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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production Software Real-time systems Stakeholders Monitoring

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Conditional score-based diffusion models for solving inverse problems in mechanics

arXiv

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arXiv 2024年

作者： Dasgupta, Agnimitra Ramaswamy, Harisankar Murgoitio-Esandi, Javier Foo, Ken Li, Runze Zhou, Qifa Kennedy, Brendan F. Oberai, Assad A. Department of Aerospace & Mechanical Engineering University of Southern California Los AngelesCA90089 United States BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre NedlandsWA6009 Australia Centre for Medical Research The University of Western Australia PerthWA6009 Australia Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia PerthWA6009 Australia Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń Grudziadzka 5 Torun87-100 Poland Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Alfred E. Mann Department of Biomedical Engineering University of Southern California Los AngelesCA90089 United States

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen’s spatially varying material properties from noisy measurements of its mechanical response to loading. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. More specifically, the score functions corresponding to multiple realizations of the measurement are approximated using a single neural network, the so-called score network, which is subsequently used to sample the posterior distribution using an appropriate Markov chain Monte Carlo scheme based on Langevin dynamics. Training the score network only requires simulating the forward model. Hence, the proposed approach can accommodate black-box forward models and complex measurement noise. Moreover, once the score network has been trained, it can be re-used to solve the inverse problem for different realizations of the measurements. We demonstrate the efficacy of the proposed approach on a suite of high-dimensional inverse problems in mechanics that involve inferring heterogeneous material properties from noisy measurements. Some examples we consider involve synthetic data, while others include data collected from actual elastography experiments. Further, our applications demonstrate that the proposed approach can handle different measurement modalities, complex patterns in the inferred quantities, non-Gaussian and non-additive noise models, and nonlinear black-box forward models. The results show that the proposed framework can solve large-scale physics-based inverse problems efficiently. © 2024, CC BY.

关键词： Markov chains

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Identifying Genes and Their Interactions from Pathway Figures and Text in Biomedical Articles

Identifying Genes and Their Interactions from Pathway Figure...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： He, Fei Thompson, Joshua Mao, Ziting Ren, Yijie Nussbaum, Yulia Kholod, Olha Shin, Dmitriy Hannink, Mark Popescu, Mihail Xu, Dong Northeast Normal University School of Information Science and Technology Changchun China University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States Institute for Data Science and Informatics University of Missouri ColumbiaMO United States University of Missouri Department of Pathology and Anatomical Sciences ColumbiaMO United States University of Missouri Department of Biochemistry ColumbiaMO United States University of Missouri Department of Health Management and Informatics ColumbiaMO United States

ISBN: (纸本)9781665401265

Many high-quality biological pathways are presented in figures and text in biomedical literature. They are great resources for studies of biological mechanisms and precision medicine practices. These pathways need to be carefully curated, reconciled, and transformed into a computable form. Current manual curation approaches are inadequate in keeping up with the pace of the literature growth. New bio-curation approaches are needed to streamline the identification of gene interactions from pathway figures and text. This paper proposes a pathway curation approach for identifying genes and their interactions using both figures and text of biomedical articles. Our method integrates deep learning-based object detection models with a Google optical character recognition service to extract genes and their interactions from pathway figures. Our pipeline was evaluated on the figures from PubMed publications with manual annotations. The results demonstrated that our model could effectively retrieve genes and their interactions in pathway figures. The proposed pipeline may accelerate various applications of the latest biomedical discoveries. We also developed a web server at http://*** to provide the gene interaction curation on uploaded pathway figures and corresponding articles. © 2021 IEEE.

关键词： Genes

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A Web and Mobile Application to Identify the Availability of Drinking Water Information During a Water Crisis

A Web and Mobile Application to Identify the Availability of...

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Suhail Razeeth Fathima Shihara Kasuni Pavithra Maran Kaliyamoorthy Samsudeen Sabraz Nawaz Rkar Kariyapper Dept. of Information and Communication Technology South Eastern University of Sri Lanka Sri Lanka Dept. of Computer Science and Informatics Uva Wellassa University of Sri Lanka Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India Department of Management and Information Technology South Eastern University of Sri Lanka Sri Lanka

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers.

