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arXiv 2024年

作者： Shan, Shanshan Gao, Yang Waddington, David E.J. Chen, Hongli Whelan, Brendan Liu, Paul Z.Y. Wang, Yaohui Liu, Chunyi Gan, Hongping Gao, Mingyuan Liu, Feng Soochow University Suzhou215123 China Image X Institute Sydney School of Health Sciences Faculty of Medicine and Health University of Sydney Sydney2015 Australia Department of Medical Physics Ingham Institute of Applied Medical Research Liverpool2170 Australia School of Computer Science and Engineering Central South University Changsha410083 China School of Electrical Engineering and Computer Science University of Queensland Brisbane4072 Australia Institute of Electrical Engineering Chinese Academy of Science Beijing100190 China School of Software Northwestern Polytechnical University Taicang215400 China

MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distortions and slow MR acquisition potentially limit the quality of the image guidance and tumour treatments. In this study, we develop an interpretable unrolled network, referred to as RebinNet, to reconstruct distortion-free images from B0 inhomogeneity-corrupted k-space for fast MRI-guided radiotherapy applications. RebinNet includes convolutional neural network (CNN) blocks to perform image regularizations and nonuniform fast Fourier Transform (NUFFT) modules to incorporate B0 inhomogeneity information. The RebinNet was trained on a publicly available MR dataset from eleven healthy volunteers for both fully sampled and subsampled acquisitions. Grid phantom and human brain images acquired from an open-bore 1T MRI-Linac scanner were used to evaluate the performance of the proposed network. The RebinNet was compared with the conventional regularization algorithm and our recently developed UnUNet method in terms of root mean squared error (RMSE), structural similarity (SSIM), residual distortions, and computation time. Imaging results demonstrated that the RebinNet reconstructed images with lowest RMSE (0.92) at four-time acceleration for simulated brain images. The RebinNet could better preserve structural details and substantially improve the computational efficiency (ten-fold faster) compared to the conventional regularization methods, and had better generalization ability than the UnUNet method. The proposed RebinNet can achieve rapid image reconstruction and overcome the B0 inhomogeneity distortions simultaneously, which would facilitate accurate and fast image guidance in radiotherapy treatments. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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3D Generative Models: A Survey

arXiv

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arXiv 2022年

作者： Shi, Zifan Peng, Sida Xu, Yinghao Geiger, Andreas Liao, Yiyi Shen, Yujun Department of Computer Science and Engineering the Hong Kong University of Science and Technology Hong Kong School of Software Technology Zhejiang University China Department of Information Engineering the Chinese University of Hong Kong Hong Kong Autonomous Vision Group University of Tubingen Tubingen AI Center Germany College of Information Science & Electronic Engineering Zhejiang University China Ant Group China

Generative models aim to learn the distribution of observed data by generating new instances. With the advent of neural networks, deep generative models, including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models (DMs), have progressed remarkably in synthesizing 2D images. Recently, researchers started to shift focus from 2D to 3D space, considering that 3D data is more closely aligned with our physical world and holds immense practical potential. However, unlike 2D images, which possess an inherent and efficient representation (i.e., a pixel grid), representing 3D data poses significantly greater challenges. Ideally, a robust 3D representation should be capable of accurately modeling complex shapes and appearances while being highly efficient in handling high-resolution data with high processing speeds and low memory requirements. Regrettably, existing 3D representations, such as point clouds, meshes, and neural fields, often fail to satisfy all of these requirements simultaneously. In this survey, we thoroughly review the ongoing developments of 3D generative models, including methods that employ 2D and 3D supervision. Our analysis centers on generative models, with a particular focus on the representations utilized in this context. We believe our survey will help the community to track the field’s evolution and to spark innovative ideas to propel progress towards solving this challenging task. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Optimal Reordering Trace Files for Improving software Testing Suitcase

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computers, Materials & Continua 2021年第4期67卷 1225-1239页

