https://reunir.unir.net/handle/123456789/14287 2025-04-27T20:07:06Z https://reunir.unir.net/handle/123456789/17627 Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments: An Empirical Study Alotaibi, Basmah K.; Khan, Fakhri Alam; Qawqzeh, Yousef; Jeon, Gwanggil; Camacho, David Federated learning, a distributive cooperative learning approach, allows clients to train the model locally using their data and share the trained model with a central server. When developing a federated learning environment, a deep/machine learning model needs to be chosen. The choice of the learning model can impact the model performance and the communication cost since federated learning requires the model exchange between clients and a central server in several rounds. In this work, we provide an empirical study to investigate the impact of using three different neural networks (CNN, VGG, and ResNet) models in image classification tasks using two different datasets (Cifar-10 and Cifar-100) in a federated learning environment. We investigate the impact of using these models on the global model performance and communication cost under different data distribution that are IID data and non-IID data distribution. The obtained results indicate that using CNN and ResNet models provide a faster convergence than VGG model. Additionally, these models require less communication costs. In contrast, the VGG model necessitates the sharing of numerous bits over several rounds to achieve higher accuracy under the IID data settings. However, its accuracy level is lower under non-IID data distributions than the other models. Furthermore, using a light model like CNN provides comparable results to the deeper neural network models with less communication cost, even though it may require more communication rounds to achieve the target accuracy in both datasets. CNN model requires fewer bits to be shared during communication than other models. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2025-01-27T16:52:16Z No. of bitstreams: 1 Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments.pdf: 2020700 bytes, checksum: c69172fdf55709aa480ba111a5668352 (MD5); Made available in DSpace on 2025-01-27T16:52:16Z (GMT). No. of bitstreams: 1 Performance and Communication Cost of Deep Neural Networks in Federated Learning Environments.pdf: 2020700 bytes, checksum: c69172fdf55709aa480ba111a5668352 (MD5) https://reunir.unir.net/handle/123456789/17349 Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family Kollmorgen, Jessica; Hinderks, Andreas; Thomaschewski, Jörg Measuring the user experience (UX) of products, systems and services is individual depending on the research question. On the one hand, the user’s goals and environment play a role in the subjective evaluation. On the other hand, different UX factors are relevant depending on the product. In this case, it is practical to have a questionnaire family as an aid, whose questionnaires are geared towards these different use cases. The User Experience Questionnaire (UEQ) family allows researchers and practitioners to choose the right tool for efficient UX measurement from three questionnaire versions. This article summarizes the UEQ, its short version (UEQ-S) and a modular framework (UEQ+) with overall 27 UX factors and purposes in over 30 different languages. In addition, specific instructions and assistance are provided for the statistical evaluation and interpretation of the questionnaire results. With the help of a key performance indicator (KPI), benchmarks and an importance-performance analysis (IPA), the realization of UX measurements is made easier for researchers and practitioners. To make it even more convenient to choose the right questionnaire from the UEQ family, influencing factors on the UX measurement and recommendations for action are given. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-04T08:58:29Z No. of bitstreams: 1 Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation.pdf: 1322255 bytes, checksum: 92d13d179b4814ac6aea197ce85e8253 (MD5); Made available in DSpace on 2024-09-04T08:58:29Z (GMT). No. of bitstreams: 1 Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation.pdf: 1322255 bytes, checksum: 92d13d179b4814ac6aea197ce85e8253 (MD5) https://reunir.unir.net/handle/123456789/17348 Automatic Surveillance of People and Objects on Railway Tracks Martínez Núñez, Domingo; López Hernández, Fernando Carlos; Rainer Granados, J. Javier This paper describes the development and evaluation of a surveillance system for the detection of people and objects on railroad tracks in real time. Firstly, the paper evaluates several background subtraction techniques including CNNs and the object detection library called YOLO. Then we describe a novel strategy to mitigate the occlusion caused by the perspective of the camera and the integration of an alarms and pre-alarms policy. To evaluate its performance, we have implemented and automated the control and notification aspects of the surveillance system using computer vision techniques. This setup, running on a standard PC, achieves an average frame rate of 15 FPS and a latency of 0.54 seconds per frame, meeting real-time expectations in terms of both false alarms and precision in operational mode. The results from experiments conducted with a publicly available recorded video dataset from Metro de Madrid facilities demonstrate significant improvements over current state-of the-art solutions. These improvements include better accident anticipation and enhanced information provided to the operator using a standard low-cost camera. Consequently, we conclude that the approach described in this paper is both effective and a more practical, cost-efficient alternative to the other solutions reviewed. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-04T08:45:08Z No. of bitstreams: 1 Automatic Surveillance of People and Objects on Railway Tracks.pdf: 1743672 bytes, checksum: 5dfd567742e3def6b4c8446fdd9ca175 (MD5); Made available in DSpace on 2024-09-04T08:45:08Z (GMT). No. of bitstreams: 1 Automatic Surveillance of People and Objects on Railway Tracks.pdf: 1743672 bytes, checksum: 5dfd567742e3def6b4c8446fdd9ca175 (MD5) https://reunir.unir.net/handle/123456789/17173 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data Galphade, Manisha; Nikam, V. B.; Banerjee, Biplab; Kiwelekar, Arvind W.; Sharma, Priyanka Currently, wind power is the fast growing area in the domain of renewable energy generation. Accurate prediction of wind power output in wind farms is crucial for addressing the challenges associated the power grid. This precise forecasting enables grid operators to enhance safety and optimize grid operations by effectively managing fluctuations in power generation, ensuring a reliable and stable energy supply. In recent years, there