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Spatial multi-criteria approaches for estimating geogenic radon hazard index

Science of the Total Environment 2024年 956卷 176419页

作者： Masoumi, Iman Maggio, Sabrina De Iaco, Sandra Ghezelbash, Reza Department of Economic Sciences University of Salento Lecce Italy National Centre for HPC Big Data and Quantum Computing Bologna Italy National Biodiversity Future Center Palermo Italy School of Mining Engineering College of Engineering University of Tehran Tehran Iran

The geogenic radon hazard index (GRHI) map plays a crucial role in evaluating radon exposure risks. The construction of this map requires a comprehensive analysis of radon levels in soil gas and some critical factors, such as uranium content in bedrock, soil permeability, and geological inhomogeneities. In this context, the spatial multi-criteria decision analysis is proposed with the aim of combining various key geological parameters and identifying high-potential radon areas. In particular, the multivariate integration involves the fuzzy gamma operator method and a hybrid multi-criteria decision-making technique, namely AHP-TOPSIS, which represents a novel approach in GRHI mapping. Thus, a comparison is provided through the definition of the GRHI maps of an unexplored study area, that is the Apulia region, located in Southern Italy. In order to evaluate the output maps, high radon potential areas are identified based on some available indoor radon measurement data. The success-rate curve, as a valid evaluation metric, is employed for the performance assessment and comparison of these two methods. The results demonstrate that although both generated GRHI maps are closely correlated with high-potential radon zones, the hybrid AHP-TOPSIS method is preferable. © 2024 The Authors

关键词： Jurassic

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Varun Kohli Bhavya Kohli Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering National University of Singapore Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a $\sim 10^{-4}$ second inference latency on a laptop CPU.

关键词： Portable computers Accuracy Random access memory Transformers Malware Graph neural networks Hardware Internet of Things Security Microprogramming

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Sentiment Detection through Emotion Classification Using Deep Learning Approach for Chinese Text

Sentiment Detection through Emotion Classification Using Dee...

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International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Yuxin Huang Shaidah Jusoh School of Computing and Data Science Xiamen University Malaysia Sepang Selangor Malaysia School of Electrical Engineering and Artficial Intelligence Xiamen University Malaysia Sepang Selangor Malaysia

Emotion classification and sentiment analysis represent crucial research areas within the field of Natural Language Processing. Previous studies have primarily focused on conducting sentiment classification and emotion classification as separate tasks. Only a limited number of researchers have delved into exploring the relationship between these two and invested efforts in deriving one from the other. This study aims to determine sentiment by employing emotion classifications. Specifically, we utilise the ERNIE Tiny deep learning model to classify emotions in Chinese texts, and detect sentiments through our devised rules. For instance, if emotions such as ‘happiness' or ‘like’ are present, the sentiment is classified as positive. Conversely, emotions like ‘sadness', ‘disgust’, ‘anger’, or ‘fear’ classify the sentiment as negative. The experimental results demonstrate the F1 score of 93.00% and 90.14% for positive and negative sentiment, respectively, in Chinese song reviews. These findings substantiate the validity and feasibility of utilising emotions to extract sentiment

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M-Graphormer: Multi-Channel Graph Transformer for Node Representation Learning

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IEEE Transactions on Big data 2024年

作者： Chang, Xinglong Wang, Jianrong Wen, Mingxiang Wang, Yingkui Huang, Yuxiao Tianjin University School of New Media and Communication Tianjin300350 China Tianjin Renai College School of Intelligent Computing Engineering Tianjin301636 China George Washington University NW Department of Data Science WashingtonDC20052 United States

In recent years, the Graph Transformer has demonstrated superiority on various graph-level tasks by facilitating global interactions among nodes. However, as for node-level tasks, the existing Graph Transformer cannot perform as well as expected. Actually, a node in a real-world graph does not necessarily have relationships with every other node, and this global interaction weakens node features. This raises a fundamental question: should we partition out an appropriate interaction channel based on graph structure so that noisy and irrelevant information will be filtered and every node can aggregate information in the optimal channel? We first perform a series of experiments on manually created graphs with varying homophily ratios. Surprisingly, we observe that different graph structures indeed require distinct optimal interaction channels. This leads us to ask whether we can develop a partitioning rule that ensures each node interacts with relevant and valuable targets. To overcome this challenge, we propose a novel Graph Transformer named Multi-channel Graphormer. The model is evaluated on six network datasets with different homophily ratios for the node classification task. Moreover, comprehensive experiments are conducted on two real datasets for the recommendation task. Experimental results show that the Multi-channel Graphormer surpasses state-of-the-art baselines, demonstrating superior performance. © 2015 IEEE.

