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9th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2021

作者： Fang, Yangxiu Liu, Jing Li, Jingbing Yi, Dan Cui, Wenfeng Xiao, Xiliang Han, Baoru Bhatti, Uzair Aslam School of Information and Communication Engineering Hainan University Haikou Hainan China State Key Laboratory of Marine Resource Utilization South China Sea Hainan University Haikou Hainan China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou Zhejiang China College of Medical Informatics Chongqing Medical University Chongqing China Nanjing Normal University Nanjing Jiangsu China

ISBN: (纸本)9789811630125

Digital watermarking technology is a good way to solve the problem of image information security. However, in previous studies, watermark is encrypted, but the host image is not encrypted, which causes the leakage of the private information of the images themselves. When the carrier image is a medical image, more attention should be paid. Therefore, a new robust zero-watermarking algorithm based on Bandelet-DCT (Bandelet Transform and Discrete Cosine Transform) for medical image in the encryption domain of DWT-DCT (Discrete Wavelet Transform and Discrete Cosine Transform) is proposed. Firstly, the original medical image is encrypted in a transform domain based on a Logistic chaotic map to enhance the security of the original medical image. Then Bandelet-DCT is applied to extract the feature sequences of encrypted medical images which were preprocessed by SIFT (Scale Invariant Feature Transform). In the stage of watermarking embedding and extraction, zero-watermarking technology is implemented to ensure that the region of interest of the medical image remains unchanged, and evaluate the robustness of the algorithm through the correlation coefficient between the original watermark and the attacked watermark. Experimental results show that the proposed algorithm has strong robustness and can effectively solve the problem of information leakage. It has a strong anti-attack ability, good robustness, and certain application prospects. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete cosine transforms

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A Zero Watermarking Scheme for Encrypted Medical Images Based on Tetrolet-DCT 9th

A Zero Watermarking Scheme for Encrypted Medical Images Base...

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9th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2021

作者： Cui, Wenfeng Liu, Jing Li, Jingbing Fang, Yangxiu Yi, Dan Xiao, Xiliang Bhatti, Uzair Aslam Han, Baoru School of Information and Communication Engineering Hainan University Haikou Hainan China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan University Haikou Hainan China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou Zhejiang China Nanjing Normal University Nanjing Jiangsu China College of Medical Informatics Chongqing Medical University Chongqing China

ISBN: (纸本)9789811630125

Aiming at the problem of medical image leakage in the transmission process, in order to enhance the concealment of patient data, a Tetrolet-DCT based encrypted medical image zero watermarking scheme is proposed. Firstly, DFT and Logistic mapping are used to encrypt the original medical image to obtain the encrypted medical image. Then, Tetrolet-DCT is used to extract the features of the encrypted medical image. After that, zero watermark technology is applied to embed and extract the watermark information of patients. Experimental results show that the proposed scheme ensures the concealment of the original medical image, and can effectively extract the watermark information. It has good robustness both against traditional attacks and geometric attacks. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hospital data processing

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Evidence-aware hierarchical interactive attention networks for explainable claim verification 29

Evidence-aware hierarchical interactive attention networks f...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wu, Lianwei Rao, Yuan Yang, Xiong Wang, Wanzhen Nazir, Ambreen Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University China Shannxi Joint Key Laboratory for Artifact Intelligence Sub-Lab of Xi'an Jiaotong University China Research Institute of Xi'an Jiaotong University Shenzhen China

ISBN: (纸本)9780999241165

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

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Contrastive Learning for Robust Android Malware Familial Classification

arXiv

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arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation

