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UNIVERSITY BALEARIC ISLANDS, Spain

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24 Papers

A Comparison Study of Cloud Environment Simulations

Topic: Cloud Computing

Authors: Adrián Jiménez , Carlos Juiz and Belen Bermejo

Affiliation: Computer Science Department, University of the Balearic Islands, Spain

Digital Object Identifier: 10.5220/0013646300003970

Proceedings of SIMULTECH 2025, ISBN: 978-989-758-759-7

Quick Abstract

Over the years, the need for cloud computing systems (virtualized) has continued to grow. For this reason, it is necessary to evaluate their performance under different workload conditions. This is typically done by benchmarking to assess their behavior with different workloads. Simulation tools offer a practical solution, allowing evaluations to be carried out at a fraction of the cost compared to real-world deployments. CloudSim is one of these tools, widely used to model complex cloud computing scenarios. In this work, we extend a previous published real-world evaluation and aim to replicate it within a reproducible and flexible simulation environment. This allows us to analyze system behavior under different workload intensities derived from real-world arrival rate patterns. Since CloudSim does not natively support time-based realistic traces or efficient data collection, we extended its functionality to address these limitations proposing a modular and reproducible simulation system based on CloudSim.

Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-Being

Topics: Software Development Methodologies; Ethics and Social Implications and SSE ; Process Improvement; SSE for Social Computing

Authors: Esperança Amengual-Alcover ¹ , Antoni Jaume-i-Capó ¹ , Miquel Miró-Nicolau ¹ , Gabriel Moyà-Alcover ¹ and Antonia Paniza-Fullana ²

Affiliations: ¹ Department of Mathematics and Computer Science, University of the Balearic Islands, Ctra. de Valldemossa, Km. 7.5, 07122 - Palma de Mallorca, Spain , ² Department of Private Law, University of the Balearic Islands, Ctra. de Valldemossa, Km. 7.5, 07122 - Palma de Mallorca, Spain

Digital Object Identifier: 10.5220/0013289600003928

Proceedings of ENASE 2025, ISBN: 978-989-758-742-9

Quick Abstract

The integration of Artificial Intelligence in the development of computer systems presents a new challenge: make intelligent systems explainable to humans. This is especially vital in the field of health and well-being, where transparency in decision support systems enables healthcare professionals to understand and trust automated decisions and predictions. To address this need, tools are required to guide the development of explainable AI systems. In this paper, we introduce an evaluation framework designed to support the development of explainable AI systems for health and well-being. Additionally, we present a case study that illustrates the application of the framework in practice. We believe that our framework can serve as a valuable tool not only for developing explainable AI systems in healthcare but also for any AI system that has a significant impact on individuals.

Combining Progressive Hierarchical Image Encoding and YOLO to Detect Fish in Their Natural Habitat

Topics: Object Detection and Localization; Vision for Robotics; Deep Learning for Visual Understanding ; Deep Learning for Tracking

Author: Antoni Burguera

Affiliation: Departament de Matemàtiques i Informàtica, Universitat de les Illes Balears, Ctra. Valldemossa Km. 7.5, 07122 Palma, Illes Balears, Spain

Digital Object Identifier: 10.5220/0012423700003660

Proceedings of VISAPP 2024, ISBN: 978-989-758-679-8

Quick Abstract

This paper explores the advantages of evaluating Progressive Image Encoding (PIE) methods in the context of the specific task for which they will be used. By focusing on a particular task —fish detection in their natural habitat— and a specific PIE algorithm — Progressive Hierarchical Image Encoding (PHIE)—, the paper investigates the performance of You Only Look Once (YOLO) in detecting fish in underwater images using PHIE-encoded images. This is particularly relevant in underwater environments where image transmission is slow. Results provide insights into the advantages and drawbacks of PHIE image encoding and decoding, not from the perspective of general metrics such as reconstructed image quality but from the viewpoint of its impact on a task —fish detection— that depends on the PHIE encoded and decoded images.

Microplankton Discrimination in FlowCAM Images Using Deep Learning

Topics: Deep Learning for Visual Understanding ; Assistive Computer Vision

Authors: Francisco Bonin-Font ¹ , Gorka Buenvaron ² , Mary Kane ³ and Idan Tuval ³

Affiliations: ¹ Systems, Robotics and Vision Group (SRVG), University of the Balearic Islands, ctra de Valldemossa km 7.5, Palma de Mallorca, Balearic Islands, Spain , ² Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), University of the Balearic Islands, ctra de Valldemossa km 7.5, Palma de Mallorca, Balearic Islands, Spain , ³ Department of Marine Ecology, Mediterranean Institute of Advanced Studies (IMEDEA), Miquel Marqués 21, Esporles, Balearic Islands, Spain

