Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing methods concentrate on learning latent representation via reconstruction tasks, but cannot directly optimize representation and are prone to oversmoothing, thus limiting the applications on downstream tasks. To alleviate these issues, we propose a novel graph embedding framework named Deep Manifold Attributed Graph Embedding (DMAGE). A node-to-node geodesic similarity is proposed to compute the inter-node similarity between the data space and the latent space and then use Bergman divergence as loss function to minimize the difference between them. We then design a new network structure with fewer aggregation to alleviate the oversmoothing problem and incorporate graph structure augmentation to improve the representation's stability. Our proposed DMAGE surpasses state-of-the-art methods by a significant margin on three downstream tasks: unsupervised visualization, node clustering, and link prediction across four popular datasets.

Due to the fact that basic uncertain information provides a simple form for decision information with certainty degree, it has been developed to reflect the quality of observed or subjective assessments. In order to study the algebra structure and preference relation of basic uncertain information, we develop some algebra operations for basic uncertain information. The order relation of such type of information has also been considered. Finally, to apply the developed algebra operations and order relations, a generalized TODIM method for multi-attribute decision making with basic uncertain information is given. The numerical example shows that the developed decision procedure is valid.

The emphasis and development of artificial intelligence (AI) is swiftly growing, with innovators across the globe trying to create more viable use-cases for this groundbreaking technology. AI's market reach has penetrated nearly every large industry, including manufacturing, retail, infrastructure, financial services, defense, and healthcare, among countless other sectors. Healthcare especially has experienced an incredible amount of attention in the AI space. The value proposition of AI in healthcare is undoubtedly extensive, especially as the industry is poised to surpass over $11 trillion in market valuation, and given that healthcare is such an inherently data-rich, innovation heavy, and operationally nuanced field. Last week, the European Union (EU) put forth its "Proposal for a Regulation on a European approach for Artificial Intelligence," intending to create "the first ever legal framework on AI, which addresses the risks of AI and positions Europe to play a leading role globally."

Big Market Research has recently added a new report to its vast depository titled Global Commerce Artificial Intelligence Market. The report studies vital factors about the Commerce Artificial Intelligence Market that are essential to be understood by existing as well as new market players. The report highlights the essential elements such as market share, profitability, production, sales, manufacturing, advertising, advancements, key market players, regional segmentation, and many more crucial aspects related to the Commerce Artificial Intelligence Market. It shows the consistent development in Commerce Artificial Intelligence Market regardless of the variances and changing business sector trends. The Commerce Artificial Intelligence Market report depends on certain significant boundaries.

Likewise, this analysis offers broad insights into technological spending across the forecast period, providing a unique viewpoint on the global Artificial Intelligence Plus Internet of Things (AIOT) market across each of the categories included in the survey. The global review of the'keyword' industry assists clients in assessing business challenges and prospects. The research includes the most recent keyword business forecast analysis for the time period in question. Furthermore, the annual industry study narrowly introduces the latest insights on technical developments and market development opportunities based on the geographic climate. The Global Artificial Intelligence Plus Internet of Things (AIOT) market also includes technology/innovation, comprehensive perspectives on future developments, research and development operations, and new products.

This has brought along several changes in This report also covers the impact of COVID-19 on the global market. The Deep Learning Market analysis summary by Reports Insights is a thorough study of the current trends leading to this vertical trend in various regions. In addition, this study emphasizes thorough competition analysis on market prospects, especially growth strategies that market experts claim. Deep Learning Market competition by top manufacturers as follow: Amazon Web Services (AWS), Google, IBM, Intel, Micron Technology, Microsoft, Nvidia, Qualcomm, Samsung Electronics, Sensory Inc., Skymind, Xilinx, AMD, General Vision, Graphcore, Mellanox Technologies, Huawei Technologies, Fujitsu, Baidu, Mythic, Adapteva, Inc., Koniku The global Deep Learning market has been segmented on the basis of technology, product type, application, distribution channel, end-user, and industry vertical, along with the geography, delivering valuable insights. To get this report at a profitable rate.: https://www.reportsinsights.com/discount/356220