A Semi-Supervised Approach with Error Reflection for Echocar...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoxiang Han Yiman Liu Jiang Shang Qingli Li Jiangang Chen Menghan Hu Qi Zhang Yuqi Zhang Yan Wang School of Communication and Information Engineering Shanghai University Shanghai P.R.China The SMART (Smart Medicine and AI-based Radiology Technology) Lab Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai P.R.China Department of Pediatric Cardiology Shanghai Children’s Medical Center School of Medicine Shanghai Jiao Tong University Shanghai P.R.China Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP) Shanghai P.R.China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai P.R.China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design methods specifically addressing the challenges posed by the poor contrast, blurred edge details and noise of echocardiography. These characteristics pose challenges to the generation of high-quality pseudo-labels in semi-supervised segmentation based on Mean Teacher. Inspired by human reflection on erroneous practices, we devise an error reflection strategy for echocardiography semi-supervised segmentation architecture. The process triggers the model to reflect on inaccuracies in unlabeled image segmentation, thereby enhancing the robustness of pseudo-label generation. Specifically, the strategy is divided into two steps. The first step is called reconstruction reflection. The network is tasked with reconstructing authentic proxy images from the semantic masks of unlabeled images and their auxiliary sketches, while maximizing the structural similarity between the original inputs and the proxies. The second step is called guidance correction. Reconstruction error maps decouple unreliable segmentation regions. Then, reliable data that are more likely to occur near high-density areas are leveraged to guide the optimization of unreliable data potentially located around decision boundaries. Additionally, we introduce an effective data augmentation strategy, termed as multi-scale mixing up strategy, to minimize the empirical distribution gap between labeled and unlabeled images and perceive diverse scales of cardiac anatomical structures. Extensive experiments on a public echocardiography dataset CAMUS, and a private clinical echocardiography dataset demonstrate the competitiveness of the proposed method.

关键词： Image segmentation Echocardiography Noise Semantics Anatomical structure Semisupervised learning data augmentation Reflection Robustness Image reconstruction

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Breaking the curse of space explosion: Towards effcient NAS with curriculum search 37

Breaking the curse of space explosion: Towards effcient NAS ...

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37th International Conference on Machine Learning, ICML 2020

作者： Guo, Yong Chen, Yaofo Zheng, Yin Zhao, Peilin Chen, Jian Huang, Junzhou Tan, Mingkui School of Software Engineering South China University of Technology China Pazhou Laboratory China Weixin Group Tencent China Tencent AI Lab Tencent China Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9781713821120

Neural architecture search (NAS) has become an important approach to automatically fnd effective architectures. To cover all possible good architectures, we need to search in an extremely large search space with billions of candidate architectures. More critically, given a large search space, we may face a very challenging issue of space explosion. However, due to the limitation of computational resources, we can only sample a very small proportion of the architectures, which provides insuffcient information for the training. As a result, existing methods may often produce suboptimal architectures. To alleviate this issue, we propose a curriculum search method that starts from a small search space and gradually incorporates the learned knowledge to guide the search in a large space. With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method signifcantly improves the search effciency and fnds better architectures than existing NAS methods. Extensive experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of the proposed method. Copyright 2020 by the author(s).

关键词： Curricula

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Breaking the moments condition barrier: no-regret algorithm for bandits with super heavy-tailed payoffs 21

Breaking the moments condition barrier: no-regret algorithm ...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Han Zhong Jiayi Huang Lin F. Yang Liwei Wang Center for Data Science Peking University Center for Data Science Peking University Pazhou Lab Department of Electrical and Computer Engineering University of California Los Angles Key Laboratory of Machine Perception MOE School of EECS Institute for Artificial Intelligence Peking University

ISBN: (纸本)9781713845393

Despite a large amount of effort in dealing with heavy-tailed error in machine learning, little is known when moments of the error can become non-existential: the random noise η satisfies Pr[|η| > |y|] ≤ 1/|y|α for some α > 0. We make the first attempt to actively handle such super heavy-tailed noise in bandit learning problems: We propose a novel robust statistical estimator, mean of medians, which estimates a random variable by computing the empirical mean of a sequence of empirical medians. We then present a generic reductionist algorithmic framework for solving bandit learning problems (including multi-armed and linear bandit problem): the mean of medians estimator can be applied to nearly any bandit learning algorithm as a black-box filtering for its reward signals and obtain similar regret bound as if the reward is sub-Gaussian. We show that the regret bound is near-optimal even with very heavy-tailed noise. We also empirically demonstrate the effectiveness of the proposed algorithm, which further corroborates our theoretical results.

