The knowledge graph is the essential infrastructure of plenty of intelligent Web applications. There are various types of knowledge graphs designed and deployed to make the applications smarter during the past decade. However, the increasing amount of product data brings new challenges to the query and retrieval of products. In this work, we present a product knowledge representation method that converts text into IDs to reduce the data transmission time. In order to decrease the retrieval time, we transform the join operation in the relational database into a matrix operation. We propose a pipeline query optimization strategy under GPUs to speed up the execution of the query. To evaluate the performance, we compare our method with the state-of-the-art RDF engine RDF-3X and gStore on the large-scale product datasets. The experimental results show that our approach can significantly improve the efficiency of the SPARQL query.
Time series data is widespread in real-world scenarios. To recover and infer missing information in practical domains, such as stock price monitoring, electricity load forecasting, traffic flows analysis, climate trend prediction, etc., the problem of time series prediction has been widely studied as a classical research topic in data mining. Over the past decade, deep learning architectures are introduced as a vital part of the next generation of time series prediction models. However, recent studies showed that deep learning models are vulnerable to adversarial attacks. In this paper, we study the adversarial attacks on the time series prediction models prospectively. We propose an attack strategy to generate adversarial samples by adding imperceptible perturbed data to the original time series with the goal of reducing the accuracy of time series prediction models. Specifically, the perturbation-based adversarial example generation algorithm is proposed using gradient information of time series prediction model. Moreover, adversarial examples should be imperceptible to humans. To address the challenge, we craft adversarial samples based on importance measuring to perturb the original data locally. We evaluate our attacks on state-of-the-art time series prediction models using three time series datasets. Our results demonstrate that our attacks can effectively evade the time series prediction models, and the adversarial attacks mechanisms can be used as robustness metric for constructing robust time series prediction models.
In this paper, using the advantages of convolutional neural networks in feature extraction, we design feature extraction modules. According to the different fusion positions of the extracted features in the network, several feature fusion schemes are designed to perform global feature fusion on infrared images and visible light images. A detection model based on the regression of the center point and the target scale is proposed. The fused feature map is used as the input of the object detection module to obtain the detection in the current image field of view. It effectively solves the problem of poor performance of visible light images in object detection caused by light intensity and weather conditions.
The rapid development of science and technology and the comprehensive application of information technology have brought electronic information engineering into the fast lane of development, and has also continuously improved the overall level of my country's electronic information engineering. Computer communication technology is an indispensable and important part of electronic information engineering, and there is a close relationship between the two in mutual influence and coordinated development. Strengthening the comprehensive application of computer communication technology in electronic information engineering can significantly improve the stability and efficiency of data and information transmission, and greatly improve people's daily life. It can have a positive meaning for the overall construction and development of society. To this end, it must be set out from a global perspective, consider how to improve the application effect of computer communication technology in electronic information engineering. Based on this, this article briefly analyzes computer communication technology and electronic information engineering, explores the development history, development status and development trend of computer communication technology, and analyzes the application ideas of computer communication technology in electronic information engineering.
Abstract:The traditional data construction in the military field mainly focuses on the vertical business field, realizing the digitization of the military business and failing to meet the application requirements of horizontal data fusion.This paper realizes the design of a special data encyclopedia system in the military field, and uses advanced big data processing technologies such as web crawler, knowledge spectrum, and full-text retrieval to realize the horizontal integration of data in the military field, mine the value of data in the military field, and improve the practicability and timeliness of data.
