Ontology embeddings map classes, relations, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic $\mathcal{EL}^{++}$, several embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations; they do not distinguish between statements that are unprovable and provably false, and therefore they may use entailed statements as negatives. Furthermore, they do not utilize the deductive closure of an ontology to identify statements that are inferred but not asserted. We evaluated a set of embedding methods for $\mathcal{EL}^{++}$ ontologies, incorporating several modifications that aim to make use of the ontology deductive closure. In particular, we designed novel negative losses that account both for the deductive closure and different types of negatives and formulated evaluation methods for knowledge base completion. We demonstrate that our embedding methods improve over the baseline ontology embedding in the task of knowledge base or ontology completion.

Data science has been described as the fourth paradigm for scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges - our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI have sparked debates about the socio-technical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for "data science for social good" (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of socio-technical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the special issue. We hope that this article and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are garnering proportionately less attention with each passing day.

International Journal of Educational Technology in Higher Education is calling for submissions to our Collection on New advances in artificial intelligence applications in higher education. There has been growing interest in the educational potential of Artificial Intelligence (AI) applications within the field of educational technology for the past decade. Despite the recent peak of excitement towards advanced features and techniques of AI-driven language models and OpenAI's ChatGPT, their actual impact on higher education (HE) institutions and participants have been largely unknown. Thus, the discussions in the field have continuously remained, mainly consisting of overstated hype and untested hypotheses, either optimistic or pessimistic, about the impact of AI applications. About three years ago, the editors of the ETHE Special Issue "Can artificial intelligence transform higher education?" However, a lot has happened since then.

March 9, 2023 (Source) – Visionstate Corp. (TSXV:VIS) ("Visionstate" or the "Company") a leading provider of smart facility-management technology, today announced that it has signed a Memorandum of Understanding (MOU) with Fluido.ai, a prominent provider of artificial-intelligence solutions. The MOU outlines the intention of both companies to explore mutually-beneficial business opportunities related to artificial intelligence (AI). This may include developing new AI-powered features such as machine learning for Visionstate's flagship WANDA platform, which leverages Internet of Things (IoT) technology to monitor and analyze restroom-usage data in real time. "We are thrilled to be partnering with Fluido.ai to delve into the exciting possibilities of AI technology," said Shannon Moore, President of Visionstate IoT Inc. "As we continue to innovate and expand our smart facility-management solutions, we believe that AI has the potential to greatly enhance our proficiencies and provide even more value to our customers." "We are excited to be working with Visionstate to explore the ways in which AI can help to improve efficiencies and reduce costs," said Wessam Gad El-Rab, CEO of Fluido.ai.

This Special Issue focuses on recent developments in the use of artificial intelligence (AI) for stroke imaging in acute and chronic phases. The use of AI has attracted widespread attention as it relates to the detection of steno-occlusive lesions in the cerebral circulation, tissue level markers of injury in ischemia and hemorrhage and perfusion imaging techniques. Manuscripts should be submitted online at www.mdpi.com Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline.

If human societies are so complex, then how can we hope to understand them? Artificial Life gives us one answer. The field of Artificial Life comprises a diverse set of introspective studies that largely ask the same questions, albeit from many different perspectives: Why are we here? Who are we? Why do we behave as we do? Starting with the origins of life provides us with fascinating answers to some of these questions. However, some researchers choose to bring their studies closer to the present day. We are after all, human. It has been a few billion years since our ancestors were self-replicating molecules. Thus, more direct studies of ourselves and our human societies can reveal truths that may lead to practical knowledge. The papers in this special issue bring together scientists who choose to perform this kind of research.

We invite submissions to a special issue on "Machine Learning and Big Data Applications in Applied Economics", in the journal Applied Economic Perspectives and Policy (AEPP). With this special issue, we aim to extend the evidence based on big data and machine learning (ML) methods across a wide range of academic disciplines and industry sectors, including the agricultural sector, food value chains, and nutrition applications. The editors encourage the use of a diverse range of big data and ML methods for addressing issues like product pricing, trade, food security, forecasting approaches, crop production, and environmental and resource evaluations. We will also consider theoretical studies that provide empirically testable and/or policy-relevant insights. Studies using data from various sources, including household surveys, simulation models, and systematic reviews are welcome.

For many years, photogrammetry has been the leading methodology to derive 3D metric and accurate information from imagery, at different scales (from satellite to aerial, terrestrial and under water) and from different sensors (linear, frame, panoramic). The inclusion of computer vision and robotics solutions has increased the level of automation in image processing and 3D data generation, leading to mainstream automatic solutions and massive 3D digitization processes. The recent advent of artificial intelligence methods based on machine and deep learning approaches is again changing the photogrammetric processes leading to unexpected automated solutions that can truly revolutionize the mapping and 3D documentation sector. This Special Issue wants to focus on this recent change for 3D geometric tasks, and is seeking high-quality papers that explore all the potentialities offered by AI in photogrammetric problems. Papers should report progresses in supporting, integrating and boosting key areas of photogrammetry with AI-based methods.

Artificial intelligence (AI) is having a major impact on healthcare. While advances in the sharing and analysis of medical data result in better and earlier diagnoses and more patient-tailored treatments, data management is also affected by trends such as increased patient-centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The way in which health services are delivered is being revolutionized through the sharing and integration of health data across organizational boundaries. Via AI, researchers can provide new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at an individual and population level. This Special Issue focuses on how AI is used in healthcare, and on related topics such as data management, data integration, data sharing, patient privacy and bioethical issues.

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