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Magnetic fluid enabled hexagonal fiber grating for vector magnetic field sensing

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Photonics Research 2025年第6期13卷 1726-1735页

作者： Siyu Chen Chen Jiang Yuehui Ma Yunhe Zhao Lilong Dai Qianqian Huang Wei Chen Chengbo Mou Yunqi Liu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai 200444 China Advanced Photonic Technology Lab College of Electronics and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 201306 China e-mail: mouc1@***

Optical fiber magnetic field sensors play a crucial role in aerospace and medical fields due to their high sensitivity, fast response time, and resistance to electromagnetic interference. Most current research primarily focuses on detecting magnetic field intensity; however, the magnetic field is a vector field with both intensity and direction, making vector magnetic field measurement significantly important in various fields. Here, we experimentally demonstrated a vector magnetic field sensor using a magnetic fluid (MF) enabled hexagonal fiber grating. Such a specialty optical fiber device features strong asymmetric evanescent field distribution along the index perturbed area, from which the overcoated MF can sense the external magnetic field. When the fiber magnetometer operated at the dispersion turning point, a maximum sensitivity of 10.48 nm/mT was achieved within a range of 0–20.7 mT, which is one order of magnitude greater than that of conventional fiber grating sensors. Utilizing the polygon optical fiber, our demonstrated device simultaneously achieves a maximum orientation sensitivity of 1.17 nm/deg within a range of 0°–30°. This hexagonal fiber grating as an excellent vector magnetic field sensor may be used in military, aerospace, medical sectors, etc.

关键词： Effective refractive index Fiber Bragg gratings Fiber optic sensors Long period fiber gratings Medical fields Optical fibers

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Indoor Sound Source Localization Algorithm Based on BP Neural Network 21

Indoor Sound Source Localization Algorithm Based on BP Neura...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Wang, Lan Zhang, Kun Shen, Chong Wang, Chai Fu, Xixi Hainan University State Key Laboratory of Marine Resources Utilization in South China Sea Hainan Haikou570228 China Data Mining and Monitoring and Early Warning Technol. of Isl. Tourism Rsrc. of Key Lab. of the Min. of Culture AndTourism of the People's Rep. of China Hainan Tropical Ocean University Hainan Sanya572022 China College of Information and Communication Engineering Hainan University Hainan Haikou570228 China

ISBN: (纸本)9781665432061

Sound source localization is an important part of the perception of things around. Sound source localization can overcome the shortcomings of visual localization, and can also locate the invisible place. The application of sound source localization in indoor is the latest trend. Starting from the application of deep learning to indoor sound source localization, this paper focuses on the analysis and research of BP neural network applied to indoor sound source localization algorithm. In this paper, an off-line sampling scheme is used to construct the network structure with 7 neurons hidden in the layer, and the BP algorithm of LevenBerg-Marquardt is used as the training function, this algorithm can solve the traditional algorithm through the study of the physical properties of sound, set up the corresponding equation, and then solve, the process is complex, to solve the difficult problem. The simulation results show that the algorithm can be implemented in 100 square meters of the house, through sampling 400 sets of data for machine training, positioning error can be controlled in a few centimeters effect. © 2021 IEEE.

关键词： Deep learning

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Multi-Task Diffusion Incentive Design for Mobile Crowdsourcing in Social Networks

arXiv

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arXiv 2023年

作者： Guo, Jianxiong Ni, Qiufen Wu, Weili Du, Ding-Zhu The Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China The Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China The School of Computers Guangdong University of Technology Guangzhou510006 China The Department of Computer Science Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Mobile Crowdsourcing (MCS) is a novel distributed computing paradigm that recruits skilled workers to perform location-dependent tasks. A number of mature incentive mechanisms have been proposed to address the worker recruitment problem in MCS systems. However, they all assume that there is a large enough worker pool and a sufficient number of users can be selected. This may be impossible in large-scale crowdsourcing environments. To address this challenge, we consider the MCS system defined on a location-aware social network provided by a social platform. In this system, we can recruit a small number of seed workers from the existing worker pool to spread the information of multiple tasks in the social network, thus attracting more users to perform tasks. In this paper, we propose a Multi-Task Diffusion Maximization (MT-DM) problem that aims to maximize the total utility of performing multiple crowdsourcing tasks under the budget. To accommodate multiple tasks diffusion over a social network, we create a multi-task diffusion model, and based on this model, we design an auction-based incentive mechanism, MT-DM-L. To deal with the high complexity of computing the multi-task diffusion, we adopt Multi-Task Reverse Reachable (MT-RR) sets to approximate the utility of information diffusion efficiently. Through both complete theoretical analysis and extensive simulations by using real-world datasets, we validate that our estimation for the spread of multitask diffusion is accurate and the proposed mechanism achieves individual rationality, truthfulness, computational efficiency, and (1 − 1/√e − Ε) approximation with at least 1 − δ probability. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

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