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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Faris Alotibi David Tipper Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh PA USA School of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (纸本)9781665435413

The growth of wind power generation is accelerating worldwide. However, wind turbines need to be carefully controlled to maintain structural stability and optimal power generation. The state of the turbine and power generated is digitized and transmitted over communication networks for physical component monitoring, control, and data management. Motivated by creating power outages, financial loss, and physical wind turbine damage, attackers could craft and inject falsified network traffic to maliciously influence control actions. In this paper, we propose a physics-informed machine learning anomaly detection approach. The mechanism is utilized at each wind turbine, where generated power signals are validated based on laws of physics and incorporated into machine learning algorithm to detect anomalies. We show that the proposed combined physics and machine learning approach improves the accuracy of detecting falsification attacks compared with using machine learning alone.

关键词： Machine learning algorithms Machine learning Telecommunication traffic Wind farms Wind power generation Wind turbines Structural engineering

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Anterior Cruciate Ligament Classification in Knee MRI using Automated Pseudo-Masking

Anterior Cruciate Ligament Classification in Knee MRI using ...

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Medical Imaging 2023: Computer-Aided Diagnosis

作者： Dadsetan, Saba Albers, Marcio Weinstock, Allison Musahl, Volker Kitamura, Gene Arefan, Dooman Wu, Shandong Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA15213 United States Department of Radiology School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Biomedical Informatics School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Bioengineering School of Engineering University of Pittsburgh PittsburghPA15213 United States Department of Orthopedic Surgery School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Radiology Loma Linda University Medical Center Loma LindaCA92354 United States

ISBN: (纸本)9781510660359

Anterior cruciate ligament (ACL) is one of the most common injuries associated with sports. Knee osseous morphology can play a role in increased knee instability. Our hypothesis is that the morphological features of the knee, as seen in knee osseous morphology, can contribute to increased knee instability and, thus, increase the likelihood of ACL tear. To test this relationship, it is necessary to segment the femur and tibia bones and extract relevant imaging features. However, manual annotation of 3D medical images, such as on magnetic resonance imaging (MRI) scans, can be a time-consuming and challenging task. In this work, we propose an automated pipeline for creating pseudo-masks of the femur and tibia bones in knee MRI. Our approach involves unsupervised segmentation and deep learning models to classify ACL integrity (intact or torn). Our results demonstrate a high agreement between the automated pseudo-masks and a radiologist's manual segmentation, which also leads to comparable AUC values for the ACL integrity classification. © 2023 SPIE.

关键词： Magnetic resonance imaging

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Sas: Speculative Locality Aware Scheduling for I/O Intensive Scientific Analysis in Clouds

SSRN

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SSRN 2022年

作者： Zahir, Ali Anjum, Ashiq Srirama, Satish Narayana Buyya, Rajkumar Department of Informatics University of Leicester United Kingdom School of Computer and Information Sciences University of Hyderabad India Lab School of Computing and Information Systems The University of Melbourne Australia

The execution of data intensive analysis workflows in a multi-cloud environment, such as WLCG at CERN, requires a large amount of input data, which is stored in multiple storage elements. Minimizing the data access time can improve the overall efficiency of data analysis process. We have used Speculative Scheduling to optimize the analysis workflows by intelligently streaming data before a task arrives for execution. We propose an Event Server which is responsible for proactively providing input data to the workflow processes. Using locality aware scheduling and prefetching algorithms, it performs Speculative Scheduling on the basis of the evaluation of historic execution logs. ES learns about the incoming jobs ahead of time and makes use of intelligent data streaming to supply data to these jobs, thus reducing the overall scheduling and data access latencies and leading to significant improvements in the overall turnaround time. We have evaluated the proposed system using a large analysis workflow from High Energy Physics (HEP) by emulating the WLCG infrastructure in a controlled environment. The results have shown that by using speculative and locality aware scheduling techniques, 30% improvements can be achieved in the execution of workflows. © 2022, The Authors. All rights reserved.

关键词： Scheduling

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20 years of network community detection

arXiv

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arXiv 2022年

作者： Fortunato, Santo Newman, M.E.J. United States Luddy School of Informatics Computing and Engineering Indiana University United States Department of Physics Center for the Study of Complex Systems University of Michigan United States

A fundamental technical challenge in the analysis of network data is the automated discovery of communities - groups of nodes that are strongly connected or that share similar features or roles. In this commentary we ...

