No Arabic abstract
In recent years, Header Bidding (HB) has gained popularity among web publishers, challenging the status quo in the ad ecosystem. Contrary to the traditional waterfall standard, HB aims to give back to publishers control of their ad inventory, increase transparency, fairness and competition among advertisers, resulting in higher ad-slot prices. Although promising, little is known about how this ad protocol works: What are HBs possible implementations, who are the major players, and what is its network and UX overhead? To address these questions, we design and implement HBDetector: a novel methodology to detect HB auctions on a website at real time. By crawling 35,000 top Alexa websites, we collect and analyze a dataset of 800k auctions. We find that: (i) 14.28% of top websites utilize HB. (ii) Publishers prefer to collaborate with a few Demand Partners who also dominate the waterfall market. (iii) HB latency can be significantly higher (up to 3x in median case) than waterfall.
Over the last decade, digital media (web or app publishers) generalized the use of real time ad auctions to sell their ad spaces. Multiple auction platforms, also called Supply-Side Platforms (SSP), were created. Because of this multiplicity, publishers started to create competition between SSPs. In this setting, there are two successive auctions: a second price auction in each SSP and a secondary, first price auction, called header bidding auction, between SSPs.In this paper, we consider an SSP competing with other SSPs for ad spaces. The SSP acts as an intermediary between an advertiser wanting to buy ad spaces and a web publisher wanting to sell its ad spaces, and needs to define a bidding strategy to be able to deliver to the advertisers as many ads as possible while spending as little as possible. The revenue optimization of this SSP can be written as a contextual bandit problem, where the context consists of the information available about the ad opportunity, such as properties of the internet user or of the ad placement.Using classical multi-armed bandit strategies (such as the origin
The debate on Net-neutrality and events pointing towards its possible violations have led to the development of tools to detect deliberate traffic discrimination on the Internet. Given the complex nature of the Internet, neutrality violations are not easy to detect, and tools developed so far suffer from various limitations. In this paper, we study many challenges in detecting the violations and discuss possible approaches to mitigate them. As a case study, we focus on the tool Wehe cite{wehe} and discuss its limitations and propose the aspects that need to be strengthened. Wehe is the most recent tool to detect neutrality violations. Despite Wehes vast utility and possible influences over policy decisions, its mechanisms are not yet fully validated by researchers other than original tool developers. We seek to fill this gap by conducting a thorough and in-depth validation of Wehe. Our validation uses the Wehe App, a client-server setup mimicking Wehes behavior and its theoretical arguments. We validated the Wehe app for its methodology, traffic discrimination detection, and operational environments. We found that the critical weaknesses of the Wehe App are due to its design choices of using port number 80, overlooking the effect of background traffic, and the direct performance comparison.
Intelligence services are playing an increasingly important role in the operation of our society. Exploring the evolution mechanism, boundaries and challenges of service ecosystem is essential to our ability to realize smart society, reap its benefits and prevent potential risks. We argue that this necessitates a broad scientific research agenda to study service ecosystem that incorporates and expands upon the disciplines of computer science and includes insights from across the sciences. We firstly outline a set of research issues that are fundamental to this emerging field, and then explores the technical, social, legal and institutional challenges on the study of service ecosystem.
Computing devices are vital to all areas of modern life and permeate every aspect of our society. The ubiquity of computing and our reliance on it has been accelerated and amplified by the COVID-19 pandemic. From education to work environments to healthcare to defense to entertainment - it is hard to imagine a segment of modern life that is not touched by computing. The security of computers, systems, and applications has been an active area of research in computer science for decades. However, with the confluence of both the scale of interconnected systems and increased adoption of artificial intelligence, there are many research challenges the community must face so that our society can continue to benefit and risks are minimized, not multiplied. Those challenges range from security and trust of the information ecosystem to adversarial artificial intelligence and machine learning. Along with basic research challenges, more often than not, securing a system happens after the design or even deployment, meaning the security community is routinely playing catch-up and attempting to patch vulnerabilities that could be exploited any minute. While security measures such as encryption and authentication have been widely adopted, questions of security tend to be secondary to application capability. There needs to be a sea-change in the way we approach this critically important aspect of the problem: new incentives and education are at the core of this change. Now is the time to refocus research community efforts on developing interconnected technologies with security baked in by design and creating an ecosystem that ensures adoption of promising research developments. To realize this vision, two additional elements of the ecosystem are necessary - proper incentive structures for adoption and an educated citizenry that is well versed in vulnerabilities and risks.
We describe an ecosystem for teaching data science (DS) to engineers which blends theory, methods, and applications, developed at the Faculty of Physical and Mathematical Sciences, Universidad de Chile, over the last three years. This initiative has been motivated by the increasing demand for DS qualifications both from academic and professional environments. The ecosystem is distributed in a collaborative fashion across three departments in the above Faculty and includes postgraduate programmes, courses, professional diplomas, data repositories, laboratories, trainee programmes, and internships. By sharing our teaching principles and the innovative components of our approach to teaching DS, we hope our experience can be useful to those developing their own DS programmes and ecosystems. The open challenges and future plans for our ecosystem are also discussed at the end of the article.