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4th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021

作者： Zou, Bangqi Zhao, Gansen Tang, Hua Nie, Ruihua Huang, Runhua Tang, Junjing South China Normal University School of Computer Science China Key Lab on Cloud Security and Assessment Technology of Guangzhou China SCNU VeChina Joint Lab on BlockChain Technology and Application Guangzhou China

ISBN: (纸本)9781665415965

Electronic archives are electronic files with very high security requirements. We use block chain technology to implement a distributed PKI system. On this basis, we build an archives system ArchivesChain based on block chain to solve the drawbacks of the current centralized storage of archives and the security problems caused by the excessive centralization of traditional *** have implemented multi-point authentication of archive data through block chain technology, including certificate registration and verification confirmation when archives are generated, ensuring data security during the process of receiving and migrating archives, and generating archive certificates on the chain, similar to handling identity cards for each electronic *** with CA authentication systems in traditional PKI, the block chain-based PKI in our system is provided by all members of the Federation to verify and authenticate the data, thus ensuring that archives information can not be tampered with and providing authentic authentication with credibility. © 2021 IEEE.

关键词： blockchain

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A Garbage Image Classification Framework Based on Deep Learning 10th

A Garbage Image Classification Framework Based on Deep Learn...

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10th International Conference on Computer Engineering and Networks, CENet 2020

作者： Lin, Chengchuang Zhao, Gansen Zhao, Lei Chen, Bingchuan School of Computer Science South China Normal University Guangzhou China Key Lab on Cloud Security and Assessment Technology of Guangzhou Guangzhou China SCNU & VeChina Joint Lab on BlockChain Technology and Application Guangzhou510631 China Guangdong University of Finance and Economics Guangzhou510320 China

ISBN: (纸本)9789811584619

In this paper, we present a garbage image classification framework to tackle the waste sorting problem which besets residents around the world every day. The proposed framework consists of two modules, a convolutional neural network backbone transferred from the ImageNet classification task and a customized network header designed for the garbage image classification task. To friendly deploy into mobile devices, the proposed method makes an artful tradeoff between classification accuracy and running efficiency. The proposed framework yields 95.62% online classification accuracy on the test dataset provided by Huawei cloud with 95ms per image inference time occupying 897 Megabytes GPU memory. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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A multi-stages chromosome segmentation and mixed classification method for chromosome automatic karyotyping 1

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17th International Conference on Web Information Systems and applications, WISA 2020

作者： Lin, Chengchuang Zhao, Gansen Yin, Aihua Ding, Bichao Guo, Li Chen, Hanbiao School of Computer Science South China Normal University Guangzhou510631 China Guangdong Women and Children Hospital Guangzhou511400 China Key Lab on Cloud Security and Assessment technology of Guangzhou Guangzhou China SCNU & VeChina Joint Lab on BlockChain Technology and Application Guangzhou510631 China

ISBN: (数字)9783030600297

ISBN: (纸本)9783030600280

The chromosome karyotyping task is vital and indispensable but tedious work for birth defect diagnosis and biomedical research. In this work, we tackle chromosome automatic karyotyping using a multi-stages chromosome segmentation and mixed classification method. Firstly, we apply a global binary threshold-based method to segment the metaphase chromosome microscope grayscale image into several image slices, consisting of chromosome instances and chromosome clusters. Afterward, we propose a mixed chromosome classification method for identifying a given image is a chromosome cluster or corresponding instance label. After that, we use a deep learning-based approach to segment chromosome cluster images into chromosome instances and apply the mixed chromosome classification model to recognize their corresponding labels. Finally, we synthesize a chromosome karyotype from all corresponding instances and labels. In the mixed classification stage, the proposed method yields 99.53± 0.23% classification accuracy on the clinical dataset. In segmentation stages, the proposed method achieves 90.81% comprehensive segmentation accuracy and 85.00% instance segmentation accuracy with 90.63% $$AP:{50}$$ precision. The experimental results show that our proposed method is promising for solving chromosome segmentation and classification task of the clinical chromosome automatic karyotyping. © Springer Nature Switzerland AG 2020.

关键词： Chromosomes

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A Garbage Image Classification Framework Based on Deep Learning

A Garbage Image Classification Framework Based on Deep Learn...

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作者： Chengchuang Lin Gansen Zhao Lei Zhao Bingchuan Chen School of Computer Science South China Normal University Key Lab on Cloud Security and Assessment Technology of Guangzhou SCNU & VeChina Joint Lab on BlockChain Technology and Application Guangdong University of Finance and Economics

In this paper, we present a garbage image classification framework to tackle the waste sorting problem which besets residents around the world every day. The proposed framework consists of two modules, a convolutional neural network backbone transferred from the ImageNet classification task and a customized network header designed for the garbage image classification *** friendly deploy into mobile devices, the proposed method makes an artful tradeoff between classification accuracy and running efficiency. The proposed framework yields 95.62% online classification accuracy on the test dataset provided by Huawei cloud with 95 ms per image inference time occupying 897 Megabytes GPU memory.

