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SSRN 2022年

作者： Yang, Zhe Bu, Ziyu Liu, Chunping Provincial Key Laboratory for Computer Information Processing Technology Suzhou215006 China Department of Computer Science and Technology Soochow University Suzhou215006 China Provincial Key Laboratory for Intelligent Engineering in Big Data Suzhou215006 China

Object detection has been an important research branch in the field of computer vision. The single-shot-detection (SSD) is an object detection model based on deep learning, which can achieve a good balance between the detection accuracy and the detection speed, but has the problem of poor recognition accuracy for small objects. To address this limitation , this paper improves the structure of the SSD feature pyramid and upsamples the shallow feature map with small object information and fuses it with the upper feature map, thus enhancing the ability of the shallow feature map to represent detailed information. In this way, not only the overall detection accuracy of the SSD is improved, but also a relatively high detection speed is maintained. The proposed model is verified by experiments on two common datasets, the Pascal VOC and MS COCO datasets. On the PASCAL VOC07+12, MS COCO14, and VOC07+12 + COCO datasets, the improved model achieves the mean average precision values of 80.1% (+3.3% compared with the conventional model), 49.9% (+6.8%), and 82.1% (+3.0%), respectively. Meanwhile, the proposed model can achieve the detection speed of 42.2 frames per second. © 2022, The Authors. All rights reserved.

关键词： Object recognition

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Leveraging tumor heterogeneity: heterogeneous graph representation learning for cancer survival prediction in whole slide images 24

Leveraging tumor heterogeneity: heterogeneous graph represen...

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

作者： Junxian Wu Xinyi Ke Xiaoming Jiang Huanwen Wu Youyong Kong Lizhi Shao School of Internet Anhui University and Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Department of Pathology State Key Laboratory of Complex Severe and Rare Disease Molecular Pathology Research Center Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Bioinformatics Chongqing University of Post and Telecommunications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education China School of Internet Anhui University

ISBN: (纸本)9798331314385

Survival prediction is a significant challenge in cancer management. Tumor microenvironment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impact the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i.e., BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods. Source code is available at https://***/wjx-error/ProtoSurv.

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Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system

arXiv

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arXiv 2024年

作者： Dong, Li Jiang, Feibo Wang, Minjie Peng, Yubo Li, Xiaolong School of Computer Science Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Information Science and Engineering Hunan Normal University Changsha China

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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ALCencryption:A Secure and Efficient Algorithm for Medical Image Encryption

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computer Modeling in Engineering & Sciences 2020年第12期125卷 1083-1100页

作者： Jiao Ge College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

With the rapid development of medical informatization and the popularization of digital imaging equipment,DICOM images contain the personal privacy of patients,and there are security risks in the process of storage and transmission,so it needs to be *** order to solve the security problem of medical images on mobile devices,a safe and efficient medical image encryption algorithm called ALCencryption is *** algorithm first analyzes the medical image and distinguishes the color image from the gray *** gray images,the improved Arnold map is used to scramble them according to the optimal number of iterations,and then the diffusion is realized by the Logistic and Chebyshev map cross-diffusion *** color image is encrypted by cross-diffusion algorithm of double chaotic *** and efficiency analysis show that the ALCencryption algorithm has the characteristics of small neighboring pixels,large key space,strong key sensitivity,high safety and short encryption *** is suitable for medical image encryption of mobile devices with high real-time requirements.

关键词： Patient privacy DICOM medical image encryption scrambling degree cross-diffusion

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Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Liu, Kaibo Zheng, Wangli Min, Huasong Lin, Yunhan School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China State Grid Electric Power Research Institute Jiangsu Nanjing211106 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Hubei Wuhan430081 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

In the application scenario of robot autonomous tasks, the robot needs to be able to complete calibration online and automatically to achieve self-maintenance, which differs from traditional robot hand-eye calibration in that the traditional robot hand-eye calibration requires a dedicated calibration board to assist offline completion. Aiming at the problem that the existing self-calibration methods cannot be optimized as a whole, which leads to low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed, which describes the solution of hand-eye self-calibration as a minimization objective function problem involving multiple stages. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Two different types of experiments were designed to validate our method. One is a single camera dataset experiment, which shows that our method is more accurate and robust than the existing self-calibration method;the other is an application platform experiment, which verifies the feasibility and availability of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Calibration

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A Photonics-aided MMW OFDM Joint Radar and Communication System with Velocity Accuracy Improvement

A Photonics-aided MMW OFDM Joint Radar and Communication Sys...