作者： Yingfu Cai Sultan Noman Qasem Harish Garg Hamïd Parvïn Kim-Hung Pho Zulkefli Mansor School of Measurement and Communication Harbin University of Science&TechnologyHarbinChina Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia Department of Computer Science Faculty of Applied ScienceTaiz UniversityTaizYemen School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatiala147004India Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsan MalaysiaUKM BangiSelangor43600Malaysia

An invariant can be described as an essential relationship between program *** invariants are very useful in software checking and *** tools that are used to detect invariants are invariant *** are two types of invariant detectors:dynamic invariant detectors and static invariant *** software is an available computer program that implements a special case of a dynamic invariant detection *** proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical *** method has some *** of its biggest drawbacks is its overwhelming time *** is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace *** is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this *** findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

关键词： Dynamic invariant detection software testing genetic algorithm

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Towards the Integration of Web of Things Applications Based on Service Discovery 20th

Towards the Integration of Web of Things Applications Based ...

引用

1st International Workshop on the Web of Things for Humans, WoT4H 2020, 2nd Semantics and the Web for Transport workshop, Sem4Tra 2020 and 6th International Workshop on Knowledge Discovery on the Web, KDWEB 2020 co-located with 20th International Conference on Web engineering, ICWE 2020

作者： Criado, Javier Boubeta-Puig, Juan Mena, Manel Llopis, Juan Alberto Ortiz, Guadalupe Iribarne, Luis Applied Computing Group University of Almeria Almería Spain Department of Computer Science and Engineering University of Cadiz Cádiz Spain

ISBN: (纸本)9783030656645

The current state of the technologies related to the Web of Things (WoT) and the Internet of Things (IoT) fosters the creation of service directories gathering resource descriptions. These directories are aimed at enabling the service discovery and supporting providers and consumers with a shared element for their communication and interoperability between the involved agents. This interoperability can be ensured by using the abstract layer of the W3C WoT recommendations. However, many of the existing approaches do not include a service discovery mechanism and those that include a WoT directory lack certain functionalities required by distributed systems specific to the WoT such as a Service-Oriented Architecture (SOA). This paper proposes a federated service discovery approach to support the management of WoT applications which ensures that the integration of the whole system components can be addressed by following a SOA. It is aimed at providing query and storage functionality for WoT resources, but also is intended to be connected to other WoT directories by applying a customizable approach based on recommender systems. Thus, we guarantee a flexible mechanism to obtain sets of ranked WoT resources to be utilized in different kinds of applications and domains. © 2020, Springer Nature Switzerland AG.

关键词： Internet of things

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A Novel Augmented Reality-Based Simulator for Enhancing Orthopedic Surgical Training

SSRN

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SSRN 2024年

作者： Wu, Luohong Seibold, Matthias Cavalcanti, Nicola A. Hein, Jonas Gerth, Tatiana Lekar, Roni Hoch, Armando Vlachopoulos, Lazaros Grabner, Helmut Zingg, Patrick Farshad, Mazda Fürnstahl, Philipp Research in Orthopedic Computer Science Balgrist University Hospital University of Zurich Lengghalde 5 Zurich Zurich8008 Switzerland Department of Orthopaedics Balgrist University Hospital University of Zurich Forchstrasse 340 Zurich Zurich8008 Switzerland Computer Vision and Geometry Group ETH Zurich Ramistrasse 101 Zurich Zurich8092 Switzerland Institute of Computer Science School of Engineering Zurich University of Applied Sciences Obere Kirchgasse 2 Winterthur Zurich8401 Switzerland Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 81 Winterthur Zurich8400 Switzerland

Background: Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for ***: The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann-Whitney U ***: The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22 ± 1.32 mm with LoA -2.37 to 2.81 mm for distance deviation, 0.94 ± 2.41 degrees with LoA -3.78 to 5.66 degrees for yaw deviation, -0.34 ± 1.30 degrees with LoA -2.90 to 2.22 degrees for pitch deviation) and p-values of Mann-Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of ***: This wor

关键词： Augmented reality

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Mechanical Vibrations on a Deployable Nanosatellite Antenna: SAR Performance Analysis

