Aeronautical University (ERAU) is working with the Air Force Research Lab (AFRL) to develop a distributed multi-layer autonomous UAS planning and control technology for gathering intelligence in Anti-Access Area Denial (A2/AD) environments populated by intelligent adaptive adversaries. These resilient autonomous systems are able to navigate through hostile environments while performing Intelligence, Surveillance, and Reconnaissance (ISR) tasks, and minimizing the loss of assets. Our approach incorporates artificial life concepts, with a high-level architecture divided into three biologically inspired layers: cyber-physical, reactive, and deliberative. Each layer has a dynamic level of influence over the behavior of the agent. Algorithms within the layers act on a filtered view of reality, abstracted in the layer immediately below. Each layer takes input from the layer below, provides output to the layer above, and provides direction to the layer below. Fast-reactive control systems in lower layers ensure a stable environment supporting cognitive function on higher layers. The cyber-physical layer represents the central nervous system of the individual, consisting of elements of the vehicle that cannot be changed such as sensors, power plant, and physical configuration. On the reactive layer, the system uses an artificial life paradigm, where each agent interacts with the environment using a set of simple rules regarding wants and needs. Information is communicated explicitly via message passing and implicitly via observation and recognition of behavior. In the deliberative layer, individual agents look outward to the group, deliberating on efficient resource management and cooperation with other agents. Strategies at all layers are developed using machine learning techniques such as Genetic Algorithm (GA) or NN applied to system training that takes place prior to the mission.

Two-party password-authenticated key exchange (2PAKE) protocols provide a natural mechanism for secret key establishment in distributed applications, and they have been extensively studied in past decades. However, only a few efforts have been made so far to design password-authenticated group key exchange (GPAKE) protocols. In a 2PAKE or GPAKE protocol, it is assumed that short passwords are preshared among users. This assumption, however, would be impractical in certain applications. Motivated by this observation, this article presents a GPAKE protocol without the password sharing assumption. To obtain the passwords, wireless devices, such as smart phones, tablets, and laptops, are used to extract short secrets at the physical layer. Using the extracted secrets, users in our protocol can establish a group key at higher layers with light computation consumptions. Thus, our GPAKE protocol is a cross-layer design. Additionally, our protocol is a compiler, that is, our protocol can transform any provably secure 2PAKE protocol into a GPAKE protocol with only one more round of communications. Besides, the proposed protocol is proved secure in the standard model.