has been a significant rise in research and investigations conducted in this field. This study aims to develop a multivariate short-term wind power forecasting (WPF) model with the objective of enhancing forecasting precision. Among the various prediction models, deep learning models such as Long Short-Term Memory (LSTM) have demonstrated outstanding performance in the field of WPF. By adding multiple layers of LSTM networks, the model can capture more complex patterns. To improve the performance, data preprocessing is carried out using two techniques such as removal of missing values and imputing missing values using Random Forest Regressor (RFR). The comparison between the proposed Stacked LSTM model and other methods including vector autoregressive (VAR), Multiple Linear Regression, Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM) has been experimented on two datasets. The experimental results show that after imputing missing values using RFR, the Stacked LSTM is optimized model for better performance than above mentioned reference models. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T09:45:34Z No. of bitstreams: 1 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.pdf: 4664292 bytes, checksum: b0bcc11428bf8e464f01f44dc7503748 (MD5); Made available in DSpace on 2024-08-07T09:45:35Z (GMT). No. of bitstreams: 1 Stacked LSTM for Short-Term Wind Power Forecasting Using Multivariate Time Series Data.pdf: 4664292 bytes, checksum: b0bcc11428bf8e464f01f44dc7503748 (MD5) https://reunir.unir.net/handle/123456789/17171 Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning Wu, Xiaoming; Cao, Yu; Wang, Yu; Li, Bing; Yang, Haitao; Raja, S.P. Curve running is a common form of training and competition. Conducting research on posture estimation during curve running can provide more accurate training and competition data for athletes. However, due to the unique nature of curve running, traditional posture estimation methods neglect the temporal changes in athlete posture, resulting in a decrease in estimation accuracy. Therefore, a posture estimation method for curve running motion using nano-biosensor and machine learning is proposed. First, the motion parameters of humans are collected by nano-biosensor, and the posture coordinates are obtained preliminarily. Second, the posture coordinates are established according to the human motion parameters, and the curve running posture data is obtained and filtered to obtain more accurate data. Finally, the Bayesian network in machine learning is used to continuously track the posture, and a nonlinear equation is established to fuse the posture angle obtained by the sensor and the posture tracked by the Bayesian network, to realize the posture estimation of curve running motion. The results show that the proposed estimation method has a good motion posture estimation effect, and the hip joint estimation error, knee joint estimation error and ankle joint estimation error are all less than 5°, and the endpoint displacement estimation offset rate is less than 2%. It can realize accurate motion posture estimation of curve running motion, and has important application value in the field of track training. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T09:33:41Z No. of bitstreams: 1 Posture Estimation of Curve Running Motion Using.pdf: 1581880 bytes, checksum: 7224bfc356a2127cf2d323af67935337 (MD5); Made available in DSpace on 2024-08-07T09:33:41Z (GMT). No. of bitstreams: 1 Posture Estimation of Curve Running Motion Using.pdf: 1581880 bytes, checksum: 7224bfc356a2127cf2d323af67935337 (MD5) https://reunir.unir.net/handle/123456789/16737 Anti-Diabetic Therapeutic Medicinal Plant Identification Using Deep Fused Discriminant Subspace Ensemble (D2 SE) Sasikaladevi, N.; Pradeepa, S.; Revathi, A.; Vimal, S.; Dhiman, Gaurav About 422 million people worldwide have diabetes, the majority living in low-and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. According to the Botanical Survey of India, India is home to more than 8,000 species of medicinal plants. The natural medications with antidiabetic activity are widely formulated because they have better compatibility with human body, are easily available and have less side effects. They may act as an alternative source of antidiabetic agents. The fused deep neural network (DNN) model with Discriminant Subspace Ensemble is designed to identify the diabetic plants from VNPlant200 data set. Here, the deep features are extracted using DenseNet201 and the matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a nearest neighbors technique is used to produce a subspace ensemble for final diabetic therapeutic medicinal plant image classification. The developed model attained the highest accuracy of 97.5% which is better compared to other state of art algorithms. Finally, the model is integrated into a mobile app for robust classification of anti-diabetic therapeutic medicinal plant with real field images. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-06-12T13:46:04Z No. of bitstreams: 1 Anti-Diabetic Therapeutic Medicinal Plant Identification Using Deep Fused Discriminant Subspace Ensemble (D2 SE).pdf: 2837535 bytes, checksum: 84a1a31340b5d4c20faa500db155e513 (MD5); Made available in DSpace on 2024-06-12T13:46:04Z (GMT). No. of bitstreams: 1 Anti-Diabetic Therapeutic Medicinal Plant Identification Using Deep Fused Discriminant Subspace Ensemble (D2 SE).pdf: 2837535 bytes, checksum: 84a1a31340b5d4c20faa500db155e513 (MD5) https://reunir.unir.net/handle/123456789/16736 User Revocation-Enabled Access Control Model Using Identity-Based Signature in the Cloud Computing Environment Kumar, Tarun; Kumar, Prabhat; Namasudra, Suyel Nowadays, a lot of data is stored in the cloud for sharing purposes across various domains. The increasing number of security issues with cloud data raises confidentiality concerns about keeping these stored or shared data. Advanced encryption and decryption techniques in cloud computing environments can be considered useful to achieve this aspect. However, an unresolved yet critical challenge in cloud data-sharing systems is the revocation of malicious users. One of the common methods for revocation involves periodically updating users' private keys. This approach increases the workload of the Key Generation Center (KGC) as the number of users increases. In this work, an efficient Revocable Identity-Based Signature (RIBS) scheme is proposed, wherein the revocation functionality is delegated to an External Revocation Server (ERS). This proposed scheme allows only the non-revoked users to access the system resources, thus, providing restricted access control. Here, the ERS generates a secret time key for signature generation based on a revoked user list. In the proposed method, a user uses its private key and secret time key to sign a message. Furthermore, to maintain data confidentiality, symmetric encryption and Elliptic Curve Cryptography (ECC) based asymmetric encryption techniques are used before outsourcing data to the cloud server. The results illustrate that the proposed scheme outperforms some of the existing schemes by providing reduced computation costs. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-06-12T13:32:42Z No. of bitstreams: 1 User Revocation-Enabled Access Control Model Using Identity-Based Signature in the Cloud Computing Environment.pdf: 889672 bytes, checksum: 40f9013559d6d5cdf1211875ce5d9189 (MD5); Made available in DSpace on 2024-06-12T13:32:42Z (GMT). No. of bitstreams: 1 User Revocation-Enabled Access Control Model Using Identity-Based Signature in the Cloud Computing Environment.pdf: 889672 bytes, checksum: 40f9013559d6d5cdf1211875ce5d9189 (MD5) https://reunir.unir.net/handle/123456789/16568 Learning Analytics Icons: Easy Comprehension of Data Treatment Amo-Filva, David; Alier, Marc; Fonseca, David; García-Peñalvo, Francisco; Casañ, María José The Learning Analytics approach adopted in education implies the gathering and processing of sensitive information and the generation of student profiles, which may have direct or indirect dire consequences for the students. The Educational institutions must manage this data processing according to the General Data Protection Regulation, respecting its principles of fairness when it comes to information gathering and processing. This implies that the students must be well informed and give explicit consent before their information is gathered and processed. The GDPR propose the usage of recognizable standardized icons to facilitate a general understanding and awareness of how personal data is deemed to be processed in each application context, like an online course. This paper presents a project that aims to provide a set of icons to inform about the treatment of educational data in the Learning Analytics processes and a survey about the student's comprehension of the icons, their meaning, and implications for their privacy and confidentiality. The result presented is a set of icons ready to be integrated into educational environments that apply Learning Analytics to increase transparency and facilitate the understanding of data processing. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-05-13T15:40:44Z No. of bitstreams: 1 Learning Analytics Icons.pdf: 3014284 bytes, checksum: 95d65cc7e996eb92d9d9269016b1c905 (MD5); Made available in DSpace on 2024-05-13T15:40:44Z (GMT). No. of bitstreams: 1 Learning Analytics Icons.pdf: 3014284 bytes, checksum: 95d65cc7e996eb92d9d9269016b1c905 (MD5) https://reunir.unir.net/handle/123456789/16266 Combating Misinformation and Polarization in the Corporate Sphere: Integrating Social, Technological and AI Strategies Tejero, Alberto; Pisoni, Galena; Lashkari, Ziba Habibi; Rios-Aguilar, Sergio In an era where misinformation and polarization present significant challenges, this research examines the root causes within social networks and assesses how corporations can use AI technologies for prompt detection. This research uses a dual approach: a "telephone game" with 225 participants from a Spanish university to study the spread of misinformation, and interviews with 15 experts from three French tech companies to investigate technological solutions. The findings indicate that almost one-third of participants inadvertently contribute to polarization, and around one-quarter propagated misinformation. The study also identifies the existing tools enhanced by AI and Machine Learning that effectively detect misinformation and polarization in corporate settings. This investigation provides crucial insights for practitioners to strengthen their strategies against misinformation and technical challenges and opportunities. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-04-04T10:34:45Z No. of bitstreams: 1 Combating Misinformation and Polarization in the Corporate Sphere.pdf: 826855 bytes, checksum: 5cb46bb4803eff5b6e621fbd28ae934d (MD5); Made available in DSpace on 2024-04-04T10:34:45Z (GMT). No. of bitstreams: 1 Combating Misinformation and Polarization in the Corporate Sphere.pdf: 826855 bytes, checksum: 5cb46bb4803eff5b6e621fbd28ae934d (MD5) https://reunir.unir.net/handle/123456789/16265 Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games Sánchez-Canella, Fernando; Pascual-Espada, Jordán; Cid-Rico, Irene Students’ motivation is one of the factors that directly affect academic performance. In recent years, teachers are looking for ways to motivate students during their training period. For example, making use of slides, videos, films, comics or games to increase students' motivation to improve their learning experience. Some research works have revealed that multiplayer games which include cooperation and competition, among other factors, are an extraordinary tool for enhancing students’ motivation. Current alternatives make it very complex for teachers to create multiplayer games for their students. The definition of the game requires many configurations and even technical knowledge. This research proposes a new platform that allows teachers to create multiplayer video games in a simple and fast way, improving the game creation process over current alternatives. The resulting games are also designed for to improve the student experience, and make it fun. These games do not only include trivia questions, but also use functional mechanisms from video games. The design of the generated games allows students to master the games in a short period of time during their classes Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-04-04T10:22:31Z No. of bitstreams: 1 Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games.pdf: 937491 bytes, checksum: 67db8e057c9c8add1aa9c7733f453ba7 (MD5); Made available in DSpace on 2024-04-04T10:22:31Z (GMT). No. of bitstreams: 1 Platform for Improving the User Experience in the Creation of Educational Multiplayer Video Games.pdf: 937491 bytes, checksum: 67db8e057c9c8add1aa9c7733f453ba7 (MD5) https://reunir.unir.net/handle/123456789/16226 Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis Zhang, Linhao; Jin, Li; Xu, Guangluan; Li, Xiaoyu; Sun, Xian; Zhang, Zequn; Zhang, Yanan; Li, Qui Aspect-based multimodal sentiment analysis under social media scenario aims to identify the sentiment polarities of each aspect term, which are mentioned in a piece of multimodal user-generated content. Previous approaches for this interdisciplinary multimodal task mainly rely on coarse-grained fusion mechanisms from the data-level or decision-level, which have the following three shortcomings:(1) ignoring the category knowledge of the sentiment target mentioned in the