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Stages Classification on Cervical Cell Images: A Comparative Study 3

Stages Classification on Cervical Cell Images: A Comparative...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Azani Mustafa, Wan Irfan Noor, Mohamad Alquran, Hiam Md Ghani, Miharaini Fahri Hanafi, Hafizul Hidayah Che Lah, Noor Mundher Adnan, Myasar Hameed Abdul Hussein, Abbas Universiti Malaysia Perlis Faculty of Electrical Engineering Technology UniCITI Alam Campus Sungai Chuchuh Perlis Padang Besar Malaysia Yarmouk University Department of Biomedical Systems and Informatics Engineering Irbid Jordan Universiti Putra Malaysia Department of Communication Faculty of Modern Language and Communication Malaysia Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Perak Tanjong Malim Malaysia The Islamic University Najaf Faculty of Engineering Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

The cancer of the cervix is called cervical cancer. An element of a woman's womb is the cervix. Among other diseases that affect women, it came in at number four on the list. According to the World Health Organization's cancer report, there are currently roughly 10 million new cases of cancer recorded year, and by 2020, that number will have doubled to 20 million. With the right screening and awareness campaign, this number can be cut in half. A quarter of cancers are said to be brought on by infections, including hepatitis B, which is connected to liver cancer, and the human papillomavirus, which is connected to cervix cancer. Deep learning techniques have been successfully applied to a wide range of image classification tasks, and have the potential to be highly effective for cervical cell image classification as well. In this project, we propose to use a deep learning-based approach to classify cervical cell images into different categories, such as normal cells, abnormal cells, or cancerous cells. To achieve this goal, we will first pre-process the images to prepare them for analysis, and then extract relevant features. These features will be used to train a deep learning model, which will be fine-tuned and optimized for the specific task of cervical cell classification. In this project, transfer learning method will be by using pre-trained classifier such as ResNet-50, GoogLeNet and EfficientNet-b0. We will evaluate the performance of the model using metrics such as accuracy and compare our results to those obtained using traditional machine learning approaches. From this project, the highest accuracy achieved are 51.49%. The goal to develop a pre-trained classifier transfer learning can be used to accurately and reliably classify cervical cell images in a clinical setting are achieved. © 2023 IEEE.

关键词： Cancer Cell Cervical Classify Image

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GCN-LG-Trust Model for Attack Detection and Cluster Optimization in Underwater Wireless Sensor Networks

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IEEE Sensors Journal 2025年

作者： Jiang, Bin Feng, Jiacheng Cui, Xuerong Luo, Fei Wang, Huihui Helen Song, Houbing Herbert College of Oceanography and Space Informatics Qingdao266580 China Ministry of Natural Resources Technology Innovation Center for Maritime Silk Road Marine Resources and Environment Networked Observation Qingdao266580 China Great Bay University School of Computing and Information Technology Dongguan523000 China Northeastern University Computing Programs 1300 17th St N 1500 ArlingtonVA22209 United States Department of Information Systems BaltimoreMD21250 United States

The researchers focus on the security issues of underwater wireless sensor networks (UWSNs) due to the specificity of network environment. In recent years, researchers have proposed trust models to assess the reliability of each node in the network. However, nodes in UWSNs are usually managed in clusters. Existing trust model evaluation algorithms and model training methods are not well adapted to the network clustering structure. For this reason, we propose a trust model optimization based on graph convolutional network (GCN) and LightGBM for cluster management in this paper, which we named GCN-LG-Trust. This trust model designs a trust parameter estimation method based on the node activity characteristics of the cluster network. And the network trust evidence is used to train the trust model by GCN-LG deep learning method. The GCN-LG-Trust model is tested in single attack and hybrid attack scenarios. The experimental results show that the proposed method has better results in terms of trust evaluation and accuracy. © 2001-2012 IEEE.

关键词： Wireless sensor networks

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Measuring Spatio-Temporal Civil War Dimensions Using Community-Based Dynamic Network Representation (CoDNet)

TechRxiv

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TechRxiv 2022年

作者： Vega-Oliveros, Didier A. Koren, Ore Institute of Computing University of Campinas Brazil The Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States The Department of Political Science Indiana University Bloomington United States

Civil war exhibits complex geospatial trends over time, which may be missed by models that rely on count-based operationalizations. Here, the spatial and temporal correlation values of monthly civil war events are transformed into their influence degree symbol, which measures geospatial concentration, spread, and intensity of civil war. We then measure variation in these degrees over time to identify relevant spatio-temporal civil war aspects. The network model is constructed using 0.5 degree grid locations as nodes, counting nearby and over time connections. We then extract the temporal community structure behind the data. We use ground truth data to visualize how our measures correlate with observed patterns, thereby illustrating our method provides accurate depictions of geospatial civil war dynamics. We also evaluate the impact of several indicators highlighted by past research and our community-based spatio-temporal measures and comparing it to the preprocessed count indicator. Our findings indicate that the relationship between state capacity and climate stress show opposite correlations with civil war as those identified by studies that use count based indicators. Counterintuitively, our results show that conflict intensifies and spreads in locations where the state is stronger and where climate conditions are improved. © 2022, CC BY-NC-SA.