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A multimodal framework for extraction and fusion of satellite images and public health data

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Scientific Data 2024年第1期11卷 634页

作者： Moukheiber, Dana Restrepo, David Cajas, Sebastián Andrés Montoya, María Patricia Arbeláez Celi, Leo Anthony Kuo, Kuan-Ting López, Diego M. Moukheiber, Lama Moukheiber, Mira Moukheiber, Sulaiman Osorio-Valencia, Juan Sebastian Purkayastha, Saptarshi Paddo, Atika Rahman Wu, Chenwei Kuo, Po-Chih Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States Departamento de Telemática Universidad del Cauca Cauca Popayán Colombia John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA United States School of Computer Science University College Dublin Dublin Ireland Grupo de Epidemiología Facultad Nacional de Salud Pública Universidad de Antioquia Medellín Colombia Department of Biostatistics Harvard TH Chan School of Public Health Boston MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science Worcester Polytechnic Institute Worcester MA United States ScienteLab Cundinamarca Bogota Colombia Department of BioHealth Informatics Indiana University Luddy School of Informatics Computing and Engineering Indianapolis IN United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health. © The Author(s) 2024.

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Towards Distributed and Intelligent Integrated Sensing and Communications for 6G Networks

arXiv

引用

arXiv 2024年

作者： Strinati, Emilio Calvanese Alexandropoulos, George C. Amani, Navid Crozzoli, Maurizio Madhusudan, Giyyarpuram Mekki, Sami Rivet, Francois Sciancalepore, Vincenzo Sehier, Philippe Stark, Maximilian Wymeersch, Henk French Atomic Energy Commission’s Electronics and Information Technologies Laboratory Minatec Campus Grenoble France Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago United States K. N. Toosi University of Technology Tehran Iran Chalmers University of Technology Gothenburg Sweden Eindhoven University of Technology Eindhoven Netherlands Wireless Access Innovation Department Orange Labs France SUP’COM Tunis Tunisia EE Department of the Bordeaux Institute of Technology IMS laboratory Bordeaux France NEC Laboratories Europe Germany Nokia Strategy and Technology in Nokia France Germany Chalmers University of Technology Sweden

This paper introduces the distributed and intelligent integrated sensing and communications (DISAC) concept, a transformative approach for 6G wireless networks that extends the emerging concept of integrated sensing and communications (ISAC). DISAC addresses the limitations of the existing ISAC models and, to overcome them, it introduces two novel foundational functionalities for both sensing and communications: a distributed architecture (enabling large-scale and energy-efficient tracking of connected users and objects, leveraging the fusion of heterogeneous sensors) and a semantic and goal-oriented framework (enabling the transition from classical data fusion to the composition of semantically selected information). © 2024, CC BY.

关键词： Semantics

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Practical limitations on robustness and scalability of quantum Internet

arXiv

引用

arXiv 2023年

作者： Sadhu, Abhishek Somayajula, Meghana Ayyala Horodecki, Karol Das, Siddhartha Raman Research Institute Karnataka Bengaluru560080 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Gachibowli Telangana Hyderabad500032 India Department of Computer Science Engineering G Narayanamma Institute of Technology and Science Telangana Hyderabad500104 India Institute of Informatics National Quantum Information Centre Faculty of Mathematics Physics and Informatics University of Gdańsk Wita Stwosza 57 Gdańsk80-308 Poland International Centre for Theory of Quantum Technologies University of Gdańsk Wita Stwosza 63 Gdańsk80-308 Poland

The realization of a desirably functional quantum Internet is hindered by fundamental and practical challenges such as high loss during transmission of quantum systems, decoherence due to interaction with the environment, fragility of quantum states under measurement, etc. We study the implications of these constraints by analyzing the limitations on the scaling and robustness of the quantum Internet. Considering quantum networks, we present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes. In particular, we show that the device-independent quantum key distribution (DI-QKD) network of isotropic states with parameter p has a finite diameter depending on p. Motivated by the power of abstraction in graph theory (in association with quantum information theory), that allows us to consider the topology of a network of users spatially separated over long distances around the globe or separated as nodes within the small space over a processor or circuit within a same framework, we consider graph-theoretic quantifiers to assess network robustness and provide critical values of communication lines for viable communication over quantum Internet. We consider some quantum networks of practical interest, ranging from satellite-based networks connecting far-off spatial locations to currently available quantum processor architectures within computers, and analyze their robustness to perform quantum information processing tasks. Some of these tasks form primitives for delegated quantum computing, e.g., entanglement distribution and quantum teleportation, that are desirable for all scale of quantum networks, be it quantum processors or quantum Internet. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph theory

