The utilization of artificial intelligence in online advertising and its perceived effectiveness

This study explores the utilization of Artificial Intelligence in online advertising process and the impact of using AI each stage in that process with the overall perceived effectiveness. It also provides a better understanding of the magnitude of using AI in the four stages of advertising online: namely consumer insights, ad creation, media planning and buying, and finally ad evaluation. Process model of AI utilization in online advertising is the conceptual model of the study, which is developed from the previous literature. A triangulation methodology is implemented to enhance the credibility of the research study and leads to a more comprehensive understanding of the topic. Online survey is conducted with digital advertisers worldwide from both agency and client side. Nonrandom sampling (N=60) was implemented to test 5 constructs from the perspective of the respondents. Three in-depth interviews were also conducted before and after the online questionnaire to analyze the findings and results and demonstrate insights on the five proposed research questions. Findings of the study showed beyond doubt that AI is stepping strongly and progressively in the four stages of the data-based online advertising process. Moreover, it significantly showed that there is a relationship between AI utilization in each stage and the following one. Finally, results indicated that using AI in each advertising stage promotes the perceived effectiveness of the overall online ad process

An Innovative Structural Solution to Failed Stabilized Earth Embankment in Multilevel Interchange

This paper summarizes a case study of a failure in a mechanically stabilized earth retaining wall ramp heading to a multilevel interchange. The failure was caused by a washout cavity resulting from a long-term leakage from unknown existing wet utilities adjacent to the mechanically stabilized earth retaining wall. The interchange is located within a very congested traffic area and has limited right of way and working space. The methodology of investigation, root cause of the failure, evaluation of the existing structure and innovative solution to the problem within fast-track design and construction are studied in this paper. Due to the necessity of maintaining the existing traffic movements at the interchange which is located at one of the most important expressway routes, timely rectification methodology was conceived, designed and implemented. This paper is prepared only to evaluate the technical innovative alternatives, solutions and fast-track construction without mentioning the interchange and project area

The personal wealth interests of politicians and government intervention in the economy

We examine whether there is a correlation between personal wealth interests of politicians and their decisions to intervene in the economy. We use the setting of the government’s support of financial institutions under the 2008 Emergency Economic Stabilization Act (ESSA). We find that the personal wealth interests of politicians are positively associated with voting in favour of the EESA

Harnessing the Power of Generative Models for Mobile Continuous and Implicit Authentication

Authenticating a user's identity lies at the heart of securing any information system. A trade off exists currently between user experience and the level of security the system abides by. Using Continuous and Implicit Authentication a user's identity can be verified without any active participation, hence increasing the level of security, given the continuous verification aspect, as well as the user experience, given its implicit nature. This thesis studies using mobile devices inertial sensors data to identify unique movements and patterns that identify the owner of the device at all times. We implement, and evaluate approaches proposed in related works as well as novel approaches based on a variety of machine learning models, specifically a new kind of Auto Encoder (AE) named Variational Auto Encoder (VAE), relating to the generative models family. We evaluate numerous machine learning models for the anomaly detection or outlier detection case of spotting a malicious user, or an unauthorised entity currently using the smartphone system. We evaluate the results under conditions similar to other works as well as under conditions typically observed in real-world applications. We find that the shallow VAE is the best performer semi-supervised anomaly detector in our evaluations and hence the most suitable for the design proposed. The thesis concludes with recommendations for the enhancement of the system and the research body dedicated to the domain of Continuous and Implicit Authentication for mobile security

Dividend Payouts and Information Shocks

This paper examines changes in firms’ dividend payouts following an exogenous shock to the information environment. Traditional signaling, agency, and voluntary disclosure models predict that the more is commonly known about a firm and its competitors in the marketplace, the less private information managers will have to reveal themselves via costly signals or cash disbursements. To test these predictions, we analyze the dividend payment behavior for a global sample of firms around the mandatory adoption of IFRS and around the initial enforcement of new insider trading laws. Both events have the potential to improve the general information environment in the economy. We find that following the two events firms are less likely to pay (or increase) cash dividends, but more likely to cut (or stop) such payments. The changes in dividend policy occur around the time of the informational shock and only in countries and for firms subject to the regulatory change. In further analyses we find that the information content of dividends, measured as three-day absolute announcement returns, is lower after the informational events. The findings underscore that firms’ payout policies, among other things, depend on the extent of information about all firms in the economy

Firm-Level Exposure to Epidemic Diseases: Covid-19, SARS, and H1N1

Using tools described in our earlier work (Hassan et al., 2019, 2020), we develop text-based measures of the costs, benefits, and risks listed firms in the US and over 80 other countries associate with the spread of Covid-19 and other epidemic diseases. We identify which firms expect to gain or lose from an epidemic disease and which are most affected by the associated uncertainty as a disease spreads in a region or around the world. As Covid-19 spreads globally in the first quarter of 2020, we find that firms’ primary concerns relate to the collapse of demand, increased uncertainty, and disruption in supply chains. Other important concerns relate to capacity reductions, closures, and employee welfare. By contrast, financing concerns are mentioned relatively rarely. We also identify some firms that foresee opportunities in new or disrupted markets due to the spread of the disease. Finally, we find some evidence that firms that have experience with SARS or H1N1 have more positive expectations about their ability to deal with the coronavirus outbreak

The diffusion of disruptive technologies

We identify novel technologies using textual analysis of patents, job postings, and earnings calls. Our approach enables us to identify and document the diffusion of 29 disruptive technologies across firms and labor markets in the U.S. Five stylized facts emerge from our data. First, the locations where technologies are developed that later disrupt businesses are geographically highly concentrated, even more so than overall patenting. Second, as the technologies mature and the number of new jobs related to them grows, they gradually spread across space. While initial hiring is concentrated in high-skilled jobs, over time the mean skill level in new positions associated with the technologies declines, broadening the types of jobs that adopt a given technology. At the same time, the geographic diffusion of low-skilled positions is significantly faster than higher-skilled ones, so that the locations where initial discoveries were made retain their leading positions among high-paying positions for decades. Finally, these technology hubs are more likely to arise in areas with universities and high skilled labor pools

Development of two reference materials for all trans-retinol, retinyl palmitate, α- and γ-tocopherol in milk powder and infant formula

AbstractVitamins are important food constituents that can be present in almost every foodstuff. Food quality and safety depends on food surveillance by reliable quantitative analysis enabled by appropriate quality control. Certified matrix reference materials are versatile tools to support quality assurance and control. However, in the case of vitamins, which are important in various foods, there is a lack of matrix reference materials. Two certified reference materials for the determination of all–trans-retinol, retinyl palmitate, and α- and γ-tocopherol in milk powder and infant formula have been developed by the National Institute of Standards, Egypt. This article presents the preparation, characterization, homogeneity, and stability testing as well as statistical treatment of data and certified value assignment. The assignment of the certified values and associated uncertainties in the prepared natural-matrix reference materials were based on the widely used approach of combining data from independent and reliable analytical methods