The Internet of Things (IoT) has become one of the most popular technologies in recent years. Advances in computing capabilities, hardware accessibility, and wireless connectivity make possible communication between people, processes, and devices for all kinds of applications and industries. However, the deployment of this technology is confined almost entirely to tech companies, leaving end users with only access to specific functionalities. This paper presents a framework that allows users with no technical knowledge to build their own IoT applications according to their needs. To this end, a framework consisting of two building blocks is presented. A friendly interface block lets users tell the system what to do using simple operating rules such as “if the temperature is cold, turn on the heater.” On the other hand, a fuzzy logic reasoner block built by experts translates the ambiguity of human language to specific actions to the actuators, such as “call the police.” The proposed system can also detect and inform the user if the inserted rules have inconsistencies in real time. Moreover, a formal model is introduced, based on fuzzy description logic, for the consistency of IoT systems. Finally, this paper presents various experiments using a fuzzy logic reasoner to show the viability of the proposed framework using a smart-home IoT security system as an example.

Nowadays, context-aware systems use data obtained from various sources to adapt and provide services of interest to users according to their needs, location or interaction with the corresponding environment. However, the use of heterogeneous sources creates a huge amount of data that may differ in format, transmission speed and may be affected by environmental noise. This generates some inconsistency in data, which must be detected in time to avoid erroneous analysis. This is done using data fusion, which is the action for integrating diverse sources to be analyzed according to a given context. In this work, we propose a scheme of data fusion of heterogeneous sources, supported by a distributed architecture and Bayesian inference as fusion method. As a practical experiment, data were collected from three DHT22 sensors, whose measurements were relative humidity and temperature. The purpose of the experiment was to analyze the variation of these measurements over 24 hours, and fusion them to obtain integrated data. This proposed of data fusion represents an important field of action for the knowledge generation of interest in context-aware systems, for example for the analysis of the environment in order to take advantage of the use of energy and provide a comfortable working environment for the users.

The paper describes the design and implementation of a semantic web service that retrieves theses and extends the keyword based-search of a DSpace repository taking into account the roles of advisors and steering committee members formally represented into a custom-made ontology. The service uses SPARQL queries and the serialization module of RDF DSpace, this links the item submission process and the ontology, thus the more theses are added into a repository, the more instances are inserted into the ontology. The paper provides empirical insights about how to reuse theses metadata and includes the results of an exploratory and self-management survey of usability heuristic evaluation of a web site that enables to access the proposed service. Heuristics were estimated with a purposive sample of students, teachers, and managers, the results indicated a high satisfaction level and showed that the service increased theses accessibility in the web environment. The service also generates semantically enriched datasets that coexist with the repository, they are of utility and value to educational organizations as they give institutional visibility.

Machine learning techniques are increasingly employed to predict material properties and reduce mechanical testing costs. This study uses tensile load‒displacement data to predict the breaking force, maximum force, ultimate tensile strength (UTS), and breaking strength of nylon PA 12 specimens manufactured via selective laser sintering (SLS). To address variations in specimen ductility, linear and fourth-degree polynomial equalisations were applied to the data. The results indicate that polynomial equalisation generally achieves superior predictive performance across various machine learning models. While all the models exhibit a decline in performance metrics with a reduction in the observation matrix data, several demonstrate strong predictive capabilities even with reduced data sets. Specifically, when using 20% of the observation matrix data (corresponding to the elastic zone) are used, the kernel ridge, elastic net, and Lasso regression models achieve R ² values of 0.987, 0.931, and 0.987, respectively, for predicting breaking strength. For maximum force prediction with 20% of the data, the kernel ridge, support vector machine, and Lasso regression models resulted in R ² values of 0.975, 0.960, and 0.930, respectively. However, the prediction accuracy for UTS and ultimate strength at break is lower, with the Elastic Net model achieving R ² values of 0.703 and 0.682, respectively, with 20% of the data. These findings suggest that while certain mechanical properties can be accurately predicted using data solely from the elastic zone, minimising specimen waste and testing time, the prediction of other properties requires data beyond the elastic limit. This study highlights the potential of machine learning to optimise and streamline the quality control process in additive manufacturing.

Program synthesis is the process of automatically generating software from a requirement specification. This paper presents a systematic literature review focused on program synthesis from specifications expressed in natural language. The research problem centers on the complexity of automatically generating accurate and robust code from high-level, ambiguous natural language descriptions – a barrier that limits the broader adoption of automatic code generation in software development. To address this issue, the study systematically examines research published between 2014 and 2024, focusing on works that explore various approaches to program synthesis from natural language inputs. The review follows a rigorous methodology, incorporating search strings tailored to capture relevant studies from five major data sources: IEEE, ACM, Springer, Elsevier, and MDPI. The selection process applied strict inclusion and exclusion criteria, resulting in a final set of 20 high-quality studies. The findings reveal significant advancements in the field, particularly in the integration of large language models (LLMs) with program synthesis techniques. The review also highlights the challenges and concludes by outlining key trends and proposing future research directions aimed at overcoming these challenges and expanding the applicability of program synthesis across various domains.

A tender process consists in competing offers from different candidate suppliers or contractors. The tender winner is supposed to supply or provide a service in better conditions than competitors. Tenders are developed using centralized unverified systems, which reduce transparency, fairness and trust on the process, it also reduces the ability to detect malicious attempts to manipulate the process. Systems that provide formal verification, decentralization, authentication, trust and transparency can mitigate these risks. Satisfiability Modulo Theories provides a formal analysis to prove correctness of tender offers properties, verified properties ensures system reliability. In addition, one technology that claims to provide decentralization is Blockchain, a chain of distributed and decentralized records linked in a way such that integrity is ensured. This paper presents a formal verified and decentralized proposal system, based on Satisfiability Modulo Theories and Blockchain technology, to make electronic procurement tenders more reliable, transparent and fair.