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The increasing deployment of robots alongside humans necessitates sophisticated communication and motion planning to ensure safety and task achievability in social navigation scenarios. Existing methods often rely heavily on historical data and extensive expert hand-coding, which limits their scalability and generalizability. This paper introduces a novel framework that models social navigation as a Markov Decision Process (MDP), utilizing Conditional Abstraction Trees (CATs) to learn dynamic abstract world representations and policies that focus on critical aspects of interaction. In the offline phase, the framework operates within a simulator, while in the online phase, it deploys the learned representations and policies in real-world scenarios for ongoing refinement and adaptation. Integral to our approach is a Dynamic Bayesian Network (DBN) based human sensor and belief model that accounts for humans’ imperfect perception to enhance the prediction of human motion. We evaluated our method through extensive simulations and user studies involving physical experiments, demonstrating its effectiveness in managing critical interactions and ensuring safety and task completion across various scenarios. 

Heterogeneous distributed systems, including the Internet of Things (IoT) or distributed cyber-physical systems (CPS), often su↵er a lack of interoperability and security, which hinders the wider deployment of such systems. Specifically, the di↵erent levels of security requirements and the heterogeneity in terms of communication models, for instance, point-to-point vs. publish-subscribe, are the example challenges of IoT and distributed CPS consisting of heterogeneous devices and applications. In this paper, we propose a working application programming interface (API) and runtime to enhance interoperability and security while addressing the challenges that stem from the heterogeneity in the IoT and distributed CPS. In our case study, we design and implement our application programming interface (API) design approach using opensource software, and with our working implementation, we evaluate the e↵ectiveness of our proposed approach. Our experimental results suggest that our approach can achieve both interoperability and security in the IoT and distributed CPS with a reasonably small overhead and better-managed software. 

In precision agriculture, integrating advanced technologies is crucial for optimizing plant growth and health monitoring. Cyber-physical system (CPS) platforms tailored to specific agricultural environments have emerged, but the diversity of these environments poses challenges in developing adaptive CPS platforms. This paper explores rapid prototyping methods to address these challenges, focusing on non-destructive techniques for estimating plant growth. We present a CPS prototype that combines sensors, microcontrollers, digital image processing, and predictive modeling to measure leaf area and biomass accumulation in hydroponic environments. Our results show that the prototype effectively monitors and predicts plant growth, highlighting the potential of rapid CPS prototyping in promoting sustainability and improving crop yields at a moderate cost of hardware. 

Transformers-based language models have achieved remarkable accuracy in various NLP tasks, employing self-attention mecha- nisms primarily based on matrix multiplication. However, their significant size leads to data movement issues, causing latency and energy efficiency challenges in conventional Von-Neumann systems. To mitigate these issues, several in-memory and near- memory architectures have been proposed. This paper introduces PACT-3D, a near-memory architecture featuring novel computing units integrated with DRAM banks. PACT-3D significantly reduces latency by 1.7× and improves energy efficiency by 18.7× compared to state-of-the-art near-memory architectures.