DeustoTeka Examinando por Autor "Almeida, Aitor"

Examinando por Autor "Almeida, Aitor"

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Addressing behavioural technologies through the human factor: a review
(Institute of Electrical and Electronics Engineers Inc., 2020-03-25) Irizar Arrieta, Ane; Gómez Carmona, Oihane; Bilbao Jayo, Aritz; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego; Almeida, Aitor
Energy-efficiency related research has reached a growing interest in recent years due to the imminent scarcity of non-renewable resources in our environment and the impending impacts their usage have on our environment. Thus, facing the reduction of energy waste and management has become a pivotal issue in our society. To cope with energy inefficiency, the scientific research community has identified the promotion of people's behaviour change as a critical field to foster environmental sustainability. However, the body of literature shows a lack of systematic methods and processes to reach a common ground when designing technology for promoting sustainable behaviour change. Therefore, this paper contributes with a thorough review and analysis of state of the art. Firstly, theoretical works related to behaviour change are collected and studied to clarify their main concepts and theories. Secondly, the different technologies, processes, methods and techniques applied in the field are reviewed to find diverse strategies in the application of the previously explained theoretical domains. Moreover, a wide range of systems developed to improve energy efficiency through human behaviour change is analysed (from augmented objects to the Internet of Things, digital applications or websites). Finally, the detected research gaps are listed to guide future research when aiming to raise the awareness of individuals through Information and Communication Technologies.
Analysing centralities for organisational role inference in online social networks
(Elsevier Ltd, 2021-03) Sánchez Corcuera, Rubén; Bilbao Jayo, Aritz; Zulaika Zurimendi, Unai; Almeida, Aitor
The intensive use of Online Social Networks (OSN) nowadays has made users expose more information without realising it. Malicious users or marketing agencies are now able to infer information that is not published on OSNs by using data from targets friends to use for their benefit. In this paper, the authors present a generalisable method capable of deducing the roles of employees of an organisation using their Twitter relationships and the features of the graph from their organisation. The authors also conduct an extensive analysis of the node centralities to study their roles in the inference of the different classes proposed. Derived from the experiments and the ablation study conducted to the centralities, the authors conclude that the latent features of the graph along with the directed relationships perform better than previously proposed methods when classifying the role of the employees of an organisation. Additionally, to evaluate the method, the authors also contribute with a new dataset consisting of three directed graphs (one for each organisation) representing the relationships between the employees obtained from Twitter.
Analysing the impact of images and text for predicting human creativity through encoders
(Science and Technology Publications, Lda, 2025) Pikatza Huerga, Amaia; Matanzas de Luis, Pablo; Fernandez De Retana Uribe, Miguel; Peña Lasa, Javier; Zulaika Zurimendi, Unai; Almeida, Aitor
This study explores the application of multimodal machine learning techniques to evaluate the originality and complexity of drawings. Traditional approaches in creativity assessment have primarily focused on visual analysis, often neglecting the potential insights derived from accompanying textual descriptions. The research assesses four target features: drawings’ originality, flexibility and elaboration level, and titles’ creativity, all labelled by expert psychologists. The research compares different image encoding and text embeddings to examine the effectiveness and impact of individual and combined modalities. The results indicate that incorporating textual information enhances the predictive accuracy for all features, suggesting that text provides valuable contextual insights that images alone may overlook. This work demonstrates the importance of a multimodal approach in creativity assessment, paving the way for more comprehensive and nuanced evaluations of artistic expression.
Analyzing the existence of organization specific languages on Twitter
(Institute of Electrical and Electronics Engineers Inc., 2021-08-05) Sánchez Corcuera, Rubén; Zubiaga, Arkaitz ; Almeida, Aitor
The presence of organisations in Online Social Networks (OSNs) has motivated malicious users to look for attack vectors, which are then used to increase the possibility of carrying out successful attacks and obtaining either private information or access to the organisation. This article hypothesised that organisations have specific languages that their members use in OSNs, which malicious users could potentially use to carry out an impersonation attack. To prove these specific languages, we propose two tasks: classifying tweets in isolation by their author's organisation and classifying users' entire timelines by organisation. To accomplish both tasks, we generate a dataset of over 15 million tweets of five organisations, and we apply language dependant models to test our hypothesis. Our results and the ablation study conclude that it is possible to classify tweets and users by organisation with more than three times the performance achieved by a traditional ML algorithm, showing a substantial potential for predicting the linguistic style of tweets.
