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Blockchain computing and Applications (BCCA), International Conference on

作者： Carmelo Felicetti Antonella Guzzo Giuseppe Manco Francesco Pasqua Ettore Ritacco Antonino Rullo Domenico Saccà Department of Informatics Modelling Electronics and Systems (DIMES) University of Calabria Rende Italy Institute for High Performance Computing and Networking National Research Council (ICAR-CNR) Rende Italy Open Knowledge Technologies (OKT) Relatech Group Rende Italy Computer Science Department University of Udine Udine Italy

Item identification is an important issue in modern supply chains where product items are traced by means of electronic tags that, despite the advantages they bring in terms of automation, are still subject to counterfeiting attacks such as tag modification, cloning, and re-application. To counter these threats (i) tags must be able to safely demonstrate their identity without giving attackers the ability to clone them or modify their content, and (ii) auxiliary identification techniques should be adopted in the case tags are removed from original items and reapplied to fake ones. Using Physically Unclonable Functions as tag “digital fingerprint” has proven to be a viable solution for tag authentication. On the other hand, artificial intelligence has proven its effectiveness in the field of object identification. In this paper we describe a tag architecture immune to cloning and modification attacks, and illustrate how this can be coupled with a product item in a supply chain scenario to confer a stable and durable identity for authentication, in order to identify the items and track them in a distributed ledger framework. We also propose a deep learning approach to perform anti-counterfeiting controls when tag reapplication attacks are in place.

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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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A novel approach for tumor segmentation for lung cancer using multi-objective genetic algorithm and connected component analysis 2nd

A novel approach for tumor segmentation for lung cancer usin...

引用

2nd International Conference on Data Engineering and Communication Technology, ICDECT 2017

作者： Choudhury, Ananya Subramanian, Rajamenakshi R. Sunder, Gaur High Performance Computing—Medical and Bioinformatics Application Group Centre for Development of Advanced Computing Pune India

ISBN: (纸本)9789811316098

Treatment in oncology relies heavily on imaging. Automatic identification of cancerous tumors is a challenging and time-taking task. This paper presents a multi-objective genetic algorithm for calculating optimal threshold value for binarization of CT lung images and segmentation of the tumor region using connected component analysis. The threshold determination method is tested on two public datasets: Data Science Bowl: Stage 1 and NSCLC lung cancer dataset. The results obtained are compared with other genetic algorithm approach and traditional Otsu’s method. The proposed method gives better threshold value compared to Otsu’s method and outperforms other genetic algorithm approach both in terms of threshold value and computation time. Connected component analysis is done on the labeled connected component in the binarized image. Based on predetermined geometric measurements, the components are marked as tumors and non-tumors. This method when applied to 240 cancerous images, correctly identified tumors for 205 images while showed no result where there are no tumors in the image. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Tumors

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WaveFormer: A Wavelet Transformer for Parkinson Disease’s Retinal Layer Segmentation in OCT

WaveFormer: A Wavelet Transformer for Parkinson Disease’s R...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yanlin Chen Xiaoqing Zhang Tianao Wang Chen Tang Haili Ye Jiang Liu Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Chinese Academy of Sciences Shenzhen China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou China The Oujiang Laboratory The Affiliated Eye Hospital Wenzhou Medical University Wenzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Pathology symptoms of Parkinson disease (PD) are different from those of retinal diseases in the retinal layers, which are subtle. However, segmenting pathology information of PD from retinal layers automatically based on optical coherence tomography (OCT) images has not been studied before. Although existing Transformer-based segmentation methods have achieved good segmentation results, they have limitations in capturing local context information. Convolutional neural networks (CNNs) can construct local context dependencies among pixels, which is complementary to Transformers. Particularly, edge information extraction is significant for accurate retinal layer segmentation, which is ignored by both Transformers and CNNs but can be captured by frequency domain learning methods. To fully leverage the advantages of Transformers, CNNs, and frequency domain learning methods, we propose a Wavelet Transformer (WaveFormer) for retinal layer segmentation based on OCT images. In the WaveFormer, we design a Wavelet Spatial Attention block to exploit the potential of frequency information. Based on these advantages, WaveFormer can be data-efficient in limited OCT images of PD. The experimental results on the OCT-PD segmentation dataset show that our WaveFormer outperforms existing Transformers and CNNs. For example, WaveFormer outperforms Swin-UNet by 3.41% of IoU.

关键词： Learning systems Image segmentation Pathology Wavelet domain Image edge detection Frequency-domain analysis Transformers

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Optical Anisotropy in van der Waals materials: Impact on Direct Excitation of Plasmons and Photons by Quantum Tunneling

引用

Light(Science & Applications) 2021年第12期10卷 2387-2398页

作者： Zhe Wang Vijith Kalathingal Thanh Xuan Hoang Hong-Son Chu Christian A.Nijhuis Department of Chemistry National University of Singapore3 Science Drive 3Singapore 117543Singapore Centre for Advanced 2D Materials National University of Singapore6 Science Drive 2Singapore 117564Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Department of Electronics and Photonics Institute of High Performance ComputingA*STAR(Agency for ScienceTechnology and Research)1 Fusionopolis WaySingapore 138632Singapore Hybrid Materials for Opto-Electronics Group Department of Molecules and MaterialsMESA+Institute for Nanotechnology and Center for Brain-Inspired Nano SystemsFaculty of Science and TechnologyUniversity of Twente7500 AE EnschedeThe Netherlands

