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arXiv 2021年

作者： Li, Zhaoqun Liang, Xu Fan, Dandan Li, Jinxing Jia, Wei Zhang, David The School of Data Science The Chinese University of Hong Kong Shenzhen The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China The Bio-Computing Research Center School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Computer and Information Hefei University of Technology Hefei230009 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

With the growing demand for hand hygiene and convenience of use, palmprint recognition with touchless manner made a great development recently, providing an effective solution for person identification. Despite many efforts that have been devoted to this area, it is still uncertain about the discriminative ability of the touchless palmprint, especially for large-scale datasets. To tackle the problem, in this paper, we build a large-scale touchless palmprint dataset containing 2334 palms from 1167 individuals. To our best knowledge, it is the largest touchless palmprint image benchmark ever collected with regard to the number of individuals and palms. Besides, we propose a novel deep learning framework for touchless palmprint recognition named 3DCPN (3D Convolution Palmprint recognition Network) which leverages 3D convolution to dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of the Gabor filter is embedded into the first layer for enhancement of curve feature extraction. With a well-designed ensemble scheme, low-level 3D features are then convolved to extract high-level features. Finally on the top, we set a region-based loss function to strengthen the discriminative ability of both global and local descriptors. To demonstrate the superiority of our method, extensive experiments are conducted on our dataset and other popular databases TongJi and IITD, where the results show the proposed 3DCPN achieves state-of-the-art or comparable performances. Copyright © 2021, The Authors. All rights reserved.

关键词： Palmprint recognition

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Social Group Query Based on Multi-fuzzy-constrained Strong Simulation

arXiv

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arXiv 2021年

作者： Liu, Guliu Li, Lei Liu, Guanfeng Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology China Department of Computing Macquarie University Australia Mininglamp Academy of Sciences Mininglamp Technology China

Traditional social group analysis mostly uses interaction models, event models, or other methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interaction between members of the group. Generally, graph pattern matching (GPM) is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this paper, a social group query problem that needs to consider the trust between members of the group is proposed. To solve this problem, we propose a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence. Aiming at the inefficiency of the NTSS algorithm when matching pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matched edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Constrained optimization

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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels

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IEEE Transactions on Mobile computing 2025年

作者： He, Changpeng Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated. © 2002-2012 IEEE.

关键词： Unsupervised learning

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Risk-based decision making: estimands for sequential prediction under interventions

arXiv

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

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Correction: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

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Journal of Cloud computing 2023年第1期12卷 1-1页

作者： Dalal, Surjeet Manoharan, Poongodi Kumar, Lilhore Umesh Seth, Bijeta alsekait, Deema Mohammed Simaiya, Sarita Hamdi, Mounir Raahemifar, Kaamran Department of Computer Science and Engineering Amity University Haryana Gurugram India Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Computing University of Louisiana Lafayette USA Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering B. M. Institute of Engineering & Technology Sonipat India Department of Computer Science and Information Technology Applied College Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia College of Information Sciences and Technology Data Science and Artificial Intelligence Program State College USA School of Optometry and Vision Science Faculty of Science University of Waterloo Waterloo Canada Faculty of Engineering University of Waterloo Waterloo Canada

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Blockchain based electronic educational document management with role-based access control using machine learning model

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Scientific Reports 2025年第1期15卷 1-19页

作者： Chinnasamy, P. Subashini, B. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Pandey, Binay Kumar Pandey, Digvijay Lelisho, Mesfin Esayas Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Engineering Srivilliputtur India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttrarakhand India Department of Technical Education (Government of Uttar Pradesh) Uttar Pradesh Kanpur India Department of Statistics College of Natural and Computational Science Mizan-Tepi University Tepi Ethiopia

The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%. © The Author(s) 2025.

