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14th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2022

作者： Chen, Yanping Peng, Jiajia Xia, Hong Wang, Zhongmin Gao, Cong Wang, Fengwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-techRoadSouth Shenzhen518057 China

ISBN: (纸本)9781665499781

In smart factories, the wide application of a variety of intelligent manufacturing devices and advanced sensors leads to the increasing demand for data exchange between different devices. In order to realize the flexible forwarding of multi-source manufacturing data in heterogeneous networks and the optimal utilization of network resources, in this article, we combined smartphone manufacturing scenarios, referred to the data flow reconstruction algorithm in related documents, analyzed the high dynamics of the data flow and proposed a data flow reconstruction algorithm based on routing-protocol, according to the particularity of different data forwarding and processing procedures, redesigned and defined the data flow in the scene. Finally, the results of Mininet simulation experiments showed that our proposed algorithm reduces the average transmission delay. © 2022 IEEE

关键词： Heterogeneous networks

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Training on the Benchmark Is Not All You Need

arXiv

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arXiv 2024年

作者： Ni, Shiwen Kong, Xiangtao Li, Chengming Hu, Xiping Xu, Ruifeng Zhu, Jia Yang, Min Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology CAS China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University China University of Science and Technology of China China Shenzhen MSU-BIT University China Harbin Institute of Technology Shenzhen China

The success of Large Language Models (LLMs) relies heavily on the huge amount of pre-training data learned in the pre-training phase. The opacity of the pre-training process and the training data causes the results of many benchmark tests to become unreliable. If any model has been trained on a benchmark test set, it can seriously hinder the health of the field. In order to automate and efficiently test the capabilities of large language models, numerous mainstream benchmarks adopt a multiple-choice format. As the swapping of the contents of multiple-choice options does not affect the meaning of the question itself, we propose a simple and effective data leakage detection method based on this property. Specifically, we shuffle the contents of the options in the data to generate the corresponding derived data sets, and then detect data leakage based on the model’s log probability distribution over the derived data sets. If there is a maximum and outlier in the set of log probabilities, it indicates that the data is leaked. Our method is able to work under gray-box conditions without access to model training data or weights, effectively identifying data leakage from benchmark test sets in model pre-training data, including both normal scenarios and complex scenarios where options may have been shuffled intentionally or unintentionally. Through experiments based on two LLMs and benchmark designs, we demonstrate the effectiveness of our method. In addition, we evaluate the degree of data leakage of 35 mainstream open-source LLMs on four benchmark datasets and give a ranking of the leaked LLMs for each benchmark, and we find that the Qwen family of LLMs has the highest degree of data leakage. Copyright © 2024, The Authors. All rights reserved.

关键词： Information leakage

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Revolutionizing the Life sciences by Developing a Holographic Digital Mannequin

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Engineering 2023年第8期27卷 14-17页

作者： Bin Cong Xin-An Liu Shiming Zhang Zhiyu Ni Liping Wang Hebei Key Laboratory of Forensic Medicine College of Forensic MedicineHebei Medical UniversityShijiazhuang 050017China College of Integrated Traditional Chinese and Western Medicine Hebei Medical UniversityShijiazhuang 050017China CAS Key Laboratory of Brain Connectome and Manipulation Shenzhen–Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Institute of Data Intelligence Bigmath(Shenzhen)Technology Co.Ltd.Shenzhen 518063China School of Basic Medical Sciences Hebei UniversityBaoding 071000China Guangdong Provincial Key Laboratory of Brain Connectome and Behavior the Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology Chinese Academy of Medical SciencesShijiazhuang 050017China

*** need to develop a holographic digital mannequin Life processes,including high intelligence,self-organization,and homeostasis,are characterized by the biological organism in the form of self-renewal,self-replication and self-regulation,metabolism,self-repair,and self-reproduction,which are all processes of multisystem coordinated movement[1].Research in the field of life sciences is not limited to the use of advanced observational methods to reveal microscopic structures at the subcellular or molecular *** based on these methods alone cannot characterize the dynamic processes of life at the microscopic and molecular level[2].

关键词： holographic coordinated multisystem

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Causal Logic Discovery and Rule Validation Using Evolutionary Integration Model Global Fit U-Net

Causal Logic Discovery and Rule Validation Using Evolutionar...