Artificial intelligence in business-to-customer fashion retail: a literature review
(Multidisciplinary Digital Publishing Institute (MDPI), 2023-06-30) Goti Elordi, Aitor ; Querejeta-Lomas, Leire; Almeida, Aitor; Gaviria de la Puerta, José ; López de Ipiña González de Artaza, Diego
Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.
Behavior modeling for a beacon-based indoor location system
(MDPI AG, 2021-07-15) Bilbao Jayo, Aritz; Almeida, Aitor ; Sergi, Ilaria; Montanaro, Teodoro; Fasano, Luca; Emaldi, Mikel ; Patrono, Luigi
In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.
Categorizing and assessing aspects of suicidal ideation detection approaches: a systematic review
(Elsevier B.V., 2025-07-09) Nikmehr, Golnaz; Bilbao Jayo, Aritz; Almeida, Aitor
Suicide remains a critical global issue and one of the leading causes of death worldwide. As this problem grows, the need for effective prevention strategies becomes increasingly urgent. Social networks and online platforms, such as Twitter, have emerged as spaces where people openly share their thoughts and emotions, including negative feelings, reflections on life, and even suicidal thoughts. This makes social media data an important resource for efforts to detect and reduce the risk of suicide. This systematic review examines 92 studies published between 2018 and 2024 on the detection of suicidal ideation. The studies are categorized using a multidimensional framework that considers three key aspects: the platforms used for data collection, the analytical techniques applied, and the specific features employed to identify suicidal ideation. By exploring these dimensions, the review highlights existing gaps and limitations in current methods, offering insights to guide future research and improve strategies for suicide prevention.
A comparative analysis of human behavior prediction approaches in intelligent environments
(MDPI, 2022-01-18) Almeida, Aitor; Bermejo Fernández, Unai ; Bilbao Jayo, Aritz ; Azkune Galparsoro, Gorka; Aguilera, Unai ; Emaldi, Mikel ; Dornaika, Fadi; Arganda-Carreras, Ignacio
Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling
A critical analysis of an IoT—aware AAL system for elderly monitoring
(Elsevier B.V., 2019-08) Almeida, Aitor; Mulero, Rubén; Rametta, Piercosimo; Urošević, Vladimir; Andrić, Marina; Patrono, Luigi
A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people's quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context's needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions
Cross-environment activity recognition using word embeddings for sensor and activity representation
(Elsevier B.V., 2020-12-22) Azkune Galparsoro, Gorka; Almeida, Aitor; Agirre Bengoa, Eneko
Cross-environment activity recognition in smart homes is a very challenging problem, specially for data-driven approaches. Currently, systems developed to work for a certain environment degrade substantially when applied to a new environment, where not only sensors, but also the monitored activities may be different. Some systems require manual labeling and mapping of the new sensor names and activities using an ontology. Ideally, given a new smart home, we would like to be able to deploy the system, which has been trained on other sources, with minimal manual effort and with acceptable performance. In this paper, we propose the use of neural word embeddings to represent sensor activations and activities, which comes with several advantages: (i) the representation of the semantic information of sensor and activity names, and (ii) automatically mapping sensors and activities of different environments into the same semantic space. Based on this novel representation approach, we propose two data-driven activity recognition systems: the first one is a completely unsupervised system based on embedding similarities, while the second one adds a supervised learning regressor on top of them. We compare our approaches with some baselines using four public datasets, showing that data-driven cross-environment activity recognition obtains good results even when sensors and activity labels significantly differ. Our results show promise for reducing manual effort, and are complementary to other efforts using ontologies
A deep learning approach to artifact removal in Transcranial Electrical Stimulation: from shallow methods to deep neural networks and state space models
(Elsevier Ltd, 2025-11-19) Fernandez De Retana Uribe, Miguel; Matanzas de Luis, Pablo; Peña Lasa, Javier; Almeida, Aitor
Transcranial Electrical Stimulation (tES) is a non-invasive neuromodulation technique that generates artifacts in simultaneous EEG recordings, hindering brain activity analysis. This study analyzes Machine Learning (ML) methods for tES noise artifact removal across three stimulation types: tDCS, tACS, and tRNS. Synthetic datasets were created by combining clean EEG data with synthetic tES artifacts. Eleven artifact removal techniques were tested and evaluated using the Root Relative Mean Squared Error (RRMSE) in the temporal and spectral domains, and the Correlation Coefficient (CC). Results indicate that method performance is highly dependent on stimulation type: for tDCS, a convolutional network (Complex CNN) performed best; while a multi-modular network (M4) based on State Space Models (SSMs) yielded the best results for tACS and tRNS. This study provides guidelines for selecting efficient artifact removal methods for different tES modalities, establishing a benchmark for future research in this area and paving the way for more robust analysis of neural dynamics in advanced clinical and neuroimaging applications.
