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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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X-ray quantification of oxygen groups on diamond surfaces for quantum applications

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Materials for Quantum Technology 2023年第4期3卷 045901-045901页

作者： Dontschuk, N. Rodgers, L.V.H. Chou, J.P. Evans, D.A. O’Donnell, K.M. Johnson, H.J. Tadich, A. Schenk, A.K. Gali, A. de Leon, N.P. Stacey, A. School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Physics National Changhua University of Education Changhua Taiwan Department of Physics Aberystwyth University AberystwythSY23 3BZ United Kingdom Royal Melbourne Hospital University of Melbourne ParkvilleVIC3010 Australia Australian Synchrotron 800 Blackburn Road ClaytonVIC3168 Australia School of Computing Engineering and Mathematical Sciences La Trobe University BundooraVIC3086 Australia Wigner Research Centre for Physics POB 49 BudapestH-1525 Hungary Department of Atomic Physics Budapest University of Technology and Economics Műegyetem rakpart 3. BudapestH-1111 Hungary Princeton Plasma Physics Laboratory 100 Stellarator Road PrincetonNJ08544 United States School of Science RMIT University MelbourneVIC3000 Australia

Identifying the surface chemistry of diamond materials is increasingly important for device applications, especially quantum sensors. Oxygen-related termination species are widely used because they are naturally abundant, chemically stable, and compatible with stable nitrogen vacancy centres near the diamond surface. Diamond surfaces host a mixture of oxygen-related species, and the precise chemistry and relative coverage of different species can lead to dramatically different electronic properties, with direct consequences for near-surface quantum sensors. However, it is challenging to unambiguously identify the different groups or quantify the relative surface coverage. Here we show that a combination of x-ray absorption and photoelectron spectroscopies can be used to quantitatively identify the coverage of carbonyl functional groups on the { 100 } diamond surface. Using this method we reveal an unexpectedly high fraction of carbonyl groups ( > 9%) on a wide range of sample surfaces. Furthermore, through a combination of ab initio calculations and spectroscopic studies of engineered surfaces, we reveal unexpected complexities in the x-ray spectroscopy of oxygen terminated diamond surfaces. Of particular note, we find the binding energies of carbonyl-related groups on diamond differs significantly from other organic systems, likely resulting in previous misestimation of carbonyl fractions on diamond surfaces. © 2023 The Author(s). Published by IOP Publishing Ltd.

关键词： Oxygen

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Forecasting Precipitable Water Vapor Using LSTMs

Forecasting Precipitable Water Vapor Using LSTMs

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USNC-URSI Radio science Meeting (Joint with AP-S Symposium)

作者： Mayank Jain Shilpa Manandhar Yee Hui Lee Stefan Winkler Soumyabrata Dev School of Computer Science University College Dublin Ireland School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore School of Computing National University of Singapore (NUS) ADAPT SFI Research Centre Dublin Ireland

ISBN: (数字)9781946815088

ISBN: (纸本)9781728161976

Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.

关键词： Forecasting Predictive models Lead Global Positioning System Delays Training Benchmark testing

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Hydrometeorological Network of Namal Valley, Pakistan

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Scientific Data 2025年第1期12卷 1-10页

作者： Muhammad Sheraz Talha Manzoor Malik Jahan Khan Usman Ali Hamza Tariq Shabeh ul Hasson Centre for Water Informatics and Technology Lahore University of Management Sciences (LUMS) Lahore Pakistan Department of Computer Science Lahore University of Management Sciences (LUMS) Lahore Pakistan School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Faculty of Computing Engineering and Media De Montfort University Leicester LE1 9BH UK HAREME Lab Institute of Geography CEN Universität Hamburg Bundesstrasse 55 Hamburg 20146 Germany

This article describes data collected from a sensor network deployed in the catchment of The Namal Lake, situated in Mianwali district, Pakistan. The lake serves as a reservoir for the Namal Dam. The sensor network consists of 14 unique observation points in a watershed spanning a 425 sq km area. The data includes measurements of precipitation at a resolution of 0.246 mm at all sensor locations and measurements of water level at a 1 mm resolution at 7 locations (including the lake itself). The first sensor installation was made in November 2020 with the last one deployed in July 2022. The associated time series for both variables is generated at a 10-minute sampling frequency. Multiple aggregations of the dataset have been made available at hourly, daily, and monthly timescales. The data can be used for calibrating hydrological models for various applications such as flood prediction, watershed management, and decision-making for reservoir operation. The dataset also contributes to understanding the hydrometeorological processes in similar small catchments at the Monsoon Margin.

