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Multimodal Transportation 2024年第2期3卷

作者： Guo, Bao Huang, Zhiren Zheng, Zhihao Zhang, Fan Wang, Pu School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Shandong Provincial Communications Planning and Design Institute Group Co. Ltd Jinan China Department of Computer Science Aalto University Helsinki Finland Department of Civil Engineering McGill University Montreal H3A 0C3 QC Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved. © 2024

关键词： Human mobility Information entropy Path flow distribution Predictability

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Neuroscience Bulletin 2022年第10期38卷 1215-1228页

作者： Liwei Sun Wenjing Zhang Mengyue Wang Songjian Wang Zhen Li Cui Zhao Meng Lin Qian Si Xia Li Ying Liang Jing Wei Xu Zhang Renji Chen Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing100069China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Capital Medical UniversityBeijing100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application School of Biomedical EngineeringCapital Medical UniversityBeijing100069China Beijing Stomatological Hospital Capital Medical UniversityBeijing100050China School of Life Science Beijing Institute of TechnologyBeijing100081China Beijing Institute of Otolaryngology-Head and Neck Surgery Beijing100005China Key Laboratory of Otolaryngology-Head and Neck Surgery(Capital Medical University) Ministry of EducationBeijing100005China Beijing Tongren Hospital Capital Medical UniversityBeijing100005China Department of Ultrasound Beijing Obstetrics and Gynecology HospitalCapital Medical UniversityBeijing100026China Peking University First Hospital Beijing100034China

Cleft lip and/or palate(CLP)are the most common craniofacial malformations in *** problems often persist even after cleft repair,such that follow-up articulation training is usually ***,the neural mechanism behind effective articulation training remains largely *** used fMRI to investigate the differences in brain activation,functional connectivity,and effective connectivity across CLP patients with and without articulation training and matched normal *** found that training promoted task-related brain activation among the articulation-related brain networks,as well as the global attributes and nodal efficiency in the functional-connectivity-based graph of the *** results reveal the neural correlates of effective articulation training in CLP patients,and this could contribute to the future improvement of the post-repair articulation training program.

关键词： Non-syndromic cleft lip and palate Articulation training fMRI Functional connectivity Graph-based network analysis

Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for aiding Diagnosis of Blood Diseases

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arXiv 2024年

作者： Chen, Yifei Zhang, Chenyan Chen, Ben Huang, Yiyu Sun, Yifei Wang, Changmiao Fu, Xianjun Dai, Yuxing Qin, Feiwei Peng, Yong Gao, Yu HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen518172 China School of Artificial Intelligence Zhejiang College of Security Technology Wenzhou325016 China National-local Joint Engineering Research Center for Digitalized Electrical Design Technology Wenzhou University Wenzhou325035 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Department of Hematology Zhejiang Hospital Hangzhou310018 China

In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients’ blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis. Contemporary leukocyte detection methods exhibit limitations in dealing with images with fewer leukocyte features and the disparity in scale among different leukocytes, leading to unsatisfactory results in most instances. To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR). To tackle the issue of leukocyte scale disparity, we designed the High-level Screening-feature Fusion Pyramid (HS-FPN), enabling multi-level fusion. This model uses high-level features as weights to filter low-level feature information via a channel attention module and then merges the screened information with the high-level features, thus enhancing the model’s feature expression capability. Further, we address the issue of leukocyte feature scarcity by incorporating a multi-scale deformable self-attention module in the encoder and using the self-attention and cross-deformable attention mechanisms in the decoder, which aids in the extraction of the global features of the leukocyte feature maps. The effectiveness, superiority, and generalizability of the proposed MFDS-DETR method are confirmed through comparisons with other cutting-edge leukocyte detection models using the private WBCDD, public LISC and BCCD datasets. Our source code and private WBCCD

关键词： Object detection

A Widespread Characteristic Estimation Analysis of Photovoltaic: Mayfly Algorithm application

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Research Square

research Square 2021年

作者： Kockanat, Serdar Department of Electrical and Electronics Engineering Sivas Cumhuriyet University Sivas58140 Turkey Artificial Intelligence Systems and Data Science Application and Research Center Sivas Cumhuriyet University Sivas58140 Turkey

