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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Haojun Zhao Yun Lin Song Gao Shui Yu College of Information and Communication Engineering Harbin Engineering University Harbin P. R. China National Pilot School of Software Yunnan University China School of Computer Science University of Technology Sydney Australia

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

The discovery of adversarial examples poses a serious risk to the deep neural networks (DNN). By adding a subtle perturbation that is imperceptible to the human eye, a well-behaved DNN model can be easily fooled and completely change the prediction categories of the input samples. However, research on adversarial attacks in the field of modulation recognition mainly focuses on increasing the prediction error of the classifier, while ignores the importance of decreasing the perceptual invisibility of attack. Aiming at the task of DNNbased modulation recognition, this study designs the Fitting Difference as a metric to measure the perturbed waveforms and proposes a new method: the Nesterov Adam Iterative Method to generate adversarial examples. We show that the proposed algorithm not only exerts excellent white-box attacks but also can initiate attacks on a black-box model. Moreover, our method decreases the waveform perceptual invisibility of attacks to a certain degree, thereby reducing the risk of an attack being detected.

关键词： Perturbation methods Modulation Measurement Iterative methods Predictive models Task analysis Convergence

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A novel approach to reducing information leakage for quantum key distribution

arXiv

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arXiv 2021年

作者： Mao, Hao-Kun Zhao, Qiang Qiao, Yu-Cheng Yan, Bing-Ze Xu, Bing-Jie Abd El-Latif, Ahmed A. Li, Qiong Department of Computer Science and Technology Harbin Institute of Technology Harbin150080 China College of Software Engineering ZhengZhou University of Light Industry ZhengZhou450053 China Guangxi Key Lab Cryptography & Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Quantum key distribution (QKD) is an important branch of quantum information science as it holds promise for unconditionally secure communication. For QKD research, a central issue is to improve the final secure key rate (SKR) and the maximal transmission distance. To address this issue, most works focused on reducing the information leakage of QKD. In this paper, we propose a novel approach to further reduce the information leakage by specially considering the overlap between the information leakage of quantum part and post-processing part. The overlap means that the information leakage of post-processing part caused solely by multi-photon pulses is considered twice in previous studies, i.e., both in quantum part and post-processing part. Since the information carried by multi-photon pulses has been considered as completely known by Eve through the photon-number-splitting attack in quantum part, there is no need to consider it in post-processing part repetitively during the SKR calculation. Therefore, our approach can theoretically reduce the information leakage of a QKD protocol. Based on this idea, we derive the formulas to calculate the amount of information leakage for decoy-BB84 and sending-or-not-sending twin-field protocols. Simulation results for these two typical protocols also demonstrate that our approach evidently improves the SKR as well as the maximal transmission distance under practical experimental parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum cryptography

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COVID-19 pneumonia level detection using deep learning algorithm

TechRxiv

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TechRxiv 2020年

作者： Ghafoor, Kayhan Zrar Ali, Abbas M. Mulahuwaish, Aos Maghdid, Halgurd S. Hussein, Safwan Mawlood Mohammed, Marwan A. Department of Software Engineering Salahaddin University-Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom Department of Computer Science and Information Systems Saginaw Valley State University University Center 7400 Bay Rd Science East 174 MI48710 United States Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq Computer Engineering Department Tishk International University Iraq

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases;in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65% and 91.304 % of CNN and kNN respectively. © 2020, CC BY.

关键词： COVID-19

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A Graph-based Methodology for Tracking Covid-19 in Time Series Datasets

A Graph-based Methodology for Tracking Covid-19 in Time Seri...

