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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

arXiv

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arXiv 2023年

作者： Liu, Yixuan Zhao, Suyun Xiong, Li Liu, Yuhan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Engineering Research Center Ministry of Education on Database and BI China Information School Renmin University of China China Department of Computer Science Emory University United States

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users’ attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users’ varying local privacy, yet only provides a central privacy guarantee equivalent to the worst-case local privacy level. Thus, achieving strong central privacy as well as personalized local privacy with a utility-promising model is a challenging problem. In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. To tighten the privacy bound, we quantify the heterogeneous contributions to the central privacy user by user. The contributions are characterized by the ability of generating "echos" from the perturbation of each user, which is carefully measured by proposed methods Neighbor Divergence and Clip-Laplace Mechanism. Furthermore, we propose a refined framework (S-APES) with the post-sparsification technique to reduce privacy loss in high-dimension scenarios. To the best of our knowledge, the impact of shuffling on personalized local privacy is considered for the first time. We provide a strong privacy amplification effect, and the bound is tighter than the baseline result based on existing methods for uniform local privacy. Experiments demonstrate that our frameworks ensure comparable or higher accuracy for the global model. © 2023, CC BY.

关键词： Learning systems

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Voxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning

Voxelwise Intensity Projection for the Spatial Representatio...

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IEEE International Symposium on Biomedical Imaging

作者： Vaibhavi S. Itkyal Anees Abrol Theodore J. LaGrow Vince D. Calhoun Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University GA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University GA Department of Neuroscience Emory University GA Department of Electrical and Computer Engineering Georgia Institute of Technology GA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Alzheimer's disease (AD) necessitates accurate early diagnosis for effective treatment. Our study explores the untapped potential of spatial information extraction from resting-state functional magnetic resonance imaging (rs-fMRI) and its integration with structural MRI (sMRI) for investigating AD-related brain alterations. Utilizing fMRI networks (independent component analysis followed by voxelwise intensity projections i.e., iVIP) outperforms traditional metrics, such as amplitude of low-frequency fluctuations (ALFF) and fractional ALFF, in capturing critical spatial maps for AD classification. A multi-channel convolutional neural network inspired by AlexNet dropout architecture effectively models spatial and temporal dependencies in integrated data. Experiments on the Alzheimer’s Disease Neuroimaging Initiative dataset demonstrate superior classification performance with 93.31% test accuracy and a 97.79 AUC score, surpassing existing methods. Fusion results generally outperform unimodal results, revealing significant differences in neurobiologically relevant regions. Saliency visualizations highlight distinctions in the hippocampus, amygdala, caudate nucleus, and thalamus, aligning with previous literature.

关键词： Deep learning Neuroimaging Measurement Accuracy Three-dimensional displays Functional magnetic resonance imaging Thalamus

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Do You Think You Can Hold Me? The Real Challenge of Problem-Space Evasion Attacks

arXiv

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arXiv 2022年

作者： Berger, Harel Dvir, Amit Hajaj, Chen Ronen, Rony Ariel Cyber Innovation Center Computer Science Department Ariel University Ariel Israel Ariel Cyber Innovation Center Data Science and Artificial Intelligence Research Center Industrial Engineering and Management Department Ariel University Ariel Israel

Android malware is a spreading disease in the virtual world. Anti-virus and detection systems continuously undergo patches and updates to defend against these threats. Most of the latest approaches in malware detection use Machine Learning (ML). Against the robustifying effort of detection systems, raise the evasion attacks, where an adversary changes its targeted samples so that they are misclassified as benign. This paper considers two kinds of evasion attacks: feature-space and problem-space. Feature-space attacks consider an adversary who manipulates ML features to evade the correct classification while minimizing or constraining the total manipulations. Problem-space attacks refer to evasion attacks that change the actual sample. Specifically, this paper analyzes the gap between these two types in the Android malware domain. The gap between the two types of evasion attacks is examined via the retraining process of classifiers using each one of the evasion attack types. The experiments show that the gap between these two types of retrained classifiers is dramatic and may increase to 96%. Retrained classifiers of feature-space evasion attacks have been found to be either less effective or completely ineffective against problem-space evasion attacks. Additionally, exploration of different problem-space evasion attacks shows that retraining of one problem-space evasion attack may be effective against other problem-space evasion attacks. © 2022, CC BY.

关键词： Virtual reality

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IMPROVED ANALYSIS OF SPARSE LINEAR REGRESSION IN LOCAL DIFFERENTIAL PRIVACY MODEL

arXiv

引用

arXiv 2023年

作者： Zhu, Liyang Ding, Meng Aggarwal, Vaneet Xu, Jinhui Wang, Di Saudi Arabia Department of Computer Science and Engineering The State University of New York Buffalo United States School of IE ECE Purdue University United States Computational Bioscience Research Center Japan SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Saudi Arabia

In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local models has focused on obtaining the lower bounds for the case where the underlying parameter is 1-sparse, and extending such bounds to the more general k-sparse case has proven to be challenging. Moreover, it is unclear whether efficient non-interactive LDP (NLDP) algorithms exist. To address these issues, we first consider the problem in the ∊ non-interactive LDP model and provide a lower bound of (formula presented) on the 2-norm estimation error for sub-Gaussian data, where n is the sample size and d is the dimension of the space. We propose an innovative NLDP algorithm, the very first of its kind for the problem. As a remarkable outcome, this algorithm also yields a novel and_ highly efficient estimator as a valuable by-product. Our algorithm achieves an upper bound of (formula presented) for the estimation error when the data is sub-Gaussian, which can be further improved by a factor of O(√d) if the server has additional public but_ unlabeled data. For the sequentially interactive LDP model, we show a similar lower bound of (formula presented). As for the upper bound, we rectify a previous method and show that it is possible to achieve a bound of (formula presented). Our findings reveal fundamental differences between the non-private case, central DP model, and local DP model in the sparse linear regression problem. © 2023, CC BY.

