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Research and application of intelligent voice food ordering system 8

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Research and application of intelligent voice food ordering ...

8th International Conference on Orange Technology, ICOT 2020

作者： Wang, Xiaogang Song, Peng Li, Lingli Changzhou College of Information Technology Software and Big Data Department Changzhou China Information Center Rizhao City Urban Drainage Management Office Rizhao China Beijing Huasheng Jingshi Information Technology Co. Ltd Computer and Software Engineering Department Beijing China

ISBN: (纸本)9781665418522

With the rapid development and popularization of Internet applications, online food ordering systems can make people's lives more convenient. However, how to choose the favorite food among various foods conveniently has become the focus of the development of online food ordering systems. Due to the rapid development of artificial intelligence technology and popularization of intelligent applications, more and more intelligent systems have been proposed. However, artificial intelligence exploiting both speech recognition and semantic understanding technology is rarely used to develop intelligent food ordering systems. We hereby propose the intelligent voice food ordering system (IVFOS), which is designed and implemented on the basis of Iflytek AIUI platform, combining speech recognition and semantic understanding technology so as to make the food ordering more convenient. We have further introduced a personalized recommendation mechanism to serve customers based on their historical ordering records. The practical application shows that our intelligent voice food ordering system has the characteristics of convenient usage and highly personalized food ordering experience. © 2020 IEEE.

关键词： Speech recognition

Performance Evaluation of Lightweight Open-source Large Language Models in Pediatric Consultations: A Comparative Analysis

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arXiv 2024年

作者： Wei, Qiuhong Cui, Ying Ding, Mengwei Wang, Yanqin Xiang, Lingling Yao, Zhengxiong Chen, Ceran Long, Ying Jin, Zhezhen Xu, Ximing Big Data Center for Children’s Medical Care Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders Chongqing China Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders China International Science and Technology Cooperation base of Child Development and Critical Disorders China Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Nephrology Children’s Hospital of Chongqing Medical University Chongqing China Department of Neonatology Children’s Hospital of Chongqing Medical University Chongqing China Department of Neurology Children’s Hospital of Chongqing Medical University Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China Department of Biostatistics Mailman School of Public Health Columbia University New YorkNY United States

Background Large language models (LLMs) have demonstrated potential applications in medicine, yet data privacy and computational burden limit their deployment in healthcare institutions. Open-source and lightweight versions of LLMs emerge as potential solutions, but their performance, particularly in pediatric settings remains underexplored. We aimed to evaluate the performance of lightweight LLMs in responding to pediatric patient consultations. Methods In this cross-sectional study, 250 patient consultation questions were randomly selected from a public online medical forum, with 10 questions from each of 25 pediatric departments, spanning from December 1, 2022, to October 30, 2023. Two lightweight open-source LLMs, ChatGLM3-6B and Vicuna-7B, along with a larger-scale model, Vicuna-13B, and the widely-used proprietary ChatGPT-3.5, independently answered these questions in Chinese between November 1, 2023, and November 7, 2023. To assess reproducibility, each inquiry was replicated once. Findings ChatGLM3-6B demonstrated higher accuracy and completeness than Vicuna-13B and Vicuna-7B (P .05), with over 98.4% of responses being rated as safe. Repetition of inquiries confirmed these findings. Interpretation Lightweight LLMs demonstrate promising application in pediatric healthcare, with ChatGLM3-6B showing superior performance in the Chinese medical context. However, the observed gap between lightweight and large-scale proprietary LLMs underscores the need for continued development efforts. Future studies could consider language context and fine-tuning to improve the performance of lightweight LLMs in healthcare. Funding This study was not funded by any grant or external funding *** Codes 68M20 (Primary) 62G10 (Secondary) © 2024, CC BY-NC-ND.

关键词： Pediatrics

Graph entropy guided Node Embedding Dimension Selection for Graph Neural Networks

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arXiv 2021年

作者： Luo, Gongxu Li, Jianxin Su, Jianlin Peng, Hao Yang, Carl Sun, Lichao Yu, Philip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Shenzhen Zhuiyi Technology Co. Ltd China Department of Computer Science Emory University United States Department of Computer Science and Engineering Lehigh University United States Department of Computer Science University of Illinois Chicago United States

Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate dimension of node embedding for a given graph still remains unsolved. The commonly used strategies for Node Embedding Dimension Selection (NEDS) based on grid search or empirical knowledge suffer from heavy computation and poor model performance. In this paper, we revisit NEDS from the perspective of minimum entropy principle. Subsequently, we propose a novel Minimum Graph Entropy (MinGE) algorithm for NEDS with graph data. To be specific, MinGE considers both feature entropy and structure entropy on graphs, which are carefully designed according to the characteristics of the rich information in them. The feature entropy, which assumes the embeddings of adjacent nodes to be more similar, connects node features and link topology on graphs. The structure entropy takes the normalized degree as basic unit to further measure the higher-order structure of graphs. Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph. Finally, comprehensive experiments with popular Graph Neural Networks (GNNs) on benchmark datasets demonstrate the effectiveness and generalizability of our proposed MinGE. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

