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arXiv 2024年

作者： Hegde, Chaitra Kiarashi, Yashar Levey, Allan I. Rodriguez, Amy D. Kwon, Hyeokhyen Clifford, Gari D. Department of Biomedical Informatics Emory University School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University AtlantaGA United States Department of Neurology Emory University AtlantaGA United States Department of Biomedical Informatics Emory University Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States

INTRODUCTION: Mild cognitive impairment (MCI) is characterized by a decline in cognitive functions beyond typical age and education-related expectations. Since, MCI has been linked to reduced social interactions and increased aimless movements, we aimed to automate the capture of these behaviors to enhance longitudinal monitoring. METHODS: Using a privacy-preserving distributed camera network, we collected movement and social interaction data from groups of individuals with MCI undergoing therapy within a 1700m2 space. We developed movement and social interaction features, which were then used to train a series of machine learning algorithms to distinguish between higher and lower cognitive functioning MCI groups. RESULTS: A Wilcoxon rank-sum test revealed statistically significant differences between high and low-functioning cohorts in features such as linear path length, walking speed, change in direction while walking, entropy of velocity and direction change, and number of group formations in the indoor space. Despite lacking individual identifiers to associate with specific levels of MCI, a machine learning approach using the most significant features provided a 71% accuracy. DISCUSSION: We provide evidence to show that a privacy-preserving low-cost camera network using edge computing framework has the potential to distinguish between different levels of cognitive impairment from the movements and social interactions captured during group activities. © 2024, CC BY-NC-SA.

关键词： Differential privacy

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Nonvolatile and SEU-Recoverable Latch Based on FeFET and CMOS for Energy-Harvesting Devices

Nonvolatile and SEU-Recoverable Latch Based on FeFET and CMO...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Aibin Yan Zhuoyuan Lin Guangzhu Liu Qingyang Zhang Zhengfeng Huang Jie Cui Xiaoqing Wen Patrick Girard School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Nonvolatile memories are widely used in emerging energy-harvesting Internet-of-Things (IoT) applications, and nonvolatile memories constructed from FeFET devices hold great promise. This paper presents a nonvolatile and single-event-upset (SEU)-recoverable latch based on FeFET and CMOS for energyharvesting devices. The latch uses n-type FeFET devices to provide nonvolatility without any additional control signals. Moreover, since the soft error problem has become increasingly severe, radiation hardening by design gains a great attention as a promising approach to mitigate the reliability issue. The latch uses feedback interlocked loops with n-type FeFETs and C-elements, enabling it to provide nonvolatility and SEU-recovery simultaneously. Simulation results with Candence Virtuoso verifies that the proposed latch design has correct functioning with excellent performance compared to the state-of-the-art designs.

关键词： Performance evaluation Latches Nonvolatile memory Radiation hardening (electronics) Circuits and systems Simulation Reliability engineering

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Dual-modal Data Attention Recognition Based on Multi-Head Self-Attention Mechanism 24

Dual-modal Data Attention Recognition Based on Multi-Head Se...

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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and Data Analysis

作者： Qi Li Mengru Du Yu Song School of Computer Science and Technology Changchun University of Science and Technology Changchun Jilin China Jilin Provincial International Joint Research Center of Brain Informatics and Intelligence Science Changchun Jilin China Zhongshan Institute of Changchun University of Science and Technology Zhongshan Guangdong China School of Computer Science and Technology Changchun University of Science and Technology Changchun Jilin China

ISBN: (纸本)9798400711831

Attention is one of the important cognitive functions of human beings, but with the prevalence of students' lack of attention, especially in the early childhood, the cultivation of attention becomes particularly critical thing. Electroencephalogram (EEG) signals can reflect the cognitive state of the brain, especially different frequency bands such as α, β, θ, δ bands are closely related to attention. Eye tracking data and attention are directly and closely related. Eye tracking data and EEG are complementary, which can reveal the characteristics of human visual attention distribution, attention transfer and distribution. Relevant studies show that the combination of EEG and eye tracking technology can more accurately assess and adjust attention. In this paper, a two-modal feature fusion model based on multi-head self-attention mechanism is proposed to learn the relationship between the two-modal features data. We input the features of multi-head self-attention fusion, the features of single mode and the features of bimodal directly splintered into multiple classifiers, and the features of multi-head self-attention fusion obtain higher classification accuracy.

