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Neumaier graphs with few eigenvalues

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arXiv 2020年

作者： Abiad, Aida de Bruyn, Bart D’haeseleer, Jozefien Koolen, Jack H. Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Belgium Department of Mathematics: Algebra and Geometry Ghent University Belgium School of Mathematical Sciences University of Science and Technology of China Hefei China CAS Wu Wen-Tsun Key Laboratory of Mathematics University of Science and Technology of China Hefei China

A Neumaier graph is a non-complete edge-regular graph containing a regular clique. In this paper we give some sufficient and necessary conditions for a Neumaier graph to be strongly regular. Further we show that there does not exist Neumaier graphs with exactly four distinct eigenvalues. We also determine the Neumaier graphs with smallest eigenvalue −2. Copyright © 2020, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

Correction to “Noise Adaptation Generative Adversarial Network for Medical Image Analysis”

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IEEE Transactions on Medical Imaging 2020年第7期39卷 2566-2567页

作者： Tianyang Miller Jun Cheng Huazhu Fu Zaiwang Gu Yuting Xiao Kang Zhou Shenghua Gao Ru Zheng Jiang Liu College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing China Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China UBTECH Robotics CORP Ltd. Shenzhen China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Information Science and Technology ShanghaiTech University Shanghai China

In the above article [1] , Tables II , III , and V and Fig. 6 are incorrect. The correct images are provided below:

关键词： Generative adversarial networks Biomedical imaging Image analysis Image segmentation Biomedical engineering computer science Image color analysis

SAHRTA: A Supervisory-Based Adaptive Human-Robot Teaming Architecture

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arXiv 2020年

作者： Heard, Jamison Fortune, Julian Adams, Julie A. Dept. of Electrical Engineering Rochester Institute of Technology RochesterNY School of Electrical Engineering and Computer Science Oregon State University CorvallisOR Collaborative Robotics and Intelligent Systems Institute Oregon State University CorvallisOR

Supervisory-based human-robot teams are deployed in various dynamic and extreme environments (e.g., space exploration). Achieving high task performance in such environments is critical, as a mistake may lead to significant monetary loss or human injury. Task performance may be augmented by adapting the supervisory interface’s interactions or autonomy levels based on the human supervisor’s workload level, as workload is related to task performance. Typical adaptive systems rely solely on the human’s overall or cognitive workload state to select what adaptation strategy to implement;however, overall workload encompasses many dimensions (i.e., cognitive, physical, visual, auditory, and speech) called workload components. Selecting an appropriate adaptation strategy based on a complete human workload state (rather than a single workload dimension) may allow for more impactful adaptations that ensure high task performance. A Supervisory-Based Adaptive Human-Robot Teaming Architecture (SAHRTA) that selects an appropriate level of autonomy or system interaction based on a complete real-time multi-dimensional workload estimate and predicted future task performance is introduced. SAHRTA was shown to improve overall task performance in a physically expanded version of the NASA Multi-Attribute Task Battery. Copyright © 2020, The Authors. All rights reserved.

关键词： Adaptive systems

Safer Motion Planning of Steerable Needles via a Shaft-to-Tissue Force Model

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arXiv 2021年

作者： Bentley, Michael Rucker, Caleb Reddy, Chakravarthy Salzman, Oren Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States The Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KnoxvilleTN37996 United States Huntsman Cancer Institute and School of Medicine University of Utah Salt Lake CityUT84112 United States Department of Computer Science Technion - Israel Institute of Technology Technion City Haifa3200003 Israel

Steerable needles are capable of accurately targeting difficult-to-reach clinical sites in the body. By bending around sensitive anatomical structures, steerable needles have the potential to reduce the invasiveness of many medical procedures. However, inserting these needles with curved trajectories increases the risk of tissue damage due to perpendicular forces exerted on the surrounding tissue by the needle's shaft, potentially resulting in lateral shearing through tissue. Such forces can cause significant damage to surrounding tissue, negatively affecting patient outcomes. In this work, we derive a tissue and needle force model based on a Cosserat string formulation, which describes the normal forces and frictional forces along the shaft as a function of the planned needle path, friction model and parameters, and tip piercing force. We propose this new force model and associated cost function as a safer and more clinically relevant metric than those currently used in motion planning for steerable needles. We fit and validate our model through physical needle robot experiments in a gel phantom. We use this force model to define a bottleneck cost function for motion planning and evaluate it against the commonly used path-length cost function in hundreds of randomly generated 3-D environments. Plans generated with our force-based cost show a 62% reduction in the peak modeled tissue force with only a 0.07% increase in length on average compared to using the path-length cost in planning. Additionally, we demonstrate the ability to plan motions with our force-based cost function in a lung tumor biopsy scenario from a segmented computed tomography (CT) scan. By planning motions for the needle that aim to minimize the modeled needle-to-tissue force explicitly, our method plans needle paths that may reduce the risk of significant tissue damage while still reaching desired targets in the body. © 2021, CC BY.

关键词： Needles

ARUSH:Automated Runtime Universal Scanner for Hybrid Cloud computing

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ARUSH:Automated Runtime Universal Scanner for Hybrid Cloud c...

