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Modeling and Optimal control for Resource Allocation in the Epidemic Monitoring of a Multi-group Population

SN computer Science 2024年第1期5卷 121页

作者： Di Giamberardino, Paolo Iacoviello, Daniela Papa, Federico Department of Computer Control and Management Engineering “A. Ruberti” Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy Institute for System Analysis and Computer Science “A. Ruberti” CNR Via dei Taurini 19 Rome 00185 Italy

In the last 3 years, the entire world has been facing the sanitary emergency due to the SARS-CoV2;it has been stressed the mutual interdependence of the human populations, as well as the strong impact of specific conditions, such as age, work, habits, on the disease spread among subgroups of a given population. Moreover, the high percentage of asymptomatic individuals, especially among young subjects, improves the infection spread among groups with higher probability of fatal consequences. The vaccination campaign has strongly contributed to reduce mortality, so bringing the epidemic scenario probably closer to the endemic state. Nevertheless, the loss of the vaccination protection, the appearance of new variants and the mixing of moving people require a surveillance action to reduce new virus waives. By suitable modeling and optimal control design, this paper addresses the planning of a swab test campaign for the surveillance of the disease spread within a given multi-group population, when resource limitation and distinctive epidemiological characteristics of the subgroups are present. The proposed approach, applied on a case study in which the population is split into four groups, stresses the importance of a suitable tuned swab test campaign, saving a significant number of infections (and therefore of human lives), by scheduling the surveillance effort on the different populations. The determination of this kind of control is useful not only in the endemic condition, but also as general surveillance strategy at the beginning of an epidemic spread, with limitations in material and logistic resources. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Epidemic monitoring Mathematical modeling of epidemics Optimal control Resource allocation

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A Cooperative Feature Removal Mechanism for Cell Outage Detection in Wireless Telecommunication Networks 19th

A Cooperative Feature Removal Mechanism for Cell Outage Det...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Wrona, Andrea Gentile, Simone De Santis, Emanuele Giuseppi, Alessandro Pietrabissa, Antonio Delli Priscoli, Francesco Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome via Ariosto 25 Lazio Rome00185 Italy Department of Electrical and Information Engineering Polytechnic of Bari via Re David 200 Puglia Bari70125 Italy

ISBN: (纸本)9783031842597

In the domain of crisis management for telecommunications infrastructures, the autonomous detection of cell outages within cellular networks is of paramount importance for prompt identification and resolution in ensuring uninterrupted connectivity to users. Traditional methods usually involve data aggregation at the core network, which is responsible for identifying cell failures. Proposing a novel approach, we leverage a Machine Learning-based distributed and cooperative feature removal mechanism in order to preserve the privacy of data and avoid any degradation in classification performance. Simulations carried out on a dataset retrieved from a real 4G-LTE rollout demonstrate that the proposed approach, through a cooperation among agents, maintains or even slightly improves accuracy, precision, recall, and F1-score in outage prediction compared to other conventional methods, showcasing its efficacy for cell outage detection purposes while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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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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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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Beyond the network-based multi-omics data integration in COPD: A pathway-centric analysis

Beyond the network-based multi-omics data integration in COP...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sibilio, Pasquale Conte, Federica Paci, Paola Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Karolinska Institutet Stockholm17177 Sweden

ISBN: (纸本)9798350386226

Chronic obstructive pulmonary disease (COPD) is a lung disease causing hundred thousand of death each year worldwide and defined as a respiratory and airflow impairment majorly due to large and small airways dysfunctions. COPD could be considered as a syndrome that includes disease axes of variable pathological and clinical conditions. Despite recent advances, the comprehension of the molecular mechanisms responsible for COPD disease spectrum is far to be reached. Moreover, we should also consider continuous smoking exposure, which could lead to variability in the disease mechanisms and progression. Network-based multi-omics data integration can help to study the association between molecular determinants on diverse biomolecular layers in a disease context. In a previous study, we leveraged lung RNA-seq and DNA-methylation data of a COPD-control cohort to build a correlation-based integrated network (called coupled network), that helped to unveil genes involved in immune and inflammatory modulation of COPD. Therefore, in our previous study we highlighted the most important genes of the coupled network and inspected their single contribution to disease-related pathways. In this study we aim to overcome this limitation by performing a pathway activity analysis by considering the expression and DNA methylation profiles of coupled network genes. Moreover, we exploit this analysis to study the possible contribution of coupled network genes to the differential disease progression between current and former smokers patients. © 2024 IEEE.

关键词： Lung cancer

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Computing Without Borders: The Way Towards Liquid Computing

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IEEE Transactions on Cloud Computing 2023年第3期11卷 2820-2838页

作者： Iorio, Marco Risso, Fulvio Palesandro, Alex Camiciotti, Leonardo Manzalini, Antonio Politecnico di Torino Department of Computer and Control Engineering Torino10129 Italy Politecnico di Torino Torino10129 Italy Consorzio TOP-IX Torino10123 Italy TIM Torino10123 Italy

Despite the de-facto technological uniformity fostered by the cloud and edge computing paradigms, resource fragmentation across isolated clusters hinders the dynamism in application placement, leading to suboptimal performance and operational complexity. Building upon and extending these paradigms, we propose a novel approach envisioning a transparent continuum of resources and services on top of the underlying fragmented infrastructure, called liquid computing. Fully decentralized, multi-ownership-oriented and intent-driven, it enables an overarching abstraction for improved applications execution, while at the same time opening up for new scenarios, including resource sharing and brokering. Following the above vision, we present liqo, an open-source project that materializes this approach through the creation of dynamic and seamless Kubernetes multi-cluster topologies. Extensive experimental evaluations have shown its effectiveness in different contexts, both in terms of Kubernetes overhead and compared to other open-source alternatives. © 2013 IEEE.

