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Onderzoekers uit Leiden en Amsterdam zetten AI in om erfelijke cholesterolziekte FH op te sporen - AD - 09-01-2025
No full textDemonstrating dark haptics scenarios for future XR advertising
Get PDFWe demonstrate the manipulative potential of haptics ('Dark Haptics') for the future of advertising in eXtended Reality (XR). Our demonstration showcases two haptic and one pseudo-haptic interactions as a XR-based advertising strategy that can subtly manipulate user actions in this dark future of advertising work. Developed in Unreal Engine 5.5 for the Meta Quest 3, this single-user Virtual Reality experience presents an initial exploration of the manipulative qualities of haptics design that can be exploited within immersive environments. By highlighting these dark design patterns, our demonstration aims to raise awareness and initiate discussion around the ethical implications of haptic feedback for the future of work in immersive environments, particularly within advertising. Our work lays the groundwork for future research and development of tools to mitigate the potential harms and risks of dark haptics as immersive technologies become more integrated into professional and commercial environments
Reading between the lines: A study of thematic bias in book recommender systems
Get PDFRecommender systems help users discover new content, but can also reinforce existing biases, leading to unfair exposure and reduced diversity. This paper introduces and investigates thematic bias in book recommendations, defined as a disproportionate favouring or neglect of certain book themes. We adopt a multi-stage bias evaluation framework using the Book-Crossing dataset to evaluate thematic bias in recommendations and its impact on different user groups.
Our findings show that thematic bias originates from content imbalances and is amplified by user engagement patterns. By segmenting users based on their thematic preferences, we find that users with niche and long-tail interests receive less personalised recommendations, whereas users with diverse interests receive more consistent recommendations. These findings suggest that recommender systems should be carefully designed to accommodate a broader range of user interests. By contributing to the broader goal of responsible AI, this work also lays the groundwork for extending thematic bias analysis to other domains
MASSXR 2025: The 3rd workshop on multi-modal affective and social behavior analysis and synthesis in Extended Reality (affiliated with IEEE VR 2025)
Get PDFThe objective of MASSXR 2025, the 3rd Workshop on Multi-modal Affective and Social Behavior Analysis and Synthesis in Extended Reality, was to bring together researchers and practitioners from fields including cybersecurity, human-computer interaction, computer graphics/animation, multi-modal machine learning, Artificial Intelligence (AI), data privacy, and socio-technical studies to discuss the state of security in Extended Reality (XR), as well as future directions and opportunities. Through this, it aimed to achieve adaptive, context-aware security measures that are both technically robust and aligned with user trust and understanding. The workshop provided an opportunity to foster collaborative research efforts and advance the state of secure social interactions within XR, setting a foundation for future innovations in the field
User centric requirements for enhancing XR use cases with machine learning capabilities
Get PDFThe combination of Extended Reality (XR) and Machine Learning (ML) will enable a new set of applications. This requires adopting a user-centric approach to address the evolving user needs. This paper addresses this gap by presenting findings from two independent focus groups specifically designed to gather user requirements for two use cases: (1) a VR Conference with an AI-enabled support agent and real-time translations, and (2) an AR Theatre featuring ML generated translation capabilities and voice-activated VFX. Both focus groups were designed using context-mapping principles. We engaged 6 experts in each of the focus groups. Participants took part in a combination of independent and group activities aimed at mapping their interaction timelines, identifying positive experiences, and highlighting pain points for each scenario. These activities were followed by open discussions in semi-structured interviews to share their experiences. The inputs were analysed using Thematic Analysis and resulted in set of user-centric requirements for both applications on Virtual Conference and Augmented Theatre respectively. Subtitles and Translations were the the most interesting and common findings in both cases. The results led to the design and development of both applications. By documenting user-centric requirements, these results contribute significantly to the evolving landscape of immersive technologies
X-ray Computed Tomography Case Study: Triangle and Pentagon Datasets with Various Sizes, Scales, and Noise Levels
No full textThe dataset consists of two laser-cut objects made from a 6-mm-thick transparent plastic material called acrylate
SMART-SPI: Towards an integrated framework for advanced computational single-pixel imaging systems
No full textWe introduce SMART-SPI, a software framework for seamless integration of advanced experimental and computational research in single-pixel imaging, such as compressed-sensing-based image reconstruction, and automatic adaptation of illumination pattern and measurement optics
Combining LLMs with a logic-based framework to explain MCTS
Get PDFIn response to the lack of trust in Artificial Intelligence (AI) for sequential planning, we design a Computational Tree Logic-guided large language model (LLM)-based natural language explanation framework designed for the Monte Carlo Tree Search (MCTS) algorithm. MCTS is often considered challenging to interpret due to the complexity of its search trees, but our framework is flexible enough to handle a wide range of free-form post-hoc queries and knowledge-based inquiries centered around MCTS and the Markov Decision Process (MDP) of the application domain. By transforming user queries into logic and variable statements, our framework ensures that the evidence obtained from the search tree remains factually consistent with the underlying environmental dynamics and any constraints in the actual stochastic control process. We evaluate the framework rigorously through quantitative assessments, where it demonstrates strong performance in terms of accuracy and factual consistency
Testing Quasiperiodicity
Get PDFA cover (or quasiperiod) of a string is a shorter string such that every position of is contained in some occurrence of as a substring. The notion of covers was introduced by Apostolico and Ehrenfeucht over 30 years ago [Theor. Comput. Sci. 1993] and it has received significant attention from the combinatorial pattern matching community. In this note, we show how to efficiently test whether admits a cover. We design an algorithm that, given , , , and oracle access to , uses letter queries to test whether has a cover of length at most or is -far from having such a cover. Our insights also lead to a simple streaming algorithm for short covers
InnateCoder: learning programmatic options with foundation models
Get PDFOutside of transfer learning settings, reinforcement learning agents start their learning process from a clean slate. As a result, such agents have to go through a slow process to learn even the most obvious skills required to solve a problem. In this paper, we present INNATECODER, a system that leverages human knowledge encoded in foundation models to provide programmatic policies that encode "innate skills" in the form of temporally extended actions, or options. In contrast to existing approaches to learning options, INNATECODER learns them from the general human knowledge encoded in foundation models in a zero-shot setting, and not from the knowledge the agent gains by interacting with the environment. Then, INNATECODER searches for a programmatic policy by combining the programs encoding these options into larger and more complex programs. We hypothesized that INNATECODER's way of learning and using options could improve the sampling efficiency of current methods for learning programmatic policies. Empirical results in MicroRTS and Karel the Robot support our hypothesis, since they show that INNATECODER is more sample efficient than versions of the system that do not use options or learn them from experience