Reconstructing the antemortem appearance of unknown human skulls, referred to as craniofacial reconstruction, is an important research topic in forensic science and criminal investigation. In response to the high data requirements, tedious acquisition steps, high storage costs, high demands for network design and training, and low accuracy of three-dimensional craniofacial reconstruction methods, this paper proposes an automated and intelligent craniofacial translation network called Skull2Skin-GAN. The generator replaces ordinary convolutions with depthwise separable convolutions based on U-Net to reduce model volume. The discriminator adopts random non-permeable data augmentation to adapt to this network structure. The SCUT-FBP5500 facial beauty prediction Dataset is introduced to calculate fine feature-aware losses. Finally, the GFPGAN was introduced to perform super-resolution reconstruction on the generated faces, restoring high-quality faces with rich geometric shape, texture, color from low-quality human faces. Experimental results show that under small samples, this network can quickly and easily generate clear and high-fidelity facial images from skull images, and can identify identity.

Hypergraphs are generalizations of graphs where the (hyper)edges can connect any number of vertices. They are powerful tools for representing complex and non-pairwise relationships. However, existing graph computation frameworks cannot accommodate hypergraphs without converting them into graphs, because they do not offer APIs that support (hyper)edges directly. This graph conversion may create excessive replicas and result in very large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a general-purpose distributed hypergraph processing framework built on top of Spark. HyperX is based on the computation paradigm “Pregel”, which is user-friendly and has been widely adopted by popular graph computation frameworks. To help create balanced workloads for distributed hypergraph processing, we further investigate the hypergraph partitioning problem and propose a novel label propagation partitioning (LPP) algorithm. We conduct extensive experiments using both real and synthetic data. The result shows that HyperX achieves an order of magnitude improvement for running hypergraph learning algorithms compared with graph conversion based approaches in terms of running time, network communication costs, and memory consumption. For hypergraph partitioning, LPP outperforms the baseline algorithms significantly in these measures as well.