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arXiv 2022年

作者： Nguyen, Quan Pham, Hieu H. Wong, Kok-Seng Le Nguyen, Phi Nguyen, Truong Thao Do, Minh N. The VinUni-Illinois Smart Health Center VinUniversity Hanoi Viet Nam Department of Informatics Technical University of Munich Munich Germany The College of Engineering and Computer Science VinUniversity Hanoi Viet Nam The School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam Tokyo Japan The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL United States

In Federated Learning (FL), the size of local models matters. On the one hand, it is logical to use large-capacity neural networks in pursuit of high performance. On the other hand, deep convolutional neural networks (CNNs) are exceedingly parameter-hungry, which makes memory a significant bottleneck when training large-scale CNNs on hardware-constrained devices such as smartphones or wearables sensors. Current state-of-the-art (SOTA) FL approaches either only test their convergence properties on tiny CNNs with inferior accuracy or assume clients have the adequate processing power to train large models, which remains a formidable obstacle in actual practice. To overcome these issues, we introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients to collaboratively train a large deep learning model by dividing it into an ensemble of several small sub-models and train them on multiple devices in parallel while maintaining privacy. In this collaborative training process, clients from the same cluster can also learn from each other, further improving their ensemble performance. In the aggregation stage, the server takes a weighted average of all the ensemble models trained by all the clusters. FedDCT reduces the memory requirements and allows low-end devices to participate in FL. We empirically conduct extensive experiments on standardized datasets, including CIFAR-10, CIFAR-100, and two real-world medical datasets HAM10000 and VAIPE. Experimental results show that FedDCT outperforms a set of current SOTA FL methods with interesting convergence behaviors. Furthermore, compared to other existing approaches, FedDCT achieves higher accuracy and substantially reduces the number of communicat

关键词： Convolutional neural networks

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From Conception to Deployment: Intelligent Stroke Prediction Framework using Machine Learning and Performance Evaluation

arXiv

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Stroke is the second leading cause of death worldwide. Machine learning classification algorithms have been widely adopted for stroke prediction. However, these algorithms were evaluated using different datasets and evaluation metrics. Moreover, there is no comprehensive framework for stroke data analytics. This paper proposes an intelligent stroke prediction framework based on a critical examination of machine learning prediction algorithms in the literature. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. Comparative analysis and numerical results reveal that the Random Forest algorithm is best suited for stroke prediction. © 2023, CC BY-NC-ND.

关键词： Data Analytics

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Superpixel-guided discriminative low-rank representation of hyperspectral images for classification

arXiv

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arXiv 2021年

作者： Yang, Shujun Hou, Junhui Jia, Yuheng Mei, Shaohui Du, Qian The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Electronics and Information Northwestern Polytechnical University Xian710129 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

In this paper, we propose a novel classification scheme for the remotely sensed hyperspectral image (HSI), namely SP-DLRR, by comprehensively exploring its unique characteristics, including the local spatial information and low-rankness. SP-DLRR is mainly composed of two modules, i.e., the classification-guided superpixel segmentation and the discriminative low-rank representation, which are iteratively conducted. Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels. According to the resulting superpixels, the pixels of the input HSI are then grouped into clusters and fed into our novel discriminative low-rank representation model with an effective numerical solution. Such a model is capable of increasing the intra-class similarity by suppressing the spectral variations locally while promoting the inter-class discriminability globally, leading to a restored HSI with more discriminative pixels. Experimental results on three benchmark datasets demonstrate the significant superiority of SP-DLRR over state-of-the-art methods, especially for the case with an extremely limited number of training pixels. © 2021, CC BY.

关键词： Spectroscopy

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Workshop Program on Disaster Prevention and Mitigation for Young Generation Utilizing Disaster information Tweeting and Mapping System 1

引用

4th IFIP TC 5 DCITDRR International Conference on information Technology in Disaster Risk Reduction, ITDRR 2019

作者： Uchida, Osamu Tajima, Sachi Kajita, Yoshitaka Utsu, Keisuke Murakami, Yuji Yamada, Sanetoshi Department of Human and Information Science Tokai University HiratsukaKanagawa Japan Center for Liberal Arts Tokai University HiratsukaKanagawa Japan Department of Civil Engineering Tokai University HiratsukaKanagawa Japan Department of Communication and Network Engineering Tokai University MinatoTokyo Japan Department of Electrical Engineering and Computer Science Tokai University Kumamoto Japan Research and Information Center Tokai University HiratsukaKanagawa Japan

ISBN: (数字)9783030489397

ISBN: (纸本)9783030489380

Various large-scale natural disasters occur every year in Japan. Then, various disaster education is conducted at schools and in local communities for alleviating the damage caused by such natural disasters. However, according to various surveys, disaster prevention awareness among young people is known to be lower than in other age groups in Japan. Therefore, we worked with Kanagawa Prefecture, Japan, on establishing a program of workshops on disaster prevention and mitigation utilizing ICT equipment such as smartphones or tablet PCs to raise awareness of disaster prevention and mitigation among young people. The program is based on town watching and group discussion using DITS and DIMS (disaster information tweeting and mapping system) proposed in the previous studies. In this paper, we introduce the contents of the developed workshop program using DITS/DIMS. Moreover, we report on the results of the workshops for disaster prevention and mitigation using this program at several junior high and high schools. The results of the post-questionnaire show that many participants of the workshops had a positive impression on the disaster prevention and mitigation workshops using DITS/DIMS. © 2020, IFIP International Federation for information Processing.

