Alejandro Martín García

Universidad Politécnica de Madrid | UPM · Departamento de Sistemas Informáticos

Ransomware is a significant security threat that poses a serious risk to the security of smartphones, and its impact on portable devices has been extensively discussed in a number of research papers. In recent times, this threat has witnessed a significant increase, causing substantial losses for both individuals and organizations. The emergence an...

With the escalation of misinformation and malicious behavior issues on social media platforms, traditional detection-based measures often fail to address the problem in time. The use of multiple accounts or the continuous creation of new accounts makes it difficult to re-detect the presence of a user who, for example, has disseminated false informa...

The appearance of complex attention‐based language models such as BERT, RoBERTa or GPT‐3 has allowed to address highly complex tasks in a plethora of scenarios. However, when applied to specific domains, these models encounter considerable difficulties. This is the case of Social Networks such as Twitter, an ever‐changing stream of information writ...

Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. The...

This research focuses on the detection of false claims in Spanish through the use of machine learning techniques. Most of the current work related to automated, or semi-automated, fake news detections are carried out for the English language, however, there is still a large room for improvement in other languages such as Spanish. The detection of f...

Despite the large number of approaches proposed for detecting malicious applications targeting platforms such as Android, malware continuously evolves in order to avoid its detection and reach the users. Likewise, malware detection engines are continuously improved, trying to detect the most modern malware. Most of these detection tools employ sign...

In scientific literature and industry, semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Human Language Understanding tasks are unquestionable, from conversational agents to the fight against disinform...

Authors writing documents imprint identifying information within their texts: vocabulary, registry, punctuation, misspellings, or even emoji usage. Finding these details is very relevant to profile authors, relating back to their gender, occupation, age, and so on. But most importantly, repeating writing patterns can help attributing authorship to...

Introducción: Los bulos antivacunas son un tipo de desinformación sanitaria con gran peligro, dados sus efectos tangibles en la sociedad. Existen investigaciones relevantes sobre tipología de bulos, discursos negacionistas en redes o popularidad de las vacunas, pero este estudio aporta una visión complementaria y pionera, centrada en el discurso an...

Introducción: Los bulos antivacunas son un tipo de desinformación sanitaria con gran peligro, dados sus efectos tangibles en la sociedad. Existen investigaciones relevantes sobre tipología de bulos, discursos negacionistas en redes o popularidad de las vacunas, pero este estudio aporta una visión complementaria y pionera, centrada en el discurso an...

Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidence about the factual veracity of a claim...

This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learn...

Both in scientific literature and in industry,, Semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Language Understanding tasks is unquestionable, from conversational agents to the fight against disinfo...

The appearance of complex attention-based language models such as BERT, Roberta or GPT-3 has allowed to address highly complex tasks in a plethora of scenarios. However, when applied to specific domains, these models encounter considerable difficulties. This is the case of Social Networks such as Twitter, an ever-changing stream of information writ...

Misinformation has long been a weapon that helps the political, social, and economic interests of different sectors. This became more evident with the transmission of false information in the COVID-19 pandemic, compromising citizens’ health by anti-vaccine recommendations, the denial of the coronavirus and false remedies. Online social networks are...

The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarising, has enabled them to be ranked as one of the best paradigms to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand c...

The presence of misinformation and harmful content on social networks is an emerging problem that endangers public health. One of the most successful approaches for detecting, assessing, and providing prompt responses to this misinformation problem is Natural Language Processing (NLP) techniques based on semantic similarity. However, language const...

Our society produces and shares overwhelming amounts of information through the Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern on every country. Allowing the public and professionals to efficiently find reliable evidence about the factual veracity of a cl...

Convolutional Neural Networks have dominated the field of computer vision for the last ten years, exhibiting extremely powerful feature extraction capabilities and outstanding classification performance. The main strategy to prolong this trend in the state-of-the-art literature relies on further upscaling networks in size. However, costs increase r...

The ability of Transformers to perform with precision a variety of tasks such as question answering, Natural Language Inference (NLI) or summarising, have enable them to be ranked as one of the best paradigms to address this kind of tasks at present. NLI is one of the best scenarios to test these architectures, due to the knowledge required to unde...

Subtitles are a key element to make any media content accessible for people who suffer from hearing impairment and for elderly people, but also useful when watching TV in a noisy environment or learning new languages. Most of the time, subtitles are generated manually in advance, building a verbatim and synchronised transcription of the audio. Howe...