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International Conference on Electronics, Control, Optimization and computer science (ICECOCS)

作者： Zakariyaa Ait El Mouden Abdeslam Jakimi Rachida Moulay Taj Moha Hajar Software Engineering & Information Systems Engineering Faculty of Sciences and Techniques Errachidia Moulay Ismail University Meknes Morocco Operational Research & Computer Science Faculty of Sciences and Techniques Errachidia Moulay Ismail University Meknes Morocco

ISBN: (数字)9781728169217

ISBN: (纸本)9781728169224

Since its first appearance in December 2019, Covid-19 has become a wide field of scientific research. Starting from biology to bioinformatic solutions, Artificial Intelligence has contributed in turn as a powerful tool for tracking and predicting the outbreak of Covid-19 using different types of datasets. Chest X-ray images are widely used in computer vision applications and Time series datasets are used for predicting the spread of the novel coronavirus. Graph analytics is a recent field of study that links the mathematical definition and operations of a graph to its application in computer science as a complex data structure, this combination has played a critical role in making graph-based applications present in different fields. One of the most powerful graph analytics is community detection which is an intelligent and unsupervised grouping of a set of graph structured data using the similarity between them. The aim of this work is to highlight the importance of graph-based algorithms in tracking Covid-19 using time series datasets, our work will also focus on Spectral Clustering (SC) as a community detection approach to extract clusters from the input datasets. Further applications are needed in order to validate the proposed theoretical approach.

关键词： COVID-19 Data models Time series analysis Clustering algorithms Laplace equations X-ray imaging Object oriented modeling

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QA-KGNet: A Language Model-Driven Knowledge Graph Question-Answering Model

引用

Ruan Jian Xue Bao/Journal of software 2023年第10期34卷

作者： Qiao, Shao-Jie Yang, Guo-Ping Yu, Yong Han, Nan Qin, Xiao Qu, Lu-Lu Ran, Li-Qiong Li, He School of Software Engineering Chengdu University of Information Technology Chengdu610225 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China School of Management Chengdu University of Information Technology Chengdu610225 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School of Computer Science and Technology XiDian University Xi’an710071 China

The question-answering system based on knowledge graphs can analyze user questions, and has become an effective way to retrieve relevant knowledge and automatically answer the given questions. The knowledge graph-based question-answering system usually uses a neural program induction model to convert natural language question into a logical form, and the answer can be obtained by executing the logical form on the knowledge graph. However, the knowledge question-answering system by using pre-trained language models and knowledge graphs involves two challenges: 1) given the QA (Question-answering) context, relevant knowledge needs to be identified from a large KG (Knowledge Graph);2) we need to perform the joint reasoning on QA context and KG. Based on these challenges, a language model-driven knowledge graph question-answering model is proposed, which connects the QA context and KG to form a joint graph, and uses a language model to calculate the relevance of the given QA context nodes and KG nodes, and a multi-head graph attention network is employed to update the node representation. Extensive experiments on the CommonsenseQA, OpenBookQA and MedQA-USMLE real datasets are conducted to evaluate the performance of QA-KGNet and the experimental results show that QA-KGNet outperforms existing benchmark models and exhibits excellent structured reasoning capability. © 2023 Chinese Academy of sciences. All rights reserved.

关键词： Knowledge graph

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Designing a Pseudo-Haptics Study for Virtual Anesthesia Skills Development

Designing a Pseudo-Haptics Study for Virtual Anesthesia Skil...

引用

IEEE Games Media Entertainment (GEM)

作者： Bill Kapralos Alvaro Quevedo KC Collins Celina Da Silva Eva Peisachovich Kamen Kanev Michael Jenkin Adam Dubrowski Fahad Alam maxSIMhealth. Ontario Tech University Oshawa Ontario Canada Software and Informatics Research Centre. Ontario Tech University Oshawa Ontario Canada Collaborative Human Immersive Interaction Lab Toronto Canada School of Information Technology Carleton University Ottawa Ontario Canada Department of Health York University Toronto Ontario Canada Research Institute of Electronics Shizuoka University Hamamatsu Japan Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Canada Faculty of Health Sciences Ontario Tech University Oshawa Ontario Canada Department of Anesthesia Sunnybrook Health Science Centre Toronto Ontario Canada

ISBN: (纸本)9781665461399

Pseudo-haptics refers to the simulation of haptic sensations without the use of haptic interfaces, using, for example, audiovisual feedback and kinesthetic cues. Given the COVID-19 pandemic and the shift to online learning, there has been a recent interest in pseudo-haptics as it can help facilitate psychomotor skills development away from simulation centers and laboratories. Here we present work-in-progress that describes the study design of a pseudo-haptics for virtual anesthesia skills development. We anticipate this work will provide greater insight to pseudo-haptics and its application to anesthesia-based training.