Early detection and prevention of malicious user behavior on Twitter using deep learning techniques
(Institute of Electrical and Electronics Engineers Inc., 2024) Sánchez Corcuera, Rubén; Zubiaga, Arkaitz; Almeida, Aitor
Organized misinformation campaigns on Twitter continue to proliferate, even as the platform acknowledges such activities through its transparency center. These deceptive initiatives significantly impact vital societal issues, including climate change, thus spurring research aimed at pinpointing and intercepting these malicious actors. Present-day algorithms for detecting bots harness an array of data drawn from user profiles, tweets, and network configurations, delivering commendable outcomes. Yet, these strategies mainly concentrate on postincident identification of malevolent users, hinging on static training datasets that categorize individuals based on historical activities. Diverging from this approach, we advocate for a forward-thinking methodology, which utilizes user data to foresee and mitigate potential threats before their realization, thereby cultivating more secure, equitable, and unbiased online communities. To this end, our proposed technique forecasts malevolent activities by tracing the projected trajectories of user embeddings before any malevolent action materializes. For validation, we employed a dynamic directed multigraph paradigm to chronicle the evolving engagements between Twitter users. When juxtaposed against the identical dataset, our technique eclipses contemporary methodologies by an impressive 40.66% in F score (F1 score) in the anticipatory identification of harmful users. Furthermore, we undertook a model evaluation exercise to gauge the efficiency of distinct system elements.
Embedding-based real-time change point detection with application to activity segmentation in smart home time series data
(Elsevier Ltd, 2021-12-15) Bermejo Fernández, Unai; Almeida, Aitor; Bilbao Jayo, Aritz; Azkune Galparsoro, Gorka
Human activity recognition systems are essential to enable many assistive applications. Those systems can be sensor-based or vision-based. When sensor-based systems are deployed in real environments, they must segment sensor data streams on the fly in order to extract features and recognize the ongoing activities. This segmentation can be done with different approaches. One effective approach is to employ change point detection (CPD) algorithms to detect activity transitions (i.e. determine when activities start and end). In this paper, we present a novel real-time CPD method to perform activity segmentation, where neural embeddings (vectors of continuous numbers) are used to represent sensor events. Through empirical evaluation with 3 publicly available benchmark datasets, we conclude that our method is useful for segmenting sensor data, offering significant better performance than state of the art algorithms in two of them. Besides, we propose the use of retrofitting, a graph-based technique, to adjust the embeddings and introduce expert knowledge in the activity segmentation task, showing empirically that it can improve the performance of our method using three graphs generated from two sources of information. Finally, we discuss the advantages of our approach regarding computational cost, manual effort reduction (no need of hand-crafted features) and cross-environment possibilities (transfer learning) in comparison to others
IEM: a unified lifecycle orchestrator for multilingual IaC deployments
(Association for Computing Machinery, Inc, 2023-04-15) Díaz de Arcaya Serrano, Josu; Osaba, Eneko ; Benguria, Gorka; Etxaniz Errazkin, Iñaki; López Lobo, Jesús ; Alonso, Juncal; Torre Bastida, Ana Isabel ; Almeida, Aitor
Over the last few years, DevOps methodologies have promoted a more streamlined operationalization of software components in production environments. Infrastructure as Code (IaC) technologies play a key role in the lifecycle management of applications, as they promote the delivery of the infrastructural elements alongside the application components. This way, IaC technologies aspire to minimize the problems associated with the environment by providing a repeatable and traceable process. However, there are a large variety of IaC frameworks, each of them focusing on a different phase of the operationalization lifecycle, hence the necessity to master numerous technologies. In this research, we present the IaC Execution Manager (IEM), a tool devoted to providing a unified framework for the operationalization of software components that encompasses the various stages and technologies involved in the application lifecycle. We analyze an industrial use case to improve the current approach and conclude the IEM is a suitable tool for solving the problem as it promotes automation, while reducing the learning curve associated with the required IaC technologies.
