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arXiv 2024年

作者： Kakooei, Mohammad Bailie, James Söderberg, Albin Becevic, Albin Daoud, Adel Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Statistics Harvard University Massachusetts United States Institute for Analytical Sociology Linköping University Sweden Center for Advanced Study in the Behavioral Sciences Stanford University United States The AI and Global Development Lab Sweden

Accurate Land Use and Land Cover (LULC) maps are essential for understanding the drivers of sustainable development, in terms of its complex interrelationships between human activities and natural resources. However, existing LULC maps often lack precise urban and rural classifications, particularly in diverse regions like Africa. This study presents a novel construction of a high-resolution rural-urban map using deep learning techniques and satellite imagery. We developed a deep learning model based on the DeeplabV3 architecture, which was trained on satellite imagery from Landsat-8 and the ESRI LULC dataset, augmented with human settlement data from the GHS-SMOD. The model utilizes semantic segmentation to classify land into detailed categories, including urban and rural areas, at a 10-meter resolution. Our findings demonstrate that incorporating LULC along with urban and rural classifications significantly enhances the model’s ability to accurately distinguish between urban, rural, and non-human settlement areas. Therefore, our maps can support more informed decision-making for policymakers, researchers, and stakeholders. We release a continent wide urban-rural map, covering the period 2016 and 2022. © 2024, CC BY.

关键词： Landsat

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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Leveraging Randomization-Inspired Hybrid Deep Learning for Enhanced Diabetic Retinopathy Detection Via Multi-Scale Image Analysis

SSRN

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SSRN 2024年

作者： Mutawa, A.M. Hemalakshmi, G.R. Prakash, N.B. Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat13060 Kuwait School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electrical and Electronics Engineering National Engineering College Tamilnadu Kovilpatti India Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha13133 Kuwait Department of Electronics and Communication Engineering Faculty of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai600117 India Universiti Malaysia Perlis Perlis Kangar02600 Malaysia

Diabetic retinopathy (DR), a critical complication of diabetes, significantly increases the risk of vision loss. Prompt detection is essential for effective intervention, yet existing diagnostic practices are resource-intensive, inconsistent, and often inaccessible. This study pioneers an innovative framework, leveraging a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, synergized with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, to fortify the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. Through sophisticated image preprocessing, including advanced noise reduction and morphological techniques, complemented by Otsu’s thresholding for precise blood vessel segmentation, we enhance the model's diagnostic capability. Our methodology exhibits superior performance in DR screening, achieving benchmarks of 96.10% precision, 95.35% sensitivity, 97.06% specificity, and 96.10% accuracy, markedly surpassing conventional diagnostic methods. Applied across various publicly available datasets, this research not only advances the precision of DR diagnostics but also contributes to the broader discourse on the potential of hybrid, randomization-inspired neural networks in medical imaging. This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.. © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Compact Millimeter Wave Massive MIMO System Utilizing ESPAR

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IEEE Transactions on Communications 2025年

作者： Zhang, Chi Shen, Shanpu Li, Hongyu Ma, Dingfei Han, Zixiang Qian, Jun Clerckx, Bruno Murch, Ross Peng Cheng Laboratory Department of Broadband Communication Shenzhen518055 China University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom Guangzhou511400 China Guangdong University of Technology College of Information Engineering Guangzhou China China Mobile Research Institute Future Research Lab Beijing100053 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Institute for Advanced Study Clear Water Bay Kowloon Hong Kong

In this work, we propose a compact millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system utilizing electronically steerable parasitic array radiator (ESPAR). We analyze the system and channel models for the compact mmWave massive MIMO system using a beamspace formulation and demonstrate that we can optimize the spectral efficiency of the compact mmWave massive MIMO system by jointly adjusting the variable reactive loads in the ESPAR with a digital beamformer. We formulate the compact mmWave massive MIMO system optimization problem to maximize spectral efficiency and propose an unconstrained optimization based algorithm with an initialization method. The spectral and energy efficiencies of the compact mmWave massive MIMO system are evaluated in comparison to conventional mmWave massive MIMO systems of the same antenna size using full-digital, fully-connected hybrid, and partially-connected hybrid beamforming. The results show that the compact mmWave massive MIMO system provides higher spectral efficiency than partially-connected hybrid beamforming. On top of this, it can provide higher energy efficiencies of around 3.21, 2.88, and 1.22 times compared to full-digital, fully-connected hybrid, and partially-connected hybrid beamforming but with lower hardware complexity and lower cost. Therefore, compact mmWave massive MIMO systems are a promising and effective alternative to conventional massive MIMO systems in millimeter wave (mmWave) communications. © 1972-2012 IEEE.

关键词： Beamforming

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FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis

arXiv

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arXiv 2022年

作者： Guo, Zihui Hou, Yonghong Wang, Pichao Gao, Zhimin Xu, Mingliang Li, Wanqing School of Electrical and Automation Engineering Tianjin University Tianjin China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Advanced Multimedia Research Lab University of Wollongong Australia

Analysis of human interaction is one important research topic of human motion analysis. It has been studied either using first person vision (FPV) or third person vision (TPV). However, the joint learning of both types of vision has so far attracted little attention. One of the reasons is the lack of suitable datasets that cover both FPV and TPV. In addition, existing benchmark datasets of either FPV or TPV have several limitations, including the limited number of samples, participant subjects, interaction categories, and modalities. In this work, we contribute a large-scale human interaction dataset, namely, FT-HID dataset. FTHID contains pair-aligned samples of first person and third person visions. The dataset was collected from 109 distinct subjects and has more than 90K samples for three modalities. The dataset has been validated by using several existing action recognition methods. In addition, we introduce a novel multi-view interaction mechanism for skeleton sequences, and a joint learning multi-stream framework for first person and third person visions. Both methods yield promising results on the FT-HID dataset. It is expected that the introduction of this vision-aligned large-scale dataset will promote the development of both FPV and TPV, and their joint learning techniques for human action analysis. The dataset and code are available at here. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

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Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval

arXiv

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arXiv 2024年

作者： Kan, Shichao Deng, Yuhai Liang, Yixiong Cen, Lihui Qu, Zhe Cen, Yigang He, Zhihai School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Automation Central South University Hunan Changsha410083 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Pengcheng Lab Shenzhen518066 China

The task of searching for visual objects in a large image dataset is difficult because it requires efficient matching and accurate localization of objects that can vary in size. Although the segment anything model (SAM) offers a potential solution for extracting object spatial context, learning embeddings for local objects remains a challenging problem. This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales. Following this, a benchmark of challenges is assembled by utilizing COCO 2017 and VOC 2007 datasets to facilitate the training and evaluation of general object retrieval models. Finally, we conduct comprehensive ablation studies and discuss the complexities faced within the domain of general object retrieval. Our object retrieval evaluations span a range of datasets, including BelgaLogos, Visual Genome, LVIS, in addition to a challenging evaluation set that we have individually assembled for open-vocabulary evaluation. These comprehensive evaluations effectively highlight the robustness of our unsupervised MS-UGCML approach, with an object level and image level mAPs improvement of up to 6.69% and 10.03%, respectively. The code is publicly available at https://***/dengyuhai/MSUGCML. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Measurement of Nd:Y2o3 Broad Band Near Infra-Red Transmittance Using Ftir System

SSRN

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SSRN 2023年

作者： Lee, Heesu Hwang, Seungjin Lee, Sungyoon Hong, Kyung Hee Oh, Hyeon-Myeong Park, Young-Jo Kim, Ha-Neul Yu, Tae Jun Department of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of HIL Lab. Inc. Pohang Korea Republic of Department of Advanced Convergence Handong Global University Pohang Korea Republic of Engineering Ceramics Department Korea Institute of Material Science Changwon Korea Republic of

We report on the measurement of transmittance in Nd:Y2O3 using a homemade Fourier transform infrared (FTIR) spectroscopy measurement system. The transmittance of Nd:Y2O3 samples in the 850–1100 nm band was measured and compared with the theoretical transmittance. A figure of merit (FOM) was defined to assess the proximity of the measured value to the theoretical value. The maximum FOM obtained was 240, corresponding to 99.6% of the theoretical transmittance at a wavelength of 1078 nm. Our results demonstrate the high accuracy and precision of the FTIR system for measuring transmittance in Nd:Y2O3 samples within the near-infrared wavelength range. © 2023, The Authors. All rights reserved.

关键词： Infrared devices

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Traffic-Aware Fast Reroute Mechanism Exploiting Disjoint Subpaths for Named Data Networks

Traffic-Aware Fast Reroute Mechanism Exploiting Disjoint Sub...

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IEEE International Conference on Communications (ICC)

作者： Boyang Zhou Chunming Wu Qiang Yang Zhen Zhu Yinghui Nie Wenjie Yu Zhejiang Lab Intelligent Networks Institute Hangzhou China College of Computer Science Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China

The popularized named data network (NDN) needs a fast reroute (FRR) to improve resilience in dealing with the norm of link faults. The resilience of the existing disjoint arborescences and paths based approaches is restricted in reroute options, due to the limited number of disjoint end-to-end routes available. Such restrictions can be weakened by exploiting of the disjoint subpaths (DSP) between two intermediate nodes in the primary path (PP), as what DSP can link together is not only the two ends. In this paper, we propose a novel NDN based traffic-aware fast reroute (TA-FRR) mechanism that exploits a new resilient subgraph (RSG) route involving multiple DSPs. TA-FRR proactively constructs an RSG with scalable computational complexity to maximize the resilience and to avoid a too high reroute stretch. In the RSG, TA-FRR efficiently computes the shortest detour path for the disconnected components actively identified according to packet arrival timeouts. We evaluated the performance of TA-FRR in the ndnSIM simulator using synthsized Internet topologies with different degrees of link faults. The results show that TA-FRR can efficiently recover serious link faults in PP with a scalable RSG construction and outperforms the existing approaches with a significantly higher resilience and the maximum stretch similar to the arborescences approach.

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Andrew Quijano Spyros T. Halkidis Kevin Gallagher Kemal Akkaya Nikolaos Samaras Dept. of Computer Science and Engineering New York University New York NY USA Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University Miami FL USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

关键词： Resistance Hands Privacy Protocols Prevention and mitigation Computational modeling Side-channel attacks Data models Timing Decision trees

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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