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arXiv 2025年

作者： Lloret-Climent, Miguel Nescolarde-Selva, Josué Antonio Alonso-Stenberg, Kristian Montoyo-Guijarro, Andrés Gutiérrez-Vázquez, Yoan Department of Applied Mathematics University of Alicante Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The framework of the present study was the destination life cycle model, a classical model that describes the development of tourist destinations. We examined mass tourism in Benidorm based on tourist accommodation supply and demand statistics over the January 2016 - October 2018 period, provided by Spain’s National Institute for Statistics. The objective was to analyze the life cycle and competitiveness of Benidorm’s tourism system, interpret whether the tourism product was sustainable, and at what stage in the cycle Benidorm is currently in. To do this, we used Smarta software, which, based on network analysis, enables to interpret the system’s virtuous cycles and analyze causality by observing relationship patterns in the system’s attractors, thus complementing typical processing based on causal maps and the study of social networks. The results obtained by this application (which has been developed by our research team), show 6 sets of attractors that mark the trends of the tourist system. Finally, the analysis of the significant variables of these attractors have helped us to justify that the tourist system of Benidorm is in the rejuvenation phase. © 2025, CC BY-NC-ND.

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Joint Multiple Efficient Neighbors and Graph Learning for Multi-view Clustering 1

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10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023

作者： Sadjadi, Fatemeh Torra, Vicenç Department of Applied Mathematics Faculty of Sciences and Modern Technologies Graduate University of Advanced Technology Kerman117-76315 Iran Department of Computing Science MIT Building Umeå University Västerbotten Umeå901 87 Sweden

ISBN: (数字)9783031467813

ISBN: (纸本)9783031467806

Graph-based multi-view clustering is a popular method for identifying informative graphs for e.g. computer vision applications. Nevertheless, optimizing sparsity and connectivity simultaneously is challenging. Multi-view clustering (MC) integrates complementary information from different views. However, most existing methods introduce noise or ignore relevant data structures. This paper introduces a Joint multiple efficient neighbors and Graph (JMEG) learning method for MC. Our approach includes a post-processing technique to optimize sparsity and connectivity by means of identifying neighbors efficiently. JMEG also uses partition space and consensus graph learning to uncover data structures efficiently. Experiments show that JMEG outperforms state-of-the-art methods with negligible additional computation cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Data structures

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Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment Analysis in Hausa Language Using AfriBERTa

arXiv

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arXiv 2025年

作者： Sani, Sani Abdullahi Muhammad, Shamsuddeen Hassan Jarvis, Devon School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Department of Computing Imperial College London United Kingdom

Sentiment analysis (SA) plays a vital role in Natural Language Processing (NLP) by identifying sentiments expressed in text. Although significant advances have been made in SA for widely spoken languages, low-resource languages such as Hausa face unique challenges, primarily due to a lack of digital resources. This study investigates the effectiveness of Language-Adaptive Fine-Tuning (LAFT) to improve SA performance in Hausa. We first curate a diverse, unlabeled corpus to expand the model’s linguistic capabilities, followed by applying LAFT to adapt AfriBERTa specifically to the nuances of the Hausa language. The adapted model is then fine-tuned on the labeled NaijaSenti sentiment dataset to evaluate its performance. Our findings demonstrate that LAFT gives modest improvements, which may be attributed to the use of formal Hausa text rather than informal social media data. Nevertheless, the pre-trained AfriBERTa model significantly outperformed models not specifically trained on Hausa, highlighting the importance of using pre-trained models in low-resource contexts. This research emphasizes the necessity for diverse data sources to advance NLP applications for low-resource African languages. We published the code and the dataset to encourage further research and facilitate reproducibility in low-resource NLP here SA for LowRes Language Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems

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M-Learning - A multi agent approach 15

M-Learning - A multi agent approach

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International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021

作者： Kristensen, Terje Solsvik Kjærvik, Joachim Coetzee Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen Norway

ISBN: (纸本)9789898704320

In this paper we have developed a framework for learning by mobile based on JADE and JESS as software components. Such a software integration makes it possible to exhibit ACL communication properties from JADE and rule-based reasoning capability from JESS. Software agents are implemented following the guidelines of the theoretical concept of Zone of Proximal Development (ZPD), as a means of supporting and promoting opportunities for effective learning. © 2021 International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： Multi agent systems

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Strategic Defense Post Allocation Using the Corona Cover Set for Enhanced Military Zone Security

Strategic Defense Post Allocation Using the Corona Cover Set...