关键词： Transfer learning Garbage classification Weakly supervised learning

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A Survey on the Efficiency, Reliability, and Security of Data Query in blockchain Systems

SSRN

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SSRN 2022年

作者： Zhang, Qizhi He, Yale Lai, Ruilin Hou, Zhihao Zhao, Gansen School of Computer Science South China Normal University Shipai Campus Guangdong Guangzhou51063 China Key Lab on Cloud Security and Assessment technology of Guangzhou Guangdong Guangzhou51063 China SCNU & VeChina Joint Lab on BlockChain Technology and Application Guangdong Guangzhou51063 China

With the rise of digital currencies, blockchain systems work for storing data efficiently and securely, creating a decentralized and tamper-proof digital platform. However, less works focus on data backtracking and supporting for many complex query functions. Therefore, we explore blockchain queries from both a technical and an application standpoint. Current methods to deliver are based on using distributed databases, data indexing structures, and various cryptographic algorithms to implement rich, verifiable, and secure query functions. As a result, the purpose of this survey is to provide some guidance on blockchain-related query technology. We analyze the issues of query efficiency, query reliability, and privacy protection of queries in blockchain. In addition we summarize some classic scenarios of query schemes in blockchain and discuss future research challenges that still need to be addressed by blockchain query technology. © 2022, The Authors. All rights reserved.

关键词： blockchain

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Translation Of DDL And DML For Constructing Flexible Schemas In Saas applications

Translation Of DDL And DML For Constructing Flexible Schemas...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Xiliang Wang Gansen Zhao Xinming Wang Xiaocong Zhou School of Computer Science South China Normal University Guangzhou China SCNU& VeChina Join Lab On Blockchain Technology And Application Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

Multi-tenancy is one of the most important features in Software as a Service(SaaS). In order to reduce cost, SaaS service vendors typically implement multi-tenant enabled database on top of conventional SQL database, and Universal Table is one of the most commonly used implementation technique. The key idea is to map all tenant data into one single table. At the application level, such a scheme-mapping is implemented by translating SQL statements. Previously, researchers have introduce methods to the translation of SQL query. In this work, we extend previous work by proposing a method that support the translation of full SQL syntax, including both data manipulation language (DML) and data definition language (DDL). Our method is based on syntax-directed translation on the context-free grammar of full SQL syntax. We have implemented our method as an automatic translation tool based on the ANTLR framework. In order to evaluate the effectiveness and efficiency of our method, we conduct an empirical study using the TPC-C benchmark. The results show that our method is scalable to the number of tenants and the size of database.

关键词： Indexes Tools Software as a service Syntactics Structured Query Language

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Detection of High-Low Risk Lung Tumors Using Semi-Supervised and Selective labeling Techniques

Detection of High-Low Risk Lung Tumors Using Semi-Supervised...

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International joint Conference on Neural Networks (IJCNN)

作者： Jinping Lao Hongwei Lin Haiyu Zhou Chengchuang Lin Zhaoliang Zheng Gansen Zhao Hua Tang Key Lab on Cloud Security and Assessment Technology of Guangzhou and SCNU VeChina Joint Lab on BlockChain Technology and Application South China Normal University Guangzhou China The First School of Clinical Medicine Guangdong Medical University Zhanjiang China Department of Thoracic Surgery Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The accurate identification of low-risk and high-risk lung tumors is essential for clinicians to develop lung cancer treatment strategies during surgery. Despite the achievements in deep learning, most medical image analysis applications are still hampered by the difficulty of obtaining large amounts of labeled data. In this paper, a semi-supervised deep learning framework, DS-FixMatch, is proposed for identifying lung tumors intra-operatively while alleviating the issue of sparse annotations. DS-FixMatch combines selective labeling and semi-supervised training: (1) For the acquired unlabeled images, a subset that best represents the distribution of the entire dataset is selected by an unsupervised algorithm. Compared to the traditional random labeling strategy, this method can avoid introducing samples that are highly influenced by the intraoperative environment for labeling. This subset is then sent to a human expert for labeling. (2) Supervised training is performed using labeled images. For the remaining unlabeled samples, DS-FixMatch utilizes model predictions to generate pseudo-labels for consistency regularization, further enhancing the model’s generalization ability. A dataset consisting of 2221 natural images, each capturing the Region of Interest (ROI) in lung tumors, is constructed to evaluate the effectiveness of the designed framework. Experiments show that DS-FixMatch leads in performance for the task of lung tumor recognition compared to other baselines.

关键词： Training Deep learning Annotations Neural networks Lung cancer Surgery Predictive models

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