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Visualization Approach for Malware Classification with ResNeXt

Visualization Approach for Malware Classification with ResNe...

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Congress on Evolutionary Computation

作者： Jin Ho Go Tony Jan Manoranjan Mohanty Om Prakash Patel Deepak Puthal Mukesh Prasad Centre for Artificial Intelligence School of Computer Science FEIT University of Technology Sydney Australia School of Information Technology and Engineering Melbourne Institute of Technology Australia Center for Forensic Science Faculty of Science University of Technology Sydney Australia Department of Computer Science and Engineering Mahindra Ecole Centrale Hyderabad India School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

The Internet has resulted in cyber-threats and cyber-crimes, which can occur anywhere at any time. Among various cyber threats, modern malware with applied metamorphosis and polymorphic technology is a concern as it can proliferate to advanced variants from its original shape. The typical malware analysis methods, including signature-based approach, remain vulnerable to such advanced variants. This paper proposes a visualization-based approach for malware analysis using the state-of-the-art Convolution Neural Network (CNN) model such as ResNeXt, which had achieved outstanding performance in image classifications with competitive computational complexity. The proposed method transforms the attributes of raw malware binary executable files to greyscale images for further analysis by well-established deep learning models. The greyscale images, which result of data transformation for visualization, are classified using ResNeXt. The experiment results show that the proposed solution achieves 98.32% and 98.86% of accuracy in malware classification on Malimg dataset and modified Malimg dataset, respectively. The proposed method outperforms other comparable methods in terms of classification accuracy and requires similar level of computational power.

关键词： Malware Data visualization computer crime Visualization Degradation Adaptation models Australia

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An Adaptive Multi-objective Salp Swarm Algorithm for Efficient Demand Side Management

An Adaptive Multi-objective Salp Swarm Algorithm for Efficie...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Zezheng Zhao Chunqiu Xia Lian Chi Xiaomin Chang Wei Li Ting Yang Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Business School Nanjing University of Information Science and Technology China Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information Engineering Tianjin University China

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

With the continuous growth in population and energy demands more attention has been paid to energy consumption issues in residential environments. At the user-end, the home energy management system (HEMS) has been proposed as a cost-effective solution to reduce the electricity cost in households, while maintaining users' comfort and reducing the pressure on energy providers. However, it is a challenge to design a cost-effective scheduling strategies for HEMS which takes many objectives into consideration while potentially benefiting both users and providers. In our work, we propose a new approach named adaptive multi-objective salp swarm algorithm (AMSSA) based on traditional multi-objective salp swarm algorithm (MSSA) to realise a multi-objective optimisation approach for the power scheduling problem. AMSSA not only fulfils the trade-off among users' comfort, electricity cost and peak to average ratio (PAR), but also enhances the convergence speed for the overall optimisation process. Moreover, we also set up a testbed by using smart appliances and implemented our design on an edge-based energy management system. The experiment results demonstrated a reduction in both electricity cost (47.55%) and PAR (45.73%), compared with the case without a scheduling scheme.

关键词： Sociology Peak to average power ratio Sensor systems Scheduling Energy management systems Statistics Optimization

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An Empirical Study on Robustness of DNNs with Out-of-Distribution Awareness

An Empirical Study on Robustness of DNNs with Out-of-Distrib...

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Asia-Pacific Conference on Software engineering

作者： Lingjun Zhou Bing Yu David Berend Xiaofei Xie Xiaohong Li Jianjun Zhao Xusheng Liu 1College of Intelligence and Computing Tianjin University Tianjin China Faculty of Information Science and Electrical Engineering Kyushu University Japan School of Computer Science and Engineering Nanyang Technological University Singapore Information Operations Centre Customer Service Centre of State Grid Corporation of China

