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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoou Zhang Yang Gao Yang Liu Yujia Zhu Peng Zhang Chuan Zhou Qingyun Liu Hongyang Chen Chinese Academy of Sciences Institute of Information Engineering School of Cyber Security University of Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University Chinese Academy of Sciences Academy of Mathematics and Systems Science Cyberspace Institute of Advanced Technology Guangzhou University Zhejiang Lab Research Center for Graph Computing

Recently link weight prediction has attracted an increasing research interest due to its merits in quantifying the strength between nodes within a graph. Nonetheless, current link weight prediction methods focus solely on graph topology, disregarding node feature information embedded in graphs. In real-world applications, we often collect heterogeneous graph data where multiple types of nodes linked by multiple types of edges are available for analysis, and it is essential and challenging to quantify the proximity of different types of nodes. To solve this challenge, we present a new model for Heterogeneous Graph Link Weight Prediction (HLWP for short). In HLWP, message passing in heterogeneous graph neural networks is described as a meta structure, which can be effectively designed by Differentiable Neural Architecture Search (DARTS) algorithms. Thus, HLWP can enhance the message passing in heterogeneous graphs by DARTS. In addition, HLWP employs a perturbation-based algorithm to enhance stability and precision. Through empirical experiments conducted on three real-world datasets, we demonstrate that HLWP achieves accurate predictions of link weights. Our results highlight the superiority of HLWP over existing methods for link weight prediction and baseline GNN models in terms of accurately predicting link weights within heterogeneous graphs.

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Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR

Towards Context-aware Support for Color Vision Deficiency: A...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Shogo Morita Yan Zhang Takuto Yamauchi Sinan Chen Jialong Li Kenji Tei School of Computing Tokyo Institute of Technology Tokyo Japan International Research Center for Neurointelligence University of Tokyo Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Center of Mathematical and Data Sciences Kobe University Kobe Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context; and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application.

关键词： Green products Color Faces Consumer electronics Augmented reality Context modeling

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Safe Online Convex Optimization with Heavy-Tailed Observation Noises 39

Safe Online Convex Optimization with Heavy-Tailed Observatio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Yunhao Xue, Bo Hao, Yunzhi Li, Ying Wan, Yuanyu School of Software Technology Zhejiang University Ningbo China Department of Computer Science City University of Hong Kong China Big Graph Center School of Computer and Computing Science Hangzhou City University Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Bangsheng Technology Co. Ltd. Hangzhou China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)157735897X

We investigate safe online convex optimization (SOCO), where each decision must satisfy a set of unknown linear constraints. Assuming that the unknown constraints can be observed with a sub-Gaussian noise for each chosen decision, previous studies have established a high-probability regret bound of O(T2/3). However, this assumption may not hold in many practical scenarios. To address this limitation, in this paper, we relax the assumption to allow any noise that admits finite (1+ϵ)-th moments for some ϵ ∈ (0, 1], and propose two algorithms that enjoy an O(Tcϵ) regret bound with high probability, where T is the time horizon and cϵ = (1+ϵ)/(1+2ϵ). The key idea of our two algorithms is to respectively utilize the median-of-means and truncation techniques to achieve accurate estimation under heavy-tailed noises. To the best of our knowledge, these are the first algorithms designed to handle SOCO with heavy-tailed observation noises. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convex optimization

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Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR

arXiv

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arXiv 2024年

作者： Morita, Shogo Zhang, Yan Yamauchi, Takuto Chen, Sinan Li, Jialong Tei, Kenji School of Computing Tokyo Institute of Technology Tokyo Japan International Research Center for Neurointelligence the University of Tokyo Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Center of Mathematical and Data Sciences Kobe University Kobe Japan

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context;and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application. © 2024, CC BY.

关键词： Color

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A Trajectory Tracking System for Zebrafish Based on Embedded Edge Artificial Intelligence

A Trajectory Tracking System for Zebrafish Based on Embedded...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chuanhao Zang Anming Dong Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Trajectory tracking of zebrafish is an important requirement in studying neurological disorders and developing new psychoactive drug. However, many challenges emerge for stable tracking, since zebrafish are similar in appearance, occlusion, agile, non-linear in moving and easy to swarm, all of which will lead to mistrack for multiple fish. And there is no precedent for tracking zebrafish through embedded edge artificial intelligence device. To overcome these difficulties, we present a tracking system for zebrafish based on RK3588-S. First, We construct an embedded edge AI hardware system consisting of two cameras driven by the RK3588-S, one for the front view and the other for top view of the fish. Then, we develop a 2D tracking algorithm based on YOLOv5 and the Observation-Centric Simple online and real-time tracking (OC-SORT) algorithm, which are transplanted to the RK3588-S for tracking the top and front views of the fish at the edge device. Compared with the previous methods, our method has fewer ID exchanges and highly real-time. Moreover, we apply the MQTT mechanism to establish communication links between the edge and the cloud to reliably transmit data to the cloud. The correspondence between cloud server and embedded AI is 1: N. Finally, we design a multi-view data fusion association algorithm to fuse the data of the two views in the cloud, which are further utilized to build the 3D tracklets of the zebrafish.

