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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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Hybrid consensus control for nonholonomic multi-robot systems

Hybrid consensus control for nonholonomic multi-robot system...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Davide Aloisi Andrea Cristofaro Department of Computer Control and Management Engineering Sapienza University of Rome Italy

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This work concerns the implementation of hybrid consensus control for nonholonomic multi-robot systems. It is shown how it is possible to attain a consensus behaviour in a multi-agent system having internal communications characterized by sampling. Furthermore, it is shown how it is possible to make the multi-agent system reach a formation with desired position and orientation by adding a virtual agent within the system. A simulation study supports and validates the theoretical results.

关键词： Protocols Consensus control Multi-robot systems Information technology Collision avoidance Multi-agent systems

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D4D: An RGBD diffusion model to boost monocular depth estimation

arXiv

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arXiv 2024年

作者： Papa, Lorenzo Russo, Paolo Amerini, Irene The Department of Computer Control and Management Engineering Sapienza University of Rome Italy

Ground-truth RGBD data are fundamental for a wide range of computer vision applications;however, those labeled samples are difficult to collect and time-consuming to produce. A common solution to overcome this lack of data is to employ graphic engines to produce synthetic proxies;however, those data do not often reflect real-world images, resulting in poor performance of the trained models at the inference step. In this paper we propose a novel training pipeline that incorporates Diffusion4D (D4D), a customized 4-channels diffusion model able to generate realistic RGBD samples. We show the effectiveness of the developed solution in improving the performances of deep learning models on the monocular depth estimation task, where the correspondence between RGB and depth map is crucial to achieving accurate measurements. Our supervised training pipeline, enriched by the generated samples, outperforms synthetic and original data performances achieving an RMSE reduction of (8.2%, 11.9%) and (8.1%, 6.1%) respectively on the indoor NYU Depth v2 and the outdoor KITTI dataset. © 2024, CC BY.

关键词： Pipelines

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On Combining Collective Entity Resolution and Repairing (Extended Abstract) 1

On Combining Collective Entity Resolution and Repairing (Ext...

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1st Workshop on AI-Driven Heterogeneous Data management: Completing, Merging, Handling Inconsistencies and Query-Answering, ENIGMA 2023

作者： Bienvenu, Meghyn Cima, Gianluca Gutiérrez-Basulto, Víctor Univ. Bordeaux CNRS Bordeaux INP LaBRI UMR 5800 Department of Computer Control and Management Engineering Sapienza University of Rome School of Computer Science and Informatics Cardiff University

This work summarizes the salient aspects of our recent work [1], about combining collective entity resolution and repairing. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons Li... 详细信息

关键词： Repair

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Speech and Language Impairment Detection by Means of AI-Driven Audio-Based Techniques 9

Speech and Language Impairment Detection by Means of AI-Driv...

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9th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2024

作者： Corvitto, Luca Faiella, Lorenzo Napoli, Christian Puglisi, Adriano Russo, Samuele Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy

Speech and Language Impairments (SLI) affect a large and heterogeneous group of people. With our work, we propose a novel, easy, and immediate detection tool to help diagnose people who suffer from SLI using speech audio signals, along with a new dataset containing English speakers affected by SLI. In this work, we experiment with feature extraction methods such as Mel Spectrogram and wav2vec 2.0, as well as classification methods such as SVM, CNN, and linear neural networks. We also work on data audio augmentation trying to overcome the very common limitations imposed by data scarcity in the medical field. The overall results indicate that the wav2vec 2.0 feature extractor, paired with a linear classifier, provides the best performance with a reasonably high accuracy of over 96%. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Speech recognition

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A comparative analysis for automated information extraction from OSHA Lockout/Tagout accident narratives with Large Language Model 6

A comparative analysis for automated information extraction ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Sabetta, Nicolò Costantino, Francesco Stabile, Sara Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Occupational and Environmental Medicine Epidemiology and Hygiene INAIL Piazzale Giulio Pastore 6 Rome00144 Italy

Analysing workplace accidents is crucial for improving occupational safety by understanding causes and preventing recurrence. However, the primary challenge in analysing accident narratives lies in the unstructured nature of the text data. This study examines the effectiveness of Large Language Models (LLMs), specifically GPT-4 Turbo, in extracting information from lockout/tagout (LOTO) accident narratives in the Occupational Safety and Health Administration (OSHA) database. It compares the extracted features, namely the degree of fatality, nature of injury, and employee's occupation, with those recorded by OSHA supervisors. Despite occasional misclassifications and hallucinations, GPT-4 Turbo shows significant potential in automating critical information extraction, reducing reliance on human interpretation. Moreover, the model achieved high accuracy rates for each feature. These findings suggest that LLMs can enhance occupational safety data analysis, though improvements in prompt design and verification are recommended for further accuracy. © 2024 The Authors.