Improving political discourse analysis on Twitter with context analysis
(Institute of Electrical and Electronics Engineers Inc., 2021-07-26) Bilbao Jayo, Aritz; Almeida, Aitor
In this study, we propose a new approach to perform political discourse analysis in social media platforms based on a widely used political categorisation schema in the field of political science, namely, the Comparative Manifestos Project's category schema. This categorisation schema has been traditionally used to perform content analysis in political manifestos, giving a code that indicates the domain or category of each of the phrases in the manifestos. Therefore, in this work we propose the application of this political discourse analysis technique in Twitter, using as training data of 100 publicly available annotated political manifestos in English with around 85,000 annotated sentences. Furthermore, we also analyse the improvement that using 5,000 annotated tweets could provide to the performance of the political discourse classifier already trained with political manifestos. Finally, we have analysed the 2016 United States presidential elections on Twitter using the proposed approach. As our main finding, we have been able to conclude that both datasets (political manifestos and annotated tweets) can be combined in order to achieve better results, achieving improvements in the F-Measure of more than 15 points. Moreover, we have also analysed if contextual information such as the previous tweet or the political affiliation of the transmitter could improve classifier's performance as it has already been proven for manifestos classification, introducing a novel method for political parties representation and finding that adding the previous tweet or the political leaning as contextual data does improve its performance.
Influence Functions for interpretable link prediction in Knowledge Graphs for Intelligent Environments
(Institute of Electrical and Electronics Engineers Inc., 2022) Zulaika Zurimendi, Unai ; Almeida, Aitor; López de Ipiña González de Artaza, Diego
Knowledge graphs are large, graph-structured databases used in many use-case scenarios such as Intelligent Environments. Many Artificial Intelligent latent feature models are used to infer new facts in Knowledge Graphs. Despite their success, the lack of interpretability remains a challenge to overcome. This paper applies influence functions to obtain the most significant facts when predicting new knowledge and allows users to understand these models. However, Influence Functions do not scale well. We present an efficient method to scale up influence functions to large Knowledge Graphs to overcome such an issue. It drastically reduces the number of training samples when computing influences and uses fast curvature matrix-vector products to linearize the computation steps required for the inverse Hessian. We conduct experiments on different sized Knowledge Graphs demonstrating the scalability of our approach and its effectiveness in measuring the most influential facts. Our method provides an intuitive understanding of link prediction behaviour in Knowledge Graphs and Intelligent Environments.