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Logarithmic-regret quantum learning algorithms for zero-sum games 23

Logarithmic-regret quantum learning algorithms for zero-sum ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Minbo Gao Zhengfeng Ji Tongyang Li Qisheng Wang State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Center on Frontiers of Computing Studies and School of Computer Science Peking University Beijing China Graduate School of Mathematics Nagoya University Nagoya Japan

We propose the first online quantum algorithm for solving zero-sum games with Õ(1) regret under the game setting. Moreover, our quantum algorithm computes an ε-approximate Nash equilibrium of an m × n matrix zero-sum game in quantum time Õ(√m + n /ε2.5). Our algorithm uses standard quantum inputs and generates classical outputs with succinct descriptions, facilitating end-to-end applications. Technically, our online quantum algorithm "quantizes" classical algorithms based on the optimistic multiplicative weight update method. At the heart of our algorithm is a fast quantum multi-sampling procedure for the Gibbs sampling problem, which may be of independent interest.

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Robust Subgraph Augmentation for Graph Convolutional Networks with Few Labeled Nodes

Robust Subgraph Augmentation for Graph Convolutional Network...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Keyu Jin Long Chen Jiahui Yu Zhimao Lu College of Computer Science and Technology Dalian University of Technology China School of Computer and Artificial Intelligence Zhengzhou University China Innovation Center for Smart Medical Technologies and Devices Binjiang Institute of Zhejiang University Hangzhou China School of Computing University of Portsmouth UK

Graph Neural Networks (GNNs) have garnered significant attention owing to their remarkable capacity to extract representations from non-Euclidean data. Despite their success, the performance of prevalent GNNs degrades significantly when structural noise is introduced into the graph data. Besides, the reliance of GNNs on a considerable volume of labeled nodes presents challenges in dealing with the low-data regime where labeled nodes are scarce. This paper aims to address a challenging scenario in which structural noise and the low-data regime occur simultaneously. Specifically, we exploit the valuable information from the local topologies, i.e. subgraphs, of a graph as the augmented supervision signals for the extensive amounts of unlabeled nodes. Moreover, to alleviate the effects of structural noise, we remove potential noisy edges linking dissimilar nodes, generating robust subgraphs for the downstream node classification tasks. We conduct a series of experimental studies on canonical node classification tasks using three graph datasets. The results of our experimental analyses demonstrate that our approach can achieve state-of-the-art performance under structural noises and the low-data regime.

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Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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A K-Nearest Centroid Neighbor with atention Classifier 5

A K-Nearest Centroid Neighbor with atention Classifier

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5th International Conference on computer science and Artificial Intelligence, CSAI 2021

作者： Huang, Rui Ma, Ying Wang, Tian Li, GuoQi Yan, Ming Department of Computer and Information Engineering Xiamen University of Technology China Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore School of Information Science and Engineering Hunan First Normal University China Department of Precision Instrument Center for Brain Inspired Computing Research

ISBN: (纸本)9781450384155

Among classic algorithms of data mining, the K-nearest neighbor based methods are simple and effective pattern classification algorithms. However, most KNN-based methods do not fully take into account the impact of different training sample points on classification, lead to inaccurate classification. To address this issue, we propose a scheme named Attention-based local mean K-Nearest Centroid Neighbor Classifier (ALMKNCN), combining nearest centroid neighbor with attention mechanism, the influence of each training sample on the query sample is fully considered. Given the query pattern, we first calculate the local centroid mean vector for each class, and then use the idea of attention mechanism to calculate the weight of pseudo-distance between each class and test sample. Finally, based on attention coefficient, the distances between the query sample and local mean vectors are weighted to determine the class of the query sample. Extensive experiments on UCI and KEEL data sets are carried out by comparing ALMKNCN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed ALMKNCN outperforms the related competitive KNN-based methods with more effectiveness. © 2021 ACM.

关键词： Data mining

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Mental Health Stigma: Strategies for Destigmatization in Healthcare Settings

Mental Health Stigma: Strategies for Destigmatization in Hea...

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Electrical Electronics and computing Technologies (ICEECT), International Conference on

作者： Aishwarya Verma Krishna Tanaya Panda Vandana Sharma Hrudaya Kumar Tripathy Mahmoud Ahmad Al-Khasawneh Ahmed Alkhayyat Kalinga Institute of Industrial Technology India Computer Science Department CHRIST University India School of Computing Skyline University College University City Sharjah Sharjah UAE Applied Science Research Center Applied Science Private University Amman Jordan Jadara University Research Center Jadara University Jordan College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Mental illness is one of the most common disabilities in the world. The term "mental illness stigma" describes harmful practices and misconceptions that lead to a detrimental effect on the mental health, motivation, and self-worth of those who suffer from mental illnesses. Health care services are important for treating and reducing the negative stigma of mental health, as they are areas where patients seek relief and support. The study aims to investigate the causes and how to reduce them. Explores ways to disrupt the health care environment, specifically the RESHAPE program, which focuses on the concept of "critical". This review paper looks at 8-10 papers on mental health and stigma and how stigma will be reduced. The results show that a large number of doctors and students are stigmatized, negatively affecting the lives of people affected by mental illness. RESHAPE, KAP, and IBH therapies are also effective ways to minimize mental health stigma. This intervention aims to educate public health workers, promote social cohesion, and integrate treatment into primary health care, improving treatment into primary health care, improving treatment quality and patient outcomes. The study draws attention to the importance of stigma reduction efforts in the long term in health education and practice emphasis.

关键词： computers Reviews Education Medical treatment Mental health Medical services Media Public healthcare

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