Recent advances in Natural Language Processing (NLP) are highly based on black-box score-based models that provide only final predictions along with their score. This opacity impedes the comprehension of their internal decision-making processes, complicating the identification of potential weaknesses. A powerful strategy for analyzing model vulnerabilities is the generation of text adversarial examples. These attacks introduce subtle text perturbations that cause victim models to make incorrect predictions while preserving the original semantic meaning. This paper presents a novel method for generating text adversarial examples through Large Language Models (LLMs). The proposed method uses the outstanding text generation capabilities of LLMs to modify the original input text at multiple granularities: character, word, and sentence level. First, sentence-level perturbations are introduced by generating paraphrases with an LLM instruction prompt. Next, further character- and word-level perturbations are introduced to words that most affect predictions using another set of LLM instruction prompts. In particular, vulnerable words are perturbed by replacing them with their synonyms or misspelled variants, or by inserting additional neutral words adjacent to them. Experiments were conducted to assess the proposal’s viability on two sentiment classification tasks: sentence-level reviews and full-length reviews. The proposal demonstrates an advantage over many well-known approaches based on LLMs. It preserves the original semantics to a similar extent, while increasing the deception of victim models by 29 − 85 % over the best-analyzed state-of-the-art methods.

Abstract Data augmentation is a widely used strategy to enhance the predictive power of machine learning (ML) models. This is the case of intent classification problems, where the end goal of a utterance needs to be categorized using text mining techniques. Nevertheless, recent augmentation methods based on general off-the-shelf Large Language Models (LLMs) have room for improvement. They can struggle to effectively capture the nuances associated with domain-specific scenarios. This paper presents a novel utterance augmentation method that uses LLMs and word embedding models to address the issue, particularly in domain-specific problems. The proposed method starts from a given reference set. Then, paraphrases are generated using LLMs to obtain new utterances that are semantically similar to the original ones, but with different word choices and syntax. Next, synonym replacement is accomplished using a previously trained domain-specific word embedding model. This entails the incorporation of relevant vocabulary to a particular topic into the final augmented dataset, effectively capturing the nuances of domain-specific problems. Experiments were conducted to assess the quality of the proposal in two intent classification problems related to financial trading compliance. The proposal has an advantage over many well-known approaches in the first problem, comprising eight reference utterances and 375 challenging examples in general language. In the second problem, with 13 reference utterances and 525 challenging examples in general and financial trading vocabulary, the proposal outperforms the best-analyzed state-of-the-art methods by up to 15%.

Medical professionals are often overwhelmed by the amount of patients they have to care for, leaving little time available to keep up to date in their respective specialities. They usually find it challenging to keep up with the vast amount of medical literature and identify the most relevant articles for their practice, especially those related to their patient’s specific conditions. Therefore, a system that proactively supports healthcare professionals in selecting relevant articles related to the characteristics of the patients is crucial. This paper presents Medical Expert Linguist for Evaluating Nosology and Diagnosis Information (MELENDI) to tackle this issue. It is a recommendation system that effectively and efficiently recommends pertinent medical articles to healthcare professionals based on their patients’ diagnoses. It combines a semantic similarity model generated using the content of discharge summaries, with a relevance estimator produced by analysing scientific publications. To test the system, 1 , 000 , 000 abstracts were obtained from PubMed and 10 discharge reports from ’Medical Information Mart for Intensive Care (MIMIC-III) were used. A group of 5 medical specialists has been involved in the system’s evaluation. These evaluations demonstrated good overall performance, supporting the implementation of the system in a real-world environment, such as a hospital information system .

Abstract In the scientific community, there is no single, objective, and precise metric for ranking the work of researchers based on their scientific merit. Most existing metrics are based on the number of publications associated with an author along with the number of citations received by those publications. However, there is no standard metric officially used to evaluate the researchers’ careers. In this paper, the Framework for Reputation Estimation of Scientific Authors (FRESA) to address this issue is depicted. It is a system able to estimate the reputation of a researcher focusing on the achieved publications. It calculates two indexes making use of the relevance and the novelty concepts in the scientific domain. The system can depict the scientific trajectories of the researchers through the proposed indexes to illustrate their evolution over time. FRESA uses web information sources and applies similarity measures, text mining techniques, and clustering algorithms to also rank and group the researchers. The presented work is experimental, rendering promising results.

Customer acquisition is an issue that continues to receive attention from companies worldwide.Various marketing campaigns using psychological methodologies have been designed to address this issue.However, once a campaign is launched, it is highly complicated to detect which sets of customers are most likely to purchase an offered product.This fact is key since it allows companies to focus their efforts on specific clients and discard others.Several selection techniques have been implemented, but most of them are usually very demanding in terms of time and human resources for the companies.Artificial Intelligence techniques appear to help to simplify the process.Thus, companies have started to use Machine Learning (ML) models trained to efficiently detect those clients with certain proneness to purchase.Toward this goal, this paper presents a novel purchase propensity detection ML system based on Sentiment Analysis techniques able to consider customer comments regarding the offered products.The tourist domain was selected for the case study, where the obtained product was successfully embedded in an initial prototype.

