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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Rajesh Singh Akkaraju Sailesh Chandra Babeetta Bbhagat Jeidy Panduro-Ramirez Anil Pandurang Gaikwad Bhasker Pant Uttaranchal Institute of Technology Uttaranchal University Uttarakhand India Doctoral Scholar School of Business and Management Christ (Deemed to be) University Bangalore Karnataka India Department of Computer Science & Information Technology Chhatrapati Shivaji Maharaj University Navi Mumbai Department of Business Universidad Tecnológica Del Perú Perú JSPM’S JSCOE-MCA Department Savitribai Phule Pune University Pune Maharashtra India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network’s perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries’ uses due to its decentralized, accessible, & safe structure. This system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations.

关键词： Training Cloud computing Technological innovation Quantum computing Machine learning algorithms Machine learning Blockchains

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ENhancing remote sensing image retrieval with triplet deep metric learning network

arXiv

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arXiv 2019年

作者： Cao, Rui Zhang, Qian Zhu, Jiasong Li, Qing Li, Qingquan Liu, Bozhi Qiu, Guoping International Doctoral Innovation Centre School of Computer Science University of Nottingham Ningbo China Ningbo315100 China Shenzhen Key Laboratory of Spatial Smart Sensing and Services Key Lab. for Geo-Environ. Monitoring of Coast. Zone of the Natl. Admin. of Surv. Mapping and Geoinformation Shenzhen University Shenzhen518060 China College of Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two publicly available remote sensing image retrieval datasets and show that our method significantly outperforms state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Content based retrieval

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Scalable Authentication and Optimal Flooding in a Quantum Network

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PRX Quantum 2022年第2期3卷 020311-020311页

作者： Naomi R. Solomons Alasdair I. Fletcher Djeylan Aktas Natarajan Venkatachalam Sören Wengerowsky Martin Lončarić Sebastian P. Neumann Bo Liu Željko Samec Mario Stipčević Rupert Ursin Stefano Pirandola John G. Rarity Siddarth Koduru Joshi Quantum Engineering Technology Labs and Quantum Engineering Centre for Doctoral Training Centre for Nanoscience and Quantum Information University of Bristol Bristol BS8 1FD United Kingdom Department of Computer Science University of York York YO10 5GH United Kingdom Quantum Engineering Technology Labs H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering University of Bristol Bristol BS8 1UB United Kingdom Institute for Quantum Optics and Quantum Information—Vienna (IQOQI) and Vienna Center for Quantum Science and Technology (VCQ) 1090 Vienna Austria Institut de Ciencies Fotoniques (ICFO) Barcelona Institute of Science and Technology Castelldefels Barcelona 08860 Spain Photonics and Quantum Optics Research Unit Center of Excellence for Advanced Materials and Sensing Devices Ruđer Bošković Institute 10000 Zagreb Croatia College of Advanced Interdisciplinary Studies NUDT Changsha 410073 China

The global interest in quantum networks stems from the security guaranteed by the laws of physics. The deployment of quantum networks means facing the challenges of scaling up the physical hardware and, more importantly, of scaling up all other network layers and optimally utilizing network resources. Here, we consider two related protocols and their experimental demonstrations on an eight-user quantum network test bed, and discuss their usefulness with the aid of example use cases. First, we consider an authentication-transfer protocol to manage a fundamental limitation of quantum communication—the need for a preshared key between every pair of users linked together on the quantum network. By temporarily trusting some intermediary nodes for a short period of time (<35min in our network), we can generate and distribute these initial authentication keys with a very high level of security. Second, when end users quantify their trust in intermediary nodes, our flooding protocol can be used to improve both end-to-end communication speeds and increase security against malicious nodes.

关键词： Quantum communication Quantum cryptography Quantum networks

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Supply Chain Framework at the Rubber Company with a Profit Sharing approach in ensuring Material Availability

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IOP Conference Series: Materials science and engineering 2019年第1期505卷

作者： Meilita Tryana Sembiring Irwan Budiman Anita Christine Sembiring Evta Indra Delima Sitanggang Dini Wahyuni Harmein Nasution Andree Chen Magister of Industrial Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia Industrial Engineering Department Faculty of Technology and Computer Sciences Universitas Prima Indonesia Indonesia Information System Department Faculty of Technology and Computer Sciences Universitas Prima Indonesia Indonesia Doctoral Programme of Industrial Engineering Faculty of Engineering Universitas Sumatera Utara Indonesia

This research was conducted in rubber factory in Indonesia. Today, businesses need a more efficient supply chain that is able to resolve complex challenges in the present. This research is aimed at increasing the company's competitive advantage by designing supply chain framework with profit sharing system to ensure material availability. This research is conducted by using data reduction method in determining quantitative data for input Stream Value Mapping and Supply Chain Management. Determination of rubber material purchase price is done by using the average trend of rubber price so that the purchase of rubber material is done flat. In order to create trusts in the supply chain, profit-sharing systems are used so that suppliers will supply the right, consistent quantity, quality, and timing of delivery. The results obtained supply chain frameworks are better able to ensure process efficiency and material availability. If applied, this framework will save up to 29.17% processing time.

