Guide to Advertising Technology

Advertising technology has built a massive technical infrastructure. The technology and motivations of advertising undergird the economy of the internet. News sites are no exception. The information we seek about our world is underpinned with, and shaped by, advertising and its needs. Journalists need to know more about these technologies, how they work, and how they influence the practice, distribution, and perception of journalism. Advertising technology may threaten the reputation and economic viability of news publishers in various ways. Ad tech promotes a specific type of audience engagement, and its incentive structures have been shown to alter how news gets produced, potentially undermining readers’ trust in publishers to provide objective coverage. Ad tech’s push of user data through opaque systems, and in some cases deposit malware onto readers’ devices, threatens reader privacy and safety and can further damage publishers’ reputations. The slow load times and distracting user experience of display ads can hamper the performance of news websites and drive readers towards walled information gardens like private apps and social-media platforms. This may siphon audiences away from professional journalism outlets and may render them more vulnerable to manipulative information operations, patterns which academics and policy-makers are only beginning to under- stand. Policies around advertising on social platforms threaten to blur the line between news and political messaging, and may incentivize so-called “influencers” to bypass publishers entirely and create their own content. Search-engine companies, meanwhile, have been accused of exploiting their power over how users find and access information. In producing this report we were driven by a set of questions, including: What’s the relationship between news publishers and advertisers? What’s changing? What’s contested? And how does the contemporary arrangement of advertising, writers, readers, and devices influence the news

Modeling Consumer Behavior For High Risk Foods

According to the Centers for Disease Control and Prevention (CDC), one in six Americans become ill or die from foodborne contaminations (CDC, 2011). Contamination (intentional or unintentional) can occur at any point in the food supply chain. Flaws in security, quality control, or transportation are some examples of how food is susceptible to intentional acts of sabotage. Certain foods are more susceptible to contamination such as meats, dairy, fruits, vegetables, and eggs. In order to build a secure and resilient food supply chain network, food producers and manufacturers need to have the ability to assess contamination risks resulting from manufacturing processes. This research quantifies risk as a function of purchasing and consumption frequency of food susceptible to recalls. A survey is constructed and administered to identify consumption and purchasing behavior of high risk foods. Using the data from the survey, a logistic regression model is developed to determine the likelihood of purchasing high risk food items based on shopping behavior and demographic information. Subsequently, a Poisson regression model is developed to predict consumers’ consumption frequency. The results of the research will lead to a better understanding of consumer behavior in relation to food choices. Furthermore, understanding purchasing and consumption behavior will enable food producers to design better policies for securing the nation’s food supply

AN INVESTIGATION INTO ADAPTIVE SEARCH TECHNIQUES FOR THE AUTOMATIC GENERATION OF SOFTWARE TEST DATA

The focus of this thesis is on the use of adaptive search techniques for the automatic generation of software test data. Three adaptive search techniques are used, these are genetic algorithms (GAs), Simulated Amiealing and Tabu search. In addition to these, hybrid search methods have been developed and applied to the problem of test data generation. The adaptive search techniques are compared to random generation to ascertain the effectiveness of adaptive search. The results indicate that GAs and Simulated Annealing outperform random generation in all test programs. Tabu search outperformed random generation in most tests, but it lost its effectiveness as the amount of input data increased. The hybrid techniques have given mixed results. The two best methods, GAs and Simulated Annealing are then compared to random generation on a program written to optimise capital budgeting, both perform better than random generation and Simulated Annealing requires less test data than GAs. Further research highlights a need for research into the control parameters of all the adaptive search methods and attaining test data which covers border conditions

The Möbius Organizational Form: Make, Buy, Cooperate, or Co-opt?

This paper examines the emerging contours of a new organizational form, in which firms move beyond the cooperative pacts of alliances to a radicalized, aggressive co-optation of external assets. Taking our point of departure from the literature on the “networked” firm, we point to an alternative to the make, buy, or cooperate decision: in the Möbius form, firms co-opt resources, unsecured by any alliances, formal or informal. Some companies are brazen in their co-optation, leveraging external assets so thoroughly that they might well be considered a core part of the firm. Enabled by developments in computing technologies, such co-optation challenges traditional models of organizational identity. These fluid boundaries recall the Möbius topological model, which we take as the metaphor for this nascent organizational form. We chart this new behavior by discussing a range of firm activities, including the functions of marketing, research and development, and managerial decision-making, as they are replaced with assets co-opted from other firms in the private sector, government agencies, and lastly the firm’s own users

Repairing Innovation: A Study of Integrating AI in Clinical Care

Over the past two years, a multi-disciplinary team of clinicians and technologists associated with Duke University and Duke Health system have developed and implemented Sepsis Watch, a sociotechnical system combining an artificial intelligence (AI) deep learning model with new hospital protocols to raise the quality of sepsis treatment. Sepsis is a widespread and deadly condition that can develop from any infection and is one of the most common causes of death in hospitals. And while sepsis is treatable, it is notoriously difficult to diagnose consistently. This makes sepsis a prime candidate for AI-based interventions, where new approaches to patient data might raise levels of detection, treatment, and, ultimately, patient outcomes in the form of fewer deaths.As an application of AI, the deep learning model tends to eclipse the other parts of the system; in practice, Sepsis Watch is constituted by a complex combination of human labor and expertise, as well as technical and institutional infrastructures. This report brings into focus the critical role of human labor and organizational context in developing an effective clinical intervention by framing Sepsis Watch as a complex sociotechnical system, not just a machine learning model

Pets, Purity and Pollution: Why Conventional Models of Disease Transmission Do Not Work for Pet Rat Owners

In the United Kingdom, following the emergence of Seoul hantavirus in pet rat owners in 2012, public health authorities tried to communicate the risk of this zoonotic disease, but had limited success. To explore this lack of engagement with health advice, we conducted in-depth, semi-structured interviews with pet rat owners and analysed them using a grounded theory approach. The findings from these interviews suggest that rat owners construct their pets as different from wild rats, and by elevating the rat to the status of a pet, the powerful associations that rats have with dirt and disease are removed. Removing the rat from the contaminated outside world moves their pet rat from being ‘out of place’ to ‘in place’. A concept of ‘bounded purity’ keeps the rat protected within the home, allowing owners to interact with their pet, safe in the knowledge that it is clean and disease-free. Additionally, owners constructed a ‘hierarchy of purity’ for their pets, and it is on this structure of disease and risk that owners base their behaviour, not conventional biomedical models of disease