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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yuwen Wu Qiang Yang Kai Tang Jianhang Zou Jianming Zhuo Naixing Feng Yijun Cai Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen University of Technology Xiamen China Xiamen Wish Information Technology Co. Ltd Xiamen China Xiamen Blue Ocean Technology Co. Ltd Xiamen China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

With regard to distance measurement in daily life, the traditional way is to measure the distance between objects through rulers. Nowadays, ultrasonic ranging, laser ranging, radar ranging, sonar ranging and other technologies have enriched the way of distance measurement. At the same time, it is possible to choose a suitable solution according to different actual environments. This paper design an intelligent ultrasonic ranging system through the research of ultrasonic characteristics and the application of existing ultrasonic ranging. The paper introduces the design ideas and process of the intelligent ultrasonic ranging system. Through the design of independent buttons, the normal mode ranging and the high-precision mode ranging are generated to achieve the division of the measurement range and accuracy. When the distance between the measured object and the intelligent ultrasonic ranging system is less than the preset value or exceeds the range of this mode, the buzzer will sound an alarm, so that the system can be used in more fields. The system detects the current ambient temperature in real time, so that the distance measurement can be compensated and modified by the temperature compensation algorithm in the system to achieve self-adaptation and reduce measurement errors.

关键词： Temperature measurement Performance evaluation Temperature distribution Laser radar Ultrasonic variables measurement Current measurement Measurement uncertainty

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Real-Time Data Collection of Wsn Based on Trajectory Optimization of Laser-Charged Uav

SSRN

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SSRN 2023年

作者： Luo, Chuanwen Hong, Yi Hou, Yunan Zhu, Yuqing Li, Deying Yu, Dongxiao Wu, Weili School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China Research Center of Applied Mathematics and Machine Intelligence Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los AngelesCA90032 United States School of Information Renmin University of China Beijing100872 China School of Computer Science and Technology Institute of Intelligent Computing Shandong University Qingdao266510 China Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV can not complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, this paper study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is used to collect data from a given WSN and replenished energy by Laser Beam Directors (LBDs). The problem aims at optimizing the flight trajectory of UAV for ensuring the real-time performance of data, which is to design the detailed hovering and traveling plans of UAV for collecting data from sensors and charged by LBDs such that all sensory data of WSN are collected by UAV and transported to the base *** prove that the RDCL problem is NP-hard. To solve the problem, we first study two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Afterwards, we propose an approximation algorithm to solve the RDCL problem based on TODC and CTO problems. Finally, the proposed algorithm is verified by extensive simulations. © 2023, The Authors. All rights reserved.

关键词： Wireless sensor networks

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Taming Stable Diffusion for MRI Cross-Modality Translation

Taming Stable Diffusion for MRI Cross-Modality Translation

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingtai Li Shuo Yang Xiaoyan Wu Shan He S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine USTC Hefei P.R. China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research USTC P.R. China iFLYTEK Research iFLYTEK Co. Ltd Hefei P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing P.R. China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this study, we explore using Stable Diffusion (SD) for unsupervised medical image-to-image translation. SD has shown remarkable performances in generating high-quality images and can be easily applied to generate custom contents by injecting standard plug-ins like LoRA, offering a promising solution to tackle the complexity caused by variations in imaging modalities, acquisition parameters, and body parts in medical imaging. However, We empirically find that existing pipelines designed for natural images fail to translate directly to medical images due to weak structural control and inappropriate color preservation. To address these issues, we propose a novel two-branch image translation pipeline. This pipeline decouples the generation of target image along the time axis and employs ControlNet to ensure precise structural preservation. Additionally, we customize SD to generate images of extreme brightness, a common feature in medical imaging. Our results on the BraTS dataset demonstrate that SD with task-specific plug-ins can generate high-quality medical images comparable to those generated by task-specific models. Since the development of these standard plug-ins can be easily done by clinicians without much knowledge of the underlying algorithm, such a mode holds the potential to significantly extend the use of medical image computing algorithms in the clinical environment.

关键词： Translation Image color analysis Computational modeling Biological system modeling Magnetic resonance imaging Pipelines Brightness Complexity theory Standards Biomedical imaging

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Efficient and adaptive kernelization for nonlinear max-margin multi-view learning

arXiv

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arXiv 2019年

作者： Du, Changying Jia He, B. Du, Changde Zhuang, Fuzhen He, Qing Long, Guoping Huawei Noah's Ark Lab Beijing100085 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institute of Software Chinese Academy of Sciences Beijing100190 China Research Center for Brain-Inspired Intelligence Institute of Automation CAS Beijing China University of Chinese Academy of Sciences Beijing100049 China

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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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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Unsupervised Muscle Fatigue Clustering Based on sEMG Signals using DeepCNN Autoencoders

Unsupervised Muscle Fatigue Clustering Based on sEMG Signals...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Mohammad Javad Javanbakht Mohammad Reza Fouji Amin Akhavan Saffar Mohammad- R. Akbarzadeh-T Alireza Akbarzadeh Dept. of Electrical Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran Dept. of Computer Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran Dept. of Electrical Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran Dept. of Mechanical Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran

Muscle fatigue assessment is key for optimizing physiotherapy, rehabilitation, and exercise. Muscle fatigue detection not only helps in reducing rehabilitation discomfort but also assists athletes in working out efficiently. The main motivation behind this research is to address the pressing need for a precise and reliable tool to assess muscle fatigue during physical activities. In addition to enhancing therapeutic interventions, this approach improves microtears-induced muscle strengthening by advancing the understanding of muscle fatigue levels. Previous studies have mainly employed supervised approaches to detect fatigue by processing surface electromyography (sEMG) signals through traditional methods in the Time, Frequency, and Time-Frequency domains. These approaches are vulnerable to challenges, such as signal sensitivity, noise interference, and inter-individual variability. Thus, the proposed method uses the spring energy model of the sEMG signal because of its excellent approximation with muscle characteristics. In order to handle noisy and ambiguous data, this paper employs a self-supervised Deep Convolutional Neural Network (DeepCNN) Autoencoder for feature extraction and K-means clustering due to their strong performance. Five healthy participants carried out two experiments using a knee rehabilitation robot (FUM-Physio). The results showcase a well-ordered clustering that closely mirrors the progression of muscle fatigue during these experiments. This indicates the robustness and generality of the proposed method.

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Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups 18th

Visitors Vis: Interactive Mining of Suspected Medical Insura...

引用

18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Dong, Rixin Liu, Hanlin Guo, Xu Zhou, Jiantao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9789819996360

As medical insurance continues to grow in size, the losses caused by medical insurance fraud cannot be underestimated. Current data mining and predictive techniques have been applied to analyze and explore the health insurance fraud population. However, previous studies only provide summary results and do not show the details of comparative analysis during the visit, resulting in the inability of auditors to quickly identify anomalous behavior. In this paper, we propose a visual analytics system for interactive medical insurance fraud detection to support the exploration and interpretation of different access processes. We propose a weighted MinDL to improve the accuracy of visit pattern classification in the time-series modeling process, and design a data analysis model and visual analysis view based on medical insurance fraud characteristics to reveal and explore the characteristics of medical insurance fraud groups. We collaborate with related organizations to design and implement an interactive visualization for medical insurance fraud group detection using real Medicare data and expert interviews The system is effective and practical in detecting and analyzing medical insurance fraud syndicates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health insurance

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An Uncovered Neurons information-Based Fuzzing Method for DNN

An Uncovered Neurons Information-Based Fuzzing Method for DN...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing Software

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Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

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Frontiers of Engineering Management 2022年第4期9卷 550-562页

作者： Haochen SUN Xiaofan LIU Zhanwei DU Ye WU Haifeng ZHANG Xiaoke XU College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China Web Mining Laboratory Department of Media and CommunicationCity University of Hong KongHong KongChina WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkHong KongChina Computational Communication Research Center Beijing Normal University(Zhuhai)Zhuhai 519087China School of Journalism and Communication Beijing Normal UniversityBeijing 100875China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical ScienceAnhui UniversityHefei 230601China

Wearing masks is an easy way to operate and popular measure for preventing *** masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains ***,we aim to investigate the epidemic prevention effect of masks in different real-life gathering *** study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering *** transmission of COVID-19 is simulated using the Monte Carlo simulation *** heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general ***,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to *** masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and ***,the use of masks alone in primary schools and hospitals cannot control *** high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for *** the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be ***,asymptomatic infection has varying effects on the prevention effect of masks in different *** effect can be weakened or eliminated by increasing the usage rate of masks in high schools and ***,the effect on primary schools and hospitals cannot be *** study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

关键词： COVID-19 masks behavioral heterogeneity asymptomatic infection

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Optical quantum sensing for agnostic environments via deep learning

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Physical Review research 2024年第4期6卷 043267-043267页

作者： Zeqiao Zhou Yuxuan Du Xu-Fei Yin Shanshan Zhao Xinmei Tian Dacheng Tao MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei 230026 China JD Explore Academy Beijing 101111 China College of Computing and Data Science Nanyang Technological University Singapore 639798 Singapore Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences University of Science and Technology of China Hefei 230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China School of Computer Science The University of Sydney NSW 2008 Australia

Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical applications. Addressing this limitation, we introduce an innovative deep-learning-based quantum sensing scheme (DQS), enabling optical quantum sensors to attain HL in agnostic environments. DQS incorporates two essential components: a graph neural network (GNN) predictor and a trigonometric interpolation algorithm. Operating within a data-driven paradigm, DQS utilizes the GNN predictor, trained on offline data, to unveil the intrinsic relationships between the optical setups employed in preparing the probe state and the resulting quantum Fisher information (QFI) after interaction with the agnostic environment. This distilled knowledge facilitates the identification of optimal optical setups associated with maximal QFI. Subsequently, DQS employs a trigonometric interpolation algorithm to recover the unknown parameter estimates for the identified optical setups. Extensive experiments are conducted to investigate the performance of DQS under different settings up to eight photons. Our findings not only offer a different lens through which to accelerate optical quantum sensing tasks but also catalyze future research integrating deep learning and quantum mechanics.

关键词： Graph neural networks

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