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15th International Conference on Social Robotics, ICSR 2023

作者： Elsayed, Saber Mabrok, Mohamed School of Systems and Computing University of New South Wales Canberra Australia Mathematics Program Department of Mathematics Statistics and Physics College of Arts and Sciences Qatar University P.O. Box 2713 Doha Qatar

ISBN: (纸本)9789819987146

In the evolving era of social robots, managing a swarm of autonomous agents to perform particular tasks has become essential for numerous industries. The task becomes more challenging for large-scale swarms and complex environments, which have not been fully explored yet. Therefore, this research introduces a methodology incorporating multiple coordinated robotic shepherds to effectively guide large-scale agent swarms in obstacle-laden terrains. The proposed framework commences with deploying an unsupervised machine-learning algorithm to categorise the swarm into clusters. Then, a shepherding algorithm with coordinated robotic shepherds drives the sub-swarms towards the goal. Also, a path planner based on an evolutionary algorithm is proposed to help robotic shepherds move in a way that minimises the dispersion of each sub-swarm and avoids potential hazards and obstructions. The proposed approach is tested on different scenarios, with the results showing a success rate of 100% in guiding swarms with sizes up to 3000 agents. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Physics-informed neural networks for functional differential equations: cylindrical approximation and its convergence guarantees 24

Physics-informed neural networks for functional differential...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Taiki Miyagawa Takeru Yokota Independent Researcher Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) RIKEN Center for Quantum Computing RIKEN

ISBN: (纸本)9798331314385

We propose the first learning scheme for functional differential equations (FDEs). FDEs play a fundamental role in physics, mathematics, and optimal control. However, the numerical analysis of FDEs has faced challenges due to its unrealistic computational costs and has been a long standing problem over decades. Thus, numerical approximations of FDEs have been developed, but they often oversimplify the solutions. To tackle these two issues, we propose a hybrid approach combining physics-informed neural networks (PINNs) with the cylindrical approximation. The cylindrical approximation expands functions and functional derivatives with an orthonormal basis and transforms FDEs into high-dimensional PDEs. To validate the reliability of the cylindrical approximation for FDE applications, we prove the convergence theorems of approximated functional derivatives and solutions. Then, the derived high-dimensional PDEs are numerically solved with PINNs. Through the capabilities of PINNs, our approach can handle a broader class of functional derivatives more efficiently than conventional discretization-based methods, improving the scalability of the cylindrical approximation. As a proof of concept, we conduct experiments on two FDEs and demonstrate that our model can successfully achieve typical L1 relative error orders of PINNs ~ 10-3. Overall, our work provides a strong backbone for physicists, mathematicians, and machine learning experts to analyze previously challenging FDEs, thereby democratizing their numerical analysis, which has received limited attention. Code is available at https://***/TaikiMiyagawa/FunctionalPINN.

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Convergence analysis of a Schrödinger problem with moving boundary

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Mathematics and Computers in Simulation 2025年 238卷 45-64页

作者： Alfaro Vigo, Daniel G. Gomes, Daniele C.R. do Carmo, Bruno A. Rincon, Mauro A. Institute of Computing Federal University of Rio de Janeiro Caixa Postal 68.530 RJ Rio de JaneiroCEP 21941-590 Brazil Graduate Program in Informatics Federal University of Rio de Janeiro Caixa Postal 68.530 RJ Rio de JaneiroCEP 21941-590 Brazil

In this article, we present the mathematical analysis of the convergence of the linearized Crank–Nicolson Galerkin method for a nonlinear Schrödinger problem related to a domain with a moving boundary. The convergence analysis of the numerical method is carried out for both semidiscrete and fully discrete problems. We establish an optimal error estimate in the L2-norm with order O(τ2+hs),2≤s≤r, where h is the finite element mesh size, τ is the time step, and r−1 represents the degree of the finite element polynomial basis. Numerical simulations are provided to confirm the consistency between theoretical and numerical results, validating the method and the order of convergence for different degrees p≥1 of the Lagrange polynomials and also for Hermite polynomials (degree p=3), which form the basis of the approximate solution. © 2025 International Association for Mathematics and Computers in Simulation (IMACS)

关键词： Convergence of numerical methods

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A Novel Approach for Commercial Opportunities Qualification Using the BANT Methodology under the Fuzzy Set Theory Framework. 28th

A Novel Approach for Commercial Opportunities Qualification ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Leite, Marcus Vinicius Abe, Jair Minoro Hoffmann Souza, Marcos Leandro Graduate Program in Production Engineering-Paulista University-UNIP Rua Dr. Bacelar 1212 São Paulo-SP04026002 Brazil Graduate Program in Applied Computing-Vale do Rio dos Sinos University-UNISINOS Av. Unisinos 950 - São LeopoldoRS-93022-000 Brazil

Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts' final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system's effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance. © 2024 The Authors.

关键词： Fuzzy set theory

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Mineralogical Classification of Sedimentary Rocks by Spectral Matching Techniques Based on Library and Field-Work References 14

Mineralogical Classification of Sedimentary Rocks by Spectra...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Zanotta, Daniel C. De Souza, Lais V. De Souza, Marcelo K. Ayres, Alysson S. Sales, Vinicius F. Racolte, Graciela R. Ibanez, Delano M. Silveira, Luiz G. Veronez, Mauricio R. UNISINOS Vizlab X-Reality and GeoInformatics Lab RS São Leopoldo Brazil Graduate Program in Applied Computing Vale do Rio dos Sinos University RS São Leopoldo Brazil RJ Brazil

ISBN: (纸本)9798331513139

Sedimentary rocks are of great importance for the oil and gas industry. Spectral matching using known references made on hyperspectral images is one of the most rapid and cost effective alternatives for the detailed analysis in these areas, since they can adequately catch the specific absorption features of the multiple minerals available. In many initiatives, scientists run the methods with standard spectral references, usually from spectral libraries or samples from other places. However, for the success of their application, they must be used along with spectral references that accurately describe the actual constituents of the surface. In this paper we scrutinize three different ways for mineralogical mapping performed by spectral matching techniques. The results showed that using reference spectra from samples collected directly on the surveyed environment can considerably improve the results, reaching high scores, pointing out that many places share common substances, but each specific material has particular differences that cannot be generalized. © 2024 IEEE.

关键词： Gas industry

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AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy 11

AI Recommendations: Friend or Foe? Unraveling the Impact on ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Dewanthi, Desyra Sukma Kristopo, Hari Qomariyah, Nunung Nurul Axel, Muhammad Business Management & Marketing Program Binus Business School International Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Technology Department Jakarta11480 Indonesia School of Computing and Creative Arts Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389654

Artificial Intelligence (AI) and marketing have transformed consumer behavior and shopping experiences, especially through Recommender Systems (RSs) in e-commerce. RSs use algorithms to provide personalized recommendations, enhancing shopping convenience. However, this raises data privacy and ethical concerns, as RSs rely on user data. This research explores the quality determinants of RSs and their impact on consumer behavior, focusing on privacy awareness and its effect on repurchase intentions. Using a qualitative approach with semi-structured interviews, the study targets Indonesian respondents aged 18-40 with e-commerce experience. Findings reveal that recommendation quality, especially self-reference and vividness, significantly influences purchasing decisions. Respondents prefer recommendations that match their preferences and are visually appealing. Positive shopping experiences are linked to efficient and accurate RSs, but there's a need for improvements in transparency, novelty, diversity, and advanced technologies like AR. Respondents are aware of digital data storage but have limited understanding of privacy concerns. They trust e-commerce platforms with basic personal details but are hesitant to share sensitive information. Despite privacy issues, e-commerce remains vital, with respondents emphasizing the need for transparency, robust security, and clear explanations for data use to maintain trust. Addressing these issues not only enhances customer experience but also demonstrates that consumer trust directly impacts the sustainability and success of the business. © 2024 IEEE.

关键词： Differential privacy

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Impact Analysis of Digital Banking Applications on Disrupting Traditional Banking Industry 7

Impact Analysis of Digital Banking Applications on Disruptin...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Siek, Michael Rukma, Luin Yatnalaksita Parasdyayatma Bina Nusantara University Faculty of Computing and Media Business Information Systems Program Information Systems Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

In the era of emerging technologies, the banks strive to provide a variety of innovations toward digital banking transformations. Hence, digital banking is the result rapid establishment of innovative digital banking applications that attracts more customers allowing for personalized and commercialized banking transactions. Due to the emerging needs of customers and changes in business models, digital banking applications have been sought to be the solutions for some drawbacks in traditional banking operations. This paper aims at identifying and determining the impacts of digital banking application in disrupting the traditional banking industry using hypothesis testing and analysis of regression on response data collected with convenient sampling technique. The data analysis utilized 265 samples gathered in May until June 2022 with the characteristics of respondents having age above 18 years old and using at least one digital banking application. The data analysis results with proper research framework demonstrate that the digital banking application has significantly disrupted the traditional banking industry, primarily due to the perceived ease of use, habit, customer satisfaction, and behavioral intention to use digital banking applications. This research result can provide useful reference and strategy for banking industry to provision innovative solutions towards banking digitalization and automation. © 2022 IEEE.

