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Women and Children Nursing 2024年第1期2卷 1-8页

作者： Elliot Mbunge Garikayi Chemhaka Tafadzwa Dzinamarira Enos Moyo Stephen Fashoto Benhildah Muchemwa Jolly Buwerimwe Ester Petrus Department of Computer Science Faculty of Science and Engineering University of Eswatini Kwaluseni Campus Kwaluseni M201 South Africa Department of Statistics and Demography Faculty of Social Sciences University of Eswatini Kwaluseni Campus Kwaluseni M201 South Africa School of Health Systems and Public Health University of Pretoria Pretoria 0002 South Africa StellenBosch University Matieland 7602 Stellenbosch South Africa Software Department Faculty of ICT International University of Management Windhoek Namibia

Background Child mortality is an important measure of a population’s health status. It is included in the third sustainable development goal that aims to improve global health by reducing under-five mortality to at least as low as 25 per 1000 live births by 2030. The study determines the factors associated with under-five child mortality in Zimbabwe. Methods Cross-sectional secondary data from the 2015 Zimbabwe Demographic Health Survey (ZDHS) were analyzed. The sample included 5,806 women aged 15–49 years of reproductive age. The Chi-square test was used to analyze the association between child death and independent variables. We identified the individual and contextual factors associated with child deaths in Zimbabwe using the Cox proportional hazard model. Results The risks of under-five mortality were highest among children of first birth order (adjusted hazard ratio (aHR) = 2.37, P = 0.04), multiple births (aHR = 2.37, P = 0.04), mothers with primary or less maternal education (aHR = 1, Ref), mothers below 18 years old (aHR = 1, Ref), apostolic mothers (aHR = 2.90, P = 0.002), mothers who do not use contraceptives (aHR = 2.20, P < 0.001), formerly married women (aHR = 6.42, P = 0.005), women with 5 or more children (aHR = 15.84, P < 0.001), women who read newspapers less than once a week (aHR = 1.75, P = 0.13), and households that use high-polluting fuels (aHR = 1.92, P = 0.023). Conclusion This study establishes that child health, maternal, socioeconomic, household, and ecological factors are important determinants of under-five mortality in Zimbabwe. Understanding these determinants is crucial for designing effective interventions and policies to reduce child mortality rates. This requires comprehensive approaches such as improving access to healthcare, education, and basic sanitation facilities; prioritizing nutrition; providing clean water; enhancing poverty reduction and immunization; and promoting breastfeeding and social empo

关键词： Child mortality Survival analysis Hazard ratio Cox proportional model

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Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

arXiv

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arXiv 2023年

作者： Chu, Nam H. Van Huynh, Nguyen Nguyen, Diep N. Hoang, Dinh Thai Gong, Shimin Shu, Tao Dutkiewicz, Eryk Phan, Khoa T. School of Electrical and Data Engineering University of Technology Sydney Australia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology La Trobe University Melbourne Australia

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Backscattering

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Role of masks in mitigating viral spread on networks

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Physical Review E 2023年第1期108卷 014306-014306页

作者： Yurun Tian Anirudh Sridhar Chai Wah Wu Simon A. Levin Kathleen M. Carley H. Vincent Poor Osman Yağan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08544 USA Thomas J. Watson Research Center IBM Yorktown Heights New York 10598 USA Department of Ecology & Evolutionary Biology Princeton University Princeton New Jersey 08544 USA Software and Societal Systems School of Computer Science Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA

Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In this work, we provide a comprehensive quantitative analysis of the impact of mask-wearing. To this end, we propose a novel agent-based model of viral spread on networks where agents may either wear no mask or wear one of several types of masks with different properties (e.g., cloth or surgical). We derive analytical expressions for three key epidemiological quantities: The probability of emergence, the epidemic threshold, and the expected epidemic size. In particular, we show how the aforementioned quantities depend on the structure of the contact network, viral transmission dynamics, and the distribution of the different types of masks within the population. Through extensive simulations, we then investigate the impact of different allocations of masks within the population and tradeoffs between the outward efficiency and inward efficiency of the masks. Interestingly, we find that masks with high outward efficiency and low inward efficiency are most useful for controlling the spread in the early stages of an epidemic, while masks with high inward efficiency but low outward efficiency are most useful in reducing the size of an already large spread. Last, we study whether degree-based mask allocation is more effective in reducing the probability of epidemic as well as epidemic size compared to random allocation. The result echoes the previous findings that mitigation strategies should differ based on the stage of the spreading process, focusing on source control before the epidemic emerges and on self-protection after the emergence.

关键词： Cascades Epidemic Percolation phase transition Spreading Multilayer & multiplex networks Poisson degree distribution networks Random graphs Social networks SIR model Spreading models Uncorrelated percolation

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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Transformative Applications and Key Challenges of Generative AI

Transformative Applications and Key Challenges of Generative...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Attila Kovari Jozsef Katona Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Department of Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI.

关键词： Power engineering Accuracy Generative AI Education Chatbots Market research Problem-solving Reliability

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Use of digital platforms for enhancing academic performance. Systematic Review

Use of digital platforms for enhancing academic performance....

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Cristina Anamaria Costescu Mălina Șogor Sabina Pălimariu Jozsef Katona Ioana Tufar Carla Maria Avram Special Education Department Babeș-Bolyai University Cluj-Napoca Romania Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills.