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A faster, cheaper and safer route to CE

World Class Design to Manufacture

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World Class Design to Manufacture 1995年第2期2卷 10-16页

作者： Stephen Evans Fiona Lettice Palminder Smart Comprehensive background in manufacturing and is currently working as a Senior Lecturer and Industrial Consultant while supervising five doctoral students and several industrial research projects. He can be contacted at the CIM Institute Cranfield University Cranfield Beds UK MK43 OAL. Tel: 01234 754073 Fax: 01234 750852. BSc Hons in technology and management science and an MSc in computer‐integrated manufacturing. She is developing her interest in concurrent engineering implementation methodologies through a PhD research programme at the CIM Institute at Cranfield. She previously spent two years working in the energy and consultancy industries. BSc Hons in social sciences and an MSc in computer‐integrated manufacturing. She has four years′ experience in production control and distribution software training consultancy. She is currently on a PhD research programme at the CIM Institute at Cranfield studying best practice concurrent engineering implementations in companies worldwide.

Describes the FAST concurrent engineering (CE) implementationmethodology that has been developed by the authors at CranfieldUniversity, through research and consultancy activities. The methodologyis presented in a paper‐based work‐book and describes three stages acompany could proceed through to take it from an initial interest in CE,to the successful implementation of a pilot CE product developmentproject and then expansion. The focus is on the introduction ofmultidisciplinary product development teams as the main vehicle forradically improving product development performance.

关键词： Concurrent engineering Implementation New product development Product development Teams

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A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention

arXiv

引用

arXiv 2024年

作者： Nunes, João D. Montezuma, Diana Oliveira, Domingos Pereira, Tania Cardoso, Jaime S. INESC TEC - Institute for Systems and Computer Engineering Technology and Science R. Dr. Roberto Frias Porto4200-465 Portugal University of Porto Faculty of Engineering R. Dr. Roberto Frias Porto4200-465 Portugal IMP Diagnostics Praça do Bom Sucesso Porto4150-146 Portugal R. Dr. António Bernardino de Almeida Porto4200-072 Portugal Doctoral Programme in Medical Sciences School of Medicine and Biomedical Sciences University of Porto ICBAS-UP Porto Portugal FCTUC - Faculty of Science and Technology University of Coimbra Coimbra3004-516 Portugal

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&E-stains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance. In this work, we hypothesise context and attention inductive biases in artificial neural networks (ANNs) could increase the performance and generalization of algorithms for cell nuclei instance segmentation and classification. To understand the advantages, use-cases, and limitations of context and attention-based mechanisms in instance segmentation and classification, we start by review works in computer vision and medical imaging. We then conduct a thorough survey on context and attention methods for cell nuclei instance segmentation and classification from H&E-stained microscopy imaging, while providing a comprehensive discussion of the challenges being tackled with context and attention. Besides, we illustrate some limitations of current approaches and present ideas for future research. As a case study, we extend both a general instance segmentation and classification method (Mask-RCNN) and a tailored cell nuclei instance segmentation and classification model (HoVer-Net) with context- and attention-based mechanisms, and do a comparative analysis on a multi-centre colon nuclei identification and

关键词： Classification (of information)

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GLSD: The Global Large-Scale Ship Database and Baseline Evaluations

arXiv

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arXiv 2021年

作者： Shao, Zhenfeng Wang, Jiaming Deng, Lianbing Huang, Xiao Lu, Tao Luo, Fang Zhang, Ruiqian Lv, Xianwei Dang, Chaoya Ding, Qing Wang, Zhiqiang The State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China The Post-Doctoral Research Center of Zhuhai Da Hengqin Science and Technology Development Co. Ltd Guangdong Hengqin New Area 519031 China The Department of Geosciences University of Arkansas FayettevilleAR72701 United States The School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China The School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan430063 China The Chinese Academy of Surveying and Mapping Beijing100036 China