关键词： Training COVID-19 Pandemics Laboratories Entertainment industry Games Media

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Improving Gamma Imaging in Proton Therapy by Sanitizing Compton Camera Simulated Patient Data using Neural Networks through the BRIDE Pipeline

Improving Gamma Imaging in Proton Therapy by Sanitizing Comp...

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IEEE International Conference on Big Data

作者： Michael O. Chen Julian Hodge Peter L. Jin Ella Protz Elizabeth Wong Ruth Obe Ehsan Shakeri Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Vijay R. Sharma Sina Mossahebi Lei Ren Stephen W. Peterson Jerimy C. Polf Departments of Mathematics Dartmouth College USA Dept. of Mathematics and Statistics U. of Maryland USA James M. Bennett High School Salisbury MD USA Department of Mathematics and Sciences Florida Atlantic University USA Department of Mathematics Brookdale Community College USA Dept. of Computer Science and of Software Engineering U. of Houston Clear Lake USA Dept. of Information Systems U. of Maryland USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa M3D Inc. USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedfoward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel ‘Big-data REU Integrated Development and Experimentation’ (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research.

关键词： Particle beams Recurrent neural networks Accuracy Pipelines Phantoms Scattering Cameras Data models software Radiation therapy

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DDU-Net: A dual dense U-structure network for medical image segmentation

引用

Applied Soft Computing 2022年 126卷

作者： Junlong Cheng Shengwei Tian Long Yu Shijia Liu Chaoqing Wang Yuan Ren Hongchun Lu Min Zhu College of Computer Science & Software Sichuan University Chengdu 610065 China College of Information Science and Engineering Xinjiang University Urumqi 830000 China College of Software Engineering Xin Jiang University Urumqi 830000 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 610031 Sichuan China

Medical image segmentation is one of the important steps in medical image analysis and has a wide range of applications and research values in medical research and practice. However, it is still a challenging task due to the characteristics of medical images with diverse lesion scales, blurred structural boundaries and numerous modalities. To address these challenges, we design a simple yet powerful dual dense u-structured network (called DDU-Net) for medical image segmentation. Specifically, we first construct dual encoders with the densely connected, where the first encoder uses DenseNet, whose backbone network is pre-trained on ImageNet, as a fixed feature extractor, and the second encoder uses a network structure similar to the first encoder. Both encoders try to encode information on the input image, and each layer is directly connected to the next layer in a feed-forward manner. This approach not only greatly reduces the number of parameters in the network, but also makes it easier to train with smaller samples. We then design up-sampling paths applicable to the dual encoder structure, and construct deeper decoders through densely connected convolutional layers, fusing the low to high level multi-scale semantic information learned by the two encoders. Finally, we employ a multiple loss function that combines boundary and content information to make DDU-Net more focus on the accuracy of boundary delineation to improve the overall segmentation performance. We have conducted comprehensive experiments on five different medical image segmentation datasets, including skin lesion segmentation, nuclei segmentation, lung segmentation, gland segmentation and vessel segmentation (IoU metrics for DDU-Net are 0.790, 0.860, 0.925, 0.812 and 0.696 respectively). Compared to baseline U-Net and other state-of-the-art methods, DDU-Net achieves competitive segmentation performance in both qualitative and quantitative evaluation.

