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arXiv 2024年

作者： Hamerly, Ryan Basani, Jasvith Raj Sludds, Alexander Vadlamani, Sri Krishna Englund, Dirk Research Laboratory of Electronics MIT 50 Vassar Street CambridgeMA02139 United States NTT Research Inc. Physics and Informatics Laboratories 940 Stewart Drive SunnyvaleCA94085 United States Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States

The scalability of many programmable photonic circuits is limited by the 2π tuning range needed for the constituent phase shifters. To address this problem, we introduce the concept of a phase-efficient circuit architecture, where the average phase shift is 2π. We derive a universal information-theoretic limit to the phase-shift efficiency of universal multiport interferometers, and propose a "3-MZI" architecture that approaches this limit to within a factor of 2×, approximately a 10× reduction in average phase shift over the prior art, where the average phase shift scales inversely with system size as O(1/√N). For non-unitary circuits, we show that the 3-MZI saturates the theoretical bound for Gaussian-distributed target matrices. Using this architecture, we show optical neural network training with all phase shifters constrained to 0.2 radians without loss of accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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Classification of Soil Images using Convolution Neural Networks

Classification of Soil Images using Convolution Neural Netwo...

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2021 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2021

作者： Chatterjee, Kajal Obaidat, Mohammad S. Samanta, Debabrata Sadoun, Balqies Islam, Sk Hafizul Chatterjee, Rajdeep University Department of Computer Science Karnataka Bangalore India University of Sharjah College of Computing and Informatics Sharjah27272 United Arab Emirates Al-Balqa' Applied University College of Engineering Al Salt Jordan Indian Institute of Information Technology Kalyani Department of CSE West Bengal 741235 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9781665432085

Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. © 2021 IEEE.

关键词： Soils

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Social Bots: Detection and Challenges

arXiv

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arXiv 2023年

作者： Yang, Kai-Cheng Varol, Onur Nwala, Alexander C. Sayyadiharikandeh, Mohsen Ferrara, Emilio Flammini, Alessandro Menczer, Filippo Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States Network Science Institute Northeastern University United States Faculty of Engineering and Natural Sciences Sabanci University Turkey Department of Applied Science William & Mary United States Thomas Lord Department of Computer Science University of Southern California United States

While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter, we focus on malicious social bots, a prominent vehicle for such manipulation. We start by discussing recent studies about the presence and actions of social bots in various online discussions to show their real-world implications and the need for detection methods. Then we discuss the challenges of bot detection methods and use Botometer, a publicly available bot detection tool, as a case study to describe recent developments in this area. We close with a practical guide on how to handle social bots in social media research. © 2023, CC BY.

关键词： Botnet

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Mechanically Laser-Induced Acoustic Cavitation in Viscoelastic Tissue: Theoretical Study

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Bulletin of the Russian Academy of Sciences: Physics 2023年第Suppl 3期87卷 S348-S355页

作者： Zain Abu Shaeer Amerah M. Hakami Abu-Bakr, Ali F. Ahmed K. Abu-Nab Department of Basic Science Higher Institute of Engineering and Technology Kafrelsheikh33511 Egypt Department of Mathematics Faculty of Science Jazan University Jazan82723 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow oblast Dolgoprudny141700 Russia

Abstract: Laser-induced acoustic cavitation microbubble dynamics have exhibited significant development in biological systems such as lithotripsy. Investigating the mechanically laser-induced acoustic cavitation microbubble in viscoelastic tissue is the goal of this work. Based on surface tension and viscous forces, the laser-induced cavitation model (also known as the modified Rayleigh–Plesset model), the theoretical model is developed and numerically solved using exponential cubic B-spline Collocation Method. In addition, the energy released in viscoelastic tissue by laser-induced acoustic cavitation as well as mechanically induced cavitation is investigated. The findings of our work provide valuable insight into the application of laser-induced acoustic cavitation microbubbles such as kidney stones and lithotripsy. © Pleiades Publishing, Ltd. 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023, Vol. 87, Suppl. 3, pp. S348–S355. Pleiades Publishing, Ltd., 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023. Pleiades Publishing, Ltd., 2023.

关键词： Tissue

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Location Functions for Self-stabilizing Byzantine Tolerant Swarms 1

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23rd International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2021

作者： Ashkenazi, Yotam Dolev, Shlomi Kamei, Sayaka Katayama, Yoshiaki Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima Japan Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Koganei Japan

ISBN: (数字)9783030910815

ISBN: (纸本)9783030910808

This paper proposes a novel framework to realize self-stabilizing Byzantine tolerant swarms. In this framework, non-Byzantine robots execute tasks while satisfying location functions, that is, the robots use a policy for their location choice, which restricts their location to satisfy the functions. We give a general Byzantine-resilient self-stabilizing algorithm based on the location function, and then provide an efficient implementation of the self-stabilizing algorithms for special classes of tasks, called polynomial-based tasks and shape-based tasks. We also demonstrate the usefulness of the proposed framework by implementing typical tasks of robots. © 2021, Springer Nature Switzerland AG.

关键词： Location

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Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization

arXiv

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arXiv 2022年

作者： Peng, Chong Liu, Yang Chen, Yongyong Wu, Xinxin Cheng, Andrew Kang, Zhao Chen, Chenglizhao Cheng, Qiang College of Computer Science and Technology Qingdao University China The College of Computer Science Harbin Institute of Technology The School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science University of Kentucky Department of Statistics and Applied Probability University of California Santa Barbara United States The Institute of Biomedical Informatics University of Kentucky

In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively. In particular, the new method adopts the log-determinant rank approximation and a novel 2,log norm, to restrict the local low-rank or column-wisely sparse properties for the component matrices, respectively. For the 2,log-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named 2,log-shrinkage operator. The new regularization and the corresponding operator can be generally used in other problems that require column-wise sparsity. Moreover, we impose the spatial-spectral total variation regularization in the log-based nonconvex RPCA model, which enhances the global piece-wise smoothness and spectral consistency from the spatial and spectral views in the recovered HSI. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectroscopy

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Challenges of deep learning methods for COVID-19 detection using public datasets

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informatics in Medicine Unlocked 2022年 30卷 100945页

作者： Hasan, Md. Kamrul Alam, Md. Ashraful Dahal, Lavsen Roy, Shidhartho Wahid, Sifat Redwan Elahi, Md. Toufick E. Martí, Robert Khanal, Bishesh Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh Nepal Applied Mathematics and Informatics Institute for Research (NAAMII) Nepal Computer Vision and Robotics Institute University of Girona Spain

Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or other common Pneumonia. Although the reported outcomes are very high in most cases, these results were obtained without an independent test set from a separate data source(s). DL models are likely to overfit training data distribution when independent test sets are not utilized or are prone to learn dataset-specific artifacts rather than the actual disease characteristics and underlying pathology. This study aims to assess the promise of such DL methods and datasets by investigating the key challenges and issues by examining the compositions of the available public image datasets and designing different experimental setups. A convolutional neural network-based network, called CVR-Net (COVID-19 Recognition Network), has been proposed for conducting comprehensive experiments to validate our hypothesis. The presented end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model that aggregates the outputs from two different encoders and their different scales to convey the final prediction probability. Three different classification tasks, such as 2-, 3-, 4-classes, are designed where the train–test datasets are from the single, multiple, and independent sources. The obtained binary classification accuracy is 99.8% for a single train–test data source, where the accuracies fall to 98.4% and 88.7% when multiple and independent train–test data sources are utilized. Similar outcomes are noticed in multi-class categorization tasks for single, multiple, and independent data sources, highlighting the challenges in developing DL models with the existing public datasets without an independent test set from a separate dataset. Such a result concludes a requirement for a better-designed dataset for developing DL tools applicable in actual clinical settings. The datas

关键词： Chest computed tomography and X-ray Convolutional neural networks COVID-19 disease Ensemble classifier

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Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

arXiv

引用

arXiv 2023年

作者： Bera, Asish Nasipuri, Mita Krejcar, Ondrej Bhattacharjee, Debotosh The Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Campus Rajasthan Pilani333031 India The Department of Computer Science and Engineering Jadavpur University Kolkata32 India The Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://***/view/syd-net/home. © 2023, CC BY.

关键词： Sports

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Enhancing Production Planning in the Internet of Vehicles: A Transformer-based Federated Reinforcement Learning Approach

Enhancing Production Planning in the Internet of Vehicles: A...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jinhua Chen Zihan Zhao Keping Yu Shahid Mumtaz Joel J. P. C. Rodrigues Mohsen Guizani Takuro Sato Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Applied Informatics Silesian University of Technology Gliwice Poland Departement of Computer Sciences Nottingham Trent University Nottingham UK Amazonas State University Manaus AM Brazil Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE Research Institute for Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers commonly used for these problems often lack scalability and fall into local optima. Moreover, security concerns about wireless data transfer arising from multi-factory manufacturing processes are garnering attention. To address these issues, this paper introduces an algorithm, TRL, which is a Transformer-based Reinforcement Learning for vehicle production planning problems. Furthermore, we propose a Transformer-based Federated Reinforcement Learning algorithm, named TFRL, tailored for large-scale manufacturing and secure wireless communication. Experimental results showcase the high performance and security of TFRL. It schedules 1000 orders in about 14 seconds and avoids exchanging plaintext during the interaction. Compared to Non-dominated Sorting Genetic Algorithm II(NSGA-II), the TFRL enhances computational speed by 95.12% and reduces constraint violation scores by 93.18%.

关键词： Wireless communication Vehicular and wireless technologies Schedules Production planning Reinforcement learning Transformers Manufacturing

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Amazon in business to customers and overcoming obstacles 2

Amazon in business to customers and overcoming obstacles

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Altrad, Abdullah Pathmanathan, P. Ravindran Al Moaiad, Yazeed Endara, Yousef Mohamed Aseh, Khairi El-Ebiary, Yousef A. Baker Farea, Mazen Mohammed Latiff, Nurul Adilah Abdul Saany, Syarilla Iryani Ahmad Faculty of Computer and Info. Tech. Al Madinah International University Malaysia International Institute of Applied Science Swiss School of Management Netherlands Faculty of Computer and Information Technology Malaysia Misurata University Libya UniSZA Faculty of Informatics and Computing Malaysia Al-Madinah International University Faculty of Finance and Admin. Sciences Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Malaysia

ISBN: (纸本)9781665432221

Electronic commerce, or people call it e-commerce, it refers to buying and selling products or services on the Internet and transferring money and data to carry out these transactions. More consumers are shifting from traditional transactions to e-commerce, as e-commerce becomes faster and cheaper. In contrast, electronic commerce provides more ease and variety in the composition of goods and services worldwide. E-commerce is the type of electronic business that has been in business for more than 25 years and is used by many users around the world. This research aims to shed light on the nature of electronic commerce and the difficulties it faces on the technical side. This research used descriptive methodology after gathering data from online and indexed research. The research also mentioned five of these obstacles, and how the Amazon overcomes them, and we will take a look at how the opportunities of the Amazon grow so that they have an effect called the Amazon effect, the research reached an important conclusion that eDesk is the leading help desk for online sellers, specifically designed to meet the exact requirements of e-commerce. The research also found solutions to some of the weaknesses faced by electronic commerce platforms. © 2021 IEEE.

关键词： Electronic commerce

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