Abstract
The PAP900 dataset centers on the semantic relationship between affective words in Portuguese. It contains 900 word pairs, each annotated by at least 30 human raters for both semantic similarity and semantic relatedness. In addition to the semantic ratings, the dataset includes the word categorization used to build the word pairs and detailed sociodemographic information about annotators, enabling the analysis of the influence of personal factors on the perception of semantic relationships. Furthermore, this article describes in detail the dataset construction process, from word selection to agreement metrics. Data was collected from Portuguese university psychology students, who completed two rounds of questionnaires. In the first round annotators were asked to rate word pairs on either semantic similarity or relatedness. The second round switched the relation type for most annotators, with a small percentage being asked to repeat the same relation. The instructions given emphasized the differences between semantic relatedness and semantic similarity, and provided examples of expected ratings of both. There are few semantic relations datasets in Portuguese, and none focusing on affective words. PAP900 is distributed in distinct formats to be easy to use for both researchers just looking for the final averaged values and for researchers looking to take advantage of the individual ratings, the word categorization and the annotator data. This dataset is a valuable resource for researchers in computational linguistics, natural language processing, psychology, and cognitive science. (c) 2025TheAuthors.

Abstract
Indoor Positioning Systems (IPSs) are essential for applications requiring accurate location awareness in indoor environments. However, achieving high precision remains challenging due to signal interference and environmental variability. This study proposes a multimodal IPS that integrates Bluetooth Received Signal Strength Indicator (RSSI) measurements and video imagery using machine learning (ML) and ensemble learning techniques. The system was implemented and deployed in the Hall of Biodiversity at the Natural History and Science Museum of the University of Porto. The venue presented significant deployment issues, namely restrictions on beacon placement and lighting conditions. We trained independent ML models on RSSI and video datasets, and combined them through ensemble learning methods. The experimental results from test scenarios, which included simulated visitor trajectories, showed that ensemble models consistently outperformed the RSSI-based and video-based models. These findings demonstrate that the use of multimodal data can significantly improve IPS accuracy despite constraints such as multipath interference, low lighting, and limited beacon infrastructure. Overall, they highlight the potential of multimodal data for deployments in complex indoor environments.

Abstract
Lock-free data structures have become increasingly significant due to their algorithmic advantages in multi-core cache-based architectures. Safe Memory Reclamation (SMR) is a technique used in concurrent programming to ensure that memory can be safely reclaimed without causing data corruption, dangling pointers, or access to freed memory. The ERA theorem states that any SMR method for concurrent data structures can only provide at most two of the three main desirable properties: Ease of use, Robustness, and Applicability. This fundamental trade-off influences the design of efficient lock-free data structures at an early stage. This work redesigns a previous lock-free hash map to fully exploit the properties of the ERA theorem and to leverage the characteristics of multi-core cache-based architectures by minimizing the number of cache misses, which are a significant bottleneck in multi-core environments. Experimental results show that our design outperforms the previous design, which was already quite competitive when compared against the Concurrent Hash Map design of the Intel's TBB library.

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
Lock-freedom offers significant advantages in terms of algorithm design, performance and scalability. A fundamental building block in software development is the usage of hash map data structures. This work extends a previous lock-free hash map to support a new simplified design that is able to take advantage of most state-of-the-art safe memory reclamation methods, thus outperforming the previous design.

Abstract
Prolog is a programming language that provides a high-level approach to software development. Python is a versatile programming language that has a vast range of libraries including support for data analysis and machine learning tasks. We present a Prolog-Python interface that aims at exploiting Prolog deduction capabilities and Python's extensive libraries. Our novel interface was built using a divide and conquer methodology. In a first step, we implemented a set of C++ classes that can be matched to Python classes; next, we used an interface generator to export the relevant classes. Finally, we use C code to actually convert between the two realms. In order to demonstrate the usefulness of the interface, we enhance an Inductive Logic Programming System with a visualization capabilities and show how to interface with a standard classifier.