Abstract
Handheld controllers are standard in immersive virtual reality (iVR), but the rise of natural hand-based interactions exposes the limitations of hand gestures, especially for point-and-click tasks with graphical user interfaces (GUI). This shows the need to explore alternative hands-free selection methods. Unlike most studies focusing on the selection task itself, this work evaluates the impact of such methods on multiple dimensions when selections occur alongside another primary task. The tested methods were: head gaze + dwell, leaning, and voice; eye gaze + dwell, leaning, blinking, and voice; and voice-only. Controllers served as the baseline. Methods were further analyzed by pointing and confirming mechanisms. Four dimensions were analyzed: (1) iVR experience, (2) user satisfaction, (3) usability, and (4) efficiency and effectiveness. With 72 participants, results show hands-free methods provide comparable experiences to controllers, suggesting selection methods have a lower impact on the user experience when users focus on a primary task.

Abstract
Deep learning and large language models (LLMs) have recently enabled studies in state-of-the-art technologies that enhance recommender systems. This research focuses on solving the next-item recommendation problem using these challenging technologies in Web applications, specifically focusing on a case study in the wine domain. This paper presents the characterization of the framework developed for the object of study: adaptive recommendation based on new modeling of the initial data to explore the user's dynamic taste profile. Following the design science research methodology, the following contributions are presented: (i) a novel dataset of wines called X-Wines; (ii) an updated recommender model called X-Model4Rec-eXtensible Model for Recommendation supported in attention and transformer mechanisms which constitute the core of the LLMs; and (iii) a collaborative Web platform to support adaptive wine recommendation to users in an online environment. The results indicate that the solutions proposed in this research can improve recommendations in online environments and promote further scientific work on specific topics.

Abstract
Recent advancements in deep learning and large language models (LLMs) have led to the development of innovative technologies that enhance recommender systems. Different heuristics, architectures, and techniques for filtering information have been proposed to obtain successful computational models for the recommendation problem; however, several issues must be addressed in online environments. This research focuses on a specific type of recommendation, which combines sequential recommendation with session-based recommendation. The goal is to solve the complex next-item recommendation problem in Web applications, using the wine domain as a case study. This paper describes a framework developed to provide adaptive recommendations by rethinking the initial data modeling to better understand users’ dynamic taste profiles. Three main contributions are presented: (a) a novel dataset of wines called X-Wines; (b) an updated recommendation model named X-Model4Rec – eXtensible Model for Recommendation, which utilizes attention and transformer mechanisms central to LLMs; and (c) a collaborative Web platform designed to support adaptive wine recommendations for users in an online environment. The results indicate that the proposed framework can enhance recommendations in online environments and encourage further scientific exploration of this topic. © The Author(s) 2025. Published by BON VIEW PUBLISHING PTE. LTD.

Abstract
E-learning has been important in higher education, enabling people to continue their education with more flexibility. Virtual laboratories play a crucial role in Computer Science distance learning degrees, by enabling students to study at their rhythm and getting practical answers to practical problems immediately. Theoretical models such as finite automata, pushdown automata, context-free grammars, Turing machines, etc., are essential for understanding the grounds of languages and computability and are also the basis for the implementation of compilers. In this paper, a new virtual laboratory is presented, UAbALL-Automata Learning Lab, developed at Universidade Aberta (UAb), the Portuguese Open University. This virtual laboratory has already been tested in the curricular unit of Languages and Computation, with good feedback from the students. A comparison to other tools was performed showing that UAbALL is more complete in terms of tools provided.

Abstract
This study investigates the impact of virtual agent realism in immersive Virtual Reality (iVR) on foreign-language vocabulary learning. Specifically, it compares the effectiveness of a realistic (human-like) pedagogical virtual agent versus an abstract (non-human-like) one in delivering instructional content. A between-subjects experiment was conducted with 17 participants, divided into two groups, were exposed to either the realistic or abstract agent in an iVR Search-and-Find vocabulary learning task. Learning outcomes were measured using pre- and post-tests (based on word matching translations for 10 German-Portuguese item pairs), while presence-related experiences were assessed via the Igroup Presence Questionnaire and Temple Presence Inventory. Both groups demonstrated significant vocabulary acquisition improvements. However, no significant differences were found between the realistic and abstract agent groups in either learning outcomes or presence scores. The findings suggest that the visual realism of virtual agents may not significantly influence language learning effectiveness or user presence in these iVR environments. These preliminary results imply that abstract agents could be as effective as realistic agents for this type of foreign-language instruction, potentially reducing development resources without compromising learning benefits.

Abstract
Developing information retrieval (IR) systems that enable access across multiple languages is crucial in multilingual contexts. In Timor-Leste, where Tetun, Portuguese, English, and Indonesian are official and working languages, no cross-lingual information retrieval (CLIR) solutions currently exist to support information access across these languages. This study addresses that gap by investigating CLIR approaches tailored to the linguistic landscape of Timor-Leste. Leveraging an existing monolingual Tetun document collection and ad-hoc text retrieval baselines, we explore the feasibility of CLIR for Tetun. Queries were manually translated into Portuguese, English, and Indonesian to create a multilingual query set. These were then automatically translated back into Tetun using Google Translate and several large language models, and used to retrieve documents in Tetun. Results show that Google Translate is the most reliable tool for Tetun CLIR overall, and the Hiemstra LM consistently outperforms BM25 and DFR BM25 in cross-lingual retrieval performance. However, overall effectiveness remains up to 26.95% points lower than that of the monolingual baseline, underscoring the limitations of current translation tools and the challenges of developing an effective CLIR for Tetun. Despite these challenges, this work establishes the first CLIR baseline for Tetun ad-hoc text retrieval, providing a foundation for future research in this under-resourced setting.