Inelastic quantum mechanical tunneling of electrons across plasmonic tunnel junctions can lead to surface plasmon polariton(SPP)and photon *** far,the optical properties of such junctions have been controlled by changing the shape,or the type of the material,of the electrodes,primarily with the aim to improve SPP or photon emission *** we show that by tuning the tunneling barrier itself,the efficiency of the inelastic tunneling rates can be improved by a factor of *** exploit the anisotropic nature of hexagonal boron nitride(hBN)as the tunneling barrier material in Au//hBN//graphene tunnel junctions where the Au electrode also serves as a plasmonic strip *** this junction constitutes an optically transparent hBN–graphene heterostructure on a glass substrate,it forms an open plasmonic system where the SPPs are directly coupled to the dedicated strip waveguide and photons outcouple to the far *** experimentally and analytically show that the photon emission rate per tunneling electron is significantly improved(~×3)in Au//hBN//graphene tunnel junction due to the enhancement in the local density of optical states(LDOS)arising from the hBN *** the dedicated strip waveguide,SPP outcoupling efficiency is quantified and is found to be∼80%stronger than the radiative outcoupling in Au//hBN//graphene due to the high LDOS of the SPP decay channel associated with the inelastic *** new insights elucidated here deepen our understanding of plasmonic tunnel junctions beyond the isotropic models with enhanced LDOS.

关键词： tunneling strip coupling

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Medical Image Registration and Its Application in Retinal Images: A Review

arXiv

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arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Application-level Benchmarking of Quantum Computers using Nonlocal Game Strategies

arXiv

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arXiv 2023年

作者： Furches, Jim Chehade, Sarah Hamilton, Kathleen Wiebe, Nathan Marrero, Carlos Ortiz College of Computing Georgia Institute of Technology AtlantaGA30332 United States Physical Detection Systems and Deployment Division Pacific Northwest National Laboratory RichlandWA99354 United States Quantum Computational Science Group Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States

In a nonlocal game, two noncommunicating players cooperate to convince a referee that they possess a strategy that does not violate the rules of the game. Quantum strategies allow players to optimally win some games by performing joint measurements on a shared entangled state, but computing these strategies can be challenging. We present a variational quantum algorithm to compute quantum strategies for nonlocal games by encoding the rules of a nonlocal game into a Hamiltonian. We show how this algorithm can generate a short-depth optimal quantum strategy for a graph coloring game with a quantum advantage. This quantum strategy is then evaluated on fourteen different quantum hardware platforms to demonstrate its utility as a benchmark. Finally, we discuss potential sources of errors that can explain the observed decreased performance of the executed task and derive an expression for the number of samples required to accurately estimate the win rate in the presence of noise. Copyright © 2023, The Authors. All rights reserved.

关键词： Hamiltonians

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Uncertainty Quantification of Signal Integrity Analysis for Neuromorphic Chips

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2022年 1卷 160-169页

作者： Hanzhi Ma Da Li Tuomin Tao Xingjian Shangguan En-Xiao Liu Jose Schutt-Aine Andreas C. Cangellaris Er-Ping Li College of Information Science and Electronic Engineering ZJU-UIUC Institute Zhejiang University Hangzhou China Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang University Hangzhou China Zhejiang-Singapore Innovation and AI Joint Research Lab China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA A*STAR Institute of High Performance Computing Singapore

A dimensionality reduction based neural network framework is introduced for uncertainty quantification of time-domain response based on system uncertain design parameters for neuromorphic chips. The proposed method firstly makes use of the singular value decomposition (SVD) method to find the basis functions and corresponding coefficients of time-domain response, of which coefficients are used as a lower dimensional target outputs in neural network model compared with time sampling points prediction. This newly proposed method then develops an integrated neural network structure to simultaneously find the mean and variance of target coefficients with a combined definition of loss function, which can be utilized together with basis functions to construct the prediction interval of time-domain response. A memrisor-based crossbar array is applied in this work to verify the performance of the proposed method with the comparison of Monte Carlo method.

关键词： Biological neural networks Uncertainty Memristors Time-domain analysis Integrated circuit modeling Neuromorphics Signal integrity Neural networks Neuromorphics

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Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation 28

Cross-lingual Machine Reading Comprehension with Language Br...

引用

28th International Conference on Computational Linguistics, COLING 2020

作者： Liu, Junhao Shou, Linjun Pei, Jian Gong, Ming Yang, Min Jiang, Daxin Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Microsoft STCA NLP Group Beijing China School of Computing Science Simon Fraser University Canada

ISBN: (纸本)9781952148279

Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. Many previous approaches use translation data by translating from a rich-source language, such as English, to low-source languages as auxiliary supervision. However, how to effectively leverage translation data and reduce the impact of noise introduced by translation remains onerous. In this paper, we tackle this challenge and enhance the cross-lingual transferring performance by a novel augmentation approach named Language Branch Machine Reading Comprehension (LBMRC). A language branch is a group of passages in one single language paired with questions in all target languages. We train multiple machine reading comprehension (MRC) models proficient in individual language based on LBMRC. Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages. Combining the LBMRC and multilingual distillation can be more robust to the data noises, therefore, improving the model’s cross-lingual ability. Meanwhile, the produced single multilingual model can apply to all target languages, which saves the cost of training, inference, and maintenance for multiple models. Extensive experiments on two CLMRC benchmarks clearly show the effectiveness of our proposed method. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Distillation

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