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SR-curvANN: Advancing 3D surface reconstruction through curvature-aware neural networks

computers & Graphics 2025年 130卷

作者： Marina Hernández-Bautista Francisco J. Melero Department of Computer Science and Artificial Intelligence University of Granada 18071 Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) Spain Department of Software Engineering University of Granada 18071 Granada Spain

Incomplete or missing data in three-dimensional (3D) models can lead to erroneous or flawed renderings, limiting their usefulness in applications such as visualization, geometric computation, and 3D printing. conventional surface-repair techniques often fail to infer complex geometric details in missing areas. Neural networks successfully address hole-filling tasks in 2D images using inpainting techniques. The combination of surface reconstruction algorithms, guided by the model’s curvature properties and the creativity of neural networks in the inpainting processes, should provide realistic results in the hole completion task. In this paper, we propose a novel method entitled SR-curvANN (Surface Reconstruction Based on curvature-Aware Neural Networks) that incorporates neural network-based 2D inpainting to effectively reconstruct 3D surfaces. We train the neural networks with images that represent planar representations of the curvature at vertices of hundreds of 3D models. Once the missing areas have been inferred, a coarse-to-fine surface deformation process ensures that the surface fits the reconstructed curvature image. Our proposal makes it possible to learn and generalize patterns from a wide variety of training 3D models, generating comprehensive inpainted curvature images and surfaces. Experiments conducted on 959 models with several holes have demonstrated that SR-curvANN excels in the shape completion process, filling holes with a remarkable level of realism and precision.

关键词： Shape completion curvature representation Parametrization Inpainting

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Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms

arXiv

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arXiv 2021年

作者： carrasco, Jacinto López, David Aguilera-Martos, Ignacio García-Gil, Diego Markova, Irina García-Barzana, Marta Arias-Rodil, Manuel Luengo, Julián Herrera, Francisco Department of Computer Science and AI Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain ArcelorMittal Global R&D New Frontier Digital Portfolio

The research in anomaly detection lacks a unified definition of what represents an anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple paradigms of algorithms design and experimentation. Predictive maintenance is a special case, where the anomaly represents a failure that must be prevented. Related time series research as outlier and novelty detection or time series classification does not apply to the concept of an anomaly in this field, because they are not single points which have not been seen previously and may not be precisely annotated. Moreover, due to the lack of annotated anomalous data, many benchmarks are adapted from supervised scenarios. To address these issues, we generalise the concept of positive and negative instances to intervals to be able to evaluate unsupervised anomaly detection algorithms. We also preserve the imbalance scheme for evaluation through the proposal of the Preceding Window ROc, a generalisation for the calculation of ROc curves for time series scenarios. We also adapt the mechanism from a established time series anomaly detection benchmark to the proposed generalisations to reward early detection. Therefore, the proposal represents a flexible evaluation framework for the different scenarios. To show the usefulness of this definition, we include a case study of Big data algorithms with a real-world time series problem provided by the company ArcelorMittal, and compare the proposal with an evaluation method. © 2021, cc0.

关键词： Anomaly detection

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Interactivity in the Generation of Test cases with Evolutionary computation

Interactivity in the Generation of Test Cases with Evolution...

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congress on Evolutionary computation

作者： Aurora Ramírez Pedro Delgado-Pérez Kevin J. Valle-Gómez Inmaculada Medina-Bulo José Raúl Romero University of Córdoba Córdoba Spain University of Cádiz Cádiz Spain Andalusian Research Institute in Data Science and Computacional Intelligence (DaSCI) University of Córdoba Córdoba Spain

Test generation is a costly but necessary testing activity to increase the quality of software projects. Automated testing tools based on evolutionary computation principles constitute an appealing modern approach to support testing tasks. However, these tools still find difficulties to detect certain types of plausible faults in real-world projects. Besides, recent studies have shown that, in general, automatically-generated tests do not resemble those manually written and, consequently, testers are reluctant to adopt them. We observe two key issues, namely the opacity of the process and the lack of cooperation with the tester, currently hampering the acceptance of automated results. Based on these findings, we explore in this paper how the interaction between current tools and expert testers would help address the test case generation problem. More specifically, we identify a number of interaction opportunities related to the object-oriented test case design driven to boost their readability and detection power. Using EvoSuite as base implementation, we present a proof of concept focused on the possibility to integrate readability assessment of the most promising test suites into a genetic algorithm.

关键词： Evolutionary computation Manuals Tools Software Proposals Test pattern generators Object recognition

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A survey on extremism analysis using natural language processing

arXiv

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arXiv 2021年

作者： Torregrosa, Javier Martinez-camara, Eugenio Bello-Orgaz, Gema Ser, Javier Del camacho, David Department of Computer Science Universidad Politecnica de Madrid Madrid28051 Spain Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain Derio48160 Spain

Extremism research has grown as an open problem for several countries during recent years, especially due to the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. Natural Language Processing (NLP) represents a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by this groups, with the final objective of detecting and preventing its spread. This survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a description and comparison of the frequently used NLP techniques, how they were applied, the insights they provided, the most frequently used NLP software tools and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested. copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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Software Product Quality Models, Developments, Trends, and Evaluation

