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Accuracy of Measuring Relative Poverty: Using Hong Kong as an Example

Journal of Social Computing 2025年第1期6卷 51-62页

作者： Chiu, Dah Ming Wong, Yu Cheung Cheng, Stephen Chun Chung Ng, Felix Sai Kit Data Science Research Centre for Social Policies and Services Saint Francis University Hong Kong The Hong Kong Polytechnic University Department of Applied Social Sciences Hong Kong

Defining poverty based on relative income, intended to identify individuals who are significantly worse off than the mainstream living standard—is widely adopted by more developed countries and regions, such as those in the Organization for Economic Co-operation and Development (OECD). There are, however, different ways income is counted, sometimes only counting earnings (e.g., from employment), sometimes counting government transfers (e.g., social welfare distributions), sometimes counting that generated from savings and investments as well. While some simpler form of income may be used for calculating relative poverty for ease of measurement (or other practical considerations), the intention of the relative poverty definition should be based on full income from all sources (including assets). This paper studies a method for evaluating the inaccuracy caused by using a simpler (and easier to measure) income distribution and understanding where the inaccuracy comes from. We test our method by using a 2000-household dataset from Hong Kong Special Administrative Region, to evaluate the relative poverty approach once adopted there. We also recommend practical alternatives: focusing on economically active households only or using disposable income instead of market income. We show how much such alternatives can improve accuracy and explain why. © 2020 Tsinghua University Press.

关键词： Economics Social computing Accuracy Standards organizations Government Employment Focusing Investment

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From Text to Knowledge Graph: Comparing Relation Extraction Methods in a Practical Context

From Text to Knowledge Graph: Comparing Relation Extraction ...

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Joint of the ESWC 2024 Workshops and Tutorials, ESWC-JP 2024

作者： Bakker, Roos M. Di Scala, Daan L. TNO Netherlands Organisation for Applied Scientific Research Department Data Science Kampweg 55 Soesterberg3769 ZG Netherlands Leiden University Centre for Linguistics Leiden University Reuvensplaats 3-4 Leiden2311 BE Netherlands

Knowledge graphs provide structure and semantic context to unstructured data. Creating them is labour intensive: it requires a close collaboration of graph developers and domain experts. Therefore, previous work has made attempts to automate (parts of) this process, utilising information extraction methods. This paper presents a comparative analysis of methods for extracting relations, with the goal of automated knowledge graph extraction. The contributions of this paper are two-fold: 1) the creation of a small dataset containing different versions of a news message annotated with triples, and 2) a comprehensive comparison of relation extraction methods within the context of this dataset. The primary objective of this paper is to assess these methods within a real-life use case scenario, where the resulting graph should aspire to the quality standards achievable through manual development. Prior methodologies often relied on automatically extracted datasets and a limited range of relation types, consequently constraining the expressivity and richness of resulting graphs. Furthermore, these datasets typically feature short or simplified sentences, failing to reflect the complexity inherent in real-world texts like news messages or research papers. The results show that GPT models demonstrate superior performance compared to the other relation extraction methods we tested. However, in the qualitative analysis performed additionally to the evaluation metrics, it was noted that alternative approaches like REBEL and KnowGL exhibit strengths in leveraging external world knowledge to enrich the graph beyond the textual content alone. This finding underscores the importance of considering a variety of methods that not only excel in extracting relations directly from text but also incorporate supplementary knowledge sources to enhance the overall richness and depth of the resulting knowledge graph. © 2024 Copyright for this paper by its authors.

关键词： Knowledge Graph Extraction Knowledge Graphs Ontology Learning Relation Extraction

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Ontology Learning from Text: an Analysis on LLM Performance 3

Ontology Learning from Text: an Analysis on LLM Performance

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3rd International Workshop on Natural Language Processing for Knowledge Graph Creation, NLP4KGC 2024

作者： Bakker, Roos M. Di Scala, Daan L. De Boer, Maaike H. T. TNO Netherlands Organisation for Applied Scientific Research Department Data Science Kampweg 55 Soesterberg3769 ZG Netherlands Leiden University Centre for Linguistics Leiden University Reuvensplaats 3-4 Leiden2311 BE Netherlands