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling

A Comparison Study on Generative Adversarial Networks (GANs)...

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IEEE Conference on systems, Process & Control (ICSPC)

作者： Brandon Kah Wei Li Muhammed Basheer Jasser Bayan Issa Samuel-Soma M. Ajibade Hui Na Chua Richard T.K. Wong Muzaffar Hamzah Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Research Centre For Human-Machine Collaboration (HUMAC)School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria Department of Computer Engineering Istanbul Ticaret University Istanbul Turkiye Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model’s effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image.

关键词： Training Photography Measurement Web services Generative adversarial networks Transformers Satellite images Image restoration Particle swarm optimization Image reconstruction

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Perceptions of Beauty in Security Ceremonies

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Philosophy and Technology 2022年第3期35卷 1-34页

作者： Bella, Giampaolo Ophoff, Jacques Renaud, Karen Sempreboni, Diego Viganò, Luca Dipartimento di Informatica Università di Catania Catania Italy Division of Cybersecurity Abertay University Dundee United Kingdom University of Cape Town Cape Town South Africa Department of Computer and Information Sciences University of Strathclyde Glasgow United Kingdom Department of Information Systems Rhodes University Grahamstown South Africa School of Computing Science University of South Africa Pretoria South Africa Department of Informatics King’s College London London United Kingdom

When we use secure computer systems, we engage with carefully orchestrated and ordered interactions called “security ceremonies”, all of which exist to assure security. A great deal of attention has been paid to improving the usability of these ceremonies over the last two decades, to make them easier for end-users to engage with. Yet, usability improvements do not seem to have endeared end users to ceremonies. As a consequence, human actors might subvert the ceremony’s processes or avoid engaging with it. Here, we consider whether beautification could be one way of making ceremonies more appealing. To explore beautification in this context, we carried out three studies. Study 1 surveyed 250 participants to derive a wide range of potential dimensions of “beautiful ceremonies”. These statements were sorted into dominant themes and converted into statements, which fed into the second study, with 309 respondents, to reveal the dominant dimensions constituting beauty. Study 3 asked 41 participants to carry out a Q-sort, which revealed the ways that people combine the identified dimensions when characterising security ceremonies as “beautiful”. These studies have allowed us to pin down the perceived dimensions of beauty in the context of security ceremonies, and also to understand how people combine these dimensions in different ways in judging security ceremonies to be beautiful, confirming the old adage of beauty being “in the eye of the beholder”. We conclude by highlighting the constraints imposed by the overarching requirement for security to be maintained in the face of any usability improvements and beautification endeavours. © 2022, The Author(s).

关键词： Beauty Human-centred security Perception Security ceremonies

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Proposed New Framework Scheme for Path Loss in Wireless Body Area Network

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for Computer Science and Mathematics 2022年第1期3卷 11-21页

作者： Al-Barazanchi, Israa Niu, Yitong Abdulshaheed, Haider Rasheed Hashim, Wahidah Alkahtani, Ammar Ahmed Daghighi, Elika Jaaz, Zahraa A. Shawkat, Shihab A. Rauf, Hafiz Tayyab College of Computing and Informatics Universiti Tenaga Nasional Malaysia Belarusian-Russian University Mira Avenue 43 Mogilev212000 Belarus Computer Engineering Techniques Department Baghdad College of Economic Sciences University Baghdad Iraq Institute of Sustainable Energy Universiti Tenga Nasional Selangor Kajang43000 Malaysia Technical and Vocational University Tehran Iran College of Science Computer Department AlNahrain University Baghdad Iraq University of Samarra Samarra Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom

Recent technical developments in wi-fi networking, microelectronic integration and programming, sensors and the Internet have enabled us to create and enforce a range of new framework schemes to fulfil the necessities of healthcare-related wireless body area network (WBAN). WBAN sensors continually screen and measure patients' indispensable signs and symptoms, and relay them to scientific monitoring for diagnosis. WBAN has a range of applications, the most necessary of which is to help patients suffering diseases to stay alive. The quality instance is the coronary heart implant sensor, whose video display unit monitors coronary heart sign and continuously transmits it. This setup eliminates the need for patients to visit the medical doctor frequently. Instead, they can take a seat at home and acquire an analysis and prescription for the disease. Today, a sizable effort is being made to increase low-power sensors and gadgets for utility in WBAN. A new framework scheme that addresses route loss in WBAN and discusses its penalties in depth is endorsed in this paper. The new framework scheme is applied to three case scenarios to obtain parameters by measuring vital information about the human body. On-body and intrabody conversation simulations are conducted. On-body conversation findings show that the route loss between transmitter and receiver rises with growing distance and frequency. © 2022 College of Education, Al-Iraqia University. All Rights Reserved.

关键词： Heart

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