We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5’s proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment.

Object detection on unmanned aerial vehicles (DAVs)-captured scenarios play an essential role in several applications such as surveillance, environmental monitoring, security, disaster response strategies, and construction of transportation systems. Images captured by DAVs are all overhead vision including too many small objects, which are difficult to detect. Besides, the high-speed and low-altitude flight process of DAVs brings in the motion blur on the densely packed objects. The average, scale transformation and scene coverage are large, which brings difficulties in extracting and identifying useful information. To address these challenges, we propose a lightweight detection model named MA-YOLO. This article has made the following improvements based on YOLOv5:1)a multi-directional weighted pyramid structure (MiFPN) is proposed for fusing information of different scales and improves the ability to detect small objects.2) A learning-capable decoupling head (AD-head) is proposed to obtain small object information in a complex environment. Extensive experiments are conducted on the challenging VisDrone-DET2021 dataset to evaluate the performance of MA-YOLO. The obtained results show that the accuracy is better than other detection algorithms and the detection speed of VisDrone-DET2021 is improved from 85FPS to 109FPS. Thus, the MA-YOLO method pursues a trade-off between speed and accuracy compared to the state-of-the-art small object detection methods and ensures practicality on drones.

The defect detection of Printed Circuit boards (PCB) is challenging due to the complex image background, various types of defects, and small size of defects. This paper develops and evaluates the SC-YOLOv5 network for accurately detecting printed circuit boards. First, we combine the spatial attention mechanism of SA with the Efficient Channel Attention Mechanism (ECA-Net) channel attention module to construct the hybrid attention mechanism module (SCA). SCA has higher defect feature expression ability and doesn't need dimensionality reduction. Second, we analyze the feature pyramid structure of YOLOv5 and construct a multi-direction dilated convolution module (MD) for the last feature layer. MD has a rich receptive field so that MD can retain more defect information during the feature pyramid downsampling process. We perform experimental evaluations on PCB Dataset and DeepPCB datasets. Experiments show that SC-YOLOv5 improves mAP by 1.9% and 1.6 %, respectively, on the two datasets, and the detection speed can reach 120FPS. Compared with the mainstream indication defect detection algorithms, SC-YOLOv5 significantly improves accuracy.

Many mechanisms, such as epidemic spread, rumor spread, and the spread of social emergencies, are closely related to complex network dynamics, and mining their key nodes plays an important role in understanding the structure and function of the network and maintaining its stable operation. In response to the problem that the key node identification methods in complex networks cannot comprehensively consider global and local information and ignore low-degree nodes, this study proposes a new method based on the voting mechanism. Firstly, the CI value of the network nodes is calculated using the CI algorithm, and initialized the voting ability of nodes by CI values, fully considering the local information of the nodes as well as the influence of low-degree nodes. Secondly, the concept of voting probability is introduced to distinguish the votes of network nodes for their different neighboring nodes through the voting probability, to consider more local information, and to comprehensively assess the importance of the nodes, and ultimately, it is more important to get nodes with the larger voting score. Comparing several classical key node identification methods, the experimental results show that this method can effectively identify key nodes and has a high accuracy rate in different complex networks.

Session-based recommendations already play an important role in platforms such as e-commerce and streaming media, which are designed to predict the next interaction item based on a given session. Most of the current recommendation models only use the interaction sequence of the session to capture the potential conversion patterns between items, often ignore the user’s multi-type interaction behavior that reflects the user’s fine-grained preferences. At present, most models of multi-type interaction behaviors only learn user-item multi-type interaction behaviors and item-item dependencies relatively independently, and ignore the problems of item cold start and data sparsity. These issues motivate us to propose a new model MKGTN in this paper, we apply multi-type user-item interaction behaviors and item-item dependencies to session recommendation via MLP. Simultaneously, using a multi-task learning MLT paradigm involving learning knowledge embeddings as an auxiliary task to facilitate the main task of SR. Evaluations on three datasets show that MKGTN outperforms state-of-the-art multi-action interaction models, demonstrating the superiority of our model’s performance.

Knowledge graph representation learning aims to obtain its vector representation by mapping entities and relations in knowledge graphs to a continuous low-dimensional vector space by learning methods. Most of the existing knowledge graph representation learning methods only consider the single-step relation between entities from the perspective of triples and fail to effectively utilize important information such as ordered multi-step relation paths and entity descriptions, thus affecting the ability of knowledge representation learning. We propose a knowledge graph representation learning model that integrates ordered relation paths and entity descriptions in response to the above problems. The model can integrate the triple representation in the knowledge graph, the semantic representation of entity description, and the representation of ordered relation paths for training. On the FB15K, WN18, FB15K-237, and WN18RR datasets, the proposed model and baselines are run on the link prediction task. Experimental results show that the model has higher accuracy than existing baselines, demonstrating the effectiveness and superiority of the method.

Knowledge graphs use triples containing head entity h, tail entity t, and relation r to represent real-world entities and their intrinsic relationships. In order to predict the actual missing triples in the knowledge graph, combine the strong triple representation ability of relational memory network with the powerful feature processing ability of capsule network, and use disperse dynamic routing that can improve the performance of capsule network, we propose a knowledge graph embedding model named RDMCapsE. First, the embedding vector is formed by encoding potential dependencies between entities and relations; then, different feature maps are generated using convolutional kernels with different window sizes and then reorganized into corresponding capsules; finally, the connection from the parent capsule to the child capsule is specified by the squash function and the disperse dynamic routing, and the credibility of the current triple is judged based on the score of the inner product of the child capsule and the weights. The experimental results show that compared with other models, the model in this paper can effectively improve the effect of knowledge graph completion and the classification accuracy of triples on WN18RR, FB15K-237, WN11, and FB13 datasets.

With the advent of the big data era, traditional industrial production and industrial manufacturing begin to turn to intelligent manufacturing, workshop production process becomes more intelligent and automated, multi-objective job shop complex networks model based on data information, one of the new fields of intelligent manufacturing research. The main contents of this paper are two points: One is to build a multiobjective job shop complex networks model using data information; Secondly, on the basis of the model, the key node of the model is found by using the fuzzy network analysis method, and the result of the key node evaluation is more objective by choosing the fuzzy network analysis method. Finally, the simulation results show that the model can be well applied to actual industrial production, and the fuzzy network analysis method can effectively mine the key nodes in the model, which proves the rationality and effectiveness of the method.

Based on the information system of Shanhua Company, this paper discusses the structure and function of intrusion detection system based on RBF, the steps and method of intrusion detection. In the experiment of network simulation, through continuous training of input normal samples and abnormal sample, keeping an eye on if the RBF neural networks can distinguish the known intrusion behavior character among the training samples with high exactness and distinguish new intrusion behavior character and mutation of known intrusion behavior character with some probability. The result of experiment proves that RBF network is better than BP network in its property of optimal approximation, classify ability and the rapidity of study, RBF can improve the detection performances of IDS.