text) in visual information. (2) unable to assess the importance of maintaining target interaction during the unimodal encoding process, which results in indiscriminative representations considering various aspect terms. (3) suffering from the semantic gap between multiple modalities. To tackle the above challenging issues, we propose an optimal target-oriented knowledge transportation network (OtarNet) for this task. Firstly, the visual category knowledge is explicitly transported through input space translation and reformulation. Secondly, with the reformulated knowledge containing the target and category information, the target sensitivity is well maintained in the unimodal representations through a multistage target-oriented interaction mechanism. Finally, to eliminate the distributional modality gap by integrating complementary knowledge, the target-sensitive features of multiple modalities are implicitly transported based on the optimal transport interaction module. Our model achieves state-of-theart performance on three benchmark datasets: Twitter-15, Twitter-17 and Yelp, together with the extensive ablation study demonstrating the superiority and effectiveness of OtarNet. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-14T08:25:07Z No. of bitstreams: 1 Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis.pdf: 4233371 bytes, checksum: ced0026a05664ba2651abb2695e17303 (MD5); Made available in DSpace on 2024-03-14T08:25:07Z (GMT). No. of bitstreams: 1 Optimal Target-Oriented Knowledge Transportation For Aspect-Based Multimodal Sentiment Analysis.pdf: 4233371 bytes, checksum: ced0026a05664ba2651abb2695e17303 (MD5) https://reunir.unir.net/handle/123456789/16225 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence Ordoñez, Hugo; Timarán-Pereira, Ricardo; González-Sanabria, Juan-Sebastián Introduction: Currently, homelessness should not be seen as just another problem, but as a reality of inequality and the absence of social justice. In this sense, homeless people are subjected to social disengagement, lack of job opportunities or the instability of these, insecurity circumstances, these aspects being one of the causes associated with the consumption or addiction to psychoactive substances. Data: To define the proposed approach, data from the Census of Street Inhabitants - CHC- 2021 of the National Administrative Department of Statistics (DANE), which contains 19,375 records and 25 columns, were used. Methodology: This article presents an artificial intelligence approach that implements a model based on machine learning algorithms for identifying addiction trends to psychoactive substances in street dwellers in Colombia. Conclusions: Based on the results obtained, it is evident that the approach can serve as a support for decision making by municipal administrations in the definition of social public policies for the street-dwelling population in Colombia. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-14T08:09:41Z No. of bitstreams: 1 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.pdf: 1336440 bytes, checksum: ec9d9c591214655d5dcfd2d716316b3e (MD5); Made available in DSpace on 2024-03-14T08:09:41Z (GMT). No. of bitstreams: 1 Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.pdf: 1336440 bytes, checksum: ec9d9c591214655d5dcfd2d716316b3e (MD5) https://reunir.unir.net/handle/123456789/16224 An Effective Prediction Approach for the Management of Children Victims of Road Accidents Saadi, F.; Baghdad, Atmani; Henni, F.; Benfriha, H.; Addou, Z.; Guerbouz, R. Road traffic generates a considerable number of accidents each year. The management of injuries caused by these accidents is becoming a real public health problem. Faced with this latter, we propose a new clinical decision making approach based on case-based reasoning (CBR) and data mining (DM) techniques to speed up and improve the care of an injured child. The main idea is to preprocess the dataset before using K Nearest Neighbor (KNN) Classification Model. In this paper, an efficient predictive model is developed to predict the admission procedure of a child victim of a traffic accident in pediatric intensive care units. The evaluation of the proposed model is conducted on a real dataset elaborated by the authors and validated by statistical analysis. This novel model executes a selection of relevant attributes using data mining technique and integrates a CBR system to retrieve similar cases from an archive of cases of patients successfully treated with the proposed treatment plan. The results revealed that the proposed approach outperformed other models and the results of previous studies by achieving an accuracy of 91.66%. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-03-14T07:52:35Z No. of bitstreams: 1 An Effective Prediction Approach for the Management of Children Victims of Road Accidents.pdf: 1178579 bytes, checksum: 0cb3e65716dc21576ea41f3d59ad934e (MD5); Made available in DSpace on 2024-03-14T07:52:35Z (GMT). No. of bitstreams: 1 An Effective Prediction Approach for the Management of Children Victims of Road Accidents.pdf: 1178579 bytes, checksum: 0cb3e65716dc21576ea41f3d59ad934e (MD5) https://reunir.unir.net/handle/123456789/16004 An Adaptive Salp-Stochastic-Gradient-Descent- Based Convolutional LSTM With MapReduce Framework for the Prediction of Rainfall Manoj, S. O.; Kumar, Abhishek; Dubey, A. K.; Ananth, J. P. Rainfall prediction is considered to be an esteemed research area that impacts the day-to-day life of Indians. The predominant income source of most of the Indian population is agriculture. It helps the farmers to make the appropriate decisions pertaining to cultivation and irrigation. The primary objective of this investigation is to develop a technique for rainfall prediction utilising the MapReduce framework and the convolutional long short-term memory (ConvLSTM) method to circumvent the limitations of higher computational requirements and the inability to process a large number of data points. In this work, an adaptive salp-stochastic-gradientdescent-based ConvLSTM (adaptive S-SGD-based ConvLSTM) system has been developed to predict rainfall accurately to process the long time series data and to eliminate the vanishing problems. To optimize the hyperparameter of the convLSTM model, the S-SGD methodology proposed combine the SGD and the salp swarm algorithm (SSA). The adaptive S-SGD based ConvLSTM has been developed by integrating the adaptive concept in S-SGD. It tunes the weights of ConvLSTM optimally to achieve better prediction accuracy. Assessment measures, such as the percentage root mean square difference (PRD) and mean square error (MSE), were employed to compare the suggested method with previous approaches. The developed system demonstrates high prediction accuracy, achieving minimal values for MSE (0.0042) and PRD (0.8450). