https://reunir.unir.net/handle/123456789/12881 2024-10-28T15:43:48Z 2024-10-28T15:43:48Z Hameed Abdulkareem, Karrar Arbaiy, Nureize Hussein Arif, Zainab Nasser Al-Mhiqani, Mohammed Abed Mohammed, Mazin Kadry, Seifedine Alkareem Alyasseri, Zaid Abdi https://reunir.unir.net/handle/123456789/13074 2022-05-11T12:07:45Z

Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations Hameed Abdulkareem, Karrar; Arbaiy, Nureize; Hussein Arif, Zainab; Nasser Al-Mhiqani, Mohammed; Abed Mohammed, Mazin; Kadry, Seifedine; Alkareem Alyasseri, Zaid Abdi Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T12:07:45Z No. of bitstreams: 1 ijimai7_2_16_0.pdf: 673766 bytes, checksum: 6f99792edfcf6637a4da6f9b2805caa7 (MD5); Made available in DSpace on 2022-05-11T12:07:45Z (GMT). No. of bitstreams: 1 ijimai7_2_16_0.pdf: 673766 bytes, checksum: 6f99792edfcf6637a4da6f9b2805caa7 (MD5)

Blanco Valencia, Xiomara Patricia https://reunir.unir.net/handle/123456789/13073 2022-05-11T11:45:44Z

Editor's Note Blanco Valencia, Xiomara Patricia The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI - provides a space in which scientists and professionals can report about new advances in Artificial Intelligence (AI). On this occasion, for the last edition of the year, I am pleased to present a regular issue including different investigations covering aspects and problems in AI and its use in various fields such as medicine, education, image analysis, protection of data, among others. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T11:45:44Z No. of bitstreams: 1 ijimai7_2_0_0.pdf: 69842 bytes, checksum: 12909db8b3fc6ff8060906ae0e1a5aa5 (MD5); Made available in DSpace on 2022-05-11T11:45:44Z (GMT). No. of bitstreams: 1 ijimai7_2_0_0.pdf: 69842 bytes, checksum: 12909db8b3fc6ff8060906ae0e1a5aa5 (MD5)

Rajinikanth, V. Kadry, Seifedine González-Crespo, Rubén Verdú, Elena https://reunir.unir.net/handle/123456789/13072 2022-07-01T10:18:32Z

A Study on RGB Image Multi-Thresholding using Kapur/Tsallis Entropy and Moth-Flame Algorithm Rajinikanth, V.; Kadry, Seifedine; González-Crespo, Rubén; Verdú, Elena In the literature, a considerable number of image processing and evaluation procedures are proposed and implemented in various domains due to their practical importance. Thresholding is one of the pre-processing techniques, widely implemented to enhance the information in a class of gray/RGB class pictures. The thresholding helps to enhance the image by grouping the similar pixels based on the chosen thresholds. In this research, an entropy assisted threshold is implemented for the benchmark RGB images. The aim of this work is to examine the thresholding performance of well-known entropy functions, such as Kapur’s and Tsallis for a chosen image threshold. This work employs a Moth-Flame-Optimization (MFO) algorithm to support the automatic identification of the finest threshold (Th) on the benchmark RGB image for a chosen threshold value (Th=2,3,4,5). After getting the threshold image, a comparison is performed against its original picture and the necessary Picture-Quality-Values (PQV) is computed to confirm the merit of the proposed work. The experimental investigation is demonstrated using benchmark images with various dimensions and the outcome of this study confirms that the MFO helps to get a satisfactory result compared to the other heuristic algorithms considered in this study. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T11:42:38Z No. of bitstreams: 1 ijimai7_2_15_0.pdf: 2567870 bytes, checksum: e38f65b0be37534802f6765f3deb16b7 (MD5); Made available in DSpace on 2022-05-11T11:42:38Z (GMT). No. of bitstreams: 1 ijimai7_2_15_0.pdf: 2567870 bytes, checksum: e38f65b0be37534802f6765f3deb16b7 (MD5)

Amo, Daniel Prinsloo, Paul Alier, Marc Fonseca, David Torres Kompen, Ricardo Canaleta, Xavier Herrero-Martín, Javier https://reunir.unir.net/handle/123456789/13071 2022-05-11T09:59:51Z

Local Technology to Enhance Data Privacy and Security in Educational Technology Amo, Daniel; Prinsloo, Paul; Alier, Marc; Fonseca, David; Torres Kompen, Ricardo; Canaleta, Xavier; Herrero-Martín, Javier In educational environments, technological adoption in the last 10 years has enabled a data-driven and decisionmaking paradigm in organizations. The integration of cloud services in schools and universities is a positive shift in the field of learning, but it also presents threats to all academic roles that need to be discussed in terms of protection, privacy, and confidentiality. Cloud storage brings the ubiquity of data to this technical transition and a delusive opportunity for cost savings. In many cases, this suggests that certain actors, beyond the control of schools and colleges, collect, handle and treat educational data on private servers and data centers. This privatization enables the manipulation of stored records, leaks, and unauthorized access. In this article, we expose the possibilities that open from the viewpoint of local technology adoption. We seek to reduce or even totally solve the detrimental effects of using cloud-based instructional and analytical technology, mixing or only using local technology. Technological methods that conform to this alternate viewpoint and new lines of study are also being suggested and created. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T09:59:51Z No. of bitstreams: 1 ijimai7_2_23_0.pdf: 616385 bytes, checksum: 1a3b22dd452061264d7aa7cab4e0873e (MD5); Made available in DSpace on 2022-05-11T09:59:51Z (GMT). No. of bitstreams: 1 ijimai7_2_23_0.pdf: 616385 bytes, checksum: 1a3b22dd452061264d7aa7cab4e0873e (MD5)

Nosek, Tijana V. Suzić, Siniša B. Pekar, Darko J. Obradović, Radovan J. Sečujski, Milan S. Delić, Vlado D. https://reunir.unir.net/handle/123456789/13070 2022-05-11T09:49:49Z

Cross-Lingual Neural Network Speech Synthesis Based on Multiple Embeddings Nosek, Tijana V.; Suzić, Siniša B.; Pekar, Darko J.; Obradović, Radovan J.; Sečujski, Milan S.; Delić, Vlado D. The paper presents a novel architecture and method for speech synthesis in multiple languages, in voices of multiple speakers and in multiple speaking styles, even in cases when speech from a particular speaker in the target language was not present in the training data. The method is based on the application of neural network embedding to combinations of speaker and style IDs, but also to phones in particular phonetic contexts, without any prior linguistic knowledge on their phonetic properties. This enables the network not only to efficiently capture similarities and differences between speakers and speaking styles, but to establish appropriate relationships between phones belonging to different languages, and ultimately to produce synthetic speech in the voice of a certain speaker in a language that he/she has never spoken. The validity of the proposed approach has been confirmed through experiments with models trained on speech corpora of American English and Mexican Spanish. It has also been shown that the proposed approach supports the use of neural vocoders, i.e. that they are able to produce synthesized speech of good quality even in languages that they were not trained on. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-11T09:49:49Z No. of bitstreams: 1 ijimai7_2_10_0.pdf: 519266 bytes, checksum: e996a82f085e590cd34e1ee6e1e1490c (MD5); Made available in DSpace on 2022-05-11T09:49:49Z (GMT). No. of bitstreams: 1 ijimai7_2_10_0.pdf: 519266 bytes, checksum: e996a82f085e590cd34e1ee6e1e1490c (MD5)

Balderas, Antonio Palomo-Duarte, Manuel Caballero-Hernández, Juan Antonio Rodriguez-Garcia, Mercedes Dodero, Juan Manuel https://reunir.unir.net/handle/123456789/13060 2022-05-10T12:33:15Z

Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations Balderas, Antonio; Palomo-Duarte, Manuel; Caballero-Hernández, Juan Antonio; Rodriguez-Garcia, Mercedes; Dodero, Juan Manuel Lecturers are often reluctant to set examinations online because of the potential problems of fraudulent behaviour from their students. This concern has increased during the coronavirus pandemic because courses that were previously designed to be taken face-to-face have to be conducted online. The courses have had to be redesigned, including seminars, laboratory sessions and evaluation activities. This has brought lecturers and students into conflict because, according to the students, the activities and examinations that have been redesigned to avoid cheating are also harder. The lecturers’ concern is that students can collaborate in taking examinations that must be taken individually without the lecturers being able to do anything to prevent it, i.e. fraudulent collaboration. This research proposes a process model to obtain evidence of students who attempt to fraudulently collaborate, based on the information in the learning environment logs. It is automated in a software tool that checks how the students took the examinations and the grades that they obtained. It is applied in a case study with more than 100 undergraduate students. The results are positive and its use allowed lecturers to detect evidence of fraudulent collaboration by several clusters of students from their submission timestamps and the grades obtained. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T12:33:15Z No. of bitstreams: 1 ijimai7_2_21_0.pdf: 800273 bytes, checksum: c4750db2120f55ac1a6e1c2443fd10fb (MD5); Made available in DSpace on 2022-05-10T12:33:15Z (GMT). No. of bitstreams: 1 ijimai7_2_21_0.pdf: 800273 bytes, checksum: c4750db2120f55ac1a6e1c2443fd10fb (MD5)

Kumari, R. Radha Kumar, V. Vijaya Naidu, K. Rama https://reunir.unir.net/handle/123456789/13059 2022-05-10T11:58:38Z

Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM Kumari, R. Radha; Kumar, V. Vijaya; Naidu, K. Rama The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T11:58:38Z No. of bitstreams: 1 ijimai7_2_14_0.pdf: 1179334 bytes, checksum: df77eba60fbb6264e4a2d75b4b7c206b (MD5); Made available in DSpace on 2022-05-10T11:58:38Z (GMT). No. of bitstreams: 1 ijimai7_2_14_0.pdf: 1179334 bytes, checksum: df77eba60fbb6264e4a2d75b4b7c206b (MD5)

Hernandez-Olivan, Carlos Beltran, Jose R. Diaz-Guerra, David https://reunir.unir.net/handle/123456789/13058 2022-05-10T11:49:12Z

Music Boundary Detection using Convolutional Neural Networks: A Comparative Analysis of Combined Input Features Hernandez-Olivan, Carlos; Beltran, Jose R.; Diaz-Guerra, David The analysis of the structure of musical pieces is a task that remains a challenge for Artificial Intelligence, especially in the field of Deep Learning. It requires prior identification of the structural boundaries of the music pieces, whose structural boundary analysis has recently been studied with unsupervised methods and supervised neural networks trained with human annotations. The supervised neural networks that have been used in previous studies are Convolutional Neural Networks (CNN) that use Mel-Scaled Log-magnitude Spectograms features (MLS), Self-Similarity Matrices (SSM) or Self-Similarity Lag Matrices (SSLM) as inputs. In previously published studies, pre-processing is done in different ways using different distance metrics, and different audio features are used for computing the inputs, so a generalised pre-processing method for calculating model inputs is missing. The objective of this work is to establish a general method to pre-process these inputs by comparing the results obtained by taking the inputs calculated from different pooling strategies, distance metrics and audio characteristics, also taking into account the computing time to obtain them. We also establish the most effective combination of inputs to be delivered to the CNN to provide the most efficient way to extract the boundaries of the structure of the music pieces. With an adequate combination of input matrices and pooling strategies, we obtain an accuracy F1 of 0.411 that outperforms a current work done under the same conditions (same public available dataset for training and testing). Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T11:49:12Z No. of bitstreams: 1 ijimai7_2_8_0.pdf: 1263458 bytes, checksum: fa397fb6164f4ce8bcd04a49851d0259 (MD5); Made available in DSpace on 2022-05-10T11:49:12Z (GMT). No. of bitstreams: 1 ijimai7_2_8_0.pdf: 1263458 bytes, checksum: fa397fb6164f4ce8bcd04a49851d0259 (MD5)

Gupta, Shikha Chug, Anuradha https://reunir.unir.net/handle/123456789/13057 2022-05-10T11:27:53Z

An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction Gupta, Shikha; Chug, Anuradha Software Maintainability is an indispensable factor to acclaim for the quality of particular software. It describes the ease to perform several maintenance activities to make a software adaptable to the modified environment. The availability & growing popularity of a wide range of Machine Learning (ML) algorithms for data analysis further provides the motivation for predicting this maintainability. However, an extensive analysis & comparison of various ML based Boosting Algorithms (BAs) for Software Maintainability Prediction (SMP) has not been made yet. Therefore, the current study analyzes and compares five different BAs, i.e., AdaBoost, GBM, XGB, LightGBM, and CatBoost, for SMP using open-source datasets. Performance of the propounded prediction models has been evaluated using Root Mean Square Error (RMSE), Mean Magnitude of Relative Error (MMRE), Pred(0.25), Pred(0.30), & Pred(0.75) as prediction accuracy measures followed by a non-parametric statistical test and a post hoc analysis to account for the differences in the performances of various BAs. Based on the residual errors obtained, it was observed that GBM is the best performer, followed by LightGBM for RMSE, whereas, in the case of MMRE, XGB performed the best for six out of the seven datasets, i.e., for 85.71% of the total datasets by providing minimum values for MMRE, ranging from 0.90 to 3.82. Further, on applying the statistical test and on performing the post hoc analysis, it was found that significant differences exist in the performance of different BAs and, XGB and CatBoost outperformed all other BAs for MMRE. Lastly, a comparison of BAs with four other ML algorithms has also been made to bring out BAs superiority over other algorithms. This study would open new doors for the software developers for carrying out comparatively more precise predictions well in time and hence reduce the overall maintenance costs. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T11:27:53Z No. of bitstreams: 1 ijimai7_2_9_0.pdf: 759011 bytes, checksum: aa9073f3d12617e05859b21b70e6c44f (MD5); Made available in DSpace on 2022-05-10T11:27:53Z (GMT). No. of bitstreams: 1 ijimai7_2_9_0.pdf: 759011 bytes, checksum: aa9073f3d12617e05859b21b70e6c44f (MD5)

Xu, Fei Wu, Tong Huang, Shali Han, Kuntong Lin, Wenwen Wu, Shizhong CB, Sivaparthipan Dinesh Jackson, Samuel R https://reunir.unir.net/handle/123456789/13056 2022-05-10T10:21:56Z

Extensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression Xu, Fei; Wu, Tong; Huang, Shali; Han, Kuntong; Lin, Wenwen; Wu, Shizhong; CB, Sivaparthipan; Dinesh Jackson, Samuel R In recent decades, the collection of visual art paintings is large, digitized, and available for public uses that are rapidly growing. The development of multi-media systems is needed due to the huge amount of digitized artwork collections for retrieving and archiving this large-scale data. This multimedia system benefits from high-level tasks and has an essential step for measuring the similarity of visual between the artistic items. For modeling the similarities between the artworks or paintings, it is essential to extract useful features of visual paintings and propose the best approach for learning these similarity metrics. The infield of visual arts education, knowing the similarities and features, makes education more attractive by enhancing cognitive development in students. In this paper, the detailed visual features are listed, and the similarity measurement between the paintings is optimized by the Sparse Metric Learning-based Kernel Regression (KR-SML). A classification model is developed using hybrid SVM-ANN for semantic-level understanding to predict painting’s genre, artist, and style. Furthermore, the Human-Computer Interaction (HCI) based formulation model is built to analyze the proposed technique. The simulation results show that the proposed model is better in terms of performance than other existing techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T10:21:56Z No. of bitstreams: 1 ijimai7_2_19_0.pdf: 727196 bytes, checksum: 4be73a56f4fce334e76982d11114a5fb (MD5); Made available in DSpace on 2022-05-10T10:21:56Z (GMT). No. of bitstreams: 1 ijimai7_2_19_0.pdf: 727196 bytes, checksum: 4be73a56f4fce334e76982d11114a5fb (MD5)

Debnath, Saswati Roy, Pinki https://reunir.unir.net/handle/123456789/13055 2022-05-10T10:09:43Z

