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Enhancing Tourist Experience via Automatic Personalized Route Suggestions 3

Enhancing Tourist Experience via Automatic Personalized Rout...

3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Ruko, Sediola Alcalde-Llergo, Jose M. Yeguas-Bolivar, Enrique Zingoni, Andrea University of Tuscia Dept. of Economics Engineering Society and Business Organization Viterbo Italy University of Cordoba Dept. of Computing and Numerical Analysis. Cordoba Spain

ISBN: (纸本)9798350378009

Navigating the streets of a new city in search of points of interest (POIs) can be challenging for tourists since they are not familiar with the place. The challenge is even more pronounced for foreigners who do not speak the language of the visited city, making navigation and understanding local signage more difficult. This article presents an approach called TourTracer for estimating tourist routes and providing optimal routing services based on the paths taken by other tourists and on their preferred POIs, enriching the overall travel experience. To achieve this goal, a novel methodology has been designed, which, modifies the Dijkstra's algorithm with the insertion of two heuristics, namely the tourists frequency and the POIs preference, which take into account the routes tourists usually traversed through a city and their personal tastes in matters of places to visit. This method aims to enhance the experience of navigating unfamiliar urban environments, making it easier for visitors to explore and discover attractions. The proposed approach was evaluated by using over 200 routes from 53 different tourists, who have been monitored while visiting the city of Viterbo (Italy). These routes were then given as input to the designed algorithm to optimize the routes of future tourists exploring the city. Afterwards, the proposed approach has been tested with 12 new tourists, to whom the system recommended different paths based on the place of different POIs. The satisfaction with the recommended routes was measured through a questionnaire, yielding very positive results. © 2024 IEEE.

关键词： Leisure

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Predictive Maintenance with Large Language Models and Transformer-based Survival analysis 50

Predictive Maintenance with Large Language Models and Transf...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Esteban, Aurora Cobilean, Victor Nawaratne, Rashmika Dept. of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Dept. of Computer Science Virginia Commonwealth University RichmondVA United States Research Center for Data Analytics and Cognition La Trobe University VIC Australia

ISBN: (纸本)9781665464543

Predictive maintenance involves proactive monitoring and prediction of equipment failures to optimize maintenance schedules to minimize downtime, reduce losses, and ensure optimal equipment performance. However, traditional methods often rely on classification or regression, overlooking the temporal aspect of failure prediction. Survival analysis is focused on modeling time-to-event data, making it well-suited for predicting the time until equipment failures. The primary goal of this paper is to evaluate the feasibility and effectiveness of Transformer-based model for survival analysis in the industrial domain. Additionally, this work aims to explore the integration of Large Language Models as virtual assistants to facilitate user interaction and decision-making in the predictive maintenance process. The proposed system was tested on a public predictive maintenance problem and the Transformer-based model obtained better survival predictions than the baseline statistical and machine learning models, with improvements of up to 20%. Furthermore, the virtual assistant based on the large language model demonstrated the ability to retrieve precise information based on natural language prompts. © 2024 IEEE.

关键词： Predictive maintenance

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Virtual Training of Healthcare Providers for Migrant Pregnant Women: A Methodological Approach 3

Virtual Training of Healthcare Providers for Migrant Pregnan...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Bisio, Alessia Yeguas-Bolívar, Enrique Aparicio-Martínez, Pilar Mezcua, Aurora Ruiz Taborri, Juri University of Cordoba Artificial Vision Applications Group Córdoba Spain University of Cordoba Dept. of Computing and Numerical Analysis Cordoba Spain University of Cordoba Dept. of Nursing Physiotherapy and Pharmacology Cordoba Spain University of Cordoba Dept. of Social Sciences Translation and Interpreting Cordoba Spain Università della Tuscia Deim Industrial Engineering Viterbo Italy

ISBN: (纸本)9798350378009

Nowadays, there is a lack of deep information and specific training for healthcare professionals about the approach they should have towards migrant pregnant women. This methodological paper presents the framework and design process of our work, whose primary objective is to implement a virtual reality (VR) lab aimed at educating sanitary professionals on effectively interacting with migrant women, showing the barriers they face on both sides and trying to overcome them in the VR scenario during a standardized task. By immersing professionals in realistic scenarios, this lab seeks to provide comprehensive training on culturally sensitive and gender-specific approaches, empowering them to deliver tailored healthcare services to this vulnerable population. At the same time, it raises awareness of minorities and provides basic cultural and linguistic tools to overcome personal barriers. Additionally, the lab aims to enhance pregnancy outcomes and the overall health and well-being of migrant women. © 2024 IEEE.

关键词： Virtual environments

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Investigation on Relative Pose Regression for Multi-Camera Setups with Neural Radiance Fields 8

Investigation on Relative Pose Regression for Multi-Camera S...

