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A Surrogate Model Based Genetic Algorithm for Complex Problem Solving

Journal of Physics: Conference Series 2020年第1期1646卷

作者： Ying Pei Hao Gao Xiaosong Han The College of Information Engineering Changchun University of Finance and Economics Changchun 130122 China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology College of Software Jilin University Changchun 130012 China

It is well known that when the fitness function is relatively complex, the optimization time cost of the genetic algorithm will be extremely huge. To address this issue, the surrogate model was employed to predict the fitness value of the optimization problem, to reduce the number of actual calculated fitness values. In this paper, BP neural network, the least square method and support vector machine were fused in the genetic algorithm to evaluate partial individuals' fitness. Sufficient benchmark numerical experiments were conducted, and the results proved that the strategy could reduce the calculating counts of fitness function on similar accuracy basis compared with simple genetic algorithm.

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Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

arXiv

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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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Advances in artificial intelligence techniques drive the application of radiomics in the clinical research of hepatocellular carcinoma

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iLIVER 2022年第1期1卷 49-54页

作者： Jingwei Wei Meng Niu Ouyang Yabo Yu Zhou Xiaoke Ma Xue Yang Hanyu Jiang Hui Hui Hongyi Cao Binwei Duan Hongjun Li Dawei Ding Jie Tian Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China Department of Interventional Radiology The First Affiliated Hospital of China Medical UniversityShenyangLiaoning110000China Beijing YouAn Hospital Capital Medical UniversityBeijing Institute of HepatologyBeijing100069China School of Life Science and Technology Xidian UniversityXi'anChina School of Computer Science and Technology Xidian UniversityXi'anShaanxiChina Department of Radiology Beijing Youan HospitalCapital Medical UniverstiyBeijing100069China Department of Radiology West China HospitalSichuan UniversityChengduSichuan 610041China Department of Pathology College of Basic Medical ScienceChina Medical UniversityShenyangLiaoning110000China The Department of General Surgery Center Beijing YouAn HospitalCapital Medical UniversityChina School of Bioengineering Beihang UniversityBeijing100191China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang UniversityBeijing100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'anShaanxi710126China

Hepatocellular carcinoma(HCC)remains the most common malignancy to threaten public health *** advances in artificial intelligence techniques,radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings,and reveals pixel-level radiological information for medical imaging big data,correlating the radiological phenotype with targeted clinical *** radiomics pipelines depend on handcrafted engineering features,and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning *** the past decade,radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation,as well as in prognosis *** this review,we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC.

关键词： Artificial intelligence Diagnosis Hepatocellular carcinoma Prognosis Radiomics

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CellMirror: Deciphering Cell Populations from Spatial Transcriptomics data by Interpretable Contrastive Learning

CellMirror: Deciphering Cell Populations from Spatial Transc...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Junjie Xia Jiangnan Cui Zhenyu Huang Shuangquan Zhang Fang Yao Yijian Zhang Chunman Zuo Department of Applied Mathematics Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Institute Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Affiliated Shenzhen Maternity and Child Healthcare Hospital Institute of Maternal and Child Medicine Southern Medical University Shenzhen China Department of General Surgery Xinhua Hospital Affiliated to Shanghai Jiao Tong University of Medicine Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial transcriptomics (ST) has enabled us elucidating tumor microenvironments, however, the technological limitations without single-cell resolution severely hinder its application. Here, we propose CellMirror, an interpretable contrastive learning model to decipher heterogeneous cell populations in ST data by single-cell RNA-sequencing (scRNA-seq) data. Specifically, CellMirror learns the disentangled shared (representing biological variations in both data) and salient features (specific to ST data) by two contrastive variational encoders, while constructing the relations between genes and features by a shared linear decoder. In various cancer samples, CellMirror outperforms other tools in learning common features for label transfer, and interpretation of features. Particularly, in breast cancer studies, CellMirror detects finer domains in ST data missed by other methods, and is robust to dissect cell populations in ST data using independent scRNA-seq data. These results demonstrate applications of CellMirror in interpreting the complex tumor structure in ST data by integrating scRNA-seq data.