Audio-Visual Automatic Speech Recognition Using PZM, MFCC and Statistical Analysis Debnath, Saswati; Roy, Pinki Audio-Visual Automatic Speech Recognition (AV-ASR) has become the most promising research area when the audio signal gets corrupted by noise. The main objective of this paper is to select the important and discriminative audio and visual speech features to recognize audio-visual speech. This paper proposes Pseudo Zernike Moment (PZM) and feature selection method for audio-visual speech recognition. Visual information is captured from the lip contour and computes the moments for lip reading. We have extracted 19th order of Mel Frequency Cepstral Coefficients (MFCC) as speech features from audio. Since all the 19 speech features are not equally important, therefore, feature selection algorithms are used to select the most efficient features. The various statistical algorithm such as Analysis of Variance (ANOVA), Kruskal-wallis, and Friedman test are employed to analyze the significance of features along with Incremental Feature Selection (IFS) technique. Statistical analysis is used to analyze the statistical significance of the speech features and after that IFS is used to select the speech feature subset. Furthermore, multiclass Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naive Bayes (NB) machine learning techniques are used to recognize the speech for both the audio and visual modalities. Based on the recognition rate combined decision is taken from the two individual recognition systems. This paper compares the result achieved by the proposed model and the existing model for both audio and visual speech recognition. Zernike Moment (ZM) is compared with PZM and shows that our proposed model using PZM extracts better discriminative features for visual speech recognition. This study also proves that audio feature selection using statistical analysis outperforms methods without any feature selection technique. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T10:09:43Z No. of bitstreams: 1 ijimai7_2_11_0.pdf: 784862 bytes, checksum: 035a4cc4bff43d9f2f2d56c0006aacd5 (MD5); Made available in DSpace on 2022-05-10T10:09:43Z (GMT). No. of bitstreams: 1 ijimai7_2_11_0.pdf: 784862 bytes, checksum: 035a4cc4bff43d9f2f2d56c0006aacd5 (MD5)

Daus, H. Backenstrass, M. https://reunir.unir.net/handle/123456789/13054 2022-05-10T09:57:04Z

Feasibility and Acceptability of a Mobile-Based Emotion Recognition Approach for Bipolar Disorder Daus, H.; Backenstrass, M. Over the past years, the mobile Health approach has motivated research projects to develop mood monitoring systems for bipolar disorder. Whereas mobile-based approaches have examined self-assessment or sensor data, so far, potentially important emotional aspects of this disease have been neglected. Thus, we developed an emotion-sensitive system that analyzes the verbal and facial expressions of bipolar patients in regard to their emotional cues. In this article, preliminary findings of a pilot study with five bipolar patients with respect to the acceptability and feasibility of the new approach are presented and discussed. There were individual differences in the usage frequency of the participants, and improvements regarding its handling were suggested. From the technical point of view, the video analysis was less dependable than the audio analysis and recognized almost exclusively the facial expressions of happiness. However, the system was feasible and well-accepted. The results indicate that further developments could facilitate the long-term analysis of expressed emotions in bipolar or other disorders without invading the privacy of patients. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-10T09:57:04Z No. of bitstreams: 1 ijimai7_2_1_0.pdf: 347042 bytes, checksum: 4f2e7d6bd582069c7e54223681bdedd6 (MD5); Made available in DSpace on 2022-05-10T09:57:04Z (GMT). No. of bitstreams: 1 ijimai7_2_1_0.pdf: 347042 bytes, checksum: 4f2e7d6bd582069c7e54223681bdedd6 (MD5)

Cobos-Guzman, S. Nuere, S. De Miguel, L. https://reunir.unir.net/handle/123456789/13051 2022-05-09T12:28:43Z

Design of a Virtual Assistant to Improve Interaction Between the Audience and the Presenter Cobos-Guzman, S.; Nuere, S.; De Miguel, L. This article presents a novel design of a Virtual Assistant as part of a human-machine interaction system to improve communication between the presenter and the audience that can be used in education or general presentations for improving interaction during the presentations (e.g., auditoriums with 200 people). The main goal of the proposed model is the design of a framework of interaction to increase the level of attention of the public in key aspects of the presentation. In this manner, the collaboration between the presenter and Virtual Assistant could improve the level of learning among the public. The design of the Virtual Assistant relies on non-anthropomorphic forms with ‘live’ characteristics generating an intuitive and self-explainable interface. A set of intuitive and useful virtual interactions to support the presenter was designed. This design was validated from various types of the public with a psychological study based on a discrete emotions’ questionnaire confirming the adequacy of the proposed solution. The human-machine interaction system supporting the Virtual Assistant should automatically recognize the attention level of the audience from audiovisual resources and synchronize the Virtual Assistant with the presentation. The system involves a complex artificial intelligence architecture embracing perception of high-level features from audio and video, knowledge representation, and reasoning for pervasive and affective computing and reinforcement learning to teach the intelligent agent to decide on the best strategy to increase the level of attention of the audience. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T12:28:43Z No. of bitstreams: 1 ijimai7_2_20_0.pdf: 957939 bytes, checksum: b1c73d047076f7adcd9f2a6149d79c46 (MD5); Made available in DSpace on 2022-05-09T12:28:43Z (GMT). No. of bitstreams: 1 ijimai7_2_20_0.pdf: 957939 bytes, checksum: b1c73d047076f7adcd9f2a6149d79c46 (MD5)

Amador-Domínguez, Elvira Serrano, Emilio Manrique, Daniel Bajo, Javier https://reunir.unir.net/handle/123456789/13050 2022-05-09T12:13:39Z

A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation Amador-Domínguez, Elvira; Serrano, Emilio; Manrique, Daniel; Bajo, Javier Case-Based Reasoning models are one of the most used reasoning paradigms in expert-knowledge-driven areas. One of the most prominent fields of use of these systems is the medical sector, where explainable models are required. However, these models are considerably reliant on user input and the introduction of relevant curated data. Deep learning approaches offer an analogous solution, where user input is not required. This paper proposes a hybrid Case-Based Reasoning, Deep Learning framework for medical-related applications, focusing on the generation of medical reports. The proposal combines the explainability and user-focused approach of case-based reasoning models with the deep learning techniques performance. Moreover, the framework is fully modular to fit a wide variety of tasks and data, such as real-time sensor captured data, images, or text, to name a few. An implementation of the proposed framework focusing on radiology report generation assistance is provided. This implementation is used to evaluate the proposal, showing that it can provide meaningful and accurate corrections, even when the amount of information available is minimal. Additional tests on the optimization degree of the case base are also performed, evidencing how the proposed framework can optimize this base to achieve optimal performance. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T12:13:39Z No. of bitstreams: 1 ijimai7_2_2_0.pdf: 1951937 bytes, checksum: b7226a70b3bb8ff7393614bff7771290 (MD5); Made available in DSpace on 2022-05-09T12:13:39Z (GMT). No. of bitstreams: 1 ijimai7_2_2_0.pdf: 1951937 bytes, checksum: b7226a70b3bb8ff7393614bff7771290 (MD5)

Kim, Jaehoon Oh, Jeongkyu Heo, Tae-Young https://reunir.unir.net/handle/123456789/13049 2022-05-09T11:55:08Z

Acoustic Classification of Mosquitoes using Convolutional Neural Networks Combined with Activity Circadian Rhythm Information Kim, Jaehoon; Oh, Jeongkyu; Heo, Tae-Young Many researchers have used sound sensors to record audio data from insects, and used these data as inputs of machine learning algorithms to classify insect species. In image classification, the convolutional neural network (CNN), a well-known deep learning algorithm, achieves better performance than any other machine learning algorithm. This performance is affected by the characteristics of the convolution filter (ConvFilter) learned inside the network. Furthermore, CNN performs well in sound classification. Unlike image classification, however, there is little research on suitable ConvFilters for sound classification. Therefore, we compare the performances of three convolution filters, 1D-ConvFilter, 3×1 2D-ConvFilter, and 3×3 2D-ConvFilter, in two different network configurations, when classifying mosquitoes using audio data. In insect sound classification, most machine learning researchers use only audio data as input. However, a classification model, which combines other information such as activity circadian rhythm, should intuitively yield improved classification results. To utilize such relevant additional information, we propose a method that defines this information as a priori probabilities and combines them with CNN outputs. Of the networks, VGG13 with 3×3 2D-ConvFilter showed the best performance in classifying mosquito species, with an accuracy of 80.8%. Moreover, adding activity circadian rhythm information to the networks showed an average performance improvement of 5.5%. The VGG13 network with 1D-ConvFilter achieved the highest accuracy of 85.7% with the additional activity circadian rhythm information. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T11:55:08Z No. of bitstreams: 1 ijimai7_2_6_0.pdf: 809756 bytes, checksum: c065207b8a017f7d4558654b23d8afb1 (MD5); Made available in DSpace on 2022-05-09T11:55:08Z (GMT). No. of bitstreams: 1 ijimai7_2_6_0.pdf: 809756 bytes, checksum: c065207b8a017f7d4558654b23d8afb1 (MD5)

Verma, Kamal Kant Singh, Brij Mohan https://reunir.unir.net/handle/123456789/13048 2022-05-09T11:48:54Z

Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms Verma, Kamal Kant; Singh, Brij Mohan Machine recognition of the human activities is an active research area in computer vision. In previous study, either one or two types of modalities have been used to handle this task. However, the grouping of maximum information improves the recognition accuracy of human activities. Therefore, this paper proposes an automatic human activity recognition system through deep fusion of multi-streams along with decision-level score optimization using evolutionary algorithms on RGB, depth maps and 3d skeleton joint information. Our proposed approach works in three phases, 1) space-time activity learning using two 3D Convolutional Neural Network (3DCNN) and a Long Sort Term Memory (LSTM) network from RGB, Depth and skeleton joint positions 2) Training of SVM using the activities learned from previous phase for each model and score generation using trained SVM 3) Score fusion and optimization using two Evolutionary algorithm such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The proposed approach is validated on two 3D challenging datasets, MSRDailyActivity3D and UTKinectAction3D. Experiments on these two datasets achieved 85.94% and 96.5% accuracies, respectively. The experimental results show the usefulness of the proposed representation. Furthermore, the fusion of different modalities improves recognition accuracies rather than using one or two types of information and obtains the state-of-art results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T11:48:54Z No. of bitstreams: 1 ijimai7_2_5_0.pdf: 1042722 bytes, checksum: 9b2093802be0e848d08c745da7e2c2f7 (MD5); Made available in DSpace on 2022-05-09T11:48:54Z (GMT). No. of bitstreams: 1 ijimai7_2_5_0.pdf: 1042722 bytes, checksum: 9b2093802be0e848d08c745da7e2c2f7 (MD5)

Lopez, Miguel Angel Terrón, Sara Lombardo, Juan Manuel González-Crespo, Rubén https://reunir.unir.net/handle/123456789/13047 2022-05-09T11:06:25Z

Towards a Solution to Create, Test and Publish Mixed Reality Experiences for Occupational Safety and Health Learning: Training-MR Lopez, Miguel Angel; Terrón, Sara; Lombardo, Juan Manuel; González-Crespo, Rubén Artificial intelligence, Internet of Things, Human Augmentation, virtual reality, or mixed reality have been rapidly implemented in Industry 4.0, as they improve the productivity of workers. This productivity improvement can come largely from modernizing tools, improving training, and implementing safer working methods. Human Augmentation is helping to place workers in unique environments through virtual reality or mixed reality, by applying them to training actions in a totally innovative way. Science still has to overcome several technological challenges to achieve widespread application of these tools. One of them is the democratisation of these experiences, for which is essential to make them more accessible, reducing the cost of creation that is the main barrier to entry. The cost of these mixed reality experiences lies in the effort required to design and build these mixed reality training experiences. Nevertheless, the tool presented in this paper is a solution to these current limitations. A solution for designing, building and publishing experiences is presented in this paper. With the solution, content creators will be able to create their own training experiences in a semiassisted way and eventually publish them in the Cloud. Students will be able to access this training offered as a service, using Microsoft HoloLens2. In this paper, the reader will find technical details of the Training-MR, its architecture, mode of operation and communication Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T11:06:25Z No. of bitstreams: 1 ijimai7_2_18_0.pdf: 576012 bytes, checksum: c6f28db410ee001b42b21c4253108fe8 (MD5); Made available in DSpace on 2022-05-09T11:06:25Z (GMT). No. of bitstreams: 1 ijimai7_2_18_0.pdf: 576012 bytes, checksum: c6f28db410ee001b42b21c4253108fe8 (MD5)

Li, Yuanfeng Deng, Jiangang Wu, Qun Wang, Ying https://reunir.unir.net/handle/123456789/13046 2022-05-09T10:43:19Z

Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks Li, Yuanfeng; Deng, Jiangang; Wu, Qun; Wang, Ying Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T10:43:19Z No. of bitstreams: 1 ijimai7_2_4_0.pdf: 1115306 bytes, checksum: 12738b8c684505e1608f4e5f72641b36 (MD5); Made available in DSpace on 2022-05-09T10:43:19Z (GMT). No. of bitstreams: 1 ijimai7_2_4_0.pdf: 1115306 bytes, checksum: 12738b8c684505e1608f4e5f72641b36 (MD5)

Bareño-Castellanos, E.F. Gaona-García, Paulo Alonso Ortiz-Guzmán, J.E. Montenegro-Marin, Carlos Enrique https://reunir.unir.net/handle/123456789/13045 2022-05-09T10:04:52Z

Using Grip Strength as a Cardiovascular Risk Indicator Based on Hybrid Algorithms Bareño-Castellanos, E.F.; Gaona-García, Paulo Alonso; Ortiz-Guzmán, J.E.; Montenegro-Marin, Carlos Enrique This article shows the application and design of a hybrid algorithm capable of classifying people into risk groups using data such as prehensile strength, body mass index and percentage of fat. The implementation was done on Python and proposes a tool to help make medical decisions regarding the cardiovascular health of patients. The data were taken in a systematic way, k-means and c-means algorithms were used for the classification of the data, for the prediction of new data two vectorial support machines were used, one for the k-means and the other for the c-means, obtaining as a result a 100% of precision in the vectorial support machine with c-means and a 92% in the one of k-means. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T10:04:52Z No. of bitstreams: 1 ijimai7_2_3_0.pdf: 523245 bytes, checksum: a61323622496bfa5d731b2fe40c15ba6 (MD5); Made available in DSpace on 2022-05-09T10:04:52Z (GMT). No. of bitstreams: 1 ijimai7_2_3_0.pdf: 523245 bytes, checksum: a61323622496bfa5d731b2fe40c15ba6 (MD5)

Basavaraju, H.T. H.T. Manjunath Aradhya, V.N. Guru, D.S. https://reunir.unir.net/handle/123456789/13044 2022-05-09T09:55:41Z

Neighborhood Structure-Based Model for Multilingual Arbitrarily-Oriented Text Localization in Images/Videos Basavaraju, H.T. H.T.; Manjunath Aradhya, V.N.; Guru, D.S. The text matter in an image or a video provides more important clue and semantic information of the particular event in the actual situation. Text localization task stands an interesting and challenging research-oriented process in the zone of image processing due to irregular alignments, brightness, degradation, and complexbackground. The multilingual textual information has different types of geometrical shapes and it makes further complex to locate the text information. In this work, an effective model is presented to locate the multilingual arbitrary oriented text. The proposed method developed a neighborhood structure model to locate the text region. Initially, the maxmin cluster is applied along with 3X3 sliding window to sharpen the text region. The neighborhood structure creates the boundary for every component using normal deviation calculated from the sharpened image. Finally, the double stroke structure model is employed to locate the accurate text region. The presented model is analyzed on five standard datasets such as NUS, arbitrarily oriented text, Hua's, MRRC and real-time video dataset with performance metrics such as recall, precision, and f-measure. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T09:55:41Z No. of bitstreams: 1 ijimai7_2_12_0.pdf: 1719075 bytes, checksum: de7f8084c7e91f0a18dbbcd61eafbf71 (MD5); Made available in DSpace on 2022-05-09T09:55:41Z (GMT). No. of bitstreams: 1 ijimai7_2_12_0.pdf: 1719075 bytes, checksum: de7f8084c7e91f0a18dbbcd61eafbf71 (MD5)

Suruliandi, A. Kasthuri, A. Raja, S. P. https://reunir.unir.net/handle/123456789/13043 2022-05-09T09:20:44Z

Deep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotation Suruliandi, A.; Kasthuri, A.; Raja, S. P. Face annotation is a naming procedure that assigns the correct name to a person emerging from an image. Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity. This may lead to mislabelling in face annotation. Consequently, an efficient method is still essential to enhance the reliability of face annotation. Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images. To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation. Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets. The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T09:20:44Z No. of bitstreams: 1 ijimai7_2_7_0.pdf: 809172 bytes, checksum: 1e76e2d158efeed6ce1d2ca5a22e329b (MD5); Made available in DSpace on 2022-05-09T09:20:44Z (GMT). No. of bitstreams: 1 ijimai7_2_7_0.pdf: 809172 bytes, checksum: 1e76e2d158efeed6ce1d2ca5a22e329b (MD5)

Seal, Ayan Karlekar, Aditya Krejcar, Ondrej Herrera-Viedma, Enrique https://reunir.unir.net/handle/123456789/13042 2022-05-09T09:04:12Z

Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering Seal, Ayan; Karlekar, Aditya; Krejcar, Ondrej; Herrera-Viedma, Enrique The size of data that we generate every day across the globe is undoubtedly astonishing due to the growth of the Internet of Things. So, it is a common practice to unravel important hidden facts and understand the massive data using clustering techniques. However, non- linear relations, which are essentially unexplored when compared to linear correlations, are more widespread within data that is high throughput. Often, nonlinear links can model a large amount of data in a more precise fashion and highlight critical trends and patterns. Moreover, selecting an appropriate measure of similarity is a well-known issue since many years when it comes to data clustering. In this work, a non-Euclidean similarity measure is proposed, which relies on non-linear Jeffreys-divergence (JS). We subsequently develop c- means using the proposed JS (J-c-means). The various properties of the JS and J-c-means are discussed. All the analyses were carried out on a few real-life and synthetic databases. The obtained outcomes show that J-c-means outperforms some cutting-edge c-means algorithms empirically. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T09:04:12Z No. of bitstreams: 1 ijimai7_2_13_0.pdf: 1127561 bytes, checksum: 81ce1c10d0a4d67f6f8b9358569277f2 (MD5); Made available in DSpace on 2022-05-09T09:04:12Z (GMT). No. of bitstreams: 1 ijimai7_2_13_0.pdf: 1127561 bytes, checksum: 81ce1c10d0a4d67f6f8b9358569277f2 (MD5)

Tlili, Ahmed Chang, Maiga Moon, Jewoong Liu, Zhichun Burgos, Daniel Chen, Nian-Shing Kinshuk https://reunir.unir.net/handle/123456789/13041 2023-03-23T10:53:25Z

A Systematic Literature Review of Empirical Studies on Learning Analytics in Educational Games Tlili, Ahmed; Chang, Maiga; Moon, Jewoong; Liu, Zhichun; Burgos, Daniel; Chen, Nian-Shing; Kinshuk Learning analytics (LA) in educational games is considered an emerging practice due to its potential of enhancing the learning process. Growing research on formative assessment has shed light on the ways in which students' meaningful and in-situ learning experiences can be supported through educational games. To understand learners' playful experiences during gameplay, researchers have applied LA, which focuses on understanding students' in-game behaviour trajectories and personal learning needs during play. However, there is a lack of studies exploring how further research on LA in educational games can be conducted. Only a few analyses have discussed how LA has been designed, integrated, and implemented in educational games. Accordingly, this systematic literature review examined how LA in educational games has evolved. The study findings suggest that: (1) there is an increasing need to consider factors such as student modelling, iterative game design and personalisation when designing and implementing LA through educational games; and (2) the use of LA creates several challenges from technical, data management and ethical perspectives. In addition to outlining these findings, this article offers important notes for practitioners, and discusses the implications of the study’s results. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T07:55:16Z No. of bitstreams: 1 ijimai7_2_22_0.pdf: 566366 bytes, checksum: 6ffdc8dea2da263c81f5f221c6dbb46e (MD5); Made available in DSpace on 2022-05-09T07:55:16Z (GMT). No. of bitstreams: 1 ijimai7_2_22_0.pdf: 566366 bytes, checksum: 6ffdc8dea2da263c81f5f221c6dbb46e (MD5)

De Garrido, L. Gómez Sanz, J.J. Pavón Mestras, Juan https://reunir.unir.net/handle/123456789/13040 2022-05-09T07:24:52Z

Foundations for the Design of a Creative System Based on the Analysis of the Main Techniques that Stimulate Human Creativity De Garrido, L.; Gómez Sanz, J.J.; Pavón Mestras, Juan This work presents the design of a computational system with creative capacity, based on the synthesis of the main methods that stimulate human creativity. When analyzing each method, a set of characteristics that the computer system must have in order to emulate a creative capacity has been suggested. In this way, by integrating all the suggestions in a structured way, it is possible to design the general architecture and functioning strategy of a computer system that has the incremental creative capacity of well-known creative methods. This computational system is designed as a multi-agent system, made up of two groups of agents, the problem solving group and the creative group, the first one exploring and evaluating paths for suitable solutions, the second implementing creative methods to generate new paths that are provided to the first group. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-09T07:24:52Z No. of bitstreams: 1 ijimai7_2_17.pdf: 982798 bytes, checksum: 8de4157a28208ebfdd5829fc0d99cd16 (MD5); Made available in DSpace on 2022-05-09T07:24:52Z (GMT). No. of bitstreams: 1 ijimai7_2_17.pdf: 982798 bytes, checksum: 8de4157a28208ebfdd5829fc0d99cd16 (MD5)

Burgos, Daniel Oviedo, Lluis Griffiths, Dai Vestrucci, Andrea https://reunir.unir.net/handle/123456789/13030 2022-05-06T08:22:56Z

Editor's Note Burgos, Daniel; Oviedo, Lluis; Griffiths, Dai; Vestrucci, Andrea Research on the relationship between computing and the meaning of human life flourishes proportionally to the increasing digitalization of our world. More and more, reflections on ethics and politics, spiritual values and religious experiences, beliefs, and practices make use of digital media in order to spread their content or express themselves. If we still consider that there is truth in the well-known dictum that “the medium is the message”, then it is worth asking how the content of these reflections and practices are changing today. Every change is the introduction of something new, and this novelty can be interpreted either as the improvement or the worsening of the current situation. Generally speaking, research on either the positive or negative interactions between the advances in AI and the dimension of spirituality and analogue thinking are based on at least three approaches. The first produces analogies between concepts from human studies and concepts from computer science; for instance, speaking of “modeling” for concepts in human sciences, or considering the universe to be intelligently organized in an algorithmic order. The second approach is the application of research on AI and computer science to develop new insights on the extents, limits, and perfectibility of spiritual topics, discussions, or even practices. Finally, the third approach applies sociological, philosophical, aesthetic, or even theological concepts to assess the changes that digitalization introduces in spiritual practices, beliefs, and cultures. This special issue analyzes the current state of the art, and it addresses all three models of the research. By doing so, the issue will place the general question of the distinction between human and machine into sharper relief. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T08:22:56Z No. of bitstreams: 1 ijimai7_1_0.pdf: 80862 bytes, checksum: 09f5c25f9992e878cc1fb9aa6c6ab27d (MD5); Made available in DSpace on 2022-05-06T08:22:56Z (GMT). No. of bitstreams: 1 ijimai7_1_0.pdf: 80862 bytes, checksum: 09f5c25f9992e878cc1fb9aa6c6ab27d (MD5)

Vestrucci, Andrea Lumbreras, Sara Oviedo, Lluis https://reunir.unir.net/handle/123456789/13029 2022-05-06T08:19:11Z

Can AI Help Us to Understand Belief? Sources, Advances, Limits, and Future Directions Vestrucci, Andrea; Lumbreras, Sara; Oviedo, Lluis The study of belief is expanding and involves a growing set of disciplines and research areas. These research programs attempt to shed light on the process of believing, understood as a central human cognitive function. Computational systems and, in particular, what we commonly understand as Artificial Intelligence (AI), can provide some insights on how beliefs work as either a linear process or as a complex system. However, the computational approach has undergone some scrutiny, in particular about the differences between what is distinctively human and what can be inferred from AI systems. The present article investigates to what extent recent developments in AI provide new elements to the debate and clarify the process of belief acquisition, consolidation, and recalibration. The article analyses and debates current issues and topics of investigation such as: different models to understand belief, the exploration of belief in an automated reasoning environment, the case of religious beliefs, and future directions of research. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T08:19:11Z No. of bitstreams: 1 ijimai7_1_3_0.pdf: 657004 bytes, checksum: 926f0d77a9f183dff708ce1b495bf438 (MD5); Made available in DSpace on 2022-05-06T08:19:11Z (GMT). No. of bitstreams: 1 ijimai7_1_3_0.pdf: 657004 bytes, checksum: 926f0d77a9f183dff708ce1b495bf438 (MD5)

Griffiths, Dai https://reunir.unir.net/handle/123456789/13028 2022-05-06T08:01:14Z

Artificial Intelligence Seen Through the Lens of Bateson’s Ecology of Mind Griffiths, Dai Gregory Bateson developed a number of ideas which are relevant to artificial intelligence, and in particular to the ascription of qualities such as mind, consciousness, spirituality and the sacred. Relevant sections of Bateson’s key works are discussed, and his intellectual framework for an ecology of mind is summarized, and in particular his concepts of mind, learning, and the sacred. These are then applied to discuss whether artificial intelligence applications can be considered to possess ‘mind’. It is concluded that symbolic artificial intelligence falls short of Bateson’s criteria for mind, as do neural networks, although approach more closely. Nor are computers based on the rules of formal logic able to engage with the sacred, which is paradoxical in nature. However, artificial intelligence applications can form part of an ecology of mind and can be involved in the experience of the sacred. Bateson’s writing remains a fertile source of ideas relevant to an understanding of the nature and capabilities of artificial intelligence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T08:01:14Z No. of bitstreams: 1 ijimai7_1_7.pdf: 852518 bytes, checksum: c112e138701f5952d979ea7881dd4598 (MD5); Made available in DSpace on 2022-05-06T08:01:14Z (GMT). No. of bitstreams: 1 ijimai7_1_7.pdf: 852518 bytes, checksum: c112e138701f5952d979ea7881dd4598 (MD5)

Graves, Mark https://reunir.unir.net/handle/123456789/13027 2022-05-06T07:56:08Z

Emergent Models for Moral AI Spirituality Graves, Mark Examining AI spirituality can illuminate problematic assumptions about human spirituality and AI cognition, suggest possible directions for AI development, reduce uncertainty about future AI, and yield a methodological lens sufficient to investigate human-AI sociotechnical interaction and morality. Incompatible philosophical assumptions about human spirituality and AI limit investigations of both and suggest a vast gulf between them. An emergentist approach can replace dualist assumptions about human spirituality and identify emergent behavior in AI computation to overcome overly reductionist assumptions about computation. Using general systems theory to organize models of human experience yields insight into human morality and spirituality, upon which AI modeling can also draw. In this context, the pragmatist Josiah Royce’s semiotic philosophy of spirituality identifies unanticipated overlap between symbolic AI and spirituality and suggests criteria for a human-AI community focused on modeling morality that would result in an emergent Interpreter-Spirit sufficient to influence the ongoing development of human and AI morality and spirituality. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:56:08Z No. of bitstreams: 1 ijimai7_1_1.pdf: 452895 bytes, checksum: 4f1b3aa0b3d1071ad7c36b0653a9fd4e (MD5); Made available in DSpace on 2022-05-06T07:56:08Z (GMT). No. of bitstreams: 1 ijimai7_1_1.pdf: 452895 bytes, checksum: 4f1b3aa0b3d1071ad7c36b0653a9fd4e (MD5)

Dorobantu, Marius https://reunir.unir.net/handle/123456789/13026 2022-05-06T07:51:34Z

Why the Future Might Actually Need Us: A Theological Critique of the ‘Humanity-As-Midwife-For-Artificial-Superintelligence’ Proposal Dorobantu, Marius If machines could one day acquire superhuman intelligence, what role would still be left for humans to play in the world? The ‘midwife proposal,’ coming from futurists like Ray Kurzweil or James Lovelock, sees the invention of AI as a fulfillment of humanity’s cosmic destiny. The universe ‘strives’ to be saturated with intelligence, and our cyborg descendants are much better equipped to advance this goal. By creating AI, humans play their humble, but instrumental, part in the grand scheme. The midwife proposal looks remarkably similar to modern Christian anthropology and cosmology, which regard humankind as “evolution becoming conscious of itself” (Pierre Teilhard de Chardin), and matter as having a predisposition to evolve toward spirit (Karl Rahner, Dumitru Stăniloae). This paper demonstrates that the similarity is only superficial. Compared to the midwife hypothesis, Christian theological accounts define the cosmic role of humanity quite differently, and they provide a more satisfactory teleology. In addition, the scientific and philosophical assumptions behind the midwife hypothesis – that the cosmos is fundamentally informational, that it intrinsically promotes higher intelligence, or that we are heading toward a technological singularity - are rather questionable, with potentially significant theological and ethical consequences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:51:34Z No. of bitstreams: 1 ijimai7_1_5.pdf: 632327 bytes, checksum: 281b28acc14ea84939893cf490de8339 (MD5); Made available in DSpace on 2022-05-06T07:51:34Z (GMT). No. of bitstreams: 1 ijimai7_1_5.pdf: 632327 bytes, checksum: 281b28acc14ea84939893cf490de8339 (MD5)

Krüger, Oliver https://reunir.unir.net/handle/123456789/13025 2022-05-06T07:47:15Z

“The Singularity is near!” Visions of Artificial Intelligence in Posthumanism and Transhumanism Krüger, Oliver Over the past 20 years, the idea of singularity has become increasingly important to the technological visions of posthumanism and transhumanism. The article first introduces key posthumanist authors such as Marvin Minsky, Ray Kurzweil, Hans Moravec, and Frank Tipler. In the following, the concept of singularity is reviewed from a cultural studies perspective, first with regard to the cosmological singularity and then to the technological singularity. According to posthumanist thinkers the singularity is marked by the emergence of a superhuman computer intelligence that will solve all of humanity’s problems. At the same time, it heralds the end of the human era. Most authors refer to the British mathematician Irving John Good’s 1965 essay Speculations Concerning the First Ultraintelligent Machine as the originator of the idea of superintelligence. Individual elements of the singularity idea such as the impenetrable event horizon, the frontier and the ongoing acceleration of progress are contextualized historically and culturally. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:47:15Z No. of bitstreams: 1 ijimai7_1_2.pdf: 531069 bytes, checksum: 728472ad62c03193016fd1faa165e18d (MD5); Made available in DSpace on 2022-05-06T07:47:15Z (GMT). No. of bitstreams: 1 ijimai7_1_2.pdf: 531069 bytes, checksum: 728472ad62c03193016fd1faa165e18d (MD5)

Burgos, Daniel https://reunir.unir.net/handle/123456789/13024 2022-05-06T07:27:53Z

Rituals and Data Analytics: A Mixed-Methods Model to Process Personal Beliefs Burgos, Daniel The goal of this research is to delve into ritual, religious, and secular phenomenology. It concentrates specifically on the relationship between pagan, cultural, celebratory, and traditional rituals and any other form of representation of a social sentiment focused on identifying, enjoying, or replacing a feeling (e.g. transcendence) as well as how these rituals overlap, replace, nourish, or make use of religious rituals bi-directionally. To achieve this goal, the research develops a semi-automatic process that leans on a mixed-methods approach, to explore the degree of ritual identity. This approach combines qualitative and quantitative research, applying a number of tools, such as systematic literature review, semi-structured interviews, data-analytics generic framework, and case studies. After a thorough systematic review of 251 publications, a semi-structured interview is designed and applied to 51 subjects. 10 significant aspects that define rituals are extracted. Subsequently, this list is completed with the 17 common elements of ritual identity from the systematic literature review. These combined indicators constitute the basis for building a data-analytics generic framework of ritual affinity through weighing each element’s relevance and presence to obtain a degree of total affinity. That framework is then applied to 34 representative case studies. The core findings reinforce the initial hypothesis, determining that rituals follow a similar pattern of structure and preparation according to a predetermined set of common elements, whether linked to religious or secular settings. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:27:53Z No. of bitstreams: 1 ijimai7_1_6.pdf: 1196512 bytes, checksum: e90905d2529040388139582eb6c2fcfb (MD5); Made available in DSpace on 2022-05-06T07:27:53Z (GMT). No. of bitstreams: 1 ijimai7_1_6.pdf: 1196512 bytes, checksum: e90905d2529040388139582eb6c2fcfb (MD5)

Calderero Hernández, José Fernando https://reunir.unir.net/handle/123456789/13023 2022-05-06T07:23:59Z