This Special Issue on Effective and Efficient Deep Learning based Solutions seeks for publications presenting new and extended applications of Deep Learning with a special focus on effectiveness and efficiency. Deep Learning has received a lot of attention in the past two decades. Although references to deep models appeared years before, it is in t...

Convolutional Networks have dominated the field of computer vision for the last ten years, exhibiting extremely powerful feature extraction capabilities and outstanding classification performance. The main strategy to prolong this trend relies on further upscaling networks in size. However, costs increase rapidly while performance improvements may...

Cloud type classification is a complex multi-class problem where total sky images are analysed to determine their category such as Stratus or Cirrus, among others. However, many properties of this domain make high classification accuracy difficult to achieve. In this paper, we design a novel fusion approach, showing that recent image classification...

Physical rehabilitation therapies for children present a challenge, and its success—the improvement of the patient’s condition—depends on many factors, such as the patient’s attitude and motivation, the correct execution of the exercises prescribed by the specialist or his progressive recovery during the therapy. With the aim to increase the benefi...

Deep Learning models have consistently provided excellent results in highly complex domains. Its deep architecture of layers allows to face problems where classical machine learning approaches fail, or simply are not able to provide good enough solutions. However, these deep models usually involve a complex topology and hyperparameters that have to...

Dear Colleagues, In recent years, Deep Learning has quickly been becoming a de facto standard for solving real world problems of very diverse kinds. Techniques such as convolutional neural networks are outstanding performers when tackling computer vision problems, LSTMs and other recurrent architectures are proficiently solving natural language pr...

Convolutional Neural Networks stands at the front of many solutions which deal with computer vision related tasks. The use and the applications of these models are growing unceasingly, as well as the complexity required to deal with bigger and highly complex problems. However, hitting the most suitable model for solving a specific task is not trivi...

Cybersecurity has become a major concern for society, mainly motivated by the increasing number of cyber attacks and the wide range of targeted objectives. Due to the popularity of smartphones and tablets, Android devices are considered an entry point in many attack vectors. Malware applications are among the most used tactics and tools to perpetra...

Android malware is increasing in spread and complexity. Advanced obfuscation, emulation detection, delayed payload activation or dynamic code loading are some of the techniques employed by current malware to hinder the use of reverse engineering techniques and anti-malware tools. This growing complexity is particularly noticeable in the evolution o...

AndroPyTool is a tool for the extraction of both, static and dynamic features from Android applications. It aims to provide Android malware analysts with an integrated environment to extract multi-source features able of modelling the behaviour of a sample and that can be used to discern its nature, whether malware or goodware. AndroPyTool integrat...

Malware writers are usually focused on those platforms which are most used among common users, with the aim of attacking as many devices as possible. Due to this reason, Android has been heavily attacked for years. Efforts dedicated to combat Android malware are mainly concentrated on detection, in order to prevent malicious software to be installe...

Machine learning classification algorithms are widely applied to different malware analysis problems because of their proven abilities to learn from examples and perform relatively well with little human input. Use cases include the labelling of malicious samples according to families during triage of suspected mal-ware. However, automated algorith...

Malware threats are growing, while at the same time, concealment strategies are being used to make them undetectable for current commercial antivirus. Android is one of the target architectures where these problems are specially alarming due to the wide extension of the platform in different everyday devices. The detection is specially relevant for...

Android platforms are known as the less security smartphone devices. The increasing number of malicious apps published on Android markets suppose an important threat to users sensitive data, compromising more devices everyday. The commercial solutions that aims to fight against this malware are based on signature methodologies whose detection ratio...

Deep Neural Networks (DNN) have become a powerful, and extremely popular mechanism, which has been widely used to solve problems of varied complexity , due to their ability to make models fitted to non-linear complex problems. Despite its well-known benefits, DNNs are complex learning models whose parametrization and architecture are made usually b...

Android malware detection represents a current and complex problem, where black hats use different methods to infect users' devices. One of these methods consists in directly upload malicious applications to app stores, whose filters are not always successful at detecting malware, entrusting the final user the decision of whether installing or not...

Malware detection has become a challenging task over the last few years. Different concealment strategies such as packing compression, polymorphic encryption and metamorphic obfuscation have produced that malware Analysts need to find more original techniques to discriminate whether a file is malware or not. One of the current benchmark techniques...

Social robots have a great potential. With high movement capabilities and large computational capacity, they allow to perform varied tasks that were usually conducted by humans. One of these tasks are physical therapies, where a therapist guides a patient through the realisation of a set of exercises. A robot, equipped with a sophisticated artifici...