Knowledge Graph-based Question Answering (KGQA) plays a pivotal role in complex reasoning tasks but remains constrained by two persistent challenges: the structural heterogeneity of Knowledge Graphs (KGs) often leads to semantic mismatch during retrieval, while existing reasoning path retrieval methods lack a global structural perspective.To address these issues, we propose Structure-Tracing Evidence Mining (STEM), a novel framework that reframes multi-hop reasoning as a schema-guided graph search task.First, we design a Semanticto-Structural Projection pipeline that leverages KG structural priors to decompose queries into atomic relational assertions and construct an adaptive query schema graph.Subsequently, we execute globally-aware node anchoring and subgraph retrieval to obtain the final evidence reasoning graph from KG.To more effectively integrate global structural information during the graph construction process, we design a Triple-Dependent GNN (Triple-GNN) to generate a Global Guidance Subgraph (Guidance Graph) that guides the construction.STEM significantly improves both the accuracy and evidence completeness of multi-hop reasoning graph retrieval, and achieves State-of-the-Art performance on multiple multi-hop benchmarks.Our source code is available at https: //github.com/PennyYu123/STEM_RAG.

In wireless electric vehicle charging systems, misalignments between a ground-assembled transmitter and a vehicle-assembled receiver are undesirable but inevitable, which may cause efficiency deterioration and even thermal risks. The recognition of horizontal misalignment, including that in the longitudinal and lateral direction, has made gradual progress in previous works. However, yaw angle, with rare attention, can also degrade the charging performance, especially for those employing noncentrosymmetric coupling coils, such as rectangular coils. In this article, a data-driven strategy based on an improved residual network (ResNet) is proposed to recognize the above multitype misalignments simultaneously, which is accurate, reliable, and of practical and general value. Large-scale data are sampled using the proposed detection coils by double-group cooperation, which features high sensitivity to misalignments of all kinds, especially yaw angle. Benefitting from the compression technique in the preprocessing phase, the number of training data and labels can be successfully reduced by three-fourths. As the core algorithm of the overall strategy, the adaptive channel parameter recalibration ResNet can effectively perceive slight differences among similar input samples and map them to proper labels. This is achieved by an adaptive operation of nonlinear transformation and recalibrated important channel features during the training process. The effectiveness of the proposed strategy is verified experimentally using a 6.6-kW prototype. Within the 24×24 cm range, 95.2% of the test cases have an error of less than 1.7 cm when recognizing horizontal misalignment, and 91.7% of the test cases have an error of less than 1.5° when recognizing yaw angle.

In Pub/Sub systems, channel-based approaches to routing the subscriptions and events have many advantages such as fewer routing messages, lower costs for subscription management, etc. But a potential issue embedded in this kind of approach, i.e. loadings on different event brokers are apt to unbalancing, is ignored more or less. In this paper, we design a load balancing mechanism and integrate it into a channel-based approach in a Pub/Sub system. In particular, we define a balancing state in a Pub/Sub system, and then propose the balancing control initiation algorithm which decides not only whether to perform load balancing among event brokers but also whether to adjust the number of event brokers. Also we present the load scheduling algorithm which can achieve load balancing by channel splitting, merging and migration. We conduct the experiments by taking loads with different distributions as input to reveal the capability of dealing with changing loads. The experimental data prove that our mechanism can help balance the system loads efficiently and dynamically start or shut down event brokers when facing overloads or insufficient loads.

For constructing a Pub/Sub middleware to detect and disseminate events in the Internet of Things (IoT), we have to pay close attention to the following requirements: (1) the IoT applications usually care about the events with both spatial and temporal extents, (2) It must address the issues brought by the IoT features, such as autonomy embedded in collaboration among things, dynamics implied by running environments. In this paper, we provide a concise and expressive formulation for the events in IoT, i.e. complex spatio-temporal events. We also present the architecture of the corresponding Pub/Sub middleware, which aims at achieving the desired functionality and efficiency.

Pub/Sub systems permit users to submit subscriptions and notify interested users of the events detected in a distributed way. Moving a Pub/Sub system to a cloud infrastructure is for high performance and scalability. This paper describes how to migrate two Pub/Sub systems i.e. PADRES and Once Pub Sub to Xen Cloud Platform, especially proposes black-box method, grey-box method and white-box method so as to take full advantage of cloud mechanisms. This paper then conducts a series of experiments on the Pub/Sub systems in the cloud to evaluate benefits and costs. The experimental results indicate that the black-box method does not always take effect although it can be implemented easily, the grey-box method is more appropriate to a Pub/Sub system if its workload features and brokers' roles are known in advance. Further, the experimental results show the white-box method, combined the load balance mechanism both in the cloud and in a Pub/Sub system, can achieve satisfying performance and scalability especially facing the workload with unidentified distribution.