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A Holistic Anti-Counterfeiting Platform Using NFC and Blockchain Technologies

computers, Materials & Continua 2025年第6期83卷 4257-4280页

作者： Rajendren Subramaniam Saaidal Razalli Azzuhri Teh Ying Wah Atif Mahmood Vimala Balakrishnan Department of Information System Faculty of Computer Science&Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia Department of Computer System&Technology Faculty of Computer Science&Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai71800Malaysia

Counterfeiting is still a pervasive global issue,affecting multiple industries and hindering industrial innovation,while causing substantial financial losses,reputational damage,and risks to consumer *** luxury goods and pharmaceuticals to electronics and automotive parts,counterfeit products infiltrate supply chains,leading to a loss of revenue for legitimate businesses and undermining consumer *** anti-counterfeiting measures,such as holograms,serial numbers,and barcodes,have proven to be insufficient as counterfeiters continuously develop more sophisticated replication *** a result,there is a growing need for more advanced,secure,and reliable methods to prevent *** paper presents a novel,holistic anti-counterfeiting platform that integrates Near Field Communication(NFC)-enabled mobile applications with blockchain technology to provide an innovative,secure,and consumer-friendly authentication *** approach addresses key gaps in existing solutions by incorporating dynamic product identifiers,which make replication significantly more *** system enables consumers to verify the authenticity of products instantly using their smartphones,enhancing transparency and trust in the supply *** technology plays a crucial role in our proposed solution by providing an immutable,decentralized ledger that records product authentication *** ensures that product verification records cannot be tampered with or altered,adding a layer of security that is absent in conventional ***,NFC technology enhances security by offering unique identification capabilities,enabling real-time product *** validate the effectiveness of the proposed system,real-world testing was conducted across different *** results demonstrated the platform’s ability to significantly reduce counterfeit products in the supply chain,offering businesses and consumers a more robust and reliable aut

关键词： Blockchain near field communication(NFC) anti-counterfeiting

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Deep Learning based Smart American Sign Language Detection in Alphabets 6

Deep Learning based Smart American Sign Language Detection i...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： RukumaniKhandhan, C. Vijayakumar, P. Sri Sabari, C. Arun, G. Chandrasekaran, N. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Erode Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai India Kongu Engineering College Department of Computer Technology - PG Tamil Nadu Perundurai India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350304480

Disabled people are of different kinds like physically challenged people, mentally challenged etc. To help the people who cannot convey their thoughts and who cannot hear others thoughts, sign language is being used to communicate with them. Sign language is something that is used to communicate through body movements mainly through hands and *** Sign Language(ASL) is an authorized sign language that is taught and used widely. ASL is difficult to teach and learn for them because it is completely new and they have no knowledge about it. In this research paper the ASL is recognized and converted into text through data mining techniques. The study uses a dataset of ASL alphabet images and applies various data mining techniques, including feature selection, classification, and clustering. The features extracted from the images include shape, size, color, and texture. The use of neural networks, decision trees, and support vector machines speeds up the data categorization process. Techniques such as k-means clustering and hierarchical grouping are used to facilitate entity grouping. The results show that the best performance is achieved using a combination of feature selection and classification techniques. The decision tree algorithm with the selected features achieves an accuracy of 96.5% on the test dataset. The system can accurately recognize all the ASL alphabets and convert them into text or speech. The system developed can be used to improve communication between deaf and hearing individuals and can be extended to recognize other ASL gestures and phrases. Future research can focus on real-time detection and recognition of ASL gestures using data mining techniques. CNN algorithm is used to recognize the image based on the images collected. The accuracy rate gets low and the detection is inaccurate. Therefore, VGG16 algorithm is used to recognize the images and it is unique in convolutional models. © 2023 IEEE.

