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arXiv 2024年

作者： Chen, Yuhong Song, Ailin Yin, Huifeng Zhong, Shuai Chen, Fuhai Xu, Qi Wang, Shiping Xu, Mingkun College of Computer and Data Science Fuzhou University Fuzhou China Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China University of Electronic Science and Technology of China Chengdu China Center for Brain Inspired Computing Research Department of Precision Instrument Tsinghua University Beijing China School of Computer Science and Technology Dalian University of Technology Dalian China

The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating the intricate cognitive procedures of the human brain processing signals sequentially. Our cerebral architecture seamlessly integrates sequential data through intricate feed-forward and feedback mechanisms. In stark contrast, traditional methods struggle to generalize effectively when confronted with data spanning diverse domains, highlighting the need for innovative strategies that can mimic the brain’s adaptability and dynamic integration capabilities. In this paper, we propose a bio-neurologically inspired multiview incremental framework named MVIL aimed at emulating the brain’s fine-grained fusion of sequentially arriving views. MVIL lies two fundamental modules: structured Hebbian plasticity and synaptic partition learning. The structured Hebbian plasticity reshapes the structure of weights to express the high correlation between view representations, facilitating a fine-grained fusion of view representations. Moreover, synaptic partition learning is efficient in alleviating drastic changes in weights and also retaining old knowledge by inhibiting partial synapses. These modules bionically play a central role in reinforcing crucial associations between newly acquired information and existing knowledge repositories, thereby enhancing the network’s capacity for generalization. Experimental results on six benchmark datasets show MVIL’s effectiveness over state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A survey on computationally efficient neural architecture search

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Journal of Automation and Intelligence 2022年第1期1卷 8-22页

作者： Shiqing Liu Haoyu Zhang Yaochu Jin Chair of Nature Inspired Computing and Engineering Faculty of TechnologyBielefeld University33619 BielefeldGermany Department of Computer Science University of SurreyGuildfordGU27XHUnited Kingdom Engineering Research Center of Digitized Textile&Apparel Technology Ministry of EducationCollege of Information Science and TechnologyDonghua UniversityShanghai 201620China

Neural architecture search(NAS)has become increasingly popular in the deep learning community recently,mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks(DNNs).However,NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS,and training DNNs is computationally *** solve this major limitation of NAS,improving the computational efficiency is essential in the design of ***,a systematic overview of computationally efficient NAS(CE-NAS)methods still *** fill this gap,we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods,together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational *** remaining challenges and open research questions are also discussed,and promising research topics in this emerging field are suggested.

关键词： Neural architecture search(NAS) One-shot NAS Surrogate model Bayesian optimization Performance predictor

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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The FORA European Training Network on Fog computing for Robotics and Industrial Automation

The FORA European Training Network on Fog Computing for Robo...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Mohammadreza Barzegaran Paul Pop Center for Pervasive Communications and Computing University of California at Irvine Irvine CA USA Department of Applied Mathematics and Computer Science Technical University of Denmark (DTU) Kongens Lynbgy Denmark

Fog computing for Robotics and Industrial Automation, FORA, was a European Training Network which focused on future industrial automation architectures and applications based on an emerging technology, called Fog computing. The research project focused on research related to Fog computing with applicability to industrial automation and manufacturing. The main outcome of the FORA project was the development of a deterministic Fog computing Platform (FCP) to be used for implementing industrial automation and robotics solutions for Industry 4.0. This paper reports on the scientific outcomes of the FORA project. FORA has proposed a reference system architecture for Fog computing, which was published as an open Architecture Analysis Design Language (AADL) model. The tech-nologies developed in FORA include fog nodes and hypervisors, resource management mechanisms and middleware for deploying scalable Fog computing applications, while guaranteeing the non-functional properties of the virtualized industrial control applications, and methods and processes for assuring the safety and security of the FCP. Several industrial use cases were used to evaluate the suitability of the FORA FCP for the Industrial IoT area, and to demonstrate how the platform can be used to develop industrial control applications and data analytics applications.

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Enhancing Cyclone center Identification in Radar Images through Deep Learning and Match Recognition

Enhancing Cyclone Center Identification in Radar Images thro...

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International Conference on computing and Networking Technology (ICCNT)

作者： Thirumurugan Shanmugam Chirag Chandrashekar Maheswari Subburaj Arun Kumar Sivaraman Ajmery Sultana Senthil Kumar Thangavel College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Solution Center Photon Interactive Inc Ontario Canada School of Computer Science and Technology Algoma University Brampton Ontario Canada Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone’s center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network’s capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from $86 \%$ to $92 \%$ or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting.

关键词： Deep learning YOLO Training Adaptation models Accuracy Machine learning algorithms Transfer learning Radar Radar imaging Cyclones

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Product Traceability Digital Tool Powered by Mathematical Model for Logistics Digital Platform 15

Product Traceability Digital Tool Powered by Mathematical Mo...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Kulba, Vladimir Medennikov, Viktor V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

This paper discusses a mathematical model for establishing a single digital logistics platform as a link between all participants in the added-value creation chain, including producers, resource and services suppliers, consumers, and logistics companies. This demonstrates that the platform in question is an opportunity to create digital tools for products and goods traceability throughout the entire production and logistics chain from the field to the end consumer's kitchen. © 2022 IEEE.

