The Situational Instructions Database (SID) addresses the need for enhanced
situational awareness in artificial intelligence (AI) systems operating in
dynamic environments. By integrating detailed scene graphs with dynamically
generated, task-specific instructions, SID provides a novel dataset that allows
AI systems to perform complex, real-world tasks with improved context
sensitivity and operational accuracy. This dataset leverages advanced
generative models to simulate a variety of realistic scenarios based on the 3D
Semantic Scene Graphs (3DSSG) dataset, enriching it with scenario-specific
information that details environmental interactions and tasks. SID facilitates
the development of AI applications that can adapt to new and evolving
conditions without extensive retraining, supporting research in autonomous
technology and AI-driven decision-making processes. This dataset is
instrumental in developing robust, context-aware AI agents capable of
effectively navigating and responding to unpredictable settings. Available for
research and development, SID serves as a critical resource for advancing the
capabilities of intelligent systems in complex environments. Dataset available
at https://github.com/mindgarage/situational-instructions-database.

Situational Instructions Database (SID) 通过将具体场景图与动态生成的任务专用指令相结合，提供了一个新颖数据集，使得 AI 系统能够在具备改进的情境感知和操作精度的基础上执行复杂的现实世界任务。该数据集利用先进的生成模型，在基于 3D 语义场景图（3DSSG）数据集的基础上模拟了各种逼真的场景，并通过提供场景特定信息，详细描述了环境相互作用和任务。SID 有助于开发能够适应新的和不断变化条件的 AI 应用程序，支持自主技术和基于 AI 的决策过程的研究。该数据集对于开发在复杂环境中具备强大上下文感知能力的智能系统至关重要。数据集可在 https://github.com/mindgarage/situational-instructions-database 获取。

情境指令数据库：动态环境中的任务指导

Situational Instructions Database: Task Guidance in Dynamic Environments

Two fundamental questions in neurolinguistics concerns the brain regions that
integrate information beyond the lexical level, and the size of their window of
integration. To address these questions we introduce a new approach named
masked-attention generation. It uses GPT-2 transformers to generate word
embeddings that capture a fixed amount of contextual information. We then
tested whether these embeddings could predict fMRI brain activity in humans
listening to naturalistic text. The results showed that most of the cortex
within the language network is sensitive to contextual information, and that
the right hemisphere is more sensitive to longer contexts than the left.
Masked-attention generation supports previous analyses of context-sensitivity
in the brain, and complements them by quantifying the window size of context
integration per voxel.

利用 GPT-2 变形金刚生成的单词嵌入来测试大脑对自然文本中背景信息的反应，结果表明语言网络中的大多数皮层对背景信息敏感，右半球对于较长场景的反应比左半球更敏感，支持以前的上下文敏感度分析并有助于量化每个体素的上下文整合窗口大小。

使用遮蔽注意力生成探测大脑上下文敏感性

Probing Brain Context-Sensitivity with Masked-Attention Generation

AI-Augmented Business Process Management Systems (ABPMSs) are an emerging
class of process-aware information systems, empowered by trustworthy AI
technology. An ABPMS enhances the execution of business processes with the aim
of making these processes more adaptable, proactive, explainable, and
context-sensitive. This manifesto presents a vision for ABPMSs and discusses
research challenges that need to be surmounted to realize this vision. To this
end, we define the concept of ABPMS, we outline the lifecycle of processes
within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a
set of challenges to realize systems with these characteristics.

本研究提出了 AI-Augmented Business Process Management Systems (ABPMSs) 的概念，并讨论了需要克服的研究挑战，以实现提高业务流程执行的可适应性、主动性、可解释性和上下文敏感性的目标。