关键词： ASL CNN Gestures VGG16

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Revisiting Active Learning in the Era of Vision Foundation Models

arXiv

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arXiv 2024年

作者： Gupte, Sanket Rajan Aklilu, Josiah Nirschl, Jeffrey J. Yeung-Levy, Serena Department of Computer Science Stanford University United States Department of Biomedical Data Science Stanford University United States Department of Pathology Stanford University United States

Foundation vision or vision-language models are trained on large unlabeled or noisy data and learn robust representations that can achieve impressive zero- or few-shot performance on diverse tasks. Given these properties, they are a natural fit for active learning (AL), which aims to maximize labeling efficiency. However, the full potential of foundation models has not been explored in the context of AL, specifically in the low-budget regime. In this work, we evaluate how foundation models influence three critical components of effective AL, namely, 1) initial labeled pool selection, 2) ensuring diverse sampling, and 3) the trade-off between representative and uncertainty sampling. We systematically study how the robust representations of foundation models (DINOv2, OpenCLIP) challenge existing findings in active learning. Our observations inform the principled construction of a new simple and elegant AL strategy that balances uncertainty estimated via dropout with sample diversity. We extensively test our strategy on many challenging image classification benchmarks, including natural images as well as out-of-domain biomedical images that are relatively understudied in the AL literature. We also provide a highly performant and efficient implementation of modern AL strategies (including our method) at https://***/sanketx/AL-foundation-models. © 2024, CC BY.

关键词： Foundations

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SWARM: Sanitizer With Attendance through Remote Monitoring 6

SWARM: Sanitizer With Attendance through Remote Monitoring

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Malla, Sai Anish Kapoor, Khushee Kejariwal, Adithya Rao, Vidya Kundapur, Poornimaa Panduranga Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal576104 India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

With Covid19 being endemic, it is very essential to continue proper physical hygiene protocols even today to avoid escalation. To ensure hygiene inside educational institutions, many governing bodies-imposed protocols to insist students wear hand gloves and facemasks. Such an implementation, however, has increased surgical waste in and around educational institutions, and also there is a rise in allergies due to the constant use of hand gloves by the students. Hence, a prototype of a hand sanitization-based attendance monitoring system has been proposed in the current research paper. This proposed sanitizer with attendance through remote monitoring (SWARM) uses Raspberry Pi devices to capture the image of a student's identity card holding the registration number and through a bar code analysis module of computer vision, the ID number is extracted. This ID number is compared with a master attendance file to mark the students' presence and then the updated file is shared with the concerned teacher via email. Such a setup is installed in the laboratory premise, thereby reducing the unnecessary use and disposal of surgical waste within the educational premise. © 2022 IEEE.

关键词： Students

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RoyaltyChain: A Blockchain-Powered Big data Framework for Accurate Music Royalties Prediction

RoyaltyChain: A Blockchain-Powered Big Data Framework for Ac...

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IEEE India Conference (INDICON)

作者： Megh H. Shah Parth V. Sheth Aparna Kumari Sudeep Tanwar Vrajesh Chawra Sunil Kumar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of AI and Data Science Guru Jambheshwar University of Science & Technology Hisar Haryana India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

The growth of the commodification of music in the present age has made royalties allocation in an efficient, straight-forward manner to the stakeholders, in general, a complex issue. To address these challenges, this paper introduces RoyaltyChain, a new framework powered by predictive analytics and blockchain. Using factors like streaming data, social media statistics, and geographical factors, Random Forest Regression is employed to predict royalties in a big data environment, with a blockchain-backed system ensuring secure and transparent distribution. Here Ethereum blockchain-based smart contracts are designed in Remix IDE and the Interplanetary File System (IPFS) is incorporated to handle data storage cost issues in blockchain. The performance evaluation shows an increase in the overall efficiency in predicting outcomes and the high scalability of our system in comparison with the related techniques with respect to accuracy, scalability, etc. RoyaltyChain provides a scalable framework to establish efficient and fair distribution of money from the consumption of music in this increasingly complex world.

关键词： Performance evaluation Accuracy Social networking (online) Scalability Music Big data Blockchains Stakeholders InterPlanetary File System Predictive analytics

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Uncertainty quantification over graph with conformalized graph neural networks 23

Uncertainty quantification over graph with conformalized gra...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Kexin Huang Ying Jin Emmanuel Candès Jure Leskovec Department of Computer Science Stanford University Department of Statistics Stanford University Department of Statistics Stanford University and Department of Mathematics Stanford University

Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncertainty estimates. Given an entity in the graph, CF-GNN produces a prediction set/interval that provably contains the true label with pre-defined coverage probability (e.g. 90%). We establish a permutation invariance condition that enables the validity of CP on graph data and provide an exact characterization of the test-time coverage. Besides valid coverage, it is crucial to reduce the prediction set size/interval length for practical use. We observe a key connection between non-conformity scores and network structures, which motivates us to develop a topology-aware output correction model that learns to update the prediction and produces more efficient prediction sets/intervals. Extensive experiments show that CF-GNN achieves any pre-defined target marginal coverage while significantly reducing the prediction set/interval size by up to 74% over the baselines. It also empirically achieves satisfactory conditional coverage over various raw and network features.