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Electrical Fault Detection for Industry 4.0 using Fusion deep Learning Algorithm 12

Electrical Fault Detection for Industry 4.0 using Fusion dee...

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12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Mihaltan, Traian Candin Neagu, Bogdan-Constantin Raboaca, Maria Simona Verma, Chaman Sandip University School of Computer Science and Engineering Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida201302 India Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania 'Gheorghe Asachi' Technical University of Iasi Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

In this paper, problems found in electrical power transmission lines are looked into and put into groups with the help of artificial neural networks. An idea is floating around for a method that uses the three-phase currents and the voltage at one end as inputs. In this process, both the feedforward neural network and the back propagation method are used. Both approaches have the ability to find errors in a system and put them into classes. So that it could be sure that the decision made by the neural network was the best one, a thorough analysis was done with a lot of different ways to set up the number of hidden layers. To design a smart model Electrical Fault Detection for Industry 4.0 using Fusion deep Learning Algorithm Simulations have shown that a neural network can reliably find and classify problems in transmission lines. © 2022 IEEE.

关键词： Electric power transmission

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An ontology-based approach for user modelling and personalization in E-Learning systems 18

An ontology-based approach for user modelling and personaliz...

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18th IEEE/ACIS International Conference on computer and Information Science, ICIS 2019

作者： Grivokostopoulou, Foteini Perikos, Isidoros Paraskevas, Michael Hatzilygeroudis, Ioannis Computer Engineering and Informatics Department University of Patras and Computer Technology Institute and Press "Diophantus" Patras Greece Department of Electrical and Computer Engineering University of Peloponnese and Computer Technology Institute and Press "Diophantus" Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (纸本)9781728108018

Intelligent tutoring systems are becoming an important mean of education delivery and have changed the ways of teaching and learning. One of their most important aspects concerns the personalization and the adaptation of the learning procedures to learners’ characteristics. This is mainly achieved with the use of student modelling which is employed to record user context and to enhance the learning experience of the students. In this work, we present a generic ontology that incorporates concepts and properties used to model user profiles in order to formulate concrete, complete and extensible user modelling in e-learning systems. The ontology consists of learners’ personal, cognitive and social information as well as information about their performance and skills. In order to keep the student model updated, semantic rules are utilized to analyse student learning and to extract knowledge regarding their performance. The ontological approach is domain independent and can be utilized as a reference model for learner representation in intelligent tutoring systems in higher and distance education. © 2019 IEEE

关键词： Ontology

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Transformers in medical image analysis

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Intelligent Medicine 2023年第1期3卷 59-78页

作者： Kelei He Chen Gan Zhuoyuan Li Islem Rekik Zihao Yin Wen Ji Yang Gao Qian Wang Junfeng Zhang Dinggang Shen Medical School of Nanjing University NanjingJiangsu 210093China National Institute of Healthcare Data Science at Nanjing University NanjingJiangsu 210093China BASIRA Laboratory Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey School of Science and Engineering ComputingUniversity of DundeeUK State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingJiangsu 210093China School of Biomedical Engineering ShanghaiTech UniversityShanghai 201210China Department of Research and Development Shanghai United Imaging Intelligence Co.Ltd.Shanghai 200030China Shanghai Clinical Research and Trial Center Shanghai 201703China

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer *** the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and *** paper aimed to promote awareness of the applications of transformers in medical image ***,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic ***,we reviewed various transformer architectures tailored for medical image applications and discuss their *** this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other *** hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

关键词： Transformer Medical image analysis Deep learning Diagnosis Registration Segmentation Image synthesis Multi-task learning Multi-modal learning Weakly-supervised learning

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