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arXiv 2024年

作者： Sahni, Yuvraj Cao, Jiannong Yang, Lei Wang, Shengwei The Department of Building Environment and Energy Engineering The Hong Kong Polytechnic University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong The School of Software Engineering South China University of Technology Guangzhou China

Collaborative edge computing has become a popular paradigm where edge devices collaborate by sharing resources. Data dissemination is a fundamental problem in CEC to decide what data is transmitted from which device and how. Existing works on data dissemination have not focused on coflow scheduling in CEC, which involves deciding the order of flows within and across coflows at network links. Coflow implies a set of parallel flows with a shared objective. The existing works on coflow scheduling in data centers usually assume a non-blocking switch and do not consider congestion at different links in the multi-hop path in CEC, leading to increased coflow completion time (CCT). Furthermore, existing works do not consider multiple flow sources that cannot be ignored, as data can have duplicate copies at different edge devices. This work formulates the multi-source coflow scheduling problem in CEC, which includes jointly deciding the source and flow ordering for multiple coflows to minimize the sum of CCT. This problem is shown to be NP-hard and challenging as each flow can have multiple dependent conflicts at multiple links. We propose a source and coflow-aware search and adjust (SCASA) heuristic that first provides an initial solution considering the coflow characteristics. SCASA further improves the initial solution using the source search and adjust heuristic by leveraging the knowledge of both coflows and network congestion at links. Evaluation done using simulation experiments shows that SCASA leads to up to 83% reduction in the sum of CCT compared to benchmarks without a joint solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Edge computing

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M3bunet:Mobile Mean Max Unet for Pancreas Segmentation on Ct-Scans

SSRN

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SSRN 2024年

作者： Juwita, Juwita Hassan, Ghulam Mubashar Akhtar, Naveed Datta, Amitava Department of Computer Science and Software Engineering The University of Western Australia Australia School of Computing and Information Systems The University of Melbourne Australia Department of Informatics Syiah Kuala University Banda Aceh Indonesia

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on NIH and MSD pancreas datasets. Our approach demonstrates performance improvement, achieving an average DSC value of up to 89.53±1.82 and IOU score of up to 81.16±0.03 for the NIH, and 88.60 ±1.48 DSC and 79.90±2.19 IOU for the MSD. © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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Data-Driven Estimation of Effectiveness of COVID-19 Non-pharmaceutical Intervention Policies

Data-Driven Estimation of Effectiveness of COVID-19 Non-phar...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Mahajan, Yash Islam, Sheikh Rabiul Amin, Mohammad Ruhul Karmaker Santu, Shubhra Kanti Auburn University Computer Science and Software Engineering Auburn United States University of Hartford Department of Computing Sciences Connecticut United States Fordham University Computer and Information Science New York United States

ISBN: (纸本)9781665480451

Non-pharmaceutical Interventions (NPIs), such as Stay-at-Home, and Face-Mask-Mandate, are essential components of the public health response to contain an outbreak like COVID-19. However, it is very challenging to quantify the individual or joint effectiveness of NPIs and their impact on people from different racial and ethnic groups or communities in general. Therefore, in this paper, we study the following two research questions: 1) How can we quantitatively estimate the effectiveness of different NPI policies pertaining to the COVID-19 pandemic?;and 2) Do these policies have considerably different effects on communities from different races and ethnicity? To answer these questions, we model the impact of an NPI as a joint function of stringency and effectiveness over a duration of time. Consequently, we propose a novel stringency function that can provide an estimate of how strictly an NPI was implemented on a particular day. Next, we applied two popular tree-based discriminative classifiers, considering the change in daily COVID cases and death counts as binary target variables, while using stringency values of different policies as independent features. Finally, we interpreted the learned feature weights as the effectiveness of COVID-19 NPIs. Our experimental results suggest that, at the country level, restaurant closures and stay-at-home policies were most effective in restricting the COVID-19 confirmed cases and death cases respectively;and overall, restaurant closing was most effective in hold-down of COVID-19 cases at individual community levels such as Asian, White, Black, AIAN and, NHPI. Additionally, we also performed a comparative analysis between race-specific effectiveness and country-level effectiveness to see whether different communities were impacted differently. Our findings suggest that the different policies impacted communities (race and ethnicity) differently. © 2022 IEEE.

