Rohit K. Dubey

The cities of tomorrow intertwine physical and virtual realms as irrevocably interconnected dimensions of urban life. Cities are complex systems and their representations result from individual experiences and cultural narratives. This is also the case when we look at digital representations enabled by data-driven smart city initiatives to produce replicas leveraging big data, visualization tools and artificial intelligence. Urban Digital Twins are a manifestation of such a holistic and truthful view of representation, envisioning synchronization and symbiotic relationships blurring the line between virtual and tangible entities. The potentials of automation and the convergence between physical and cyberspace are unlimited to the transformation of urban environments and human experiences within them. However, as cities evolve as products of both physical and virtual realities, the ethical implications for individuals and communities are increasingly emphasizing how technology is not per se the solution for wicked problems and social divides. This chapter explores future cities as a convergence of physical and virtual worlds, highlighting the potentials and complexities for future societies.

Human path-planning operates differently from deterministic AI-based path-planning algorithms due to the decay and distortion in a human's spatial memory and the lack of complete scene knowledge. Here, we present a cognitive model of path-planning that simulates human-like learning of unfamiliar environments, supports systematic degradation in spatial memory, and distorts spatial recall during path-planning. We propose a Dynamic Hierarchical Cognitive Graph (DHCG) representation to encode the environment structure by incorporating two critical spatial memory biases during exploration: categorical adjustment and sequence order effect . We then extend the “Fine-To-Coarse” (FTC), the most prevalent path-planning heuristic, to incorporate spatial uncertainty during recall through the DHCG. We conducted a lab-based Virtual Reality (VR) experiment to validate the proposed cognitive path-planning model and made three observations: (1) a statistically significant impact of sequence order effect on participants’ route-choices, (2) approximately three hierarchical levels in the DHCG according to participants’ recall data, and (3) similar trajectories and significantly similar wayfinding performances between participants and simulated cognitive agents on identical path-planning tasks. Furthermore, we performed two detailed simulation experiments with different FTC variants on a Manhattan-style grid. Experimental results demonstrate that the proposed cognitive path-planning model successfully produces human-like paths and can capture human wayfinding's complex and dynamic nature, which traditional AI-based path-planning algorithms cannot capture.

In a continuous opportunity, Blockchain technology (BCT) was able to receive a great consideration outside the financial area. The chain concepts of blocks are more passed than the furniture and the rhythm of encryption. Block the universe chain Research of internal probabilities of assets several applications in BCT is frequently achieved. In this document, we participate in the two BCT prospects to add the future scope of these two points of view and future transactions in the tokenization of assets. This document is intended to investigate a part of the chain of blocks in the maintainable transaction with real money and dispersed through the network of block chains.

Signage systems are critical for communicating spatial information during wayfinding among a plethora of noise in the environment. A proper signage system can improve wayfinding performance and user experience by reducing the perceived complexity of the environment. However, previous models of sign-based wayfinding do not incorporate realistic noise or quantify the reduction in perceived complexity from the use of signage. Drawing upon concepts from information theory, we propose and validate a new agent-signage interaction model that quantifies available wayfinding information from signs for wayfinding. We conducted two online crowd-sourcing experiments to compute the distribution of a sign’s visibility and an agent’s decision-making confidence as a function of observation angle and viewing distance. We then validated this model using a virtual reality (VR) experiment with trajectories from human participants. The crowd-sourcing experiments provided a distribution of decision-making entropy (conditioned on visibility) that can be applied to any sign/environment. From the VR experiment, a training dataset of 30 trajectories was used to refine our model, and the remaining test dataset of 10 trajectories was compared with agent behavior using dynamic time warping (DTW) distance. The results revealed a reduction of 38.76% in DTW distance between the average trajectories before and after refinement. Our refined agent-signage interaction model provides realistic predictions of human wayfinding behavior using signs. These findings represent a first step towards modeling human wayfinding behavior in complex real environments in a manner that can incorporate several additional random variables (e.g., environment layout).