Game engine architecture (GEA) underpins the design of complex, performance-critical interactive systems in modern game development. This paper presents the design and evolution of a fourth-year elective course on \emph{Game Engine Architecture} taught within a five-year integrated Master’s engineering program in Game Technology at Blekinge Institute of Technology, Sweden. The course combines lectures, programming assignments, and a final project to support architectural analysis, subsystem implementation, and system-level integration. The paper outlines how the course has been iteratively refined across multiple offerings, including changes to assignments, lecture content, and assessment. It summarizes student evaluation data and discusses instructor reflections on learning patterns, challenges, and course adaptations, providing a grounded account of teaching GEA as an advanced software architecture subject.

Integrating artificial intelligence (AI) with immersive analytics (IA) represents a promising means of leveraging advanced computational techniques to enhance data visualization and analysis. This study examines the state-of-the-art of AI-IA integration by addressing three key research issues: the significant application domains, the AI techniques used and their combinations, and current challenges and future directions. Results of reviewing 43 relevant studies reveal that AI-IA integration is still in its early stages, as existing research has mainly focused on a limited range of data types and application scenarios. By analyzing the application domains, this systematic literature review supports previous findings of important applications in the fields of education, manufacturing, and healthcare. At the same time, it identifies emerging applications that have progressed from XR and AI domains to AI-IA integration, such as sports events, assistive systems, urban planning, and disaster management. We contribute to extending established visual analytics (VA) pipelines into XR environments with integrated AI techniques. AI techniques are identified as contributing in five ways to this IA pipeline. Our contribution also includes identifying four key challenges and seven opportunities for future exploration. The review concludes that combining AI and IA holds the potential to create innovative applications using advanced AI and immersive visualization techniques. We present an overview of these applications and address key issues for future development. 

Recent advancements in semantic 3D city building reconstruction have achieved high levels of geometric detail, enabling a wide range of applications in urban planning, digital twins, and smart city systems. However, the state of the art (SOTA) has three glaring gaps. Firstly, building and road reconstruction are often treated as decoupled tasks, overlooking the mutual benefits of integrating both within a unified modeling framework. The integration is beneficial, as roads could serve as reliable spatial priors that aid in the determination of the optimal layout of buildings. Secondly, despite the avail-ability of large-scale point clouds from airborne and mobile LiDAR (Light Detection and Ranging) systems, most existing workflows remain manual or semi-automated, relying on rule-based modeling and expert intervention. Finally, there is a lack of open datasets for an integrated 3D building-road reconstruction. A scoping review of the existing work is much needed to identify these gaps. This comprehensive unifying review of existing techniques includes both building and road reconstruction from point clouds, spanning both parametric and data-driven approaches. Further, a few key building and road reconstruction methods are implemented on an example open dataset to identify challenges. This pipeline treats road modeling as a prior; thus, it is implemented before the reconstruction of buildings. This example highlights key chal-lenges, such as pose correction, ambiguity in footprint estimation from MLS data, and semantic inconsistencies in the reconstructed meshes, that need to be studied further.

Federated learning (FL) offers a promising solution for human activity recognition (HAR) in healthcare by enabling model training on decentralized data, thereby preserving privacy in compliance with regulations such as GDPR and HIPAA. This study investigates the privacy vs performance trade-offs of FL with centralized machine learning (CML) using the UCI HAR dataset. We focus on three aggregation methods: federated averaging (FedAvg), federated proximal (FedProx), and Krum, under both independent and identically distributed (IID) and non-IID data settings. We evaluate their robustness to poisoning attacks and the impact of local differential privacy (LDP).

Our results show that FL outperforms CML in HAR tasks. In non-IID settings, FedAvg achieves up to 97\% accuracy, outperforming FedProx (91\%) and Krum (88\%). Interestingly, non-IID data yields better performance across all methods. While Krum demonstrates strong resilience against poisoning attacks in the absence of LDP, FedProx maintains greater stability when LDP is applied. However, higher privacy levels reduce accuracy to 58–65\%. These findings position FedProx as a balanced option for privacy-preserving healthcare HAR, emphasizing the importance of carefully tuning privacy mechanisms to maintain optimal performance.

