The increasing use of mobile technologies in healthcare has driven significant advancements in patient care management while raising critical concerns about data security and privacy. This study addresses the challenges and solutions for ensuring security and privacy in mobile health applications, emphasizing the importance of integrating Privacy by Design (PbD) principles from the development phase. By implementing a framework in Flutter, the proposed approach focuses on safeguarding sensitive data through measures such as screenshot prevention and data encryption, ensuring strict compliance with regulations like the General Data Protection Law (LGPD). The results demonstrate that adopting PbD not only meets legal requirements but also strengthens user trust by effectively protecting personal data. The proposed framework establishes a new standard for developing mobile health applications, ensuring that security and privacy are integral throughout the entire design and operational process.

Large Language Models (LLMs) have become a significant milestone in the history of artificial intelligence, representing a powerful technology that drives advancements in natural language understanding and generation. In this paper, we propose an approach in which LLMs are utilized to support the task of translating natural language arguments into computational representations. Our approach is grounded in using argumentation schemes to classify arguments, providing context to LLMs for performing the proposed task. Our results demonstrate that LLMs, even with a short context, can handle simple argument structures. Moreover, our findings suggest that a larger context would likely enhance the performance, particularly when dealing with more complex argument structures.

The investigation of stateful service mobility across networking infrastructures is becoming increasingly important as applications require stateful services capable of migrating from centralized cloud data centers to edge computing infrastructures. State-of-the-art approaches propose either machine learning solutions for stateless service placement or stateful service mobility using static and inflexible state management strategies. We believe these approaches fall short of addressing the full length of the stateful service mobility problem. In this paper, we revisit an emerging concept named self-distributing systems, where a local executing application manages to detach some of its constituent (often stateful) components and place them in remote machines as a solution for stateful service mobility. In previous work, a machine learning approach to support self-distributing systems has not been thoroughly investigated. We model the distribution of stateful components across networking infrastructures as a multi-armed bandits problem and use the UCB1 algorithm to solve it as a first attempt at a flexible solution for stateful service mobility. We conclude the paper by discussing the main challenges and opportunities in this area.

One of the main challenges in integrating different smart applications is security. Among the problems related to security, data access control is one of the most important, given that it involves end-users' privacy. In this paper, we propose an approach to the modelling of data access control interfaces using argumentation-based agents. In particular, we introduce argumentation schemes for data access control, which are based on some of the most relevant models for data access control currently available. Our approach considers not only the usual access control policies, describing which category of agents has access to which category of information, but also emergency policies, describing situations where special emergency access control rules apply. Using argumentation-based agents as access control interfaces allows us to deal with the uncertainty about external information, allowing a correct categorisation of agents that request access to information, as well as allowing agents to expose emergency situations in which emergency access control rules may apply.

Recently, argumentation frameworks have been extended in order to consider trust when defining preferences between arguments, given that arguments (or information that supports the arguments) from more trustworthy sources may be preferred to arguments from less trustworthy sources. Although such literature presents interesting results on argumentation-based reasoning and how agents define preferences between arguments, there is little work taking into account agent strategies for argumentation-based dialogues using such information. In this work, we propose an argumentation framework in which agents consider how much the recipient of an argument trusts others in order to choose the most suitable argument for that particular recipient, i.e., arguments constructed using information from those sources that the recipient trusts. Our approach aims to allow agents to construct more effective arguments, depending on the recipients and on their views on the trustworthiness of potential sources.

Several advantages can be obtained by allowing multi-agent systems to easily access ontologies, for example, in scenarios where agents make their decisions based on knowledge provided by ontologies. Thus, this paper presents an infrastructure to allow the use of web ontologies in different agent-oriented platforms. The agents use this infrastructure layer as a tool for storing, accessing and querying domain-specific OWL ontologies. As a result, this layer allows an integration of agent platforms with semantic web data and ontologies. We exemplify in practice how agents, coded in one such platform, can use the proposed access layer to ontological reasoning engines, as well as which features can be obtained from it. We evaluated and compared performance and memory consumption of this semantic infrastructure against usual knowledge representation in agent programming.