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-02-06T09:12:23Z No. of bitstreams: 1 ip2024_01_003_0.pdf: 2711710 bytes, checksum: a8f9f550ee68dfb95eeeff37d0dd99cd (MD5); Made available in DSpace on 2024-02-06T09:12:23Z (GMT). No. of bitstreams: 1 ip2024_01_003_0.pdf: 2711710 bytes, checksum: a8f9f550ee68dfb95eeeff37d0dd99cd (MD5) https://reunir.unir.net/handle/123456789/16003 TKU-PSO: An Efficient Particle Swarm Optimization Model for Top-K High-Utility Itemset Mining Carstensen, Simen; Chun-Wei Lin, Jerry Top-k high-utility itemset mining (top- HUIM) is a data mining procedure used to identify the most valuable patterns within transactional data. Although many algorithms are proposed for this purpose, they require substantial execution times when the search space is vast. For this reason, several meta-heuristic models have been applied in similar utility mining problems, particularly evolutionary computation (EC). These algorithms are beneficial as they can find optimal solutions without exploring the search space exhaustively. However, there are currently no evolutionary heuristics available for top-k HUIM. This paper addresses this issue by proposing an EC-based particle swarm optimization model for top-k HUIM, which we call TKU-PSO. In addition, we have developed several strategies to relieve the computational complexity throughout the algorithm. First, redundant and unnecessary candidate evaluations are avoided by utilizing explored solutions and estimating itemset utilities. Second, unpromising items are pruned during execution based on a thresholdraising concept we call minimum solution fitness. Finally, the traditional population initialization approach is revised to improve the model’s ability to find optimal solutions in huge search spaces. Our results show that TKU-PSO is faster than state-of-the-art competitors in all datasets tested. Most notably, existing algorithms could not complete certain experiments due to excessive runtimes, whereas our model discovered the correct solutions within seconds. Moreover, TKU-PSO achieved an overall accuracy of 99.8% compared to 16.5% with the current heuristic approach, while memory usage was the smallest in 2/3 of all tests. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-02-06T08:40:05Z No. of bitstreams: 1 ip2024_01_002_1.pdf: 1652224 bytes, checksum: bc066ea9a8c4b0578d7ab427b76390ee (MD5); Made available in DSpace on 2024-02-06T08:40:05Z (GMT). No. of bitstreams: 1 ip2024_01_002_1.pdf: 1652224 bytes, checksum: bc066ea9a8c4b0578d7ab427b76390ee (MD5) https://reunir.unir.net/handle/123456789/16002 How Does the Visualization Technique Affect the Design Process? Using Sketches, Real Products and Virtual Models to Test the User’s Emotional Response Alonso-García, María; Palacios-Ibáñez, Almudena; de-Cózar-Macías, Óscar D.; Marín-Granados, Manuel D. Testing products during the design process can help design teams anticipate user needs and predict a positive emotional response. Emerging technologies, e.g., Virtual Reality (VR), allow designers to test products in a more sophisticated manner alongside traditional approaches like sketches, photographs or physical prototypes. In this paper, we present the results of a study conducted to evaluate the feasibility of seven visualization techniques for product assessment within the framework of emotional design, suggesting that the user’s perception depends on the visualization technique used to present the product. This research provides recommendations for product evaluation using physical, virtual, or conceptual prototypes to analyze the user’s emotional response throughout 19 parameters. Our results indicate that the use of virtual environments, including VR and VR with Passive Haptics (VRPH), can facilitate user participation in the design process, although these visualization techniques may also exaggerate the emotions perceived by users. In this context, VRPH tends to overstate the tactile perception of the product. Additionally, our results reveal that both virtual and conceptual environments can amplify a user’s likelihood to purchase a product. However, the latter setting could also potentially lead to confusion among users in regards to their perception of the product’s weight, dimensions, and cost. Based on these findings, the authors encourage industrial designers to develop new methodologies to optimize design process and minimize costs. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-02-06T08:28:08Z No. of bitstreams: 1 ip2024_01_001.pdf: 4676066 bytes, checksum: 9e451573d743d11b000f8576f98d05cc (MD5); Made available in DSpace on 2024-02-06T08:28:08Z (GMT). No. of bitstreams: 1 ip2024_01_001.pdf: 4676066 bytes, checksum: 9e451573d743d11b000f8576f98d05cc (MD5) https://reunir.unir.net/handle/123456789/15784 Traffic Optimization Through Waiting Prediction and Evolutive Algorithms García, Francisco; Hernández, Helena; Moreno-García, María N.; de Paz Santana, Juan F.; López, Vivian F.; Bajo, Javier Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests. For this reason, this project proposes the development of traffic optimizations based on the estimation of vehicle waiting times through the use of different prediction techniques and the use of this estimation to subsequently apply evolutionary algorithms that allow the optimizations to be carried out. The combination of these two techniques leads to a considerable reduction in calculation time, which makes it possible to apply this system at runtime. The tests have been carried out on a real traffic junction on which different traffic volumes have been applied to analyze the performance of the system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2024-01-02T08:21:54Z No. of bitstreams: 1 ip2023_12_001_0.pdf: 1895463 bytes, checksum: 20ee704a77e5452f0f776492b7dab0c5 (MD5); Made available in DSpace on 2024-01-02T08:21:54Z (GMT). No. of bitstreams: 1 ip2023_12_001_0.pdf: 1895463 bytes, checksum: 20ee704a77e5452f0f776492b7dab0c5 (MD5) https://reunir.unir.net/handle/123456789/15693 A Review of Bias and Fairness in Artificial Intelligence González-Sendino, Rubén; Serrano, Emilio; Bajo, Javier; Novais, Paulo Automating decision systems has led to hidden biases in the use of artificial intelligence (AI). Consequently, explaining these decisions and identifying responsibilities has become a challenge. As a result, a new field of research on algorithmic fairness has emerged. In this area, detecting biases and mitigating them is essential to ensure fair and discrimination-free decisions. This paper contributes with: (1) a categorization of biases and how these are associated with different phases of an AI model’s development (including the data-generation phase); (2) a revision of fairness metrics to audit the data and AI models trained with them (considering agnostic models when focusing on fairness); and, (3) a novel taxonomy of the procedures to mitigate biases in the different phases of an AI model’s development (pre-processing, training, and post-processing) with the addition of transversal actions that help to produce fairer models. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-12-11T13:20:45Z No. of bitstreams: 1 ip2023_11_001.pdf: 1082876 bytes, checksum: 4f7737e73ded1b47a4fbf7fdb60cd437 (MD5); Made available in DSpace on 2023-12-11T13:20:45Z (GMT). No. of bitstreams: 1 ip2023_11_001.pdf: 1082876 bytes, checksum: 4f7737e73ded1b47a4fbf7fdb60cd437 (MD5) https://reunir.unir.net/handle/123456789/15692 Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation Martínez-Comesaña, Miguel; Martínez-Torres, Javier; Eguía-Oller, Pablo; López-Gómez, Javier Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-12-11T12:39:29Z No. of bitstreams: 1 ip2023_11_002.pdf: 3321570 bytes, checksum: 9b631febbc75d5a2914ef233fb5f9013 (MD5); Made available in DSpace on 2023-12-11T12:39:29Z (GMT). No. of bitstreams: 1 ip2023_11_002.pdf: 3321570 bytes, checksum: 9b631febbc75d5a2914ef233fb5f9013 (MD5) https://reunir.unir.net/handle/123456789/15691 Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach Diaz-Pacheco, Angel; Álvarez-Carmona, Miguel A.; Rodríguez-González, Ansel Y.; Carlos, Hugo; Aranda, Ramón Promoting a destination is a major task for Destination Marketing Organizations (DMOs). Although DMOs control, to some extent, the information presented to travelers (controlled sources), there are other different sources of information (uncontrolled sources) that could project an unfavorable image of the destination. Measuring differences between information sources would help design strategies to mitigate negative factors. In this way, we propose a deep learning-based approach to automatically measure the changes between images from controlled and uncontrolled information sources. Our approach exempts experts from the time-consuming task of assessing enormous quantities of pictures to track changes. To our best knowledge, this work is the first work that focuses on this issue using technological paradigms. Notwithstanding this, our approach paves novel pathways to acquire strategic insights that can be harnessed for the augmentation of destination development, the refinement of recommendation systems, the analysis of online travel reviews, and myriad other pertinent domains. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-12-11T12:26:20Z No. of bitstreams: 1 ip2023_10_003_0.pdf: 4406116 bytes, checksum: 08e69141ea39a9c47231ca9ac0609575 (MD5); Made available in DSpace on 2023-12-11T12:26:20Z (GMT). No. of bitstreams: 1 ip2023_10_003_0.pdf: 4406116 bytes, checksum: 08e69141ea39a9c47231ca9ac0609575 (MD5) https://reunir.unir.net/handle/123456789/15534 Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets Bobadilla, Jesús; Gutiérrez, Abraham The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datasets. We have tested the GANRS method by creating multiple synthetic datasets from three different real datasets taken as a source. Experiments include variations in the number of users in the synthetic datasets, as well as a different number of samples. We have also selected six state-of-the-art collaborative filtering deep learning models to test both their comparative performance and the GANRS method. The results show a consistent behavior of the generated datasets compared to the source ones; particularly, in the obtained values and trends of the precision and recall quality measures. The tested deep learning models have also performed as expected on all synthetic datasets, making it possible to compare the results with those obtained from the real source data. Future work is proposed, including different cold start scenarios, unbalanced data, and demographic fairness. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-11-02T17:30:16Z No. of bitstreams: 1 ip2023_10_002.pdf: 5390765 bytes, checksum: 4edb2ac46e74d982a71f964a97162377 (MD5); Made available in DSpace on 2023-11-02T17:30:16Z (GMT). No. of bitstreams: 1 ip2023_10_002.pdf: 5390765 bytes, checksum: 4edb2ac46e74d982a71f964a97162377 (MD5) https://reunir.unir.net/handle/123456789/15340 Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality Izquierdo-Domenech, Juan; Linares-Pellicer, Jordi; Ferri-Molla, Isabel Augmented reality (AR) has become a powerful tool for assisting operators in complex environments, such as shop floors, laboratories, and industrial settings. By displaying synthetic visual elements anchored in real environments and providing information for specific tasks, AR helps to improve efficiency and accuracy. However, a common bottleneck in these environments is introducing all necessary information, which often requires predefined structured formats and needs more ability for multimodal and Natural Language (NL) interaction. This work proposes a new method for dynamically documenting complex environments using AR in a multimodal, non-structured, and interactive manner. Our method employs Large Language Models (LLMs) to allow experts to describe elements from the real environment in NL and select corresponding AR elements in a dynamic and iterative process. This enables a more natural and flexible way of introducing information, allowing experts to describe the environment in their own words rather than being constrained by a predetermined structure. Any operator can then ask about any aspect of the environment in NL to receive a response and visual guidance from the AR system, thus allowing for a more natural and flexible way of introducing and retrieving information. These capabilities ultimately improve the effectiveness and efficiency of tasks in complex environments. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-10-02T15:57:32Z No. of bitstreams: 1 ip2023_09_002.pdf: 1947156 bytes, checksum: 5cbf3c0aa9d4ca318f0f478bfedc6825 (MD5); Made available in DSpace on 2023-10-02T15:57:32Z (GMT). No. of bitstreams: 1 ip2023_09_002.pdf: 1947156 bytes, checksum: 5cbf3c0aa9d4ca318f0f478bfedc6825 (MD5) https://reunir.unir.net/handle/123456789/15166 Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique Lakshmi, H. R.; Borra, Surekha With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-31T10:18:07Z No. of bitstreams: 1 ip2023_08_002.pdf: 4918772 bytes, checksum: d4644403b3cc2f380a852da7aac8b994 (MD5); Made available in DSpace on 2023-08-31T10:18:07Z (GMT). No. of bitstreams: 1 ip2023_08_002.pdf: 4918772 bytes, checksum: d4644403b3cc2f380a852da7aac8b994 (MD5) https://reunir.unir.net/handle/123456789/15135 The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data Irfan, Muhammad; Shahrestani, Seyed; ElKhodr, Mahmoud Detecting Alzheimer’s disease (AD) in its early stages is essential for effective management, and screening for Mild Cognitive Impairment (MCI) is common practice. Among many deep learning techniques applied to assess brain structural changes, Magnetic Resonance Imaging (MRI) and Convolutional Neural Networks (CNN) have grabbed research attention because of their excellent efficiency in automated feature learning of a variety of multilayer perceptron. In this study, various CNNs are trained to predict AD on three different views of MRI images, including Sagittal, Transverse, and Coronal views. This research use T1-Weighted MRI data of 3 years composed of 2182 NIFTI files. Each NIFTI file presents a single patient's Sagittal, Transverse, and Coronal views. T1-Weighted MRI images from the ADNI database are first preprocessed to achieve better representation. After MRI preprocessing, large slice numbers require a substantial computational cost during CNN training. To reduce the slice numbers for each view, this research proposes an intelligent probabilistic approach to select slice numbers such that the total computational cost per MRI is minimized. With hyperparameter tuning, batch normalization, and intelligent slice selection and cropping, an accuracy of 90.05% achieve with the Transverse, 82.4% with Sagittal, and 78.5% with Coronal view, respectively. Moreover, the views are stacked together and an accuracy of 92.21% is achived for the combined views. In addition, results are compared with other studies to show the performance of the proposed approach for AD detection. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T12:26:24Z No. of bitstreams: 1 ip2023_07_009.pdf: 2326497 bytes, checksum: 9461bad16202051f6035b98f55c7c36f (MD5); Made available in DSpace on 2023-08-28T12:26:24Z (GMT). No. of bitstreams: 1 ip2023_07_009.pdf: 2326497 bytes, checksum: 9461bad16202051f6035b98f55c7c36f (MD5) https://reunir.unir.net/handle/123456789/15130 Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering Saadi, F.; Atmani, Baghdad; Henni, F. Case-based reasoning (CBR), which is a classical reasoning methodology, has been put to use. Its application has allowed significant progress in resolving problems related to the diagnosis, therapy, and prediction of diseases. However, this methodology has shown some complicated problems that must be resolved, including determining a representation form for the case (complexity, uncertainty, and vagueness of medical information), preventing the case base from the infinite growth of generated medical information and selecting the best retrieval technique. These limitations have pushed researchers to think about other ways of solving problems, and we are recently witnessing the integration of CBR with other techniques such as data mining. In this article, we develop a new approach integrating clustering (Fuzzy C-Means (FCM) and K-Means) in the CBR cycle. Clustering is one of the crucial challenges and has been successfully used in many areas to develop innate structures and hidden patterns for data grouping [1]. The objective of the proposed approach is to solve the limitations of CBR and improve it, particularly in the search for similar cases (retrieval step). The approach is tested with the publicly available immunotherapy dataset. The results of the experimentations show that the integration of the FCM algorithm in the retrieval step reduces the search space (the large volume of information), resolves the problem of the vagueness of medical information, speeds up the calculation and response time, and increases the search efficiency, which further improves the performance of the retrieval step and, consequently, the CBR system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-08-28T11:27:41Z No. of bitstreams: 1 ip2023_07_002.pdf: 1057175 bytes, checksum: 1c18333d633c197eb724c2cb2574d39b (MD5); Made available in DSpace on 2023-08-28T11:27:41Z (GMT). No. of bitstreams: 1 ip2023_07_002.pdf: 1057175 bytes, checksum: 1c18333d633c197eb724c2cb2574d39b (MD5) https://reunir.unir.net/handle/123456789/15033 Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction Su, Zhan; Yu, Ruiyun; Zou, Shihao; Guo, Bingyang; Cheng, Li Human-Object Interaction (HOI) detection focuses on human-centered visual relationship detection, which is a challenging task due to the complexity and diversity of image content. Unlike most recent HOI detection works that only rely on paired instance-level information in the union range, our proposed Spatial-aware Multilevel Parsing Network (SMPNet) uses a multi-level information detection strategy, including instance-level visual features of detected human-object pair, part-level related features of the human body, and scene-level features extracted by the graph neural network. After fusing the three levels of features, the HOI relationship is predicted. We validate our method on two public datasets, V-COCO and HICO-DET. Compared with prior works, our proposed method achieves the state-of-the-art results on both datasets in terms of mAProle, which demonstrates the effectiveness of our proposed multi-level information detection strategy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-07-11T13:51:43Z No. of bitstreams: 1 ip2023_06_004.pdf: 8063452 bytes, checksum: 52340a7c1fb803e53fbd3505555ae8c8 (MD5); Made available in DSpace on 2023-07-11T13:51:43Z (GMT). No. of bitstreams: 1 ip2023_06_004.pdf: 8063452 bytes, checksum: 52340a7c1fb803e53fbd3505555ae8c8 (MD5) https://reunir.unir.net/handle/123456789/15032 Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review Suárez-Cetrulo, Andrés L.; Quintana, David; Cervantes, Alejandro Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-07-11T11:48:04Z No. of bitstreams: 1 ip2023_06_003.pdf: 997078 bytes, checksum: fd331c4e733574393c764b1b317d2e13 (MD5); Made available in DSpace on 2023-07-11T11:48:04Z (GMT). No. of bitstreams: 1 ip2023_06_003.pdf: 997078 bytes, checksum: fd331c4e733574393c764b1b317d2e13 (MD5) https://reunir.unir.net/handle/123456789/15031 Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study Llauró, A.; Fonseca, David; Villegas, E.; Aláez, M.; Romero, S. The field of research on the phenomenon of university dropout and the factors that promote it is of the utmost relevance, especially in the current context of the Covid-19 pandemic. Students who have started degrees in the last two years have completed their university studies in periods of lockdown and unlike traditional education, this has often involved taking online classes. In this scenario, the students' motivation and the way they are able to cope with the difficulties of the first year of a university course are very relevant, especially in technical degrees. Previous studies show that a large number of undergraduate students drop out prematurely. In order to act to reduce dropout rates, schools, especially technical schools, should be able to map the entry profile of students and identify the factors that promote early dropout. This paper focuses on identifying, categorizing and evaluating a number of indicators according to the perception of tutors and the field of study, based on the application of quantitative and qualitative techniques. The results support the approach taken, as they show how tutors can identify students at risk of dropping out at the beginning of the course and act proactively to monitor and motivate them. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-07-11T11:19:29Z No. of bitstreams: 1 ip2023_06_002.pdf: 513302 bytes, checksum: 3b2ddb78e187bd1926c84bd4f22b25f2 (MD5); Made available in DSpace on 2023-07-11T11:19:29Z (GMT). No. of bitstreams: 1 ip2023_06_002.pdf: 513302 bytes, checksum: 3b2ddb78e187bd1926c84bd4f22b25f2 (MD5) https://reunir.unir.net/handle/123456789/14591 Reliability of IBM’s Public Quantum Computers Pérez-Antón, Raquel; Corbi, Alberto; López-Sánchez, José Ignacio; Burgos, Daniel One of the challenges of the current ecosystem of quantum computers (QC) is the stabilization of the coherence associated with the entanglement of the states of their inner qubits. In this empirical study, we monitor the reliability of IBM’s public-access QCs network on a daily basis. Each of these state-of-the-art machines has a totally different qubit association, and this entails that for a given (same) input program, they may output a different set of probabilities for the assembly of results (including both the right and the wrong ones). Although we focus on the computing structure provided by the “Big Blue” company, our survey can be easily transferred to other currently available quantum mainframes. In more detail, we probe these quantum processors with an ad hoc designed computationally demanding quaternary search algorithm. As stated, this quantum program is executed every 24 hours (for nearly 100 days) and its goal is to put to the limit the operational capacity of this novel and genuine type of equipment. Next, we perform a comparative analysis of the obtained results according to the singularities of each computer and over the total number of executions. In addition, we subsequently apply (for 50 days) an improvement filtering to perform noise mitigation on the results obtained proposed by IBM. The Yorktown 5-qubit computer reaches noise filtering of up to 33% in one day, that is, a 90% confidence level is reached in the expected results. From our continuous and long-term tests, we derive that room still exists regarding the improvement of quantum calculators in order to guarantee enough confidence in the returned outcomes. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T10:09:15Z No. of bitstreams: 1 ip2023_04_005.pdf: 1799939 bytes, checksum: dfac3fefecc5e4a8a6ba36ec4e5cc70f (MD5); Made available in DSpace on 2023-05-03T10:09:15Z (GMT). No. of bitstreams: 1 ip2023_04_005.pdf: 1799939 bytes, checksum: dfac3fefecc5e4a8a6ba36ec4e5cc70f (MD5) https://reunir.unir.net/handle/123456789/14590 Aligning Figurative Paintings With Their Sources for Semantic Interpretation Aslan, Sinem; Steels, Luc This paper reports steps in probing the artistic methods of figurative painters through computational algorithms. We explore a comparative method that investigates the relation between the source of a painting, typically a photograph or an earlier painting, and the painting itself. A first crucial step in this process is to find the source and to crop, standardize and align it to the painting so that a comparison becomes possible. The next step is to apply different low-level algorithms to construct difference maps for color, edges, texture, brightness, etc. From this basis, various subsequent operations become possible to detect and compare features of the image, such as facial action units and the emotions they signify. This paper demonstrates a pipeline we have built and tested using paintings by a renowned contemporary painter Luc Tuymans. We focus in this paper particularly on the alignment process, on edge difference maps, and on the utility of the comparative method for bringing out the semantic significance of a painting. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-05-03T09:31:36Z No. of bitstreams: 1 ip2023_04_004_0.pdf: 5966615 bytes, checksum: 10749d690dd51e22ada8b5c8bc621930 (MD5); Made available in DSpace on 2023-05-03T09:31:36Z (GMT). No. of bitstreams: 1 ip2023_04_004_0.pdf: 5966615 bytes, checksum: 10749d690dd51e22ada8b5c8bc621930 (MD5) https://reunir.unir.net/handle/123456789/14484 Use of Data Mining for Intelligent Evaluation of Imputation Methods la Red, David L.; Primorac, Carlos R. In real-world situations, researchers frequently face the difficulty of missing values (MV), i.e., values not observed in a data set. Data imputation techniques allow the estimation of MV using different algorithms, by means of which important data can be imputed for a particular instance. Most of the literature in this field deals with different imputation methods. However, few studies deal with a comparative evaluation of the different methods as to provide more appropriate guidelines for the selection of the method to be applied to impute data for specific situations. The objective of this work is to show a methodology for evaluating the performance of imputation methods by means of new metrics derived from data mining processes, using quality metrics of data mining models. We started from the complete dataset that was amputated with different amputation mechanisms to generate 63 datasets with MV; these were imputed using Median, k-NN, k-Means and Hot-Deck imputation methods. The performance of the imputation methods was evaluated using new metrics derived from quality metrics of the data mining processes, performed with the original full file and with the imputed files. This evaluation is not based on measuring the error when imputing (usual operation), but on considering the similarity of the values of the quality metrics of the data mining processes obtained with the original file and with the imputed files. The results show that –globally considered and according to the new proposed metric, the imputation methods that showed the best performance were k-NN and k-Means. An additional advantage of the proposed methodology is that it provides predictive data mining models that can be used a posteriori. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2023-04-05T08:31:53Z No. of bitstreams: 1 ip2023_03_002.pdf: 3509410 bytes, checksum: 4784064166409fee926d2e983625eab2 (MD5); Made available in DSpace on 2023-04-05T08:31:54Z (GMT). No. of bitstreams: 1 ip2023_03_002.pdf: 3509410 bytes, checksum: 4784064166409fee926d2e983625eab2 (MD5)