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Women in computing (InCoWoCo), International Conference for

作者： Niveditha. P G. Mahadevan C. Sivagnanam Department of Mathematics The Gandhigram Rural Institute (Deemed to be University) Gandhigram Tamilnadu India Mathematics and Computing Skills Unit University of Technology and Applied Sciences-Sur Sultanate of Oman

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Defense forces are vital for national safety, peace-keeping, disaster response, and economic support. They require strong support systems, medical access, rapid deployment, and effective command centers to operate efficiently. Proper placement and a well-structured defense strategy focusing on sustainability and readiness are essential for ensuring safety and stability for both military and civilians. Recently G. Mahadevan *** established the concept of Corona covering number of a graph and obtained many results. A vertex cover set $S\subseteq V(G)$ is a corona cover set if every vertex $v\in S$ such that $d_{\langle S\rangle}(v)=1$ or there exist a vertex $u\in S$ with $d_{\langle S\rangle}(u)=1$ and $uv\in E$ . The least cardinality of a corona cover set is the corona covering number of a graph and it is expressed as $\tau_{C}(G)$ . In this article, the investigation of corona covering number in the strategic placement of defense posts within military zones, aiming to enhance operational efficiency and effectively deter unauthorized incursions has been attained.

关键词： Protected areas Systematics Military computing Focusing Corona Security Resource management Sustainable development Monitoring Resilience

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Human-Machine Pilot Navigation Method Using Nonlinear Model Predictive Control, Simulation and Inflight Testing

Human-Machine Pilot Navigation Method Using Nonlinear Model ...

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Conference on Digital Avionics Systems (DASC)

作者： Jeremy Bertoncini Adrian Dudek Johannes Ostler Martin Russ Matthias Gerdts Institute of Applied Mathematics and Scientific Computing Department of Aerospace Engineering Institute of Flight Systems University of the Bundeswehr Munich Neubiberg Germany

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

In military or civil aviation, depending on the mission to be accomplished, an aircraft may need to avoid prohibited or dangerous areas that are not always within line of sight. However, these areas can be identified by external information and communicated from a ground station. The aim of this work is to provide an optimal control method that generates trajectories and assists the pilot in navigating. Using a human-machine interface, the pilot has access to a digital twin of the actual airspace, where the route is modeled using a tunnel-in-the-sky method. The research can be applied to airborne mission management, airport maneuver management or military operations. The path planning task is formulated as an optimal control problem and solved on a fixed time horizon using a nonlinear model predictive controller (NMPC) [5].

关键词： Navigation Human-machine systems Atmospheric modeling Optimal control Predictive models Military aircraft Trajectory Digital twins Testing Predictive control

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An Automatic Sleep Arousal Detection Method by Enhancing U-Net with Spatial-channel Attention

An Automatic Sleep Arousal Detection Method by Enhancing U-N...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Su, Yen-Ning Kuo, Chih-En National Chung Hsing University Institute of Data Science and Information Computing Taichung Taiwan National Chung Hsing University Department of Applied Mathematics Taichung Taiwan

ISBN: (纸本)9781665480451

Sleep apnea is the most prevalent sleep disorder. In severe cases, it can even lead to sudden death. To diagnose sleep apnea, it is critical to measure the number of sleep arousal times per hour. Current clinical standard procedures require patients to visit a sleep center or hospital for polysomnography (PSG), in which the patient's physiological signals are recorded during the night, and a sleep technician then interprets these signals manually to determine the sleep-wake pattern. With the rising number of sleep disorders, sleep technologists cannot afford such a high workload, which makes the development of an automated sleep arousal detection system critical to reducing medical costs. When PSG is recorded, a large number of wires will also interfere with the patient and cause measurement distortion. In this study, a spatial-channel attention (SCA) U-Net for automatic sleep arousal detection was proposed, which was trained and tested on PhysioNet 2018 sleep PSG dataset. Considering clinical applicability and reducing patient disturbance when recording physiological signals, only five physiological signal channels were required for our proposed model. It is much lower than other methods in the literature. The A AUPRC of our proposed model was and 39%. The results showed the proposed method is highly accurate and requires fewer channel signals, that also demonstrated the clinical applicability and robustness of the proposed method. © 2022 IEEE.

