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2022 Engaging Citizen science Conference, CitSci 2022

作者： Wolkorte, Ria Wildevuur, Sabine Health Technology Services Research Department Technical Medical Centre Faculty of Behavioural Management and Social Sciences University of Twente Enschede Netherlands DesignLab Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands ECSA Working Group Citizen Science for Health Netherlands

Monitoring and evaluating projects is important to ensure that the project proceeds smoothly and desired goals and deliverables are met. Several monitoring and evaluation frameworks have been developed to evaluate citizen science projects. Citizen science for health is an upcoming domain within citizen science;however, citizen science for health differs from other domains, such as biodiversity or geo-science, with regards to ethics and privacy of health data. Therefore, it is imperative that a framework be developed that can appropriately monitor and evaluate citizen science for health projects. Based upon one of the existing monitoring and evaluation frameworks, a first outline of such a framework has been developed. A roundtable session at the Engaging Citizen science Conference 2022 was used to elicit possible topics for the ways in which citizen science for health distinguishes itself from other domains of citizen science. The topics mentioned by the participants were, amongst others: ethics, reciprocity, data privacy, transdisciplinary cooperation, personal and societal health outcomes, education of different stakeholders, and working towards sustainability and taking steps to create an impact with the outcomes of citizen science research. The roundtable discussions provided relevant input on areas where the current outline might be adapted when working towards a framework for the monitoring and evaluation of citizen science for health and wellbeing. Focus groups are foreseen to co-create a framework 1.0 for monitoring and evaluating citizen science for health projects. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Biodiversity

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A Perception-Aware NMPC for Vision-Based Target Tracking and Collision Avoidance with a Multi-Rotor UAV

arXiv

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arXiv 2023年

作者： Dmytruk, Andriy Silano, Giuseppe Bicego, Davide Licea, Daniel Bonilla Saska, Martin Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Netherlands

A perception-aware Nonlinear Model Predictive Control (NMPC) strategy aimed at performing vision-based target tracking and collision avoidance with a multi-rotor aerial vehicle is presented in this paper. The proposed control strategy considers both realistic actuation limits at the torque level and visual perception constraints to enforce the visibility coverage of a target while complying with the mission objectives. Furthermore, the approach allows to safely navigate in a workspace area populated by dynamic obstacles with a ballistic motion. The formulation is meant to be generic and set upon a large class of multi-rotor vehicles that covers both coplanar designs like quadrotors as well as fully-actuated platforms with tilted propellers. The feasibility and effectiveness of the control strategy are demonstrated via closed-loop simulations achieved in MATLAB. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Efficient Data Optimisation for Harmonic Inpainting with Finite Elements 19th

Efficient Data Optimisation for Harmonic Inpainting with Fin...

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19th International Conference on computer Analysis of Images and Patterns, CAIP 2021

作者： Chizhov, Vassillen Weickert, Joachim Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Campus E 1.7 Saarland University Saarbrücken66041 Germany

ISBN: (纸本)9783030891305

Harmonic inpainting with optimised data is very popular for inpainting-based image compression. We improve this approach in three important aspects. Firstly, we replace the standard finite differences discretisation by a finite element method with triangle elements. This does not only speed up inpainting and data selection, but even improves the reconstruction quality. Secondly, we propose highly efficient algorithms for spatial and tonal data optimisation that are several orders of magnitude faster than state-of-the-art methods. Last but not least, we show that our algorithms also allow working with very large images. This has previously been impractical due to the memory and runtime requirements of prior algorithms. © 2021, Springer Nature Switzerland AG.

关键词： Finite element method

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A Hybrid SARIMAX Model in Conjunction with Neural Networks for the Forecasting of Life Insurance Industry Growth in Thailand 9

A Hybrid SARIMAX Model in Conjunction with Neural Networks f...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Huadsri, Supika Mekruksavanich, Sakorn Jitpattanakul, Anuchit Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao56000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798350318241

This article is conducted with the primary aim of comparing various forecasting models to identify the optimal model for forecasting the growth of the life insurance industry in Thailand. The models under consideration include the SARIMAX model, the Recurrent Neural Network (RNN), and a hybrid model introduced as RNN-SARIMAX. The dataset utilized for this investigation is extensive, consisting of monthly direct premium data from the Thai life insurance sector spanning January 2003 to December 2022. The dataset is segregated into two distinct segments. Initially, data from January 2003 to December 2021 is employed for constructing the forecasting model. Subsequently, data from January 2022 to December 2022 is utilized for a comparative analysis of the forecasting models, to determine the most suitable and accurate model for predicting the growth of the life insurance industry in Thailand. The evaluation criteria for comparing models include the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R-squared). The consistent findings from our analysis indicate that the SARIMAX model consistently outperforms both the RNN and RNN-SARIMAX models. © 2024 IEEE.

关键词： Recurrent neural networks

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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A Modular Open-Source Cryptographic Co-Processor for Internet of Things

A Modular Open-Source Cryptographic Co-Processor for Interne...

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Euromicro Symposium on Digital System Design

作者： Dina Hesse Maël Gay Ilia Polian Elif Bilge Kavun Owen Millwood Witali Bartsch Hörst Gortz Institute for IT Security Ruhr University Bochum Bochum Germany Institute of Computer Architecture and Computer Engineering University of Stuttgart Stuttgart Germany Faculty of Computer Science and Mathematics University of Passau Passau Germany Department of Computer Science The University of Sheffield Sheffield UK WIZnet Germany GmbH Kleinniedesheim Germany

The security of computer systems can be increased effectively by using cryptographic co-processors to encapsulate secrets and speed-up the computationally intensive cryptographic functions. This can be especially advantageous for Internet of Things devices, as they usually have to be very efficient in cost, space and timing. However, these devices are also at greater risk of becoming targets of hardware attacks, as they handle sensitive data and are physically exposed to a nearly unrestricted population of users. This paper describes a modular cryptographic co-processor, allowing it to be applied in different scenarios and easily adjusted to concrete system specifications. The co-processor design is also open-source and freely available for anyone to further applications and modifications. It implements the basic cryptographic functions of symmetric encryption, hashing and a pseudo random number generation, with an interface to a true random number generator. In addition, the co-processor offers additional interfaces for key generation. A specific realization is presented in detail, compared to existing solutions, and its resilience against various attacks is discussed.

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Generalised Diffusion Probabilistic Scale-Spaces

arXiv

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arXiv 2023年

作者： Peter, Pascal Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University Campus E1.7 Saarbrücken Saarland66041 Germany

Diffusion probabilistic models excel at sampling new images from learned distributions. Originally motivated by drift-diffusion concepts from physics, they apply image perturbations such as noise and blur in a forward process that results in a tractable probability distribution. A corresponding learned reverse process generates images and can be conditioned on side information, which leads to a wide variety of practical applications. Most of the research focus currently lies on practice-oriented extensions. In contrast, the theoretical background remains largely unexplored, in particular the relations to drift-diffusion. In order to shed light on these connections to classical image filtering, we propose a generalised scale-space theory for diffusion probabilistic models. Moreover, we show conceptual and empirical connections to diffusion and osmosis filters. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Orthology and Near-Cographs in the Context of Phylogenetic Networks

arXiv

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arXiv 2025年

作者： Lindeberg, Anna Scholz, Guillaume E. Wieseke, Nicolas Hellmuth, Marc Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Swarm Intelligence and Complex Systems Group Department of Computer Science Universität Leipzig Augustusplatz 10 LeipzigD-04109 Germany

