The unique artistic style is crucial to artists' occupational
competitiveness, yet prevailing Art Commission Platforms rarely support
style-based retrieval. Meanwhile, the fast-growing generative AI techniques
aggravate artists' concerns about releasing personal artworks to public
platforms. To achieve artistic style-based retrieval without exposing personal
artworks, we propose FedStyle, a style-based federated learning crowdsourcing
framework. It allows artists to train local style models and share model
parameters rather than artworks for collaboration. However, most artists
possess a unique artistic style, resulting in severe model drift among them.
FedStyle addresses such extreme data heterogeneity by having artists learn
their abstract style representations and align with the server, rather than
merely aggregating model parameters lacking semantics. Besides, we introduce
contrastive learning to meticulously construct the style representation space,
pulling artworks with similar styles closer and keeping different ones apart in
the embedding space. Extensive experiments on the proposed datasets demonstrate
the superiority of FedStyle.

提出 FedStyle：一种基于风格的联邦学习众包框架，允许艺术家训练本地风格模型并共享模型参数以进行合作，从而实现基于艺术风格的检索。通过学习艺术家的抽象风格表示并与服务器对齐，FedStyle 解决了艺术家之间严重的模型漂移问题。此外，引入对比学习来精心构建风格表示空间，在嵌入空间中将具有类似风格的艺术品拉近，保持不同风格的艺术品分离。对提出的数据集进行的大量实验证明了 FedStyle 的优越性。

FedStyle: 基于风格的艺术委托联邦学习众包框架

FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art  Commissions

The prevalence of ubiquitous location-aware devices and mobile Internet
enables us to collect massive individual-level trajectory dataset from users.
Such trajectory big data bring new opportunities to human mobility research but
also raise public concerns with regard to location privacy. In this work, we
present the Conditional Adversarial Trajectory Synthesis (CATS), a
deep-learning-based GeoAI methodological framework for privacy-preserving
trajectory data generation and publication. CATS applies K-anonymity to the
underlying spatiotemporal distributions of human movements, which provides a
distributional-level strong privacy guarantee. By leveraging conditional
adversarial training on K-anonymized human mobility matrices, trajectory global
context learning using the attention-based mechanism, and recurrent bipartite
graph matching of adjacent trajectory points, CATS is able to reconstruct
trajectory topology from conditionally sampled locations and generate
high-quality individual-level synthetic trajectory data, which can serve as
supplements or alternatives to raw data for privacy-preserving trajectory data
publication. The experiment results on over 90k GPS trajectories show that our
method has a better performance in privacy preservation, spatiotemporal
characteristic preservation, and downstream utility compared with baseline
methods, which brings new insights into privacy-preserving human mobility
research using generative AI techniques and explores data ethics issues in
GIScience.

利用无处不在的位置感知设备和移动互联网收集用户的海量个体轨迹数据，基于深度学习的 GeoAI 方法论框架 —— 条件对抗轨迹合成（CATS）提供了保护隐私的轨迹数据生成和发布解决方案，该方法应用 K - 匿名来保护人类移动行为的时空分布，通过条件对抗训练、注意力机制和轨迹拓扑的重建，生成高质量的合成轨迹数据，同时提供更好的隐私保护性能和空间 - 时间特征保持，为基于生成性人工智能技术的隐私保护人类移动研究带来新的视角，并探讨地理信息科学中的数据伦理问题。

CATS: 条件对抗轨迹合成 用于隐私保护轨迹数据发布的深度学习方法

CATS: Conditional Adversarial Trajectory Synthesis for  Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches

The recent progress in generative AI techniques has significantly influenced
software engineering, as AI-driven methods tackle common developer challenges
such as code synthesis from descriptions, program repair, and natural language
summaries for existing programs. Large-scale language models (LLMs), like
OpenAI's Codex, are increasingly adopted in AI-driven software engineering.
ChatGPT, another LLM, has gained considerable attention for its potential as a
bot for discussing source code, suggesting changes, providing descriptions, and
generating code. To evaluate the practicality of LLMs as programming assistant
bots, it is essential to examine their performance on unseen problems and
various tasks.
In our paper, we conduct an empirical analysis of ChatGPT's potential as a
fully automated programming assistant, emphasizing code generation, program
repair, and code summarization. Our study assesses ChatGPT's performance on
common programming problems and compares it to state-of-the-art approaches
using two benchmarks. Our research indicates that ChatGPT effectively handles
typical programming challenges. However, we also discover the limitations in
its attention span: comprehensive descriptions can restrict ChatGPT's focus and
impede its ability to utilize its extensive knowledge for problem-solving.
Surprisingly, we find that ChatGPT's summary explanations of incorrect code
provide valuable insights into the developer's original intentions. This
insight can be served as a foundation for future work addressing the oracle
problem. Our study offers valuable perspectives on the development of LLMs for
programming assistance, specifically by highlighting the significance of prompt
engineering and enhancing our comprehension of ChatGPT's practical applications
in software engineering.

我们通过实证分析了 ChatGPT 在无人辅助编程助手方面的潜力，并强调了其相对程序生成、程序修复、代码摘要方面的表现，并对其在常见编程问题上的表现进行了评估，这证明 ChatGPT 有效地处理典型的编程挑战，但我们也发现，综合的描述可能会限制 ChatGPT 的关注点并阻碍其利用其广泛的知识进行问题解决。