Do you want to publish a course? Click here

Hide and seek in Slovakia: utilizing tracking code data to uncover untrustworthy website networks

127   0   0.0 ( 0 )
 Added by Jozef Michal Mintal
 Publication date 2021
and research's language is English




Ask ChatGPT about the research

The proliferation of misleading or false information spread by untrustworthy websites has emerged as a significant concern on the public agenda in many countries, including Slovakia. Despite the influence ascribed to such websites, their transparency and accountability remain an issue in most cases, with published work on mapping the administrators and connections of untrustworthy websites remaining limited. This article contributes to this body of knowledge (i) by providing an effective open-source tool to uncover untrustworthy website networks based on the utilization of the same Google Analytics/AdSense IDs, with the added ability to expose networks based on historical data, and (ii) by providing insight into the Slovak untrustworthy website landscape through delivering a first of its kind mapping of Slovak untrustworthy website networks. Our approach is based on a mix-method design employing a qualitative exploration of data collected in a two wave study conducted in 2019 and 2021, utilizing a custom-coded tool to uncover website connections. Overall, the study succeeds in exposing multiple novel website ties. Our findings indicate that while some untrustworthy website networks have been found to operate in the Slovak infosphere, most researched websites appear to be run by multiple mutually unconnected administrators. The resulting data also demonstrates that untrustworthy Slovak websites display a high content diversity in terms of connected websites, ranging from websites of local NGOs, an e-shop selling underwear to a matchmaking portal.



rate research

Read More

115 - K. Sneppen 2004
Signaling pathways and networks determine the ability to communicate in systems ranging from living cells to human society. We investigate how the network structure constrains communication in social-, man-made and biological networks. We find that human networks of governance and collaboration are predictable on teat-a-teat level, reflecting well defined pathways, but globally inefficient. In contrast, the Internet tends to have better overall communication abilities, more alternative pathways, and is therefore more robust. Between these extremes the molecular network of Saccharomyces cerevisea is more similar to the simpler social systems, whereas the pattern of interactions in the more complex Drosophilia melanogaster, resembles the robust Internet.
We train embodied agents to play Visual Hide and Seek where a prey must navigate in a simulated environment in order to avoid capture from a predator. We place a variety of obstacles in the environment for the prey to hide behind, and we only give the agents partial observations of their environment using an egocentric perspective. Although we train the model to play this game from scratch, experiments and visualizations suggest that the agent learns to predict its own visibility in the environment. Furthermore, we quantitatively analyze how agent weaknesses, such as slower speed, effect the learned policy. Our results suggest that, although agent weaknesses make the learning problem more challenging, they also cause more useful features to be learned. Our project website is available at: http://www.cs.columbia.edu/ ~bchen/visualhideseek/.
As several large single-dish radio surveys begin operation within the coming decade, a wealth of radio data will become available and provide a new window to the Universe. In order to fully exploit the potential of these data sets, it is important to understand the systematic effects associated with the instrument and the analysis pipeline. A common approach to tackle this is to forward-model the entire system - from the hardware to the analysis of the data products. For this purpose, we introduce two newly developed, open-source Python packages: the HI Data Emulator (HIDE) and the Signal Extraction and Emission Kartographer (SEEK) for simulating and processing single-dish radio survey data. HIDE forward-models the process of collecting astronomical radio signals in a single-dish radio telescope instrument and outputs pixel-level time-ordered-data. SEEK processes the time-ordered-data, removes artifacts from Radio Frequency Interference (RFI), automatically applies flux calibration, and aims to recover the astronomical radio signal. The two packages can be used separately or together depending on the application. Their modular and flexible nature allows easy adaptation to other instruments and data sets. We describe the basic architecture of the two packages and examine in detail the noise and RFI modeling in HIDE, as well as the implementation of gain calibration and RFI mitigation in SEEK. We then apply HIDE & SEEK to forward-model a Galactic survey in the frequency range 990 - 1260 MHz based on data taken at the Bleien Observatory. For this survey, we expect to cover 70% of the full sky and achieve a median signal-to-noise ratio of approximately 5 - 6 in the cleanest channels including systematic uncertainties. However, we also point out the potential challenges of high RFI contamination and baseline removal when examining the early data from the Bleien Observatory.
We propose Hide-and-Seek a general purpose data augmentation technique, which is complementary to existing data augmentation techniques and is beneficial for various visual recognition tasks. The key idea is to hide patches in a training image randomly, in order to force the network to seek other relevant content when the most discriminative content is hidden. Our approach only needs to modify the input image and can work with any network to improve its performance. During testing, it does not need to hide any patches. The main advantage of Hide-and-Seek over existing data augmentation techniques is its ability to improve object localization accuracy in the weakly-supervised setting, and we therefore use this task to motivate the approach. However, Hide-and-Seek is not tied only to the image localization task, and can generalize to other forms of visual input like videos, as well as other recognition tasks like image classification, temporal action localization, semantic segmentation, emotion recognition, age/gender estimation, and person re-identification. We perform extensive experiments to showcase the advantage of Hide-and-Seek on these various visual recognition problems.
We use $sim$83,000 star-forming galaxies at $0.04<z<0.3$ from the Sloan Digital Sky Survey to study the so-called fundamental metallicity relation (FMR) and report on the disappearance of its anti-correlation between metallicity and star formation rate (SFR) when using the new metallicity indicator recently proposed by Dopita et al. In this calibration, metallicity is primarily sensitive to the emission line ratio [NII]$lambda$6584 / [SII]$lambdalambda$6717, 6731 that is insensitive to dilution by pristine infalling gas that may drive the FMR anti-correlation with SFR. Therefore, we conclude that the apparent disappearance of the FMR (using this new metallicity indicator) does not rule out its existence.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا