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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Honglei Zhu Anming Dong Jiguo Yu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security 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

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor’s prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it’s hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement.

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Prediction of Compressive Strength of Green Concrete by Artificial Neural Network 1st

Prediction of Compressive Strength of Green Concrete by Arti...

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1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Verma, Manvendra Upreti, Kamal Dadhich, Priyanka Ghosh, Soumi Khatri, Vishal Singh, Prashant Department of Civil Engineering GLA University Uttar Pradesh Mathura India Department of Computer Science and Engineering Akhilesh Das Gupta Institute of Technology and Management Delhi India Department of Big Data Analytics Asia Pacific Institute of Management Delhi India Department of Information Technology Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India Department of Computer Science and Engineering Sunder Deep Engineering College Ghaziabad India

ISBN: (纸本)9783031250873

The concrete business has just come out with a brand-new product called geopolymer concrete. Because of its resilience in harsh environments and its high strength, it has the potential to revolutionise the whole construction industry. It is an excellent substitute for the traditional kind of concrete. It outperforms traditional concrete in terms of sustainability, ecology, durability, and economic viability. In this day and age, the use of machine learning strategies is not just the future of the research and development industry, but of all industries. These methods provide predictions about the outcomes by analysing the facts from the past. The process of determining the outcomes or worth of anything in the construction sector is very challenging, time consuming, and arduous. Without having to conduct destructive experiments or take samples, the strength of a mix design may now be more accurately predicted thanks to these procedures. By using deep learning and the random forest method, the purpose of this research is to predict the compressive strength of flyash-based geopolymer concrete. This will be accomplished by comparing the two algorithms with varying degrees of error and coefficient correlation. It has been shown via the use of simulated data that the random forest algorithm is the method that is most suited for making predictions about compressive strength. After the developing a model, the various errors were found for accuracy. The mean absolute error, root mean square error, relative absolute error, and root relative squared error are 3.46%, 5.94%, 64.32%, and 82.97%, respectively for the deep learning predicted compressive strength. The errors provide the proof of model accuracy to predict the compressive strength on the basis of ingredients proportions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Compressive strength

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2d Human Pose Estimation with Explicit Anatomical Keypoints Structure Constraints

SSRN

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SSRN 2023年

作者： Ji, Zhangjian Wang, Zilong Zhang, Ming Chen, Yapeng Qian, Yuhua No.92 Wucheng Rd. Xiaodian District Taiyuan China School of Computer & Information Technology Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

Recently, human pose estimation mainly focuses on how to design a more effective and better deep network structure as human features extractor, and most designed feature extraction networks only introduce the position of each anatomical keypoint to guide their training process. However, we found that some human anatomical keypoints kept their topology invariance, which can help to localize them more accurately when detecting the keypoints on the feature map. But to the best of our knowledge, there is no literature that has specifically studied it. Thus, in this paper, we present a novel 2D human pose estimation method with explicit anatomical keypoints structure constraints, which introduces the topology constraint term that consisting of the differences between the distance and direction of the keypoint-to-keypoint and their groundtruth in the loss object. More importantly, our proposed model can be plugged in the most existing bottom-up or top-down human pose estimation methods and improve their performance. The extensive experiments on the benchmark dataset: COCO keypoint dataset, show that our methods perform favorably against the most existing bottom-up and topdown human pose estimation methods, especially for Lite-HRNet, when our model is plugged into it, its AP scores separately raise by 2.9% and 3.3% on COCO val2017 and test-dev2017 datasets. © 2023, The Authors. All rights reserved.

关键词： Topology

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Training of Spiking Neural Network joint Curriculum Learning Strategy

arXiv

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arXiv 2023年

作者： Tang, Lingling Chu, Jielei Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL. Copyright © 2023, The Authors. All rights reserved.

关键词： Curricula

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On efficient expanding training datasets of breast tumor ultrasound segmentation model

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Computers in Biology and Medicine 2024年 183卷 109274-109274页

作者： Zhang, Caicai Mei, Mei Mei, Zhuolin Wu, Bin Chen, Shasha Lu, Minfeng Lu, Chenglang School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering 528 Binwen Road Binjiang District Zhejiang Hangzhou310053 China Department of Ultrasound The Second Affiliated Hospital Zhejiang University School of Medicine 88 Jiefang Road Shangcheng District Zhejiang Hangzhou310009 China School of Computer and Big Data Science Jiujiang University 551 Qianjin East Road Jiangxi Jiujiang332005 China

Automatic segmentation of breast tumor ultrasound images can provide doctors with objective and efficient references for lesions and regions of interest. Both dataset optimization and model structure optimization are crucial for achieving optimal image segmentation performance, and it can be challenging to satisfy the clinical needs solely through model structure enhancements in the context of insufficient breast tumor ultrasound datasets for model training. While significant research has focused on enhancing the architecture of deep learning models to improve tumor segmentation performance, there is a relative paucity of work dedicated to dataset augmentation. Current data augmentation techniques, such as rotation and transformation, often yield insufficient improvements in model accuracy. The deep learning methods used for generating synthetic images, such as GANs is primarily applied to produce visually natural-looking images. Nevertheless, the accuracy of the labels for these generated images still requires manual verification, and the images exhibit a lack of diversity. Therefore, they are not suitable for the training datasets augmentation of image segmentation models. This study introduces a novel dataset augmentation approach that generates synthetic images by embedding tumor regions into normal images. We explore two synthetic methods: one using identical backgrounds and another with varying backgrounds. Through experimental validation, we demonstrate the efficiency of the synthetic datasets in enhancing the performance of image segmentation models. Notably, the synthetic method utilizing different backgrounds exhibits superior improvement compared to the identical background approach. Our findings contribute to medical image analysis, particularly in tumor segmentation, by providing a practical and effective dataset augmentation strategy that can significantly improve the accuracy and reliability of segmentation models. © 2024 Elsevier Ltd

关键词： Ultrasonic imaging

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One Person, One Model-Learning Compound Router for Sequential Recommendation

arXiv

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arXiv 2022年

作者： Liu, Zhiding Cheng, Mingyue Li, Zhi Liu, Qi Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China Shenzhen International Graduate School Tsinghua University Shenzhen China

Deep learning has brought significant breakthroughs in sequential recommendation (SR) for capturing dynamic user interests. A series of recent research revealed that models with more parameters usually achieve optimal performance for SR tasks, inevitably resulting in great challenges for deploying them in real systems. Following the simple assumption that light networks might already suffice for certain users, in this work, we propose CANet, a conceptually simple yet very scalable framework for assigning adaptive network architecture in an input-dependent manner to reduce unnecessary computation. The core idea of CANet is to route the input user behaviors with a light-weighted router module. Specifically, we first construct the routing space with various submodels parameterized in terms of multiple model dimensions such as the number of layers, hidden size and embedding size. To avoid extra storage overhead of the routing space, we employ a weight-slicing schema to maintain all the submodels in exactly one network. Furthermore, we leverage several solutions to solve the discrete optimization issues caused by the router module. Thanks to them, CANet could adaptively adjust its network architecture for each input in an end-to-end manner, in which the user preference can be effectively captured. To evaluate our work, we conduct extensive experiments on benchmark datasets. Experimental results show that CANet reduces computation by 55 ∼ 65% while preserving the accuracy of the original model. Our codes are available at https://***/icantnamemyself/CANet. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Equivariant Graph Hierarchy-Based Neural Networks

arXiv

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arXiv 2022年

作者： Han, Jiaqi Huang, Wenbing Xu, Tingyang Rong, Yu Department of Computer Science and Technology Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tencent AI Lab China

Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems. Existing EGNs conduct flat message passing, which, yet, is unable to capture the spatial/dynamical hierarchy for complex systems particularly, limiting substructure discovery and global information fusion. In this paper, we propose Equivariant Hierarchy-based Graph Networks (EGHNs) which consist of the three key components: generalized Equivariant Matrix Message Passing (EMMP), E-Pool and E-UnPool. In particular, EMMP is able to improve the expressivity of conventional equivariant message passing, E-Pool assigns the quantities of the low-level nodes into high-level clusters, while E-UnPool leverages the high-level information to update the dynamics of the low-level nodes. As their names imply, both E-Pool and E-UnPool are guaranteed to be E(n)-equivariant to meet the physical symmetry. Considerable experimental evaluations verify the effectiveness of our EGHN on several applications including multi-object dynamics simulation, motion capture, and protein dynamics modeling. © 2022, CC BY.