Artificial Intelligence and Spirituality Calderero Hernández, José Fernando Drawing from a conceptual review of the terms ‘mind’, ‘intelligence’, ‘spirit’, ‘spirituality’, ‘spiritual intelligence’ and their possible interrelations, an approach to the concept ‘human nature’ is made in relation to transhumanism and post-humanism. In addition, through a reflection on the nature and meaning of the terms ‘datum’, ‘coding’, ‘language’, ‘energy’, ‘concrete’, and ‘abstract’, some dimensions of ‘artificial intelligence’ (AI) and their analogies and differences with ‘the spiritual’ are shown. After a brief foray into the concept of ‘reality’ and its probable ‘fuzziness’, we discuss their intrinsic and inherent mutability, and the possible existential dependence of some of their parts on the intentional activity of personal beings. We point out the dangers, for intellectual rigor and therefore for life in general, and human life in particular, of reductionist interpretations of reality that, arguing at having been scientifically proven, are intended to provide a closed and indisputable explanation of facts and phenomena of diverse aetiology, ignoring the need for ‘management of the unknown’. Consequently, an open, synergetic, harmonious vision of the role of technology and the humanities, especially those most focused on the study of the intangible, is necessary for the progress of knowledge and, therefore, for the mutually beneficial care of humanity and nature. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-06T07:23:59Z No. of bitstreams: 1 ijimai7_1_4.pdf: 645923 bytes, checksum: ff560293f40dc22e74e47726f67c5f59 (MD5); Made available in DSpace on 2022-05-06T07:23:59Z (GMT). No. of bitstreams: 1 ijimai7_1_4.pdf: 645923 bytes, checksum: ff560293f40dc22e74e47726f67c5f59 (MD5)

Chun-Wei Lin, Jerry Srivastava, Gautam Tseng, Vicent S. https://reunir.unir.net/handle/123456789/13005 2022-05-03T12:30:24Z

Editor's Note Chun-Wei Lin, Jerry; Srivastava, Gautam; Tseng, Vicent S. In today’s world, we have witnessed an onset of multimedia content being uploaded/downloaded and shared through a multitude of platforms both online and offline. In support of this trend, multimedia processing and analyzing has become very popular in all kinds of information extraction and attracts research interest from both academia and industry. This is to be expected as the multimedia digital world is worth trillions of dollars worldwide. However, multimedia information is hard to encode, interpret and recognize because it is combined with many complex components. Recently, there are many research areas related to the overall notion of intelligent multimedia processing. Therefore, the collected papers in this special issue provide a systematic overview and state-of-the-art research in the field of intelligent multimedia processing and analyzing system and outline new developments in fundamental, theorems, approaches, methodologies, software systems, recommendations, and real-world applications in this area. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T12:30:24Z No. of bitstreams: 1 ijimai6_7_0.pdf: 85925 bytes, checksum: ba27e470d7b795be7800324d985cad96 (MD5); Made available in DSpace on 2022-05-03T12:30:24Z (GMT). No. of bitstreams: 1 ijimai6_7_0.pdf: 85925 bytes, checksum: ba27e470d7b795be7800324d985cad96 (MD5)

Chan Chiu, Po Selamat, Ali Krejcar, Ondrej Kuok Kuok, King Herrera-Viedma, Enrique Fenza, Giuseppe https://reunir.unir.net/handle/123456789/13004 2022-05-03T12:26:22Z

Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network Chan Chiu, Po; Selamat, Ali; Krejcar, Ondrej; Kuok Kuok, King; Herrera-Viedma, Enrique; Fenza, Giuseppe Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SCFFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T12:26:22Z No. of bitstreams: 1 ijimai6_7_4.pdf: 2067955 bytes, checksum: 2af2444d93e5c0c255b6cad33152c6a1 (MD5); Made available in DSpace on 2022-05-03T12:26:22Z (GMT). No. of bitstreams: 1 ijimai6_7_4.pdf: 2067955 bytes, checksum: 2af2444d93e5c0c255b6cad33152c6a1 (MD5)

Juan, Chun-Jung Wang, Chen-Shu Lee, Bo-Yi Chiang, Shang-Yu Yeh, Chun-Chang Cho, Der-Yang Shen, Wu-Chung https://reunir.unir.net/handle/123456789/13003 2022-05-03T12:05:15Z

Integration of Genetic Programming and TABU Search Mechanism for Automatic Detection of Magnetic Resonance Imaging in Cervical Spondylosis Juan, Chun-Jung; Wang, Chen-Shu; Lee, Bo-Yi; Chiang, Shang-Yu; Yeh, Chun-Chang; Cho, Der-Yang; Shen, Wu-Chung Cervical spondylosis is a kind of degenerative disease which not only occurs in elder patients. The age distribution of patients is unfortunately decreasing gradually. Magnetic Resonance Imaging (MRI) is the best tool to confirm the cervical spondylosis severity but it requires radiologist to spend a lot of time for image check and interpretation. In this study, we proposed a prediction model to evaluate the cervical spine condition of patients by using MRI data. Furthermore, to ensure the computing efficiency of the proposed model, we adopted a heuristic programming, genetic programming (GP), to build the core of refereeing engine by combining the TABU search (TS) with the evolutionary GP. Finally, to validate the accuracy of the proposed model, we implemented experiments and compared our prediction results with radiologist’s diagnosis to the same MRI image. The experiment found that using clinical indicators to optimize the TABU list in GP+TABU got better fitness than the other two methods and the accuracy rate of our proposed model can achieve 88% on average. We expected the proposed model can help radiologists reduce the interpretation effort and improve the relationship between doctors and patients. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T12:05:15Z No. of bitstreams: 1 ijimai6_7_11.pdf: 2984088 bytes, checksum: 9d394359a3de2e44d7cbf1bac731ef61 (MD5); Made available in DSpace on 2022-05-03T12:05:15Z (GMT). No. of bitstreams: 1 ijimai6_7_11.pdf: 2984088 bytes, checksum: 9d394359a3de2e44d7cbf1bac731ef61 (MD5)

Wu, Yufeng Zhang, Longfei Ding, Gangyi Xue, Tong Zhang, Fuquan https://reunir.unir.net/handle/123456789/13002 2022-05-03T11:58:51Z

Modeling of Performance Creative Evaluation Driven by Multimodal Affective Data Wu, Yufeng; Zhang, Longfei; Ding, Gangyi; Xue, Tong; Zhang, Fuquan Performance creative evaluation can be achieved through affective data, and the use of affective featuresto evaluate performance creative is a new research trend. This paper proposes a “Performance Creative—Multimodal Affective (PC-MulAff)” model based on the multimodal affective features for performance creative evaluation. The multimedia data acquisition equipment is used to collect the physiological data of the audience, including the multimodal affective data such as the facial expression, heart rate and eye movement. Calculate affective features of multimodal data combined with director annotation, and defined “Performance Creative—Affective Acceptance (PC-Acc)” based on multimodal affective features to evaluate the quality of performance creative. This paper verifies the PC-MulAff model on different performance data sets. The experimental results show that the PC-MulAff model shows high evaluation quality in different performance forms. In the creative evaluation of dance performance, the accuracy of the model is 7.44% and 13.95% higher than that of the single textual and single video evaluation. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T11:58:51Z No. of bitstreams: 1 ijimai6_7_9.pdf: 4988076 bytes, checksum: 9c5e1818386608466023e1d750104196 (MD5); Made available in DSpace on 2022-05-03T11:58:51Z (GMT). No. of bitstreams: 1 ijimai6_7_9.pdf: 4988076 bytes, checksum: 9c5e1818386608466023e1d750104196 (MD5)

Chang, Shin-Hung Tsai, Min-Lun Lee, Meng-Huang Ho, Jan-Ming https://reunir.unir.net/handle/123456789/13001 2022-05-03T11:40:28Z

Optimal QoE Scheduling in MPEG-DASH Video Streaming Chang, Shin-Hung; Tsai, Min-Lun; Lee, Meng-Huang; Ho, Jan-Ming DASH is a popular technology for video streaming over the Internet. However, the quality of experience (QoE), a measure of humans’ perceived satisfaction of the quality of these streamed videos, is their subjective opinion, which is difficult to evaluate. Previous studies only considered network-based indices and focused on them to provide smooth video playback instead of improving the true QoE experienced by humans. In this study, we designed a series of click density experiments to verify whether different resolutions could affect the QoE for different video scenes. We observed that, in a single video segment, different scenes with the same resolution could affect the viewer’s QoE differently. It is true that the user’s satisfaction as a result of watching high-resolution video segments is always greater than that when watching low-resolution video segments of the same scenes. However, the most important observation is that low-resolution video segments yield higher viewing QoE gain in slow motion scenes than in fast motion scenes. Thus, the inclusion of more high-resolution segments in the fast motion scenes and more low-resolution segments in the slow motion scenes would be expected to maximize the user’s viewing QoE. In this study, to evaluate the user’s true experience, we convert the viewing QoE into a satisfaction quality score, termed the Q-score, for scenes with different resolutions in each video segment. Additionally, we developed an optimal segment assignment (OSA) algorithm for Q-score optimization in environments characterized by a constrained network bandwidth. Our experimental results show that application of the OSA algorithm to the playback schedule significantly improved users’ viewing satisfaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T11:40:28Z No. of bitstreams: 1 ijimai6_7_7.pdf: 5765364 bytes, checksum: 48622abe49860609c898f21c6f7b9269 (MD5); Made available in DSpace on 2022-05-03T11:40:28Z (GMT). No. of bitstreams: 1 ijimai6_7_7.pdf: 5765364 bytes, checksum: 48622abe49860609c898f21c6f7b9269 (MD5)

Chen, Shih-Hsin Wang, Chun-Wei Tai, I-Hsin Weng, Ken-Pen Chen, Yi-Hui Hsieh, Kai-Sheng https://reunir.unir.net/handle/123456789/13000 2022-05-03T11:09:38Z

Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images Chen, Shih-Hsin; Wang, Chun-Wei; Tai, I-Hsin; Weng, Ken-Pen; Chen, Yi-Hui; Hsieh, Kai-Sheng Doctors conventionally analyzed echocardiographic images for diagnosing congenital heart diseases (CHDs). However, this process is laborious and depends on the experience of the doctors. This study investigated the use of deep learning algorithms for the image detection of the ventricular septal defect (VSD), the most common type. Color Doppler echocardiographic images containing three types of VSDs were tested with color doppler ultrasound medical images. To the best of our knowledge, this study is the first one to solve this object detection problem by using a modified YOLOv4–DenseNet framework. Because some techniques of YOLOv4 are not suitable for echocardiographic object detection, we revised the algorithm for this problem. The results revealed that the YOLOv4–DenseNet outperformed YOLOv4, YOLOv3, YOLOv3–SPP, and YOLOv3–DenseNet in terms of metric mAP-50. The F1-score of YOLOv4-DenseNet and YOLOv3-DenseNet were better than those of others. Hence, the contribution of this study establishes the feasibility of using deep learning for echocardiographic image detection of VSD investigation and a better YOLOv4-DenseNet framework could be employed for the VSD detection. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T11:09:38Z No. of bitstreams: 1 ijimai6_7_10.pdf: 2345492 bytes, checksum: 2425a3a4bbc97ba66c00fefb13bfe324 (MD5); Made available in DSpace on 2022-05-03T11:09:38Z (GMT). No. of bitstreams: 1 ijimai6_7_10.pdf: 2345492 bytes, checksum: 2425a3a4bbc97ba66c00fefb13bfe324 (MD5)

Kaliyugarasan, Satheshkumar Lundervold, Arvid Lundervold, Alexander Selvikvåg https://reunir.unir.net/handle/123456789/12999 2022-05-03T11:00:30Z

Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI Kaliyugarasan, Satheshkumar; Lundervold, Arvid; Lundervold, Alexander Selvikvåg We construct a convolutional neural network to classify pulmonary nodules as malignant or benign in the context of lung cancer. To construct and train our model, we use our novel extension of the fastai deep learning framework to 3D medical imaging tasks, combined with the MONAI deep learning library. We train and evaluate the model using a large, openly available data set of annotated thoracic CT scans. Our model achieves a nodule classification accuracy of 92.4% and a ROC AUC of 97% when compared to a “ground truth” based on multiple human raters subjective assessment of malignancy. We further evaluate our approach by predicting patient-level diagnoses of cancer, achieving a test set accuracy of 75%. This is higher than the 70% obtained by aggregating the human raters assessments. Class activation maps are applied to investigate the features used by our classifier, enabling a rudimentary level of explainability for what is otherwise close to “black box” predictions. As the classification of structures in chest CT scans is useful across a variety of diagnostic and prognostic tasks in radiology, our approach has broad applicability. As we aimed to construct a fully reproducible system that can be compared to new proposed methods and easily be adapted and extended, the full source code of our work is available at https://github.com/MMIV-ML/Lung-CT-fastai-2020. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T11:00:30Z No. of bitstreams: 1 ijimai6_7_8.pdf: 4159041 bytes, checksum: 2d9ab0ea8671e64fe47a484809bd380d (MD5); Made available in DSpace on 2022-05-03T11:00:30Z (GMT). No. of bitstreams: 1 ijimai6_7_8.pdf: 4159041 bytes, checksum: 2d9ab0ea8671e64fe47a484809bd380d (MD5)

Hui-Ye Chiu, Terry Wu, Chienwen Chen, Chun-Hao https://reunir.unir.net/handle/123456789/12998 2022-05-03T10:36:21Z

A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms Hui-Ye Chiu, Terry; Wu, Chienwen; Chen, Chun-Hao Wine is an exciting and complex product with distinctive qualities that makes it different from other manufactured products. Therefore, the testing approach to determine the quality of wine is complex and diverse. Several elements influence wine quality, but the views of experts can cause the most considerable influence on how people view the quality of wine. The views of experts on quality is very subjective, and may not match the taste of consumer. In addition, the experts may not always be available for the wine testing. To overcome this issue, many approaches based on machine learning techniques that get the attention of the wine industry have been proposed to solve it. However, they focused only on using a particular classifier with a specific set of wine dataset. In this paper, we thus firstly propose the generalized wine quality prediction framework to provide a mechanism for finding a useful hybrid model for wine quality prediction. Secondly, based on the framework, the generalized wine quality prediction algorithm using the genetic algorithms is proposed. It first encodes the classifiers as well as their hyperparameters into a chromosome. The fitness of a chromosome is then evaluated by the average accuracy of the employed classifiers. The genetic operations are performed to generate new offspring. The evolution process is continuing until reaching the stop criteria. As a result, the proposed approach can automatically find an appropriate hybrid set of classifiers and their hyperparameters for optimizing the prediction result and independent on the dataset. At last, experiments on the wine datasets were made to show the merits and effectiveness of the proposed approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T10:36:21Z No. of bitstreams: 1 ijimai6_7_6.pdf: 2091621 bytes, checksum: 8181aed690d44bb4cf32cd015ab0582d (MD5); Made available in DSpace on 2022-05-03T10:36:21Z (GMT). No. of bitstreams: 1 ijimai6_7_6.pdf: 2091621 bytes, checksum: 8181aed690d44bb4cf32cd015ab0582d (MD5)

Afzal, Sitara Maqsood, Muazzam Khan, Umair Mehmood, Irfan Nawaz, Hina Aadil, Farhan Song, Oh-Young Yunyoung, Nam https://reunir.unir.net/handle/123456789/12997 2022-05-03T10:29:48Z

Alzheimer Disease Detection Techniques and Methods: A Review Afzal, Sitara; Maqsood, Muazzam; Khan, Umair; Mehmood, Irfan; Nawaz, Hina; Aadil, Farhan; Song, Oh-Young; Yunyoung, Nam Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T10:29:48Z No. of bitstreams: 1 ijimai6_7_3.pdf: 1256360 bytes, checksum: c7087c8a9916347c36d1dbc9f1985568 (MD5); Made available in DSpace on 2022-05-03T10:29:48Z (GMT). No. of bitstreams: 1 ijimai6_7_3.pdf: 1256360 bytes, checksum: c7087c8a9916347c36d1dbc9f1985568 (MD5)

Biswas, Nirmal Kr. Banerjee, Sourav Biswas, Utpal https://reunir.unir.net/handle/123456789/12996 2022-05-03T09:50:01Z

Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation Biswas, Nirmal Kr.; Banerjee, Sourav; Biswas, Utpal Multimedia computing (MC) is rising as a nascent computing paradigm to process multimedia applications and provide efficient multimedia cloud services with optimal Quality of Service (QoS) to the multimedia cloud users. But, the growing popularity of MC is affecting the climate. Because multimedia cloud data centers consume an enormous amount of energy to provide services, it harms the environment due to carbon dioxide emissions. Virtual machine (VM) migration can effectively address this issue; it reduces the energy consumption of multimedia cloud data centers. Due to the reduction of Energy Consumption (EC), the Service Level Agreement violation (SLAV) may increase. An efficient VM selection plays a crucial role in maintaining the stability between EC and SLAV. This work highlights a novel VM selection policy based on identifying the Maximum value among the differences of the Sum of Squares Utilization Rate (MdSSUR) parameter to reduce the EC of multimedia cloud data centers with minimal SLAV. The proposed MdSSUR VM selection policy has been evaluated using real workload traces in CloudSim. The simulation result of the proposed MdSSUR VM selection policy demonstrates the rate of improvements of the EC, the number of VM migrations, and the SLAV by 28.37%, 89.47%, and 79.14%, respectively. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T09:50:01Z No. of bitstreams: 1 ijimai6_7_5.pdf: 1978034 bytes, checksum: d3cbcf721b71de8db1dddea4644721f4 (MD5); Made available in DSpace on 2022-05-03T09:50:01Z (GMT). No. of bitstreams: 1 ijimai6_7_5.pdf: 1978034 bytes, checksum: d3cbcf721b71de8db1dddea4644721f4 (MD5)

