Institution
Qatar Computing Research Institute
About: Qatar Computing Research Institute is a based out in . It is known for research contribution in the topics: Computer science & Social media. The organization has 270 authors who have published 1633 publications receiving 51720 citations.
Topics: Computer science, Social media, Machine translation, Context (language use), Task (project management)
Papers published on a yearly basis
Papers
TL;DR: The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users.
Abstract: Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan (
http://rayyan.qcri.org
), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature. Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users. Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.
16,415 citations
TL;DR: An R/Bioconductor package called TCGAbiolinks is developed to address bioinformatics challenges of the Cancer Genome Atlas data by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data.
Abstract: The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.
3,531 citations
Proceedings Article•
1 Jan 20142,493 citations
1 Aug 2017
TL;DR: Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask.
Abstract: This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.
1,460 citations
Book•
26 Aug 2021
TL;DR: The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection.
Abstract: The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.
1,266 citations
Authors
Showing all 270 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Othmane Bouhali | 113 | 1060 | 66738 |
| Muhammad Imran | 94 | 3053 | 51728 |
| Divyakant Agrawal | 72 | 458 | 19789 |
| Mohammed J. Zaki | 65 | 297 | 22904 |
| Carlos Castillo | 62 | 240 | 16975 |
| Ahmed K. Elmagarmid | 62 | 277 | 19411 |
| Bernard J. Jansen | 61 | 388 | 17502 |
| Preslav Nakov | 61 | 387 | 14335 |
| Ahmed Ali | 61 | 728 | 15197 |
| D. M. Bramich | 55 | 277 | 12180 |
| Gautam Das | 54 | 253 | 11363 |
| Mohamed F. Mokbel | 53 | 237 | 11362 |
| Alessandro Moschitti | 52 | 308 | 11378 |
| Ingmar Weber | 49 | 257 | 9175 |
| Jaideep Srivastava | 48 | 382 | 17000 |
Related Institutes (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2026 | 2 |
| 2025 | 34 |
| 2024 | 16 |
| 2023 | 39 |
| 2022 | 1 |
| 2021 | 126 |