In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks. The designed GLSD database includes a total of 212,357 annotated instances from 152,576 images. Based on the collected images, we propose 13 ship categories that widely exist in international routes. These categories include Sailing boat, Fishing boat, Passenger ship, Warship, General cargo ship, Container ship, Bulk cargo carrier, Barge, Ore carrier, Speed boat, Canoe, Oil carrier, and Tug. The motivations of developing GLSD include the following: 1) providing a refine and extensive ship detection database that benefits the object detection community, 2) establishing a database with exhaustive labels (bounding boxes and ship class categories) in a uniform classification scheme, and 3) providing a large-scale ship database with geographic information (covering more than 3000 ports and 33 routes) that benefits multi-modal analysis. In addition, we discuss the evaluation protocols corresponding to image characteristics in GLSD and analyze the performance of selected state-of-the-art object detection algorithms on GSLD, aiming to establish baselines for future studies. More information regarding the designed GLSD can be found at https://***/jiaming-wang/GLSD. © 2021, CC BY.

关键词： Object detection

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E-Commerce Business Model Analysis and Success in Urban Areas Using AI-Distributed Machine Learning

E-Commerce Business Model Analysis and Success in Urban Area...

引用

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Akey Sungheetha B Bharathi D. Ganesan T. Karthikeyan N. Bindu Madhavi Chairma Lakshmi K R Dept. of CSE Alliance College of Engineering and Design Alliance University Bangalore Karnataka India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Tamilnadu India Department of Management Studies Sona College of Technology Salem Tamilnadu India Department of Information Technology University of Technology and Applied Sciences Salalah Sultanate of Oman KL Business School & Programme Coordinator (MBA) KL Centre for Distance & Online Education(CDOE) Koneru Lakshmaiah Education Foundation (Deemed to be University) Andhra Pradesh India Department of Electronics and Instrumentation Engineering R.M.K. Engineering College India

This study presents a comprehensive analysis of the e-commerce business model success in urban areas through the integration of AI distributed machine learning techniques. The rapid growth of e-commerce in urban settings has led to increased competition among businesses striving to capture market share. To achieve a competitive edge, companies are increasingly turning to AI and machine learning to enhance various aspects of their operations. This research examines the symbiotic relationship between e-commerce success and AI technologies, specifically focusing on distributed machine learning role in optimizing operations, personalizing user experiences, and predicting market trends. By leveraging real-world case studies and empirical data, this study sheds light on the mechanisms through which AI distributed machine learning contributes to the sustainable development of e-commerce enterprises in urban environments. The findings of this study provide valuable insights for e-commerce businesses, urban planners, and policymakers to formulate strategies that foster a conducive ecosystem for e-commerce success in urban areas.

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A Reliable Method to Estimate the Bispectral Index Value Using a Single Frontal EEG Channel for Intra and Inter Subject Variability

A Reliable Method to Estimate the Bispectral Index Value Usi...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Mohammad Shahbakhti Matin Beiramvand Roza Krycinska Erfan Nasiri Wei Chen Jordi Solé-Casals Michal Wierzchon Anna Broniec-Wójcik Piotr Augustyniak Vaidotas Marozas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Faculty of Information Technology and Communication Tampere University Tempere Finland Doctoral School in Medical and Health Sciences Jagiellonian University Medical College Krakow Poland Department of Interdisciplinary Intensive Care Jagiellonian University Collegium Medicum Krakow Poland Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Center for Intelligent Medical Electronics School of Information Science and Technology Fudan University Shanghai China Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

Objective: Monitoring the depth of anesthesia (DoA) plays an important role for administering the drug injection during a surgery, i.e., preventing undesired awareness and inordinate anesthetic depth. Although the bispectral index (BIS) monitor is the golden standard system for the DoA monitoring, it is still not affordable for the developing countries. Alternatively, a low-cost electroencephalogram (EEG) headband can be used. The objective of this paper is to present a new algorithm for estimating the BIS values using a single frontal EEG channel. Method: In the first step, the EEG signal is filtered for the elimination of artifacts and is split into its sub-bands. In the second step, several linear and nonlinear features are extracted from each sub-band and fed to a random forest regression model in order to estimate the BIS. The performance of the proposed algorithm is assessed using EEG data recorded from twenty-four subjects during the general anesthesia and is validated in terms of correlation coefficient (CC) and absolute error (AE) between the reference and estimated BIS values. Results: The proposed algorithm achieved the mean CC of 0.83 and AE of 6.5 for intra subject variability and mean CC of 0.87 and AE of 5.5 for inter subject variability. Significance: Given the similar results for both intra and inter subject variability, the proposed algorithm has the potential to be used in the real-world scenario.

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SYNAPSE: An international roadmap to large brain imaging

引用

Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

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