关键词： Generative AI Health Administration (OSHA) Industrial Safety LLM LOTO Occupational Safety

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Multi-criteria Optimization Scheduling of Surgical Units: A Case Study at Policlinico Umberto I of Rome

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Operations Research Forum 2025年第2期6卷 1-26页

作者： Croella, Anna Livia Tronci, Edoardo Maria Nastasi, Alberto Palagi, Laura Petitti, Federico Romano, Ferdinando Department of Computer Control and Management Engineering Sapienza University of Rome Via Aroisto Rome 00185 Italy Accenture S.p.A. Rome Italy CNR-IASI Rome Italy AOU-Policlinico Umberto I Rome Italy

Nowadays, managing Operating Rooms (ORs) in healthcare systems presents a significant challenge, with underutilization of surgical facilities being a key concern. Several decision factors must be considered, including the number of ORs, operating hours, target utilization rates, and the possibility of patient transfers between surgical units (SUs). This study presents a multi-criteria mixed-integer linear optimization model designed for the specific context of Policlinico Umberto I in Rome. The model uses an open scheduling approach to determine the optimal day, time, and OR allocation for weekly surgeries. Using real data from two SUs and four ORs over twelve weeks, we assess the impact of different operating time policies on hospital Key Performance Indicators. Additionally, we introduce randomly generated data to simulate surgery priorities and further validate our model. Results demonstrate the effectiveness of mathematical optimization in improving OR utilization and overall healthcare efficiency. © The Author(s) 2025.

关键词： Health care management Mixed integer linear optimization Multi-criteria optimization Operating room scheduling

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Transforming Training with New Enabling Technologies: A Proposal to Verify the Efficacy of Virtual Reality Tools in the Occupational Health and Safety Sector 8th

Transforming Training with New Enabling Technologies: A Prop...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Bentivenga, Rosina Bernabei, Margherita Carli, Marco Colabianchi, Silvia Costantino, Francesco Ferrarotti, Anna Neri, Michael Pietrafesa, Emma Sorrentino, Edvige Stabile, Sara Department of Occupational and Environmental Medicine Epidemiology and Hygiene INAIL Rome Italy Department of Mechanical and Aerospace Engineering Sapienza University of Rome Rome Italy Department of Industrial Electronic and Mechanical Engineering University of Roma Tre Rome Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome Italy

ISBN: (纸本)9789819793266

In the context of digital transformation in the occupational health and safety sector, this research focuses on the efficacy of virtual reality training tools in ensuring workplace safety during maintenance activities. A comprehensive methodology, including focus groups and workshops, is utilized to define a study protocol and evaluate the proposed virtual reality solution. The study suggests that immersive virtual reality training, which integrates the Skill-Rule-Knowledge framework, has the potential to enhance workers’ ability to manage unforeseen situations. It emphasizes the importance of practical, scenario-based training and outlines a detailed evaluation process. The conclusion highlights the need for ongoing validation and future steps to extend the application to supervisors, fostering improved health and safety management in the workplace. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Composing Like an Ancient Chinese Poet:Learn to Generate Rhythmic Chinese Poetry

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Journal of computer Science & Technology 2023年第6期38卷 1272-1287页

作者：何明陈艳赵洪科刘淇吴乐崔羽曾贵华刘贵全 Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China AI Laboratory Lenovo ResearchBeijing 100193China Baidu Incorporated Beijing 100193China College of Management and Economics Tianjin UniversityTianjin 300072China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Risk Control Department OPayBeijing 100010China

Automatic generation of Chinese classical poetry is still a challenging problem in artificial ***-cently,Encoder-Decoder models have provided a few viable methods for poetry ***,by reviewing the pri-or methods,two major issues still need to be settled:1)most of them are one-stage generation methods without further polishing;2)they rarely take into consideration the restrictions of poetry,such as tone and ***,some an-cient Chinese poets tended first to write a coarse poem underlying aesthetics and then deliberated its semantics;while oth-ers first create a semantic poem and then refine its *** this basis,in order to better imitate the human creation procedure of poems,we propose a two-stage method(i.e.,restricted polishing generation method)of which each stage fo-cuses on the different aspects of poems(i.e.,semantics and aesthetics),which can produce a higher quality of generated *** this way,the two-stage method develops into two symmetrical generation methods,the aesthetics-to-semantics method and the semantics-to-aesthetics *** particular,we design a sampling method and a gate to formulate the tone and rhyme restrictions,which can further improve the rhythm of the generated *** results demon-strate the superiority of our proposed two-stage method in both automatic evaluation metrics and human evaluation met-rics compared with baselines,especially in yielding consistent improvements in tone and rhyme.

关键词： aesthetics poetry generation polishing semantics two-stage method

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Dynamic Topology Optimization for Efficient and Decentralised Federated Learning

Dynamic Topology Optimization for Efficient and Decentralise...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Menegatti, Danilo Giuseppi, Alessandro Poli, Cecilia Pietrabissa, Antonio Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy Istituto Superiore di Sanità National Centre for Innovative Technologies in Public Health Department Rome Italy

ISBN: (纸本)9798350362480

Topology optimization in decentralised federated learning settings enables the design of policies aimed at minimizing the number of communication rounds needed to reach algorithmic convergence. Given a federation of autonomous agents, finding the optimal topology which guarantees that the underlying graph is connected is still an open issue. This paper proposes a novel energy-aware topology optimization algorithm with the goal to derive an optimal topology which maximizes the algebraic connectivity of the corresponding graph in presence of energy and communication constraints. The effectiveness of the proposed approach is validated in the context of a consensus-based federated learning algorithm over an e-Health scenario. © 2024 IEEE.

关键词： Federated learning

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