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An ethics assessment list for geoinformation ecosystems: revisiting the integrated geospatial information framework of the United Nations

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International Journal of Digital Earth 2023年第1期16卷 1418-1438页

作者： Stefano Calzati Bastiaan van Loenen Urban Data Science Department of UrbanismTechnology University of DelftDelftThe Netherlands

To achieve sustainable development goals,georeferenced data and geographic information systems play a crucial ***,the way in which these data and systems are summoned upon rests on positivist assumptions which overlook both epistemological and ethical *** is epitomized by the integrated geospatial information framework(IGIF)of the United Nations,which,from the perspective of sustainable development,aims to provide guidance for the management of geoinformation and related tools,considering these as mirrors of the physical *** this respect,the article has three main ***,it delivers an epistemological and ethical critique of the IGIF,by highlighting its internal ***,it suggests how the IGIF and similar geoinformation initiatives can benefit from an ethical reflection that allows to conduct georeferenced practices in a fair(er)***,it designs an ethics assessment list for self-evaluating the ethical robustness of geoinformation initiatives as ecosystems.

关键词： Sustainable development goals geoinformation GISs data ecosystem data ethics integrated geospatial information framework

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Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT:A Survey

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Chinese Journal of Electronics 2024年第4期33卷 859-874页

作者： Jiwei HUANG Fangzheng LIU Jianbing ZHANG Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Department of Computer Science and Technology China University of Petroleum

With the evolvement of the Internet of things(IoT), mobile edge computing(MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements on quality of service(QoS) have been growing rapidly, making QoS a multi-dimensional concept including several attributes, such as performance, dependability, energy efficiency, and economic factors. To guarantee the QoS of IoT applications, theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT,which have attracted significant attention from both academia and industry. This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT, and provide insights and guidance for future research in this field. This paper summarizes the multi-dimensional and multi-attribute QoS metrics in Io T scenarios, and then several QoS evaluation methods are presented. For QoS optimization, the main research problems in this field are summarized, and optimization models as well as their corresponding solutions are elaborated. We take notice of the booming of edge intelligence in artificial intelligence-empowered Io T scenarios, and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization. We discuss the challenges and future research directions.

关键词： Internet of things Mobile edge computing Quality of service Quality of service evaluation Quality of service optimization

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Computing Persistent Homology by Spanning Trees and Critical Simplices

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Research 2024年第2期2023卷 519-527页

作者： Dinghua Shi Zhifeng Chen Chuang Ma Guanrong Chen Department of Mathematics Cllege of ScienceShanghai UniversityShanghaiChina School of Big Data Fuzhou University of International Studies and TradeFuzhouChina Department of Data Science and Big Data Technology School of InternetAnhui UniversityHefeiChina Department of Electrical Engineering City University of Hong KongHong KongChina

Topological data analysis can extract effective information from higher-dimensional *** mathematical basis is persistent *** persistent homology can calculate topological features at different spatiotemporal scales of the dataset,that is,establishing the integrated taxonomic relation among points,lines,and ***,the simplicial network composed of all-order simplices in a simplicial complex is *** the sequence of nested simplicial subnetworks can be regarded as a discrete Morse function from the simplicial network to real values,a method based on the concept of critical simplices can be developed by searching all-order spanning *** this new method,not only the Morse function values with the theoretical minimum number of critical simplices can be obtained,but also the Betti numbers and composition of all-order cavities in the simplicial network can be calculated ***,this method is used to analyze some examples and compared with other methods,showing its effectiveness and feasibility.

