Exploring tourism networks in the Guangxi mountainous area using mobility data from user generated content

Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism literature lacks empirical evidence of the tourism network in lessdeveloped mountainous regions where the development of transport infrastructure is more variable.This paper aims to provide such evidence using Guangxi Zhuang Autonomous Region in China as a case study.Using User Generated Content(UGC)data,this study constructs a tourism network in Guangxi.By integrating social network analysis with spatial interaction modelling,we compared the impact of two different transport infrastructures,highway and high-speed railway,on tourist flows,particularly in less-developed mountainous regions.It was found that the product of node centrality and flow could best describe the significant pushing and pulling forces on the flow of tourists.The tourism by high-speed railway was sensitive to the position of trip destination on the whole tourism network but self-drive tourism was more sensitive to travelling time.The increase of high-speed railway density is crucial to promote local tourism-led economic development,however,large-scale karst landforms in the study area present a significant obstacle to the construction of high-speed railways.

基于精确扩散反演的生成式图像内生水印方法

扩散模型在图像生成方面取得了显著成就,但生成的图像真假难辨,因此滥用扩散模型将引发隐私安全、法律伦理等社会问题。对生成模型的输出添加水印可以追踪生成内容版权,防止人工智能生成内容造成潜在危害。对于去噪扩散模型,在初始噪声向量中添加水印的内生水印方法可直接生成含水印图像,版权验证时通过反向扩散重建初始向量以提取水印。但扩散模型中的采样过程并不是严格可逆,重建的噪声向量与原始噪声存在较大误差,很难保证水印的准确提取。通过引入基于耦合变换的精确反向扩散,可以更加准确地重建初始噪声向量,提升水印提取的准确性。通过实验验证了引入基于耦合变换的精确反向扩散对于生成式图像内生水印的性能提升,实验结果表明,内生水印可以在生成图像中嵌入不可见水印,嵌入的水印可通过精确反向扩散被准确提取,并具有一定的稳健性。

Cogeneration of Innovative Audio-visual Content: A New Challenge for Computing Art

In recent years,computing art has developed rapidly with the in-depth cross study of artificial intelligence generated con-tent(AIGC)and the main features of artworks.Audio-visual content generation has gradually been applied to various practical tasks,including video or game score,assisting artists in creation,art education and other aspects,which demonstrates a broad application pro-spect.In this paper,we introduce innovative achievements in audio-visual content generation from the perspective of visual art genera-tion and auditory art generation based on artificial intelligence(Al).We outline the development tendency of image and music datasets,visual and auditory content modelling,and related automatic generation systems.The objective and subjective evaluation of generated samples plays an important role in the measurement of algorithm performance.We provide a cogeneration mechanism of audio-visual content in multimodal tasks from image to music and display the construction of specific stylized datasets.There are still many new op-portunities and challenges in the field of audio-visual synesthesia generation,and we provide a comprehensive discussion on them.

Relationship between scores and tags for Chinese books—In the case of Douban Book

Purpose:Currently,social tagging behavior,including social tag,online review and score information,has been investigated extensively,however,there are very few works about the relationship among them.In this paper,we have investigated the problem using Douban Website as the research object.Design/methodology/approach:Firstly,we divided social tags into those with high and low frequency counts,respectively,divided books into popular and unpopular books according to books’popularity,and chose core tags in terms of distribution;Secondly,we conducted an investigation on the relationship between social tags and books scores including comprehensive analyses and assorted analyses.Findings:The more popular the books become,the higher scores they will get.Tag frequency is not related with book scores directly,and neither does the tag distribution weight.Tags in books of'fashion'category are relatively disordered,which may associate with books miscellany and readers diversity.Research limitations:Social tags are growing dramatically,strategies and researches to this respect are just experimental exploration.Open source books,data and educational resources are not consummate.Comparative studies are necessary,but the result may be affected by researches based on data analyses.In addition,this research has been conducted only on one website,namely Douban,and the tags provided by Douban Book are not complete.All these factors could influence the versatility of the results.Practical implications:There are very a few studies that have been conducted on the relationship between tags and scores,and this research could bring a certain practical significance to popular books prediction and tags’quality research.Originality/value:Less attention has been paid to Chinese books while analyzing relationship between scores and tags of user generated content.Analyses based on the Chinese books may fill in the gap of better understanding the relationship between the two objects.

Prompt learning in computer vision: a survey

Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.

AIGC challenges and opportunities related to public safety:A case study of ChatGPT

Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intelligence,generative AI,as exemplified by ChatGPT,exhibits characteristics that increasingly resemble human-level comprehension and creation processes.This paper provides a detailed technical framework and history of ChatGPT,followed by an examination of the challenges posed to political security,military security,economic security,cultural security,social security,ethical security,legal security,machine escape problems,and information leakage.Finally,this paper discusses the potential opportunities that AIGC presents in the realms of politics,military,cybersecurity,society,and public safety education.