Abstract
Current methods for optimization of low-density parity-check (LDPC) codes analyze the degree distribution pair asymptotically as block length approaches infinity. This effectively ignores the discrete nature of the space of valid degree distribution pairs for LDPC codes of finite block length. While large codes are likely to conform reasonably well to the infinite block length analysis, shorter codes have no such guarantee. We present and analyze an algorithm for completely enumerating the space of all valid degree distribution pairs for a given block length, code rate, maximum variable node degree, and maximum check node degree. We then demonstrate this algorithm on an example LDPC code of finite block length. Finally, we discuss how the result of this algorithm can be utilized by discrete optimization routines to form novel methods for the optimization of small block length LDPC codes.

Abstract
Due to the pervasiveness of Interconnected devices, large amounts of heterogeneous data types are being continuously collected. Regardless of the benefits that come from sharing data, exposing sensitive and private information arises serious privacy concerns. To prevent unwanted disclosures and, hence, to protect users' privacy, several privacy-preserving mechanisms have been proposed. However, the data heterogeneity and the inherent correlations among the different data types have been disregarded when developing such mechanisms. Our goal is to develop privacy-preserving mechanisms that are suitable for data heterogeneity and data correlation. These aspects will also be considered to develop mechanisms to achieve private learning. © 2021 Owner/Author.

Abstract
Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allows a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground truth data with a significantly large quantity of annotated posts, so that the models can learn to detect relevance over all the spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test data set labelled by experts' show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.

Abstract
In this article we discuss how social tagging can be used to improve the methodology used for clustering evaluation. We analyze the impact of the integration of tags in the clustering process and its effectiveness. Following the semiotic theory, the own nature of tags allows the reflection of which ones should be considered depending on the interpretant (community of users, or tag writer). Using a case with the community of users as the interpretant, our novel clustering algorithm (k-C), which is based on community detection on a network of tags, was compared with the standard k-means algorithm. The results indicate that the k-C algorithm created more effective clusters. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The emergence of online social networks provided users with an easy way to publish and disseminate content, reaching broader audiences than previous platforms (such as blogs or personal websites) allowed. However, malicious users started to take advantage of these features to disseminate unreliable content through the network like false information, extremely biased opinions, or hate speech. Consequently, it becomes crucial to try to detect these users at an early stage to avoid the propagation of unreliable content in social networks’ ecosystems. In this work, we introduce a methodology to extract large corpus of unreliable posts using Twitter and two databases of unreliable websites (OpenSources and Media Bias Fact Check). In addition, we present an analysis of the content and users that publish and share several types of unreliable content. Finally, we develop supervised models to classify a twitter account according to its reliability. The experiments conducted using two different data sets show performance above 94% using Decision Trees as the learning algorithm. These experiments, although with some limitations, provide some encouraging results for future research on detecting unreliable accounts on social networks. © 2020, Springer Nature Switzerland AG.

Abstract