Kishor, Amit Chakraborty, Chinmay Jeberson, Wilson https://reunir.unir.net/handle/123456789/12995 2022-05-03T09:22:41Z

A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning Kishor, Amit; Chakraborty, Chinmay; Jeberson, Wilson In the recent scenario, the most challenging requirements are to handle the massive generation of multimedia data from the Internet of Things (IoT) devices which becomes very difficult to handle only through the cloud. Fog computing technology emerges as an intelligent solution and uses a distributed environment to operate. The objective of the paper is latency minimization in e-healthcare through fog computing. Therefore, in IoT multimedia data transmission, the parameters such as transmission delay, network delay, and computation delay must be reduced as there is a high demand for healthcare multimedia analytics. Fog computing provides processing, storage, and analyze the data nearer to IoT and end-users to overcome the latency. In this paper, the novel Intelligent Multimedia Data Segregation (IMDS) scheme using Machine learning (k-fold random forest) is proposed in the fog computing environment that segregates the multimedia data and the model used to calculate total latency (transmission, computation, and network). With the simulated results, we achieved 92% as the classification accuracy of the model, an approximately 95% reduction in latency as compared with the pre-existing model, and improved the quality of services in e-healthcare. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T09:22:41Z No. of bitstreams: 1 ijimai6_7_1.pdf: 2233977 bytes, checksum: 28018ceafc10d13be7a3337606e0dd14 (MD5); Made available in DSpace on 2022-05-03T09:22:41Z (GMT). No. of bitstreams: 1 ijimai6_7_1.pdf: 2233977 bytes, checksum: 28018ceafc10d13be7a3337606e0dd14 (MD5)

Srivastava, Varun Gupta, Shilp Chaudhary, Gopal Balodi, Arun Khari, Manju García-Díaz, Vicente https://reunir.unir.net/handle/123456789/12994 2022-05-03T09:07:16Z

An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs Srivastava, Varun; Gupta, Shilp; Chaudhary, Gopal; Balodi, Arun; Khari, Manju; García-Díaz, Vicente Content Based Image Retrieval (CBIR) techniques based on texture have gained a lot of popularity in recent times. In the proposed work, a feature vector is obtained by concatenation of features extracted from local mesh peak valley edge pattern (LMePVEP) technique; a dynamic threshold based local mesh ternary pattern technique and texture of the image in five different directions. The concatenated feature vector is then used to classify images of two datasets viz. Emphysema dataset and Early Lung Cancer Action Program (ELCAP) lung database. The proposed framework has improved the accuracy by 12.56%, 9.71% and 7.01% in average for data set 1 and 9.37%, 8.99% and 7.63% in average for dataset 2 over three popular algorithms used for image retrieval. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-05-03T09:07:16Z No. of bitstreams: 1 ijimai6_7_2.pdf: 1271084 bytes, checksum: 0aa7a702bd7f3d0dcc56163946b02a5a (MD5); Made available in DSpace on 2022-05-03T09:07:16Z (GMT). No. of bitstreams: 1 ijimai6_7_2.pdf: 1271084 bytes, checksum: 0aa7a702bd7f3d0dcc56163946b02a5a (MD5)

Martínez Torres, Javier https://reunir.unir.net/handle/123456789/12985 2022-04-29T10:24:42Z

Editor's Note Martínez Torres, Javier The International Journal of Interactive Multimedia and Artificial Intelligence – IJIMAI (ISSN 1989-1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. The present volume, June volume, consists of 24 articles of diverse applications of great impact in different fields, always having as a common element the use of artificial intelligence techniques or mathematical models with an artificial intelligence base. As is logical, COVID is present in several manuscripts of this volume, always focused on the prediction and estimation of the presence of the disease. In addition to this expected presence, there are manuscripts of a semantic or syntactic analysis nature as well as works in the field of management and recommender systems. It is also worth mentioning several works in the field of video compression and signal processing. Of course, the Internet of Things and text analysis for several applications could not be missed in this volume. Finally, different manuscripts on usability and satisfaction, investments, solar panels, malware detection, video analysis, audio analysis and learning can also be found in this volume. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T10:24:42Z No. of bitstreams: 1 ijimai_6_6_0.pdf: 457899 bytes, checksum: 341ee1b31869f052722760878a3d09a1 (MD5); Made available in DSpace on 2022-04-29T10:24:42Z (GMT). No. of bitstreams: 1 ijimai_6_6_0.pdf: 457899 bytes, checksum: 341ee1b31869f052722760878a3d09a1 (MD5)

Conde, Miguel Á. Rodríguez-Sedano, Francisco J. Hernández-García, Ángel Gutiérrez-Fernández, Alexis Guerrero-Higueras, Ángel M. https://reunir.unir.net/handle/123456789/12984 2022-04-29T10:21:53Z

Your Teammate Just Sent You a New Message! The Effects of Using Telegram on Individual Acquisition of Teamwork Competence Conde, Miguel Á.; Rodríguez-Sedano, Francisco J.; Hernández-García, Ángel; Gutiérrez-Fernández, Alexis; Guerrero-Higueras, Ángel M. Students’ acquisition of teamwork competence has become a priority for educational institutions. The development of teamwork competence in education generally relies in project-based learning methodologies and challenges. The assessment of teamwork in project-based learning involves, among others, assessing students’ participation and the interactions between team members. Project-based learning can easily be handled in small-size courses, but course management and teamwork assessment become a burdensome task for instructors as the size of the class increases. Additionally, when project-based learning happens in a virtual space, such as online learning, interactions occur in a less natural way. This study explores the use of instant messaging apps (more precisely, the use of Telegram) as team communication space in project-based learning, using a learning analytics tool to extract and analyze student interactions. Further, the study compares student interactions (e.g., number of messages exchanged) and individual teamwork competence acquisition between traditional asynchronous (e.g., LMS message boards) and synchronous instant messaging communication environments. The results show a preference of students for IM tools and increased participation in the course. However, the analysis does not find significant improvement in the acquisition of individual teamwork competence. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T10:21:53Z No. of bitstreams: 1 ijimai_6_6_23.pdf: 909024 bytes, checksum: 90460b7f5f70b467031371f78ea08dff (MD5); Made available in DSpace on 2022-04-29T10:21:53Z (GMT). No. of bitstreams: 1 ijimai_6_6_23.pdf: 909024 bytes, checksum: 90460b7f5f70b467031371f78ea08dff (MD5)

Cervantes-Perez, Francisco Navarro-Perales, Joaquin Franzoni-Velázquez, Ana L. de-la-Fuente-Valentín, Luis https://reunir.unir.net/handle/123456789/12983 2023-03-23T13:08:14Z

Bayesian Knowledge Tracing for Navigation through Marzano’s Taxonomy Cervantes-Perez, Francisco; Navarro-Perales, Joaquin; Franzoni-Velázquez, Ana L.; de-la-Fuente-Valentín, Luis In this paper we propose a theoretical model of an ITS (Intelligent Tutoring Systems) capable of improving and updating computer-aided navigation based on Bloom’s taxonomy. For this we use the Bayesian Knowledge Tracing algorithm, performing an adaptive control of the navigation among different levels of cognition in online courses. These levels are defined by a taxonomy of educational objectives with a hierarchical order in terms of the control that some processes have over others, called Marzano’s Taxonomy, that takes into account the metacognitive system, responsible for the creation of goals as well as strategies to fulfill them. The main improvements of this proposal are: 1) An adaptive transition between individual assessment questions determined by levels of cognition. 2) A student model based on the initial response of a group of learners which is then adjusted to the ability of each learner. 3) The promotion of metacognitive skills such as goal setting and self-monitoring through the estimation of attempts required to pass the levels. One level of Marzano's taxonomy was left in the hands of the human teacher, clarifying that a differentiation must be made between the tasks in which an ITS can be an important aid and in which it would be more difficult. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T10:13:40Z No. of bitstreams: 1 ijimai_6_6_24.pdf: 721459 bytes, checksum: 1120f6837e112c002e2ef61b9a79ce15 (MD5); Made available in DSpace on 2022-04-29T10:13:40Z (GMT). No. of bitstreams: 1 ijimai_6_6_24.pdf: 721459 bytes, checksum: 1120f6837e112c002e2ef61b9a79ce15 (MD5)

García-Peñalvo, Francisco Vázquez-Ingelmo, Andrea García-Holgado, Alicia Sampedro-Gómez, Jesús Sánchez-Puente, Antonio Vicente-Palacios, Víctor Dorado-Díaz, P. Ignacio Sánchez, Pedro L. https://reunir.unir.net/handle/123456789/12982 2022-04-29T09:35:07Z

Application of Artificial Intelligence Algorithms Within the Medical Context for Non-Specialized Users: the CARTIER-IA Platform García-Peñalvo, Francisco; Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; Sampedro-Gómez, Jesús; Sánchez-Puente, Antonio; Vicente-Palacios, Víctor; Dorado-Díaz, P. Ignacio; Sánchez, Pedro L. The use of advanced algorithms and models such as Machine Learning, Deep Learning and other related approaches of Artificial Intelligence have grown in their use given their benefits in different contexts. One of these contexts is the medical domain, as these algorithms can support disease detection, image segmentation and other multiple tasks. However, it is necessary to organize and arrange the different data resources involved in these scenarios and tackle the heterogeneity of data sources. This work presents the CARTIER-IA platform: a platform for the management of medical data and imaging. The goal of this project focuses on providing a friendly and usable interface to organize structured data, to visualize and edit medical images, and to apply Artificial Intelligence algorithms on the stored resources. One of the challenges of the platform design is to ease these complex tasks in a way that non-AI-specialized users could benefit from the application of AI algorithms without further training. Two use cases of AI application within the platform are provided, as well as a heuristic evaluation to assess the usability of the first version of CARTIER-IA. Year of Publication 2021 Journal International Journal of Interactive Multimedia and Artificial Intelligence Volume 6 Issue Regular Issue Number 6 Number of Pages 46-53 Date Published 06/2021 ISSN Number 1989-1660 URL https://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_5.pdf DOI 10.9781/ijimai.2021.05.005 DOI Google Scholar BibTeX EndNote X3 XML EndNote 7 XML Endnote tagged Marc RIS Attachment ijimai_6_6_5.pdf 932.11 KB Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T09:35:07Z No. of bitstreams: 1 ijimai_6_6_5.pdf: 959452 bytes, checksum: e608a8b469e8938f546009d1144c3771 (MD5); Made available in DSpace on 2022-04-29T09:35:07Z (GMT). No. of bitstreams: 1 ijimai_6_6_5.pdf: 959452 bytes, checksum: e608a8b469e8938f546009d1144c3771 (MD5)

Martín, Carlos Quintana, David Isasi, Pedro https://reunir.unir.net/handle/123456789/12981 2022-04-29T09:27:23Z

Dynamic Generation of Investment Recommendations Using Grammatical Evolution Martín, Carlos; Quintana, David; Isasi, Pedro The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T09:27:23Z No. of bitstreams: 1 ijimai_6_6_11.pdf: 907215 bytes, checksum: 59cf972c00417070817819dee6853ca6 (MD5); Made available in DSpace on 2022-04-29T09:27:23Z (GMT). No. of bitstreams: 1 ijimai_6_6_11.pdf: 907215 bytes, checksum: 59cf972c00417070817819dee6853ca6 (MD5)

Khattak, Muhammad Irfan Al-Hasan, Mu’ath Jan, Atif Saleem, Nasir Verdú, Elena Khurshid, Numan https://reunir.unir.net/handle/123456789/12980 2022-04-29T09:03:57Z

Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer- Spatial Convolutional Neural Networks Khattak, Muhammad Irfan; Al-Hasan, Mu’ath; Jan, Atif; Saleem, Nasir; Verdú, Elena; Khurshid, Numan The novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T09:03:57Z No. of bitstreams: 1 ijimai_6_6_2.pdf: 1585230 bytes, checksum: 48ce6957e4c5c2a1f6b3eeb0aab59ae4 (MD5); Made available in DSpace on 2022-04-29T09:03:57Z (GMT). No. of bitstreams: 1 ijimai_6_6_2.pdf: 1585230 bytes, checksum: 48ce6957e4c5c2a1f6b3eeb0aab59ae4 (MD5)

Prada, J. Gala, Y. Sierra, A. L. https://reunir.unir.net/handle/123456789/12979 2022-04-29T08:33:44Z

COVID-19 Mortality Risk Prediction Using X-Ray Images Prada, J.; Gala, Y.; Sierra, A. L. The pandemic caused by coronavirus COVID-19 has already had a massive impact in our societies in terms of health, economy, and social distress. One of the most common symptoms caused by COVID-19 are lung problems like pneumonia, which can be detected using X-ray images. On the other hand, the popularity of Machine Learning models has grown exponentially in recent years and Deep Learning techniques have become the state-of-the-art for image classification tasks and is widely used in the healthcare sector nowadays as support for clinical decisions. This research aims to build a prediction model based on Machine Learning, including Deep Learning, techniques to predict the mortality risk of a particular patient given an X-ray and some basic demographic data. Keeping this in mind, this paper has three goals. First, we use Deep Learning models to predict the mortality risk of a patient based on this patient X-ray images. For this purpose, we apply Convolutional Neural Networks as well as Transfer Learning techniques to mitigate the effect of the reduced amount of COVID19 data available. Second, we propose to combine the prediction of this Convolutional Neural Network with other patient data, like gender and age, as input features of a final Machine Learning model, that will act as second and final layer. This second model layer will aim to improve the goodness of fit and prediction power of our first layer. Finally, and in accordance with the principle of reproducible research, the data used for the experiments is publicly available and we make the implementations developed easily accessible via public repositories. Experiments over a real dataset of COVID-19 patients yield high AUROC values and show our two-layer framework to obtain better results than a single Convolutional Neural Network (CNN) model, achieving close to perfect classification. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T08:33:44Z No. of bitstreams: 1 ijimai_6_6_1.pdf: 939424 bytes, checksum: 6025ed2d968c7b8d55675ed8f445b809 (MD5); Made available in DSpace on 2022-04-29T08:33:44Z (GMT). No. of bitstreams: 1 ijimai_6_6_1.pdf: 939424 bytes, checksum: 6025ed2d968c7b8d55675ed8f445b809 (MD5)

Gupta, Akansha Ghanshala, Kamal Joshi, R. C. https://reunir.unir.net/handle/123456789/12978 2022-04-29T08:27:49Z

Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping Gupta, Akansha; Ghanshala, Kamal; Joshi, R. C. This article offers a thorough analysis of the machine learning classifiers approaches for the collected Received Signal Strength Indicator (RSSI) samples which can be applied in predicting propagation loss, used for network planning to achieve maximum coverage. We estimated the RMSE of a machine learning classifier on multivariate RSSI data collected from the cluster of 6 Base Transceiver Stations (BTS) across a hilly terrain of Uttarakhand-India. Variable attributes comprise topology, environment, and forest canopy. Four machine learning classifiers have been investigated to identify the classifier with the least RMSE: Gaussian Process, Ensemble Boosted Tree, SVM, and Linear Regression. Gaussian Process showed the lowest RMSE, R- Squared, MSE, and MAE of 1.96, 0.98, 3.8774, and 1.3202 respectively as compared to other classifiers. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T08:27:49Z No. of bitstreams: 1 ijimai_6_6_16.pdf: 1680641 bytes, checksum: 670693000493b4e0a77b5ea2cbd27745 (MD5); Made available in DSpace on 2022-04-29T08:27:49Z (GMT). No. of bitstreams: 1 ijimai_6_6_16.pdf: 1680641 bytes, checksum: 670693000493b4e0a77b5ea2cbd27745 (MD5)

Tlili, Ahmed Hattab, Sarra Essalmi, Fathi Chen, Nian-Shing Huang, Ronghuai Kinshuk Chang, Maiga Burgos, Daniel https://reunir.unir.net/handle/123456789/12977 2023-03-23T10:46:41Z

