During the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users. In this scenario, the disruption of fake news providers and bot accounts for spreading propaganda information as well as sensitive content throughout the network has fostered applied research to automatically measure the reliability of social networks accounts via Artificial Intelligence (AI). In this paper, we present a multilingual approach for addressing the bot identification task in Twitter via Deep learning (DL) approaches to support end-users when checking the credibility of a certain Twitter account. To do so, several experiments were conducted using state-of-the-art Multilingual Language Models to generate an encoding of the text-based features of the user account that are later on concatenated with the rest of the metadata to build a potential input vector on top of a Dense Network denoted as Bot-DenseNet. Consequently, this paper assesses the language constraint from previous studies where the encoding of the user account only considered either the metadata information or the metadata information together with some basic semantic text features. Moreover, the Bot-DenseNet produces a low-dimensional representation of the user account which can be used for any application within the Information Retrieval (IR) framework.

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable.

The continuous evolution of multimedia applications is fostering applied research in order to dynamically enhance the services provided by platforms such as Spotify, Lastfm, or Billboard. Thus, innovative methods for retrieving specific information from large volumes of data related with music arises as a potential challenge within the Music Information Retrieval (MIR) framework. Moreover, despite the existence of several musical-based datasets, there is still a lack of information to properly assess an accurate estimation of the impact or the popularity of a song within a platform. Furthermore, the aforementioned platforms measure the popularity in various manners, thus increasing the difficulties in performing generalized and comparable models. In this paper, the creation of SpotGenTrack Popularity Dataset (SPD) is presented as an alternative solution to existing datasets that will facilitate researchers when comparing and promoting their models. In addition, an innovative multimodal end-to-end Deep Learning architecture named as HitMusicNet is presented for predicting popularity in music recordings. Experiments conducted show that the proposed architecture outperforms previous studies in the State-of-the-Art by incorporating three main modalities to the analysis, such as audio, lyrics and meta-data as well as a preliminary compression stage via autoencoder to better the capability of the model when predicting the popularity.

Indoor Localization and Tracking have become an attractive research topic because of the wide range of potential applications. These applications are highly demanding in terms of estimation accuracy and rise a challenge due to the complexity of the scenarios modeled. Approaches for these topics are mainly based on either deterministic or probabilistic methods, such as Kalman or Particles Filter. These techniques are improved by fusing information from different sources, such as wireless or optical sensors. In this paper, a novel MUlti-sensor Fusion using adaptive fingerprint (MUFAF) algorithm is presented and compared with several multi-sensor indoor localization and tracking methods. MUFAF is mainly divided in four phases: first, a target position estimation (TPE) process is performed by every sensor; second, a target tracking process stage; third, a multi-sensor fusion combines the sensor information; and finally, an adaptive fingerprint update (AFU) is applied. For TPE, a complete environment characterization in combination with a Kernel density estimation technique is employed to obtain object position. A Modified Kalman Filter is applied to TPE output in order to smooth target routes and avoid outliers effect. Moreover, two fusion methods are described in this paper: track-to-track fusion and Kalman sensor group fusion. Finally, AFU will endow the algorithm with responsiveness to environment changes by using Kriging interpolation to update the scenario fingerprint. MUFAF is implemented and compared in a test bed showing that it provides a significant improvement in estimation accuracy and long-term adaptivity to condition changes.

Indoor person tracking attracts a considerable effort from the research community as it allows to perform Human Behaviour Analysis tasks, where wireless technologies play a key role. However, complex signal propagation effects in indoor environments are the main issue to face when performing accurate indoor positioning and tracking. The advances in machine and deep learning models, applied to improve the estimation of the position captured by wireless sensors, can provide a more precise tracking and positioning, an open field for research which has been used to improve the prior art. In this paper, a novel framework for Adaptive Indoor Tracking using Recurrent models, in combination with Generative networks for new data generation (recovery), is presented (RecTrack-GAN). Firstly, a Received Signal Strength Indicator RSSI Fingerprinting database is collected. Secondly, a Recurrent Neural Network (RNN) takes as input the RSSI parameters collected by a Wireless Sensor Network (WSN) and estimates of both orientation and velocity using devices equipped with Inertial Measurement Unit (IMU) sensors, and learns to model the human movement based on these parameters. Thirdly, a Conditional Generative Adversarial Network (CGAN) is used to perform data recovering when no measurements are received and to update the Fingerprinting database taking into account the day time. The experiments performed showed that RecTrack-GAN improves accuracy performance and reduces error deviation for tracking up to 15% compared to the prior art in the literature.