In recent years, traditional energy sources have begun to give way to alternative and renewable energy sources such as solar energy. Therefore, the interest in the use of solar cells in energy production is increasing by researchers and practitioners. In literature, performance and control improvement studies are carried out in order to generate more energy by using solar cells effectively. To increase performance and control, several solar cell models are developed such as one diode ad double diode models. They are very effective but there is a problem of accurate and efficient estimation of parameters in these models. Recently, to overcome this problem, metaheuristic algorithms have been used in literature. In this paper, for the first time, a novel swarm based Mayfly algorithm (MA) is proposed to characteristic parameter estimation of photovoltaic models. To analyze the estimation performance of MA, single and double diode model are applied for silicon R.T.C. France, poly-crystalline Photowatt-PWP-201 and mono-crystalline STM-40/36 solar cells. Obtained results are compared with those of the well-known algorithms suggested in literature. Also, current-voltage and power-voltage characteristic are constructed using estimated parameters by MA. The results show that the MA is right choice for accurate and effective parameter estimation of photovoltaic cells. © 2021, CC BY.

关键词： Parameter estimation

TradeFL: A Trading Mechanism for Cross-Silo Federated Learning

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TradeFL: A Trading Mechanism for Cross-Silo Federated Learni...

International Conference on Distributed Computing Systems

作者： Shijing Yuan Hongtao Lv Hongze Liu Chentao Wu Song Guo Zhi Liu Hongyang Chen Jie Li Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Software & Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University Department of Computing The Hong Kong Polytechnic University Hong Kong The University of Electro-Communications Tokyo Japan Zhejiang Lab Research Center for Graph Computing China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Yancheng Blockchain Research Institute China

Cross-silo federated learning (CFL) is a distributed learning paradigm that allows organizations (e.g., financial or medical entities) to train a global model on siloed data. Recent studies on mechanisms designed for CFL, however, rarely jointly consider the potential inter-organizational competition and the lack of credibility between organizations, which may discourage organizational participation. In this paper, we investigate the problem of inter-organizational competition and credibility assurance. We propose a distributed trading mechanism, called $TradeFL$ , to incentivize organizations to contribute data and computational resources through mutual trading among organizations. Technically, TradeFL characterizes the competition among organizations and compensates for their damage incurred by competition. TradeFL runs on distributed organizations and provides credibility guarantees for compensation through a customized smart contract 1 1 Illustration of the prototype: https://***/user10963.. We prove that the interaction among organizations that contribute resources to maximize personal payoffs is a weighted potential game. Then, we propose a centralized algorithm and a distributed algorithm to determine the optimal resource contribution. Simulation results and evaluations based on real-world datasets demonstrate that our scheme achieves higher social welfare, increases the amount of contributed data by up to 64%, and improves the accuracy of the global model by at most 23.2%.

关键词：

BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models

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arXiv 2024年

作者： Sun, Yifei Chen, Zhanghao Zheng, Hao Deng, Wenming Liu, Jin Min, Wenwen Elazab, Ahmed Wan, Xiang Wang, Changmiao Ge, Ruiquan Hangzhou Dianzi University Hangzhou China National Cancer Center National Clinical Research Center for Cancer Cancer Hospital&Shenzhen Hospital Chinese Academy of Medical Sciences Peking Union Medical College Shenzhen China Central South University Changsha China Yunnan University Kunming China School of Biomedical Engineering Shenzhen University Shenzhen China Computer Science Department Misr Higher Institute for Commerce and Computers Mansoura Egypt Shenzhen Research Institute of Big Data Shenzhen China

Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to their accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often hindered by overlapping bone structures in CXR images, leading to potential misdiagnoses. To address this issue, we developed an end-to-end framework called BS-LDM, designed to effectively suppress bone in high-resolution CXR images. This framework is based on conditional latent diffusion models and incorporates a multi-level hybrid loss-constrained vector-quantized generative adversarial network which is crafted for perceptual compression, ensuring the preservation of details. To further enhance the framework’s performance, we introduce offset noise and a temporal adaptive thresholding strategy. These additions help minimize discrepancies in generating low-frequency information, thereby improving the clarity of the generated soft tissue images. Additionally, we have compiled a high-quality bone suppression dataset named SZCH-X-Rays. This dataset includes 818 pairs of high-resolution CXR and dual-energy subtraction soft tissue images collected from a partner hospital. Moreover, we processed 241 data pairs from the JSRT dataset into negative images, which are more commonly used in clinical practice. Our comprehensive experimental and clinical evaluations reveal that BS-LDM excels in bone suppression, underscoring its signifi- cant clinical value. © 2024, CC BY.