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Author Correction: Multiple similarly effective solutions exist for biomedical feature selection and classification problems

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Scientific reports 2018年第1期8卷 838页

作者： Jiamei Liu Cheng Xu Weifeng Yang Yayun Shu Weiwei Zheng Fengfeng Zhou College of Software Jilin University Changchun Jilin 130012 China. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. College of Software Jilin University Changchun Jilin 130012 China. fengfengzhou@***. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. fengfengzhou@***.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

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Filter Pruning for Efficient CNNs via knowledge-driven Differential Filter Sampler

arXiv

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arXiv 2023年

作者： Lin, Shaohui Huang, Wenxuan Xie, Jiao Zhang, Baochang Shen, Yunhang Yu, Zhou Han, Jungong Doermann, David The School of Computer Science and Technology East China Normal University Shanghai China The Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China The Department of Automation School of Aerospace Engineering Xiamen University Xiamen China The Institute of Artificial Intelligence Beihang University Beijing China Zhongguancun Laboratory Beijing China The Youtu Lab Tencent Shanghai China The School of Statistics East China Normal University Shanghai China The Department of Computer Science University of Sheffield United Kingdom The University at Buffalo BuffaloNY United States

Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs, which can be effectively applied to edge devices and cloud services. In this paper, we propose a novel knowledge-driven Differential Filter Sampler (KDFS) with Masked Filter Modeling (MFM) framework for filter pruning, which globally prunes the redundant filters based on the prior knowledge of a pre-trained model in a differential and non-alternative optimization. Specifically, we design a differential sampler with learnable sampling parameters to build a binary mask vector for each layer, determining whether the corresponding filters are redundant. To learn the mask, we introduce masked filter modeling to construct PCA-like knowledge by aligning the intermediate features from the pre-trained teacher model and the outputs of the student decoder taking sampling features as the input. The mask and sampler are directly optimized by the Gumbel-Softmax Straight-Through Gradient Estimator in an end-to-end manner in combination with global pruning constraint, MFM reconstruction error, and dark knowledge. Extensive experiments demonstrate the proposed KDFS’s effectiveness in compressing the base models on various datasets. For instance, the pruned ResNet-50 on ImageNet achieves 55.36% computation reduction, and 42.86% parameter reduction, while only dropping 0.35% Top-1 accuracy, significantly outperforming the state-of-the-art methods. The code is available at https://***/Osilly/KDFS. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Publisher Correction: Multiple similarly effective solutions exist for biomedical feature selection and classification problems

引用

Scientific reports 2018年第1期8卷 4218页

作者： Jiamei Liu Cheng Xu Weifeng Yang Yayun Shu Weiwei Zheng Fengfeng Zhou College of Software Jilin University Changchun Jilin 130012 China. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. College of Software Jilin University Changchun Jilin 130012 China. FengfengZhou@***. College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. FengfengZhou@***.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

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Localization algorithm based on optimal nodes for wireless sensor networks

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2011年第SUPPL. 1期41卷 208-214页

作者： Shen, Xuan-Jing Li, Cheng-Yue Wang, Shuo Qian, Qing-Ji College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Unit 89 The Chinese People's Liberation Army 95900 Troops Kaifeng 475003 China College of Physics Jilin University Changchun 130015 China

Because of restricted energy of the sensor nodes, the location error, costs of communication and computation should be considered in localization algorithms. The DV-Hop algorithm was detailedly analyzed and the main reasons for the error were pointed out, aiming at the different position anchor nodes' effect on location error, a novel localization algorithm called DV-Hop_Bon (DV-Hop based on optimal nodes) based on optimal nodes was put forward. Finally, it was simulated on Matlab, the results of the simulation show that the novel localization algorithm improves the localization precision with a short communication radius. Therefore it can be applied to wireless sensor networks widely.

关键词： Sensor nodes

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Secure and Privacy-preserving data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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SSM-DTA: Breaking the Barriers of data Scarcity in Drug-Target Affinity Prediction

arXiv

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arXiv 2022年

作者： Pei, Qizhi Wu, Lijun Zhu, Jinhua Xia, Yingce Xie, Shufang Qin, Tao Liu, Haiguang Liu, Tie-Yan Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China No.59 Zhong Guan Cun Avenue Haidian District Beijing100872 China Microsoft Research AI4Science No.5 Dan Ling Street Haidian District Beijing100080 China CAS Key Laboratory of GIPAS EEIS Department University of Science and Technology of China No.96 JinZhai Road Baohe District Anhui Province Hefei230026 China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the SSM-DTA framework, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling (MLM) using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS, and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations, and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes. Our code is available at https://***/QizhiPei/SSM-DTA. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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