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8th International ISPRS Workshop LowCost 3D - Sensors, Algorithms, Applications

作者： Kullmann, Timo Hübner, Patrick Wirth, Tristan Kuijper, Arjan Iwaszczuk, Dorota Technical University of Darmstadt Dept. of Civil and Environmental Engineering Sciences Remote Sensing and Image Analysis Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Mathematical and Applied Visual Computing Darmstadt Germany Fraunhofer IGD Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Interactive Graphics Systems Darmstadt Germany

Neural Radiance Fields (NeRFs) are a novel approach that is being intensively investigated in 3D scene reconstruction and similar fields to overcome challenges of conventional methods. In this paper, we address the problem of estimating missing camera poses in a six degrees of freedom setting, pushing the capabilities of NeRFs to address scenarios where only the primary camera's pose is known. Specifically, we focus on dual-camera setups with this constraint. Our core contribution is a novel pose correction model that operates alongside an unmodified NeRF model, for which we have chosen Nerfacto in the Nerfstudio framework. The pose correction model learns the necessary relative translation and rotation adjustments for the secondary camera solely through the NeRF loss. This allows us to integrate our correction model directly into the Nerfacto training pipeline without altering the core functionality. Through extensive experiments on different camera configurations in a synthetic scene, we rigorously evaluate our model's performance across diverse scenarios, pushing it to its limits. Our findings reveal that our model can effectively learn pose correction parameters within a constrained range, with increased sensitivity to larger translations and particular challenges in rotation corrections. This research highlights the potential of NeRFs for machine learning-driven 3D reconstruction on dual- and multi-camera platforms, expanding the applicability of NeRFs to more complex, real-world setups despite the inherent challenges. Copyright: © 2024 Timo Kullmann et al.

关键词： 3D reconstruction

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Artificial intelligence to automate the systematic review of scientific literature

arXiv

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arXiv 2024年

作者： de la Torre-López, José Ramírez, Aurora Romero, José Raúl Dept. Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Córdoba14071 Spain

Artificial intelligence (AI) has acquired notorious relevance in modern computing as it effectively solves complex tasks traditionally done by humans. AI provides methods to represent and infer knowledge, efficiently manipulate texts and learn from vast amount of data. These characteristics are applicable in many activities that human find laborious or repetitive, as is the case of the analysis of scientific literature. Manually preparing and writing a systematic literature review (SLR) takes considerable time and effort, since it requires planning a strategy, conducting the literature search and analysis, and reporting the findings. Depending on the area under study, the number of papers retrieved can be of hundreds or thousands, meaning that filtering those relevant ones and extracting the key information becomes a costly and error-prone process. However, some of the involved tasks are repetitive and, therefore, subject to automation by means of AI. In this paper, we present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies. This survey also provides a historical perspective of the evolution of the field and the role that humans can play in an increasingly automated SLR *** Codes 68T01 © 2024, CC BY.

关键词： Machine learning

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A VR Serious Game to Increase Empathy Towards Students with Phonological Dyslexia 2

A VR Serious Game to Increase Empathy Towards Students with ...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Alcalde-Llergo, José M. Yeguas-Bolívar, Enrique Aparicio-Martínez, Pilar Zingoni, Andrea Taborri, Juri Pinzi, Sara Viterbo Italy University of Córdoba Computing and Numerical Analysis Córdoba Spain University of Córdoba Nursing Physiotherapy and Pharmacology Córdoba Spain University of Córdoba Dept. Physical Chemistry and Applied Thermodynamics Córdoba Spain

ISBN: (纸本)9798350300802

Dyslexia is a neurodevelopmental disorder that is estimated to affect about 5-10% of the population. In particular, phonological dyslexia causes problems in connecting the sounds of words with their written forms. This results in difficulties such as slow reading speed, inaccurate reading, and difficulty decoding unfamiliar words. Moreover, dyslexia can also be a challenging and frustrating experience for students as they may feel misunderstood or stigmatized by their peers or educators. For these reasons, the use of compensatory tools and strategies is of crucial importance for dyslexic students to have the same opportunities as non-dyslexic ones. However, generally, people underestimate the problem and are not aware of the importance of support methodologies. In the light of this, the main purpose of this paper is to propose a virtual reality (VR) serious game through which teachers, students and, in general, non-dyslexic people could understand which are some of the issues of student with dyslexia and the fundamental utility of offering support to them. In the game, players must create a potion by following a recipe written in an alphabet that is specifically designed to replicate the reading difficulties experienced by individuals with dyslexia. The task must be solved first without any help and then by receiving supporting tools and strategies with the idea that the player can put himself in the place of the dyslexic person and understand the real need for support methodologies. © 2023 IEEE.

