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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Lei Lei Yaxiong Yuan Yang Yang Yu Luo Lina Pu Symeon Chatzinotas School of Information and Communications Engineering Xi’ an Jiaotong University China Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Competence Center for High Performance Computing Fraunhofer ITWM Germany Department of Electrical and Computer Engineering Mississippi State University USA Department of Computer Science University of Alabama USA

ISBN: (纸本)9781665435413

Federated Learning (FL), as an effective decentral-ized approach, has attracted considerable attention in privacy-preserving applications for wireless edge networks. In practice, edge devices are typically limited by energy, memory, and computation capabilities. In addition, the communications be-tween the central server and edge devices are with constrained resources, e.g., power or bandwidth. In this paper, we propose a joint sparsification and optimization scheme to reduce the energy consumption in local training and data transmission. On the one hand, we introduce sparsification, leading to a large number of zero weights in sparse neural networks, to alleviate devices' computational burden and mitigate the data volume to be uploaded. To handle the non-smoothness incurred by sparsification, we develop an enhanced stochastic gradient descent algorithm to improve the learning performance. On the other hand, we optimize power, bandwidth, and learning parameters to avoid communication congestion and enable an energy-efficient transmission between the central server and edge devices. By collaboratively deploying the above two components, the numerical results show that the overall energy consumption in FL can be significantly reduced, compared to benchmark FL with fully-connected neural networks.

关键词： Wireless communication Training Energy consumption Federated learning Neural networks Bandwidth Energy efficiency

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Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification

arXiv

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arXiv 2024年

作者： Yuan, Han Hong, Chuan Jiang, Pengtao Zhao, Gangming Tran, Nguyen Tuan Anh Xu, Xinxing Yan, Yet Yen Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Biostatistics and Bioinformatics Duke University United States College of Computer Science Nankai University China Faculty of Engineering The University of Hong Kong Hong Kong Department of Diagnostic Radiology Singapore General Hospital Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Department of Radiology Changi General Hospital Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Institute of Data Science National University of Singapore Singapore

Background: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax diagnoses made by DL models. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. Method: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of these explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template’s boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). Results: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. Conclusions: In the context of pneumothorax diagnos

关键词： Diagnosis

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Incoherent Digital Holography using Spiral Rotating Point Spread Functions Created by Double-helix Beams

Incoherent Digital Holography using Spiral Rotating Point Sp...

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Digital Holography and Three-Dimensional Imaging, DH 2022

作者： Dubey, Nitin Anand, Vijayakumar Khonina, Svetlana Kumar, Ravi Reddy, Andra Naresh Kumar Rosen, Joseph School of Electrical and Computer Engineering Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva8410501 Israel Institute of Physics University of Tartu Tartu50411 Estonia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn MelbourneVIC3122 Australia Samara National Research University Samara443086 Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences Samara443001 Russia Hee Photonic Labs RigaLV-1002 Latvia Department of Physics of Complex Systems Weizmann Institute of Science Rehovot7610001 Israel

A new incoherent 3D imaging system with a rotating point spread function has been developed. Different computational reconstruction methods such as non-linear reconstruction and the Lucy-Richardson-Rosen algorithm wer... 详细信息

ISBN: (纸本)9781957171128

关键词： Optical transfer function

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A Critical Investigation in Analysing the Main Factors Influencing the Network Security Strategies By Applying the Big Data Context Using Multivariate Analysis of Variance (MANOVA) 2

A Critical Investigation in Analysing the Main Factors Influ...

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2nd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Khanna, M.Rajesh Arun, M. Othman, Bestoon Abdulmaged Johar, Siti Sarawati Bhattacharya, Sumanta Suribabu, Potnuri Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology India School of Computing Kalasalingam Academy of Research and Education Krishnankoil India Knowledge University-kurdistan Region- Iraq. Business Administration Koya Technical Institute Erbil Polytechnic University College of Administration & Financial Sciences Accounting Department Erbil Iraq Universiti Tun Hussein Onn Malaysia Centre for General Studies and Co-curricular Department of Social Science Johor Darul Takzim Batu Pahat86400 Malaysia Department of Science and Technology and Biotechnology MAKAUT India Department of Computer Science and Engineering Jntu Hyderabad India