Ontologies provide a structured framework to represent and integrate domain knowledge. Developing them is a complex and time-consuming task, requiring domain expertise to ensure accuracy and consistency. Ontology learning aims to automate this process by learning full ontologies, or parts of them, from sources such as textual data. In this paper, we research the potential of Large Language Models (LLMs), specifically GPT-4o, in ontology learning, using a real-world use case. We introduce a manually constructed ontology based on knowledge in a news article, and compare it to ontologies extracted using three different prompting approaches over multiple runs. The resulting ontologies are evaluated both quantitatively and qualitatively, to ensure that differences in performance due to modelling choices are also considered. The results show that, while the LLM effectively identifies important classes and individuals, it often does not include properties between classes, and adds inconsistent and incorrect properties between individuals. Prompting on a sentence level leads to more correct individuals and properties, however, quantitative evaluation shows more hallucinations and incorrect triples. Despite these issues, LLMs advance previous ontology learning methods by considering classes, individuals, and properties as a whole, creating a more complete ontology rather than isolated elements. This provides a new perspective on ontology learning and highlights the potential of LLMs to offer a first version of an ontology or an extension to an existing one based on new information. © 2024 Copyright for this paper by its authors.

关键词： Contrastive Learning

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Enhancing high-school dropout identification: a collaborative approach integrating human and machine insights

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Discover Education 2024年第1期3卷 1-21页

作者： Bulut, Okan Wongvorachan, Tarid He, Surina Lee, Soo Centre for Research in Applied Measurement and Evaluation University of Alberta 6-110 Education Centre North 11210 87 Ave NW Edmonton T6G 2G5 AB Canada Measurement Evaluation and Data Science University of Alberta Edmonton AB Canada American Institutes for Research Arlington VA United States

Despite its proven success in various fields such as engineering, business, and healthcare, human–machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human–machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school dropout prediction serves as a case study for examining human–machine collaboration’s efficacy. Unlike previous research prioritizing high accuracy with immutable predictors, this study seeks to bridge gaps by identifying actionable factors for dropout prediction through a framework of human–machine collaboration. Utilizing a large dataset from the High School Longitudinal Study of 2009 (HSLS:09), two machine learning models were developed to predict 9th-grade students’ high school dropout history. Results indicated that the Random Forest algorithm outperformed the deep learning algorithm. Model explainability revealed the significance of actionable variables such as students’ GPA in the 9th grade, sense of school belonging, self-efficacy in mathematics and science, and immutable variables like socioeconomic status in predicting high school dropout history. The study concludes with discussions on the practical implications of human–machine partnerships for enhancing student success. © The Author(s) 2024.

关键词： Explainable AI High school dropout Human–machine collaboration Machine learning

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

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Computer Modeling in Engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Contrast Enhanced Mammography (CEM): is experience necessary? An inter and intra-reader agreement study for lesion classification and breast density

Contrast Enhanced Mammography (CEM): is experience necessary...

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Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment

作者： Altokhais, Reham S. Eiada, Riham Khoumais, Nuha Zeitouni, Leena Abunayyan, Deema Alfuraih, Abdulrahman Alghamdi, Bandar Alghamdi, Reem Harkness, Elaine F. Astley, Susan Division of informatics Imaging and Data science University of Manchester United Kingdom College of Applied Medical Sciences Radiological sciences King Saud University Saudi Arabia King Faisal Specialist Hospital & Research Centre Riyadh Saudi Arabia

ISBN: (纸本)9781510671621

Introduction: The aim of this study is to measure reader agreement for i) lesion classification and ii) breast density in Contrast Enhanced Mammography (CEM). Methods: Two experienced and two inexperienced CEM readers reported 60 examinations. Kappa was used to assess inter-reader agreement between experienced and inexperienced readers for lesion classification (benign/malignant) and breast density (dense/non-dense). Weighted kappa was used to assess agreement for BI-RADS categories (1-5) and BIRADS density (A-D). Intraclass correlation coefficient (ICC) measured agreement for breast density using Visual Analog scale (VAS). Intra-reader agreement for one experienced and one inexperienced reader was measured after a three month interval. Results: Agreement between experienced readers was substantial (κ=0.66) for benign/malignant, and moderate (κ=0.57) for BI-RADS categories. Agreement for inexperienced readers was moderate for benign/malignant and BI-RADS categories (κ=0.52, κ=0.47 respectively). Breast density (dense/non-dense) agreement was almost perfect for experienced readers (κ=0.83) and substantial for BI-RADS (κ=0.70). Inexperienced reader agreement was moderate for dense/non-dense (κ=0.50) and BIRADS (κ=0.49). ICC for VAS was moderate for experienced (ICC=0.60) and good (ICC=0.84) for inexperienced readers. Intra-reader agreement for benign/malignant classification was almost perfect for both experienced and inexperienced readers respectively (κ=0.83, κ=0.91). Conclusion: Experienced readers showed substantial agreement for lesion classification and almost perfect agreement for breast density. While inexperienced reader agreement was moderate for both lesion classification and breast density, their agreement for VAS was higher than experienced readers, suggesting that CEM may have a short learning curve and that radiologists could potentially be trained for CEM interpretation, which would help its implementation in other clinical practices in Saudi Arabia. ©