Digital Object Identifier: 10.5220/0012460200003660

Proceedings of VISAPP 2024, ISBN: 978-989-758-679-8

Quick Abstract

Marine plankton are omnipresent throughout the oceans, and the Mediterranean Sea is no exception. Innovation on microscopy technology for observing marine plankton over the last several decades has enabled scientist to obtain large quantities of images. While these new instruments permit generating and recording large amounts of visual information about plankton, they have produced a bottleneck and overwhelmed our abilities to provide meaningful taxonomic information quickly. The development of methods based on Artificial Intelligence or Deep Learning to process these images in efficient, cost-effective manners is an active area of continued research. In this study, Convolutional Neural Networks (CNNs) were trained to analyze images of natural assemblages of microplankton (< 100µm) and laboratory monocultures. The CNN configurations and training were focused on differentiating phytoplankton, zooplankton, and zooplankton consuming phytoplankton. Experiments reveal high performance in the discrimination of these different varieties of plankton, in terms of Accuracy, Precision, F1 scores and mean Average Precision.

Towards a General Metric for Energy Efficiency in Cloud Computing Data Centres: A Proposal for Extending of the ISO/IEC 30134-4

Topics: Cloud Standards; Cloud Quality and Performance; Virtualization Technologies; Energy Efficient Cloud Computing

Authors: Carlos Juiz ¹ , Belen Bermejo ¹ , Alejandro Fernández-Montes ² and Damián Fernández-Cerero ²

Affiliations: ¹ Department of Computer Science, Universitat de les Illes Balears, Palma, Spain , ² Department of Computer Languages and Systems, University of Seville, Spain

Digital Object Identifier: 10.5220/0012707600003711

Proceedings of CLOSER 2024, ISBN: 978-989-758-701-6

Quick Abstract

For some years now, energy efficiency has been one of the concerns of cloud system administrators. To improve energy efficiency, in recent years standards such as ISO/IEC 30134-4 and ISO/IEC 21836 have emerged. Both standards are focused on the evaluation of physical servers, taking into account the power consumed and the maximum peak of performance, under running a SPEC benchmark. In this way, the server consolidation through virtualization is not considered in these standards, being the consolidation of servers as one of the most applied techniques to improve energy efficiency in cloud data centres. This work proposes an extension of the methodology proposed in these standards to measure energy efficiency in consolidated servers. As a result, it has been demonstrated through real experimentation that the proposed generical methodology considers the consolidation of servers in any type of virtualization environment. This methodology helps system administrators to manage cloud data centres and servers more efficiently.

Robust Underwater Visual Graph SLAM using a Simanese Neural Network and Robust Image Matching

Topics: Image Registration; Tracking and Visual Navigation; Vision for Robotics; Simultaneous Localization and Mapping

Authors: Antoni Burguera ^{1

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Affiliations: ¹ Departament de Matemàtiques i Informàtica, Universitat de les Illes Balears, 07122 Palma, Spain , ² Institut d’Investigació Sanitària Illes Balears (IdISBa), 07010 Palma, Spain

Digital Object Identifier: 10.5220/0010889100003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Quick Abstract

This paper proposes a fast method to robustly perform Visual Graph SLAM in underwater environments. Since Graph SLAM is not resilient to wrong loop detections, the key of our proposal is the Visual Loop Detector, which operates in two steps. First, a lightweight Siamese Neural Network performs a fast check to discard non loop closing image pairs. Second, a RANSAC based algorithm exhaustively analyzes the remaining image pairs and filters out those that do not close a loop. The accepted image pairs are then introduced as new graph constraints that will be used during the graph optimization. By executing RANSAC only on a previously filtered set of images, the gain in speed is considerable. The experimental results, which evaluate each component separately as well as the whole Visual Graph SLAM system, show the validity of our proposal both in terms of quality of the detected loops, error of the resulting trajectory and execution time.

Towards Visual Loop Detection in Underwater Robotics using a Deep Neural Network

Topics: Image Registration; Machine Learning Technologies for Vision; Vision for Robotics; Deep Learning for Visual Understanding ; Simultaneous Localization and Mapping; Mobile and Egocentric Localization

Authors: Antoni Burguera and Francisco Bonin-Font

Affiliation: Universitat de les Illes Balears, Ctra. Valldemossa Km. 7.5, Palma (Illes Balears), 07122, Spain

Digital Object Identifier: 10.5220/0009162806670673

Proceedings of VISAPP 2020/VISIGRAPP, ISBN: 978-989-758-402-2

Quick Abstract

This paper constitutes a first step towards the use of Deep Neural Networks to fast and robustly detect underwater visual loops. The proposed architecture is based on an autoencoder, replacing the decoder part by a set of fully connected layers. Thanks to that it is possible to guide the training process by means of a global image descriptor built upon clusters of local SIFT features. After training, the NN builds two different descriptors of the input image. Both descriptors can be compared among different images to decide if they are likely to close a loop. The experiments, performed in coastal areas of Mallorca (Spain), evaluate both descriptors, show the ability of the presented approach to detect loop candidates and favourably compare our proposal to a previously existing method.

