With the development of modern browsing, the convenience brought by rich browser features has also produced a large number of features, which are called browser fingerprints. This article surveys the latest research results on browser fingerprinting, hoping to provide a convenient navigation for newcomers to research or apply this technology in the future. This paper first briefly introduces the browser fingerprinting technology itself, then classifies the related research on browsers, and analyzes the development of different research directions of browser fingerprinting in detail. And through the analysis of the existing results, the problems faced by different research directions are pointed out. After that, this paper introduces the application of browser fingerprint technology in detail and discusses the application achievements and technical challenges of this technology. Next, this paper introduces the theoretical tools related to the research of browser fingerprinting technology and introduces the application of different theoretical tools and practical significance. Finally, the research achievements of browser fingerprint recognition are summarized, and the future development trend is pointed out.
Traffic prediction is an important component of intelligent transportation system. Since traffic data is typical spatiotemporal data with spatial attributes and temporal attributes, how to integrate the information of temporal and spatial dimension to model traffic data and make effective prediction is an important way to improve the prediction effect. In terms of temporal modeling, most of the existing research uses RNN-based methods, which cannot effectively capture long-term sequence features. In terms of spatial modeling, the GCN model is used to model the static spatial structure, which cannot accurately reflect the dynamic relationship between the nodes in the graph structure, and in the multi-layer structure, the prediction error of each layer is easy to spread through the gradient to generate error accumulation. In view of the above deficiencies, we propose a traffic prediction model based on dynamic temporal graph convolutional networks. For temporal attribute modeling, dilated causal convolution is used to construct temporal relationships, and the influence of global temporal features on the extraction of temporal relationships is considered. For modeling the spatial relationship, a dynamic adjacency matrix is obtained by learning the relationship between the nodes in the graph through the attention mechanism, so that the model can capture the dynamic relationship between the nodes. At the same time, a Translate module is added between each spatiotemporal layer to reduce the propagation of prediction errors between spatiotemporal modules of each layer. The experimental results show that on the METR-LA dataset and the XIAN-TAXI dataset, compared with other mainstream traffic prediction methods, Our model achieves better prediction performance.
This research paper investigates the impact of e-commerce on the offline retail industry, examining both the challenges and opportunities it presents. The research draws on various sources, including industry reports, academic literature, and a case study of Costco, to provide an in-depth analysis of the topic. The paper begins by exploring the evolution of e-commerce and its effects on offline retailers, followed by a discussion of strategies offline retailers can employ to adapt to the changing retail landscape. These strategies include adopting an omnichannel approach, enhancing in-store experiences, utilizing data analytics and AI, and fostering strategic partnerships. The paper concludes with an outlook on the future of the offline retail industry, suggesting that continual innovation and customer-centric approaches are key for success. The findings of this research can provide valuable insights for offline retailers seeking to navigate the rapidly evolving retail environment in the age of e-commerce.
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