关键词： Attention

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Reducing the Number of Qubits from n2 to nlog2(n) to Solve the Traveling Salesman Problem with Quantum computers: A Proposal for Demonstrating Quantum Supremacy in the NISQ Era

arXiv

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arXiv 2024年

作者： Ramezani, Mehdi Salami, Sadegh Shokhmkar, Mehdi Moradi, Morteza Bahrampour, Alireza Department of Physics Sharif University of Technology Tehran14588 Iran Centre for Quantum Engineering and Photonics Technology Sharif University of Technology Tehran14588 Iran Department of Computer Engineering Sharif University of Technology Tehran14588 Iran Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Banacha 2 Warsaw02-097 Poland

In our pursuit of quantum supremacy during the NISQ era, this research introduces a novel approach rooted in the Quantum Approximate Optimization Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By strategically reducing the requisite qubit count from n2 to n log2 (n), our QAOA-based algorithm not only contributes to the ongoing discourse on qubit efficiency but also demonstrates improved performance based on established metrics, underscoring its potential for achieving NISQ-era supremacy in solving real-world optimization challenges. © 2024, CC BY-NC-ND.

关键词： Traveling salesman problem

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Weighted Sum of Segmented Correlation: an Efficient Method for Spectra Matching in Hyperspectral Images

Weighted Sum of Segmented Correlation: an Efficient Method f...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sampriti Soor Priyanka Kumari B. S. Daya Sagar Amba Shetty School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Water Resources and Ocean Engineering NIT Karnataka India Systems Science and Informatics Unit Indian Statistical Institute Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Matching a target spectrum with known spectra in a spectral library is a common method for material identification in hyperspectral imaging research. Hyperspectral spectra exhibit precise absorption features across different wavelength segments, and the unique shapes and positions of these absorptions create distinct spectral signatures for each material, aiding in their identification. Therefore, only the specific positions can be considered for material identification. This study introduces the Weighted Sum of Segmented Correlation method, which calculates correlation indices between various segments of a library and a test spectrum, and derives a matching index, favoring positive correlations and penalizing negative correlations using assigned weights. The effectiveness of this approach is evaluated for mineral identification in hyperspectral images from both Earth and Martian surfaces.

关键词： Image segmentation Correlation Absorption Shape Geoscience and remote sensing Libraries Iron

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Transforming Experimental Cobot Cell to Industrial Realization – an Ethical AI Approach

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IFAC-PapersOnLine 2023年第2期56卷 7335-7341页

作者： Istvan Mezgár József Váncza Imre Paniti József Tóth EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Technology Budapest University of Technology and Economics Budapest Hungary Hepenix Ltd. Diósd Hungary

Collaborative Robot (cobot) cells are getting more and more integrative building blocks of Cyber Physical Enterprises. These cells integrate the advantages of human workers with the special capabilities of robots in a safe manner. Cobots need advanced, in many cases artificial intelligence (AI) based control systems to harmonize the collaborative activities. When transferring/transforming experimental setups into industrial application, not only technological and business related, but also ethical aspects have to be taken into consideration. The paper introduces a novel workflow supporting this transformation and presents its application in a case-study of a cobot cell which uses advanced sensing, symbolic AI planning and mixed reality techniques for planning and explaining visually the operation of the cell. The work which takes the responsible artificial intelligence (RAI) approach combines the actual relevant AI standards with the explicit requirements of industrial practitioners.