关键词： Morse establishing topological

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Flexible,efficient,and accurate tests for epidemics

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science China Mathematics 2024年第8期67卷 1881-1898页

作者： Linjiajie Fang Bing-Yi Jing Shen Ling Qiyue Wang Qing Yang Data Science and Analytics Thrust The Hong Kong University of Science and TechnologyHong KongChina Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China Department of Mathematics and Statistics Dalhousie UniversityHalifaxNS B 3H 4R 2Canada Department of Mathematics The Hong Kong University of Science and TechnologyHong KongChina

Group testing involves discovering a small subset of distinguished subjects from a large population while efficiently reducing the total number of *** has been widely used for industrial testing,information technology,and biology,especially epidemic ***,in reality,are noisy for the presence of false *** tests are accurate but time-consuming,while others are cheaper but less *** which test to use is constrained by various considerations,such as availability,cost,accuracy,and *** this paper,we propose flexible,efficient,and accurate tests(FEATs).FEATs are based on group testing with simple but careful designs by incorporating ideas such as close contact cliques and repeated *** could dramatically improve the efficiency or accuracy of existing *** example,for accurate but slow tests,the FEAT can improve efficiency multiple times without compromising *** the other hand,for fast but inaccurate tests,the FEAT can sharply reduce the false-negative rate(FNR)and significantly increase *** justifications are *** point out some scenarios where the FEAT can be effectively employed.

关键词： epidemiology control blood testing testing efficiency testing accuracy

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Software defect density prediction using grey system theory and fuzzy logic

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Soft Computing 2024年第21期28卷 12897-12916页

作者： Azzeh, Mohammad Elsheikh, Yousef Alqasrawi, Yousef Department of Data Science Princess Sumaya University for Technology Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

Defect Density (DD) is a cornerstone metric in software quality assessment, influencing decisions across quality planning, testing strategies, and resource allocation. However, inherent uncertainties within software module data significantly impede accurate prediction of DD. This paper proposes a novel predictive framework that leverages the strengths of grey system theory and fuzzy logic to address data imprecision in software defect measurement. Our approach introduces a fuzzy grey relational similarity metric that capitalizes on the benefits of both fuzzy logic membership functions and grey relational analysis for similarity assessment. We perform a rigorous evaluation comparing our model with eight established machine learning algorithms across 28 publicly available software datasets. The proposed framework demonstrates superior performance, particularly in scenarios characterized by high sparsity levels in the DD variable. We further analyze the model's efficacy across varying data sparsity levels, categorizing datasets into four groups based on sparsity ratios. Our findings reveal the model's dominance over alternative prediction methods in datasets exhibiting high and very high sparsity. Notably, traditional techniques like multi-linear regression and multi-layer perceptron exhibit limitations in handling such challenges within this specific problem domain and data landscape. Conversely, ensemble learning techniques emerge as viable alternatives for datasets with lower sparsity levels. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： System theory

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Modelling Mobile-X Architecture for Offloading in Mobile Edge Computing

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Intelligent Automation & Soft Computing 2023年第4期36卷 617-632页

作者： G.Pandiyan E.Sasikala Department of Data Science and Business System SRM Institute of Science and TechnologyChennaiIndia

Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close *** edge servers are not allocated efﬁciently according to the dynamic nature of the *** leads to processing delay,and the tasks are dropped due to time *** researchersﬁnd it difﬁcult and complex to determine the ofﬂoading decision because of uncertain load dynamic condition over the edge *** challenge relies on the ofﬂoad-ing decision on selection of edge nodes for ofﬂoading in a centralized *** study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge ***,a task-ofﬂoading problem needs to be formulated based on the communication and *** ofﬂoading decision problem is solved by deep analysis on taskﬂow in the network and feedback from the devices on edge *** signiﬁcance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory(b-LSTM).The simulation is done in the Edgecloudsim environment,and the outcomes show the signiﬁcance of the proposed *** processing time of the anticipated model is 6.6 *** following perfor-mance metrics,improved server utilization,the ratio of the dropped task,and number of ofﬂoading tasks are evaluated and compared with existing learning *** proposed model shows a better trade-off compared to existing approaches.

