Abstract
This research contributes to the field of Human-Robot Collaboration (HRC) within dynamic and unstructured environments by extending the previously proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) architecture to handle the uncertainty and irregularity inherent in real-world sensor data. Recognising the challenges posed by low-cost sensors, which are highly susceptible to environmental conditions and often fail to provide regular periodic readings, this paper introduces additional pre-processing blocks. These include two indirect Kalman filters and an additional LSTM network, which together enhance the input variables for the fuzzification process. The enhanced FS-LSTM approach is evaluated using real-world data, demonstrating its effectiveness in extracting meaningful information and accurately recognising human activities. This work underscores the potential of robotics in addressing global challenges, particularly in labour-intensive and hazardous tasks. By improving the integration of humans and robots in unstructured environments, this research contributes to the broader exploration of robotics in new societal applications, fostering connections and collaborations across diverse fields.

Abstract
Background: The coffee industry is one of the most important world supply chains, with an estimated consumption of two billion cups daily, making it the most consumed beverage worldwide. Coffee beans are primarily grown in tropical countries, with Brazil accounting for almost 50% of the production. The objective of this study is to examine the Brazilian trade between 2018 and 2022, focusing on state producers, logistical corridors, and importer countries. Methods: The methodology approach revolves around a quantitative method using Social Network Analysis measures. Results: The results reveal a massive concentration in local production (99.5%-Minas Gerais), port movements (99.9%-Santos, Itaguai, and Rio de Janeiro), and country buyers (80.9%-the United States, United Kingdon, and Japan). Conclusions: The study concludes that the Brazilian green coffee supply chain relies on a fragile and overloaded logistical network. Due to that, this study indicates that the stakeholders and decision-makers involved must consider this high concentration of production in some areas and companies. They must also address the bottlenecks in logistical corridors and the fierce competition involved in acquiring and processing Brazilian coffee production because these factors can drastically affect the revenue of the companies operating in this sector.

Abstract
Humans are good at understanding subjective or vague statements which, however, are hard to express in classical logic. Fuzzy logic is an evolution of classical logic that can cope with vague terms by handling degrees of truth and not just the crisp values true and false. Logic is the formal basis of computing, enabling the formal design of systems supported by tools such as model checkers and theorem provers.This paper shows how a model checker such as Alloy can evolve to handle both classical and fuzzy logic, enabling the specification of high-level quantitative relational models in the fuzzy domain. In particular, the paper showcases how QAlloy-F (a conservative, general-purpose quantitative extension to standard Alloy) can be used to tackle fuzzy problems, namely in the context of validating the design of fuzzy controllers. The evaluation of QAlloy-F against examples taken from various classes of fuzzy case studies shows the approach to be feasible.

Abstract
Formal contracts and assertions are effective methods to enhance software quality by enforcing preconditions, postconditions, and invariants. However, the adoption and impact of contracts in the context of mobile application development, particularly of Android applications, remain unexplored. We present the first large-scale empirical study on the presence and use of contracts in Android applications, written in Java or Kotlin. We consider 2,390 applications and five categories of contract elements: conditional runtime exceptions, APIs, annotations, assertions, and other. We show that most contracts are annotation-based and are concentrated in a small number of applications.

Abstract
The latest technological advances drive the emergence of countless real-time data streams fed by users, sensors, and devices. These data sources can be mined with the help of predictive and classification techniques to support decision-making in fields like e-commerce, industry or health. In particular, stream-based classification is widely used to categorise incoming samples on the fly. However, the distribution of samples per class is often imbalanced, affecting the performance and fairness of machine learning models. To overcome this drawback, this paper proposes Bplug, a balancing plug-in for stream-based classification, to minimise the bias introduced by data imbalance. First, the plugin determines the class imbalance degree and then synthesises data statistically through non-parametric kernel density estimation. The experiments, performed with real data from Wikivoyage and Metro of Porto, show that Bplug maintains inter-feature correlation and improves classification accuracy. Moreover, it works both online and offline.

Abstract
We live in an era where robotics and IoT represent a significant transition towards a unified and automated world. Nonetheless, this convergence faces challenges, including system compatibility and device interoperability. The lack of flexibility of conventional robotic architectures amplifies these obstacles, highlighting the urgency for solutions. Furthermore, the complexity of adopting new technologies can be overwhelming. To address these challenges, this article features a Robot Operating System (ROS2)-centered middleware, referred to as Gateway since it applies the concept of a gateway, designed to ease the robot integration. Focusing on the payload module and fostering several types of external communication, it enhances modularity and interoperability. Developers can select payloads and communication modes through a console, which the middleware subsequently configures, guaranteeing flexibility. The goal is to highlight this middleware's potential to overcome robotics limitations, allowing a flexible integration of robots. This work contributes to the Internet of Robotic Things (IoRT) matter, underscoring the importance of modular payload engineering and interoperable communication in robotics and IoT.