关键词： Disasters

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Integrated curriculum framework design for embedded systems 2

Integrated curriculum framework design for embedded systems

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2nd International Conference on computer science and Educational Informatization, CSEI 2020

作者： Hu, Wei Wu, Jing Liu, Fang Hong, Guo Jiang, Min College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China National Demonstration Center for Experimental Computer Education Wuhan University of Science and Technology Wuhan China City College of Wuhan University of Science and Technology Information Engineering Department Wuhan China

ISBN: (纸本)9781728170077

With the continuous expansion of the application field, embedded systems have been used more and more widely. How to develop embedded system teaching in colleges and universities is still an important issue in current curriculum construction. We propose an integrated curriculum framework design proposal for embedded systems including undergraduate and graduate. This proposal focuses on embedded technology and based on the differences of student objectives and teaching goals in the undergraduate and graduate education. The teaching of embedded systems is individualized and continuous, which plays an important role in the training of high level embedded talents. At the same time, the proposal also provides important reference for other curriculum or professional development of courses. © 2020 IEEE.

关键词： Curricula

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Optimization on multi-user physical layer security of intelligent reflecting surface-aided VLC

arXiv

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arXiv 2022年

作者： Sun, Shiyuan Yang, Fang Song, Jian Han, Zhu Department Of Electronic Engineering Beijing National Research Center For Information Science And Technology Tsinghua University Beijing100084 China Key Laboratory Of Digital TV System Of Guangdong Province And Shenzhen City Research Institute Of Tsinghua University In Shenzhen Shenzhen518057 China Department Of Electrical And Computer Engineering The University Of Houston HoustonTX77004 United States Department Of Computer Science And Engineering Kyung Hee University Seoul446-701 Korea Republic of

This letter investigates physical layer security in intelligent reflecting surface (IRS)-aided visible light communication (VLC). Under the point source assumption, we first elaborate the system model in the scenario with multiple legitimate users and one eavesdropper, where the secrecy rate maximization problem is transformed into an assignment problem by objective function approximation. Then, an iterative Kuhn-Munkres algorithm is proposed to optimize the transformed problem, and its computational complexity is in the second-order form of the numbers of IRS units and transmitters. Moreover, numerical simulations are carried out to verify the approximation performance and the VLC secrecy rate improvement by IRS. Copyright © 2022, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Automatic firmware emulation through invalidity-guided knowledge inference (extended version)

arXiv

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arXiv 2021年

作者： Zhou, Wei Guan, Le Liu, Peng Zhang, Yuqing National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China Department of Computer Science University of Georgia United States College of Information Sciences and Technology The Pennsylvania State University United States School of Cyber Engineering Xidian University China School of Computer Science and Cyberspace Security Hainan University China

Emulating firmware for microcontrollers is challenging due to the tight coupling between the hardware and firmware. This has greatly impeded the application of dynamic analysis tools to firmware analysis. The state-of-the-art work automatically models unknown peripherals by observing their access patterns, and then leverages heuristics to calculate the appropriate responses when unknown peripheral registers are accessed. However, we empirically found that this approach and the corresponding heuristics are frequently insufficient to emulate firmware. In this work, we propose a new approach called µEmu to emulate firmware with unknown peripherals. Unlike existing work that attempts to build a general model for each peripheral, our approach learns how to correctly emulate firmware execution at individual peripheral access points. It takes the image as input and symbolically executes it by representing unknown peripheral registers as symbols. During symbolic execution, it infers the rules to respond to unknown peripheral accesses. These rules are stored in a knowledge base, which is referred to during the dynamic firmware analysis. µEmu achieved a passing rate of 95% in a set of unit tests for peripheral drivers without any manual assistance. We also evaluated µEmu with real-world firmware samples and new bugs were discovered. Copyright © 2021, The Authors. All rights reserved.

关键词： Firmware

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Multidimensional clinical data denoising via Bayesian CP factorization

引用

science China(Technological sciences) 2020年第2期63卷 249-254页

作者： CUI GaoChao ZHU Li GUI LiHua ZHAO QiBin ZHANG JianHai CAO JianTing Graduate School of Engineering Saitama Institute of TechnologySaitama 3690293Japan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Cognitive Science Department School of Information Science and EngineeringXiamen UniversityXiamen 361005China Tensor Learning Unit Center for Advanced Intelligence ProjectRIKENSaitama 1030027Japan School of Automation Guangdong University of TechnologyGuangzhou 510006China

CANDECOMP/PARAFAC(CP) tensor factorization is an efficient technique for incomplete tensor-data processing through capturing the multilinear latent factors. Based on the incorporate a sparsity-inducing prior over multiple latent factors and appropriate hyper-priors over all hyper-parameters, a Bayesian-based hierarchical probabilistic CP factorization model could be formed. By this way, the rank of the incomplete tensor can be determined automatically. In this paper, we explored the tensor completion method in processing incomplete multidimensional electroencephalogram(EEG) and magnetic resonance imaging(MRI) clinical data. The empirical results indicated that the Bayesian CP tensor factorization of incomplete data method can effectively recover EEG signal with missing data and denoised the noisy MRI data.

关键词： electroencephalogram(EEG) magnetic resonance imaging(MRI) Bayesian tensor factorization CANDECOMP/PARAFAC(CP)

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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...

引用

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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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

引用

arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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