关键词： Mobile edge computing cloud ofﬂoading delay task drop reinforcement learning mobile-X architecture

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Incorporating Relative Error Criterion to Conformal Prediction for Positive data

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Communications in Mathematics and Statistics 2024年第1期12卷 157-186页

作者： Yuxiang Luo Yang Wei Zhouping Li Bing-Yi Jing School of Mathematics and Statistics Center for Data ScienceLanzhou UniversityLanzhou 730000People’s Republic of China Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055People’s Republic of China

Positive data are very common in many scientific fields and applications;for these data,it is known that estimation and inference based on relative error criterion are superior to that of absolute error *** prediction problems,conformal prediction provides a useful framework to construct flexible prediction intervals based on hypothesis testing,which has been actively studied in the past *** view of the advantages of the relative error criterion for regression problems with positive responses,in this paper,we combine the relative error criterion(REC)with conformal prediction to develop a novel REC-based predictive inference method to construct prediction intervals for the positive *** proposed method satisfies the finite sample global coverage guarantee and to some extent achieves the local *** conduct extensive simulation studies and two real data analysis to demonstrate the competitiveness of the new proposed method.

关键词： Conformal prediction Neural network Positive responses Relative error criterion Regression data

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FedLRes: enhancing lung cancer detection using federated learning with convolution neural network (ResNet50)

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Neural Computing and Applications 2025年第14期37卷 8273-8284页

作者： Usharani, C. Selvapandian, A. Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering Gnanamani College of Technology Namakkal India

Lung cancer remains a leading global cause of mortality, necessitating efficient early detection. Lung cancer image analysis plays a pivotal role, yet current manual segmentation by oncologists is laborious. Our innovative (FedLRes) approach proposes a comprehensive system for automated diagnoses of lung cancer using federated learning with ResNet50. The lung cancer dataset from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases was gathered by the IQ-OTH/NCCD. The dataset consists of 1097 images, split into training (822 images) and validation (275 images) sets. The training set includes 312 normal, 420 malignant, and 90 benign cases. The training set's data are further enhanced by using data augmentation techniques, which are then applied to normalise images differences before being fed into the federated learning with ResNet50 architecture. This approach combines deep learning models trained on different datasets allowing for improvising accuracy and generalisation. The federated learning approach enables the use of distributed data while ensuring data privacy and security. The proposed approach compared with different state of art algorithms. Through rigorous experimentation, our system showcases remarkable advancements a classification accuracy of 99.40%. This innovative approach, utilising 3D input CT scan data, offers a potent and precise tool for early detection and effective treatment strategies against the scourge of lung cancer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Thrackles, Superthrackles and the Hanani-Tutte Theorem

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Journal of Graph Algorithms and Applications 2024年第1期28卷 95-127页

作者： Dehkordi, Hooman R. Farr, Graham Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia

A thrackle is a drawing of a graph in which any two vertex-disjoint edges cross exactly once and incident edges do not cross. A graph that has a thrackle drawing is thracklable. In the past three decades, mathematicians investigated a number of variations of thrackles by relaxing or changing the required number of crossings on edges or restricting the placement of vertices on the surface (e.g., restricting to outerdrawings, in which vertices are restricted to a boundary curve of the surface). A graph is superthracklable if it can be drawn so that any two edges cross exactly once. We provide a simple proof for the following: a graph can be drawn on the plane so that every two edges cross an odd number of times if and only if it is superthracklable on the plane. A graph is outersuperthracklable if it has a drawing on a disc in which every vertex is on the boundary of the disc and every pair of edges cross exactly once. We introduce some natural generalisations of outersuperthracklable graphs and we show that these classes of graphs are equivalent. Lastly, using the Hanani-Tutte characterisation of planar graphs we show that for any surface Σ, there is a relationship between the class of graphs that are not embed-dable on Σ and the class of graphs that are not superthracklable with respect to Σ. More specifically, we show how to construct, from any forbidden minor G for embed-dability in Σ, two infinite families of graphs that are not superthracklable with respect to Σ. This sheds further light on the relationship between some of Archdeacon and Stor’s forbidden configurations for superthrackles and Kuratowski’s forbidden minors for planarity. © 2024, Brown University. All rights reserved.

关键词： Graphic methods

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