关键词： Medical image segmentation Dual dense U-structure network Up-sampling path Multiple loss function

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Learning sampling policy for faster derivative free optimization

arXiv

引用

arXiv 2021年

作者： Zhai, Zhou Gu, Bin Huang, Heng School of Computer & Software Nanjing University of Information Science & Technology Nanjing China department ofmachine learning Mohamed bin Zayed University of Artificial Intelli-gence United Arab Emirates JD Finance America Corporation Mountain ViewCA94043 United States Department of Electrical & Computer Engineering University of Pittsburgh United States JD Finance America Corporation

Zeroth-order (ZO, also known as derivative-free) methods, which estimate the gradient only by two function evaluations, have attracted much attention recently because of its broad applications in machine learning community. The two function evaluations are normally generated with random perturbations from standard Gaussian distribution. To speed up ZO methods, many methods, such as variance reduced stochastic ZO gradients and learning an adaptive Gaussian distribution, have recently been proposed to reduce the variances of ZO gradients. However, it is still an open problem whether there is a space to further improve the convergence of ZO methods. To explore this problem, in this paper, we propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling. To find the optimal policy, an actor-critic RL algorithm called deep deterministic policy gradient (DDPG) with two neural network function approximators is adopted. The learned sampling policy guides the perturbed points in the parameter space to estimate a more accurate ZO gradient. To the best of our knowledge, our ZO-RL is the first algorithm to learn the sampling policy using reinforcement learning for ZO optimization which is parallel to the existing methods. Especially, our ZO-RL can be combined with existing ZO algorithms that could further accelerate the algorithms. Experimental results for different ZO optimization problems show that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by learning a sampling policy, and converge faster than existing ZO algorithms in different scenarios. © 2021, CC BY.

关键词： Gaussian distribution

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Evaluating the Deep Convolutional Neural Network for Thyroid Nodule Detection on Vietnamese Ultrasound Dataset 16th

Evaluating the Deep Convolutional Neural Network for Thyroid...

引用

16th International Conference on Intelligent information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of information Technology, Applications and Tools, FITAT 2020

作者： Pham, Tri-Cong Doucet, Antonie Hoang, Van-Dung Nguyen, Quang-Hoa Phan, Trong-Bach Tran, Cong-Thanh Bui, Tran-Tien Luong, Chi-Mai Bui, Van-Giang School of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam ICTLab University of Science and Technology of Hanoi VAST: Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam FPT Software Hanoi Viet Nam Laboratory L3i University La Rochelle La Rochelle France Ho Chi Minh City University of Technology and Education Ho Chi Minh City Viet Nam Institute of Information Technology VAST: Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi Viet Nam Center of Radiology Campus 3 of K Hospital Tan Trieu Thanh Tri Ha Noi Viet Nam

ISBN: (纸本)9789813367562

Nowadays, computer-aided diagnosis (CAD) systems play an important role in supporting doctors and improving the performance of thyroid nodule detection which able to reduce the physician’s examination burden. In addition, medical data have certain characteristics that make their application very challenging on the existing system. In this paper, we propose an ultrasound image-based of thyroid nodule detection method using state-of-the-art object detectors. We employed radiologist knowledge from clinical studies such as size and aspect ratio distributions of real nodules to generate better region proposals and improve the detection accuracy. Additionally, we contribute a new Vietnamese thyroid nodule dataset, which is collected from Vietnamese Hospitals and validated by experienced radiologists. With this dataset, we expect to provide an essential medical imaging research resource for CAD development and validation. Through experimental results, our detection network FasterIncResnet** that was optimized with radiologist’s knowledge yielded the highest performance compared with original-based methods. Our optimized detection sensitivity, specificity, and AUC were 0.87, 0.86, and 0.89, respectively, on the Vietnamese dataset. The overall performance improves ~2.4% compared with the original FasterIncResnet method. The result proved the effectiveness of the proposed method on a new Vietnamese dataset in order to build an accurate system for thyroid nodule detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Medical imaging

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