Orthologous genes, which arise through speciation, play a key role in comparative genomics and functional inference. In particular, graph-based methods allow for the inference of orthology estimates without prior knowledge of the underlying gene or species trees. This results in orthology graphs, where each vertex represents a gene, and an edge exists between two vertices if the corresponding genes are estimated to be orthologs. Orthology graphs inferred under a tree-like evolutionary model must be cographs. However, real-world data often deviate from this property, either due to noise in the data, errors in inference methods or, simply, because evolution follows a network-like rather than a tree-like process. The latter, in particular, raises the question of whether and how orthology graphs can be derived from or, equivalently, are explained by phylogenetic networks. In this work, we study the constraints imposed on orthology graphs when the underlying evolutionary history follows a phylogenetic network instead of a tree. We show that any orthology graph can be represented by a sufficiently complex level-k network. However, such networks lack biologically meaningful constraints. In contrast, level-1 networks provide a simpler explanation, and we establish characterizations for level-1 explainable orthology graphs, i.e., those derived from level-1 evolutionary histories. To this end, we employ modular decomposition, a classical technique for studying graph structures. Specifically, an arbitrary graph is level-1 explainable if and only if each primitive subgraph is a near-cograph (a graph in which the removal of a single vertex results in a cograph). Additionally, we present a linear-time algorithm to recognize level-1 explainable orthology graphs and to construct a level-1 network that explains them, if such a network exists. Finally, we demonstrate the close relationship of level-1 explainable orthology graphs to the substitution operation, weakly chordal and perfe

关键词： Trees (mathematics)

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Analysis of Public Sentiment regarding COVID-19 Vaccines on the Social Media Platform Reddit

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Journal of computer and Communications 2024年第2期12卷 80-108页

作者： Lucien Dikla Ngueleo Jules Pagna Disso Armel Ayimdji Tekemetieu Justin Moskolaï Ngossaha Michael Nana Kameni African Institute for Mathematical Sciences (AIMS)/AIMS-Cameroon Limbe Cameroon Department of Mathematics and Computer Science Faculty of Science University of Douala Douala Cameroon WMG (Warwick Manufacturing Group) University of Warwick Coventry UK School of Information Studies McGill University Montreal Canada

This study undertakes a thorough analysis of the sentiment within the r/Corona-virus subreddit community regarding COVID-19 vaccines on Reddit. We meticulously collected and processed 34,768 comments, spanning from November 20, 2020, to January 17, 2021, using sentiment calculation methods such as TextBlob and Twitter-RoBERTa-Base-sentiment to categorize comments into positive, negative, or neutral sentiments. The methodology involved the use of Count Vectorizer as a vectorization technique and the implementation of advanced ensemble algorithms like XGBoost and Random Forest, achieving an accuracy of approximately 80%. Furthermore, through the Dirichlet latent allocation, we identified 23 distinct reasons for vaccine distrust among negative comments. These findings are crucial for understanding the community’s attitudes towards vaccination and can guide targeted public health messaging. Our study not only provides insights into public opinion during a critical health crisis, but also demonstrates the effectiveness of combining natural language processing tools and ensemble algorithms in sentiment analysis.

关键词： COVID-19 Vaccine TextBlob Twitter-RoBERTa-Base-Sentiment Sentiment Analysis Latent Dirichlet Allocation

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Characterizing Zagreb Index Bounds in Trees with Specified Metric Dimension

arXiv

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arXiv 2024年

作者： Ali, Waqar Husin, Mohamad Nazri Bin Nadeem, Muhammad Faisal Special Interest Group on Modeling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Department of Mathematics COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan

Consider a simple graph G = (V, E), where V are the vertices and E are the edges. The first Zagreb index, M1(G) = Pv∈V ψG(v)2. The second Zagreb index, M2(G) = Puv∈E ψG(u)ψG(v). The metric dimension of a graph refers to the smallest subset of vertices in a resolving set such that the distances from these vertices to all others in the graph uniquely identify each vertex. In this paper, we characterize bounds for the Zagreb indices of trees, based on the order of the tree and its metric dimension. Furthermore, we identify the trees that achieve these extremal bounds, offering valuable insights into how the metric dimension influences the behavior of the Zagreb indices in tree structures. © 2024, CC BY.

关键词： Trees (mathematics)

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