1,720,962 research outputs found
Secure Multi-Tenant Architectures in Microsoft Fabric: A Zero-Trust Perspective
No full textMicrosoft Fabric is a new cloud-based Analytics Service, built in Azure, that is designed to be much simpler than its predecessors. Which made use of separate services to perform Analytics tasks, like the ingestion, the preparation, the warehousing, the real-time streaming, the data science, and the business use task. All these separate services worked in the Azure platform ecosystem but used different pipelines, and required different services to interconnect, creating a complex integration system, and at times setting a bottleneck. Managing work across the previous tools in Microsoft Azure was demanding and often fragmented. Microsoft Fabric set a new vision to make things much easier, making software as a service integral solution that will reduce the burden of building, operating, and sharing advanced analytics solutions.https://rspublication.com/ijst/2025/may25/13.htm
Artificial Intelligence for Cognitive Systems: Deep Learning, Neuro- symbolic Integration, and Human-Centric Intelligence
No full textArtificial intelligence quickly changed from a theory to a practical power - it spreads through every part of modern life. As people go from specific uses to more general kinds of intelligence, they must face a main change. This change involves what machines do and how people think about intelligence. The book, Cognitive AI - From Deep Learning to Artificial General Intelligence, looks at that change. This writing serves a wide, serious group of people - it is for graduate students and researchers in artificial intelligence and cognitive science. Educators along with industry workers also read this to get a better grasp of the path from current AI systems to future cognitive architectures. We do not just list technologies. We deal with the concepts, morals, technical issues as well as societal problems that sit at the core of creating machines that think. The chapters lay out this story bit by bit; they start with basic learning systems. They move to cognitive modeling and designs. The book finishes with important questions about governance, combining fields along with how people will work in the future. Throughout the text, the reader learns about current subjects. Some of these are large language models, explaining how systems work, reasoning with symbols plus networks, the safety of general artificial intelligence, and people working with machines. I appreciate the researchers, collaborators along with students who inspired this work. The growing group of thinkers also recognizes that making intelligent systems requires scientific exactness and philosophical thought. My hope is that this book guides plus starts talks for anyone who wants AI to develop responsibly and creatively.https://www.deepscienceresearch.com/dsr/catalog/book/20
A Streaming Tensor Decomposition Analysis for Earth Science Informatics
No full textToday, many application domains from science, sports, social media, health give rise to streaming multi-way data that can be naturally represented and analyzed via tensors.
Time sequences produced from measurements at different locations, made from a variety
of platforms, over a wide range of events, lend themselves to important Tensor
decomposition (TD) applications. TD is any scheme for expressing a "data tensor" (Mway array or M-nodes) as a sequence of elementary outer product operations acting on
other, often simpler tensors. TDs have applications in data analysis, signal processing,
machine learning and data mining. We apply the Shaden Smith (SPLATT) algorithm, to
form a tensor decomposition that provides a leading coefficient for each outer product
term. The value of the leading coefficients for each outer product are analogous to the
eigenvalues in the singular value decomposition of any matrix. We computed TD for the
FROSTT streaming data test modules, streaming aerosol concentration profiling from a
network of ceilometers, weather research forecast model (WRF) output analysis and 40
years of hourly climate observation analysis. We applied TD to a data set of aerosol concentration used to predict air quality. We
implemented a multi-sensor ground-based observatory network consisting of three lidar
x
firing ceilometers distributed along a 650 km corridor along the east coast, which
provided near real-time, streaming of high-resolution aerosol concentration profiles from
the ground up to 15 km for a one-year period. Daily variations of the aerosol
concentration are used to determine the planetary boundary layer heights (PBLH). We
determined the Planetary Boundary Layer Height (PBLH) acquired from our observation
network in near real time and over 1-year. We applied TD to the WRF model simulated
outputs over the entire continental US to study the time dependence of the dominant
components of PBLH. Results obtained by TD were compared with the ceilometer
observations as an accuracy assessment of model generated PBLH. A second application
of TD was applied to ERA5, a global reanalysis of 40 years of atmospheric and model
data, that enabled the study of the dominant components of the PBLH on a planetary
scale. We further applied TD to global warming data over the entire 40 years. Finally, we
examine the power spectral distribution of the leading coefficients associated with the
maximum tensor rank of the PBLH and the surface wind speeds for any similarities to
fluid turbulence power laws
AI for Climate Change and Sustainability
No full textClimate change stands as one of the most significant existential threats of the 21st century, with far-reaching impacts on ecosystems, economies, and the well-being of humanity. Despite the existence of global awareness and policy frameworks, such as the Paris Agreement, efforts for effective mitigation and adaptation are still progressing at a slow and inadequate pace. In this landscape, Artificial Intelligence (AI) has emerged as a transformative technology that holds the potential to tackle climate-related challenges on a large scale. This paper investigates the ways in which AI can aid in climate change mitigation, adaptation, and sustainability across diverse sectors. We start by delving into the scientific foundations and socioeconomic ramifications of climate change to establish a comprehensive understanding of the crisis at hand. Following this, we examine the contributions of AI in various areas, including climate modeling, predictive analytics, early warning systems, agricultural practices, land management, and disaster response mechanisms. Case studies from the real world illustrate successful applications of AI in fields such as renewable energy management, intelligent urban infrastructure, and ecosystem monitoring. Nevertheless, we also critically assess the shortcomings of existing AI systems, with particular emphasis on challenges related to data quality, algorithmic bias, and ethical considerations in deployment. The paper advocates for the advancement of AI systems that are less biased, more inclusive, and incorporate human judgment, ensuring alignment with planetary health objectives and supporting informed decision-making in regions that are particularly vulnerable. By integrating developments in AI and climate science, this research provides a multi-faceted perspective on how intelligent systems can enhance climate resilience and sustainability initiatives. While AI alone is not a panacea, when developed with care and applied ethically, it can be instrumental in fostering a more sustainable future.https://zenodo.org/records/1571649
Optimizing Reliability and Sustainability: Smart Energy Integration in Core Systems
No full textAdding smart energy systems to foundational infrastructure seems like a key way to build a strong and long-lasting future. Through the smooth merging of smart energy technologies into important core systems, this study looks into the many ways that dependability and sustainability can be improve. So that we can meet the growing need for energy while also minimizing our effect on the environment, we need new ideas that go beyond the usual ways of thinking. Specifically, our study looks at how smart energy technologies and key systems work together and how they can improve operating dependability and sustainability by working together. To make vital infrastructure more resilient, we look into how clever energy management, grid optimization, and local energy output could change things. We suggest a system for dynamic energy adaptation that uses real-time data analytics, machine learning algorithms, and advanced devices to make sure that usefulness stays the same while also leaving the smallest possible environmental impact. Also, the study looks closely at how smart energy integration can work economically and on a large scale, figuring out how it can help towns and businesses in the long run. Adopting smart energy solutions has real benefits, such as using less energy and putting out fewer greenhouse gases. Case studies and models show these benefits. When it comes to making energy systems that are stable and last a long time, the results show how important technology, policy frameworks, and teamwork between stakeholders are.https://actaenergetica.org/index.php/journal/article/view/50
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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