Multi-agent synthesis generated cross-referencing arXiv, PubMed, StatCan, and open web data for: “free website”.
Extracted & Cosine-Filtered Evidence
- [arXiv] Uploading User-Defined Functions onto the AMIDAS Website
(Cosine Sim: 0.2317)
The AMIDAS website has been established as an online interactive tool for running simulations and analyzing data in direct Dark Matter detection experiments. At the first phase of the website building, only some commonly used WIMP velocity distribution functions and elastic nuclear form factors have been involved in the AMIDAS code. In order to let the options for velocity distribution as well as for nuclear form factors be more flexible, we have extended the AMIDAS code to be able to include user-uploaded files with their own functions. In this article, I describe the preparation of files of user-defined functions onto the AMIDAS website. Some examples will also be given. - [arXiv] Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning
(Cosine Sim: 0.2018)
In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over $1%$ higher true positive rate (TPR) than state-of-the-art attacks while achieving $4times$ lower false positive rate (FPR). Var-CNN's improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by $3.12%$ while increasing the TPR by $13%$. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues. - [arXiv] Analysis of the Visitor Data of a Higher Education Institution Website
(Cosine Sim: 0.1599)
In todays world, the internet affects every aspect of human life; it has caused changes in corporate websites as well as in many other areas. Corporate websites should be more dynamic, more interactive, and more compatible with new technologies. The interaction of the website with users, search engines, and other devices has to be examined by experts, and improvements and changes should be made for this interaction. In this study, a higher education institution website was examined. Visitor data collected between 2013 and 2019 were used for the analysis. In the study, which includes a wide range of examinations and data, important findings from traffic analysis to development suggestions were included. In particular, useful information has been obtained through the compatibility of the site with mobile devices, optimization of pictures and videos, geographical features of users, language options, and density analysis of the content accessed over time.