关键词： Collaborative robotics Artificial intelligence Ethics Design methodology

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可信联邦学习进化优化算法综述

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Engineering 2024年第3期34卷 23-42页

作者： Qiqi Liu Yuping Yan Yaochu Jin Xilu Wang Peter Ligeti Guo Yu Xueming Yan School of Engineering Westlake UniversityHangzhou 310030China Faculty of Informatics Department of Computer AlgebraEötvös Loránd UniversityBudapest 1053Hungary Faculty of Technology Bielefeld UniversityBielefeld 33619Germany Institute of Intelligent Manufacturing Nanjing Tech UniversityNanjing 211816China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China

With the development of edge devices and cloud computing,the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past *** a privacy-preserving distributed machine learning method,federated learning(FL)has become popular in the last few ***,the data privacy issue also occurs when solving optimization problems,which has received little attention so *** survey paper is concerned with privacy-preserving optimization,with a focus on privacy-preserving data-driven evolutionary *** aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and *** provide a formal definition of security and privacy in learning,followed by a comprehensive review of FL schemes and cryptographic privacy-preserving ***,we present ideas on the emerging area of privacy-preserving optimization,ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization(BO).We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active *** the basis of the above,an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these ***,we conclude the survey by outlining open questions and remaining challenges in federated data-driven *** hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.

关键词： Federated learning Privacy-preservation Security Evolutionary optimization Data-driven optimization Bayesian optimization

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EffNet-CNN:A Semantic Model for Image Mining&Content-Based Image Retrieval

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computer Modeling in Engineering & Sciences 2025年第5期143卷 1971-2000页

作者： Rajendran Thanikachalam Anandhavalli Muniasamy Ashwag Alasmari Rajendran Thavasimuthu Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Informatics and Computer Systems College of Computer ScienceKing Khalid University Abha61421Saudi Arabia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Sustainable Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai602105India

Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various *** image retrieval system mainly relies on the efficiency and accuracy of the classification *** research addresses the challenge of enhancing the image retrieval system by developing a novel approach,EfficientNet-Convolutional Neural Network(EffNet-CNN).The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification,image mining,and *** novelty of the proposed EffNet-CNN model includes the integration of different techniques and *** model includes the Mahalanobis distance metric for feature matching,which enhances the similarity *** model extends EfficientNet architecture by incorporating additional convolutional layers,batch normalization,dropout,and pooling layers for improved hierarchical feature extraction.A systematic hyperparameter optimization using SGD,performance evaluation with three datasets,and data normalization for improving feature *** EffNet-CNN is assessed utilizing precision,accuracy,F-measure,and recall metrics across MS-COCO,CIFAR-10 and 100 *** model achieved accuracy values ranging from 90.60%to 95.90%for the MS-COCO dataset,96.8%to 98.3%for the CIFAR-10 dataset and 92.9%to 98.6%for the CIFAR-100 dataset.A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior *** results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.

关键词： Image mining CBIR semantic features EffNet-CNN image retrieval

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A SEMI-AUTOMATIC CRANIAL IMPLANT DESIGN TOOL BASED ON RIGID ICP TEMPLATE ALIGNMENT AND VOXEL SPACE RECONSTRUCTION

arXiv

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arXiv 2024年

作者： Lackner, Michael Puladi, Behrus Kleesiek, Jens Egger, Jan Li, Jianning Germany Institute of Computer Graphics and Vision Graz University of Technology Austria Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Germany Institute of Medical Informatics University Hospital RWTH Aachen Germany

In traumatic medical emergencies, the patients heavily depend on cranioplasty - the craft of neurocranial repair using cranial implants. Despite the improvements made in recent years, the design of a patient-specific implant (PSI) is among the most complex, expensive, and least automated tasks in cranioplasty. Further research in this area is needed. Therefore, we created a prototype application with a graphical user interface (UI) specifically tailored for semi-automatic implant generation, where the users only need to perform high-level actions. A general outline of the proposed implant generation process involves setting an area of interest, aligning the templates, and then creating the implant in voxel space. Furthermore, we show that the alignment can be improved significantly, by only considering clipped geometry in the vicinity of the defect border. The software prototype will be open-sourced at https://***/3Descape/Cranial_Implant_Design. © 2024, CC BY.

关键词： Graphical user interfaces

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Konstantinos Kyritsis Charalampos M. Liapis Nikolaos Spatiotis Isidoros Perikos Michael Paraskevas Electrical & Computer Engineering Dept. University of Peloponnese Patras Greece Mathematics Dept. University of Patras Patras Greece Computer Technology Institute & Press “Diophantus” Patras Greece Computer Engineering & Informatics Dept. University of Patras Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification.

关键词： Training Analytical models Text categorization Transformers

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