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Considerations for Using Artificial Intelligence to Manage Authorized Push Payment (APP) Scams

IEEE Engineering Management Review 2023年第3期51卷 166-179页

作者： Ma, Katelyn Wan Fei Dhot, Tarundeep Raza, Mohsen York University Science and Technology Studies Graduate Program TorontoONM3J 1P3 Canada Td Bank TorontoONM5K 1A2 Canada

Artificial Intelligence (AI)-based security intelligence modeling can be used to prevent, detect, and manage cyber threats. Data-driven AI solutions are currently undergoing rigorous research and design changes in their own field, but few scholars or practitioners frame authorized push payment (APP) scams as a unique cybersecurity concern, or tailor technical solutions based on local regulatory contexts. Drawing on a recent consultation publication by the UK Payment Systems Regulator on APP scams (November 2021), this article shows how AI can be leveraged to manage APP scams and explores some of the opportunities and risks one should consider when adopting such an approach. We highlight three scenarios: 1) Liability on payment service provider;2) Liability on payor;and 3) Liability on payor with substantial public sector involvement. These examples illustrate how sociotechnical systems can play a design role, and consequently assist industry leaders and engineering management in prioritizing investment focus, strategic approaches, and technical solutions. © 1973-2011 IEEE.

关键词： Cybersecurity

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Elevated temperature-driven coordinative reconstruction of an unsaturated single-Ni-atom structure with low valency on a polymer-derived matrix for the electrolytic oxygen evolution reaction

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Nanoscale 2024年第15期16卷 7467-7479页

作者： Patil, Rahul Rajput, Anubha Matsagar, Babasaheb M. Chen, Norman C. R. Ujihara, Masaki Salunkhe, Rahul R. Yadav, Praveen Wu, Kevin C.-W. Chakraborty, Biswarup Dutta, Saikat Electrochemical Energy & Sensor Research Laboratory Amity Institute of Click Chemistry Research & Studies Amity University Noida India Department of Chemistry Indian Institute of Technology New Delhi India Department of Chemical Engineering National Taiwan University Taipei10617 Taiwan Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica Taiwan National Taiwan University Taiwan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei Taiwan Materials Research Laboratory Department of Physics Indian Institute of Technology Jammu India Synchrotron X-ray Facility Raja Ramanna Centre for Advanced Technology Rajendra Nagar Madhya Pradesh 452013 Indore India

A high-temperature pyrolysis-controlled coordination reconstruction resulted in a single-Ni-atom structure with a Ni-Nx-C structural unit (x = N atom coordinated to Ni). Pyrolysis of Ni-phen@ZIF-8-RF at 700 °C resulted in NiNP-NC-700 with predominantly Ni nanoparticles. Upon elevating the pyrolysis temperature from 700 to 900 °C, a coordination reconstruction offers Ni-Nx atomic sites in NiSA-NC-900. A combined investigation with X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and soft X-ray L3-edge spectroscopy suggests the stabilization of low-valent Niδ+ (0 −2 was achieved. The cell potential recorded with Pt(−)/(+)NiSA-NC-900/NF was much lower than that obtained for other cells, i.e., Pt(−)/NF and NF(−)/(+)NF, which enhances the potentials of low-valent NiSAs for insightful understanding of the OER. At a constant applied potential of 1.61 V (vs. RHE) for 12 h, an small increase in current for initial 0.6 h followed by a constant current depicts the fair stability of catalyst for 12 h. Our results offer an insightful angle into the OER with a coordinatively reconstructed single-Ni-atom structure at lower valency ( © 2024 The Royal Society of Chemistry.

关键词： Reaction kinetics

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Parent-Child Interaction in Game-Based Learning 16

Parent-Child Interaction in Game-Based Learning

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Li, Ming-Chaun Tsai, Chin-Chung Hou, Huei-Tse Graduate Institute of Library Information and Archival Studies National Chengchi University Taipei City Taiwan Program of Learning Sciences and Institutions for Research Excellence in Learning Sciences National Taiwan Normal University Taipei City Taiwan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei City Taiwan

ISBN: (纸本)9798350377903

This study is aimed to explore parent-child interaction in a game-based learning activity. The participants were 10 parent-child dyads. The children were 10.4 years old in average and the parents were 45.4 years old. Parent-child interaction behaviors and discourses during gameplay were recorded by both digital video recorders and screen recording software. To explore the patterns of parent-child interactions, a quantitative content analysis was conducted. The parent-child interaction of the game success group and the game fail group were compared. According to the results of quantitative content analysis, the parents of the success group showed more observation and teaching behaviors, and the parents of the fail group mainly observed and gave feedbacks to their children. © 2024 IEEE.

