Enabling on-device intelligence that supports AI inference, has become a main trend in mobile application development. To enhance intelligence on resource-limited smartphones in mobile cellular networks, edge computing offloads tasks to powerful servers. However, current AI inference models exhibit significant heterogeneity across various platforms, posing challenges to edge offloading implementation. The complexity of managing these heterogeneous models is exacerbated by the super-large scale of smartphones and the need for realtime information exchange. These challenges, stemming from prohibitive communication overheads, render current offloading approaches economically undesirable. This paper proposes LOSA, a decentralized task-offloading scheme for edge-assisted AI inference systems with three main components. First, a performance estimator to measure the gains in offloading diverse AI model execution to the edge. Second, a workload forecaster that predicts the future workload based on historical traces. Last, a scheduler determines the optimal place to execute the model by solving a stochastic optimization problem with local model performance and workload prediction. Simulation results show how LOSA outperforms the current solutions while maintaining low communication overhead.

With the rapid growth of low earth orbit (LEO) satellites, enabling LEO AI inference becomes a fast-increasing trend. However, due to resource heterogeneity, scheduling complexity, and fast movement, how to decide the place of executing each AI inference task is nontrivial in LEO systems. In this paper, we propose LEOEdge, an edge-assisted AI inference system for LEO satellites. We first introduce the adaptive modeling technologies that automatically generate the model for each satellite according to its computation resources. We then propose a layered scheduling optimization scheme to schedule the AI inference task in a distributed manner. LEOEdge also designs a seamless data transmission scheme to avoid transmission failure due to the LEO satellite movement. We conduct a series of simulation tests to validate the performance of the proposed LEOEdge, in terms of the neural network searching efficiency, average time execution latency, and delivery latency.

As healthcare popularity increases, more people use food therapy for nourishment and healing. However, without scientific guidance, it's difficult to select appropriate foods for specific needs. To address the issue, we extract knowledge from TCMSP and professional books and fuse the data from different sources. Next, the Food Therapy Knowledge Graph (FTKG) is constructed. Finally, a food therapy system is developed that integrates the concept of TCMSP and FTKG, which uses the efficient knowledge retrieval and knowledge reasoning ability of the knowledge graph. It provides scientific food therapy solutions by analyzing symptoms and substituting traditional Chinese medicine with food, s address individual health needs.

Traditional Chinese medicine (TCM) has a unique advantage of preventive treatment of diseases, and adopting the concept of early intervention can effectively prevent diseases. Using knowledge graph is an effective way while the knowledge in the field of TCM is huge and messy. However, the structure of the TCM knowledge graph is often relatively sparse, which makes it highly limited. To this end, a rule-based compositional representation learning (RCRL) model is proposed. RCRL uses the implicit rules in the TCM knowledge graph, which solves the problem of poor representation learning due to the sparse structure of the TCM knowledge graph to a certain extent. Extensive experiments are conducted on the TCM knowledge graph and public datasets, and they are compared with other baselines. Experimental results show that RCRL is superior to other baselines, with improved learning accuracy and interpretability, and can be used for various downstream tasks.