关键词： Topology

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Application of a digital intelligent assistant to support industrial processes: the case of adaptive allocation in the face of cyber attacks

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Procedia computer Science 2025年 253卷 1555-1564页

作者： Silvia Colabianchi Department of Computer control and management engineering “Antonio Ruberti” Via Ariosto 25 Rome 00185 Italy

The research proposes the application of Digital Intelligent Assistants (DIAs) as proactive agents that can support employees in dealing with cybersecurity issues in sustainable industrial processes underlying the importance of a fruitful Human-Artificial Intelligence collaboration. Cyber-attacks around the world are constantly increasing. DIAs are becoming more effective, also thanks to the use of Large Language Models. Users are required to recall security procedures and rules. Moreover, attacks are constantly evolving and following different patterns. The study presents how a DIA can be a backup agent during and after an attack. The application of digital intelligent assistance technology helps to reduce the cognitive load and pressure that users feel during downtime. In addition, the solution enhances attack reporting by decreasing the shame experienced by the victims. The research proposes a methodological design defining the agent’s technical and functional characteristics and its adaptive relationship with human characteristics. The solution is developed using the RASA framework and evaluated through a case study based on a phishing attack scenario.

关键词： chatbot adaptive automation industry 4.0 cybersecurity

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Dynamic Multihead Attention for Enhancing Neural Machine Translation Performance 5th

Dynamic Multihead Attention for Enhancing Neural Machine Tra...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9789819719228

The research paper delves into the realm of "Attention-Based Neural Machine Translation for Multilingual Communication", focusing on the revolutionary impact of attention mechanisms in enhancing cross-language understanding. Through dynamic allocation of focus to pertinent segments of source sequences, attention mechanisms have significantly improved the precision and coherence of translations, playing a pivotal role in multilingual communication. The paper underscores the importance of this technology in addressing the complexities of aligning words across diverse languages and maintaining grammatical accuracy, crucial for effective cross-lingual interactions. The study also presents a visual flowchart outlining the core components of attention-based NMT, encompassing the encoder, decoder, and attention context. Moreover, the paper extrapolates the future trajectory of attention-based NMT, identifying areas for continued exploration and innovation. Researchers are urged to refine attention models to cater to intricate linguistic structures, potentially unveiling novel mechanisms that strike a balance between capturing long-range dependencies and computational efficiency. Furthermore, the paper anticipates the integration of attention-based NMT with multimodal inputs, unlocking the potential for comprehensive communication by incorporating visual and auditory cues. These advancements hold the promise of revolutionising multilingual communication, shaping a future where language barriers are transcended in various domains, from global diplomacy to education and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural machine translation

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Advancing Network Anomaly Detection: Comparative Analysis of Machine Learning Models

Advancing Network Anomaly Detection: Comparative Analysis of...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russia

ISBN: (纸本)9789819706402

In the rapidly evolving realm of cyber-security, the detection of network anomalies serves as a pivotal line of defense against a myriad of malicious activities and cyberthreats. This research undertakes the task of enhancing the accuracy and efficacy of network anomaly detection by employing a comparative analysis of various individual machine learning models. The study delves into the performance of distinct models, including Random Forest, Gradient Boosting, AdaBoost, neural networks, and SVM, meticulously scrutinizing their capabilities in detecting network anomalies. The crux of this study lies in its meticulous evaluation of each individual model on the revered NSL-KDD dataset—an established benchmark within the field of network intrusion detection. Through a systematic blend of rigorous mathematical frameworks, precise model implementations, and comprehensive experimental assessments, this research offers a deep understanding of the inner workings of each algorithm. The pivotal aspect of this study revolves around the comprehensive comparative analysis of these standalone models. Going beyond the mere quantifica-tion of accuracy, the exploration delves into aspects of precision, recall, F1-score, and more, shedding light on their diverse facets of performance. With achieved accuracies of 99.2419% for Random Forest, 99.5197% for Gradient Boosting, 86.6044% for AdaBoost, 84.00% for neural networks, and 87.00% for SVM, this research underlines the distinctive attributes and potential of each model in the context of network anomaly detection. As the study unravels the distinct strengths and limitations of each model, it contributes to the broader landscape of cyber-security by providing insights into the efficacy of individual machine learning approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Automatic Recognition of Whale and Dolphin Whistles Based on Visual Feature Extraction Approach 2

Automatic Recognition of Whale and Dolphin Whistles Based on...

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2nd IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2022

作者： Wu, Wei Feng, Huibin Junejo, Naveed Ur Rehman Qiu, Yuquan Yan, Jiaquan College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China University of Lahore Department of Computer Engineering Lahore Pakistan

ISBN: (纸本)9781665442763

Automatic recognition of marine mammals plays a significant role in the protection of endangered marine species, generally utilizing their vocalizations. In this paper, we propose a novel visual feature extraction approach for whale and dolphin signals incorporating variational mode decomposition (VMD) and Hilbert transform (VMD-HT) to construct the high-resolution time-frequency signatures and using gray level co-occurrence matrix (GLCM) to extort image features. Firstly, the objected signals are decomposed by VMD algorithm into multiple intrinsic mode functions (IMFs), which can capture local frequency features precisely. Secondly, the time-frequency signature of the objected signal with high resolution is constructed by linear superposition operation of each IMF time-frequency image depicted through Hilbert transform (HT). Thirdly, the image features are extracted using GLCM. Eventually, support vector machine (SVM) is adopted to validate the classification performance of the proposed method. The experimental results verify that the resolution improvement of time-frequency image attains the correct recognition rate and the proposed method accuracy is higher than the compared method about 11.11% © 2022 IEEE.

关键词： Variational mode decomposition

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