An IoT-aware architecture for collecting and managing data related to elderly behavior
(Hindawi Limited, 2017-12-28) Almeida, Aitor ; Fiore, Alessandro; Mainetti, Luca; Mulero, Rubén ; Patrono, Luigi; Rametta, Piercosimo
The world population will be made up of a growing number of elderly people in the near future. Aged people are characterized by some physical and cognitive diseases, like mild cognitive impairment (MCI) and frailty, that, if not timely diagnosed, could turn into more severe diseases, like Alzheimer disease, thus implying high costs for treatments and cares. Information and Communication Technologies (ICTs) enabling the Internet of Tings (IoT) can be adopted to create frameworks for monitoring elderly behavior which, alongside normal clinical procedures, can help geriatricians to early detect behavioral changes related to such pathologies and to provide customized interventions. As part of the City4Age project, this work describes a novel approach for collecting and managing data about elderly behavior during their normal activities. The data capturing layer is an unobtrusive and low-cost sensing infrastructure abstracting the heterogeneity of physical devices, while the data management layer easily manages the huge quantity of sensed data, giving them semantic meaning and fostering data shareability. This work provides a functional validation of the proposed architecture and introduces how the data it manages can be used by the whole City4Age platform to early identify risks related to MCI/frailty and promptly intervene.
Location based indoor and outdoor lightweight activity recognition system
(MDPI, 2022-01-25) Bilbao Jayo, Aritz ; Cantero López, Xabier; Almeida, Aitor ; Fasano, Luca; Montanaro, Teodoro; Sergi, Ilaria; Patrono, Luigi
In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure.
LWP-WL: link weight prediction based on CNNs and the Weisfeiler–Lehman algorithm
(Elsevier Ltd, 2022-05-01) Zulaika Zurimendi, Unai; Sánchez Corcuera, Rubén; Almeida, Aitor; López de Ipiña González de Artaza, Diego
We present a new technique for link weight prediction, the Link Weight Prediction Weisfeiler–Lehman (LWP-WL) method that learns from graph structure features and link relationship patterns. Inspired by the Weisfeiler–Lehman Neural Machine, LWP-WL extracts an enclosing subgraph for the target link and applies a graph labelling algorithm for weighted graphs to provide an ordered subgraph adjacency matrix into a neural network. The neural network contains a Convolutional Neural Network in the first layer that applies special filters adapted to the input graph representation. An extensive evaluation is provided that demonstrates an improvement over the state-of-the-art methods in several weighted graphs. Furthermore, we conduct an ablation study to show how adding different features to our approach improves our technique's performance. Finally, we also perform a study on the complexity and scalability of our algorithm. Unlike other approaches, LWP-WL does not rely on a specific graph heuristic and can perform well in different kinds of graphs
Machine learning approaches for predicting heart failure readmissions
(Oxford University Press, 2025-07-06) Pikatza Huerga, Amaia; Almeida, Aitor; Quirós López, Raúl; Larrea, Nere; Legarreta Olabarrieta, María José; Zulaika Zurimendi, Unai; García, Rodrigo Damián; García Gutiérrez, Susana
Purpose: This study aims to develop and evaluate machine learning (ML) models to predict the likelihood of hospital readmission within 30 days after discharge for patients with heart failure (HF). The goal is to compare the predictive accuracy of ML models with traditional methods such as those based on Cox proportional hazards and logistic regression, to improve clinical outcomes and reduce hospital costs. Methods: We conducted a prospective cohort study of patients discharged from five hospitals following admission for HF. Data were collected on variables including sociodemographic characteristics, medical history, admission details, patient-reported outcomes, and clinical parameters. ML techniques were employed to analyse the data and predict readmission risk, incorporating strategies to handle class imbalance and missing data. Model performance was assessed based on accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F1 score. Results: Ensemble methods with Synthetic Minority Over-sampling Technique balancing and bagging improved the predictive performance of ML models compared with traditional models. The best-performing ensemble model, using decision trees, Gaussian Naïve Bayes, and neural networks, achieved an AUC of 0.81. In contrast, Cox and logistic regression models showed significantly poorer performance (AUC of 0.58 and 0.50, respectively). SHapley Additive exPlanations analysis revealed that frailty, anxiety, and depression were critical in predicting readmission. Conclusion: ML models, particularly those using ensemble methods, significantly outperform traditional models in predicting short-term readmission for patients with HF. These findings highlight the potential of ML to improve clinical decision-making and resource allocation in HF management.

Examinando por Autor "Almeida, Aitor"

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