ISBN: (纸本)9781665437899

Organizations may use big data to analyze enormous and complicated information in order to uncover hidden trends and correlations, as well as to distribute information across a network. Information that travels via an organization's network is frequently delicate, necessitating the use of a platform that is both efficient and safe. As a result, network security has indeed been elevated to the beginning stages of consideration in the big data world. Consequently, network security systems must handle with a large amount of data that is both complicated and varied in order to detect and prevent possible threats in actual time. Unfortunately, since these systems are frequently built on old methodologies, they are problematic when it comes to protecting large amounts of information. In this article, we will primarily discuss network security and information protection solutions for large amounts of information. First and foremost, we discuss the elements that influence network security architecture in the Big Data world. Following that, we will discuss several big data solutions that allow for the protection of data throughout networks, as well as new study findings. © 2022 IEEE.

关键词： Big data

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A Contrastive Learning-based PPC-UNet for Colorectal Histopathology Whole Slide Image Segmentation

A Contrastive Learning-based PPC-UNet for Colorectal Histopa...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Yuxuan Li, Xuechen Liu, Jingxin Shen, Linlin Sun, Kunming Wang, Suying AI Research Centre for Medical Image Analysis and Diagnosis Marshall Laboratory of Biomedical Engineering Shenzhen University School of Computer Science and Software Engineering Shenzhen China Xi'An Jiaotong-Liverpool University Histo Pathology Diagnostic Center Shanghai School of AI and Advanced Computing Suzhou China Histo Pathology Diagnostic Center Shanghai China Ningbo Diagnostic Pathology Center Department of Pathology Ningbo China

ISBN: (纸本)9781665401265

Colorectal cancer (CRC) is the third most common cancer and is usually diagnosed using colonoscopy and biopsy. Diagnosis of pathological biopsy requires professional knowledge and technology. computer-aided gland and lesion segmentation systems have been proposed to help pathologists in diagnosis of CRC. However, to the best of our knowledge, there has not been a literature work trying to segment different levels of intraepithelial neoplasia in CRC pathological image. To reduce such a research gap, in this paper, we firstly collect a colorectal cancer biopsy histopathology whole slide image (WSI) dataset, named Histo-CRC Biopsy dataset, for algorithm evaluation. We further propose a PPC-UNet network to segment high level, low level intraepithelial neoplasia and normal tissues. The proposed PPC-UNet consists of two modules i.e., a UNet-based network for segmentation, and a pixel-to-propagation consistency (PPC) contrastive learning-based network for UNet encoder pre-training. As the important feature can be learned from the unannotated data during pre-training, our approach can consistently improve the Dice of UNet by around 2% when different ratios of the training data are labeled. © 2021 IEEE.

关键词： Image segmentation

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Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

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IEEE International Symposium on Biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

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Industrial Internet of Things (IIoT): Transforming Industrial Operations through Advanced Connectivity

Industrial Internet of Things (IIoT): Transforming Industria...

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Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S. Viveka Vivek Justus Sangeetha J Desidi Narsimha Reddy V. Samuthira Pandi S. Banumathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE S. A. Engineering College Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The Industrial Internet of Things (IIoT) seamlessly integrates operational technology with modern connectivity technologies, ushering in a new age in industrial operations. This paper examines IIoT's revolutionary potential in changing industrial landscapes through smart sensors, actuators, and intelligent devices that enable real-time data sharing and process automation. This paper uses case studies from numerous sectors to demonstrate how IIoT may improve operational efficiency, downtime, and decision-making. Manufacturing, energy, and logistics are discussed. The report also examines IIoT technologies like cloud, edge, and machine learning techniques. These help enterprises exploit enormous data sets for predictive maintenance, supply chain optimization, and product quality. The article discusses IIoT's risks and challenges, including data privacy, cybersecurity, and the need for skilled personnel. A detailed research shows IIoT's strategic importance in the fourth industrial revolution. It also discusses how industrial networking will enable smart and sustainable operations.