A Smart Collaborative Educational Game with Learning Analytics to Support English Vocabulary Teaching Tlili, Ahmed; Hattab, Sarra; Essalmi, Fathi; Chen, Nian-Shing; Huang, Ronghuai; Kinshuk; Chang, Maiga; Burgos, Daniel Learning Analytics (LA) approaches have proved to be able to enhance learning process and learning performance. However, little is known about applying these approaches for second language acquisition using educational games. Therefore, this study applied LA approaches to design a smart collaborative educational game, to enhance primary school children learning English vocabularies. Specifically, the game provided dashboards to the teachers about their students in a real-time manner. A pilot experiment was conducted in a public primary school where the students’ data from experimental and control groups, namely learning and motivation test scores, interview and observation, were collected and analyzed. The obtained results showed that the experimental group (who used the smart game with LA) had significantly higher motivation and performance for learning English vocabularies than the control group (who used the smart game without LA). The findings of this study can help researchers and practitioners incorporate LA in their educational games to help students enhance language acquisition. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T08:22:06Z No. of bitstreams: 1 ijimai_6_6_22.pdf: 817089 bytes, checksum: 20fbc2f29f5b6ea1a2615094fd41a6f8 (MD5); Made available in DSpace on 2022-04-29T08:22:06Z (GMT). No. of bitstreams: 1 ijimai_6_6_22.pdf: 817089 bytes, checksum: 20fbc2f29f5b6ea1a2615094fd41a6f8 (MD5)

Meana-Llorián, Daniel González García, Cristian Pelayo García-Bustelo, B. Cristina Cueva-Lovelle, Juan Manuel https://reunir.unir.net/handle/123456789/12976 2023-11-02T12:24:25Z

BILROST: Handling Actuators of the Internet of Things through Tweets on Twitter using a Domain- Specific Language Meana-Llorián, Daniel; González García, Cristian; Pelayo García-Bustelo, B. Cristina; Cueva-Lovelle, Juan Manuel In recent years, many investigations have appeared that combine the Internet of Things and Social Networks. Some of them addressed the interconnection of objects as Social Networks interconnect people, and others addressed the connection between objects and people. However, they usually used interfaces created for that purpose instead of using familiar interfaces for users. Why not integrate Smart Objects in traditional Social Networks? Why not control Smart Objects through natural interactions in Social Networks? The goal of this paper is to make easier to create applications that allow non-experts users to control Smart Objects actuators through Social Networks through the proposal of a novel approach to connect objects and people using Social Networks. This proposal will address how to use Twitter so that objects could perform actions based on Twitter users’ posts. Moreover, it will be presented a Domain-Specific language that could help in the task of defining the actions that objects could perform when people publish specific content on Twitter. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T08:13:49Z No. of bitstreams: 1 ijimai_6_6_14.pdf: 1246461 bytes, checksum: 5542390fb3da78d960a26cf9bd55b936 (MD5); Made available in DSpace on 2022-04-29T08:13:49Z (GMT). No. of bitstreams: 1 ijimai_6_6_14.pdf: 1246461 bytes, checksum: 5542390fb3da78d960a26cf9bd55b936 (MD5)

Arronte Alvarez, Aitor Gómez, Francisco https://reunir.unir.net/handle/123456789/12975 2022-04-29T07:23:45Z

Motivic Pattern Classification of Music Audio Signals Combining Residual and LSTM Networks Arronte Alvarez, Aitor; Gómez, Francisco Motivic pattern classification from music audio recordings is a challenging task. More so in the case of a cappella flamenco cantes, characterized by complex melodic variations, pitch instability, timbre changes, extreme vibrato oscillations, microtonal ornamentations, and noisy conditions of the recordings. Convolutional Neural Networks (CNN) have proven to be very effective algorithms in image classification. Recent work in large-scale audio classification has shown that CNN architectures, originally developed for image problems, can be applied successfully to audio event recognition and classification with little or no modifications to the networks. In this paper, CNN architectures are tested in a more nuanced problem: flamenco cantes intra-style classification using small motivic patterns. A new architecture is proposed that uses the advantages of residual CNN as feature extractors, and a bidirectional LSTM layer to exploit the sequential nature of musical audio data. We present a full end-to-end pipeline for audio music classification that includes a sequential pattern mining technique and a contour simplification method to extract relevant motifs from audio recordings. Mel-spectrograms of the extracted motifs are then used as the input for the different architectures tested. We investigate the usefulness of motivic patterns for the automatic classification of music recordings and the effect of the length of the audio and corpus size on the overall classification accuracy. Results show a relative accuracy improvement of up to 20.4% when CNN architectures are trained using acoustic representations from motivic patterns. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T07:23:45Z No. of bitstreams: 1 ijimai_6_6_21.pdf: 811793 bytes, checksum: 7b2f371b80260dbb4db6c5483b1d4980 (MD5); Made available in DSpace on 2022-04-29T07:23:45Z (GMT). No. of bitstreams: 1 ijimai_6_6_21.pdf: 811793 bytes, checksum: 7b2f371b80260dbb4db6c5483b1d4980 (MD5)

Houari, Nawal Sad Taghezout, Noria https://reunir.unir.net/handle/123456789/12974 2022-04-29T06:50:21Z

An Effective Tool for the Experts' Recommendation Based on PROMETHEE II and Negotiation: Application to the Industrial Maintenance Houari, Nawal Sad; Taghezout, Noria In this article, we propose an expert recommendation tool that relies on the skills of experts and their interventions in collaboration. This tool provides us with a list of the most appropriate (effective) experts to solve business problems in the field of industrial maintenance. The proposed system recommends experts using an unsupervised classification algorithm that takes into account the competences of the experts, their preferences and the stored information in previous collaborative sessions. We have tested the performance of the system with K-means and C-means algorithms. To fix the inconsistencies detected in business rules, the PROMETHEE II multi-criteria decision support method is integrated into the extended CNP negotiation protocol in order to classify the experts from best to worst. The study is supported by the well known petroleum company in Algeria namely SONATRACH where the experimentations are operated on maintenance domain. Experiments results show the effectiveness of our approach, obtaining a recall of 86%, precision of 92% and F-measure of 89%. Also, the proposed approach offers very high results and improvement, in terms of response time (154.28 ms), space memory (9843912 bytes) and negotiation rounds. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T06:50:21Z No. of bitstreams: 1 ijimai_6_6_7.pdf: 875379 bytes, checksum: 9eb309c53b8e247d9edca06143403261 (MD5); Made available in DSpace on 2022-04-29T06:50:21Z (GMT). No. of bitstreams: 1 ijimai_6_6_7.pdf: 875379 bytes, checksum: 9eb309c53b8e247d9edca06143403261 (MD5)

Galán, Julia García-García, Carlos Felip, Francisco Contero, Manuel https://reunir.unir.net/handle/123456789/12973 2022-04-29T06:45:50Z

Does a presentation Media Influence the Evaluation of Consumer Products? A Comparative Study to Evaluate Virtual Reality, Virtual Reality with Passive Haptics and a Real Setting Galán, Julia; García-García, Carlos; Felip, Francisco; Contero, Manuel Technologies based on image offer a high potential to present consumers with products by focusing on their visual characteristics, but lack the capacity to physically interact with an object, which can compromise how consumer products are evaluated. The present study aims to analyse the influence of different presentation media on how users perceive the product by comparing the evaluation of a piece of furniture made by a sample of 203 users, which was presented in three different settings: a real setting (R), a Virtual Reality setting (VR) and a Virtual Reality with Passive Haptics setting (VRPH). To evaluate the product in the different settings, a semantic differential scale was built that comprised 12 bipolar pairs of adjectives. To study the results, the descriptive statistics for the semantic differential scales were analysed, a study about the frequency of repetition was conducted of each evaluation, a Kruskal-Wallis test was conducted and Dunn’s post hoc tests were performed. The results showed that the presentation media of a piece of furniture influenced the evaluation of how users perceived it. These results also revealed that the haptic interaction with a product influenced how users perceived it compared to an exclusively visual interaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-29T06:45:50Z No. of bitstreams: 1 ijimai_6_6_20.pdf: 1021591 bytes, checksum: 35be3ab88dc1b0d7a61aae9cbb6892bd (MD5); Made available in DSpace on 2022-04-29T06:45:50Z (GMT). No. of bitstreams: 1 ijimai_6_6_20.pdf: 1021591 bytes, checksum: 35be3ab88dc1b0d7a61aae9cbb6892bd (MD5)

Schrepp, Martin Otten, Raphael Blum, Kerstin Thomaschewski, Jörg https://reunir.unir.net/handle/123456789/12966 2023-06-26T09:28:59Z

What Causes the Dependency between Perceived Aesthetics and Perceived Usability? Schrepp, Martin; Otten, Raphael; Blum, Kerstin; Thomaschewski, Jörg Several studies reported a dependency between perceived beauty and perceived usability of a user interface. But it is still not fully clear which psychological mechanism is responsible for this dependency. We suggest a new explanation based on the concept of visual clarity. This concept describes the perception of order, alignment and visual complexity. A high visual clarity supports a fast orientation on an interface and creates an impression of simplicity. Thus, visual clarity will impact usability dimensions, like efficiency and learnability. Visual clarity is also related to classical aesthetics and the fluency effect, thus an impact on the perception of aesthetics is plausible. We present two large studies that show a strong mediator effect of visual clarity on the dependency between perceived aesthetics and perceived usability. These results support the proposed explanation. In addition, we show how visual clarity of a user interface can be evaluated by a new scale embedded in the UEQ+ framework. Construction and first evaluation results of this new scale are described. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:42:38Z No. of bitstreams: 1 ijimai_6_6_8.pdf: 1347702 bytes, checksum: 8429bd93ebee30b2c886f79235fd468f (MD5); Made available in DSpace on 2022-04-28T08:42:38Z (GMT). No. of bitstreams: 1 ijimai_6_6_8.pdf: 1347702 bytes, checksum: 8429bd93ebee30b2c886f79235fd468f (MD5)

Gil, Jesús Martínez Torres, Javier González-Crespo, Rubén https://reunir.unir.net/handle/123456789/12965 2022-04-28T08:36:59Z

The Application of Artificial Intelligence in Project Management Research: A Review Gil, Jesús; Martínez Torres, Javier; González-Crespo, Rubén The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and major efforts in terms of development is required within the scientific community. In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence techniques used today and the areas of project management in which agents are being applied. This work should serve as a starting point for researchers who wish to work in the exciting world of artificial intelligence in relation to project leadership and management. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:36:59Z No. of bitstreams: 1 ijimai_6_6_6.pdf: 789648 bytes, checksum: f48d708495661640a57dcc5467899211 (MD5); Made available in DSpace on 2022-04-28T08:36:59Z (GMT). No. of bitstreams: 1 ijimai_6_6_6.pdf: 789648 bytes, checksum: f48d708495661640a57dcc5467899211 (MD5)

Ebadi, M. J. Ebrahimi, A. https://reunir.unir.net/handle/123456789/12964 2022-04-28T08:31:18Z

Video Data Compression by Progressive Iterative Approximation Ebadi, M. J.; Ebrahimi, A. In the present paper, the B-spline curve is used for reducing the entropy of video data. We consider the color or luminance variations of a spatial position in a series of frames as input data points in Euclidean space R or R3. The progressive and iterative approximation (PIA) method is a direct and intuitive way of generating curve series of high and higher fitting accuracy. The video data points are approximated using progressive and iterative approximation for least square (LSPIA) fitting. The Lossless video data compression is done through storing the B-spline curve control points (CPs) and the difference between fitted and original video data. The proposed method is applied to two classes of synthetically produced and naturally recorded video sequences and makes a reduction in the entropy of both. However, this reduction is higher for syntactically created than those naturally produced. The comparative analysis of experiments on a variety of video sequences suggests that the entropy of output video data is much less than that of input video data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:31:18Z No. of bitstreams: 1 ijimai_6_6_19.pdf: 1544242 bytes, checksum: 488c0981b7182ecf93ffd699ea5fde42 (MD5); Made available in DSpace on 2022-04-28T08:31:18Z (GMT). No. of bitstreams: 1 ijimai_6_6_19.pdf: 1544242 bytes, checksum: 488c0981b7182ecf93ffd699ea5fde42 (MD5)

Rezk, Hegazy Arfaoui, Jouda Gomaa, Mohamed R. https://reunir.unir.net/handle/123456789/12963 2022-04-28T08:13:19Z

Optimal Parameter Estimation of Solar PV Panel Based on Hybrid Particle Swarm and Grey Wolf Optimization Algorithms Rezk, Hegazy; Arfaoui, Jouda; Gomaa, Mohamed R. The performance of a solar photovoltaic (PV) panel is examined through determining its internal parameters based on single and double diode models. The environmental conditions such as temperature and the level of radiation also influence the output characteristics of solar panel. In this research work, the parameters of solar PV panel are identified for the first time, as far as the authors know, using hybrid particle swarm optimization (PSO) and grey wolf optimizer (WGO) based on experimental datasets of I-V curves. The main advantage of hybrid PSOGWO is combining the exploitation ability of the PSO with the exploration ability of the GWO. During the optimization process, the main target is minimizing the root mean square error (RMSE) between the original experimental data and the estimated data. Three different solar PV modules are considered to prove the superiority of the proposed strategy. Three different solar PV panels are used during the evaluation of the proposed strategy. A comparison of PSOGWO with other state-of-the-art methods is made. The obtained results confirmed that the least RMSE values are achieved using PSOGWO for all case studies compared with PSO and GWO optimizers. Almost a perfect agreement between the estimated data and experimental data set is achieved by PSOGWO. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:13:19Z No. of bitstreams: 1 ijimai_6_6_15.pdf: 1592860 bytes, checksum: ba41698fc48f67f9dbc1ecb7cc33f714 (MD5); Made available in DSpace on 2022-04-28T08:13:19Z (GMT). No. of bitstreams: 1 ijimai_6_6_15.pdf: 1592860 bytes, checksum: ba41698fc48f67f9dbc1ecb7cc33f714 (MD5)

Bobadilla, Jesús Lara-Cabrera, Raúl González-Prieto, Ángel Ortega, Fernando https://reunir.unir.net/handle/123456789/12962 2022-04-28T08:01:38Z

DeepFair: Deep Learning for Improving Fairness in Recommender Systems Bobadilla, Jesús; Lara-Cabrera, Raúl; González-Prieto, Ángel; Ortega, Fernando The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy. Furthermore, in the recommendation stage, this balance does not require an initial knowledge of the users’ demographic information. The proposed architecture incorporates four abstraction levels: raw ratings and demographic information, minority indexes, accurate predictions, and fair recommendations. Last two levels use the classical Probabilistic Matrix Factorization (PMF) model to obtain users and items hidden factors, and a Multi-Layer Network (MLN) to combine those factors with a ‘fairness’ (ß) parameter. Several experiments have been conducted using two types of minority sets: gender and age. Experimental results show that it is possible to make fair recommendations without losing a significant proportion of accuracy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T08:01:38Z No. of bitstreams: 1 ijimai_6_6_9_0.pdf: 1368857 bytes, checksum: de147688d06e532da619d5e1821d9651 (MD5); Made available in DSpace on 2022-04-28T08:01:38Z (GMT). No. of bitstreams: 1 ijimai_6_6_9_0.pdf: 1368857 bytes, checksum: de147688d06e532da619d5e1821d9651 (MD5)

Singh, Ritu Rajpal, Navin Mehta, Rajesh https://reunir.unir.net/handle/123456789/12961 2022-04-28T07:48:37Z

An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification Singh, Ritu; Rajpal, Navin; Mehta, Rajesh The aberration in human electrocardiogram (ECG) affects cardiovascular events that may lead to arrhythmias. Many automation systems for ECG classification exist, but the ambiguity to wisely employ the in-built feature extraction or expert based manual feature extraction before classification still needs recognition. The proposed work compares and presents the enactment of using machine learning and deep learning classification on time series sequences. The two classifiers, namely the Support Vector Machine (SVM) and the Bi-directional Long Short-Term Memory (BiLSTM) network, are separately trained by direct ECG samples and extracted feature vectors using multiresolution analysis of Maximal Overlap Discrete Wavelet Transform (MODWT). Single beat segmentation with R-peaks and QRS detection is also involved with 6 morphological and 12 statistical feature extraction. The two benchmark datasets, multi-class, and binary class, are acquired from the PhysioNet database. For the binary dataset, BiLSTM with direct samples and with feature extraction gives 58.1% and 80.7% testing accuracy, respectively, whereas SVM outperforms with 99.88% accuracy. For the multi-class dataset, BiLSTM classification accuracy with the direct sample and the extracted feature is 49.6% and 95.4%, whereas SVM shows 99.44%. The efficient statistical workout depicts that the extracted feature-based selection of data can deliver distinguished outcomes compared with raw ECG data or in-built automatic feature extraction. The machine learning classifiers like SVM with knowledge-based feature extraction can equally or better perform than Bi-LSTM network for certain datasets. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:48:37Z No. of bitstreams: 1 ijimai_6_6_3.pdf: 1652687 bytes, checksum: 49e79fe25dd77e54c9e2bb1a1286ec90 (MD5); Made available in DSpace on 2022-04-28T07:48:37Z (GMT). No. of bitstreams: 1 ijimai_6_6_3.pdf: 1652687 bytes, checksum: 49e79fe25dd77e54c9e2bb1a1286ec90 (MD5)