关键词： Computational electromagnetics

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Working but hungry: precarious employment and household food insecurity in Ghana

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International Review of Economics 2024年第4期71卷 875-916页

作者： Opoku, Alexander Nunoo, Jacob Kofinti, Raymond Elikplim Department of Economic Studies School of Economics University of Cape Coast Department of Applied Economics School of Economics University of Cape Coast Department of Data Science and Economic Policy School of Economics University of Cape Coast African Centre of Excellence for Inequality Research (ACEIR) University of Nairobi

Workers and food systems are integral to economic progress in Africa, where food insecurity is rising alongside exacerbating levels of precarious employment conditions. However, empirical literature remains scant on this relationship. This study examines the link between precarious employment and food insecurity in Ghana using the country’s first Annual Household Income and Expenditure Panel Survey. Precarious employment is a multidimensional index of income inadequacy, employment insecurity, lack of rights and protection, and an unsafe or unhealthy work environment, whereas food insecurity is measured using the Food Insecurity Experience Scale. The main finding, adjusted for endogeneity, indicates that precarious employment increases household food insecurity, a finding that is robust to alternative measures of precarious employment and food insecurity. We further find that precarious employment increases food insecurity more among female-headed households in rural areas. Relatively, employment insecurity has the biggest effect in increasing food insecurity followed by lack of rights and protection, income inadequacy, and unsafe or unhealthy work environment. These findings further demonstrate that health shocks and household asset accumulation are important channels through which precarious employment affects food insecurity. We suggest policies to mitigate health shocks and bolster household asset accumulation among workers in precarious employment to safeguard household food security. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Food insecurity Gender Ghana I32 I38 J21 O55 Precarious employment Rural

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Real-time Hand Gesture Recognition Using Python and Web Application 1

Real-time Hand Gesture Recognition Using Python and Web Appl...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Patel, Meenu Rao, Saksham Chauhan, Shweta Kumar, Bibek Chandigarh University Apex Institute of Technology CSE Punjab Mohali India University Centre for Research & Development Chandigarh University Apex Institute of Technology CSE Punjab Mohali India Symbiosis University of Applied Sciences School of Data Science Madhya Pradesh Indore India

ISBN: (纸本)9798350356816

The recognition and tracking of hand gestures are essential elements in human-computer interaction systems, providing intuitive control and facilitating interaction with a wide range of devices and applications. We have developed a hand gesture detection system using Python and JavaScript. Our system uses MediaPipe, an open-source framework for machine learning-based perception tasks, to accurately identify and classify hand gestures. MediaPipe handles hand tracking, interpreting the hand curl and movement of hand gestures with high precision. We've built the frontend interface using HTML, CSS, and JavaScript. By combining these technologies, we've created a user-friendly interface where you can interact with the system through hand gestures captured via a webcam. Additionally, we've added our custom machine-learning methods to detect and recognize various hand *** system has numerous potential applications, including hand-sign translation and gesture-based control for smart devices. By offering an efficient and accessible solution, our project advances human-computer interaction paradigms, enabling more natural and intuitive interactions between users and machines. © 2024 IEEE.

关键词： Gesture recognition

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Handwritten Digits Prediction System using Convolution Neural Network for MINIST dataset 1

Handwritten Digits Prediction System using Convolution Neura...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Chauhan, Shweta Kumar, Rahul Kumar, Bibek Apex Institute of Technology CSE University Centre for Research and Development Chandigarh University Mohali140413 India GLA University Mathura ECE Deaprtment Uttar Pardesh 281406 India Symbiosis University of Applied Sciences School of Data Science Madhya Pradesh Indore India

ISBN: (纸本)9798350356816

A dramatic change has been occurred in machine learning with the development of Artificial Neural Network (ANN), making it more artificially intelligent in recent years. In addition to surveillance, health, medicine, sports, robotics, and drones, deep learning is used in many other fields as well due to its diverse range of applications. The Convolutional Neural Network (CNN), which blends Artificial Neural Networks and current deep learning strategies, is at the center of phenomenal advances in deep learning. Using varying numbers of hidden layers and epochs, we investigate the variation in accuracy of CNN to classify handwritten numbers and make comparisons between the different accuracy levels. A stochastic gradient descent algorithm and backpropagation has been used to train the network model. This experiment has been designed to evaluate the performance of CNN using the MNIST dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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