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arXiv 2024年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Department of Software and IT Engineering Ecole de Technologie Superieure Montreal Canada Artificial Intelligence & Cyber Systems Research Center Department of CSM Lebanese American University Beirut Lebanon Department of Electrical Engineering Ecole de Technologie Superieure Montreal Canada University Beirut Lebanon Department of CS Khalifa University Abu Dhabi United Arab Emirates KU 6G Research Center Department of CS Khalifa University United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada KU 6G Research Center Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Department of ML Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 2024, CC BY-NC-SA.

关键词： Mobile telecommunication systems

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Determinants of under-five mortality in Zimbabwe: Evidence from the 2015–2016 Zimbabwe demographic Health Survey data

Women and Children Nursing

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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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On the Correctness of Speculative Consensus

arXiv

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arXiv 2022年

作者： Hellings, Jelle Rahnama, Sajjad Gupta, Suyash Sadoghi, Mohammad Department of Computing and Software McMaster University HamiltonON Canada Exploratory Systems Lab Department of Computer Science University of California Davis DavisCA United States RISELab Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States

The introduction of Bitcoin fueled the development of blockchain-based resilient data management systems that are resilient against failures, enable federated data management, and can support data provenance. The key factor determining the performance of such resilient data management systems is the consensus protocol used by the system to replicate client transactions among all participants. Unfortunately, existing high-throughput consensus protocols are costly and impose significant latencies on transaction processing, which rules out their usage in responsive high-performance data management systems. In this work, we improve on this situation by introducing the Proof-of-Execution consensus protocol (PoE), a consensus protocol designed for high-performance low-latency resilient data management. PoE introduces speculative execution, which minimizes latencies by starting execution before consensus is reached, and PoE introduces proof-of-executions to guarantee successful execution to clients. Furthermore, PoE introduces a single-round check-commit protocol to reduce the overall communication costs of consensus. Hence, we believe that PoE is a promising step towards flexible general-purpose low-latency resilient data management systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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Proposing Model for Security of IoT Devices in Smart Logistics: A Review 3

Proposing Model for Security of IoT Devices in Smart Logisti...

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3rd International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2020

作者： Abbas, Arbab Waseem Nawaz Khan Marwat, Safdar Ahmed, Salman Hafeez, Abdul Ullah, Khalil Khan, Ihsan Ullah University of Engineering Technology Department of Computer Systems Engg Peshawar Pakistan Department of Computer Science Pakistan University of Malakand Department of Software Engg Dir Lower Pakistan Islamabad Pakistan

ISBN: (纸本)9781728149707

The information technology's new revolution is Internet of Things (IoT), where physical world is connected to the internet. IoT applications are diversified such as smart health, smart industry, smart homes and most importantly smart logistics for perishable items conservation. Although IoT devices have revolutionized world but due to their constrained nature they are at the cost of a greater risk of personal data loss, security breaches and misuse. In this research, a review is presented for proposing two state of the art modules i.e. communication model of smart logistics and security of IoT devices in Smart Logistics. As the smart logistics have perishable items i.e. fruits, vegetables, meat, medicine, chemicals, and cosmetics etc. which are sensitive to temperature, humidity and pressure etc. If the sensors monitoring these items have been handled abnormally due to security attacks, it will give huge loss. Therefore, security of smart logistics is of paramount importance in this research. In order to expedite research in this emergent area of IoT, in this research paper a brief review on progress of IoT in smart logistics, and its security has been presented. © 2020 IEEE.

关键词： Cybersecurity

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Research of the Particle Swarm Method for Generating Nonlinear Substitutions

Research of the Particle Swarm Method for Generating Nonline...

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2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2021

作者： Kuznetsov, Alexandr Myroshnychenko, Volodymyr Derevianko, Yaroslav Bulhakova, Olha Department of information systems and technologies security V. N. Karazin Kharkiv National University Kharkiv Ukraine Department of Economic and Information Security Dnipropetrovsk State University of Internal Affairs Dnipro Ukraine Department of Computer Science and Software Engineering University of Customs and Finance Dnipro Ukraine

ISBN: (纸本)9781665426527

Nonlinear substitutions (S-boxes) have an important role in ensuring security of symmetric cryptographical transformations. In this article, we consider a computational particle swarm optimization method (PSO) to generate S-boxes. We have implemented two algorithms for generating substitutions that reproduce a certain simulation of particle motion in the space of possible states to make experimental research. We perform a computational search and show the efficiency of these two implementations. Our proposed improved implementation allows us to confidently generate Sboxes with nonlinearity 104, delta uniformity 8, linear redundancy 0 and algebraic immunity 3. © 2021 UkrMiCo 2021 - 2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics, Proceedings. All rights reserved.

关键词： Particle swarm optimization (PSO)

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F2A2: flexible fully-decentralized approximate actor-critic for cooperative multi-agent reinforcement learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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Agile Meets Quantum: A Novel Genetic Algorithm Model for Predicting the Success of Quantum software Development Project

arXiv

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arXiv 2024年

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta53851 Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

Context: Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. Objective: This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum software Project Success Prediction Model (AQSSPM). Methodology: Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Results: Utilizing GA with NBC, project success probability improved from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly, GA with LR increased success rates from 55.52% to 98.99%, and costs decreased from 0.496% to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs=0.955) in causes ranking, with no significant difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project. © 2024, CC BY.

关键词： Genetic algorithms

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A comparative analysis of machine learning algorithms to build a predictive model for detecting diabetes complications

引用

Informatica (Slovenia) 2021年第1期45卷 117页

作者： Abaker, Ali A. Saeed, Fakhreldeen A. Department of Accounting Information Systems Faculty of Computer Science Al-Neelain University Khartoum Sudan Department of Software Engineering Faculty of Computer Science Al-Neelain University Khartoum Sudan

Diabetes complications have a significant impact on patients’ quality of life. The objective of this study was to predict which patients were more likely to be in a complicated health condition at the time of admission to allow for the early introduction of medical interventions. The data were 644 electronic health records from Alsukari Hospital collected from January 2018 to April 2019. We used the following machine learning methods: logistic regression, random forest, and k-nearest neighbor (KNN). The logistic regression algorithm performed better than the other algorithms achieving an accuracy of 81%, recall of 81%, and F1 score of 75%. Also, attributes such as infection years, swelling, diabetic ketoacidosis, and diabetic septic foot were significant in predicting diabetes complications. This model can be useful for the identification of patients requiring additional care to limit the complications and help practitioners in making decisions on whether the patient should be hospitalized or sent home. Furthermore, we used the sequential feature selection (SFS) algorithm which reduced the features to six, which is fewer than any model built before to predict diabetes complications. The primary goal of this study was achieved. The model had fewer attributes which means we have a simple and understandable model in addition to, it has a better performance. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Decision trees

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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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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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