关键词： COVID-19

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Cloud-Assisted Secure and Cost-Effective Authenticated Solution for Remote Wearable Health Monitoring System

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IEEE Transactions on Network Science and engineering 2023年第5期10卷 2710-2718页

作者： Mahmood, Khalid Obaidat, Mohammad S. Ghaffar, Zahid Alzahrani, Bander A. Shamshad, Salman Saleem, Muhammad Asad Hussain, Shafiq Lahore Campus Lahore55150 Pakistan University of Padua Department of Mathematics Padua35131 Italy University of Jordan KASIT Amman11942 Jordan University of Science and Technology Beijing100083 China Barani Institute of Sciences Department of Computer Science Sahiwal57000 Pakistan King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia The University of Lahore Department of Software Engineering Lahore54590 Pakistan COMSATS University Islamabad Department of Computer Science Sahiwal Campus Sahiwal57000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan

Security and privacy are the leading solicitude in cloud computing since users have restricted privilege on the data maintained by distinct service providers at remote locations. The situation becomes more strenuous when a huge amount of data is sensed and produced by wearable devices as it is usually considered more sensitive and heterogeneous. The majority of existing authenticated solutions for such a challenging environment either suffer due to high computation and communication costs or due to the fact that they are susceptible to known cyber threats making them impractical for utilization. Therefore we introduced a cost-effective and secure authenticated scheme for the cloud-assisted remote wearable health monitoring system. Informal security proof is discussed to showcase the security strength of the proposed scheme against known security threats. After that a detailed performance analysis of the proposed scheme with related protocols reveals the fact that it can reduce the computation and communication costs by more than 37% and 40%, respectively. Moreover, our scheme offers some added security features like physical and desynchronization attacks prevention. © 2013 IEEE.

关键词： Authentication

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A Hybrid Transformer Topology for Distribution Network Voltage Regulation

A Hybrid Transformer Topology for Distribution Network Volta...

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2021 IEEE Southern Power Electronics Conference, SPEC 2021

作者： Power, Rupert Mithani, Alykhan Madawala, Udaya Baguley, Craig The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Auckland University of Technology Department of Engineering Computing and Mathematical Sciences Auckland New Zealand

ISBN: (纸本)9781665436236

Worldwide, distributed generation sources are being installed in increasing numbers within distribution networks. This poses new challenges for distribution network operators, including voltage fluctuations beyond permissible limits that can differ in different parts of the same distribution network. The Hybrid Transformer (HT) is an effective solution that can mitigate voltage fluctuations continuously, dynamically and in a local manner, through combining a standard distribution transformer with a power electronic converter. In this paper a retrofittable HT topology is proposed. It utilises a second, fractionally-rated line frequency transformer (LFT) to enable the use of low-voltage power electronics with low power ratings for reduced costs and complexity. The proposed topology is modelled and a scaled hardware prototype is used to verify theory and simulation results. The simulation and experimental results show the efficacy of the proposed topology, its applicability to mitigate voltage fluctuations, and its benefits over alternative approaches. © 2021 IEEE.

关键词： Distributed power generation

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M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans

arXiv

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arXiv 2024年

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on the widely known NIH pancreas dataset and MSD pancreas dataset. Our approach demonstrates a considerable performance improvement, achieving an average Dice Similarity Coefficient (DSC) value of up to 89.53%±1.82 and an Intersection Over Union (IOU) score of up to 81.16±0.03% for the NIH pancreas dataset, and 88.60%±1.48 DSC and 79.90%±2.19 IOU for the MSD Pancreas dataset. © 2024, CC BY-NC-SA.

关键词： Computerized tomography

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Database Scaling on Kubernetes 3

Database Scaling on Kubernetes

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3rd International Conference on Advancements in computing, ICAC 2021

作者： Perera, H.C.S. De Silva, T.S.D. Wasala, W.M.D.C. Rajapakshe, R.M.P.R.L. Kodagoda, N. Samaratunge, Udara Srimath S. Jayanandana, H.H.N.C. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe10115 Sri Lanka

ISBN: (纸本)9781665408622

Kubernetes is a hot topic in the field of software engineering and Distributed computing. When compared to previous methods, the principle underlying Kubernetes, which is containerization, has altered how applications are created and delivered. However, when considering the state, particularly the databases, with Kubernetes, there is a scalability and data synchronization barrier. The most frequently used approach is to host the database outside of Kubernetes and maintain connectivity with the cluster. Kubernetes inherent capabilities are sufficient for hosting databases. But that requires high domain knowledge to do the configurations and maintain the databases on Kubernetes. The purpose of this research is to fulfil that gap by introducing a solution for managing highly available databases on Kubernetes. The solution is limited to managing PostgreSQL databases on Kubernetes using auto-scaling. A novel algorithm is proposed for auto-scaling, as previous algorithms do not take database requests into account when determining the scaling need. The drawbacks of data synchronization and auto-scaling will be solved in this research, and the end user will be able to access the service without interruption for the majority of the time, as the final solution makes the database cluster highly available for the service layer. © 2021 IEEE.