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Natural Language Processing (ICNLP), International Conference on

作者： Xuelian Yang Fangyuan Du Kunjun Chen Hao Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to explore the correlation between different MRI sequences and the results of U-Net segmentation of glioma subregions, this paper proposes an interpretable method based on an evolutionary integration algorithm for logical discovery of the segmentation process of U-Net. Our approach consists of three steps: 1) Global fitting of the U-Net model to the segmentation results of gliomas using a dual evolutionary algorithm to generate a fitted model with both accuracy and interpretability.2) Extracting decision rules from the fitted model according to a specific target interpretable region and generating a complete set of interpretable rules after optimisation.3) Proposing a decision path integrator modeling method for the target region of decision paths for experimental validation. In this study, 293 patients from the BraTS2020 dataset are used as research data, and the accuracy of the fitted model is obtained to be 0.92, which is basically the same as that of Random Forest, but the model in this study has a better and simpler internal structure. At the same time, this study validated the relationship between Flair sequence and the edema region of glioma, and the experimental results showed that our extracted decision paths have a certain auxiliary effect on the segmentation of U-Net, and also proved the effectiveness of our proposed interpretability method.

关键词： Accuracy Correlation Magnetic resonance imaging Fitting Evolutionary computation data models Natural language processing Logic Random forests

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LFD-RSNet: Lightweight Face Detection network for Rainy Weather Scenes

LFD-RSNet: Lightweight Face Detection Network for Rainy Weat...

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Natural Language Processing (ICNLP), International Conference on

作者： Xiaoying Pan Yamie Ding Nijuan Zhang Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In response to the problem of low detection accuracy of lightweight models for face detection in complex rainy scenes, this paper proposes a face detection network FDL-RSNet for complex rainy scenes. This network integrates an adaptive image denoising module, an optimizer module, an HCA attention module, and a balanced branching strategy. The FDL-RSNet network can effectively address various challenges in complex rainy backgrounds, such as raindrop noise, differences in image lighting and contrast, and face detection challenges caused by low-quality images with dense targets. Experimental results on the UFDD Rain datasets show that FDL-RSNet has superior face detection capabilities in real-world unconstrained complex rainy scenes, Proved its effectiveness and reliability under such highly challenging conditions.

关键词： Adaptation models Rain Adaptive systems Noise Lighting Natural language processing Face detection Reliability Meteorology Image denoising

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FPPNet: Fast Privacy-Preserving Neural network via Three-Party Arithmetic Secret Sharing 15th

FPPNet: Fast Privacy-Preserving Neural Network via Three-Pa...

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15th EAI International Conference on Mobile Multimedia Communications, MobiMedia 2022

作者： Bi, Renwan Xiong, Jinbo Li, Qi Liu, Ximeng Tian, Youliang Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal University Hangzhou311121 China Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9783031239014

Jointing multi-source data for model training can improve the accuracy of neural network. To solve the raising privacy concerns caused by data sharing, data are generally encrypted and outsourced to a group of cloud servers for computing and processing. In this client-cloud architecture, we propose FPPNet, a fast and privacy-preserving neural network for secure inference on sensitive data. FPPNet is deployed in three cloud servers, who collaboratively execute privacy computing via three-party arithmetic secret sharing. We develop the secure conversion method between additive shares and multiplicative shares, and propose three secure protocols to calculate non-linear functions, such as comparison, exponent and division that are superior to prior three-party works. Some secure modules for running convolutional, ReLU, max-pooling and Sigmoid layers are designed to implement FPPNet. We theoretically analyze the security and complexity of the proposed protocols. With MNIST dataset and two types of neural networks, experimental results validate that our FPPNet is faster than the related works, and the accuracy is the same as that of plaintext neural network. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Sensitive data

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ASAFPN:An End-to-End Defect Detector with Adaptive Spatial Attention Method

ASAFPN:An End-to-End Defect Detector with Adaptive Spatial A...

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2021 International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation technology, MEMAT 2021

作者： Ouyang, Sen Shan Li, Yucheng Cao, Ming Sheng Chen, Chen Network and Data Security Key Laboratory of Sichuan Provence China University of Electronic Science and Technology of China China Co. Ltd China

Industrial defect inspection aims to detect unqualified products from those samples under detection, which plays a vital role in ensuring the quality of products. In this paper, an end-to-end defect detection network is proposed to locate the defect position of steel plate images and predict the defect category. In the detection network, we propose a novel feature pyramid network module, which is called the adaptive spatial attention feature pyramid. It can effectively fuse the texture features at low levels with semantic features at high levels. Based on the proposed module, persuasive experiments are carried out using different backbone networks. Experimental data shows that the feature pyramid module can effectively extract different levels of information and adaptively fuse multiple hierarchical features to improve the detection effect of the network. © Published under licence by IOP Publishing Ltd.

关键词： Defects

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3D Hand Pose Estimation Based on External Attention

3D Hand Pose Estimation Based on External Attention

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Natural Language Processing (ICNLP), International Conference on

作者： Shoukun Li Xiaoying Pan Beibei Wang Jialong Gao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

3D hand pose for a single depth image is an important topic in computer vision and human-computer interaction, and although significant progress has been made in this field in recent years, there is still room for improvement in accuracy for some specific application scenarios. To address this problem, a 3D hand pose estimation algorithm based on external attention is proposed. First, the target features are extracted by an hourglass network; then, a HEM (Hard Example Mining) loss based on a mean-variance loss function is proposed, which firstly calculates the L2 loss values of all N keypoints, and then sorts these loss values, and back-propagates the gradient only to the first m loss values. Meanwhile, external attention is introduced to enhance the ability to perceive the global information of the target, and the recognition ability of the features is improved by giving the features different influences through the attention weights. Experimental results show that the algorithm achieves an average distance error of 5.42 mm on the ICVL dataset and 7.11 mm on the MSRA dataset, which further improves the detection performance of the 3D hand pose estimation algorithm.

关键词： Human computer interaction Computer vision Three-dimensional displays Accuracy Target recognition Pose estimation Feature extraction Prediction algorithms Natural language processing

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TSARNet :Integrated spatio-temporal attention mechanism for multivariate time series prediction deep learning framework

TSARNet :Integrated spatio-temporal attention mechanism for ...

引用

Natural Language Processing (ICNLP), International Conference on

作者： Xiaoying Pan Hao Wang Jia Sun Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Multivariate time series forecasting is particularly critical in a number of areas involving air forecasting, electricity consumption and exchange rate transformations. The time series data in these fields often contain present multivariate nature, fusing long and short-term patterns, and traditional forecasting methods are often difficult to work. To address this challenge, this paper proposes a new deep learning framework, TSARNet, which combines gated recurrent units (GRUs) and an adaptive scale processing layer to effectively capture short-term localized dependency patterns among multivariate variables and reveal long-term trends through a spatio-temporal attention mechanism. In addition, an autoregressive model is incorporated to enhance the network's sensitivity to time series scale changes. Comprehensive experiments on four publicly available datasets show that the proposed TSARNet algorithm outperforms other comparative methods in most cases in terms of mean absolute error (MAE) and root mean square error (RMSE) in the long term prediction of different datasets. The TSARNet method also achieves optimal results for doing long-term prediction for each dimension of the power transformer temperature dataset.

关键词： Deep learning Attention mechanisms Accuracy Sensitivity Time series analysis Predictive models Market research Prediction algorithms Forecasting Root mean square

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FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction

arXiv

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arXiv 2024年

作者： Xu, Muhao Zhu, Zhenfeng Li, Youru Zheng, Shuai Zhao, Yawei He, Kunlun Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China Medical Big Data Research Center Chinese PLA General Hospital Beijing China

Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's health status, supporting various predictive healthcare tasks. Recently, several studies have embraced the multitask learning approach in the healthcare domain, exploiting the inherent correlations among clinical tasks to predict multiple outcomes simultaneously. However, existing methods necessitate samples to possess complete labels for all tasks, which places heavy demands on the data and restricts the flexibility of the model. Meanwhile, within a multitask framework with multimodal inputs, how to comprehensively consider the information disparity among modalities and among tasks still remains a challenging problem. To tackle these issues, a unified healthcare prediction model, also named by FlexCare, is proposed to flexibly accommodate incomplete multimodal inputs, promoting the adaption to multiple healthcare tasks. The proposed model breaks the conventional paradigm of parallel multitask prediction by decomposing it into a series of asynchronous single-task prediction. Specifically, a task-agnostic multimodal information extraction module is presented to capture decorrelated representations of diverse intra- and inter-modality patterns. Taking full account of the information disparities between different modalities and different tasks, we present a task-guided hierarchical multimodal fusion module that integrates the refined modality-level representations into an individual patient-level representation. Experimental results on multiple tasks from MIMIC-IV/MIMIC-CXR/MIMIC-NOTE datasets demonstrate the effectiveness of the proposed method. Additionally, further analysis underscores the feasibility and potential of employing such a multitask strategy in the healthcare domain. The source code is available at https://***/mhxu1998/FlexCare. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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