Visual computation of surface lightness: Local contrast vs. frames of reference

Seeing black, white and gray surfaces, called lightness perception, might seem simple because white surfaces reflect 90% of the light they receive while black surfaces reflect only 3%, and the human retina is composed of light sensitive cells. The problem is that, because illumination varies from time to time and from place to place, any amount of light can be reflected from any shade of gray. Thus the amount of light reflected by an object, called luminance, says nothing about its lightness. Experts agree that the lightness of a surface can be computed only by using the surrounding context, but they disagree about how the context is used. We have tested an image in which two major classes of theory, contrast theories and frame-of-reference theories, make very different predictions regarding what gray shades will be seen by human observers. We show that when frame-of-reference is varied while contrast is held constant, lightness varies strongly. But when contrast is varied but frame-of-reference is held constant, little or no variation is seen. These results suggest that efforts to discover the exact algorithm by which the human visual system segments the image received by the retina into frames of reference should be given high priority

The interaction of artificial intelligence and design thinking In the development of HR and decision-making trends

This paper is a qualitative exploratory study, and a suggestive theory that aims to explore contemporary trends in HR policies in relation to technology. More precisely, the paper is a content-analysis research, aimed to explore the relationship between decision-making and data-driven business environment, and the extent to which AI and DT augment decision-making, if at all. Artificial intelligence is a physical concept which is used to describe and examine the impact that technology has on HRM practices. Design thinking is an abstract concept used to describe and examine the evolution of best leadership practices in terms of HRM processes. Before I started conducting this research, my focus was on AI, as a concept bound to change the face of traditional decision-making. Copious amount of data that is produced, extracted and stored daily, requires respective analysis. As such, I approached my respondents with the knowledge I gathered through personal research and during the creation of theoretical framework. As the research were advancing, I began to realise the extent to which these concepts provide insights into the relationship between the culture of design (thinking) and notion of artificial (intelligence) in decision-making. These two concepts were used to test the extent to which decision-making can be augmented with their use, and how they influence organisational hierarchy. From the side of the technology, AI is looking into the nature of Big Data and how it is used to exploit information for competitive HRM. DT is used to exploit the extent to which Big Data is used to broaden decision-making solutions. Together, this paper is examining the potential of these relationships, and if it in fact renders greater decision-making advantage, by accelerating the process with AI and disrupting traditional decision-making with DT. The paper used the Big Data Maturity Model (BDMM) to filter the findings and study this relationship accordingly. The model is comprised of five interconnected stages which test big data maturity of companies, as well as of their employees. Stages were divided according to goals of the paper and the two concepts. Moreover, the codes that were used to filter the findings served as additional differentiating points in the stages. The research provides insights into the synthesis of AI and DT and how they are perceived by decision-makers. The conclusions give an overview of advantages and challenges faced by HR managers when implementing AI and DT in decision-making and the subsequent room for research of this relationship

Reference Model and Architecture for the Post-Platform Economy

The primary goal of this thesis is to propose a reference model and an accompanying software system architecture, which together can serve as a guiding framework for the analysis, design, and implementation of distributed market spaces. The benefit of such a framework is considered two-fold: On the one hand, it provides insights essential for understanding various aspects and elements of self-organized and strictly decentralized online structures to facilitate the emergence of the post-platform economy. On the other hand, it serves as a blueprint for designing and implementing a distributed marketplace instance for a specific application context. It thus allows consumers and providers to set up and expand market spaces themselves, in which they can engage directly and reliably with complex product scenarios

Fracture Conductivity Modelling in Experimental Shale Rock Interactions with Aqueous CO\u3csub\u3e2\u3c/sub\u3e

In large scale subsurface injection of carbondioxide (CO ) as obtainable in carbon sequestration programs and in environmentally friendly hydraulic fracturing processes (using supercritical CO ), long term rock-fluid interaction can affect reservoir and seal rocks properties which are essential in monitoring the progress of these operations. The mineralogical components of sedimentary rocks are geochemically active particularly under enormous earth stresses, which generate high pressure and temperature conditions in the subsurface. While geomechanical properties such as rock stiffness, Poisson\u27s ratio and fracture geometry largely govern fluid flow characteristics in deep micro-fractured formations. Simulation results suggested that influx-induced mineral dissolution/precipitation reactions within clay-based sedimentary rocks can continuously close micro-fracture networks, though injection pressure and effective-stress transformation first rapidly expand the fractures. This experimental modelling research investigated the impact of in-situ geochemical precipitation on conductivity of fractures under geomechanical stress conditions. Bulk rock geomechanical hardness was determined using Vickers\u27 micro-indentation. Differential pressure drop data across fractured composite core were also measured with respect to time over a five day period. This was used in estimating the conductivity of the artificially fractured cores with 25 μm-bore microtubings embedded. Three experimental runs per sample types were carried out in order to check the repeatability of observed changes. The results showed that most significant diagenetic changes in shale rocks after flooding with CO -brine, reflect in the effluent fluid with predominantly calcium based minerals dissolving and precipitating under experimental conditions. Micro-indentation results suggested slight reduction in the hardness of the shale rocks and this reduction appears dependent on diagenetic quartz content. Estimated fracture conductivity indicated that reactive transport of dissolved minerals can occlude micro-fracture flow paths, thereby improving caprock seal integrity with respect to leakage risk under CO sequestration conditions. 2 2 2