The scientific and business communities are proposing new authentication methods more robust than traditional solutions relying on a single security point such as passwords (i.e. “something you know”). User and Entity Behavior Analysis (UEBA) has postulated as an excellent solution to improve authentication systems by performing continuous authentication to extend the authentication process over time. UEBA is based on detecting anomalies in the intrinsic behaviour of each user or entity (i.e. it is based on “something you are/do”). This paper presents a method for performing continuous authentication using UEBA techniques that allows combining information from multiple sources at the feature level. This combination is achieved through a novel Symbolic Aggregate approximation (SAX) using Random Trees Embeddings for each information source, producing a sequence of symbols. Then, these sequences of symbols are combined into a single sequence using temporal information. The resulting sequence of symbols feeds a density-based clustering model that uses a distance based on DNA sequence alignment techniques to extract behavioural cores. Finally, new samples are compared against these cores to detect anomalies using a risk model that evaluates if a behaviour is anomalous (suspected user impersonation). The model has been extensively tested and evaluated against well-known state-of-the-art datasets.

User and Entity Behaviour Analytics (UEBA) mechanisms rely on statistical techniques and Machine Learning to determine when a significant deviation from patterns or trends established as a standard for users and entities is occurring. These mechanisms are beneficial within cybersecurity contexts because they allow managers and administrators to have early alerts warning about potential security incidents. This paper proposes the utilisation of UEBA to improve the security of Federated Identity Management (FIM) solutions. The proposed UEBA workflow allows Relying Parties within identity federations to build a session fingerprint characterising each user’s behaviour from available information. Furthermore, it enables anomaly detection based on this fingerprint, integrating raised alerts within current identity management specifications. The proposed workflow is validated and evaluated in a real use case based on a web chat application using OpenID Connect for identity management.

Voice transmission is no longer the main usage of mobile phones. Data transmissions, in particular Internet access, are very common actions that we might perform with these devices. However, the spectacular growth of the mobile data demand in 5 G mobile communication systems leads to a reduction of the resources assigned to each device. Therefore, to avoid situations in which the Quality of Experience (QoE) would be negatively affected, an automated system for degradation detection of video streaming is proposed. This approach is named QoE Management for Mobile Users (QoEMU). QoEMU is composed of several modules to perform a real-time analysis of the network traffic, select a mitigation action according to the information of the traffic and to some predefined policies, and apply these actions. In order to perform such tasks, the best Key Performance Indicators (KPIs) for a given set of video traces are selected. A QoE Model is trained to define a global QoE for the set of traces. When an alert regarding degradation in the quality appears, a proper mitigation plan is activated to mitigate this situation. The performance of QoEMU has been evaluated over a degradation situation experiments with different video users.

Road traffic is a pervasive aspect in modern societies that affects millions people.The study of its multiple aspects is a very demanding task.Due to its complexity, traffic simulations become a key tool.Their development demands multidisciplinary teams, where communication problems are frequent.Modeldriven engineering alleviates this situation providing graphical instruments for designing Modelling Languages (MLs) and semiautomatic transformations.This work presents a model-driven infrastructure composed by an integrative ML, a model editor, and a code generator.The ML is based on related literature and facilitates modelling different theories and simulations based on them.It considers the roles of individuals involved in road traffic, and partially adopts agent-based methodologies to model their decision-making.A case study shows how to produce a simulation specification adapting an existing traffic theory to the ML, and adjust this specification to a simulation platform for testing.It provides the basis for comparison with related work.

Traffic has an important impact in many aspects of our everyday life, from healthcare to transport regulation or urban planning. Given its complexity, the study in real settings is frequently limited, so researchers resort to simulations. However, realistic simulations are still complex systems. Its development frequently requires multidisciplinary groups, where misunderstandings are frequent, and there is a great variety of potential theories and platforms to consider. In order to reduce the impact of these issues, the Model-Driven Engineering (MDE) of simulations has been proposed. It is focused on developing mainly through models and their semi-automated transformation. Nevertheless, an effective approach of this kind requires the availability of infrastructures that include modelling languages, transformations, tools, and processes to use them. This work presents a MDE process for traffic simulations. It introduces a modelling language and makes uses of available infrastructures in its tasks. The process guides users in creating tailored models for their simulations, and transforming these to code. A case study that uses an existing model for driversâ?? behaviour and an already available platform to develop a simulation illustrates the approach.