Mechanical Vibrations on a Deployable Nanosatellite Antenna:...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Andrew C. M. Austin Timothy Pelham Simone Mencarelli Annalisa Tresoldi Mohammed Dabboor Guglielmo Aglietti Michael J. Neve Communication Systems and Networks Group University of Bristol Bristol UK Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Te Pūnaha Ātea—Auckland Space Institute University of Auckland Auckland New Zealand Science and Technology Branch Environment and Climate Change Canada Dorval QC Canada

The mechanical vibrations of a high-strain glass-fibre composite deployable X-band synthetic aperture radar (SAR) antenna structure are investigated using a finite element method. The antenna is approximately $\mathrm{2}\ \mathrm{m}\times \mathrm{0}.\mathrm{3}\ \mathrm{m}$ , and exhibits natural frequencies at 2.8 Hz and 7.6 Hz. The radiation patterns for a reflectarray formed from the structure are calculated using a ray-tracing approach for the nominal and mechanically deformed cases. The mechanically deformed antenna pattern is defocused, and a SAR system model shows this reduces the signal-to-noise ratio of a point scattering target by approximately 6 dB.

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RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures

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The Journal of Supercomputing 2022年第6期78卷 8890-8924页

作者： Rehman, Eid Haseeb-ud-Din, Muhammad Malik, Arif Jamal Khan, Tehmina Karmat Abbasi, Aaqif Afzaal Kadry, Seifedine Khan, Muhammad Attique Rho, Seungmin Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Computer Science HITEC University Taxila Taxila Pakistan Chung-Ang University - Seoul Campus: Chung-Ang University Seoul Korea

Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT security is significant. The nature of attacks is dynamic, and addressing this requires designing dynamic methods and a self-adaptable scheme to discover security attacks from malicious use of IoT equipment. The best detection mechanism against attacks from compromised IoT devices includes machine learning techniques. This study emphasizes the latest literature on attack types and uses a scheme based on machine learning for network support in IoT and intrusion detection. Therefore, the current work includes a thorough analysis of multiple intelligence methods and their deployed architectures of network intrusion detection, focusing on IoT attacks and machine learning-based intrusion detection schemes. Moreover, it explores methods based on machine learning appropriate for identifying IoT devices associated with cyber attacks.

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Technical Debt Management: The Road Ahead for Successful software Delivery

arXiv

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arXiv 2024年

作者： Avgeriou, Paris Ozkaya, Ipek Chatzigeorgiou, Alexander Ciolkowski, Marcus Ernst, Neil A. Koontz, Ronald J. Poort, Eltjo Shull, Forrest Dept. of Computing Science University of Groningen Groningen Netherlands Software Engineering Institute Carnegie Mellon University PittsburghPA United States Dept. of Applied Informatics University of Macedonia Thessaloniki Greece QAware GmbH München Germany Department of Computer Science University of Victoria Victoria Canada The Boeing Company MesaAZ United States CGI Rotterdam Netherlands

Technical Debt, considered by many to be the 'silent killer' of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a system, either deliberately or inadvertently. We understand Technical Debt is not all derogatory, often serving the purpose of expediency. But, it is associated with a clear risk, especially for large and complex systems with extended service life: if we do not properly manage Technical Debt, it threatens to "bankrupt" those systems. software engineers and organizations that develop softwareintensive systems are facing an increasingly more dire future state of those systems if they do not start incorporating Technical Debt management into their day to day practice. But how? What have the wins and losses of the past decade of research and practice in managing Technical Debt taught us and where should we focus next? In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt;we subsequently distill the gaps in industrial practice and the research shortcomings, and synthesize them to define and articulate a vision for what Technical Debt management looks like five years hence. © 2024, CC BY-NC-ND.

关键词： Industrial research

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A Novel Deep Learning Approach for Capturing Time Series Dependencies and Improving Short-term Weather Forecasting

A Novel Deep Learning Approach for Capturing Time Series Dep...