关键词： Federated learning

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Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil)

Natural Hazards Research

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Natural Hazards Research 2025年第2期5卷 247-261页

作者： Alcântara, Enner Baião, Cheila Flávia Guimarães, Yasmim Carvalho Mantovani, José Roberto Marengo, José Antonio Institute of Science and Technology São Paulo State University (Unesp) SP São José Dos Campos Brazil Graduate Program in Natural Disasters (Unesp/CEMADEN) SP São José Dos Campos Brazil Graduate School of International Studies Korea University Seoul South Korea

This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions. © 2024 National Institute of Natural Hazards, Ministry of Emergency Management of China

关键词： Brazil Land use change Landslide susceptibility Machine learning São sebastião

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Effect of Sn addition on electrochemical activity of ribbons of Mg-Al-Ca anode material for rechargeable magnesium batteries fabricated by rapid solidification

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Keikinzoku/Journal of Japan Institute of Light Metals 2025年第1期75卷 9-13页

作者： Fukuta, Yoshitaka Kikura, Kensei Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan Graduate School of Science and Engineering University of Toyama Toyama-shi Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawagun Toyama Japan Saitama Industrial technology Center Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

Magnesium-based batteries are potential candidates for next-generation rechargeable batteries due to the divalent nature of magnesium cations and the natural abundance of magnesium resources. In this study, the electrochemical activity of Mg-6mass%Al-3mass%Ca anodes combined with Sn was investigated. Thin ribbons of the anode specimens were prepared by a single-roll rapid solidification method. The results showed that the electrochemical activity increased upon adding 1 mass% Sn, while it decreased for 3 mass% Sn. Based on the results of an X-ray diffraction analysis, the addition of Sn led to the formation of Mg2Sn and Ca2Sn phases, which increased the lattice distortion. Scanning electron microscopy observations indicated that these secondary phases were homogeneously distributed for 1 mass% Sn addition, while they were coarsely distributed for 3 mass% Sn. © 2025 The Japan Institute of Light Metals.

关键词： X ray diffraction analysis

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Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam

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Asian Pacific Journal of Tropical Medicine 2024年第3期17卷 119-128,I0001,I0002页

作者： Huynh Ngoc Linh Nguyen The Tan Le Thi Minh Thu Nguyen Tu Loan Nguyen Thi To Uyen Le Thanh Thao Trang Truong Thanh Nam Doan Hoang Phu Faculty of Medicine Ca Mau Medical CollegeCa Mau 98000Vietnam Ca Mau Obstetrics and Pediatrics Hospital Ca Mau 98000Vietnam Faculty of Basic Science and Public Health Dong Thap Medical CollegeDong Thap 81000Vietnam Graduate University of Sciences and Technology Vietnam Academy of Science and TechnologyHa Noi 10000Vietnam Community Public Health Research Program School of Public HealthWalailak UniversityNakhon Si Thammarat 80160Thailand Faculty Public Health Can Tho University of Medicine and PharmacyCan Tho City 94000Vietnam Doctoral Program in Health Sciences College of Graduate StudiesWalailak UniversityNakhon Si Thammarat 80160Thailand

Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from ***:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was *** correlation coefficients assessed associations among pairs of long COVID ***,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and ***:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in *** symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two ***,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven ***:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.

关键词： Long COVID Prevalence Risk factors Children Adolescent Vietnam

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Understanding Data Valuation: Valuing Google's Data Assets

IEEE Transactions on Technology and Society

引用

IEEE Transactions on technology and Society 2024年第2期5卷 183-190页

作者： Birch, Kean Marquis, Sarah Cavalcante Silva, Guilherme York University Institute for Technoscience and Society TorontoONM3J 1P3 Canada University of Ottawa Centre for Law Technology and Society OttawaONK1H 8M5 Canada York University Graduate Program in Science and Technology Studies TorontoONM3J 1P3 Canada

Digital personal data are increasingly understood as a key asset in our digital economies. But how should we value such data? Numerous policymakers, regulators, and stakeholders are trying to work out how to manage the collection, use, and valuation of data in order to balance the advantages and disadvantages of its collection and use. The negative implications of data practices may include privacy loss, data breaches, or declining market competition, while social and economic benefits include improved service delivery, more efficient welfare systems, or better products. Increasingly, data are conceptualized as an asset. To understand the value of data as an asset means understanding how data are configured as an asset;data value does not reflect ownership and property rights per se, but rather diverse modes of access and use restrictions (usually delineated by opaque contractual agreements). Data are increasingly controlled by a few, large digital technology firms, especially so-called 'Big Tech' firms. In this paper, we use Google as a case study of how Big Tech firms configure and value digital data as an asset. We analyse how Google understands, frames, values, and monetizes the data they collect from users. We qualitatively analyse an extensive dataset of financial documentary materials produced by and about Google to identify the different modes of access and use restrictions that Google deploys to turn digital data into a valuable asset. We conclude that, despite being highly ambiguous, Google's approach to data value focuses on monetizing users. © 2024 The Authors.

