Space: the Final FrontierPaper @CoNext 2023
Dissecting the Performance of Satellite Network Operators
The rapid growth of satellite network operators (SNOs) has revolutionized broadband communications, enabling global connectivity and bridging the digital divide. As these networks expand, it is important to evaluate their performance and efficiency. This paper presents the first comprehensive study of SNOs. We take an opportunistic approach and devise a methodology which allows to identify public network measurements performed via SNOs. We apply this methodology to both M-Lab and RIPE public datasets which allowed us to characterize low level performance and footprint of up to 18 SNOs operating in different orbits. Finally, we identify and recruit paid testers on three popular SNOs (Starlink, HughesNet, and ViaSat) to evaluate the performance of popular applications like web browsing and video streaming.
Apple's AirTag vs. Samsung's Galaxy SmartTagPaper @ACM IMC 2023
I Tag, You Tag, Everybody Tags!
We study the performance of the two most popular location tags (Apple's AirTag and Samsung's SmartTag) through controlled experiments – with a known large distribution of location-reporting devices – as well as in-the-wild experiments – with no control on the number and kind of reporting devices encountered, thus emulating real-life use-cases. We find that both tags achieve similar performance, e.g., they are located 60% of the times in about 10 minutes within a 100 meter radius. It follows that real time stalking via location tags is impractical, even when both tags are concurrently deployed which achieves comparable accuracy in half the time. Nevertheless, half of a victim's movements can be backtracked accurately (10 meter error) with just a one-hour delay.
Invited talk at the IETF-18 MAPRG.
YouYube's Political RecommendationPaper @PNAS Nexus 2023
YouTube's recommendation algorithm is left-leaning in the United States
We analyze YouTube's recommendation algorithm by constructing archetypal users with varying political personas, and examining videos recommended during four stages of each user's life cycle: (i) after their account is created; (ii) as they build a political persona through watching videos of a particular political leaning; (iii) as they try to escape their political persona by watching videos of a different leaning; (iv) as they watch videos suggested by the recommendation algorithm. We find that while the algorithm pulls users away from political extremes, this pull is asymmetric, with users being pulled away from Far-Right content faster than from Far-Left. These findings raise questions on whether recommendation algorithms should exhibit political biases, and the societal implications that such biases could entail.
Mobile Networks PerformancePaper @TMA 2023
A WorldWide Look Into Mobile Access Networks Through The Eyes of Amigos
proposes a novel testbed design called "AmiGo", which relies on travelers carrying mobile phones to act as vantage points and collect data on mobile network performance. The AmiGo design has three key advantages: it is easy to deploy, has realistic user mobility, and runs on real Android devices. We further developed a suite of measurement tools for AmiGo to perform network measurements, e.g., pings, speedtests, and webpage loads. We leverage these tools to measure the performance of 24 mobile networks across five continents over a month via an AmiGo deployment involving 31 students. We find that 50% of networks face a 40-70% chance of providing low data rates, only 20% achieve low latencies, and networks in Asia, Central/South America, and Africa have significantly higher CDN download times than in Europe. Most news websites load slowly, while YouTube performs well.
LLMs in EducationPaper @Scientific reports 2023
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
Given the recent emergence of conversational artificial intelligence tools, educational institutions worldwide are facing the significant challenge of addressing the integration of artificial intelligence into educational frameworks. Yet, the literature lacks a systematic study evaluating the performance of such tools on university-level courses and their susceptibility to detection, and also lacks an examination of students' and educators' perspectives on the use of such tools in educational contexts. This work fills these gaps, providing timely and vital insights into the performance of the latest such tool—ChatGPT—and the threat of "AI-plagiarism" that it entails. Our findings can inform policy discussions of how to shape student evaluation frameworks in the age of artificial intelligence.
Homework in the Age of AIPaper @IEEE Intelligent Systems 2023
Rethinking Homework in the Age of Artificial Intelligence
The evolution of natural language processing techniques has led to the development of advanced conversational tools such as ChatGPT, capable of assisting users with a variety of activities. Media attention has centered on ChatGPT's potential impact, policy implications, and ethical ramifications, particularly in the context of education. As such tools become more accessible, students across the globe may use them to assist with their homework. However, it is still unclear whether ChatGPT's performance is advanced enough to pose a serious risk of plagiarism. We fill this gap by evaluating ChatGPT on two introductory and two advanced university-level courses. We find that ChatGPT receives near-perfect grades on the majority of questions in the introductory courses but has not yet reached the level of sophistication required to pass in advanced courses. These findings suggest that, at least for some courses, current artificial intelligence tools pose a real threat that can no longer be overlooked by educational institutions.