关键词： Convolutional neural networks

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Optimized Vaccine Selection Using Machine Learning and Genetic Algorithms: A Study on Side Effects of COVID-19 Vaccines 12th

Optimized Vaccine Selection Using Machine Learning and Genet...

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12th International Conference on Soft computing for Problem Solving, SocProS 2023

作者： Soni, Vishal Joshi, Shubham Deep, Kusum Pant, Millie Department of Mechatronics Indian Institute of Information Technology Bihar Bhagalpur813210 India Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Mathematics Indian Institute of Technology Uttarakhand Roorkee247667 India Mehta Family School for Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

ISBN: (纸本)9789819732913

The COVID-19 pandemic has necessitated large-scale vaccination campaigns to control the virus's spread. Given the scale of these efforts, it's crucial to scrutinize potential adverse effects, especially in vulnerable population groups. This study combines machine learning and Genetic Algorithms (GA) to analyze the patterns of patient recovery post-vaccination. We use a range of classifiers, including the Gradient Boosting Decision Tree (GBDT), to identify key features and symptom categories that influence patient recovery. We also use Principal Component Analysis (PCA) to reduce dimensionality and uncover patterns within high-dimensional data. The data used in this study have taken from the Vaccine Adverse Event Reporting System (VAERS). With the help of GA, we aim to guide optimal vaccine selection for susceptible demographics, minimizing the risk and severity of side effects. Our research paves the way for more personalized vaccination strategies, promoting healthier outcomes and reducing risks associated with mass vaccination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Invariants for level-1 phylogenetic networks under the random walk 4-state Markov model

arXiv

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arXiv 2024年

作者： Frohn, Martin Holtgrefe, Niels van Iersel, Leo Jones, Mark Kelk, Steven Department of Advanced Computing Sciences Maastricht University Maastricht Netherlands Institute of Applied Mathematics Delft University of Technology Delft Netherlands

Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees, such as hybridization, introgression, and lateral gene transfer. Studying phylogenetic networks under a statistical model of DNA sequence evolution can aid the inference of phylogenetic networks. Most notably Markov models like the Jukes-Cantor or Kimura-3 model can been employed to infer a phylogenetic network using phylogenetic invariants. In this article we determine all quadratic invariants for sunlet networks under the random walk 4-state Markov model, which includes the aforementioned models. Taking toric fiber products of trees and sunlet networks, we obtain a new class of invariants for level-1 phylogenetic networks under the same model. Furthermore, we apply our results to the identifiability problem of a network parameter. In particular, we prove that our new class of invariants of the studied model is not sufficient to derive identifiability of quarnets (4-leaf networks). Moreover, we provide an efficient method that is faster and more reliable than the state-of-the-art in finding a significant number of invariants for many level-1 phylogenetic networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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A Unified Norm Error Analysis of SAV-BDF Schemes for the Incompressible Navier–Stokes Equations

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Journal of Scientific computing 2024年第1期100卷 1-25页

作者： Ji, Bingquan Liao, Hong-lin Institute of Applied Physics and Computational Mathematics Beijing China School of Mathematics Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles (NUAA) MIIT Nanjing China

This article is concerned with the $$L^2$$ norm error analysis of high-order BDF methods for the incompressible Navier–Stokes equation subjected to no-slip boundary conditions. To mitigate the complexity of high-order time-stepping algorithms caused by decoupling strategies, we combine the prediction–correction technique with a generalized scalar auxiliary variable approach to devise a family of linear, energy stable BDF schemes up to fifth-order accuracy in time. All the proposed schemes can be decoupled into a sequence of Poisson-type equations for the pressure and velocity fields, which significantly reduces the size of the linear systems at each time step. To deal with the essential difficulty raised by the non-A-stability of high-order BDF schemes, we introduce a class of discrete orthogonal convolution kernels to develop a unified framework for the $$L^2$$ norm convergence analysis of the proposed high-order BDF schemes. The velocity attains optimal rate of convergence in the $$L^2$$ norm under some mild mesh restrictions. Furthermore, building on the recent results concerning the positive definiteness of high-order BDF convolution kernels, we also provide an error estimate of the pressure in a weak $$L^2$$ norm. Benchmark examples including the Taylor–Green vortex problem and the regularized lid-driven cavity flow with Reynolds numbers up to 5000 are included to verify the stability and high accuracy of our numerical schemes.

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