IEEE Globecom Workshops

作者： Jatin Luthra Arjun Choudhary Abhishek Sharma Dushantha Nalin K. Jayakody The LNM Institute of Information Technology Jaipur India Centre for Cyber Security Sardar Patel University of Police Security and Criminal Justice Jodhpur India Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka Sri Lanka School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Universidade Autónoma de Lisboa Lisboa Portugal Autonoma TechLab Lisboa Portugal

ISBN: (纸本)9781665423915

With the shift of leading companies to the cloud, so comes the risk of data leaks and breaches because of many possible misconfigurations in the setup of various cloud computing environments. Though many solutions have come up, each is platform-specific, and none of them targets multiple or hybrid cloud platforms all together. This work proposes one such solution where three major public cloud platforms are supported, and the main focus is on the storage services provided by them. Since there is no such open-source solution that supports multiple cloud platforms, the program in this work is supposed to provide such a solution. Each cloud platform provides native SDK libraries for issuing API calls to the platform. These API calls aim to query the currently employed configuration for the storage services at the very least if not change. The solution can combine all the libraries into a single driver program. The program can facilitate security management of storage accounts across multiple as well hybrid cloud services and opens possibilities for automation and routine security and audit checks.

关键词： Cloud computing Runtime Automation Security management Conferences Cloud computing security Companies

Bitcoin Price Forecasting: A Comparative Study Between Statistical and Machine Learning Methods

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Bitcoin Price Forecasting: A Comparative Study Between Stati...

International Conference on computer & Information science (ICCIS)

作者： Waddah Waheeb Habib Shah Mohammed Jabreel Domenec Puig School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Bukit Jalil Malaysia College of Computer Science King Khalid University Abha Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona Spain

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154688

This paper presents a comparative study between statistical and machine learning methods in forecasting Bitcoin's closing prices. Thirteen forecasting methods namely average, naive, drift, auto-regressive integrated moving-average, simple exponential smoothing (SES), Holt, and damped exponential smoothing, the average of SES, Holt and damped methods, exponential smoothing (ETS), bagged ETS, Theta, multilayer perceptron, and extreme learning machines (ELM) were used to forecast the closing prices for the next 14 days. The findings of this study are three folds. First, there are seven forecasting methods outperformed the naive method namely MLP, ELM, damped exponential smoothing, simple exponential smoothing, Theta, ETS, and ARIMA. Second, MLP and ELM showed better forecasting accuracy on both validation and out-of-sample data among the forecasting methods used in this study. Third, the size of the training data is essential factor that should be considered when training forecasting methods.

关键词： Forecasting Bitcoin Training data Smoothing methods Training Time series analysis Support vector machines

P-WAE: Generalized patch-wasserstein autoencoder for anomaly screening

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arXiv 2021年

作者： Zhang, Hui Chen, Yurong Wang, Yaonan Cao, Yihong Wu, Q.M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha Hunan410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University OntarioP7B 5E1 Canada Vector Institute TorontoM5G 1M1 Canada

To mitigate the inspector’s workload and improve the quality of the product, computer vision-based anomaly detection (AD) techniques are gradually deployed in real-world industrial scenarios. Recent anomaly analysis benchmarks progress to generative models. The aim is to model the defect-free distribution so that anomalies can be classified as out-of-distribution samples. Nevertheless, there are two disturbing factors that need researchers and deployers to prioritize: (i) the simplistic prior latent distribution inducing limited expressive capability;(ii) the collapsed mutual-dependent features resulting in poor generalization. In this paper, we propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to alleviate those challenges. In particular, a patch-wise variational inference model coupled with solving the jigsaw puzzle is designed, which is a simple yet effective way to increase the expressiveness and complexity of the latent manifold. This alleviates the blurry reconstruction problem. In addition, the Hilbert-Schmidt Independence Criterion (HSIC) bottleneck is introduced to constrain the over-regularization representation. Comprehensive experiments, conducted on the MVTec AD dataset, demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Anomaly detection

An Improved Big Data Analytics Architecture for Intruder Classification Using Machine Learning

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Security and Communication Networks 2023年第1期2023卷

作者： Muhammad Babar Sarah Kaleem Adnan Sohail Muhammad Asim Muhammad Usman Tariq Robotics and Internet of Things Lab Prince Sultan University Riyadh 11586 Saudi Arabiapsu.edu.sa Computing and Technology Department Iqra University Islamabad 44000 Pakistaniqra.edu.pk EIAS Data Science Lab Prince Sultan University Riyadh 11586 Saudi Arabiapsu.edu.sa School of Computer Science and Technology Guangdong University of Technology Guangzhou 510006 *** Abu Dhabi University Abu Dhabi 59911 UAEadu.ac.ae

The approval of retrieving information on the Internet originates several network securities matters. Intrusion recognition is a critical study in network security to spot unauthorized admission or occurrences on protected networks. Intrusion detection has a fully-fledged reputation in the current era. Research emphasizes several datasets to upsurge system precision and lessen the false-positive proportion. This article proposes a new intrusion detection system using big data analytics and deep learning to address some of the misuse and irregularity detection limitations. The proposed method could identify any odd activities in a network to recognize malicious or unauthorized action and permit a response during a confidentiality break. The proposed system utilizes the big data analytics platform based on parallel and distributed mechanisms. The parallel and distributed platforms improve the training time along with the accuracy. The experimentation appropriately classifies the information as either normal or abnormal. The proposed system has a recognition proportion of 96.11% that pointedly expands overall recognition accuracy related to existing strategies.

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