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4th International Conference on Blockchain technology and information Security, ICBCTIS 2024

作者： Li, Yan Zhang, Guofeng Feng, Bin Yang, Shihao Zhao, Chuan Wang, Peng Shen, Cong Zhang, Yanan School of Information Science and Engineering University of Jinan Shandong Jinan250022 China School of Information Science and Technology Taishan University Shandong Taian271000 China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Shandong Jinan250022 China College of Electronic Information Shandong Xiandai University Shandong Jinan250104 China

ISBN: (纸本)9798331528324

Medical data sharing can provide secure, trust-worthy and high-quality health care services to a wide range of patients and healthcare providers, but the data sharing process faces the risk of identity and information security leakage. This study begins by examining access control technologies for health data sharing, emphasizing the analysis of blockchain-based solutions for securing various types of medical data. It reviews and assesses the current state of these technologies across four dimensions-attributes, signatures, roles, and multi-smart contracts-and further develops a blockchain-based system for controlling access to medical data. Finally, it summarizes and analyzes the challenges faced by medical data sharing, and provides an outlook on the future development of the technology. This study is of great sig- nificance in guiding the deep integration of blockchain technology and medical digitization, and guaranteeing the secu-rity of medical data throughout its life cycle. © 2024 IEEE.

关键词： Electronic health record

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Cross-chain Architecture of Blockchain Integrating Notary Mechanism and Relay-chain technology 4

Cross-chain Architecture of Blockchain Integrating Notary Me...

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4th International Conference on Blockchain technology and information Security, ICBCTIS 2024

作者： Yang, ShiHao Zhang, GuoFeng Feng, Bin Li, Yan Zhao, Chuan Wang, Peng Shen, Cong Zhang, YaNan School of Information Science and Engineering University of Jinan Shandong Jinan250022 China School of Information Science and Technology Taishan University Shandong Taian271000 China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Shandong Jinan250022 China College of Electronic Information Shandong Xiandai University Shandong Jinan250104 China

ISBN: (纸本)9798331528324

Due to the independent nature of Blockchain systems in different organizations, data sharing and asset transfer between these Blockchain systems face extreme challenges. The phenomenon of data silos is gradually emerging, and Blockchain cross chain technology provides important technical support for bridging data silos, achieving interoperability and data sharing between Blockchains. The focus of this article is on the systematic study and comparison of four widely used cross-chain technologies and platforms, and designs a Blockchain cross-chain architecture that integrates notary and relay chain technologies based on the analysis results. Compared with using a single notary mechanism or a single relay cross-chain technology, it has better scalability, stronger customization, and higher security. This work has a certain promoting effect on the development of cross chain technology. © 2024 IEEE.

关键词： Blockchain

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Speculative decoding with CTC-based draft model for LLM inference acceleration 24

Speculative decoding with CTC-based draft model for LLM infe...

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

作者： Zhuofan Wen Shangtong Gui Yang Feng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences and State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences and Key Laboratory of Al Safety Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331314385

Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usually introduces a draft model to assist the base LLM where the draft model produces drafts and the base LLM verifies the draft for acceptance or rejection. In this framework, the final inference speed is decided by the decoding speed of the draft model and the acceptance rate of the draft provided by the draft model. Currently the widely used draft models usually generate draft tokens for the next several positions in a non-autoregressive way without considering the correlations between draft tokens. Therefore, it has a high decoding speed but an unsatisfactory acceptance rate. In this paper, we focus on how to improve the performance of the draft model and aim to accelerate inference via a high acceptance rate. To this end, we propose a CTC-based draft model which strengthens the correlations between draft tokens during the draft phase, thereby generating higher-quality draft candidate sequences. Experiment results show that compared to strong baselines, the proposed method can achieve a higher acceptance rate and hence a faster inference speed.

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M3GPT: an advanced multimodal, multitask framework for motion comprehension and generation 24

M3GPT: an advanced multimodal, multitask framework for motio...

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

作者： Mingshuang Luo Ruibing Hou Zhuo Li Hong Chang Zimo Liu Yaowei Wang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China and Peng Cheng Laboratory China and University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China WeChat Tencent Inc Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China and University of Chinese Academy of Sciences China Peng Cheng Laboratory China Peng Cheng Laboratory China and Harbin Institute of Technology Shenzhen

ISBN: (纸本)9798331314385

This paper presents M3 GPT, an advanced Multimodal, Multitask framework for Motion comprehension and generation. M3 GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for multimodal conditional signals, such as text, music and motion/dance, enabling seamless integration into a large language model (LLM) with a single vocabulary. The second involves modeling motion generation directly in the raw motion space. This strategy circumvents the information loss associated with a discrete tokenizer, resulting in more detailed and comprehensive motion generation. Third, M3 GPT learns to model the connections and synergies among various motion-relevant tasks. Text, the most familiar and well-understood modality for LLMs, is utilized as a bridge to establish connections between different motion tasks, facilitating mutual reinforcement. To our knowledge, M3 GPT is the first model capable of comprehending and generating motions based on multiple signals. Extensive experiments highlight M3GPT's superior performance across various motion-relevant tasks and its powerful zero-shot generalization capabilities for extremely challenging tasks. Project page: https://***/M3GPT/.