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Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture 9

Image-Based Soil Health Analysis Using Deep Learning for Pre...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Saranya, V.S. Mangali, Saikiran Srinija, K. Medabalimi, Galeiah Devi, Meena Venkata Ramana, N.R. Lakkshmanan, Ajanthaa School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603203 India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Mlrit Department: Data science Dundigal India Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Chinna Amiram Andhra Pradesh Bhimavaram India Kcg college of technology Department of electrical and electronics engineering Chennai600097 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India

ISBN: (纸本)9798350377972

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management. © 2024 IEEE.

关键词： Convolutional neural networks

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Sharp Matrix Empirical Bernstein Inequalities

arXiv

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arXiv 2024年

作者： Wang, Hongjian Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

We present two sharp empirical Bernstein inequalities for symmetric random matrices with bounded eigenvalues. By sharp, we mean that both inequalities adapt to the unknown variance in a tight manner: the deviation captured by the first-order 1/√n term asymptotically matches the matrix Bernstein inequality exactly, including constants, the latter requiring knowledge of the variance. Our first inequality holds for the sample mean of independent matrices, and our second inequality holds for a mean estimator under martingale dependence at stopping times. Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

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Sampling from Mean-Field Gibbs Measures via Diffusion Processes

arXiv

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arXiv 2023年

作者： Alaoui, Ahmed El Montanari, Andrea Sellke, Mark Department of Statistics and Data Science Cornell University United States Department of Statistics Department of Mathematics Stanford University United States Department of Statistics Harvard University United States

We consider Ising mixed p-spin glasses at high-temperature and without external field, and study the problem of sampling from the Gibbs distribution µ in polynomial time. We develop a new sampling algorithm with complexity of the same order as evaluating the gradient of the Hamiltonian and, in particular, at most linear in the input size. We prove that, at sufficiently high-temperature, it produces samples from a distribution µalg which is close in normalized Wasserstein distance to µ. Namely, there exists a coupling of µ and µalg such that if (x, xalg) ∈ {−1, +1}n × {−1, +1}n is a pair drawn from this coupling, then n−1 E{kx − xalgk22} = on(1). For the case of the Sherrington-Kirkpatrick model, our algorithm succeeds in the full replica-symmetric phase. Previously, [ABXY22, AJK+23] showed that Glauber dynamics succeeds in sampling under a stronger assumption on the temperature. (However, these works prove sampling in total variation distance.) We complement this result with a negative one for sampling algorithms satisfying a certain ‘stability’ property, which is verified by many standard techniques. No stable algorithm can approximately sample at temperatures below the onset of shattering, even under the normalized Wasserstein metric. Further, no algorithm can sample at temperatures below the onset of replica symmetry breaking. Our sampling method implements a discretized version of a diffusion process that has become recently popular in machine learning under the name of ‘denoising diffusion.’ We derive the same process from the general construction of stochastic localization. Implementing the diffusion process requires to efficiently approximate the mean of the tilted measure. To this end, we use an approximate message passing algorithm that, as we prove, achieves sufficiently accurate mean estimation. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Sustainable allocation of greenhouse gas emission permits for …rms with Leontief technologies

arXiv

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arXiv 2024年

作者： Gutiérrez, Elisabeth Llorca, Natividad Sánchez-Soriano, Joaquin Mosquera, Manuel A. CIO and Department of Statistics Mathematics and Computer Science University Miguel Hernández of Elche Spain Department of Statistics and Operations Research University of Vigo Spain

In this paper we deal with production situations where a cap or limit to the amount of greenhouse gas emissions permitted is imposed. Fixing a tax for each ton of pollutant emitted is also considered. We use bankruptcy rules to de…ne cooperative games with externalities associated with these situations and analyze the existence of coalitionally stable allocations of the emission permits. We prove that the constrained equal awards (CEA) rule provides stable allocations and as a direct mechanism, it is incentive compatible. These two facts have interesting managerial implications to control pollution *** Codes 91A12 (Primary) 90B30, 91A40, 91A80, 91B32 (Secondary) © 2024, CC BY-NC-ND.

关键词： Game theory

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