关键词： Blockchain Electronic educational document management Remora swarm optimization Role-based access Temporal neural network

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Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

arXiv

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arXiv 2023年

作者： Cheng, Sibo Quilodrán-Casas, César Ouala, Said Farchi, Alban Liu, Che Tandeo, Pierre Fablet, Ronan Lucor, Didier Iooss, Bertrand Brajard, Julien Xiao, Dunhui Janjic, Tijana Ding, Weiping Guo, Yike Carrassi, Alberto Bocquet, Marc Arcucci, Rossella Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Earth Science and Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong IMT Atlantique Lab-STICC UMR CNRS 6285 France and Odyssey Inria/IMT France RIKEN Center for Computational Science Kobe Japan CEREA École des Ponts and EDF R&D île-de-France France The Laboratoire Interdisciplinaire des Sciences du Numérique CNRS Paris-Saclay University OrsayF-91403 France 78401 Chatou France Institut de Mathématiques de Toulouse Toulouse31062 France SINCLAIR AI Lab Saclay France Bergen Norway School of Mathematical Sciences Tongji University Shanghai200092 China Mathematical Institute for Machine Learning and Data Science KU Eichstätt-Ingolstadt Bavaria Germany School of Information Science and Technology Nantong University Nantong226019 China Department of Physics and Astronomy Augusto Righi University of Bologna Bologna40124 Italy

data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed. © 2023, CC BY-NC-SA.

关键词： Dynamical systems

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Retraction notice to “Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference” [Optik 270 (2022) 169956]

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Optik 2023年 288卷

作者： Romany F. Mansour Eatedal Alabdulkreem Heba F. Eid K. Sathishkumar Mohd Abdul Rahim Khan Anil Kumar Department of Mathematics Faculty of Science New Valley University El-Kharja 72511 Egypt Department of Computer Science College of Computer and Information Science Princess Nourah Bint Abdulrahman University P.O.Box 84428 Riyadh 11671 Saudi Arabia Faculty of Science AL-Azhar University Cairo Egypt Department of Computer Science Gobi Arts & Science College(Autonomous) Gobichettipalayam Tamilnadu India Department of Computer Science College of Computer and Information Sciences Majmaah University Al-Majmaah 11952 Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India

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Evaluating the Use of Large Language Model to Identify Top Research Priorities in Hematology

Evaluating the Use of Large Language Model to Identify Top R...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Huiqin Chen Fangwan Huang Xuanyun Liu Yinli Tan Dihua Zhang Dongqi Li Glen M. Borchert Ming Tan Jingshan Huang Department of Pediatrics The Third Affiliated Hospital Sun Yatsen University Guangzhou China College of Computer and Data Science Fuzhou University Fuzhou China Department of Nephrology The First Affiliated Hospital Sun Yat-sen University Guangzhou China Department of Economics & School of Information and Computer Sciences University of California Irvine Irvine U.S.A. Department of Pharmacology College of Medicine University of South Alabama Mobile U.S.A. Institute of Biochemistry and Molecular Biology Institute of Biomedical Sciences China Medical University Taichung Taiwan School of Computing College of Medicine University of South Alabama Mobile U.S.A.

The field of hematology is highly progressive and dynamic requiring researchers to commit significant amounts of time and effort towards staying abreast of the most crucial research areas. As such, in this work we assess the potential of ChatGPT for identifying research priorities within five key topics in hematology: acute lymphocytic leukemia, immunotherapy, targeted therapy, hematopoietic stem cell transplantation, and acute myeloid leukemia. After ChatGPT was employed to generate specific research questions in these areas, a panel of seven experienced hematologists independently reviewed and rated resultant research questions based on four parameters: relevance, originality, clarity, and specificity on a scale of 1 to 5, with 5 denoting the highest score. Excitingly, the mean and median grades of the four parameters were all above 4, indicating that the hematologists strongly agreed that the problems generated by ChatGPT were generally highly specific, clear, relevant, and original. As such, although further work will clearly be required, we suggest our current study indicates that Large Language Models (LLMs), such as ChatGPT, may very well represent valuable new tools for more efficiently identifying and prioritizing impactful research questions in the field of hematology.

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