This paper presents a conceptual design for a virtual reality (VR)-based furniture arrangement system, proposed to leverage immersive technologies and generative AI (GenAI) with intuitive interaction methods. The system utilizes a head-mounted display (HMD) to provide an immersive and intuitive user experience (UX) with multimodal interaction methods through hand and eye-tracking controls. Additionally, GenAl enables the virtual agent to engage in natural conversations with users and interpret and respond contextually, aiming to enhance personalization by understanding conversations. Users' interests and preferences are analyzed and predicted from the conversation and eye-gaze data, which provides recommendations for relevant furniture items and real-time personalized feedback. By combining these techniques, this design aims to create a seamless, interactive, intelligent, personalized virtual interior design and furniture arrangement experience within the immersive virtual environment (VE). The implementation of several key features demonstrates a proof of concept for our virtual furniture arrangement system. 

This paper presents Geometry-Independent Path Planning (GIPP) in real time for games and other dynamic, interactive worlds using the GPU. The first contribution is the automatic generation of a geometry-independent 2D navigation mesh, termed GINT, at a user-defined resolution. GINT can be computed efficiently for complex scenes containing millions of triangles, without manual preprocessing. The second contribution is HALOS, a parallel, line-of-sight-inspired path planning algorithm that operates entirely on the GPU using GINT. Both GINT generation and HALOS execution are integrated into the GPU pipeline for optimal performance. GIPP scales well with large numbers of agents targeting the same goal and robustly adapts to dynamic environments, overcoming key limitations of many traditional path planning methods. Importantly, GIPP's real-time, high-resolution capabilities and GPU-based design make it especially valuable in virtual environments, where immersive, responsive, and large-scale agent navigation is crucial for presence, realism, and interactivity.

The 'Human-Centered Intelligent Realities' (HINTS) laboratory is a strategic infrastructure project aiming to support research that advances the development of immersive, user-aware, and intelligent digital environments by integrating augmented reality (AR), virtual reality (VR), extended reality (XR), artificial intelligence (AI), and machine learning (ML). By combining virtual reality and communication-computing continuums, the HINTS environment seeks to create innovative concepts, methods, and tools that empower users to engage with digital systems in novel, efficient, and effective ways. Research in the HINTS laboratory focuses on experience assessment, new digital environments and interaction techniques, visual analytics, adaptive AI, and networking. This paper presents the HINTS laboratory, ongoing activities, and opportunities and challenges for the future.

Heart disease remains the leading cause of death globally, and early detection plays a vital role in reducing mortality. Phonocardiograms (PCGs), which record heart sounds via digital stethoscopes, offer a portable and cost-effective diagnostic modality. However, manual PCG analysis is time-consuming and requires expertise. In this study, we present a machine learning-based pipeline to classify heart sounds as normal or abnormal for early disease detection, crucial for cardiac diagnostics. We propose a twofold approach: (i) a deep learning model using a one-dimensional convolutional neural network (1D-CNN) trained directly on raw PCG recordings, and (ii) traditional machine learning models, support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost), trained on clinically meaningful features. The dataset comprises 3,240 PCG recordings from the PhysioNet Challenge 2016. The models were evaluated using k-fold cross-validation, and performance was assessed via accuracy, precision, recall, and F1-score. Explainability techniques such as gradient-weighted class activation mapping (Grad-CAM) and local interpretable model-agnostic explanations (LIME) were applied to improve clinical interpretability. Our results show that the feature-based models outperform the CNN in transparency and time efficiency, highlighting the value of interpretable artificial intelligence (AI) systems in healthcare diagnostics.

This research investigates the needs and preferences of low-income angioplasty patients and their caregivers in India during post-angioplasty recovery. Through in-depth interviews and contextual inquiries, the study uncovers critical informational, physical, and emotional needs. Patients often lack access to reliable health information, leading to misconceptions about care and medication adherence. Pain management and emotional support are significant concerns for both patients and caregivers. The study proposes the integration of digital health solutions to address these challenges, providing a platform for reliable information, communication, and support. This research emphasizes the need for context-sensitive interventions to improve patient outcomes and enhance the quality of life for vulnerable populations in developing countries.

In this paper, we investigate the current state and development of personalized smart immersive extended reality environments (PSI-XR). PSI-XR has gained increasing traction across various fields such as education, entertainment, and healthcare, offering customized immersive experiences that address users’ personalized needs. This study performs a systematic literature review by collecting and analyzing related journal and conference papers in the domain. Following a comprehensive search across three databases, which yielded 1276 papers, a refined selection of 94 publications was made to conduct an in-depth analysis of cutting-edge research in the field of PSI-XR. This review focused on examining application domains, relevant technologies, and smart techniques, including artificial intelligence, with particular emphasis on advancements in personalization. The study provides insights into prospective advancements while also identifying the opportunities and challenges in this evolving field. This review is beneficial for both researchers and developers interested in exploring the state-of-the-art personalized perspective in a smart immersive extended reality environment.