关键词： Message passing

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Regional adversarial training for better robust generalization

arXiv

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arXiv 2021年

作者： Song, Chuanbiao Fan, Yanbo Yang, Yichen Wu, Baoyuan Li, Yiming Li, Zhifeng He, Kun Huazhong University of Science and Technology Tencent The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Tsinghua University

Adversarial training (AT) has been demonstrated as one of the most promising defense methods against various adversarial attacks. To our knowledge, existing AT-based methods usually train with the locally most adversarial perturbed points and treat all the perturbed points equally, which may lead to considerably weaker adversarial robust generalization on test data. In this work, we introduce a new adversarial training framework that considers the diversity as well as characteristics of the perturbed points in the vicinity of benign samples. To realize the framework, we propose a Regional Adversarial Training (RAT) defense method that first utilizes the attack path generated by the typical iterative attack method of projected gradient descent (PGD), and constructs an adversarial region based on the attack path. Then, RAT samples diverse perturbed training points efficiently inside this region, and utilizes a distance-aware label smoothing mechanism to capture our intuition that perturbed points at different locations should have different impact on the model performance. Extensive experiments on several benchmark datasets show that RAT consistently makes significant improvement on standard adversarial training (SAT), and exhibits better robust generalization. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China

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International Journal of Digital Earth 2022年第1期15卷 748-769页

作者： Zhe Chen Ruichun Chang Wenbo Zhao Sijia Li Huadong Guo Keyan Xiao Lin Wu Dong Hou Lu Zou College of Mathematics and Physics Chengdu University of TechnologyChengduPeople’s Republic of China Digital Hu Line Research Institute Chengdu University of TechnologyChengduPeople’s Republic of China Geomathematics Key Laboratory of Sichuan Province ChengduPeople’s Republic of China Key Laboratory of Digital Earth Science Chinese Academy of SciencesAerospace Information Research InstituteBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China International Research Centre of Big Data for Sustainable Development Goals (CBAS) BeijingPeople’s Republic of China MNR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral ResourcesChinese Academy of Geological SciencesBeijingPeople’s Republic of China

The geothermal resources in the southwest section of the Mid-Spine Belt of Beautiful China are abundant,but the quantitative prediction and evaluation of geothermal resources are very difficult. Based on geographic information system (GIS) and remote sensing (RS) platforms,six impact factors,namely land surface temperature,fault density,Gutenberg–Liszt B value,formation combination entropy,distance to river and aeromagnetic anomaly were selected. Through the establishment of the certainty factor model (CF),weights of the information entropy certainty factor model (ICF) and weights of the evidence certainty factor model (ECF),the geothermal potential in the study area were predicted quantitatively. Based on the ECF results,the six main geothermal resource areas were delineated. The results show that (1) ECF had high prediction accuracy (success index is 0.00405%,area ratio is 0.867);(2) The geothermal resource areas obtained were Ganzi–Ya’an–Liangshan,Panzhihua–Liangshan,Dali–Chuxiong,Nujiang–Baoshan,Diqing–Dali,and Lijiang–Diqing. The results provide a basis for the effective development and utilization of geothermal resources in the southwest section of the mid-ridge belt.

关键词： Mid-Spine Belt of Beautiful China certainty factor geothermal resource area weights of evidence

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Towards on Blockchain data Privacy Protection with Cryptography and Software Architecture Approach 2nd

Towards on Blockchain Data Privacy Protection with Cryptogra...

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2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020

作者： Wang, Zexu Wen, Bin Luo, Ziqiang School of Information Science and Technology Hainan Normal University Haikou China Cloud Computing and Big Data Research Center Hainan Normal University Haikou China Key Laboratory of Data Science and Intelligence Education of Ministry of Education Hainan Normal University Haikou571158 China

ISBN: (纸本)9789811592126

The essence of blockchain is to solve trust problems and realize value transfer. The traditional centralized processing method adopts centralized transmission and storage of users’ data privacy, which improves the security and reliability of processing. The non central blockchain technology uses distributed ledger technology to realize the characteristics of disintermediation, data tampering, traceability, work audit, etc. encryption algorithm is used to encrypt the data, and consensus mechanism makes the data sharing of blockchain system more fair and stable. The current passive data privacy schemes are basically based on cryptography. On the premise of satisfying the constraint mechanism of blockchain, this paper studies the technical framework, encryption mechanism and empirical analysis of blockchain, and discusses the information privacy protection method of hiding the original big data as much as possible, so as to improve the system performance and protect the security and privacy of user data. At the same time, we study data privacy protection from the perspective of software architecture, and propose a data privacy protection scheme through algorithm decomposition multi center collaboration method, which provides guidance for big data sharing and transaction based on blockchain. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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