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Ethical and legal challenges with IoT in home digital twins

MethodsX 2025年 14卷

作者： D. Dhinakaran S. Edwin Raja A. Ramathilagam G. Vennila A. Alagulakshmi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Artificial Intelligence and Machine Learning School of Computing Mohan Babu University Tirupati India Department of Information Technology Ramco Institute of Technology Rajapalayam India

Home Digital Twins represent a transformative application of IoT in the home environment, turning conventional living spaces into intelligent ecosystems. This paper explores the ethical and legal challenges associated with these technologies, focusing on critical issues such as privacy, data security, and accountability. The study integrates real-world case studies of privacy controversies and cybersecurity breaches to illustrate potential vulnerabilities in IoT-enabled systems. Furthermore, it examines the complexities of regulatory compliance, including cross-border data flows and liability concerns in the event of system failures, with a focus on frameworks such as GDPR, CCPA, and India’s Digital Personal Data Protection Bill. The methodology includes an in-depth analysis of existing legal frameworks, industry best practices, and technical mitigation strategies to propose actionable guidelines for addressing these challenges. Key findings emphasize the necessity of robust legal frameworks, user-centered design principles, and transparent data practices to foster trust and security in IoT systems. By advocating for a balance between technological innovation and ethical accountability, this paper highlights opportunities for sustainable and responsible IoT development that upholds user rights and societal values.

关键词： Internet of Things Home digital twins Privacy Data security Trust Cybersecurity Ethico-Legal IoT Compliance Framework (ELICF)

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Ant Colony Optimization based Medical Image Preservation and Segmentation

Ant Colony Optimization based Medical Image Preservation and...

引用

Advanced Technologies in Intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： Priya Nandihal Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna V Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics Image segmentation Ant colony optimization Biomedical optical imaging Optical design Image edge detection Metaheuristics

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MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions

MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Zili Chen Yilong Peng Yaokai Yang Jiadong Jiao Laigang Wang Aiguo Wang Jianjun Liu Wei Lin Yan Guo College of Computer and Information Engineering Henan Normal University Xinxiang China School of Tourism Henan Normal University Xinxiang China Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences Zhengzhou China Zhengzhou Tobacco Research Institute of CNTC Zhengzhou China Henan Provincial Tobacco Company of CNTC Zhengzhou China Henan Provincial Key Laboratory of Educational Artificial Intelligence and Personalized Learning Xinxiang China Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology Ministry of Agriculture and Rural Affairs Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

For tobacco leaf disease spots with varying scales and shapes causing difficulties in segmentation and low accuracy, a novel MD_Unet algorithm is proposed for precise segmentation of tobacco leaf disease spots, aiding in scientific prevention and control of tobacco diseases. Innovative design includes Multi-scale Convolution Module (MC) and Dense Residual Dilated Convolution Module (MVCR) to enhance feature extraction of multiscale disease spots and perception of global contextual information. Utilizing Residual Unit Fusion Framework (RUFF) in the upsampling section effectively compensates for spatial details loss and strengthens network representation capability. Additionally, integration of Channel Spatial Attention Mechanism (CBAM) and Attention Gate (AG) further enhances the network's focusing ability on key information and fine-grained feature extraction capability. The MD_Unet algorithm achieves significant improvements in spot CPA, Recall, IoU, F1 metrics with 92.75%, 90.94 % ,84.93 % ,91.81% respectively, and an overall Dice score of 94.67%. Compared to mainstream methods like Unet, PSP, DeepLab v3+, FCN, SegNet, UNET++, DoubleU-Net, it shows remarkable enhancements, providing theoretical basis and technical support for precise segmentation of tobacco leaf disease spots and other plant diseases.

关键词： Location awareness Accuracy Shape Convolution Signal processing algorithms Logic gates Feature extraction Robustness Lesions Diseases

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Intelligent FORecasting Model for Climate Variations (InFORM): An Urban Climate Case Study

Intelligent FORecasting Model for Climate Variations (InFORM...

引用

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Pralhad P. Teggi Santhi Natarajan Bharathi Malakreddy Computer Science and Engineering BMS Institute of Technology and Management Bangalore INDIA Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Bangalore INDIA

ISBN: (数字)9789380544380

ISBN: (纸本)9781728129587

When perceived as a deeply connected socio-ecological system, Bangalore which is popularly addressed as the Silicon Valley of India faces the major challenge of integrating the systems of water, climate, energy and waste management. Climate changes have resulted in extreme variations in temperature, relative humidity and precipitation levels, thus altering the monsoon patterns in Bangalore over the last few years. The alarming increase in pollution levels results in decreased visibility considering increased traffic and emission. A complex and accurate climate modelling is essential to enable precise weather forecasting in this scenario. Through this paper, we present InFORM, an Intelligent Forecasting Model for Climatic Variations. InFORM presents an approach to forecast daily weather parameters like temperature, relative humidity and visibility using time series ARIMA model. The approach is built as a pipeline that makes forecasting predictions for multiple variables using a unique singular algorithm, which can be integrated to the complex climatic models to make them more accurate and precise.

