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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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HOPE-L: A Lossless database Watermarking Method in Homomorphic Encryption Domain

HOPE-L: A Lossless Database Watermarking Method in Homomorph...

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Smart City and Informatization (iSCI), IEEE International Conference on

作者： Xueqi Zhang Haiyong Xie Hui Lin University of Science and Technology of China Anhui China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Beijing China

ISBN: (纸本)9781665400411

Cloud computing has been widely adopted in the Internet economy; however, this poses numerous risks including illegal data copying and digital copyright infringement, leading to the challenge on protecting the security and copyright of key data stored in databases. In this paper, We propose a novel method, Lossless database Watermarking in Homomorphic Encryption domain (HOPE-L), to address this challenge. Our method combines the database encryption (i.e., homomorphic encryption, order-preserving encryption) and information hiding (i.e., lossless databased watermarking) technologies. More specifically, the information hiding algorithm leverages the homomorphic properties of the homomorphic encryption algorithm to embed secret data. No distortion is introduced throughout the data embedding process. Therefore, we can embed the watermark into the encrypted data losslessly. The watermark can be used to authenticate the copyright, and the recipients can directly recover the original database without loss. Analysis and theorem prove that HOPE-L can achieve more embedding space and no distortion. Extensive experiments show that the whole operation process is time-efficient, the embedded watermark is robust, and HOPE-L performs more robust than the existing algorithms and can resist common database attacks.

关键词： data privacy databases Smart cities Conferences Watermarking Resists Copyright protection

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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An efficient face recognition attack method based on generative adversarial networks and cosine metrics 5

An efficient face recognition attack method based on generat...

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5th Asian Conference on Artificial Intelligence technology, ACAIT 2021

作者： Ding, Hu He, Shumeng Wu, Yanwen Jin, Yongli Gan, Lin Xu, Gaodi Yang, Houqun Hainan University School of Computer Science and Technology Hainan Haikou China Chongqing University School of Big Data and Software Engineering Chongqing China Anhui University School of Computer Science and Technology Anhui Hefei China Purdue University Elmore Family School of Computer and Electrical Engineering West Lafayette United States Hainan Association for Artificial Intelligence Hainan Haikou China

ISBN: (纸本)9781665426305

Deep neural networks are vulnerable to attacks on adversarial samples. These attacks are caused by adding small magnitude perturbations to the input samples, which may lead to misclassification of the deep neural network. Based on the study of the adversarial sample attack network model, we propose an attack sample based on the generative adversarial network HNUGAN, incorporating the cosine metric of disparity recognition, for the features of the face dataset, to construct an attack sample to attack the face recognition system. Using these adversarial samples can reduce the recognition accuracy of models such as GoogleNet and ResNet to a very low level, and thus complete the attack on the target model. Interestingly, we can obtain a batch of adversarial samples through adversarial training to expand the dataset and retrain the target model to improve its robustness and resistance to attacks. © 2021 IEEE.

关键词： Face recognition

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A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

A residual attention-based privacy-preserving biometrics mod...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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Predicting Transcription Factor Binding Signals Based on a Novel Encoder-Decoder Framework 2nd

Predicting Transcription Factor Binding Signals Based on a N...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wang, Siguo Li, Zhipeng Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China

ISBN: (纸本)9789819619061

The transcription process is the initial step in gene expression and the core of the regulatory mechanism, in which transcription factors control gene expression through interactions with particular DNA sequences. Therefore, identification of transcription factor binding sites (TFBSs) provides theoretical basis for further dissection of transcriptional regulatory mechanisms and disease diagnosis. To address the limitations of current research on TFBSs, a novel symmetric Encoder-Decoder framework is proposed for predicting transcription factor binding signals values at base sites. Our approach utilizes word vectors to encode DNA sequences, followed by encoders that progressively extract feature representations from these sequences. We then employ Gated Recurrent Unit (GRU) to capture the contextual dependencies within the sequences, ultimately using decoders to reconstruct the binding signal values for the DNA sequence sites. In order to access the effectiveness of the proposed symmetric Encoder-Decoder framework, some experiments were conducted on 20 ChIP-seq datasets, and the results show that the proposed novel method outperformers several existing advanced methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： DNA sequences

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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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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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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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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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