The increasing interest surrounding 3D human heads for digital avatars and simulations has highlighted the need for accurate internal modeling rather than solely focusing on external approximations. Existing approaches rely on traditional optimization techniques applied to explicit 3D representations like point clouds and meshes, leading to computational inefficiencies and challenges in capturing local geometric features. To tackle these problems, we propose a novel modeling method called DDF-ISM. It leverages a probabilistic Directed Distance Field for Internal Structure Modeling, facilitating efficient and anatomically accurate deformation of different parts of the human head. DDF-ISM comprises two key components: 1) a probabilistic DDF network for implicit representation of the target model to provide crucial local geometric information, and 2) a conditioned deformation network guided by the local geometry. Additionally, we introduce a large-scale dataset of human heads with internal structures derived from high-quality Computed Tomography (CT) scans, along with well-designed template models encompassing skull, mandible, brain, and head surface. Evaluation on this dataset showcases the superiority of our approach over existing methods, exhibiting superior performance in both modeling quality and efficiency.

Abstract This paper presents a secure communication application called DiscoverFriends. Its purpose is to securely communicate to a group of online friends while bypassing their respective social networking servers under a mobile ad hoc network environment. DiscoverFriends leverages Bloom filters and a hybrid encryption technique with a self‐organized public‐key management scheme to securely identify friends and provide authentication. Additionally, DiscoverFriends enables anonymous location check‐ins by utilizing a new cryptographic primitive called function secret sharing. Finally, to the best of our knowledge, DiscoverFriends implements and evaluates the first Android multi‐hop WiFi direct protocol using IPv6. Copyright © 2016 John Wiley & Sons, Ltd.

This paper presents a secure communication application called DiscoverFriends. Its purpose is to communicate to a group of online friends while bypassing their respective social networking servers under a mobile ad hoc network environment. DiscoverFriends leverages Bloom filters and a hybrid encryption technique with a self-organized public-key management scheme to securely identify friends and provide authentication. Firstly, Bloom filters provide a space-efficient means of security for friend discovery. Secondly, a combination of asymmetric and symmetric encryptions algorithms utilizes both benefits to provide increased security at lower computational cost. Thirdly, a self-organized public-key management scheme helps authenticate users using a trust graph in an infrastructureless setting. With the use of Wi-Fi Direct technology, an initiator is able to establish an ad hoc network where friends can connect to within the application. DiscoverFriends was analyzed under two threat models: replay attacks and eavesdropping by a common friend. Finally, the paper evaluates the application based on storage usage and processing.

In ad hoc wireless networks, nodes communicate through a cooperative network in which other nodes function as relays. Since resources are frequently constrained, incentives must be provided to entice nodes to relay. The correct amount of incentive is essential to the efficient and optimal operation of the network. Excessive incentive results in widespread altruism, leading to diminished system lifetime. In contrast, insufficient incentive leads to selfish nodes that dramatically decrease network throughput. Nodes are assumed to be rational, and seek to maximize their own utilities. We use virtual credits as the incentive to stimulate cooperative behavior between nodes. The result of this research is fee arbitrated incentive architecture (FAIR), an application layer protocol that enhances fairness and collaboration in ad hoc wireless networks. FAIR utilizes autonomous feedback mechanisms to configure itself to the changing demands of the nodes, users, and network conditions. PriceSim, a network simulator implementing the FAIR protocol, was created to evaluate FAIR's performance.