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StegTransX: A lightweight deep steganography method for high-capacity hiding and JPEG compression resistance

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Information Sciences 2025年 716卷

作者： Duan, Xintao Wang, Zhao Li, Sen Qin, Chuan College of Computer and Information Engineering Henan Normal University Xinxiang453007 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Henan Normal University Xinxiang453007 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Image steganography refers to the concealment of multiple secret images within a cover image of the same resolution. This technique must ensure not only sufficient security but also high capacity and robustness. In this paper, we introduce StegTransX, which is a lightweight deep steganography framework that combines local and global modeling to improve data embedding performance. StegTransX can hide one or multiple secret images within a cover image of the same size while maintaining the visual quality of the stego image and the reconstruction quality of the secret images. Furthermore, considering that images typically undergo compression during channel transmission, we introduce a JPEG compression attack module to increase the robustness of secret information recovery under realistic compression scenarios in actual information transmission. Additionally, we propose an effective multiscale loss and constraint loss to preserve the quality of the stego image and improve the reconstruction quality of the secret images. The experimental results demonstrate that StegTransX outperforms existing state-of-the-art (SOTA) steganography models. In the case of single-image hiding, the PSNRs of both the cover/stego images and the secret/recovered images improved by more than 4 dB. Moreover, StegTransX also outperforms the state-of-the-art (SOTA) steganography models in multi-image hiding and JPEG compression resistance. Our code will be released at https://***/QQ-Stars/StegTransX © 2025 Elsevier Inc.

关键词： Steganography

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Inter-GPS: Interpretable geometry problem solving with formal language and symbolic reasoning

arXiv

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arXiv 2021年

作者： Lu, Pan Gong, Ran Jiang, Shibiao Qiu, Liang Huang, Siyuan Liang, Xiaodan Zhu, Song-Chun Center for Vision Cognition Learning and Autonomy UCLA College of Computer Science and Technology Zhejiang University School of Intelligent Systems Engineering Sun Yat-sen University Beijing Institute of General Artificial Intelligence Peking University Tsinghua University

Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods.1 © 2021, CC BY.

关键词： Formal languages

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Federated learning for Inference at Anytime and Anywhere

arXiv

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arXiv 2022年

作者： Liu, Zicheng Li, Da Fernandez-Marques, Javier Laskaridis, Stefanos Gao, Yan Dudziak, Lukasz Li, Stan Z. Hu, Shell Xu Hospedales, Timothy School of Information Science & Electronic Engineering Zhejiang University Hangzhou China Machine Learning & Data Intelligence Samsung AI Center Cambridge United Kingdom Automated AI Samsung AI Center Cambridge United Kingdom Distributed AI Samsung AI Center Cambridge United Kingdom Department of Computer Science and Technology The University of Cambridge Cambridge United Kingdom School of Engineering Westlake University Hangzhou China School of Informatics The University of Edinburgh Edinburgh United Kingdom

Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities. However, there is no unified framework that addresses all these problems together. This paper studies the challenges and opportunities of exploiting pre-trained Transformer models in FL. In particular, we propose to efficiently adapt such pre-trained models by injecting a novel attention-based adapter module at each transformer block that both modulates the forward pass and makes an early prediction. Training only the lightweight adapter by FL leads to fast and communication-efficient learning even in the presence of heterogeneous data and devices. Extensive experiments on standard FL benchmarks, including CIFAR-100, FEMNIST and SpeechCommandsv2 demonstrate that this simple framework provides fast and accurate FL while supporting heterogenous device capabilities, efficient personalization, and scalable-cost anytime inference. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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An application review on internet of things and machine learning based smart agriculture

引用

AIP Conference Proceedings 2024年第1期3161卷

作者： G. Lavanya S. Vignesh K. Sundaramurthy S. Sivakumar Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India 602105 Department of Mechanical Engineering Saveetha Engineering College Chennai India 602105 Department of Mechanical Engineering Paavai Engineering College Namakkal India 637018 School of Engineering Asia Pacific University of Technology and Innovation Wilayah Persekutuan Kuala Lumpur Malaysia

Smart farming is an approach to governance which concentrates on equipping the farming sector with the necessary tools to employ cutting-edge technologies such as big data, the internet of things (IoT), and the use of cloud computing for observing, tracking, computerized, and evaluating processes. IoT intelligent farming technologies use cameras and automated monitoring and control systems to aid in farming field monitoring. As a result, farmers and associated companies may easily track field conditions virtually anywhere. machine learning (ML) approaches are widely accepted as viable replacements and supplements to commonly used modelling strategies in agriculture and other associated sectors. machine learning may help agriculture at every level, including handling soils, identification of illnesses, crop oversight, livestock administration, and furthermore. This review paper summarizes the importance on smart agriculture, components involved in the IoT, ML based smart agriculture and its recent trends.

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Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices

arXiv

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arXiv 2022年

作者： Peng, Jiangjun Wang, Yao Zhang, Hongying Wang, Jianjun Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaan'xi Xi’an710049 China The Center for Intelligent Decision-making and Machine Learning School of Management Xian Jiaotong University Shaan'xi Xi’an China The College of Artificial Intelligence Southwest University Chongqing400715 China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa China

It is known that the decomposition in low-rank and sparse matrices (L+S for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local smoothness (LSS) is a vitally essential prior for many real-world matrix data such as hyperspectral images and surveillance videos, which makes such matrices have low-rankness and local smoothness property at the same time. This poses an interesting question: Can we make a matrix decomposition in terms of L&LSS +S form exactly? To address this issue, we propose in this paper a new RPCA model based on three-dimensional correlated total variation regularization (3DCTV-RPCA for short) by fully exploiting and encoding the prior expression underlying such joint low-rank and local smoothness matrices. Specifically, using a modification of Golfing scheme, we prove that under some mild assumptions, the proposed 3DCTV-RPCA model can decompose both components exactly, which should be the first theoretical guarantee among all such related methods combining low rankness and local smoothness. In addition, by utilizing Fast Fourier Transform (FFT), we propose an efficient ADMM algorithm with a solid convergence guarantee for solving the resulting optimization problem. Finally, a series of experiments on both simulations and real applications are carried out to demonstrate the general validity of the proposed 3DCTV-RPCA model. Copyright © 2022, The Authors. All rights reserved.

关键词： Fast Fourier transforms

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