Semantic rule checking on RDFS/OWL data has been widely used in the construction industry. At present, semantic rule checking is mainly performed on static models. There are still challenges in integrating temporal models and semantic models for combined rule checking. In this paper, Semantic Petri-Net (SPN) is proposed as a novel temporal modeling and validating method, which implements the states and transitions of the Colored Petri-Net directly based on RDFS and SPARQL, and realizes two-way sharing of knowledge between domain semantic webs and temporal models in the runtime. Several cases are provided to demonstrate the possible applications in digital twins with concurrent state changes and dependencies.

We are in an era of large catalogs and, thus, statistical analysis tools for large data sets, such as machine learning, play a fundamental role. One example of such a survey is the Sloan Moving Object Catalog (MOC), which lists the astrometric and photometric information of all moving objects captured by the Sloan field of view. One great advantage of this telescope is represented by its set of five filters, allowing for taxonomic analysis of asteroids by studying their colors. However, until now, the color variation produced by the change of phase angle of the object has not been taken into account. In this paper, we address this issue by using absolute magnitudes for classification. We aim to produce a new taxonomic classification of asteroids based on their magnitudes that is unaffected by variations caused by the change in phase angle. We selected 9481 asteroids with absolute magnitudes of Hg, Hi and Hz, computed from the Sloan Moving Objects Catalog using the HG12 system. We calculated the absolute colors with them. To perform the taxonomic classification, we applied a unsupervised machine learning algorithm known as fuzzy C-means. This is a useful soft clustering tool for working with {data sets where the different groups are not completely separated and there are regions of overlap between them. We have chosen to work with the four main taxonomic complexes, C, S, X, and V, as they comprise most of the known spectral characteristics. We classified a total of 6329 asteroids with more than 60% probability of belonging to the assigned taxonomic class, with 162 of these objects having been characterized by an ambiguous classification in the past. By analyzing the sample obtained in the plane Semimajor axis versus inclination, we identified 15 new V-type asteroid candidates outside the Vesta family region.

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers about a hierarchical semantic compositional model (HSCM) which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects including semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning.

Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.

In her 2018 book "Double Negative: The Black Image and Popular Culture," Racquel Gates explores the disruptive potential of stereotypical or so-called negative images of Black people onscreen: Flavor Flav on VH1's "Flavor of Love," for example, and the stars of "ratchet" reality shows such as "Basketball Wives." These images, Gates argues, intervene against narratives of racial uplift that are overly tethered to white and middle-class definitions of respectability. In her acknowledgments section, Gates, a professor of film and media studies at Columbia, invokes a scene from "Love & Hip Hop," in which an aspiring singer tells an entertainment manager, "I want to be on your roster." Gates writes, "While I was tempted to quote this bit of dialogue to my editor, Ken Wissoker, during our first meeting, I erred on the side of caution." Wissoker, who has been an editor at Duke University Press since 1991, has a formidable roster, and one could easily imagine a reality show about junior scholars fighting for a chance to work with him.

Research in multi-agent teaming has increased substantially over recent years, with knowledge-based systems to support teaming processes typically focused on delivering functional (communicative) solutions for a team to act meaningfully in response to direction. Enabling humans to effectively interact and team with a swarm of autonomous cognitive agents is an open research challenge in Human-Swarm Teaming research, partially due to the focus on developing the enabling architectures to support these systems. Typically, bi-directional transparency and shared semantic understanding between agents has not prioritised a designed mechanism in Human-Swarm Teaming, potentially limiting how a human and a swarm team can share understanding and information\textemdash data through concepts and contexts\textemdash to achieve a goal. To address this, we provide a formal knowledge representation design that enables the swarm Artificial Intelligence to reason about its environment and system, ultimately achieving a shared goal. We propose the Ontology for Generalised Multi-Agent Teaming, Onto4MAT, to enable more effective teaming between humans and teams through the biologically-inspired approach of shepherding.

Given a deep learning model trained on data from a source site, how to deploy the model to a target hospital automatically? How to accommodate heterogeneous medical coding systems across different hospitals? Standard approaches rely on existing medical code mapping tools, which have significant practical limitations. To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework. We evaluate AutoMap using several deep learning models with two real-world EHR datasets: eICU and MIMIC-III. Results show that AutoMap achieves relative improvements up to 3.9% (AUC-ROC) and 8.7% (AUC-PR) for mortality prediction, and up to 4.7% (AUC-ROC) and 3.7% (F1) for length-of-stay estimation. Further, we show that AutoMap can provide accurate mapping across coding systems. Lastly, we demonstrate that AutoMap can adapt to the two challenging scenarios: (1) mapping between completely different coding systems and (2) between completely different hospitals.

This course brings the software architecture skills required by an enterprise architect. In the lectures, we go through the engineering requirements and how to deal with gained information. Although the course is not about to show you how to build a web/desktop/mobile app with programming, but you have a great tool to create blueprint of your system. You will learn modern way to create your own design pattern or use common and useful architecture patterns. As it is said in the videos, by creating a blueprint of you system before starting to build it, you can then easily edit/modify/update/upgrade the system even after lot of years.

Federated Analytics is the latest feature added to Raven Distribution Framework that allows for the safe dynamic aggregation of statistics such as mean, variance, and standard deviation across data that is privately held on several clients. RDF's Ravop library now supports the creation of federated operations which developers can leverage to conduct analysis without directly observing a client's private data. Federated analytics is a new approach to data analysis in which key statistics like mean, variance, and standard deviation can be calculated across various private datasets without compromising privacy. It operates similarly to federated learning in that it runs local calculations over each client device's data and only makes the aggregated findings -- never any data from a specific device -- available to developers. Sensitive data like medical records, financial transactions, employee data, and others can be analyzed without leaving the premise.

The current release of Raven Distribution Framework (RDF v0.3)provides an easy to use library that allows developers to build mathematical algorithms or models and computes these operations by distributing them across multiple clients. This provides an increase in speed and efficiency when dealing with a large number of mathematical operations. Distributed Computing is the linking of various computing resources like PCs and smartphones to share and coordinate their processing power for a common computational requirement, such as the training of a large Machine Learning model. These resources or nodes communicate with a central server and in some cases with each other, such that each node receives some data and completes a subset of a task. These nodes can coordinate their computations to complete a large and complex computational requirement in a fast and efficient manner.