关键词： Database systems

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KidSwatch: Children Development Observation System

KidSwatch: Children Development Observation System

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Advancements in computing (ICAC), International Conference on

作者： P.A.L.N Oshadhini K.A.D.H.O Siriwardana H.K Peshala Ranmini S.R Sandeepa Lokesha Weerasinghe Thamali Kelegama Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The kidSwatch is a mobile application designed specifically for grade 1 children aged six years. It serves as a comprehensive framework for observing and evaluating children's developmental progress. The application focuses on the main components of children's development: Cognitive development, Emotional development, Language development, and Learning development. By analyzing the results obtained from observing these four components, parents can gain valuable insights into their children's development within a matter of minutes. The kidSwatch employs a range of observational techniques, including direct observation, structured interviews, and standardized tests, to gather information on a child's talents, skills, and behaviors. Trained observers proficient in the kidSwatch framework carefully record and analyze the child's interactions, activities, problem-solving abilities, emotional expressions, and social interactions. Ultimately, the kidSwatch empowers parents with a quick and effective means of understanding and assessing their child's development. To satisfy the above features there are a Hand number detection algorithm, letter recognition algorithm, Emotion recognition algorithm, and voice-based answering algorithm They perform an accuracy between 75%-90% on CNN.

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Intrusion detection system for malicious traffic using evolutionary search algorithm

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Recent Advances in Computer Science and Communications 2021年第5期14卷 1381-1389页

作者： Al-Saqqa, Samar Al-Fayoumi, Mustafa Qasaimeh, Malik Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Science King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Department of Software Engineering King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan

Introduction: Intrusion detection systems play a key role in system security by identifying potential attacks and giving appropriate responses. As new attacks are always emerging, intrusion detection systems must adapt to these attacks, and more work is continuously needed to develop and propose new methods and techniques that can improve efficient and effective adaptive intrusion systems. Feature selection is one of the challenging areas that need more work because of its importance and impact on the performance of intrusion detection systems. This paper applies an evolutionary search algorithm in feature subset selection for intrusion detection systems. Methods: The evolutionary search algorithm for the feature subset selection is applied and two clas-sifiers are used, Naïve Bayes and decision tree J48, to evaluate system performance before and after features selection. NSL-KDD dataset and its subsets are used in all evaluation experiments. Results: The results show that feature selection using the evolutionary search algorithm enhances the intrusion detection system with respect to detection accuracy and detection of unknown attacks. Furthermore, time performance is achieved by reducing training time, which is reflected positively in overall system performance. Discussion: The evolutionary search applied to select IDS algorithm features can be developed by modifying and enhancing mutation and crossover operators and applying new enhanced techniques in the selection process, which can give better results and enhance the performance of intrusion detection for rare and complicated attacks. Conclusion: The evolutionary search algorithm is applied to find the best subset of features for the intrusion detection system. In conclusion, it is a promising approach to be used as a feature selection method for intrusion detection. The results showed better performance for the intrusion detection system in terms of accuracy and detection rate. © 2021 Bentham Science Publisher

关键词： Feature Selection

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Medical Diagnosis Using Machine Learning:A Statistical Review

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Computers, Materials & Continua 2021年第4期67卷 107-125页

作者： Kaustubh Arun Bhavsar Jimmy Singla Yasser D.Al-Otaibi Oh-Young Song Yousaf Bin Zikria Ali Kashif Bashir Lovely Professional University Jalandhar144411India Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Department Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUK

Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate *** learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease *** academic papers involving the use of machine learning for disease diagnosis have been increasingly getting ***,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this *** carry out the review,six different databases are *** and exclusion criteria are employed to limit the ***,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and *** the selected articles are analyzed to show the impact of ML methods in improving the disease *** ndings of this study show the most used ML methods and the most common diseases that are focused on by *** also shows the increase in use of machine learning for disease diagnosis over the *** results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.

关键词： Decision making disease diagnosis machine learning medical disciplines

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