Algebraic Formal Modelling for EIGRP using ACP - Formal Description Modelling on EIGRP Routing Protocol

Topic: Conceptual Modeling

Authors: Pedro Juan Roig ¹ , Salvador Alcaraz ² , Katja Gilly ² and Carlos Juiz ³

Affiliations: ¹ Department of Physics and Computer Architecture, Miguel Hernández University, 03202 Elche (Alicante), Spain, Department of Computer Science, University of the Balearic Islands, 07122 Palma de Mallorca and Spain , ² Department of Physics and Computer Architecture, Miguel Hernández University, 03202 Elche (Alicante) and Spain , ³ Department of Computer Science, University of the Balearic Islands, 07122 Palma de Mallorca and Spain

Digital Object Identifier: 10.5220/0006838802590266

Proceedings of SIMULTECH 2018, ISBN: 978-989-758-323-0

Quick Abstract

Fast-converging routing protocols are necessary in order to keep up with the interconnected world we are living in and one of the quickest ones is EIGRP. In this paper, we are going to design two models for network devices running EIGRP by focusing on the main events happening on them. First, a non-timing model is going to be formally described, hence just studying the aforesaid main events without any time constraints. Then, a timing model is going to extend the former with the proper time values associated with each particular event. Both models are going to be formally described by means of manual algebraic derivations using Algebra of Communicating Processes (ACP).

Related Ontology Subjects/Areas/Topics (3)

Conceptual Modeling

Formal Methods

Simulation and Modeling

OSPF Algebraic Formal Modelling using ACP - A Formal Description on OSPF Routing Protocol

Topic: Network Protocols

Authors: Pedro Juan Roig ¹ , Salvador Alcaraz ² , Katja Gilly ² and Carlos Juiz ³

Digital Object Identifier: 10.5220/0006838702210232

Proceedings of ICETE 2018, ISBN: 978-989-758-319-3

Quick Abstract

OSPF may well be the most popular routing protocol within Autonomous Systems, being used in all kind of networks around the world. In this paper, we first design a basic model by focusing on the main tasks of a router running OSPF, hence being neighbour discovery and a simplified route management, by means of algebraic derivations using Algebra of Communicating Processes (ACP). Taking this model as a base case scenario, we extend it by adding up some timing behaviour present in real OSPF implementations and by detailing the packet exchanges involved in route management.

Related Ontology Subjects/Areas/Topics (3)

Data Communication Networking

Network Protocols

Telecommunications

Towards a Pre-diagnose of Surgical Wounds through the Analysis of Visual 3D Reconstructions

Topics: Image-Based Modeling and 3D Reconstruction; Stereo Vision and Structure from Motion; Medical Image Applications

Authors: Neus Muntaner Estarellas ¹ , Francisco Bonin-Font ² , Juan J. Segura-Sampedro ³ , Andres Jiménez Ramírez ¹ , Pep L. Negre Carrasco ² , Miquel Massot Campos ² , Francesc X. Gonzalez-Argenté ³ and Gabriel Oliver Codina ²

Affiliations: ¹ University of Sevilla, Spain , ² University of the Balearic Islands, Spain , ³ University Hospital of Son Espases, Spain

Digital Object Identifier: 10.5220/0006628505890595

Proceedings of VISAPP 2018/VISIGRAPP, ISBN: 978-989-758-290-5

Quick Abstract

This paper presents a new methodology to pre-diagnose the state of post-surgical abdominal wounds based on visual information. The process consist of four major phases: a) building dense 3D reconstruction of the abdominal area around the wound, b) selecting an area close to the wound to fit a plane, c) calculating the distance from each point of the 3D model to the plane, d) analyzing this map of distances to infer if the wound is inflamed or not. This method needs to be wrapped in an application to be used by patients in order to save unnecessary visits to the medical center.

Related Ontology Subjects/Areas/Topics (10)

Applications	Image-Based Modeling	Pattern Recognition
Applications and Services	Medical Image Applications	Software Engineering
Computer Vision, Visualization and Computer Graphics	Motion, Tracking and Stereo Vision	Stereo Vision and Structure from Motion
Geometry and Modeling

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