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Peng, Lanfeng Luo, Dongxiang Xiao, Yaoqiang Li, Fan School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou510275 China Institute of Semiconductors South China Normal University Guangzhou510631 China School of Chemistry and Chemical Engineering Huangpu Hydrogen Innovation Center Guangzhou University Guangzhou510006 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9781665461634

Photonics-aided millimeter wave (MMW) Orthogonal frequency division multiplexing (OFDM) system is a novel approach to realize the joint radar and communication system. The phase noise introduced by the laser will degrade the system performance. In this paper, we analyze the mechanism of phase noise acting on OFDM radar system. A method that combines chirp-Z transform (CZT) and compensation enabled by a reference signal is adopted to improve the velocity accuracy. RF pilot (RFP) and blind phase search (BPS) algorithm are adopted to improve communication performance. A simulation is demonstrated at 30 GHz with 2 GHz communication bandwidth. A twodimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 0.980 m/s and a communication capacity of 6.4 Gbps are obtained in the proposed joint radar and communication system. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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FTG: Score-based black-box watermarking by fragile trigger generation for deep model integrity verification

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Journal of information and Intelligence 2024年第1期2卷 28-41页

作者： Heng Yin Zhaoxia Yin Zhenzhe Gao Hang Su Xinpeng Zhang Bin Luo Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui UniversityHefei 230000China Shanghai key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai 200241China School of Communication and Electronic Engineering East China Normal UniversityShanghai 200241China Department of Computer Science and Technology Tsinghua UniversityBeijing 100000China School of Computer Science and Technology Fudan UniversityShanghai 200241China

Deep neural networks(DNNs)are widely used in real-world applications,thanks to their exceptional performance in image ***,their vulnerability to attacks,such as Trojan and data poison,can compromise the integrity and stability of DNN ***,it is crucial to verify the integrity of DNN models to ensure their *** research on model watermarking for integrity detection has encountered the issue of overexposure of model parameters during embedding and extraction of the *** address this problem,we propose a novel score-based black-box DNN fragile watermarking framework called fragile trigger generation(FTG).The FTG framework only requires the prediction probability distribution of the final output of the classifier during the watermarking *** generates different fragile samples as the trigger,based on the classification prediction probability of the target classifier and a specified prediction probability mask to watermark *** prediction probability masks can promote the generation of fragile samples in corresponding distribution *** whole watermarking process does not affect the performance of the target *** verifying the watermarking information,the FTG only needs to compare the prediction results of the model on the samples with the previous *** a result,the required model parameter information is reduced,and the FTG only needs a few samples to detect slight modifications in the *** results demonstrate the effectiveness of our proposed method and show its superiority over related *** FTG framework provides a robust solution for verifying the integrity of DNN models,and its effectiveness in detecting slight modifications makes it a valuable tool for ensuring the security and stability of DNN applications.

关键词： Neural network Sensitive sample Fragile watermarking Model integrity Fragile trigger

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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A Parallel Algorithm to Construct Edge Independent Spanning Trees on the Line Graphs of Conditional Bijective Connection Networks

SSRN

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SSRN 2022年

作者： Pan, Zhiyong Cheng, Baolei Fan, Jianxi Wang, Yan Li, Xiajing School of Computer Science and Technology Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

The line graphs of some well-known graphs, such as genralized hypercube and crossed cube, have been adopted for the logic graphs of data center networks (DCNs). Conditional bijective connection networks (conditional BC networks) are a class of networks which have been proved include hypercube, crossed cube, locally twisted cube and Möbius cube, etc. Hence, the research on the line graphs of conditional BC networks can be applied to DCNs. Edge independent spanning trees (EISTs) in a graph have received extensive attention because of their applications in reliable communication, fault-tolerant broadcasting and secure message distribution. Since the line graphs of conditional BC networks, denoted as L ( XCn), are (2 n −2)-regular, whether there exist 2 n −2 EISTs on L (XCn) is an open question. In this paper, we first propose a parallel algorithm to construct 2 n −2 EISTs rooted at an arbitrary node in L (XCn), where n ≥ 1. Then, we prove the correctness of our algorithm. © 2022, The Authors. All rights reserved.

关键词： Geometry

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MFGB: Molecular Properties Prediction leveraging Self-supervised Morgan Fingerprint Representation Learning

MFGB: Molecular Properties Prediction leveraging Self-superv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuyan Fang Yu Liu Song Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan University of Science and Technology Wuhan China

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improve the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules.

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