关键词： Dyslexia Empathy Inclusion Serious game Simulation Virtual reality

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Training Program on Sign Language: Social Inclusion Through Virtual Reality in ISENSE Project 2

Training Program on Sign Language: Social Inclusion Through ...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Bisio, Alessia Yeguas-Bolívar, Enrique Aparicio-Martínez, Pilar Redel-Macías, Maria Dolores Pinzi, Sara Rossi, Stefano Taborri, Juri Politecnico di Torino Dept. of Mechanical and Aerospace Engineering Torino Italy University of Cordoba Dept. of Computing and Numerical Analysis Córdoba Spain University of Cordoba Dept. of Nursing Physiotherapy and Pharmacology Córdoba Spain University of Cordoba Dept. of Rural Engineering Córdoba Spain University of Cordoba Dept. Physical Chemistry and Applied Thermodynamics Córdoba Spain Università della Tuscia Deim Industrial Engineering Viterbo Italy

ISBN: (纸本)9798350300802

Structured hand gestures that incorporate visual motions and signs are used in sign language. Sign language is a valuable means of daily communication for individuals who are deaf or have speech impairments, but it is still rare among hearing people, and fewer are capable of understand it. Within the academic context, parents and teachers play a crucial role in supporting deaf students from childhood by facilitating their learning of sign language. In the last years, among all the teaching tools useful for learning sign language, the use of Virtual Reality (VR) has increased, as it has been demonstrated to improve retention, memory and attention during the learning process. The ISENSE project has been created to assist students with deafness during their academic life by proposing different technological tools for teaching sign language to the hearing community in the academic context. As part of the ISENSE project, this work aims to develop an application for Spanish and Italian sign language recognition that exploits the VR environment to quickly and easily create a comprehensive database of signs and an Artificial Intelligence (AI)-based software to accurately classify and recognize static and dynamic signs: from letters to sentences. © 2023 IEEE.

关键词： Deafness Machine Learning Virtual Reality

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VR-based Silent Reading and Rosenberg Tests: Machine-Learning Approach to Identify Learning Disorders

VR-based Silent Reading and Rosenberg Tests: Machine-Learnin...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Michele Materazzini Gianluca Morciano José Manuel Alcalde-Llergo Enrique Yeguas-Bolivar Andrea Zingoni Juri Taborri Dept. of Economics Engineering Society and Business Organization University of Tuscia Viterbo Italy Computing and Numerical Analysis University of Córdoba Córdoba Spain

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

This study explores the intersection of self-esteem assessment, specific learning disorders (SLDs), and emerging technologies, particularly virtual reality (VR) and machine learning (ML). Through this scope, the research involved the development of novel VR-based tools for administering silent reading and Rosenberg tests, designed to simulate real-world academic challenges and social interactions. A sample of 40 Italian university students, including those diagnosed with SLDs and a control group, participated in the study. Data collected from VR were analyzed using ML algorithms, with a focus on dyslexia detection. The findings underscored the critical role of self-esteem in academic performance and overall well-being for individuals with SLDs. ML models, particularly support vector machines (SVM), demonstrated efficacy in identifying dyslexic tendencies based on data from VR. This multidisciplinary approach contributes to advancing personalized intervention strategies for individuals with SLDs, emphasizing early diagnosis and targeted support systems.

关键词： Support vector machines Solid modeling Machine learning algorithms Extended reality Neural engineering Machine learning Metrology Superluminescent diodes Dyslexia Data models

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Towards Explainable Test Case Prioritisation with Learning-to-Rank Models

arXiv

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arXiv 2024年

作者： Ramírez, Aurora Berrios, Mario Romero, José Raúl Feldt, Robert Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Softw. Eng. Division Dept. of Computer Science and Engineering Chalmers University of Technology Goteborg Sweden

Test case prioritisation (TCP) is a critical task in regression testing to ensure quality as software evolves. Machine learning has become a common way to achieve it. In particular, learning-to-rank (LTR) algorithms provide an effective method of ordering and prioritising test cases. However, their use poses a challenge in terms of explainability, both globally at the model level and locally for particular results. Here, we present and discuss scenarios that require different explanations and how the particularities of TCP (multiple builds over time, test case and test suite variations, etc.) could influence them. We include a preliminary experiment to analyse the similarity of explanations, showing that they do not only vary depending on test case-specific predictions, but also on the relative ranks. © 2024, CC BY-NC-ND.

关键词： Machine learning

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Convolutional and Deep Learning based techniques for Time Series Ordinal Classification

arXiv

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arXiv 2023年

作者： Ayllón-Gavilán, Rafael Guijo-Rubio, David Gutiérrez, Pedro Antonio Bagnall, Anthony Hervás-Martínez, César Dept. of Clinical-Epidemiological Research in Primary Care IMIBIC Spain Dept. of Computer Science and Numerical Analysis University of Cordoba Spain Dept. of Electronics and Computer Science University of Southampton United Kingdom

Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the field bridging this gap, yet unexplored in the literature. There are a wide range of time series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this paper presents the first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art. Both convolutional- and deep learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems has been made. In this way, this paper contributes to the establishment of the state-of-the-art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems. © 2023, CC BY-NC-ND.

关键词： Convolution

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