Being able to infer the goal of people we observe, interact with, or read stories about is one of the hallmarks of human intelligence. A prominent idea in current goal-recognition research is to infer the likelihood of an agent's goal from the estimations of the costs of plans to the different goals the agent might have. Different approaches implement this idea by relying only on handcrafted symbolic representations. Their application to real-world settings is, however, quite limited, mainly because extracting rules for the factors that influence goal-oriented behaviors remains a complicated task. In this paper, we introduce a novel idea of using a symbolic planner to compute plan-cost insights, which augment a deep neural network with an imagination capability, leading to improved goal recognition accuracy in real and synthetic domains compared to a symbolic recognizer or a deep-learning goal recognizer alone.
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学电子计算学系）
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Huawei Heisenberg Research Center（华为海森堡研究中心）；Zhejiang University（浙江大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；TU Berlin（柏林工业大学）；Kiel University（基尔大学）
RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition
University of Sydney Australia（悉尼大学澳大利亚分校）；University of Sri Jayewardenepura Sri Lanka（Sri Jayewardenepura大学 Sri Lanka）；Curtin University Australia（Curtin大学澳大利亚分校）；Colorado State University USA（科罗拉多州立大学美国分校）
RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；MThreads, Inc.（MThreads公司）；Nanyang Technological University（南洋理工大学）
Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models
Department of Biomedical Informatics, School of Medicine, Emory University（生物医学信息学系，医学院，埃默里大学）；Department of Computer Science, Emory University（计算机科学系，埃默里大学）；National Library of Medicine, National Institutes of Health（国家医学图书馆，国立卫生研究院）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（生物医学工程系，佐治亚理工学院和埃默里大学）
SignRAG: A Retrieval-Augmented System for Scalable Zero-Shot Road Sign Recognition
Computer Engineering The Ohio State University（计算机工程 俄亥俄州立大学）；Applied Research Transportation Research Center Inc.（应用研究 交通运输研究中心公司）；Computer Engineering The Ohio State University Columbus, Ohio, USA（计算机工程 俄亥俄州立大学 哥伦布俄亥俄州）；Applied Research Transportation Research Center Inc. East Liberty, Ohio, USA（应用研究 交通运输研究中心公司 埃斯特利比 俄亥俄州）
A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition
Missouri University of Science and Technology（密苏里科技大学）；Ain Shams University（艾因夏姆斯大学）；The American University in Cairo（开罗美国大学）；German University in Cairo（开罗德国大学）
NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning
Shenzhen Graduate School, Peking University（北京大学深圳研究生院）；Baidu, Inc.（百度公司）；Shenzhen University（深圳大学）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