关键词： Bone

Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

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arXiv 2022年

作者： Zhao, Bowen Chen, Wei-Neng Wei, Feng-Feng Liu, Ximeng Pei, Qingqi Zhang, Jun Guangzhou Institute of Technology Xidian University Guangzhou China The School of Computer Science and Engineering South China University of Technology Guangzhou China The College of Computer and Data Science Fuzhou University Fujian China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China The Department of Electrical and Electronic Engineering Hanyang University Ansan15588 Korea Republic of

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough capability to implement EAs to solve COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, whilst it suffers from privacy concerns. To this end, this paper proposes a novel computing paradigm, evolution as a service (EaaS), where a cloud server renders evolutionary computation services for users without sacrificing users' privacy. Inspired by the idea of EaaS, this paper designs PEGA, a novel privacy-preserving GA for COPs. Specifically, PEGA enables users outsourcing COPs to the cloud server holding a competitive GA and approximating the optimal solution in a privacy-preserving manner. PEGA features the following characteristics. First, any user without expertise and enough resources can solve her COPs. Second, PEGA does not leak contents of optimization problems, i.e., users' privacy. Third, PEGA has the same capability as the conventional GA to approximate the optimal solution. We implements PEGA falling in a twin-server architecture and evaluates it in the traveling salesman problem (TSP, a widely known COP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suit of secure computing protocols to support evolutionary operators of GA on encrypted data. Privacy analysis demonstrates that PEGA does not disclose the contents of the COP to the cloud server. Experimental evaluation results on four TSP datasets show that PEGA is as effective as the conventional GA in approximating the optimal solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Genetic algorithms

Vflh: A Following-the-Leader-History Based Algorithm for Adaptive Online Convex Optimization with Stochastic Constraints

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SSRN

SSRN 2022年

作者： Yang, Yifan Chen, Lin Zhou, Pan Ding, Xiaofeng Department of Computer Science University of California Santa BarbaraCA93106 United States National Engineering Research Center for Big Data Technology and System Lab 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 School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430072 China

This paper considers online convex optimization (OCO) with generated i.i.d. stochastic constraints, where the distribution of environment is changing and the performance is measured by \textit{adaptive regret}. The stochastic constraints are disclosed at each round to the learner after the decision is made. Different from the previous non-adaptive constrained OCO algorithm which is generalized based on the traditional online gradient descent algorithm, we propose a novel \textit{Virtual queue based Following-the-Leader-History} (VFLH) strategy to make the constrained OCO algorithm adaptive and design the VFLH algorithm. In this setting, the algorithm firstly generalizes all the experts in the specified time interval, then decides the optimal decision with each expert and gives out the final choice by combining the predictions of active experts. In terms of the algorithm performance, there are two performance metrics to be evaluated, i.e., the bounds of constraint violation and regret. For the constraint violation, we bring in the multi-objective drift analysis approach to deal with the constraints via a virtual queue which provides a new method to bound the constraints violations. For the regret, we give out its upper bound and then achieve a corresponding lower bound that proves the upper bound is sufficiently tight. Finally, we provide some numerical experiments to validate the feasibility of our algorithm. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

FedDBL: Communication and data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

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arXiv 2023年

作者： Deng, Tianpeng Huang, Yanqi Han, Guoqiang Shi, Zhenwei Lin, Jiatai Dou, Qi Liu, Zaiyi Guo, Xiao-Jing Chen, C.L. Philip Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China The Department of Computer Science and Engineering The Chinese University of Hong Kong 999077 Hong Kong The Department of Breast Pathology and Lab Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center of Cancer Key Laboratory of Cancer Prevention and Therapy Tianjin Tianjin’s Clinical Research Center for Cancer Key Laboratory of Breast Cancer Prevention and Therapy Tianjin Medical University Ministry of Education Tianjin300060 China

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning-based models have achieved superior performance but centralized training with data centralization suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, but existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their practicability in the real-world clinical scenario. In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply associating a pre-trained deep learning feature extractor, a fast and lightweight broad learning inference system and a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6GB to only 276.5KB per client using the ResNet-50 backbone at 50-round training. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://***/tianpeng-deng/FedDBL. © 2023, CC BY.

关键词： Tissue

Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication

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Towards Cluster-Based Split Federated Learning Approach for ...

Cyber Security in Networking Conference (CSNet)

作者： Mohamad Wazzeh Mohamad Arafeh Hakima Ould-Slimane Chamseddine Talhi Azzam Mourad Hadi Otrok Department of Software and IT Engineering Ecole de Technologie Supérieure (ÉTS) Montreal Canada Department of Mathematics and Computer Science Université du Québec à Trois-Riviéres (UQTR) Trois-Rivières Canada Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.

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