WiFi sensing technology has shown superiority in smart homes among various
sensors for its cost-effective and privacy-preserving merits. It is empowered
by Channel State Information (CSI) extracted from WiFi signals and advanced
machine learning models to analyze motion patterns in CSI. Many learning-based
models have been proposed for kinds of applications, but they severely suffer
from environmental dependency. Though domain adaptation methods have been
proposed to tackle this issue, it is not practical to collect high-quality,
well-segmented and balanced CSI samples in a new environment for adaptation
algorithms, but randomly-captured CSI samples can be easily collected.
{\color{black}In this paper, we firstly explore how to learn a robust model
from these low-quality CSI samples, and propose AutoFi, an annotation-efficient
WiFi sensing model based on a novel geometric self-supervised learning
algorithm.} The AutoFi fully utilizes unlabeled low-quality CSI samples that
are captured randomly, and then transfers the knowledge to specific tasks
defined by users, which is the first work to achieve cross-task transfer in
WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for
evaluation. The AutoFi transfers knowledge from randomly collected CSI samples
into human gait recognition and achieves state-of-the-art performance.
Furthermore, we simulate cross-task transfer using public datasets to further
demonstrate its capacity for cross-task learning. For the UT-HAR and Widar
datasets, the AutoFi achieves satisfactory results on activity recognition and
gesture recognition without any prior training. We believe that the AutoFi
takes a huge step toward automatic WiFi sensing without any developer
engagement.

本文提出了一种基于新颖的几何自监督学习算法的自动 WiFi 感知模型 AutoFi，该模型可以利用不需要额外标注的低质量 CSI 样本进行学习，并将学习的知识传递到特定的用户定义任务中进行交叉任务转移，从而实现自动的 WiFi 感知。在人类步态识别任务和公共数据集上，AutoFi 实现了领先的性能，并展示了跨任务学习的能力。

AutoFi：基于几何自监督学习的自动 WiFi 人体感知技术

AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent
years, which typically involves four fundamental sentiment elements, including
the aspect category, aspect term, opinion term, and sentiment polarity.
Existing studies usually consider the detection of partial sentiment elements,
instead of predicting the four elements in one shot. In this work, we introduce
the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all
sentiment elements in quads for a given opinionated sentence, which can reveal
a more comprehensive and complete aspect-level sentiment structure. We further
propose a novel \textsc{Paraphrase} modeling paradigm to cast the ASQP task to
a paraphrase generation process. On one hand, the generation formulation allows
solving ASQP in an end-to-end manner, alleviating the potential error
propagation in the pipeline solution. On the other hand, the semantics of the
sentiment elements can be fully exploited by learning to generate them in the
natural language form. Extensive experiments on benchmark datasets show the
superiority of our proposed method and the capacity of cross-task transfer with
the proposed unified \textsc{Paraphrase} modeling framework.

本文提出了基于语义生成以及 ASQP 任务的方案，能够通过一个句子实现四个情感因素的预测，从而揭示一个更全面的方面级别情感结构。同时该方案可以以端到端的方式从生成的角度来解决 ASQP 任务，充分利用情感元素的语义信息，取得了很好的实验效果及其在其他任务上的良好通用性。