关键词： Actuators Supply chains Real-time systems Product design Telecommunication computing Quality assessment Personnel Intelligent sensors Industrial Internet of Things Predictive maintenance

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Optimizing Handwritten Numeral Recognition for English and Devanagari Using MNIST and CPAR Data

Optimizing Handwritten Numeral Recognition for English and D...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Kamred Udham Singh Ankit Kumar Gaurav Kumar Teekam Singh Tanupriya Choudhury Ketan Kotecha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University Mathura UP India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun CSE Dept. Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

In this paper, a technique for the categorization of offline handwritten digits in the Devanagari script as well as the Roman script (English numbers) is presented. Finding out how to get the best possible recognition results while concurrently working with several scripts is the primary purpose of this study. The approach that has been described makes use of an easy profiling method for the extraction of features, together with an innovative implementation of Linear Discriminant Analysis (LDA), and a neural network architecture that has been adapted specifically for numeral classification. An exhaustive series of trials including 36,000 examples of handwritten numerals were carried out as part of the evaluation of the effectiveness of this approach. These samples were chosen at random from the CPAR datasets, with 22,000 being put to use in the construction of the training set and the remaining 14,000 being assigned to the role of the test set. In addition, the well-known MNIST database was used, which provided 60,000 samples for training purposes and 10,000 samples specifically for testing. It was determined via thorough testing that the LDA classifier produced variable results. These varying results are essential in emphasising the success of the method as well as possible areas for improvement for the method. According to the results, this strategy, which combines profile-based feature extraction with advanced classification algorithms, has the potential to lead to major breakthroughs in the area of handwritten number identification.

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IoT Security Frameworks: A Comparative Review with a Focus on Privacy

IoT Security Frameworks: A Comparative Review with a Focus o...

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Cyber Resilience (ICCR), International Conference on

作者： Abduljaleel Al-Hasnawi Yitong Niu Jamal F. Tawfeq Manas Ranjan Pradhan Mohammed Salahat Taher M. Ghazal Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Communication Technology Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq School of Aeronautical Engineering AnYang University Anyang China Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Computer Science Department Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The privacy of sensitive data within the Internet of Things (IoT) is at risk due to potential privacy violations. These violations can occur through illegal access by external entities, as well as inadvertent disclosures or intentional attacks by those within the system who exceed their authorized access privileges. Furthermore, there needs to be more control experienced by data owners regarding their sensitive data as it transitions from their domain to other entities within the Internet of Things (IoT) ecosystem. Numerous methods have been presented in the academic literature over the past few years to effectively tackle the privacy concerns associated with the Internet of Things (IoT). This paper aims to classify existing literature by organizing it according to the specific technology or technique employed to safeguard privacy. The classification encompasses four primary areas of privacy solutions, specifically, solutions based on encryption, solutions based on identity management, solutions based on policy enforcement, and solutions focused on self-protection. The paper examines various state-of-the-art solutions within each category. It begins by providing a broad comparison between these solutions and our work. Subsequently, it focuses on a specific comparison of the privacy objectives of the offered PEFM solutions.

关键词： Privacy Data privacy Reviews Ecosystems Encryption Internet of Things Resilience

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Transition Metal Layer Substitution in Mo2cs2 Mxene for Improving Li Ion Surface Kinetics

SSRN

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SSRN 2023年

作者： Papadopoulou, Konstantina A. Christopoulos, Stavros-Richard G. Department of Physics and Astronomy Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QL United Kingdom Faculty of Engineering Environment and Computing Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Computer Science School of Computing and Engineering University of Huddersfield HuddersfieldHD4 6DJ United Kingdom Centre for Computational Science and Mathematical Modelling Coventry University CoventryCV1 2TU United Kingdom

We study the adsorption and mobility of a Li ion on the surface of the Mo2CS2 MXene by means of Density Functional Theory. We find that by substituting the Mo atoms of the upper MXene layer with V, the mobility of the Li ion can be improved up to 95% while the material retains its metallic character. This fact indicates that MoVCS2 is a promising candidate for anode electrode in Li-ion batteries, where the materials need to be conductive and the Li ion to have small migration barrier. © 2023, The Authors. All rights reserved.

关键词： Diffusion barriers

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