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arXiv 2022年

作者： Fan, Mingyuan Guo, Wenzhong Yu, Shengxing Ying, Zuobin Liu, Ximeng College of Computer and Data Science Fuzhou University China Faculty of Data Science City University of Macau China School of Electronics Engineering and Computer Science Peking University China

Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model. However, under such a challenging but practical setting, the crafted adversarial examples are always prone to overfitting to the proxy model employed, presenting poor transferability. In this paper, we suggest alleviating the overfitting issue from a novel perspective, i.e., designing a fitted model architecture. Specifically, delving the bottom of the cause of poor transferability, we arguably decompose and reconstruct the existing model architecture into an effective model architecture, namely multi-track model architecture (MMA). The adversarial examples crafted on the MMA can maximumly relieve the effect of model-specified features to it and toward the vulnerable directions adopted by diverse architectures. Extensive experimental evaluation demonstrates that the transferability of adversarial examples based on the MMA significantly surpass other state-of-the-art model architectures by up to 40% with comparable overhead. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images

Generative Adversarial Networks for Spatio-Spectral Compress...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Martin Hermann Paul Fuchs Akshara Preethy Byju Alisa Walda Behnood Rasti Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCs E ); and ii) HiFiC with 3D convolutions (denoted as HiFiC 3D ) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCs E and HiFiC 3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC.

关键词： Convolutional codes Solid modeling Image coding Three-dimensional displays Convolution Redundancy Bit rate Transform coding Image reconstruction Hyperspectral imaging

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Parallelization of Software Systems Test Case Selection Algorithm Based on Singular Value Decomposition

arXiv

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arXiv 2022年

作者： Moghaddam, Mahdi Movahedian Department of Data and Computer Science Faculty of Mathematical Science Shahid Beheshti University Tehran Iran

When developing a software system, a change in one part of the system may lead to unwanted changes in other parts of the system. These affected parts may interfere with system performance, so regression testing is used to deal with these disorders. This test seeks to re-measure these sections to prevent these abnormalities, but it is difficult to identify these sections for re-examination. We try to cluster the changes of our software system based on the system functions by singular value decomposition, to be able to use to identify these parts during a new change, to perform the test again. In order to increase speedup, our calculations were performed in parallel on shared memory systems so that by increasing the scale of software systems, an optimal answer could be obtained. © 2022, CC BY.

关键词： Singular value decomposition

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NFT Appraisal Using Machine Learning 23

NFT Appraisal Using Machine Learning

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5th Asia Pacific Information Technology Conference, APIT 2023

作者： Dawod, Ahmed Dawod Mohammed Munkhdalai, Lkhagvadorj Park, Kwang Ho Ryu, Keun Ho Pham, Van Huy Database and Bioinformatics Laboratory College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399500

Non-Fungible Tokens (NFTs) are digital assets based on a blockchain and those are characterized as unique cryptographic tokens and non-interchangeable. To date, research into the NFT marketplace has been relatively limited. As it is an emerging platform with many unique elements, The NFT market has been impacted due to recent fluctuations in crypto-asset markets more broadly. This current bear market cycle has shed light on concerns around the value of NFTs, profit-based motivation, and environmental sustainability. However, periods of volatility and cyclicality are to be expected with any nascent technology as it develops a product-market fit. consequently, the appraisal of real-price for NFT collections is essential for individual financial security and investment making. In this study, we evaluate the machine learning algorithms to appraise their real-price based on NFT item's characteristics, market event information, and their rarity score data acquired by retrieved from the biggest marketplace OpenSea. Furthermore, the procedures were applied to meet the objectives of this study we built prediction models based on various machine-learning algorithms ranging from Random Forest, XGBoost, SVM, Lasso, ElasticNet, Ridge, Linear Polynomial Regression, TabNet, CatBoost, and LightGBM models. From the results, LightGBM regression model outperformed the other by RMSE around 0.905. The best R2 is only found in this model, which has a value of 0.917. © 2023 ACM.

