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Register a new userWhoTracks .Me: Shedding light on the opaque world of online tracking
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Added by
Arjaldo Karaj
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Online tracking has become of increasing concern in recent years, however our understanding of its extent to date has been limited to snapshots from web crawls. Previous at-tempts to measure the tracking ecosystem, have been done using instrumented measurement platforms, which are not able to accurately capture how people interact with the web. In this work we present a method for the measurement of tracking in the web through a browser extension, as well as a method for the aggregation and collection of this information which protects the privacy of participants. We deployed this extension to more than 5 million users, enabling measurement across multiple countries, ISPs and browser configurations, to give an accurate picture of real-world tracking. The result is the largest and longest measurement of online tracking to date based on real users, covering 1.5 billion page loads gathered over 12 months. The data, detailing tracking behaviour over a year, is made publicly available to help drive transparency around online tracking practices.
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Websites with hyper-partisan, left or right-leaning focus offer content that is typically biased towards the expectations of their target audience. Such content often polarizes users, who are repeatedly primed to specific (extreme) content, usually reflecting hard party lines on political and socio-economic topics. Though this polarization has been extensively studied with respect to content, it is still unknown how it associates with the online tracking experienced by browsing users, especially when they exhibit certain demographic characteristics. For example, it is unclear how such websites enable the ad-ecosystem to track users based on their gender or age. In this paper, we take a first step to shed light and measure such potential differences in tracking imposed on users when visiting specific party-lines websites. For this, we design and deploy a methodology to systematically probe such websites and measure differences in user tracking. This methodology allows us to create user personas with specific attributes like gender and age and automate their browsing behavior in a consistent and repeatable manner. Thus, we systematically study how personas are being tracked by these websites and their third parties, especially if they exhibit particular demographic properties. Overall, we test 9 personas on 556 hyper-partisan websites and find that right-leaning websites tend to track users more intensely than left-leaning, depending on user demographics, using both cookies and cookie synchronization methods and leading to more costly delivered ads.
News recommendation and personalization is not a solved problem. People are growing concerned of their data being collected in excess in the name of personalization and the usage of it for purposes other than the ones they would think reasonable. Our experience in building personalization products for publishers while adhering to safeguard user privacy led us to investigate more on the user perspective of privacy and personalization. We conducted a survey to explore peoples experience with personalization and privacy and the viewpoints of different age groups. In this paper, we share our major findings with publishers and the community that can inform algorithmic design and implementation of the next generation of news recommender systems, which must put the human at its core and reach a balance between personalization experiences and privacy to reap the benefits of both.
In the standard view of massive star evolution, luminous blue variables (LBVs) are transitional objects between the most massive O-type stars and Wolf-Rayet (WR) stars. With short lifetimes, these stars should all be found near one another. A recent study of LBVs in the Large Magellanic Cloud (LMC) found instead that LBVs are considerably more isolated than either O-type stars or WRs, with a distribution intermediate between that of the WRs and red supergiants (RSGs). A similar study, using a more restricted sample of LBVs, reached the opposite conclusion. Both studies relied upon the distance to the nearest spectroscopically identified O-type star to define the degree of isolation. However, our knowledge of the spectroscopic content of the LMC is quite spotty. Here we re-examine the issue using carefully defined photometric criteria to select the highest mass unevolved stars (bright blue stars, or BBSs), using spatially complete photometric catalogs of the LMC, M31, and M33. Our study finds that the LBVs are no more isolated than BBSs or WRs. This result holds no matter which sample of LBVs we employ. A statistical test shows that we can rule out the LBVs having the same distribution as the RSGs, which are about 2x more isolated. We demonstrate the robustness of our results using the second-closest neighbor. Furthermore, the majority of LBVs in the LMC are found in or near OB associations as are the BBS and WRs; the RSGs are not. We conclude that the spatial distribution of LBVs therefore is consistent with the standard picture of massive star evolution.
The Light-Up puzzle, also known as the AKARI puzzle, has never been solved using modern artificial intelligence (AI) methods. Currently, the most widely used computational technique to autonomously develop solutions involve evolution theory algorithms. This project is an effort to apply new AI techniques for solving the Light-up puzzle faster and more computationally efficient. The algorithms explored for producing optimal solutions include hill climbing, simulated annealing, feed-forward neural network (FNN), and convolutional neural network (CNN). Two algorithms were developed for hill climbing and simulated annealing using 2 actions (add and remove light bulb) versus 3 actions(add, remove, or move light-bulb to a different cell). Both hill climbing and simulated annealing algorithms showed a higher accuracy for the case of 3 actions. The simulated annealing showed to significantly outperform hill climbing, FNN, CNN, and an evolutionary theory algorithm achieving 100% accuracy in 30 unique board configurations. Lastly, while FNN and CNN algorithms showed low accuracies, computational times were significantly faster compared to the remaining algorithms. The GitHub repository for this project can be found at https://github.com/rperera12/AKARI-LightUp-GameSolver-with-DeepNeuralNetworks-and-HillClimb-or-SimulatedAnnealing.
During its first six years of operation, the Fermi Large Area Telescope (LAT) has detected >30 MeV gamma-ray emission from more than 40 solar flares, nearly a factor of 10 more than those detected by EGRET. These include detections of impulsive and sustained emissions, extending up to 20 hours in the case of the 2012 March 7 X-class flares. We will present an overview of solar flare detections with LAT, highlighting recent results and surprising features, including the detection of >100 MeV emission associated with flares located behind the limb. Such flares may shed new light on the relationship between the sites of particle acceleration and gamma-ray emission.
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