Knowledge graphs (KGs) are machine-readable representations of cyber-physical systems (CPS) which can be incomplete due to the size and complexity of CPS. Knowledge graph completion (KGC) models can predict missing edges, but perform poorly and fail to generalize on CPS KGs due to the high heterogeneity and small size of those graphs. In this work, we propose an ontology-informed heterogeneous Graph Neural Network (GNN) architecture that integrates hierarchical parent-class layers to enhance generalization in CPS KGs. Our approach outperforms traditional heterogeneous GNNs and Node2Vec-based methods in edge prediction tasks, offering a promising solution for CPS KGs.

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and evolutions of GenAI, we capture a snapshot of student attitudes towards and uses of yet emerging GenAI, in a period of time before university policies had reacted to these technologies. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists; (2) describe computing students' GenAI-related needs and concerns for their education and careers; and (3) discuss GenAI influences on CS pedagogy, curriculum, culture, and policy. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education.

Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open datasets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats. To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.

Operating systems and applications in the built environment have relied upon central authorization and management mechanisms that restrict their scalability, especially with respect to administrative overhead. We propose a new set of primitives encompassing syndication, security, and service execution that unifies the management of applications and services across the built environment, while enabling participants to individually delegate privilege across multiple administrative domains with no loss of security or manageability. We show how to leverage a decentralized authorization syndication platform to extend the design of building operating systems beyond the single administrative domain of a building. The authorization system leveraged is based on blockchain smart contracts to permit decentralized and democratized delegation of authorization without central trust. Upon this, a publish/subscribe syndication tier and a containerized service execution environment are constructed. Combined, these mechanisms solve problems of delegation, federation, device protection and service execution that arise throughout the built environment. We leverage a high-fidelity city-scale emulation to verify the scalability of the authorization tier, and briefly describe a prototypical democratized operating system for the built environment using this foundation. This is an extension of work presented in Ref. [3].

Brick is a recently proposed metadata schema and ontology for describing building components and the relationships between them. It represents buildings as directed labeled graphs using the RDF data model. Using the SPARQL query language, building-agnostic applications query a Brick graph to discover the set of resources and relationships they require to operate. Latency-sensitive applications, such as user interfaces, demand response, and model-predictive control, require fast queries—conventionally less than 100ms. We benchmark a set of popular open source and commercial SPARQL databases against three real Brick models using seven application queries and find that none of them meet this performance target. This lack of performance can be attributed to design decisions that optimize for queries over large graphs consisting of billions of triples but give poor spatial locality and join performance on the small dense graphs typical of Brick. We present the design and evaluation of HodDB, a RDF/SPARQL database for Brick built over a node-based index structure. HodDB performs Brick queries 3--700× faster than leading SPARQL databases and consistently meets the 100ms threshold, enabling the portability of important latency-sensitive building applications. This article is an extension of a previously published work [16].

Modern IoT prototyping platforms fall short in terms of energy efficiency, connectivity and software programming practices. We present the design of a new hardware and software platform that addresses these shortcomings by bringing together Mobile, Wearable, Maker and Wireless Sensor Network technologies to enable rapid prototyping with a high degree of synergy and energy efficiency. This is achieved in part by leveraging the Memory Protection Unit on modern microcontrollers along with a novel syscall interface to provide kernel / user isolation and a clean concurrency model. Such a design allows a wide range of languages to be used for application development without significant adaptation. We demonstrate how careful choice of application language allows the naturally asynchronous nature of embedded programming to be expressed cleanly and powerfully. Finally we evaluate the platform in several integrated use cases, providing examples of the capabilities introduced by Synergy.

In this demonstration, we present a novel system of building control and simulation focused on the integration of the physical and virtual worlds. Actuations and schedules can be manifested either in a physical space or in a virtualization of that space, allowing for more natural interactions with simulations and easier transferring of schedules and configurations from the simulated virtual environment to a real-world deployment. We provide an implementation using a widely used game engine (Unity 3D) and sMAP (Simple Measurement and Actuation Profile), a developed time series database and metadata store.