关键词： Blockchain

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MorphText: Deep Morphology Regularized Accurate Arbitrary-shape Scene Text Detection

arXiv

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arXiv 2024年

作者： Xu, Chengpei Jia, Wenjing Wang, Ruomei Luo, Xiaonan He, Xiangjian Faculty of Engineering and IT University of Technology Sydney Australia The Department of Computer Science University of Nottingham Ningbo China School of Data and Computer Science National Engineering Research Center of Digital Life Sun Yat-sen University China The School of Computer Science and Information Security Guilin University of Electronic Technology China

Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i.e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments. Targeting these two problems, we propose a novel approach, named "MorphText", to capture the regularity of texts by embedding deep morphology for arbitrary-shape text detection. Towards this end, two deep morphological modules are designed to regularize text segments and determine the linkage between them. First, a Deep Morphological Opening (DMOP) module is constructed to remove false text segment detections generated in the feature extraction process. Then, a Deep Morphological Closing (DMCL) module is proposed to allow text instances of various shapes to stretch their morphology along their most significant orientation while deriving their connections. Extensive experiments conducted on four challenging benchmark datasets (CTW1500, Total-Text, MSRA-TD500 and ICDAR2017) demonstrate that our proposed MorphText outperforms both top-down and bottom-up state-of-the-art arbitrary-shape scene text detection approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Morphology

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Prediction of COVID-19 Transmission in the United States Using Google Search Trends

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computers, Materials & Continua 2022年第4期71卷 1751-1768页

作者： Meshrif Alruily Mohamed Ezz Ayman Mohamed Mostafa Nacim Yanes Mostafa Abbas Yasser El-Manzalawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Faculty of Engineering Al-Azhar UniversityCairo11651Egypt Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt RIADI Laboratory La Manouba UniversityManouba2010Tunisia Department of Translational Data Science and Informatics GeisingerDanvillePA17822USA

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health *** to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the *** accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access *** objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related ***,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms *** the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,*** the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,*** 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,*** found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 *** also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

关键词： Forecasting COVID-19 transmission and mortality in the US stacked LSTM SARS-COV-2 and google COVID-19 search trends

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Lymphoma Ultrasound Image Classification with Causal Attention and Feature Fusion 19

Lymphoma Ultrasound Image Classification with Causal Attenti...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Dehua Han, Yingkang Dong, Yijie Zhou, Xiaokang Donghua University School of Computer Science and Technology Shanghai China Shanghai Jiaotong University School of Medicine Shanghai China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Riken Tokyo Japan

ISBN: (纸本)9798350346558

Medical image classification aids in diagnosis eases the work of physicians. In this paper, we propose an image classification method for lymphoma ultrasound image classification. We make the model more focused on the lymphoma lesion region by fusing the features of the original image and the image of the lesion region obtained through semantic segmentation in parallel, which is used to solve the problem that the grayscale values of the lymphoma lesion region and the background region are similar and difficult to distinguish. The problem of generalization of different lymphoma ultrasound images obtained from different instrument scans is handled by adding a causal attention mechanism to the model. Finally, we use counterfactual interpretation to perturb both the original image and the lesion image to obtain the instance-level interpretation. It is demonstrated experimentally that the method effectively improves the performance of classification. © 2022 IEEE.

关键词： Image classification

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Efficient Federated Learning with Adaptive Channel Pruning for Edge Devices 19

Efficient Federated Learning with Adaptive Channel Pruning f...

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作者： Jia, Yongzhe Zhang, Xuyun Liu, Bowen Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China Shenzhen China Macquarie University Department of Computing SydneyNSW Australia Shiga University Faculty of Data Science Hikone Japan

ISBN: (纸本)9798350346558

Federated learning (FL) is an emerging machine learning paradigm that allows distributed participants to train a global model collaboratively. In edge networks, FL involves numerous edge devices updating the local models with the assistance of a central server while keeping the local data on the devices. However, edge devices are commonly resource-limited while neural network models used in FL require significant resource consumption to reach a satisfactory accuracy. Moreover, different devices are various in terms of system capabilities, and employing a uniform model on all devices leads to the derogation of model accuracy. Several pioneer work integrate model pruning techniques into the FL process and focus on reducing the resource consumption on edge devices, whereas neglecting the impact of model generality on accuracy. In our work, we propose FedACP, an efficient FL approach with adaptive channel pruning for edge devices to achieve better resource-accuracy trade-offs. Specifically, we design a two-phase channel pruning approach to adaptively prune local models, in which both the resource constraints and model generalities are taken into consideration. In addition, we also design an aggregation algorithm for aggregating heterogeneous local models produced by the two-phase channel pruning approach. To evaluate the performance of the FedACP, we implement it with a real FL framework FedML and compare it with several state-of-the-art methods. The experimental results show that FedACP achieves better model accuracy while simultaneously reducing 22.41% to 27.34% model parameters. © 2022 IEEE.

关键词： Economic and social effects

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Thermal Simulation for Magnetic Coupler of Wireless Power Transfer Electric Vehicles by Using Heat Sink and Thermoelectric Cooler

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Progress In Electromagnetics Research M 2024年 126卷 89-98页

作者： Farooq, Umar Shafique, Shahryar Asif, Muhammad Arslan, Muhammad Wongjom, Poramed Ullah, Rizwan Zhilenkov, Anton Mobayen, Saleh Pijitrojana, Wanchai Electrical and Computer Engineering Department Thammasat School of Engineering Thammasat University Rangsit Campus Klong Luang Pathum Thani 12120 Thailand Ministry of IT & Telecommunications Islamabad Pakistan Department of Electrical Engineering University of Science and Technology Main Campus Bannu23200 Pakistan Department of Mechanical Engineering University of Engineering and Technology Lahore Pakistan Division of Physics Faculty of Science and Technology Thammasat University Rangsit Campus Klongluang Pathum Thani 12120 Thailand Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan

In challenging operational environments such as underground buildings beneath roadways, reliability and performance of wireless power transfer (WPT) systems for electric vehicles (EVs) heavily hinge on the operating temperature of the magnetic couplers. Addressing this, this study introduces a novel approach employing heat sink and thermoelectric cooler technologies to mitigate temperature rise in magnetic couplers, which is particularly crucial for high-power applications. Utilizing ANSYS simulation, the study evaluates a WPT high-power application coil model with a total output power of 2 KW and an 18 cm air gap, with a 3.5 cm adjacent alignment to enhance thermal performance on both transmitter and receiver sides. Results demonstrate significant thermal enhancement, reducing the temperature of coils from 63◦ C to 54◦ C solely with the heat sink and further down to 48◦ C with the combined implementation of both heat sink and thermoelectric cooler. These measures effectively dissipate heat from the coils into the surrounding air, ensuring system efficiency and stability while facilitating functionality of system components. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Heat sinks

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Privacy-Enhanced Role-Based Access Control for IoT Systems

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IEEE Internet of Things Journal 2025年

作者： Zhang, Yawei Chen, Lanxiang Zhu, Yizhao Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China City University of Macau Faculty of Data Science China

Unauthorized accessing private information poses a significant security risk in IoT. Although role-based access control can partially address the problem, there is a lack of studies on protecting the privacy of roles. This work proposes a novel privacy-enhanced role-based access control (PE-RBAC) scheme for resource-limited cloud-based IoT systems. Our PE-RBAC scheme enhances the privacy of traditional RBAC by allowing the cloud platform (CP) to learn only the necessary roles of IoT devices rather than their entire role sets. IoT devices remain unaware of the cloud platform’s roles through the incorporation of the authorized private set intersection (APSI) protocol. Our scheme enables role authorization to prevent unauthorized requests by integrating an authorization center (AC) with an authentication scheme. It achieves lightweight computation and communication overhead using a garbled Bloom filter (GBF), independent of IoT roles. It requires only XOR operations and random number generation, making it ideal for IoT devices with limited computing capacity and bandwidth. Through formal security model definitions and rigorous security analysis, it achieves unlinkability and ciphertext indistinguishability for IoT device privacy and unforgeability for both the cloud platform and IoT devices. It demonstrates high practicality through real-world-like experiments considering varying role sizes and the number of concurrent IoT devices. Experimental results show a throughput of 1,100 requests per second, with storage overhead remaining linear with the number of devices. © 2014 IEEE.

关键词： Cloud platforms

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