Humans watch more than a billion hours of video per day. Most of this video
was edited manually, which is a tedious process. However, AI-enabled
video-generation and video-editing is on the rise. Building on text-to-image
models like Stable Diffusion and Imagen, generative AI has improved
dramatically on video tasks. But it's hard to evaluate progress in these video
tasks because there is no standard benchmark. So, we propose a new dataset for
text-guided video editing (TGVE), and we run a competition at CVPR to evaluate
models on our TGVE dataset. In this paper we present a retrospective on the
competition and describe the winning method. The competition dataset is
available at this https URL

利用 AI 实现文本引导下的视频编辑的方法在该研究中进行了描绘和竞赛评估，并提出了一种新的数据集用于文本引导下的视频编辑任务。

CVPR 2023 文本引导的视频编辑竞赛

CVPR 2023 Text Guided Video Editing Competition

Differentiating malware is important to determine their behaviors and level
of threat; as well as to devise defensive strategy against them. In response,
various anti-malware systems have been developed to distinguish between
different malwares. However, most of the recent malware families are Artificial
Intelligence (AI) enable and can deceive traditional anti-malware systems using
different obfuscation techniques. Therefore, only AI-enabled anti-malware
system is robust against these techniques and can detect different features in
the malware files that aid in malicious activities. In this study we review two
AI-enabled techniques for detecting malware in Windows and Android operating
system, respectively. Both the techniques achieved perfect accuracy in
detecting various malware families.

该研究评估了两种基于人工智能技术的恶意软件检测技术，分别适用于 Windows 和 Android 操作系统，结果表明这两种技术均可高度精确地检测出不同家族的恶意软件。

基于深度学习的 Android 和 Windows 系统恶意软件检测综述

Review of Deep Learning-based Malware Detection for Android and Windows  System

With the proliferation of open-sourced Large Language Models (LLMs) and
efficient finetuning techniques, we are on the cusp of the emergence of
numerous domain-specific LLMs that have been finetuned for expertise across
specialized fields and applications for which the current general-purpose LLMs
are unsuitable. In academia, this technology has the potential to revolutionize
the way we conduct systematic literature reviews (SLRs), access knowledge and
generate new insights. This paper proposes an AI-enabled methodological
framework that combines the power of LLMs with the rigorous reporting
guidelines of the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA). By finetuning LLMs on domain-specific academic papers
that have been selected as a result of a rigorous SLR process, the proposed
PRISMA-DFLLM (for Domain-specific Finetuned LLMs) reporting guidelines offer
the potential to achieve greater efficiency, reusability and scalability, while
also opening the potential for conducting incremental living systematic reviews
with the aid of LLMs. Additionally, the proposed approach for leveraging LLMs
for SLRs enables the dissemination of finetuned models, empowering researchers
to accelerate advancements and democratize cutting-edge research. This paper
presents the case for the feasibility of finetuned LLMs to support rigorous
SLRs and the technical requirements for realizing this. This work then proposes
the extended PRISMA-DFLLM checklist of reporting guidelines as well as the
advantages, challenges, and potential implications of implementing
PRISMA-DFLLM. Finally, a future research roadmap to develop this line of
AI-enabled SLRs is presented, paving the way for a new era of evidence
synthesis and knowledge discovery.

本文提出了一种结合大型语言模型和系统性文献综述的方法论框架 PRISMA-DFLLM，以实现更高效、可重复性和可扩展性的文献综述并开展增量式的无限制系统性文献综述。

PRISMA-DFLLM: 一种基于领域特定调优大语言模型的系统文献综述扩展方法

PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews  using Domain-specific Finetuned Large Language Models

DECICE is a Horizon Europe project that is developing an AI-enabled open and
portable management framework for automatic and adaptive optimization and
deployment of applications in computing continuum encompassing from IoT sensors
on the Edge to large-scale Cloud / HPC computing infrastructures. In this
paper, we describe the DECICE framework and architecture. Furthermore, we
highlight use-cases for framework evaluation: intelligent traffic intersection,
magnetic resonance imaging, and emergency response.

该论文介绍了 DECICE 框架和体系架构，针对智能交通，磁共振成像和应急响应等场景进行了框架评估。

DECICE：设备边缘云智能协同框架

DECICE: Device-Edge-Cloud Intelligent Collaboration Framework

A clinical trial is an essential step in drug development, which is often
costly and time-consuming. In silico trials are clinical trials conducted
digitally through simulation and modeling as an alternative to traditional
clinical trials. AI-enabled in silico trials can increase the case group size
by creating virtual cohorts as controls. In addition, it also enables
automation and optimization of trial design and predicts the trial success
rate. This article systematically reviews papers under three main topics:
clinical simulation, individualized predictive modeling, and computer-aided
trial design. We focus on how machine learning (ML) may be applied in these
applications. In particular, we present the machine learning problem
formulation and available data sources for each task. We end with discussing
the challenges and opportunities of AI for in silico trials in real-world
applications.

本文系统综述了三个主要主题：临床模拟，个体化预测建模和计算机辅助试验设计，重点关注机器学习在这些应用中的应用，讨论了 AI 在实际应用中 in silico 试验的挑战和机遇。