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16th IEEE International Conference on computer Science and Information Technologies, CSIT 2021

作者： Turbal, Yuriy Babych, Sergii Kunanets, Nataliia Melnyk, Leonid Pasichnyk, Volodymyr National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine College of National University of Life and Environmental Sciences of Ukraine Software Engineering Rivne Ukraine National University 'Lviv Polytechnic' Department of Information Systems and Networks Lviv Ukraine National University of Water and Environmental Engineering Department of Finance and Economic Security Rivne Ukraine

ISBN: (纸本)9781665442572

An algorithm for solving the problem of scheduling meetings in the process of working on a project is proposed in the paper. The problem of project managers meetings with working groups is considered under certain restrictions., the possibility of a simultaneous meeting of the project manager with one or several project groups is considered. The proposed procedure for finding the permanent allows to build all possible combinations of the meeting schedule at a given time. The specificity of the proposed approach is the ability to take into account any number of additional conditions. © 2021 IEEE.

关键词： Managers

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MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation

arXiv

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arXiv 2025年

作者： Molino, Daniele Feola, Francesco Di Faiella, Eliodoro Fazzini, Deborah Santucci, Domiziana Shen, Linlin Guarrasi, Valerio Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Roma Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Diagnostic Imaging and Stereotactic Radiosurgey Centro Diagnostico Italiano S.p.A. Milano Italy Department of Radiology and Interventional Radiology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Research Unit of Radiology and Interventional Radiology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Artificial Intelligence is revolutionizing medical practice, enhancing diagnostic accuracy and healthcare delivery. However, its adaptation in medical settings still faces significant challenges, related to data availability and privacy constraints. Synthetic data has emerged as a promising solution to mitigate these issues, addressing data scarcity while preserving privacy. Recently, Latent Diffusion Models have emerged as a powerful tool for generating high-quality synthetic data. Meanwhile, the integration of different modalities has gained interest, emphasizing the need of models capable of handle multimodal medical data. Existing approaches struggle to integrate complementary information and lack the ability to generate modalities simultaneously. To address this challenge, we present MedCoDi-M, a 6.77-billion-parameter model, designed for multimodal medical data generation, that, following Foundation Model paradigm, exploits contrastive learning and large quantity of data to build a shared latent space which capture the relationships between different data modalities. Further, we introduce the Multi-Prompt training technique, which significantly boosts MedCoDi-M’s generation under different settings. We extensively validate MedCoDi-M: first we benchmark it against five competitors on the MIMIC-CXR dataset, a state-of-the-art dataset for Chest X-ray and radiological report generation. Secondly, we perform a Visual Turing Test with expert radiologists to assess the realism and clinical relevance of the generated data, ensuring alignment with real-world scenarios. Finally, we assess the utility of MedCoDi-M in addressing key challenges in the medical field, such as anonymization, data scarcity and imbalance learning. The results are promising, demonstrating the applicability of MedCoDi-M in medical contexts. Project page is at https://***/MedCoDi-M/. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

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Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance

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Tsinghua Science and Technology 2020年第1期25卷 1-11页

作者： Jiachen Yang Chaofan Ma Jiabao Man Huifang Xu Gan Zheng Houbing Song School of Electrical and Information Engineering Tianjin UniversityTianjin 300000China Wolfson School of Mechanical Electrical and Manufacturing EngineeringLoughborough UniversityLoughboroughLE11 3TUUK Department of Electrical ComputerSoftwareand Systems EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachFL 32114USA.

The proliferation of mobile devices that support the acceleration of data services(especially smartphones)has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary ***, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching.

关键词： cooperative cognitive radio network content caching power allocation

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Fractional discrete neural networks with variable order: solvability, finite time stability and synchronization

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The European Physical Journal Special Topics 2024年 1-14页

作者： Hioual, Amel Alomari, Saleh Al-Tarawneh, Hassan Ouannas, Adel Grassi, Giuseppe Laboratory of Dynamical Systems and Control University of Oum El-Bouaghi Oum El Bouaghi Algeria Faculty of Science and Information Technology Software Engineering Jadara University Irbid Jordan Department of Data Sciences and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Mathematics and Computer Science University of Oum EL-Bouaghi Oum El Bouaghi Algeria Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy

Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.

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A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

arXiv

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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

引用

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

引用

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...

引用

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...

引用

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

引用

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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