关键词： Data privacy

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Majorization-minimization Bregman proximal gradient algorithms for nonnegative matrix factorization with the Kullback–Leibler divergence

arXiv

引用

arXiv 2024年

作者： Takahashi, Shota Tanaka, Mirai Ikeda, Shiro Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan The Institute of Statistical Mathematics Tokyo Japan Statistical Science Program The Graduate University for Advanced Studies Tokyo Japan

Nonnegative matrix factorization (NMF) is a popular method in machine learning and signal processing to decompose a given nonnegative matrix into two nonnegative matrices. In this paper, to solve NMF, we propose new algorithms, called majorization-minimization Bregman proximal gradient algorithm (MMBPG) and MMBPG with extrapolation (MMBPGe). MMBPG and MMBPGe minimize an auxiliary function majorizing the Kullback–Leibler (KL) divergence loss by the existing Bregman proximal gradient algorithms. While existing KL-based NMF methods update each variable alternately, proposed algorithms update all variables simultaneously. The proposed MMBPG and MMBPGe are equipped with a separable Bregman distance that satisfies the smooth adaptable property and that makes its subproblem solvable in closed forms. We also proved that even though these algorithms are designed to minimize an auxiliary function, MMBPG and MMBPGe monotonically decrease the objective function and a potential function, respectively. Using this fact, we establish that a sequence generated by MMBPG(e) globally converges to a Karush–Kuhn–Tucker (KKT) point. In numerical experiments, we compared proposed algorithms with existing algorithms on synthetic *** Codes 90C26, 49M37, 15A23 Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

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Efficiency Analysis of Major Airlines: Exploring the Operational Performance Determinants in Aviation

Efficiency Analysis of Major Airlines: Exploring the Operati...

引用

International Symposium for Production Research, ISPR 2023

作者： Çınar Yalçın, Kübra Kuşakcı, Ali Osman Tatoğlu, Ekrem School of Graduate Studies Air Transport Management MSc Program Ibn Haldun University Istanbul Turkey School of Business Department of Management Ibn Haldun University Istanbul Turkey Gulf University for Science & Technology College of Business Administration Department of Business Administration Hawally Kuwait

ISBN: (纸本)9783031539909

This paper evaluates the performances and dynamics of productivity changes of 38 airlines from different regions of the world between 2015 and 2019, assessing the effects of business model, alliance membership, the economic development level of the home country, and size on the operational performance of the airlines. In this regard, first, constant returns to scale (CRS) and variable returns to scale (VRS) input-oriented data envelopment analysis (DEA) models with three inputs and two outputs are implemented. The input-oriented CRS model super-efficiency DEA is also used to discriminate between efficient airlines. The Malmquist Productivity Index (MPI) estimates efficiency dynamics across time. Further, the determinants of operational efficiencies are assessed by applying Mann-Whitney rank-sum tests. The analysis results show that most airlines suffer from scale inefficiency. MPI signifies productivity deterioration in airlines’ performance. It is found that low-cost carriers (LCC) have performed better than full-service carriers and alliance member airlines are less efficient than non-member airlines. Additionally, developed country airlines show better performance than their emerging country counterparts. Lastly, the size of airlines measured in available seat kilometers (ASK) is found to have no statistically significant effect on performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Efficiency

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Effect of chemical composition and microstructure on the thermal conductivity of Mg-Al-Ca alloys

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Keikinzoku/Journal of Japan Institute of Light Metals 2024年第1期74卷 9-13页

作者： Kirimoto, Yuichi Kondo, Natsuki Ito, Mizuki Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Department of Nano and Functional Material Science Graduate School of Science and Engineering for Education University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan School of Sustainable Design Materials and Engineering University of Toyama Toyama-shi Toyama Japan Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama Toyama-shi Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawa-gun Toyama Japan Saitama Industrial Technology Center Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

In recent years, the increasing demand for electric vehicles, digital devices such as PCs and smartphones, and renewable energy storage systems has created a need for higher-performance batteries. Accordingly, the thermal conductivity of the case that houses the battery is becoming increasingly important. In this study, compositions with high thermal conductivity and good castability were investigated, focusing on Mg-Al-Ca alloys, and the relationship between the two was examined through thermal conductivity measurements and microstructural observations. As a result, the Mg-6%Al-4%Ca alloy was optimal in terms of castability and thermal conductivity. It was confirmed that a lower Al solid solution capacity and a higher purity of the matrix phase are important to improve the thermal conductivity. It is also suggested that the second phase, platelet Al2Ca, is likely to be recognized and enriched in the matrix, contributing to the improvement of thermal conductivity. The amount of Ca solid solution in the matrix was suggested to be reduced by the enrichment of Al in the matrix. The Ca solid solution in the matrix was found to be less effective in reducing thermal conductivity than the Al solid solution. © 2024 The Japan Institute of Light Metals.

关键词： Magnesium alloys

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