关键词： Digital devices

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Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

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computers, Materials & Continua 2021年第4期67卷 761-777页

作者： Afzan Adam Abdul Hadi Abd Rahman Nor Samsiah Sani Zaid Abdi Alkareem Alyessari Nur Jumaadzan Zaleha Mamat Basela Hasan Faculty of Information Science and Technology Center for Artificial Intelligence TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43300Malaysia Faculty of Science and Technology School of Mathematical SciencesUniversiti Kebangsaan MalaysiaBangiSelangor43300Malaysia Department of Information Technology and Computing Faculty of Computer StudiesArab Open UniversityAmmanJordan

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling *** morphological pattern of an epithelial layer is greatly ***,the shape deformation model can normalise the distortions,but it needs a 2D *** theory,on the other hand,is not yet tested on digital pathology ***,this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial *** boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight *** epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus ***,the textural and spatial features along the estimated layer are used for dysplastic tissue *** proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic *** result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth *** improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of ***,quantifying and classifying the morphological patterns for dysplasia can be *** textural and spatial features on the detected epithelial layer can capture the changes in tissue.

关键词： Digital pathology grading dysplasia tissue boundary effect

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SHARED ANCESTRY GRAPHS AND SYMBOLIC ARBOREAL MAPS

arXiv

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arXiv 2023年

作者： Huber, Katharina T. Moulton, Vincent Scholz, Guillaume E. School of Computing Sciences University of East Anglia Norwich United Kingdom Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany

A network N on a finite set X, |X| ≥ 2, is a connected directed acyclic graph with leaf set X in which every root in N has outdegree at least 2 and no vertex in N has indegree and outdegree equal to 1;N is arboreal if the underlying unrooted, undirected graph of N is a tree. Networks are of interest in evolutionary biology since they are used, for example, to represent the evolutionary history of a set X of species whose ancestors have exchanged genes in the past. For M some arbitrary set of symbols, (Equation presented) is a symbolic arboreal map if there exists some arboreal network N whose vertices with outdegree two or more are labelled by elements in M and so that (Equation presented) is equal to the label of the least common ancestor of x and y in N if this exists and else. Important examples of symbolic arboreal maps include the symbolic ultrametrics, which arise in areas such as game theory, phylogenetics and cograph theory. In this paper we show that a map (Equation presented) is a symbolic arboreal map if and only if d satisfies certain 3- and 4-point conditions and the graph with vertex set X and edge set consisting of those pairs (Equation presented) with d({x, y}) ≠ ☉ is Ptolemaic (i.e. its shortest path distance satisfies Ptolemy's inequality). To do this, we introduce and prove a key theorem concerning the shared ancestry graph for a network N on X, where this is the graph with vertex set X and edge set consisting of those (Equation presented) such that x and y share a common ancestor in N. In particular, we show that for any connected graph G with vertex set X and edge clique cover K in which there are no two distinct sets in K with one a subset of the other, there is some network with |K| roots and leaf set X whose shared ancestry graph is *** Codes 05C05, 05C20, 05C90 Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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Comparative Analysis of Unsupervised Pre-Training Models for LULC Classification With Limited Labelled Data

Comparative Analysis of Unsupervised Pre-Training Models for...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Deeksha Aggarwal Uttam Kumar Rikhil Gupta Spatial Computing Laboratory Center for Data Sciences International Institute of Information Technology Bangalore (IIIT Bangalore) Bangalore India Department of Computer Science BITS Pilani Pilani India

Accurate classification of land use and land cover (LULC) categories from satellite imagery remains a pivotal task in remote sensing and geospatial analysis. The efficacy of classification models is often hindered by the scarcity of labeled samples and the degree of noise present in the labeled data, posing prevalent challenges in the real-world applications. This paper presents a study aimed at addressing these challenges by systematically comparing six combined generative pre-training and discriminative models with two standalone discriminative models in the context of LULC classification. Through meticulous empirical analysis, this study evaluates the performance of both generative and discriminative models when confronted with limited labeled data. The comparison not only highlighted the strengths and weaknesses of each model, but also provided insights into their adaptability and utility under constrained and noisy labeling resources, which are common scenarios in real-world LULC classification tasks. Additionally, the comparison offer insights into how generative models can leverage unlabeled data to augment their predictive capabilities, while also high-lighting the efficiency of traditional discriminative models when dealing with limited labeled data.

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Generating Coupling Interfaces for Multiphysics Simulations with ExaStencils and waLBerla

Generating Coupling Interfaces for Multiphysics Simulations ...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Richard Angersbach Sebastian Kuckuk Harald Köstler Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg National High Performance Computing Center Friedrich-Alexander-Universität Erlangen-Nürnberg

Domain-specific languages (DSLs) and code generation technology are adopted more and more by the high performance computing (HPC) community to tackle the performance, portability, and productivity requirements for HPC software. They share their inherent specialization in specific scientific domains with traditional HPC frameworks. In this regard, multiphysics simulations pose a strong challenge as they usually require components from multiple domains. A promising option for retaining these requirements is the combination of code generation with HPC frameworks through automatically created couplings. This work demonstrates the fusion of waLBerla with generated code from ExaStencils. waLBerla is a massively parallel C++ framework focused on computational fluid dynamics (CFD) simulations using the Lattice Boltzmann method (LBM). ExaStencils generates whole simulation codes using geometric multigrid solvers from specifications in its own DSL. We showcase the coupling of generated solvers with LBM simulations, including the mapping of data structures and augmentations to convert quantities units on-the-fly.

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