Hassan, Loay Saleh, Adel Abdel-Nasser, Mohamed Omer, Osama A. Puig, Domenec https://reunir.unir.net/handle/123456789/12960 2022-04-28T07:42:59Z

Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs Hassan, Loay; Saleh, Adel; Abdel-Nasser, Mohamed; Omer, Osama A.; Puig, Domenec Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology. It is a fundamental task for different applications, such as cancer cell type classification, cancer grading, and cancer subtype classification. However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different cell nuclei, that limit their performance. In this paper, we present promising deep semantic nuclei segmentation models for multi-institutional WSI images (i.e., collected from different scanners) of different organs. Specifically, we study the performance of pertinent deep learning-based models with nuclei segmentation in WSI images of different stains and various organs. We also propose a feasible deep learning nuclei segmentation model formed by combining robust deep learning architectures. A comprehensive comparative study with existing software and related methods in terms of different evaluation metrics and the number of parameters of each model, emphasizes the efficacy of the proposed nuclei segmentation models. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:42:59Z No. of bitstreams: 1 ijimai_6_6_4.pdf: 1262221 bytes, checksum: 55709fdab7acf58482eb320e00e33189 (MD5); Made available in DSpace on 2022-04-28T07:42:59Z (GMT). No. of bitstreams: 1 ijimai_6_6_4.pdf: 1262221 bytes, checksum: 55709fdab7acf58482eb320e00e33189 (MD5)

Kumar, Pravin Dayal, Mohit Khari, Manju Fenza, Giuseppe Gallo, Mariacristina https://reunir.unir.net/handle/123456789/12959 2022-04-28T07:35:14Z

NSL-BP: A Meta Classifier Model Based Prediction of Amazon Product Reviews Kumar, Pravin; Dayal, Mohit; Khari, Manju; Fenza, Giuseppe; Gallo, Mariacristina In machine learning, the product rating prediction based on the semantic analysis of the consumers' reviews is a relevant topic. Amazon is one of the most popular online retailers, with millions of customers purchasing and reviewing products. In the literature, many research projects work on the rating prediction of a given review. In this research project, we introduce a novel approach to enhance the accuracy of rating prediction by machine learning methods by processing the reviewed text. We trained our model by using many methods, so we propose a combined model to predict the ratings of products corresponding to a given review content. First, using k-means and LDA, we cluster the products and topics so that it will be easy to predict the ratings having the same kind of products and reviews together. We trained low, neutral, and high models based on clusters and topics of products. Then, by adopting a stacking ensemble model, we combine Naïve Bayes, Logistic Regression, and SVM to predict the ratings. We will combine these models into a two-level stack. We called this newly introduced model, NSL model, and compared the prediction performance with other methods at state of the art. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:35:14Z No. of bitstreams: 1 ijimai_6_6_10.pdf: 1918604 bytes, checksum: f6fbe7f06eb20d287f632d22bb5f9daa (MD5); Made available in DSpace on 2022-04-28T07:35:14Z (GMT). No. of bitstreams: 1 ijimai_6_6_10.pdf: 1918604 bytes, checksum: f6fbe7f06eb20d287f632d22bb5f9daa (MD5)

Dhanith, P. R. Joe Surendiran, B. Raja, S. P. https://reunir.unir.net/handle/123456789/12958 2022-04-28T07:29:30Z

A Word Embedding Based Approach for Focused Web Crawling Using the Recurrent Neural Network Dhanith, P. R. Joe; Surendiran, B.; Raja, S. P. Learning-based focused crawlers download relevant uniform resource locators (URLs) from the web for a specific topic. Several studies have used the term frequency-inverse document frequency (TF-IDF) weighted cosine vector as an input feature vector for learning algorithms. TF-IDF-based crawlers calculate the relevance of a web page only if a topic word co-occurs on the said page, failing which it is considered irrelevant. Similarity is not considered even if a synonym of a term co-occurs on a web page. To resolve this challenge, this paper proposes a new methodology that integrates the Adagrad-optimized Skip Gram Negative Sampling (A-SGNS)-based word embedding and the Recurrent Neural Network (RNN).The cosine similarity is calculated from the word embedding matrix to form a feature vector that is given as an input to the RNN to predict the relevance of the website. The performance of the proposed method is evaluated using the harvest rate (hr) and irrelevance ratio (ir). The proposed methodology outperforms existing methodologies with an average harvest rate of 0.42 and irrelevance ratio of 0.58. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:29:30Z No. of bitstreams: 1 ijimai_6_6_13.pdf: 1266941 bytes, checksum: 08f79b1ecc9b9c8f7c4a575d89faf9c2 (MD5); Made available in DSpace on 2022-04-28T07:29:30Z (GMT). No. of bitstreams: 1 ijimai_6_6_13.pdf: 1266941 bytes, checksum: 08f79b1ecc9b9c8f7c4a575d89faf9c2 (MD5)

Dhalaria, Meghna Gandotra, Ekta https://reunir.unir.net/handle/123456789/12957 2022-04-28T07:24:13Z

A Hybrid Approach for Android Malware Detection and Family Classification Dhalaria, Meghna; Gandotra, Ekta With the increase in the popularity of mobile devices, malicious applications targeting Android platform have greatly increased. Malware is coded so prudently that it has become very complicated to identify. The increase in the large amount of malware every day has made the manual approaches inadequate for detecting the malware. Nowadays, a new malware is characterized by sophisticated and complex obfuscation techniques. Thus, the static malware analysis alone is not enough for detecting it. However, dynamic malware analysis is appropriate to tackle evasion techniques but incapable to investigate all the execution paths and also it is very time consuming. So, for better detection and classification of Android malware, we propose a hybrid approach which integrates the features obtained after performing static and dynamic malware analysis. This approach tackles the problem of analyzing, detecting and classifying the Android malware in a more efficient manner. In this paper, we have used a robust set of features from static and dynamic malware analysis for creating two datasets i.e. binary and multiclass (family) classification datasets. These are made publically available on GitHub and Kaggle with the aim to help researchers and anti-malware tool creators for enhancing or developing new techniques and tools for detecting and classifying Android malware. Various machine learning algorithms are employed to detect and classify malware using the features extracted after performing static and dynamic malware analysis. The experimental outcomes indicate that hybrid approach enhances the accuracy of detection and classification of Android malware as compared to the case when static and dynamic features are considered alone. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:24:13Z No. of bitstreams: 1 ijimai_6_6_18.pdf: 1394422 bytes, checksum: f2b6587a369f4cb2b5cef17c4e46925c (MD5); Made available in DSpace on 2022-04-28T07:24:13Z (GMT). No. of bitstreams: 1 ijimai_6_6_18.pdf: 1394422 bytes, checksum: f2b6587a369f4cb2b5cef17c4e46925c (MD5)

Huddar, Mahesh G. Sannakki, Sanjeev S. Rajpurohit, Vijay S. https://reunir.unir.net/handle/123456789/12956 2022-04-28T07:20:14Z

Attention-based Multi-modal Sentiment Analysis and Emotion Detection in Conversation using RNN Huddar, Mahesh G.; Sannakki, Sanjeev S.; Rajpurohit, Vijay S. The availability of an enormous quantity of multimodal data and its widespread applications, automatic sentiment analysis and emotion classification in the conversation has become an interesting research topic among the research community. The interlocutor state, context state between the neighboring utterances and multimodal fusion play an important role in multimodal sentiment analysis and emotion detection in conversation. In this article, the recurrent neural network (RNN) based method is developed to capture the interlocutor state and contextual state between the utterances. The pair-wise attention mechanism is used to understand the relationship between the modalities and their importance before fusion. First, two-two combinations of modalities are fused at a time and finally, all the modalities are fused to form the trimodal representation feature vector. The experiments are conducted on three standard datasets such as IEMOCAP, CMU-MOSEI, and CMU-MOSI. The proposed model is evaluated using two metrics such as accuracy and F1-Score and the results demonstrate that the proposed model performs better than the standard baselines. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:20:14Z No. of bitstreams: 1 ijimai_6_6_12.pdf: 1521302 bytes, checksum: e5032e24c7880cae152f0b23b2530372 (MD5); Made available in DSpace on 2022-04-28T07:20:14Z (GMT). No. of bitstreams: 1 ijimai_6_6_12.pdf: 1521302 bytes, checksum: e5032e24c7880cae152f0b23b2530372 (MD5)

Kasihmuddin, Mohd Shareduwan Bin Mohd Mansor, Mohd Asyraf Bin Abdulhabib Alzaeemi, Shehab Sathasivam, Saratha https://reunir.unir.net/handle/123456789/12955 2022-04-28T07:10:04Z

Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm Kasihmuddin, Mohd Shareduwan Bin Mohd; Mansor, Mohd Asyraf Bin; Abdulhabib Alzaeemi, Shehab; Sathasivam, Saratha Radial Basis Function Neural Network (RBFNN) is a variant of artificial neural network (ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology and science. 2 Satisfiability (2SAT) programming has been coined as a prominent logical rule that defines the identity of RBFNN. In this research, a swarm-based searching algorithm namely, the Artificial Bee Colony (ABC) will be introduced to facilitate the training of RBFNN. Worth mentioning that ABC is a new population-based metaheuristics algorithm inspired by the intelligent comportment of the honey bee hives. The optimization pattern in ABC was found fruitful in RBFNN since ABC reduces the complexity of the RBFNN in optimizing important parameters. The effectiveness of ABC in RBFNN has been examined in terms of various performance evaluations. Therefore, the simulation has proved that the ABC complied efficiently in tandem with the Radial Basis Neural Network with 2SAT according to various evaluations such as the Root Mean Square Error (RMSE), Sum of Squares Error (SSE), Mean Absolute Percentage Error (MAPE), and CPU Time. Overall, the experimental results have demonstrated the capability of ABC in enhancing the learning phase of RBFNN-2SAT as compared to the Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-28T07:10:04Z No. of bitstreams: 1 ijimai_6_6_17.pdf: 765951 bytes, checksum: 6a4766f4022778bd5a16faac29e622be (MD5); Made available in DSpace on 2022-04-28T07:10:04Z (GMT). No. of bitstreams: 1 ijimai_6_6_17.pdf: 765951 bytes, checksum: 6a4766f4022778bd5a16faac29e622be (MD5)

Alonso-Betanzos, Amparo Cabalar, Pedro Dimuro, Gracaliz P. García, Marcos Hernández-Orallo, José Hervás, Raquel Manjarés, Ángeles Martínez-Plumed, Fernado Mora-Jiménez, Inmaculada Sànchez-Marrè, Miquel https://reunir.unir.net/handle/123456789/12920 2022-05-19T06:47:10Z

Editor's Note Alonso-Betanzos, Amparo; Cabalar, Pedro; Dimuro, Gracaliz P.; García, Marcos; Hernández-Orallo, José; Hervás, Raquel; Manjarés, Ángeles; Martínez-Plumed, Fernado; Mora-Jiménez, Inmaculada; Sànchez-Marrè, Miquel Artificial Intelligence has become nowadays one of the main relevant technologies that is driven us to a new revolution, a change in society, just as well as other human inventions, such as navigation, steam machines, or electricity did in our past. There are several ways in which AI might be developed, and the European Union has chosen a path, a way to transit through this revolution, in which Artificial Intelligence will be a tool at the service of Humanity. That was precisely the motto of the 2020 European Conference on Artificial Intelligence (“Paving the way towards Human-Centric AI”), of which these special issue is a selection of the best papers selected by the organizers of some of the Workshops in ECAI 2020. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:38:07Z No. of bitstreams: 1 ijimai_6_5_0.pdf: 497691 bytes, checksum: ed2c7b345639776423b1760faae18f47 (MD5); Made available in DSpace on 2022-04-25T09:38:07Z (GMT). No. of bitstreams: 1 ijimai_6_5_0.pdf: 497691 bytes, checksum: ed2c7b345639776423b1760faae18f47 (MD5)

Zhang, Sicui Genga, Laura Yan, Hui Nie, Hongchao Lu, Xudong Kaymak, Uzay https://reunir.unir.net/handle/123456789/12919 2022-04-25T09:29:59Z

Towards Multi-perspective Conformance Checking with Fuzzy Sets Zhang, Sicui; Genga, Laura; Yan, Hui; Nie, Hongchao; Lu, Xudong; Kaymak, Uzay Nowadays organizations often need to employ data-driven techniques to audit their business processes and ensure they comply with laws and internal/external regulations. Failing in complying with the expected process behavior can indeed pave the way to inefficiencies or, worse, to frauds or abuses. An increasingly popular approach to automatically assess the compliance of the executions of organization processes is represented by alignment-based conformance checking. These techniques are able to compare real process executions with models representing the expected behaviors, providing diagnostics able to pinpoint possible discrepancies. However, the diagnostics generated by state of the art techniques still suffer from some limitations. They perform a crisp evaluation of process compliance, marking process behavior either as compliant or deviant, without taking into account the severity of the identified deviation. This hampers the accuracy of the obtained diagnostics and can lead to misleading results, especially in contexts where there is some tolerance with respect to violations of the process guidelines. In the present work, we discuss the impact and the drawbacks of a crisp deviation assessment approach. Then, we propose a novel conformance checking approach aimed at representing actors’ tolerance with respect to process deviations, taking it into account when assessing the severity of the deviations. As a proof of concept, we performed a set of synthetic experiments to assess the approach. The obtained results point out the potential of the usage of a more flexible evaluation of process deviations, and its impact on the quality and the interpretation of the obtained diagnostics. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:29:59Z No. of bitstreams: 1 ijimai_6_5_13.pdf: 1071887 bytes, checksum: 32b33cd6018b9ff2afe672e989a3c615 (MD5); Made available in DSpace on 2022-04-25T09:29:59Z (GMT). No. of bitstreams: 1 ijimai_6_5_13.pdf: 1071887 bytes, checksum: 32b33cd6018b9ff2afe672e989a3c615 (MD5)

Hernàndez-Carnerero, Àlvar Sànchez-Marrè, Miquel Mora-Jiménez, Inmaculada Soguero-Ruiz, Cristina Martínez-Agüero, Sergio Álvarez-Rodríguez, Joaquín https://reunir.unir.net/handle/123456789/12918 2022-04-25T09:24:37Z

Antimicrobial Resistance Prediction in Intensive Care Unit for Pseudomonas Aeruginosa using Temporal Data-Driven Models Hernàndez-Carnerero, Àlvar; Sànchez-Marrè, Miquel; Mora-Jiménez, Inmaculada; Soguero-Ruiz, Cristina; Martínez-Agüero, Sergio; Álvarez-Rodríguez, Joaquín One threatening medical problem for human beings is the increasing antimicrobial resistance of some microorganisms. This problem is especially difficult in Intensive Care Units (ICUs) of hospitals due to the vulnerable state of patients. Knowing in advance whether a concrete bacterium is resistant or susceptible to an antibiotic is a crux step for clinicians to determine an effective antibiotic treatment. This usual clinical procedure takes approximately 48 hours and it is named antibiogram. It tests the bacterium resistance to one or more antimicrobial families (six of them considered in this work). This article focuses on cultures of the Pseudomonas Aeruginosa bacterium because is one of the most dangerous in the ICU. Several temporal data-driven models are proposed and analyzed to predict the resistance or susceptibility to a determined antibiotic family previously to know the antibiogram result and only using the available past information from a data set. This data set is formed by anonymized electronic health records data from more than 3300 ICU patients during 15 years. Several data-driven classifier methods are used in combination with several temporal modeling approaches. The results show that our predictions are reasonably accurate for some antimicrobial families, and could be used by clinicians to determine the best antibiotic therapy in advance. This early prediction can save valuable time to start the adequate treatment for an ICU patient. This study corroborates the results of a previous work pointing that the antimicrobial resistance of bacteria in the ICU is related to other recent resistance tests of ICU patients. This information is very valuable for making accurate antimicrobial resistance predictions. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:24:37Z No. of bitstreams: 1 ijimai_6_5_12_0.pdf: 1767884 bytes, checksum: 2e4cbefa7dbbdd510a6f3472de7e0bc5 (MD5); Made available in DSpace on 2022-04-25T09:24:37Z (GMT). No. of bitstreams: 1 ijimai_6_5_12_0.pdf: 1767884 bytes, checksum: 2e4cbefa7dbbdd510a6f3472de7e0bc5 (MD5)