Reliable and frequent population estimation is key for making policies around vaccination and planning infrastructure delivery. Since censuses lack the spatio-temporal resolution required for these tasks, census-independent approaches, using remote sensing and microcensus data, have become popular. We estimate intercensal population count in two pilot districts in Mozambique. To encourage sustainability, we assess the feasibility of using publicly available datasets to estimate population. We also explore transfer learning with existing annotated datasets for predicting building footprints, and training with additional `dot' annotations from regions of interest to enhance these estimations. We observe that population predictions improve when using footprint area estimated with this approach versus only publicly available features.

INTRODUCTION With increasing awareness of the societal risks of algorithmic bias and encroaching automation, issues of fairness, accountability, and transparency in data-driven AI systems have received growing academic attention in multiple high-stakes contexts, including healthcare, loan-granting, and hiring (e.g., Barocas & Selbst, 2016; Holstein, Wortman Vaughan, Daumé III, Dudik, & Wallach, 2019; Veale, Van Kleek, & Binns, 2018). Given these noble intentions, why might AIEd systems have inequitable impacts? In this chapter, we ask whether AIEd systems will ultimately serve to A mplify I nequities in Ed ucation, or alternatively, whether they will help to A lleviate existing inequities. We discuss four lenses that can be used to examine how and why AIEd systems risk amplifying existing inequities: (1) factors inherent to the overall socio-technical system design; (2) the use of datasets that reflect historical inequities; (3) factors inherent to the underlying algorithms used to drive machine learning and automated decision-making, and (4) factors that emerge through a complex interplay between automated and human decision-making. Building from these lenses, we then outline possible paths towards more equitable futures for AIEd, while highlighting debates surrounding each proposal. In doing so, we hope to provoke new conversations around the design of equitable AIEd, and to push ongoing conversations in the field forward. PATHWAYS TOWARD INEQUITY IN AIED We begin by presenting four lenses to understand how AIEd systems might amplify existing inequities or even create new ones (cf. While each lens provides a different way of examining pathways towards inequity in AIEd, all are pointed at the same underlying socio-technical system. Figure 1 provides a coarse-grained overview of the broader social-technical systems in which AIEd systems are embedded, and some of the components we will refer to in the four lenses. The accumulated, collective decisions of designers, researchers, policy-makers, and other stakeholders shape these systems' designs. In addition to using or being affected by AIEd systems, on-the-ground stakeholders such as students, teachers, or school administrators may also play a role in shaping their designs; whether directly, through participatory design processes, or indirectly through the passive generation of training data while interacting with an AIEd interface. In turn, decisions regarding what data is used to shape an AIEd system's design (e.g., when used as training data for use with machine learning methods) can shape an AIEd system's algorithmic behavior (e.g., instructional policies learned from data).

To understand these manipulations of the quantum world, we need to understand the... Harvey pulled Einstein's brain out of the skull. Then put it in a formalin jar.... To understand these manipulations of the quantum world, we need to understand the... What is the most coveted and prestigious award in the world? Scientists recently developed this new process in the lab. Let's take a look at what Bill Gates was saying about the epidemic that we have... The whole world is now pointing to the Chinese wildlife market as a possible source... Today we are going to discuss Marie Tharp and Marie Tharp's historical map.

If there's one thing the United States military gets right, it's lethality. Yet even once the US military has you in its sights, it may not know who you actually are -- such are, these so-called "signature strikes" -- even as that wrathful finger of God is called down from upon on high. As Kate Crawford, Microsoft Research principal and co-founder of the AI Now Institute at NYU, lays out in this fascinating excerpt from her new book, Atlas of AI, the military-industrial complex is alive and well and now leveraging metadata surveillance scores derived by IBM to decide which home/commute/gender reveal party to drone strike next. And if you think that same insidious technology isn't already trickling down to infest the domestic economy, I have a credit score to sell you. Excerpted from Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford, published by Yale University Press.