ISBN: (数字)9781728195537

ISBN: (纸本)9781728195544

The state-of-the-art deep neural network (DNN) achieves impressive performance on the input that is similar to training data. However, it fails to make reasonable decisions on the input that is quite different from training data, i.e., out-of-distribution (OOD) examples. Although many techniques have been proposed to detect OOD examples in recent years, it is still a lack of a systematic study about the effectiveness and robustness of different techniques as well as the performance of OOD-aware DNN models. In this paper, we conduct a comprehensive study to unveil the mystery of current OOD detection techniques, and investigate the differences between OOD-unaware/-aware DNNs in model performance, robustness, and uncertainty. We first compare the effectiveness of existing detection techniques and identify the best one. Then, evasion attacks are performed to evaluate the robustness of techniques. Furthermore, we compare the accuracy and robustness between OOD-unaware/-aware DNNs. At last, we study the uncertainty of different models on various kinds of data. Empirical results show OOD-aware detection modules have better performance and are more robust against random noises and evasion attacks. OOD-awareness seldom degrades the accuracy of DNN models in training/test datasets. In contrast, it makes the DNN model more robust against adversarial attacks and noisy inputs. Our study calls for attention to the development of OOD-aware DNN models and the necessity to take data distribution into account when robust and reliable DNN models are desired.

关键词： Training Deep learning Uncertainty Systematics Training data Robustness Data models

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Indoor Navigation with Deep Reinforcement Learning

Indoor Navigation with Deep Reinforcement Learning

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International Conference on Inventive Computation Technologies (ICICT)

作者： Vijayalakshmi A Bakale Yeshwanth Kumar V S Vivekanand C Roodagi Yashaswini N Kulkarni Mahesh S Patil Satyadhyan Chickerur School of Computer Science and Engineering K L E Technological University Hubballi Karnataka INDIA Centre for High Performance Computing K L E Technological University Hubballi Karnataka INDIA

ISBN: (数字)9781728146850

ISBN: (纸本)9781728146867

Indoor navigation solutions are essential for navigating within the building where GPS based solutions cannot be used. The applications of indoor navigations are robotics, drones, and few gaming apps. Deep reinforcement learning algorithms are used for building indoor navigation solutions, but such algorithms are computationally intensive and practically infeasible. But it is feasible to use reinforcement learning by reducing the scope and indoor scene representation information to propose an indoor navigation solution, which is the focus of the proposed work. The proposed work uses deep learning to detect objects in the given indoor scene and find a path using deep reinforcement learning. The proposed solution gives the navigation path in 2 minutes 10 seconds approximately, which is comparable with previous works. The authors also indicate the research work to be carried out in the future.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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B-VNF: Blockchain-enhanced Architecture for VNF Orchestration in MEC-5G Networks

B-VNF: Blockchain-enhanced Architecture for VNF Orchestratio...

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2020 IEEE 3rd 5G World Forum (5GWF)

作者： Raaj Anand Mishra Anshuman Kalla Kaustubh Shukla Avishek Nag Madhusanka Liyanage School of Computing and Information Technology Manipal University Jaipur India School of Electrical and Electronic Engineering University College Dublin Ireland School of Computer Science University College Dublin Ireland Centre for Wireless Communications University of Oulu Finland

ISBN: (数字)9781728172996

ISBN: (纸本)9781728173009

The roll-out of 5G technology will nurture the realization of broadband, ultra-reliable, and zero latency services. Network Function Virtualization (NFV) and Multi-Access Edge computing (MEC) are among the key enablers for 5G. The synergy between NFV and MEC allows migration of Virtual Network Functions (VNF) from cloud to the edge of the network thereby adding agility to the softwarized 5G networks. The overall orchestration of VNF includes, but is not limited to, processing VNF requests, selecting appropriate VNF, migrating VNF from cloud to MEC, instantiating migrated VNF at MEC, settling payment according to a VNF's usage, maintaining VNF's reputation, etc. The orchestration is not foolproof and raises doubts about its trustworthiness. To address all the existing issues in a unified manner, we leverage Blockchain technology as yet another enabling technology for MEC-enabled 5G. Thus, we propose a Blockchain-enhanced architecture for secure VNF orchestration such that issues like authenticity, integrity, confidentiality, reputation, payment transfer, and many more are resolved. To furnish a Proof-of-Concept (PoC), we develop a prototypical DApp (Decentralized Application) using Ethereum Blockchain and Suricata as an exemplar VNF. Further, we discuss the strong resiliency of the proposed architecture against numerous well-known attacks.

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