Lite-WebPaper @PNAS 2023
Towards a World Wide Web without digital inequality
Developing regions suffer from poor Internet connection and over reliance on low-end phones, which violates net neutrality—the idea that all Internet traffic should be treated equally. We sent participants to 56 countries to measure global variation in web-browsing experience, revealing significant inequality in mobile data cost and page load time. We also show that popular webpages are increasingly tailored to high-end phones, thereby exacerbating the inequality. Our solution, Lite-Web, makes webpages faster to load and easier to process on low-end phones. Evaluating Lite-Web on the ground reveals that it transforms the browsing experience of Pakistani villagers with low-end phones to that of Dubai residents with high-end phones. These findings call attention from researchers and policy makers to mitigate digital inequality.
Videoconferencing in the WildPaper @ACM IMC 2022
Performance characterization of videoconferencing in the wild
One important question that we tackle in this paper is: what is the performance of videoconferencing in the wild? Answering this generic question is challenging because it requires, ideally, a world-wide testbed composed of diverse devices (mobile, desktop), operating systems (Windows, MacOS, Linux) and network accesses (mobile and WiFi). In this paper, we present such a testbed that we develop to evaluate videoconferencing performance in the wild via automation for Android and Chromium-based browsers.Read paper
MuzeelPaper @ACM IMC 2022
JSAnalyzerPaper @ACM TWEB 2022
JSAnalyzer: A Web Developer Tool for Simplifying Mobile Web Pages
slimWebPaper @ICTD 2022
To Block or Not to Block: Accelerating Mobile Web Pages On-The-Fly Through JS Classification
QLUEPaper @The Webconf 2022
QLUE: A Computer Vision Tool for Uniform Qualitative Evaluation of Web Pages
QLUE (QuaLitative Uniform Evaluation) is a tool that automates the qualitative evaluation of web pages generated by web complexity solutions with respect to their original versions using computer vision. QLUE evaluates the content and the functionality of these pages separately using two metrics: QLUE's Structural Similarity, to assess the former, and QLUE's Functional Similarity to assess the latter---a task that is proven to be a challenging for humans given the complex functional dependencies in modern pages.Read paper
ALCCPaper @JSYS 2022
ALCC: Migrating Congestion Control to the Application Layer in Cellular Networks
Application Layer Congestion Control (ALCC) is a framework that allows any new CC protocol to be implemented easily at the application layer, within or above an application-layer protocol that sits atop a legacy TCP stack. It drives it to deliver approximately the same as the native performance. The ALCC socket sits on top of a traditional TCP socket. Still, it can leverage the large congestion windows opened by TCP connections to carefully execute an application-level CC within the window bounds of the underlying TCP connection.Read paper
MDIPaper @Sigcomm CCR 2021
The Case for Model-Driven Interpretability of Delay-Based Congestion Control Protocols
Model-Driven Interpretability (MDI) is congestion control framework, which derives a model version of a delay-based protocol by simplifying a congestion control protocol's response into a guided random walk over a two-dimensional Markov model. We demonstrate the case for the MDI framework by using MDI to analyze and interpret the behavior of two delay-based protocols over cellular channels: Verus and Copa.Read paper
PQualDemo @UIST 2020
PQual: Automating Web Pages Qualitative Evaluation
A tool that enables the automation of the qualitative evaluation of web pages using computer vision. In comparison to humans, PQual can effectively evaluate all the functionality of a web page, whereas the users might skip many of the functional elements during the evaluation.Read paper
JSCleanerPaper @The Webconf 2020
Adaptive Congestion Control for Unpredictable Cellular Networks
An adaptive congestion control protocol designed for cellular networks. Verus leverages the relation ship between the sending window and the observed network delay by using the delay profile curve. Verus is a delay-based congestion control protocol.Read paper
Be part of the team
We are always on the look out for talented people to join the lab. Whether a research summer internship, a research visit, or a longer term position as a post-doc or research assistant. For inquiries please email yasir.zaki (at) nyu.edu.
Hazem Ibrahim has joined our lab as a research assistant. Welcome on board Hazem.
Dr. Moumena Shaqfa has joined our lab as a postdoctoral associate. Welcome on board.