关键词： Performance evaluation Power engineering Databases Personal digital devices Tablet computers Libraries

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A 3d convolutional neural network for bacterial image classification 3rd

A 3d convolutional neural network for bacterial image classi...

引用

3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019

作者： Mhathesh, T.S.R. Andrew, J. Martin Sagayam, K. Henesey, Lawrence Department of Bioinformatics Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Systems and Software Engineering School of Engineering Blekinge Institute of Technology Karlskrona Sweden

ISBN: (纸本)9789811552847

Identification and analysis of biological microscopy images need high focus and years of experience to master the art. The rise of deep neural networks enables analyst to achieve the desired results with reduced time and cost. Light sheet fluorescence microscopies are one of the types of 3D microcopy images. Processing microscopy images is tedious process as it consists of low-level features. It is necessary to use proper image processing techniques to extract the low-level features of the biological microscopy images. Deep neural networks (DNN) are efficient in extracting the features of images and able to classify with high accuracy. Convolutional neural networks (CNN) are one of the types of neural networks that can provide promising results with less error rates. The ability of CNN to extract the low-level features of images makes it popular for image classification. In this paper, a CNN-based 3D bacterial image classification is proposed. 3D images contain more in-depth features than 2D images. The proposed CNN model is trained on 3D light sheet fluorescence microscopy images of larval zebrafish. The proposed CNN model classifies the bacterial and non-bacterial images effectively. Intense experimental analyses are carried out to find the optimal complexity and to get better classification accuracy. The proposed model provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc. The details of network architecture, regularization, and hyperparameter optimization techniques are also presented. © Springer Nature Singapore Pte Ltd 2021.

关键词： Image classification

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A Perspective on the Role of Human Behaviors in software Development: Voice and Silence

arXiv

引用

arXiv 2023年

作者： Sánchez-Gordón, Mary Colomo-Palacios, Ricardo Akbar, Muhammad Azeem Holone, Monica Kristiansen Department of Computer Science and Communication Øsfold University College Halden Norway Escuela Técnica Superior de Ingenieros Informáticos Universidad Politécnica de Madrid Madrid Spain Software Engineering Department and Systems LUT University Lappeenranta Finland

Context: Most software companies strive to have high-performing teams and mitigate withdrawal behaviors like being present but unproductive. In this context, psychological safety and developers’ perceived impact are suggested as potential drivers of voice and silence behaviors. However, understanding these social aspects of software development entails the incorporation of social science theories . Objective: This study aims to empirically demonstrate whether such a new theory about voice and silence at work actually applies to the software development context. Method: We plan to use a survey questionnaire design. This study will collect data from software development teams and analyze the result using structural equation modeling (SEM) technique. It can contribute to extent of the body of knowledge about the topic. Copyright © 2023, The Authors. All rights reserved.

关键词： Social aspects

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Authentic Learning on DevOps Security with Labware: Git Hooks To Facilitate Automated Security Static Analysis

Authentic Learning on DevOps Security with Labware: Git Hook...

引用

IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Md Mostafizur Rahman MD Abdul Barek Mst Shapna Akter Abm Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Information Technology University of West Florida Florida USA Department of Intelligent Systems and Robotics University of West Florida Florida USA Center for Cyber Security University of West Florida Florida USA Dept. of Computer Science and Software Engineering Auburn University USA Tuskegee University Alabama USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through prac-tical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities.

关键词： Learning systems DevOps Codes Education Static analysis Dynamic scheduling software

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Fast Symbolic Algorithms for Omega-Regular Games under Strong Transition Fairness

arXiv

引用

arXiv 2022年

作者： Banerjee, Tamajit Majumdar, Rupak Mallik, Kaushik Schmuck, Anne-Kathrin Soudjani, Sadegh Department of Computer Science and Engineering Indian Institute of Technology Delhi India Max Planck Institute for Software Systems Germany Institute of Science and Technology Austria Austria Newcastle University United Kingdom

We consider fixpoint algorithms for two-player games on graphs with ω-regular winning conditions, where the environment is constrained by a strong transition fairness assumption. Strong transition fairness is a widely occurring special case of strong fairness. It requires that any execution is strongly fair with respect to a specified set of live edges: whenever the source vertex of a live edge is visited infinitely often along a play, the edge itself is traversed infinitely often along the play as well. We show that, surprisingly, strong transition fairness retains the algorithmic characteristics of the fixpoint algorithms for ω-regular games-the new algorithms have the same alternation depth as the classical algorithms but invoke a new type of predecessor operator. For example, for Rabin games with k pairs under strong transition fairness, the complexity of the new algorithm is O(nk+2k!) symbolic steps, which is independent of the number of live edges in the strong transition fairness assumption. In contrast, strong fairness necessarily requires increasing the alternation depth depending on the number of fairness assumptions. We get symbolic algorithms for (generalized) Rabin, parity, and GR(1) objectives under strong transition fairness assumptions as well as a direct symbolic algorithm for qualitative winning in stochastic ω-regular games that runs in O(nk+2k!) symbolic steps, improving the state of the art. Previous approaches for handling fairness assumptions would either increase the alternation depth of the fixpoint algorithm or require an up-front automata-theoretic construction that would increase the state space, or both. We have implemented a BDD-based synthesis engine based on our algorithm. We show on a set of synthetic and real benchmarks that our algorithm is scalable, parallelizable, and outperforms previous algorithms by orders of magnitude. © 2022, CC BY.

关键词： Stochastic systems

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