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SN computer science 2020年第3期1卷 1-24页

作者： Galli, Tamas chiclana, Francisco Siewe, Francois Institute of Artificial Intelligence (IAI) Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain Software Technology Research Laboratory (STRL) Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom

Software product quality models have evolved in their abilities to capture and describe the abstract notion of software quality since the 1970s. Many models constructed deal with a specific part of software quality only which makes them ineligible to assess the quality of software products as a whole. Former publications failed to thoroughly examine and list all the available models which attempt to describe each known property of software product quality. This paper discovers such complete software product quality models published since 2000;moreover, it endeavours to measure the relevance of each model quantitatively by introducing indicators with regard to the scientific and industrial community. The identified 23 software product quality model classes differ significantly in terms of publication intensity, publication range, quality score average, relevance score and the 12-month average of the Google Relative Search Index. The results offer a foundation for selecting the appropriate software product quality model for use or for extension if newly identified quality properties need to be connected to a general context. Furthermore, the experiences accumulated on the field of software product quality modelling motivated researchers to successfully transfer the concepts to other areas where abstract entities need to be compared or assessed including the quality of higher educational teaching and business processes, which is also briefly highlighted in the paper. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Execution tracing Quality assessment software engineering Software product quality model

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Fuzzychain: An equitable consensus mechanism for blockchain networks

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Journal of Network and computer Applications 2025年 241卷

作者： Ramos-cruz, Bruno Andreu-Perez, Javier Quesada-Real, Francisco J. Martínez, Luis Computer Science Department University of Jaen Jaen Jaen23071 Spain Centre for Computational Intelligencer School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Jaen Jaen23071 Spain

Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher rewards. Our proposed novel solution, Fuzzychain, leverages fuzzy sets to define stake semantics, introducing a degree of softness in validator selection. This approach mitigates rigid threshold-based decision-making by allowing gradual transitions between stake levels, reducing sharp disparities among validators. By incorporating this enhanced stake evaluation, Fuzzychain promotes a more adaptive and distributed selection process, ensuring a fairer and more inclusive blockchain network. A thorough assessment of a real-time multi-agent blockchain system to examine validator selection and reduce inequality, promoting a more equitable distribution of stakes among validators compared to other consensus mechanisms. This fosters a more inclusive selection process and a more equitably distributed network. © 2025 The Authors

关键词： consensus algorithm

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Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology: Using Natural Language Processing and Deep Learning for Decision Aid

arXiv

引用

arXiv 2020年

作者： Zuheros, cristina Martínez-cámara, Eugenio Herrera-Viedma, Enrique Herrera, Francisco Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOc-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual expert evaluations are aggregated via a criteria weighting through the attention of the experts. We evaluate the methodology in a restaurant decision problem, hence we build the TripR-2020 dataset of restaurant reviews, which we manually annotate and release. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector. copyright © 2020, The Authors. All rights reserved.

关键词： Sentiment analysis

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Automatic detection of lung nodule in cT scan slices using cNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International conference on Machine Learning and data Engineering, IcMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the convolutional-Neural-Network (cNN) segmentation methodology to extract the LN in various lung cT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) cNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained cNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The cancer Imaging Archive (TcIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other cNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

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classification of Breast Thermal Images into Healthy/cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast cancer (Bc) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to Bc. Bc is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. compared to other methods, it keeps Bc early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting Bc using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization classification

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Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptations

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Information Fusion 2025年 123卷

作者： Germán-Morales, M. Rivera-Rivas, A.J. del Jesus Díaz, M.J. carmona, c.J. Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science University of Jaen Jaen E-23071 Spain Leicester School of Pharmacy DeMontfort University Leicester LE1 7RH United Kingdom

Foundational Models are an emerging widely used technique of Generative Artificial intelligence. These models are distinguished by their scalability and the ease with which they can be adapted through the exploitation of Transfer Learning. The availability of high computational power and large datasets have supported their development, achieving a high generalization capacity due to the enormous and heterogeneous amounts of data used in their initial training. These characteristics contribute to a solid base that can be adapted or adjusted to a wide range of tasks, increasing their applicability. This study proposes the methodology LLIAM, a straightforward adaptation of a kind of Foundational Models, Large Language Models, for the Time Series Forecasting task. An adequate time-series prompting schema and Low-Rank Adaptations are used to enhance the knowledge of the model with diverse time series datasets, known as the fine-tuning phase. A study divided in two stages has been performed for evaluating the effectiveness of the proposed methodology. Initially, a comparison was made between the performance of LLIAM and different state-of-the-art Deep Learning algorithms, including Recurrent Neural Networks and Temporal convolutional Networks, as well as a LLM-based method, TimeLLM. Following this, a zero-shot study is presented in order to evaluate the generalization capacity of the proposed methodology with time series datasets from unknown domains not considered in the model training. The outcomes of this investigation demonstrate the efficacy of LLIAM, highlighting that this straightforward and general approach can attain competent results without the necessity for applying complex modifications. This work also encourages the use of available resources (such as these pre-trained models) and efficient fine-tuning techniques to avoid unnecessary and costly training, narrowing the gap between the goals of traditional Artificial intelligence and Green Artificial Intellige

关键词： Transfer learning

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