引用

International Conference on Image Information Processing

作者： Shiva Johri M. G Divyajyothi S Anitha M.Sandya Rani Thejovathi Murari N Shirisha Department of Management Oriental College of Management Oriental Group of Institutes Bhopal Madhya Pradesh India Department of Information Technology University of Technology and Applied Sciences - Al Mussanah Al Muladha Muscat Sultanate of Oman Department of Computer Science Engineering Vels Institute of Science Technology and Advanced Studies Krishnapuram Pallavaram Chennai Tamil Nadu India St. Martin’s Engineering College Dhulapally Secunderabad Telangana India Department of Computer Science and Engineering Acharya Nagarjuna University Guntur Andhra Pradesh India Department of CSE MLR Institute of Technology Dundigal Hyderabad India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Weather forecasting is a critical and challenging task that requires accurate predictions based on historical data and intricate dependencies between time series. Traditional neural networks, such as Back Propagation through Time (BPTT) trained RNNs, struggle to effectively capture these dependencies, leading to suboptimal performance in weather forecasting. In this study, we suggest a novel Integrated_Stack based Bi-LSTM Model, which associations the strengths of LSTM and Bi-LSTM networks, to address these limitations and enhance the accuracy of short-term weather predictions. The primary objective of this research is to develop a versatile deep learning model capable of maintaining long-term dependances and reading any length of sequence, while simultaneously providing real-time short-term weather forecasts. To achieve this, the Integrated_Stack based Bi-LSTM Model is designed to exploit the versatility and non-linear adaptive processing ability of neural networks. The proposed model’s architecture involves the integration of LSTM and Bi-LSTM layers. By incorporating both forward and reverse direction processing, the model gains a comprehensive understanding of complex patterns present in time-series data. This bidirectional approach enables the model to capture dependencies effectively, which is essential for accurate weather predictions. To estimate the presentation of the Integrated_Stack based Bi-LSTM Model, we conducted experiments using real-world weather data. We compared the model’s predictive capabilities against traditional LSTM models and other state-of-the-art weather forecasting methods. The assessment metrics included accuracy, root mean square error (RMSE), and mean absolute error (MAE).

关键词： Deep learning Adaptation models Time series analysis Weather forecasting Predictive models Data models Reliability

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Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type

arXiv

引用

arXiv 2023年

作者： Müller, Romy Dürschmidt, Marcel Ullrich, Julian Knoll, Carsten Weber, Sascha Seitz, Steffen Faculty of Psychology Chair of Engineering Psychology and Applied Cognitive Research TUD Dresden University of Technology Dresden Germany Faculty of Electrical and Computer Engineering Chair of Fundamentals of Electrical Engineering TUD Dresden University of Technology Dresden Germany Faculty of Mathematics and Natural Sciences Department of Computer Science Machine Learning Group Heinrich-Heine-Universität Düsseldorf Düsseldorf Germany

Deep Learning models like Convolutional Neural Networks (CNN) are powerful image classifiers, but what factors determine whether they attend to similar image areas as humans do? While previous studies have focused on technological factors, little is known about the role of factors that affect human attention. In the present study, we investigated how the tasks used to elicit human attention maps interact with image characteristics in modulating the similarity between humans and CNN. We varied the intentionality of human tasks, ranging from spontaneous gaze during categorization over intentional gaze-pointing up to manual area selection. Moreover, we varied the type of image to be categorized, using either singular, salient objects, indoor scenes consisting of object arrangements, or landscapes without distinct objects defining the category. The human attention maps generated in this way were compared to the CNN attention maps revealed by explainable artificial intelligence (Grad-CAM). The influence of human tasks strongly depended on image type: For objects, human manual selection produced maps that were most similar to CNN, while the specific eye movement task has little impact. For indoor scenes, spontaneous gaze produced the least similarity, while for landscapes, similarity was equally low across all human tasks. To better understand these results, we also compared the different human attention maps to each other. Our results highlight the importance of taking human factors into account when comparing the attention of humans and CNN. © 2023, CC BY.

关键词： Eye movements

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