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A Cascaded Broad Learning System for Manipulator Motion Control

A Cascaded Broad Learning System for Manipulator Motion Cont...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Guoyu Zuo Shuaifeng Dong Jiyong Zhou Shuangyue Yu Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

intelligent control methods have led to a significant simplification of the robotic arm modeling and control tuning process, and thus they have been widely used. To further improve the precision of robotic arm motion control, this paper proposes a robotic arm motion control strategy based on a cascaded feature-enhanced elastic-net broad learning system (CFE-EN-BLS). This will fully extract data features to improve motion control accuracy. Moreover, ElasticNet regression is introduced to reduce feature redundancy. Finally, Lyapunov stability theory is introduced to constrain the learning parameters of the proposed learning method to enhance the convergence of the control strategy. The simulation and experiment show that the proposed control strategy can realize high-precision trajectory tracking control of the robotic arm.

关键词： Learning systems Accuracy Manipulators Feature extraction Stability analysis Motion control Robots Tuning intelligent control Convergence

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Cross-Domain Retinopathy Classification with OCT Images via Disentangling Representation and Adaptation Networks

Cross-Domain Retinopathy Classification with OCT Images via ...

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Optoelectronics Global Conference (OGC)

作者： Wickramaarachchi Minidu Thiranjaya Yuan Li Lei Tao Yuemei Luo Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Future Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350391466

ISBN: (纸本)9798350391473

Deep learning methods have shown significant potential in retinopathy classification using optical coherence tomography (OCT) images. However, substantial challenges arise due to domain shift issues, which are attributable to heterogeneity in data collection devices, variations in subject populations, and discrepancies in imaging protocols and parameters. To address these, we propose a novel approach leveraging Disentangling Representation and Adaptation Networks (DRANet) to enhance the robustness and generalization of retinopathy classification models across different OCT datasets. Our methodology involves developing DRANet, a specialized network architecture designed to disentangle representations of data and facilitate domain adaptation. DRANet includes components for learning invariant representations across domains through adversarial training, ensuring robustness against domain shifts. DRANet encodes individual representations of content and style from both source and target images. Then, it adapts the domain by incorporating the transferred style factor into the content factor, along with learnable weights specified for each domain. This learning framework allows bidirectional domain. After conducting extensive experiments conducted on three public datasets achieved accuracies of $\mathrm{8 8 \%, ~} \mathrm{9 0 \%}$, and $\mathrm{9 2 \%}$ under three cross-domain scenarios, demonstrating the efficacy of our approach in mitigating domain shift effects and improving retinopathy classification performance.

关键词： Training Deep learning Protocols Retinopathy Optical coherence tomography Imaging Coherence Network architecture Data collection Robustness

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Design of a cross-frame barrier-crossing and wall-climbing cleaning robot

Design of a cross-frame barrier-crossing and wall-climbing c...

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第35届中国控制与决策会议

作者： Naigong Yu Wanhu Feng Fan Zhang Hejie Yu Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computing Intelligence and Intelligent System

The photovoltaic curtain wall is a combination of an outer photovoltaic module plate and inner vacuum glass,which forms a narrow cavity with obstacles such as beams and junction boxes *** the cavity of the photovoltaic curtain wall is contaminated,it is impossible to enter *** the problem of cavity cleaning,a cross-frame barrier-crossing and wallclimbing cleaning robot is *** robot body mechanism is designed as a cross-frame motion mechanism to achieve flexible movement in narrow cavities;its adsorption device is designed as a littable 3*5 vacuum suction cup set to achieve stable adsorption on the vacuum glass surface and the function of crossing obstacles;its cleaning structure is designed as a foldable brush cleaning structure to achieve the cleaning function on the photovoltaic module *** on the kinematic model,the robot motion simulation analysis was completed;the robot prototype was developed,and the adsorption and movement experiments of the robot were carried *** experimental results show that the robot can achieve stable adsorption and autonomous movement on the vertical wall surface.

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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Dynamic Prediction of Intraoperative Hypotension Based on Hemodynamic Monitoring Data With a Transformer-Based Deep Learning Model

Dynamic Prediction of Intraoperative Hypotension Based on He...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kai Yang Mucheng Ren Jun Xu Xian Zeng Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Timely prediction and intervention for Intraoperative Hypotension (IOH), a prevalent complication associated with general anesthesia, is crucial to prevent severe postoperative outcomes. While existing machine learning methods for IOH prediction have shown promise, they face limitations such as reliance on single data sources and disregard for crucial features like the trend in Arterial Blood Pressure (ABP) waveforms. To address these challenges, this paper proposes a novel multichannel deep learning framework that combines Convolutional Neural Networks (CNN) and Transformers for automatic IOH prediction. The model leverages four types of physiological waveforms, including ABP, ElectroCardioGram (ECG), photoplethysmography (PLE), and Carbon Dioxide (CO2), to capture both local and global information, enhancing information representation. Experimental results on retrospective data of 14,140 adult patients undergoing non-cardiac surgery from VitalDB, a public data repository of vital signs taken during surgeries in 10 operating rooms at Seoul National University Hospital (from January 6, 2005 to March 1, 2014), demonstrate that the proposed model consistently outperforms other methods, achieving superior AUROC values of 0.943, 0.928, and 0.923 at 5, 10, and 15 minutes before the event, respectively. These results demonstrate the powerfulness of multi-modal data source as well as the importance of ABP waveform trends. Additionally, the incorporation of multi-task learning and the attention mechanism validates the effectiveness and superiority of our model, highlighting its potential for proactive clinical interventions and improved postoperative patient outcomes.

关键词： Analytical models Soft sensors Surgery Predictive models Transformers Market research Data models Biomedical monitoring Monitoring Hypotension

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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