关键词： Customer satisfaction

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Data-Driven Modelling For Tsunami Forecasting Using Computational Intelligence 6

Data-Driven Modelling For Tsunami Forecasting Using Computat...

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6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Siek, Michael Rafles, Alfriyadi Bina Nusantara University Business Information Systems Program Faculty of Computing and Media Information Systems Department Jakarta11480 Indonesia

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

Numerous tsunami disasters have happened in Indonesia as located across Ring of Fire, and it brings casualties to both the economy and welfare of the people in the event of their occurrence. Frequent tectonic earthquakes in the oceanic areas may lead to tsunami disasters that can cause significant damages to the infrastructures and people. Therefore, the development and implementation of a significantly improved early warning system's performance is essential. This paper presents the research on finding an appropriate machine learning algorithm for provisioning fast and accurate tsunami forecasts using spatiotemporal data of tsunami event in Aceh occurred on December 26th, 2004. A mixture of two modelling paradigms: physically based and data-driven modelling was explored and developed by utilizing 3D numerical models with essential measurement data. The outputs of numerical computations are in the form of time series datasets with various time windows and forecast horizons. Three machine learning algorithms namely fully connected neural network (FCNN), convolutional neural networks (CNN), and recurrent neural network (CNN) with long short-term memory (LSTM) were employed and compared to achieve accurate tsunami wave forecasts, evaluated according to specific set of evaluation metrics. The model forecast comparison with window size of 15 minutes and forecast horizon of 1 minute indicate that FCNN model outperform the CNN and RNN with LSTM models, with RMSE of 0.299. This modelling results show that the proposed modelling framework has been able to support in provisioning towards fast and accurate tsunami early warning system. © 2022 IEEE.

关键词： Long short-term memory

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Perpetual Crossers without Sensory Delay: Revisiting the Perceptual Crossing Simulation Studies

Perpetual Crossers without Sensory Delay: Revisiting the Per...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Severino, Gabriel J. Merritt, Haily Cognitive Science Program Indiana University Bloomington Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

We revisit the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition by studying multi-agent real-time interactions. To date, all of these simulation studies have reported that it is practically impossible to evolve artificially a robust behavioral strategy without introducing temporal delays into the simulation. Also, all of the studies report on a single strategy: a perpetually crossing agent pair. Here, we systematically report on the evolutionary success of neural circuits on the perceptual crossing task, with and without sensory delay. We also report on two different strategies in the ensemble of successful solutions, only one of which had been discussed in the literature previously. © Artificial Life Conference Proceedings 2022.

关键词： Delay circuits

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SPLORT: Evaluation of a Web-based Approach for Descriptive and Exploratory Analysis of Multivariate Spatio-temporal Data 21

SPLORT: Evaluation of a Web-based Approach for Descriptive a...

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14th International Symposium on Visual Information Communication and Interaction, VINCI 2021

作者： Feijóo-García, Miguel Alfonso Hernández-Peñalosa, José Tiberio Program of Systems and Computing Engineering Universidad de Los Andes Colombia and Program of Systems Engineering Universidad El Bosque Colombia Program of Systems and Computing Engineering Universidad de Los Andes Colombia

ISBN: (纸本)9781450386470

The multivariate Spatiooral data challenges the analysts along the navigation and exploration of the information, using Data Analytics techniques. We present SPLORT, as a user-centered web-based solution for segmented Visual Analytics. The tool involves a "What-Where-When"pyramidal paradigm to drive Spatiooral analysis towards the findings of new insights. It features multi-layered coordinated views to help the analyst navigate and explore multivariate data simultaneously. We evaluated this web-based tool, and reported the findings and results, considering users' performance and perceptions, basing our analysis on their Likert scale (0-5) responses, regarding their experience and the tool's usability, and the sentiment of their feedback, by using Natural Language Processing (NLP) techniques. Our findings from the pilot study held, suggest that SPLORT successfully allows the users to visually query, explore and analyze contextless multi-scale attributes within space and time, which quickly drives them to new insights and to support easier decision-making processes. Moreover, the tool's flexibility, due to its multi-layered GUI, supports better and more friendly visual analytics. © 2021 ACM.

关键词： Data Analytics

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