关键词： Linear regression

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Crack Catcher ai - Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI - Enabling Smart Fracture Mechanics Approac...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Preston, Gabriel Gonzolez, Greer Karnyoto, Andrea S. Mozar, Fitya S. Henry, Matthew M. Isnan, Mahmud Nugroho, Kuncahyo S. Candra, Henry Doris, Muhamad Putri, Dianing N. N. Sari, Tyas K Pardamean, Bens Djuana, Endang Speaks, Derrric Budiman, Arief S. Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls 97601 OR United States Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls 97601 OR United States Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta 11480 Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas Trisakti Jakarta 11440 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia Industrial Eng. Dept. Bina Nusantara University Jakarta 11480 Indonesia Computer Sci. Dept. Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

ISBN: (纸本)9798331504779

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher ai uses novel smart fracture mechanics approach with Artificial Intelligence (ai) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability. © 2025 IEEE.

关键词： 3D Integrated Devices/Packages Fracture Mechanics Machine Learning Approaches Silicon Crack

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RapidAuth: Fast Authentication for Sustainable IoT 1

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1st Eai International Conference on Forthcoming Networks and Sustainability in the IoT Era, FoNeS 2020

作者： Aman, Muhammad Naveed Chaudhry, Shehzad Ashraf Al-Turjman, Fadi Department of Computer Science National University of Singapore Singapore Singapore Department of Computer Engineering Faculty of Engineering and Architecture Istanbul Gelisim University Istanbul Turkey Artificial Intelligence Department Near East University Nicosia Mersin 10 Turkey Research Center for AI and IoT Near East University Nicosia Mersin 10 Turkey

ISBN: (数字)9783030694319

ISBN: (纸本)9783030694302

The exponential growth in the number of Internet of Things (IoT) devices, the sensitive nature of data they produce, and the simple nature of these devices makes IoT systems vulnerable to a wide range cyber-threats. Physical attacks are one of the major concerns for IoT device security. Security solutions for the IoT have to be accurate and quick since many real time applications depend on the data generated by these devices. In this article, we undertake the IoT authentication problem by proposing a fast protocol RapidAuth, which also restricts physical attacks. The proposed protocol uses Physical Unclonable Functions to achieve the security goals and requires the exchange of only two messages between the server and an IoT device. The analysis of RapidAuth proves its’ robustness against various types of attacks as well as its’ efficiency in terms of computation, communication, memory overheads and energy consumption. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Authentication

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A Secure Identity Authentication Protocol for Edge data in Smart Grid Environment 8

A Secure Identity Authentication Protocol for Edge Data in S...

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8th IEEE International Conference on Cyber Security and Cloud Computing and 2021 7th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2021

作者： Xiao, Lijun Cai, Jiahong Qiu, Meikang Liu, Meiqin Guangzhou College of Technology and Business Big Data Development and Research Center Guangzhou China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Texas AM University-Commerce Department of Computer Science CommerceTX75428 United States Zhejiang University College of Electrical Engineering Hangzhou310027 China

ISBN: (纸本)9781665443777

Edge computing uses devices deployed at the edge of the network to preprocess data to reduce the computing pressure on the central server. Through edge computing, smart grid can process large amounts of data faster, meeting real-time, reliability, and high-efficiency requirements. However, as the number of edge devices continues to increase, the problem of leakage and tampering of private data is gradually exposed. In order to solve this problem, this paper proposes a lightweight identity authentication scheme suitable for smart grid environment based on Elliptic Curve Encryption (ECC) technology. In the proposed protocol, we encrypt the identity and key information through ECC, and use the timestamp to verify the validity of the session. In addition, the proposed protocol is encrypted with a random number for each round of authentication, and an attacker cannot crack our protocol through a replay attack. Experimental analysis shows that the proposed protocol is safety and key information will not be leaked. Finally, the cost analysis shows that the proposed protocol has lower communication and lower calculation cost than other that of related protocols, and is suitable for deployment in large-scale intelligent smart grid environments. © 2021 IEEE.

关键词： Cryptography

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Position: Bayesian deep learning is needed in the age of large-scale ai 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Omni-Granularity Embedding Network for Text-to-Image Person Retrieval

Omni-Granularity Embedding Network for Text-to-Image Person ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chengji Wang Zhiming Luo Shaozi Li Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning National Language Resources Monitoring and Research Center for Network Media School of Computer Science Central China Normal University Wuhan China Department of Artificial Intelligence Xiamen University Xiamen China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry WuyiShan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Text-to-image person retrieval aims to identify the desired individual based on a textual description. As an instance-level retrieval problem, it has a large intra-class variance and a small inter-class variance. Although significant progress has been made, the omni-granularity matching issue remains unaddressed. Omni-granularity matching involves aligning words with multi-granularity image regions, challenging models to learn in an omni-granularity embedding space. In this paper, we introduce a novel Omni-Granularity Embedding Network (OGEN) for person representation learning. It addresses the omni-granularity matching issue by developing a Cross-Granularity Aggregation Module (CGAM). This module dynamically consolidates diverse granularity features for learning granularity-dependent and omni-granularity person representations. Additionally, a teacher-student knowledge transfer framework is introduced to minimize the inter-modality discrepancy, allowing CGAM to focus on modality-shared semantics. Due to the effectiveness of CGAM and the knowledge transfer framework, our OGEN enhances the Rank-1 accuracy of the Baseline by 8.54%, 9.89%, and 11.09% on three public datasets, respectively.

关键词： Representation learning Accuracy Semantics Text to image Knowledge transfer

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