This paper relies on 1 prerequisite mathematical and architectural foundations.
Virtual Reality (VR) applications are increasingly used in domains such as surgical training and industrial marketing, yet their longterm adoption remains limited by the lack of effective, systematic, and reproducible testing approaches tailored to VR. To address this gap, we present UltraInstinctVR, a novel tool that automates the generation and execution of concrete system-level tests for VR applications, helping developers efficiently validate interactions and behaviors. We evaluated UltraInstinctVR against state-of-the-art automated VR testing tools across 10 open-source VR applications, where it outperformed existing approaches in detecting unique failures and uncovering real-world bugs. In this paper, we further extended this evaluation through an empirical user study with ten VR developers. Results highlight strong dependability and efficiency while identifying opportunities for improvement in other user experience dimensions. Overall, these findings position UltraInstinctVR as a promising step toward more practical, effective, and scalable VR testing.
Addresses critical efficiency and scalability bottlenecks in modern computing systems, providing concrete empirical evidence and reproducible designs.
To address this gap, we present UltraInstinctVR, a novel tool that automates the generation and execution of concrete system-level tests for VR applications, helping developers efficiently validate interactions and behaviors.
To address this gap, we present UltraInstinctVR, a novel tool that automates the generation and execution of concrete system-level tests for VR applications, helping developers efficiently validate interactions and behaviors.
We evaluated UltraInstinctVR against state-of-the-art automated VR testing tools across 10 open-source VR applications, where it outperformed existing approaches in detecting unique failures and uncovering real-world bugs.
Trade-offs between peak accuracy and memory overhead remain under investigation across diverse hardware environments.
With the increasing tendency of incorporating technology into environments and activities in everyday life, new methods are being proposed to better integrate devices and mankind.Networks designed for monitoring areas through video and image systems are being implemented in several applications.Recent studies have shown interest from both the academy and industry to integrate wireless surveillance networks with lowcost and long range transmission technologies, such as LoRa (Long Range).With the intention to explore this topic, this work presents the development of a system prototype for intelligent monitoring as a basis to future implementation of low-cost LoRabased Wireless Image Sensor Networks (WISN).The developed system consists of a processing unit, a data routing interface and multiple sensor nodes.Its operation is autonomous and initiated through the detection of human presence, by the sensor node, capturing an image of the individuals present at each detection.The data is transmitted through LoRa devices to the central hub, where python-based methods of facial detection and recognition are employed.Several tests were performed to both adjust the system and to verify its efficiency.The results achieved indicate the viability of the proposed low-cost LoRa-based WISN.
Aligning agentic AI with user intent is critical for delegating complex, socially embedded tasks, yet user preferences are often implicit, evolving, and difficult to specify upfront. We present DoubleAgents, a system for human–agent alignment in coordination tasks, grounded in distributed cognition. DoubleAgents integrates three components: (1) a coordination agent that maintains state and proposes plans and actions, (2) a dashboard visualization that makes the agent’s reasoning legible for user evaluation, and (3) a policy module that transforms user edits into reusable alignment artifacts, including coordination policies, email templates, and stop hooks, which improve system behavior over time. We evaluate DoubleAgents through a two-day in-lab interactive simulation study (n=10), three real-world deployments, and a technical evaluation. Participants’ comfort in offloading tasks and reliance on DoubleAgents both increased over time, correlating with the three distributed cognition components. Participants still required control at points of uncertainty — edge-case flagging and context-dependent actions. We contribute a distributed cognition approach to human-agent alignment in socially embedded tasks. We further introduce interactive simulation as a methodological testbed for rapid iteration and alignment testing of agentic systems.
