Abstract
This study explores prompt engineering for automated white-box integration testing of RESTful APIs using Large Language Models (LLMs). Four versions of prompts were designed and tested across three OpenAI models (GPT-3.5 Turbo, GPT-4 Turbo, and GPT-4o) to assess their impact on code coverage, token consumption, execution time, and financial cost. The results indicate that different prompt versions, especially with more advanced models, achieved up to 90% coverage, although at higher costs. Additionally, combining test sets from different models increased coverage, reaching 96% in some cases. We also compared the results with EvoMaster, a specialized tool for generating tests for REST APIs, where LLM-generated tests achieved comparable or higher coverage in the benchmark projects. Despite higher execution costs, LLMs demonstrated superior adaptability and flexibility in test generation.

Abstract
With the huge amount of data that has been collected over time, many methods are being developed to allow better understanding and forecasting in several domains. Time series analysis is a powerful tool to achieve this goal. Despite being a well-established area, there are some gaps, and new methods are emerging to overcome these limitations, such as visibility graphs. Visibility graphs allow the analyses of times series as complex networks and make possible the use of more advanced techniques from another well-established area, network science. In this paper, we present two new efficient approaches for computing natural visibility graphs from times series, one for online scenarios in.O(n log n) and the other for offline scenarios in.O(nm), the latter taking advantage of the number of different values in the time series (m).

Abstract
Aquaculture is the world's fastest-growing food production sector, yet it lags behind other industries in adopting upcoming digital technologies. Challenges, such as integrating multimodal data and maintaining reliable network connectivity, have hindered the development of digital twins for monitoring aquaculture systems. This paper addresses these challenges through two main contributions: (i) a novel edge-based architecture for digital twinning that enables distributed, localized monitoring and actuation, reducing dependence on centralized systems and robust networks; and (ii) a three-stage algorithmic approach for mortality monitoring tailored to edge computing environments. This approach enables early detection of rising mortality rates using data fused from diverse sources, including directly monitored environmental parameters (e.g. pH and temperature), and novel optical biosensors that make use of lightweight computer vision and machine learning techniques for the estimation of bacterial concentrations within edge devices. The algorithmic strategy was tested in a real-world recirculating aquaculture system for Solea senegalensis, where bacterial concentration was estimated with an F1-score of 0.83 across five concentration levels using biosensor imagery. Moreover, a multimodal drift detection algorithm successfully identified abnormal data trends aligned with significant changes in input distributions, with preemptive drift signals preceding critical 7-day mortality spikes.

Abstract
In this study, we analyzed federated learning (FL) for ECG and PCG data from the PhysioNet 2016 challenge dataset. We tested multiple approaches of FL and evaluated how these approaches affect the performance metrics of cardiac abnormality detection while preserving data privacy. We compared the performance of the centralized and federated models with two and four clients. The results demonstrated that multimodal federated models using both ECG and PCG data consistently outperformed centralized single-modality ECG or PCG models; in fact the gains provided by multimodal approaches can compensate for the loss in performance induced by distributed learning. These findings highlight the potential of multimodal federated learning to not only provide decentralization advantages but also to achieve comparable performance with the centralized single-modality approaches.

Abstract
Hash maps are a widely used and efficient data structure for storing and accessing data organized as key-value pairs. Multithreading with hash maps refers to the ability to concurrently execute multiple lookup, insert, and delete operations, such that each operation runs independently while sharing the underlying data structure. One of the main challenges in hash map implementation is the management of collisions. Arguably, separate chaining is among the most well-known strategies for collision resolution. In this paper, we present a comprehensive study comparing two common approaches to implementing separate chaining-linked lists and dynamic arrays-in a multithreaded environment using a lock-based concurrent hash map design. Our study includes a performance evaluation covering parameters such as cache behavior, energy consumption, contention under concurrent access, and resizing overhead. Experimental results show that dynamic arrays maintain more predictable memory access and lower energy consumption in multithreaded environments.

Abstract