The modern surge in camera usage alongside widespread computer vision
technology applications poses significant privacy and security concerns.
Current artificial intelligence (AI) technologies aid in recognizing relevant
events and assisting in daily tasks in homes, offices, hospitals, etc. The need
to access or process personal information for these purposes raises privacy
concerns. While software-level solutions like face de-identification provide a
good privacy/utility trade-off, they present vulnerabilities to sniffing
attacks. In this paper, we propose a hardware-level face de-identification
method to solve this vulnerability. Specifically, our approach first learns an
optical encoder along with a regression model to obtain a face heatmap while
hiding the face identity from the source image. We also propose an
anonymization framework that generates a new face using the privacy-preserving
image, face heatmap, and a reference face image from a public dataset as input.
We validate our approach with extensive simulations and hardware experiments.

通过硬件级别的人脸去识别方法，解决了软件级别的人脸去识别在隐私安全上的漏洞问题。

面部去识别中提升保护的隐私保护光学技术

Privacy-preserving Optics for Enhancing Protection in Face  De-identification

In 2024, we will hold a research paper competition (the third Human
Understanding AI Paper Challenge) for the research and development of
artificial intelligence technologies to understand human daily life. This
document introduces the datasets that will be provided to participants in the
competition, and summarizes the issues to consider in data processing and
learning model development.

2024 年我们将举办第三届人工智能研究论文竞赛，旨在研发能够理解人类日常生活的人工智能技术。本文介绍了竞赛参与者将获得的数据集，并总结了数据处理和学习模型开发中需要考虑的问题。

2024 年人类理解 AI 论文挑战赛 -- 数据集设计

Human Understanding AI Paper Challenge 2024 -- Dataset Design

This brief discusses evolutionary game theory as a powerful and unified
mathematical tool to study evolution of collective behaviours. It summarises
some of my recent research directions using evolutionary game theory methods,
which include i) the analysis of statistical properties of the number of
(stable) equilibria in a random evolutionary game, and ii) the modelling of
safety behaviours' evolution and the risk posed by advanced Artificial
Intelligence technologies in a technology development race. Finally, it
includes an outlook and some suggestions for future researchers.

本文讨论了进化博弈理论作为一种强大而统一的数学工具，用于研究集体行为的演化，总结了作者最近通过进化博弈理论方法所涉及的一些研究方向，包括：i）随机进化博弈中（稳定）均衡数量的统计性质分析，以及 ii）在技术发展竞赛中建模安全行为的演化及先进人工智能技术带来的风险。最后，对未来研究者提出了展望和一些建议。

进化博弈论：进化与集体行为的数学

Evolutionary game theory: the mathematics of evolution and collective  behaviours

Massive emerging applications are driving demand for the ubiquitous
deployment of computing power today. This trend not only spurs the recent
popularity of the \emph{Computing and Network Convergence} (CNC), but also
introduces an urgent need for the intelligentization of a management platform
to coordinate changing resources and tasks in the CNC. Therefore, in this
article, we present the concept of an intelligence-endogenous management
platform for CNCs called \emph{CNC brain} based on artificial intelligence
technologies. It aims at efficiently and automatically matching the supply and
demand with high heterogeneity in a CNC via four key building blocks, i.e.,
perception, scheduling, adaptation, and governance, throughout the CNC's life
cycle. Their functionalities, goals, and challenges are presented. To examine
the effectiveness of the proposed concept and framework, we also implement a
prototype for the CNC brain based on a deep reinforcement learning technology.
Also, it is evaluated on a CNC testbed that integrates two open-source and
popular frameworks (OpenFaas and Kubernetes) and a real-world business dataset
provided by Microsoft Azure. The evaluation results prove the proposed method's
effectiveness in terms of resource utilization and performance. Finally, we
highlight the future research directions of the CNC brain.

基于人工智能技术，本文提出了一种用于计算和网络融合的智能内生管理平台 ——CNC 大脑，通过感知、调度、适应和治理等四个关键构建模块，在整个计算和网络融合生命周期中实现高异构性的供需匹配，通过在 OpenFaas、Kubernetes 和 Microsoft Azure 的真实业务数据集上进行评估，证明了该方法在资源利用和性能方面的有效性，并提出了 CNC 大脑的未来研究方向。