Single-Carrier Delay Alignment Modulation for Multi-IRS Aided Communication

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arXiv 2022年

作者： Lu, Haiquan Zeng, Yong Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Delay alignment modulation (DAM) is a promising technology to achieve inter-symbol interference (ISI)-free single-carrier communication, by leveraging delay compensation and path-based beamforming, rather than the conventional channel equalization or multi-carrier transmission. In particular, when there exist a few strong time-dispersive channel paths, DAM is able to effectively align different propagation delays and achieve their constructive superposition, thus especially appealing for intelligent reflecting surfaces (IRSs)-aided communications with controllable multi-paths. In this paper, we apply single-carrier DAM to multi-IRS aided communication and study its design and achievable performance. We first provide an asymptotic analysis showing that when the number of base station (BS) antennas is much larger than the number of IRSs, an ISI-free channel can be established from the BS to the user with appropriate delay pre-compensation and the simple path-based maximal-ratio transmission (MRT) beamforming. We then consider the general system setup and study the problem of joint path-based beamforming design at the BS and phase shifts design at the IRSs for DAM transmission, by considering the three classical beamforming techniques on a per-path basis, namely the low-complexity path-based MRT beamforming to maximize the desired signal power, the path-based zero-forcing (ZF) beamforming for ISI-free DAM communication, and the optimal path-based minimum mean-square error (MMSE) beamforming to maximize the signal-to-interference-plus-noise ratio (SINR). As a comparison, orthogonal frequency-division multiplexing (OFDM)-based multi-IRS aided communication is considered for benchmarking. Simulation results are provided which demonstrate the significant performance gain of DAM over OFDM, in terms of spectral efficiency and bit error rate (BER), as well as its lower peak-to-average-power ratio (PAPR). Copyright © 2022, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation

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arXiv 2024年

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Chen, Zhen Wang, Yi Li, Chenxin He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labor-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2024, CC BY.

关键词： Decoding

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

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arXiv 2022年

作者： Zhao, Chen Zhang, Jialu Hou, Xiaoyue Yeung, Chi Ho Zeng, An College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Shijiazhuang050024 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong School of Systems Science Beijing Normal University Beijing100875 China

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Leveraging Herpangina data to Enhance Hospital-level Prediction of Hand-Foot-and-Mouth Disease Admissions Using UPTST

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arXiv 2023年

作者： Yu, Guoqi Yao, Hailun Zheng, Huan Xu, Ximing The Big Data Center for Children’s Medical Care Children’s Hospital Chongqing Medical University Chongqing401122 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China The National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders Chongqing401122 China The Department of Orthopedics Ministry of Education Key Laboratory of Child Development and Disorders National Clinical Research Center for Child Health and Disorders China International Science and Technology Cooperation Base of Child Development and Critical Disorders Chongqing Key Laboratory of Pediatrics Children’s Hospital Chongqing Medical University Chongqing400014 China The School of Finance Nankai University Tianjin300350 China The Big Data Center for Children’s Medical Care Children’s Hospital Chongqing Medical University Chongqing401122 China

Outbreaks of hand-foot-and-mouth disease (HFMD) have been associated with significant morbidity and, in severe cases, mortality. Accurate forecasting of daily admissions of pediatric HFMD patients i1s therefore crucial for aiding the hospital in preparing for potential outbreaks and mitigating nosocomial transmissions. To address this pressing need, we propose a novel transformer-based model with a U-net shape, utilizing the patching strategy and the joint prediction strategy that capitalizes on insights from herpangina, a disease closely correlated with HFMD. This model also integrates representation learning by introducing reconstruction loss as an auxiliary loss. The results show that our U-net Patching Time Series Transformer (UPTST) model outperforms existing approaches in both long- and short-term prediction accuracy of HFMD at hospital-level. Furthermore, the exploratory extension experiments show that the model's capabilities extend beyond prediction of infectious disease, suggesting broader applicability in various domains. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

SUGAR: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism

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arXiv 2021年

作者： Sun, Qingyun Li, Jianxin Peng, Hao Wu, Jia Ning, Yuanxing Yu, Phillip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Department of Computing Macquarie University Sydney Australia Department of Computer Science University of Illinois at Chicago Chicago United States Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching view of graphs, neglecting finer substructures' semantics, and suffering from interpretation enigmas. This paper presents a novel hierarchical subgraph-level selection and embedding-based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way. SUGAR reconstructs a sketched graph by extracting striking subgraphs as the representative part of the original graph to reveal subgraph-level patterns. To adaptively select striking subgraphs without prior knowledge, we develop a reinforcement pooling mechanism, which improves the generalization ability of the model. To differentiate subgraph representations among graphs, we present a self-supervised mutual information mechanism to encourage subgraph embedding to be mindful of the global graph structural properties by maximizing their mutual information. Extensive experiments on six typical bioinformatics datasets demonstrate a significant and consistent improvement in model quality with competitive performance and interpretability. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

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arXiv 2023年

作者： Huang, Shijie Zhang, Xukun Cui, Zhiming Zhang, He Chen, Geng Shen, Dinggang School of Biomedical Engineering ShanghaiTech University Shanghai China Academy for Eng. & Tech. Fudan University Shanghai China Department of Radiology Obstetrics and Gynecology Hospital of Fudan University Shanghai China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence company Ltd. Shanghai200030 China Shanghai Clinical Research Trial Center Shanghai201210 China

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality volumes to thick-slice scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance