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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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Computer Systems science & Engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

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arXiv 2023年

作者： Lauscher, Anne Nozza, Debora Crowley, Archie Miltersen, Ehm Hovy, Dirk Data Science Group Universität Hamburg Germany Department of Computing Sciences Bocconi University Italy Linguistics University of South Carolina United States School of Culture and Communication Aarhus University Denmark

As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals (Dev et al., 2021). In this "reality check", we study how three commercial MT systems translate 3rd-person pronouns. Concretely, we compare the translations of gendered vs. gender-neutral pronouns from English to five other languages (Danish, Farsi, French, German, Italian), and vice versa, from Danish to English. Our error analysis shows that the presence of a gender-neutral pronoun often leads to grammatical and semantic translation errors. Similarly, gender neutrality is often not preserved. By surveying the opinions of affected native speakers from diverse languages, we provide recommendations to address the issue in future MT research. © 2023, CC BY-SA.

关键词： Error analysis

SCIENTIFIC MACHINE LEARNING FOR CLOSURE MODELS IN MULTISCALE PROBLEMS: A REVIEW

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arXiv 2024年

作者： Sanderse, Benjamin Stinis, Panos Maulik, Romit Ahmed, Shady E. Scientific Computing Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA99352 United States College of Information Sciences and Technology The Pennsylvania State University Westgate Building University ParkPA16802 United States

Closure problems are omnipresent when simulating multiscale systems, where some quantities and processes cannot be fully prescribed despite their effects on the simulation’s accuracy. Recently, scientific machine learning approaches have been proposed as a way to tackle the closure problem, combining traditional (physics-based) modeling with data-driven (machine-learned) techniques, typically through enriching differential equations with neural networks. This paper reviews the different reduced model forms, distinguished by the degree to which they include known physics, and the different objectives of a priori and a posteriori learning. The importance of adhering to physical laws (such as symmetries and conservation laws) in choosing the reduced model form and choosing the learning method is discussed. The effect of spatial and temporal discretization and recent trends toward discretization-invariant models are reviewed. In addition, we make the connections between closure problems and several other research disciplines: inverse problems, Mori-Zwanzig theory, and multi-fidelity methods. In conclusion, much progress has been made with scientific machine learning approaches for solving closure problems, but many challenges remain. In particular, the generalizability and interpretability of learned models is a major issue that needs to be addressed *** Codes 65MXX, 76MXX, 68T07 © 2024, CC BY-NC-SA.

关键词： Inverse problems

High Performance computing Framework for Variable Selection on Genome-wide Association Studies

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High Performance Computing Framework for Variable Selection ...

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Xiang Liu, Xueling Diao, Jing Zheng, Mengyao Li, Jihe Wei, Dehui Xie, Qipeng Li, Xia Jiang, Linshan National University Health System Group Technology Office Singapore National University of Singapore School of Computing Singapore Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Department of Preventive Dentistry China Harvard University Harvard T.H. Chan School of Public Health United States Peking University School of Software and Microelectronics China Hong Kong University of Science and Technology IoT Thrust Information Hub Guangzhou China Eth Zurich Department of Computer Science Switzerland National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798350386226

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response *** address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers. © 2024 IEEE.

关键词： Confounding factors genome-wide association study structured relatedness variable selection

SMOG: Accelerating Subgraph Matching on GPUs

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SMOG: Accelerating Subgraph Matching on GPUs

IEEE Conference on High Performance Extreme computing (HPEC)

作者： Zhibin Wang Ziheng Meng Xue Li Xi Lin Long Zheng Chen Tian Sheng Zhong State Key Laboratory for Novel Software Technology Nanjing University Alibaba Group“ National Engineering Research Center for Big Data Technology and System/ Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Zhejiang Lab

Subgraph matching is a crucial problem in graph theory with diverse applications in fields, such as bioinformatics, social networks and recommendation systems. Accelerating subgraph matching can be greatly facilitated by GPUs, which offer exceptional parallelism and high memory bandwidth. By leveraging the power of multiple GPU cards, subgraph matching can be scaled to achieve unprecedented levels of performance. In this paper, we propose SMOG, an abbreviation for. Subgraph Matching On Multi-Card GPUs. It is a general, high-performance and scalable sub graph matching system that utilizes multi-card GPUs. To address the issue of duplication resulting from subgraph automorphism, SMOG introduces a two-step approach. Firstly, it analyzes the symmetry within the subgraph. Then, it adaptively adjusts the graph preprocessing and generates subgraph-aware GPU codes tailored to the given subgraph. Furthermore, SMOG leverages multi-level parallelism by designing the specific strategy for each level, enabling it to scale from 1 to 1,024 GPU cards, resulting in an extraordinary $553\times$ speedup. We evaluate SMOG on various subgraph queries and datasets. The experimental results demonstrate that SMOG outperforms the triangle-specific system TRUST with an average speedup of $2.94\times$ . And it performs significantly better than the subgraph matching system RPS by $203.55\times$ and the graph processing system Gunrock by 35, $455.52\times$ on average.

关键词：

REAL-TIME DIGITAL TWIN PLATFORM: A CASE STUDY ON CORE NETWORK SELECTION IN AERONAUTICAL AD-HOC NETWORKS

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arXiv 2024年

作者： Cakir, Lal Verda Kocak, Mihriban Özdem, Mehmet Canberk, Berk School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom BTS Group Turkey Department of Computer Science Aalto University Finland Turk Telekom Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Turkey

The development of Digital Twins (DTs) is hindered by a lack of specialized, open-source solutions that can meet the demands of dynamic applications. This has caused state-of-the-art DT applications to be validated using offline data. However, this approach falls short of integrating real-time data, which is one of the most important characteristics of DTs. This can limit the validating effectiveness of DT applications in cases such as aeronautical ad-hoc networks (AANETs). Considering this, we develop a Real-Time Digital Twin Platform and implement core network selection in AANETs as a case study. In this, we implement microservice-based architecture and design a robust data pipeline. Additionally, we develop an interactive user interface using open-source tools. Using this, the platform supports real-time decision-making in the presence of data retrieval failures. Copyright © 2024, The Authors. All rights reserved.

关键词： Ad hoc networks

SpaceSeg: A High-Precision Intelligent Perception Segmentation Method for Multi-Spacecraft On-Orbit Targets

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arXiv 2025年

作者： Liu, Hao Guo, Pengyu Yang, Siyuan Jiang, Zeqing Hu, Qinglei Li, Dongyu Hangzhou International Innovation Institute Beihang University Hangzhou311115 China School of Cyber Science and Technology Beihang University Beijing100191 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Ant Group Shanghai200122 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

With the continuous advancement of human exploration into deep space, intelligent perception and high-precision segmentation technology for on-orbit multi-spacecraft targets have become critical factors for ensuring the success of modern space missions. However, the complex deep space environment, diverse imaging conditions, and high variability in spacecraft morphology pose significant challenges to traditional segmentation methods. This paper proposes SpaceSeg, an innovative vision foundation model-based segmentation framework with four core technical innovations: First, the Multi-Scale Hierarchical Attention Refinement Decoder (MSHARD) achieves high-precision feature decoding through cross-resolution feature fusion via hierarchical attention. Second, the Multi-spacecraft Connected Component Analysis (MS-CCA) effectively resolves topological structure confusion in dense targets. Third, the Spatial Domain Adaptation Transform framework (SDAT) eliminates cross-domain disparities and resist spatial sensor perturbations through composite enhancement strategies. Finally, a custom Multi-Spacecraft Segmentation Task Loss Function is created to significantly improve segmentation robustness in deep space scenarios. To support algorithm validation, we construct the first multi-scale on-orbit multi-spacecraft semantic segmentation dataset SpaceES, which covers four types of spatial backgrounds and 17 typical spacecraft targets. In testing, SpaceSeg achieves state-of-the-art performance with 89.87% mIoU and 99.98% mAcc, surpassing existing best methods by 5.71 percentage points. The dataset and code are open-sourced at https://***/Akibaru/SpaceSeg to provide critical technical support for next-generation space situational awareness systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

A Failure-Free and Efficient Discrete Laplace Distribution for Differential Privacy in MPC

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arXiv 2025年

作者： Tjuawinata, Ivan Wang, Jiabo Yang, Mengmeng Lyu, Shanxiang Wang, Huaxiong Lam, Kwok-Yan Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore Information Security and Privacy Group Data61 CSIRO Australia College of Cyber Security Jinan University Guangzhou China Division of Mathematical Sciences Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore

In an MPC-protected distributed computation, although the use of MPC assures data privacy during computation, sensitive information may still be inferred by curious MPC participants from the computation output. This can be observed, for instance, in the inference attacks on either federated learning or a more standard statistical computation with distributed inputs. In this work, we address this output privacy issue by proposing a discrete and bounded Laplace-inspired perturbation mechanism along with a secure realization of this mechanism using MPC. The proposed mechanism strictly adheres to a zero failure probability, overcoming the limitation encountered on other existing bounded and discrete variants of Laplace perturbation. We provide analyses of the proposed differential privacy (DP) perturbation in terms of its privacy and utility. Additionally, we designed MPC protocols to implement this mechanism and presented performance benchmarks based on our experimental setup. The MPC realization of the proposed mechanism exhibits a complexity similar to the state-of-the-art discrete Gaussian mechanism, which can be considered an alternative with comparable efficiency while providing stronger differential privacy guarantee. Moreover, efficiency of the proposed scheme can be further enhanced by performing the noise generation offline while leaving the perturbation phase online. © 2025, CC BY-NC-ND.

关键词： Differential privacy

A Framework Optimization in Social Media using Xampp: A Systematic Approach 4

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A Framework Optimization in Social Media using Xampp: A Syst...

International Conference on 4th Industrial Revolution Based Technology and Practices, ICFIRTP 2022

作者： Joshi, Kapil Kumar, Rajiv Kumar, Anil Reshi, Jagdeep Sharma, Aditi Dumka, Ankur Uttaranchal University Uttaranchal Institute of Technology Department of CSE Dehradun India Uttaranchal University Uttaranchal School of Computing Sciences Dehradun India DIT University Data Science Research Group School of Computing Dehradun India School of Engineering and Digital Sciences Nazarbayev University Kazakhstan Women Institute of Technology Department of CSE Dehradun India Graphic Era Deemed to Be University Department of CSE Dehradun India

ISBN: (纸本)9798350345919

XAMPP is an acronym that stands for Cross-Platform, Apache, MySQL, PHP, and Perl, with the Ps standing for PHP and Perl, respectively. This article investigates the various helpful assets and procedures which are utilized in site creation. In a discussion, chiefly dependent on a nearby host named Xampp stage, we likewise address the procedure that follows. Next, we break down different web application creation frameworks. Furthermore,, We're discussing life cycle model and web application engineering creation. Frequently remembered for this article is the result of various research articles for buyers to consider the issues they that experience. This paper discusses the innovation utilized in this turn of events, PHP and with screen captures clarified its highlights with Xampp subsequently. It is normal that it would have significant criticism context for the strategy. © 2022 IEEE.

关键词： FACENET Map reading MTCNN Xampp