关键词： Graph transformer homophily node classification

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Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification

Revolutionizing Influenza A Detection in Humans: Unleashing ...

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Women in Innovation, Technology & Entrepreneurship (ICWITE), IEEE International Conference for

作者： V Nikhil S A Sahaaya Arul Mary B Natarajan A. Bajulunisha R.S. Anuradha K Venkatraman Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering VIT University Chennai India Department of Artificial Intelligence & Data Science M.A.M College of Engineering and Technology Tiruchirappalli India Department of Artificial Intelligence & Data Science Panimalar Engineering College Chennai India

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model’s capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems.

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SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis

arXiv

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arXiv 2025年

作者： Bai, Runci Xu, Guibao Shi, Yanze Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing100080 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China

Brain tumors can lead to neurological dysfunction, cognitive and psychological changes, increased intracranial pressure, and seizures, posing significant risks to health. The You Only Look Once (YOLO) series has shown superior accuracy in medical imaging object detection. This paper presents a novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The SCConv module optimizes convolutional efficiency by reducing spatial and channel redundancy, enhancing image feature learning. We examine the effects of different attention mechanisms with YOLOv9 for brain tumor detection using the Br35H dataset and our custom dataset (Brain_Tumor_dataset). Results indicate that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art performance in brain tumor detection. Copyright © 2025, The Authors. All rights reserved.

关键词： Health risks

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Converter Networks for Multi-Scale Tokens-Aware Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model 2

Converter Networks for Multi-Scale Tokens-Aware Multi-Region...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Mahalle, Parikshit N. Chandra Lohani, Manoj Hemelatha, S. Sharma, Divya Chitra, S. Jayakumar, Sujayaraj Samuel Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Graphic Era Hill University School of Computing Haldwani India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India School of Sciences Forensic Science Karnataka Bangalore India

ISBN: (纸本)9798350388916

Synthetic computed tomography (sCT) based on magnetic resonance imaging (MRI) streamlines the planning of radiation therapy treatments by doing away with CT simulation and error-prone picture registration, which in turn reduces radiation dosage for patients and setup uncertainty. To create sCT images that matched the MRI anatomy, an optimally trained MultiHead Attention was used in conjunction with the reverse diffusion process. We generated sCT using MRI on two datasets - one for the brain and one for the prostate - to test the suggested strategy. The following metrics were used for qualitative evaluation: ground truth CTs and sCTs normalized cross correlation (NCC) indices, multi-scale Structure Similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE) of Hounsfield unit (HU). A new method for creating CT images from standard MRIs utilizing a transformer-based convolutional neural network (CNN) has been designed and verified by us. Synthetic CT (sCT) pictures of excellent quality and robustness may be produced in a few minutes using this model, which successfully depicts the intricate interaction among CT and MRI images. By cutting down on patient time spent on treatment planning, improving accuracy of treatment delivery, and doing away with the necessity for extra CT scans, this method may considerably streamline radiation treatment planning. © 2024 IEEE.

关键词： Radiotherapy

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques 38

Suppress Content Shift: Better Diffusion Features via Off-th...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

arXiv

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arXiv 2025年

作者： Sun, Yan Ding, Tiansheng Huang Liang Shen, Li Tao, Dacheng Faculty of Engineering The University of Sydney Australia School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University China The college of Computing & Data Science Nanyang Technological University Singapore

Zeroth-order optimization (ZO) has demonstrated remarkable promise in efficient fine-tuning tasks for Large Language Models (LLMs). In particular, recent advances incorporate the low-rankness of gradients, introducing low-rank ZO estimators to further reduce GPU memory consumption. However, most existing works focus solely on the low-rankness of each individual gradient, overlooking a broader property shared by all gradients throughout the training, i.e., all gradients approximately reside within a similar subspace. In this paper, we consider two factors together and propose a novel low-rank ZO estimator, TeZO, which captures the low-rankness across both the model and temporal dimension. Specifically, we represent ZO perturbations along the temporal dimension as a 3D tensor and employ Canonical Polyadic Decomposition (CPD) to extract each low-rank 2D matrix, significantly reducing the training cost. TeZO can also be easily extended to the Adam variant while consuming less memory than MeZO-SGD, and requiring about only 35% memory of MeZO-Adam. Both comprehensive theoretical analysis and extensive experimental research have validated its efficiency, achieving SOTA-comparable results with lower overhead of time and memory. © 2025, CC BY.

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