Generative text-to-image models have gained great popularity among the public
for their powerful capability to generate high-quality images based on natural
language prompts. However, developing effective prompts for desired images can
be challenging due to the complexity and ambiguity of natural language. This
research proposes PromptMagician, a visual analysis system that helps users
explore the image results and refine the input prompts. The backbone of our
system is a prompt recommendation model that takes user prompts as input,
retrieves similar prompt-image pairs from DiffusionDB, and identifies special
(important and relevant) prompt keywords. To facilitate interactive prompt
refinement, PromptMagician introduces a multi-level visualization for the
cross-modal embedding of the retrieved images and recommended keywords, and
supports users in specifying multiple criteria for personalized exploration.
Two usage scenarios, a user study, and expert interviews demonstrate the
effectiveness and usability of our system, suggesting it facilitates prompt
engineering and improves the creativity support of the generative text-to-image
model.

开发有效的提示对于生成高质量图像的强大能力具有挑战性，因此本研究提出了 PromptMagician，一个视觉分析系统，通过推荐模型和多层次可视化来帮助用户探索和优化生成图像的输入提示。研究通过用户研究和专家访谈证明了该系统的有效性和可用性，从而改善生成图文模型的创造力支持。

PromptMagician：文本到图像创作的交互式提示工程

PromptMagician: Interactive Prompt Engineering for Text-to-Image  Creation

The problem of revealing botnet activity through Domain Generation Algorithm
(DGA) detection seems to be solved, considering that available deep learning
classifiers achieve accuracies of over 99.9%. However, these classifiers
provide a false sense of security as they are heavily biased and allow for
trivial detection bypass. In this work, we leverage explainable artificial
intelligence (XAI) methods to analyze the reasoning of deep learning
classifiers and to systematically reveal such biases. We show that eliminating
these biases from DGA classifiers considerably deteriorates their performance.
Nevertheless we are able to design a context-aware detection system that is
free of the identified biases and maintains the detection rate of state-of-the
art deep learning classifiers. In this context, we propose a visual analysis
system that helps to better understand a classifier's reasoning, thereby
increasing trust in and transparency of detection methods and facilitating
decision-making.

通过解释性人工智能方法，我们分析深度学习分类器的推理过程，揭示其偏差，并设计了一个去除偏差的上下文感知检测系统，保持了最先进深度学习分类器的检测率，并提出了一个可视化分析系统，增强了对检测方法的理解、信任和透明度，促进决策制定。

虚假的安全感：利用 XAI 分析无上下文 DGA 分类器的推理和真实性能

False Sense of Security: Leveraging XAI to Analyze the Reasoning and  True Performance of Context-less DGA Classifiers

A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays,
more and more investors and collectors (NFT traders), who participate in
transactions of NFTs, have an urgent need to assess the performance of NFTs.
However, there are two challenges for NFT traders when analyzing the
performance of NFT. First, the current rarity models have flaws and are
sometimes not convincing. In addition, NFT performance is dependent on multiple
factors, such as images (high-dimensional data), history transactions
(network), and market evolution (time series). It is difficult to take
comprehensive consideration and analyze NFT performance efficiently. To address
these challenges, we propose NFTVis, a visual analysis system that facilitates
assessing individual NFT performance. A new NFT rarity model is proposed to
quantify NFTs with images. Four well-coordinated views are designed to
represent the various factors affecting the performance of the NFT. Finally, we
evaluate the usefulness and effectiveness of our system using two case studies
and user studies.

本文设计了一个视觉分析系统 NFTVis 来量化和评估 NFT 的性能，这个系统包含了一个新的 NFT 稀有度模型和四个协调一致的视图来表示影响 NFT 性能的各种因素，并使用两个案例研究和用户研究来验证其效用和有效性。