Zoe Cremer, Carla Whittlestone, Jess https://reunir.unir.net/handle/123456789/12917 2022-04-25T09:18:24Z

Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI Zoe Cremer, Carla; Whittlestone, Jess We propose a method for identifying early warning signs of transformative progress in artificial intelligence (AI), and discuss how these can support the anticipatory and democratic governance of AI. We call these early warning signs ‘canaries’, based on the use of canaries to provide early warnings of unsafe air pollution in coal mines. Our method combines expert elicitation and collaborative causal graphs to identify key milestones and identify the relationships between them. We present two illustrations of how this method could be used: to identify early warnings of harmful impacts of language models; and of progress towards high-level machine intelligence. Identifying early warning signs of transformative applications can support more efficient monitoring and timely regulation of progress in AI: as AI advances, its impacts on society may be too great to be governed retrospectively. It is essential that those impacted by AI have a say in how it is governed. Early warnings can give the public time and focus to influence emerging technologies using democratic, participatory technology assessments. We discuss the challenges in identifying early warning signals and propose directions for future work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:18:24Z No. of bitstreams: 1 ijimai_6_5_8.pdf: 811659 bytes, checksum: effdef00ca187bdcd3b848a656965925 (MD5); Made available in DSpace on 2022-04-25T09:18:24Z (GMT). No. of bitstreams: 1 ijimai_6_5_8.pdf: 811659 bytes, checksum: effdef00ca187bdcd3b848a656965925 (MD5)

Rao S B, Pooja Agnihotri, Manish Babu Jayagopi, Dinesh https://reunir.unir.net/handle/123456789/12916 2022-04-25T09:02:55Z

Improving Asynchronous Interview Interaction with Follow-up Question Generation Rao S B, Pooja; Agnihotri, Manish; Babu Jayagopi, Dinesh The user experience of an asynchronous video interview system, conventionally is not reciprocal or conversational. Interview applicants expect that, like a typical face-to-face interview, they are innate and coherent. We posit that the planned adoption of limited probing through follow-up questions is an important step towards improving the interaction. We propose a follow-up question generation model (followQG) capable of generating relevant and diverse follow-up questions based on the previously asked questions, and their answers. We implement a 3D virtual interviewing system, Maya, with capability of follow-up question generation. Existing asynchronous interviewing systems are not dynamic with scripted and repetitive questions. In comparison, Maya responds with relevant follow-up questions, a largely unexplored feature of irtual interview systems. We take advantage of the implicit knowledge from deep pre-trained language models to generate rich and varied natural language follow-up questions. Empirical results suggest that followQG generates questions that humans rate as high quality, achieving 77% relevance. A comparison with strong baselines of neural network and rule-based systems show that it produces better quality questions. The corpus used for fine-tuning is made publicly available. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T09:02:55Z No. of bitstreams: 1 ijimai_6_5_8.pdf: 811659 bytes, checksum: effdef00ca187bdcd3b848a656965925 (MD5); Made available in DSpace on 2022-04-25T09:02:55Z (GMT). No. of bitstreams: 1 ijimai_6_5_8.pdf: 811659 bytes, checksum: effdef00ca187bdcd3b848a656965925 (MD5)

Song, Hao Flach, Peter https://reunir.unir.net/handle/123456789/12915 2022-04-25T08:26:43Z

Efficient and Robust Model Benchmarks with Item Response Theory and Adaptive Testing Song, Hao; Flach, Peter Progress in predictive machine learning is typically measured on the basis of performance comparisons on benchmark datasets. Traditionally these kinds of empirical evaluation are carried out on large numbers of datasets, but this is becoming increasingly hard due to computational requirements and the often large number of alternative methods to compare against. In this paper we investigate adaptive approaches to achieve better efficiency on model benchmarking. For a large collection of datasets, rather than training and testing a given approach on every individual dataset, we seek methods that allow us to pick only a few representative datasets to quantify the model’s goodness, from which to extrapolate to performance on other datasets. To this end, we adapt existing approaches from psychometrics: specifically, Item Response Theory and Adaptive Testing. Both are well-founded frameworks designed for educational tests. We propose certain modifications following the requirements of machine learning experiments, and present experimental results to validate the approach. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T08:26:43Z No. of bitstreams: 1 ijimai_6_5_11_0.pdf: 1553782 bytes, checksum: 3902c228651abcbb54eedf8f00911863 (MD5); Made available in DSpace on 2022-04-25T08:26:43Z (GMT). No. of bitstreams: 1 ijimai_6_5_11_0.pdf: 1553782 bytes, checksum: 3902c228651abcbb54eedf8f00911863 (MD5)

Criado, Natalia Ferrer, Xavier Such, José M. https://reunir.unir.net/handle/123456789/12914 2022-04-25T08:22:14Z

Attesting Digital Discrimination Using Norms Criado, Natalia; Ferrer, Xavier; Such, José M. More and more decisions are delegated to Machine Learning (ML) and automatic decision systems recently. Despite initial misconceptions considering these systems unbiased and fair, recent cases such as racist algorithms being used to inform parole decisions in the US, low-income neighborhood's targeted with high-interest loans and low credit scores, and women being undervalued by online marketing, fueled public distrust in machine learning. This poses a significant challenge to the adoption of ML by companies or public sector organisations, despite ML having the potential to lead to significant reductions in cost and more efficient decisions, and is motivating research in the area of algorithmic fairness and fair ML. Much of that research is aimed at providing detailed statistics, metrics and algorithms which are difficult to interpret and use by someone without technical skills. This paper tries to bridge the gap between lay users and fairness metrics by using simpler notions and concepts to represent and reason about digital discrimination. In particular, we use norms as an abstraction to communicate situations that may lead to algorithms committing discrimination. In particular, we formalise non-discrimination norms in the context of ML systems and propose an algorithm to attest whether ML systems violate these norms. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-25T08:22:14Z No. of bitstreams: 1 ijimai_6_5_2_0.pdf: 1547136 bytes, checksum: ae3fea797aa35805462bbd0ce54b2d59 (MD5); Made available in DSpace on 2022-04-25T08:22:14Z (GMT). No. of bitstreams: 1 ijimai_6_5_2_0.pdf: 1547136 bytes, checksum: ae3fea797aa35805462bbd0ce54b2d59 (MD5)

Boeschoten, Laura van Kesteren, Erik-Jan Bagheri, Ayoub Oberski, Daniel L. https://reunir.unir.net/handle/123456789/12889 2022-04-21T09:51:33Z

Achieving Fair Inference Using Error-Prone Outcomes Boeschoten, Laura; van Kesteren, Erik-Jan; Bagheri, Ayoub; Oberski, Daniel L. Recently, an increasing amount of research has focused on methods to assess and account for fairness criteria when predicting ground truth targets in supervised learning. However, recent literature has shown that prediction unfairness can potentially arise due to measurement error when target labels are error prone. In this study we demonstrate that existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest, when an error-prone proxy target is used. As a solution to this problem, we suggest a framework that combines two existing fields of research: fair ML methods, such as those found in the counterfactual fairness literature and measurement models found in the statistical literature. Firstly, we discuss these approaches and how they can be combined to form our framework. We also show that, in a healthcare decision problem, a latent variable model to account for measurement error removes the unfairness detected previously. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T09:51:33Z No. of bitstreams: 1 ijimai_6_5_1_0.pdf: 1416724 bytes, checksum: 5967c9d0ef87c8fe583c0a5ce961bd2d (MD5); Made available in DSpace on 2022-04-21T09:51:33Z (GMT). No. of bitstreams: 1 ijimai_6_5_1_0.pdf: 1416724 bytes, checksum: 5967c9d0ef87c8fe583c0a5ce961bd2d (MD5)

Gonçalo Oliveira, Hugo Sousa, Tiago Alves, Ana https://reunir.unir.net/handle/123456789/12888 2022-04-21T09:40:33Z

Assessing Lexical-Semantic Regularities in Portuguese Word Embeddings Gonçalo Oliveira, Hugo; Sousa, Tiago; Alves, Ana Models of word embeddings are often assessed when solving syntactic and semantic analogies. Among the latter, we are interested in relations that one would find in lexical-semantic knowledge bases like WordNet, also covered by some analogy test sets for English. Briefly, this paper aims to study how well pretrained Portuguese word embeddings capture such relations. For this purpose, we created a new test, dubbed TALES, with an exclusive focus on Portuguese lexical-semantic relations, acquired from lexical resources. With TALES, we analyse the performance of methods previously used for solving analogies, on different models of Portuguese word embeddings. Accuracies were clearly below the state of the art in analogies of other kinds, which shows that TALES is a challenging test, mainly due to the nature of lexical-semantic relations, i.e., there are many instances sharing the same argument, thus allowing for several correct answers, sometimes too many to be all included in the dataset. We further inspect the results of the best performing combination of method and model to find that some acceptable answers had been considered incorrect. This was mainly due to the lack of coverage by the source lexical resources and suggests that word embeddings may be a useful source of information for enriching those resources, something we also discuss. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T09:40:33Z No. of bitstreams: 1 ijimai_6_5_4.pdf: 740435 bytes, checksum: 4dc5d11063ba9a0dead09ca7c9ec2f31 (MD5); Made available in DSpace on 2022-04-21T09:40:33Z (GMT). No. of bitstreams: 1 ijimai_6_5_4.pdf: 740435 bytes, checksum: 4dc5d11063ba9a0dead09ca7c9ec2f31 (MD5)

Pitt, Steph van Meelis Lacey, Marlína Scaife, Ed Pitt, Jeremy https://reunir.unir.net/handle/123456789/12887 2022-04-21T09:07:05Z

No App is an Island: Collective Action and Sustainable Development Goal-Sensitive Design Pitt, Steph; van Meelis Lacey, Marlína; Scaife, Ed; Pitt, Jeremy The transformation to the Digital Society presents a challenge to engineer ever more complex socio-technical systems in order to address wicked societal problems. Therefore, it is essential that these systems should be engineered with respect not just to conventional functional and non-functional requirements, but also with respect to satisfying qualitative human values, and assessing their impact on global challenges, such as those expressed by the UN sustainable development goals (SDGs). In this paper, we present a set of sets of design principles and an associated meta-platform, which focus design of socio-technical systems on the potential interaction of human and artificial intelligence with respect to three aspects: firstly, decision-support with respect to the codification of deep social knowledge; secondly, visualisation of community contribution to successful collective action; and thirdly, systemic improvement with respect to the SDGs through impact assessment and measurement. This methodology, of SDG-Sensitive Design, is illustrated through the design of two collective action apps, one for encouraging plastic re-use and reducing plastic waste, and the other for addressing redistribution of surplus food. However, as with the inter-connectedness of the SDGs, we conclude by arguing that the inter-connectedness of the Digital Society implies that system development cannot be undertaken in isolation from other systems. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T09:07:05Z No. of bitstreams: 1 ijimai_6_5_3.pdf: 1257169 bytes, checksum: 49b23252669790ab29a032be89e83f70 (MD5); Made available in DSpace on 2022-04-21T09:07:05Z (GMT). No. of bitstreams: 1 ijimai_6_5_3.pdf: 1257169 bytes, checksum: 49b23252669790ab29a032be89e83f70 (MD5)

Susaiyah, Allmin Härmä, Aki Reiter, Ehud Petković, Milan https://reunir.unir.net/handle/123456789/12886 2022-04-21T08:51:32Z

Neural Scoring of Logical Inferences from Data using Feedback Susaiyah, Allmin; Härmä, Aki; Reiter, Ehud; Petković, Milan Insights derived from wearable sensors in smartwatches or sleep trackers can help users in approaching their healthy lifestyle goals. These insights should indicate significant inferences from user behaviour and their generation should adapt automatically to the preferences and goals of the user. In this paper, we propose a neural network model that generates personalised lifestyle insights based on a model of their significance, and feedback from the user. Simulated analysis of our model shows its ability to assign high scores to a) insights with statistically significant behaviour patterns and b) topics related to simple or complex user preferences at any given time. We believe that the proposed neural networks model could be adapted for any application that needs user feedback to score logical inferences from data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T08:51:32Z No. of bitstreams: 1 ijimai_6_5_9.pdf: 1542068 bytes, checksum: 0b7f144dee74038863f4319af98fcb00 (MD5); Made available in DSpace on 2022-04-21T08:51:32Z (GMT). No. of bitstreams: 1 ijimai_6_5_9.pdf: 1542068 bytes, checksum: 0b7f144dee74038863f4319af98fcb00 (MD5)

Philipp, Tobias Roland, Valentin Schweizer, Lukas https://reunir.unir.net/handle/123456789/12885 2022-04-21T08:44:55Z

Smoke Test Planning using Answer Set Programming Philipp, Tobias; Roland, Valentin; Schweizer, Lukas Smoke testing is an important method to increase stability and reliability of hardware- gramming, Testing depending systems. Due to concurrent access to the same physical resource and the impracticality of the use of virtualization, smoke testing requires some form of planning. In this paper, we propose to decompose test cases in terms of atomic actions consisting of preconditions and effects. We present a solution based on answer set programming with multi-shot solving that automatically generates short parallel test plans. Experiments suggest that the approach is feasible for non-inherently sequential test cases and scales up to thousands of test cases. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T08:44:55Z No. of bitstreams: 1 ijimai_6_5_6.pdf: 1421291 bytes, checksum: dd56f403e28117df1c8d95bf34057b55 (MD5); Made available in DSpace on 2022-04-21T08:44:55Z (GMT). No. of bitstreams: 1 ijimai_6_5_6.pdf: 1421291 bytes, checksum: dd56f403e28117df1c8d95bf34057b55 (MD5)

Costantini, Stefania De Gasperis, Giovanni De Lauretis, Lorenzo https://reunir.unir.net/handle/123456789/12884 2022-04-21T08:25:49Z

An Application of Declarative Languages in Distributed Architectures: ASP and DALI Microservices Costantini, Stefania; De Gasperis, Giovanni; De Lauretis, Lorenzo In this paper we introduce an approach to the possible adoption of Answer Set Programming (ASP) for the definition of microservices, which are a successful abstraction for designing distributed applications as suites of independently deployable interacting components. Such ASP-based components might be employed in distributed architectures related to Cloud Computing or to the Internet of Things (IoT), where the ASP microservices might be usefully coordinated with intelligent logic-based agents. We develop a case study where we consider ASP microservices in synergy with agents defined in DALI, a well-known logic-based agent-oriented programming language developed by our research group. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T08:25:49Z No. of bitstreams: 1 ijimai_6_5_7.pdf: 798062 bytes, checksum: abbe89b464aadcbe68089ed6649c2a0b (MD5); Made available in DSpace on 2022-04-21T08:25:49Z (GMT). No. of bitstreams: 1 ijimai_6_5_7.pdf: 798062 bytes, checksum: abbe89b464aadcbe68089ed6649c2a0b (MD5)

Travé Allepuz, Esther del Fresno Bernal, Pablo Mauri Martí, Alfred Medina Gordo, Sonia https://reunir.unir.net/handle/123456789/12883 2022-04-21T08:11:57Z

The Semantics of History. Interdisciplinary Categories and Methods for Digital Historical Research Travé Allepuz, Esther; del Fresno Bernal, Pablo; Mauri Martí, Alfred; Medina Gordo, Sonia This paper aims at introducing and discussing the data modelling and labelling methods for interdisciplinary and digital research in History developed and used by the authors. Our approach suggests the development of a conceptual framework for interdisciplinary research in history as a much-needed strategy to ensure that historians use all vestiges from the past regardless of their origin or support for the construction of historical discourse. By labelling Units of Topography and Actors in a wide range of historical sources and exploiting the obtained data, we use the Monastery of Sant Genís de Rocafort (Martorell, Spain) as a lab example of our method. This should lead researchers to the development of an integrated historical discourse maximizing the potential of interdisciplinary and fair research and minimizing the risks of bias. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-04-21T08:11:57Z No. of bitstreams: 1 ijimai_6_5_5_0.pdf: 2719671 bytes, checksum: 6892ed37b8ab2c895160236b59719993 (MD5); Made available in DSpace on 2022-04-21T08:11:57Z (GMT). No. of bitstreams: 1 ijimai_6_5_5_0.pdf: 2719671 bytes, checksum: 6892ed37b8ab2c895160236b59719993 (MD5)