Posts by Collection


Bridging Educational Gaps through Volunteers: Implementation, Problems and Their Solutions

Published in ACM International Conference on Information and Communication Technologies and Development (ICTD), 2016

Teachers play a central role in education. However, there are many factors like non-teaching activities given to teachers besides the teaching workload, and the low or insufficient qualification of teachers, which can lower the quality of education imparted by teachers, especially in the developing countries. In this paper, we have proposed a volunteer based solution named EDUCATION ASAAN in Pakistan where volunteers will be placed with teachers of different public sector and private sector educational institutes to share the workload of teachers there, to complement the learning process as well as to improve the quality of education. The paper mentions semi-structured interviews from teachers to understand the kind of work burdens they face as well as their feedback on our proposed solution. We discuss the findings of different surveys conducted in different educational institute from different students and potential volunteers as well. With each of the feedback, the paper also tries to discuss the problems in different phases of selection, placement and evaluation of these volunteers and their possible solutions. Read more

Experimental study of link quality in IEEE 802.15. 4 using Z1 Motes

Published in IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), 2016

In low-power low-rate wireless networks, IEEE 802.15.4 is a standard protocol for communication. Devices in such networks are usually battery-operated so radio-transceiver component in such networks are typically of low range but most power consuming. Proper antenna orientation, distance between nodes and channel selection are among the ways to achieve reliability in these networks. But these arrangements can affect the link qualities and achieved communication performance in IoT applications. In this work, we have evaluated the performance of IEEE 802.15.4 links using Zolertia Z1 motes experimentally through indoor/outdoor real scenarios. We have found that RSSI decreases with distance and is effected by the height of the motes. The antenna polarization drastically affects the RSSI whereas LQI and packet delivery ratio is not much affected. We also found that the non-interfering channels 26 and channel 15 are effected by Wi-Fi in the same way. Moreover the contiki MAC performs better than XMAC protocol. Read more

A Bibliometric Analysis of Publications in Computer Networking Research

Published in Springer Scientometrics Journal, 2019

Computer networking is a major research discipline in computer science, electrical engineering, and computer engineering. The feld has been actively growing, in terms of both research and development, for the past hundred years. This study uses the article content and metadata of four important computer networking periodicals—IEEE Communications Surveys and Tutorials (COMST), IEEE/ACM Transactions on Networking (TON), ACM Special Interest Group on Data Communications (SIGCOMM), and IEEE International Conference on Computer Communications (INFOCOM)—obtained using ACM, IEEE Xplore, Scopus and CrossRef, for an 18-year period (2000–2017) to address important bibliometrics questions. All of the venues are prestigious, yet they publish quite different research. The frst two of these periodicals (COMST and TON) are highly reputed journals of the felds while SIGCOMM and INFOCOM are considered top conferences of the feld. SIGCOMM and INFOCOM publish new original research. TON has a similar genre and publishes new original research as well as the extended versions of diferent research published in the conferences such as SIGCOMM and INFOCOM, while COMST publishes surveys and reviews (which not only summarize previous works but highlight future research opportunities). In this study, we aim to track the co-evolution of trends in the COMST and TON journals and compare them to the publication trends in INFOCOM and SIGCOMM. Our analyses of the computer networking literature include: (a) metadata analysis; (b) content-based analysis; and (c) citation analysis. In addition, we identify the signifcant trends and the most infuential authors, institutes and countries, based on the publication count as well as article citations. Through this study, we are proposing a methodology and framework for performing a comprehensive bibliometric analysis on computer networking research. To the best of our knowledge, no such study has been undertaken in computer networking until now. Read more

Five decades of the ACM special interest group on data communications (SIGCOMM) a bibliometric perspective

Published in ACM SIGCOMM Computer Communication Review, 2019

The ACM Special Interest Group on Data Communications (SIGCOMM) has been a major research forum for fifty years. This community has had a major impact on the history of the Internet, and therefore we argue its exploration may reveal fundamental insights into the evolution of networking technologies around the globe. Hence, on the 50th anniversary of SIGCOMM, we take this opportunity to reflect upon its progress and achievements, through the lens of its various publication outlets, e.g., the SIGCOMM conference, IMC, CoNEXT, HotNets. Our analysis takes several perspectives, looking at authors, countries, institutes and papers. We explore trends in co-authorship, country-based productivity, and knowledge flow to and from SIGCOMM venues using bibliometric techniques. We hope this study will serve as a valuable resource for the computer networking community. Read more

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Published in IEEE Transactions on Artificial Intelligence, 2020

COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies.As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works. Read more

Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security

Published in Frontiers in Big Data, 2020

With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party cloud services for outsourcing training of DL models, which requires substantial costly computational resources (e.g., high-performance graphics processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a wide range of attack surfaces for adversaries to exploit the ML/DL system to achieve malicious goals. In this article, we conduct a systematic evaluation of literature of cloud-hosted ML/DL models along both the important dimensions—attacks and defenses—related to their security. Our systematic review identified a total of 31 related articles out of which 19 focused on attack, six focused on defense, and six focused on both attack and defense. Our evaluation reveals that there is an increasing interest from the research community on the perspective of attacking and defending different attacks on Machine Learning as a Service platforms. In addition, we identify the limitations and pitfalls of the analyzed articles and highlight open research issues that require further investigation. Read more

A First Look at COVID-19 Messages on WhatsApp in Pakistan

Published in ASONAM 2020, 2020

The worldwide spread of COVID-19 has prompted extensive online discussions, creating an infodemic on social media platforms such as WhatsApp and Twitter. However, the information shared on these platforms is prone to be unreliable and/or misleading. In this paper, we present the first analysis of COVID-19 discourse on public WhatsApp groups from Pakistan. Building on a large scale annotation of thousands of messages containing text and images, we identify the main categories of discussion. We focus on COVID-19 messages and understand the different types of images/text messages being propagated. By exploring user behavior related to COVID messages, we inspect how misinformation is spread. Finally, by quantifying the flow of information across WhatsApp and Twitter, we show how information spreads across platforms and how WhatsApp acts as a source for much of the information shared on Twitter. Read more



Lady and tramp Nextdoor: Online manifestations of real-world inequalities

Published in 34th Multi-Service Networks workshop (Coseners-MSN 2022), 2022

Abstract: In recent years, assessing socioeconomic differences in society has become a significant concern for policymakers and scholars. Income disparity is one of the most often used metrics for gauging these social inequalities. These income disparities affect several socioeconomic facets of society, such as crime and public opinion. Although these patterns of income-based social inequality can be identified using official data sources, a critical question remains: Can similar social inequalities be observed in freely and abundantly available Internet user activity? We analyse this hypothesis using the two key research questions; (i) How does a neighbourhood’s income influence the crime discussion user sentiment? and (ii) Can this user-generated data accurately forecast a neighbourhood’s income? To answer these questions, we collected a dataset of 2.5 Million posts from 64283 neighbourhoods in the United States (USA) and 3325 neighbourhoods in the ten most populous cities in the United Kingdom (UK) using the Nextdoor platform between November 2020 and September 2021. We also utilise datasets from the various official data sources of the United States and the United Kingdom to obtain official crime and income figures. To the best of our knowledge, this is the first study to measure online manifestations of social inequalities using location-based social network data. See more info here Read more