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Fri, Apr 17, 2020 @ 11:00 AM - 12:00 PM
Information Sciences Institute
The first webinar will feature a double-header of talks by two researchers from USC Viterbi's Information Sciences Institute: Emilio Ferrara and Kristina Lerman. Their talks will cover tracking COVID-19 on social media, and the network science behind the spread of COVID-19. Please find abstracts for these talks and the speaker bios below:
Talk 1: Charting COVID-19 Chatter on Social Media, by Emilio Ferrara
Abstract: Social Networks have dramatically changed the way we experience the world. Information access and broadcasting have been revolutionized. The Internet, the Web, and online platforms bring us together: our society is experiencing the effects, both positive and negative, of ubiquitous and unparalleled connectivity. In this talk, I will overview the implications of COVID-19 on online platforms for our society, democracy, and public health. Our preliminary work illustrates our data collection, detection of malicious actors, etc.. I'll also overview how conspiracy theories about vaccines, epidemic outbreaks, and other health-related rumors can have adverse effects and contribute toward public health crises. I'll conclude by discussing the tools we developed to understand and combat online misinformation, detect bots and trolls, and characterize their activity, behavior, and strategies, suggesting how they are changing the way researchers and the public study communication networks in the era of automation and artificial intelligence.
Bio: Dr. Emilio Ferrara from USC Viterbi is Research Assistant Professor of Computer Science, Research Team Leader at the Information Sciences Institute, and Associate Director of Data Science Master and Undergraduate programs. His research focus has been at the intersection between developing theory and methods for network analysis and applying them to study socio-technical systems and information networks. He is concerned with understanding the implications of technology and communication networks on human behavior, and their effects on society at large. His work spans from studying the Web and social networks, to collaboration systems and academic networks, from team science to online crowds. Ferrara has published over 130 articles on social networks, machine learning, and network science appeared in venues like the Proceeding of the National Academy of Sciences, and Communications of the ACM. Ferrara received accolades including the 2016 DARPA Young Faculty Award, the 2016 Complex Systems Society Junior Scientific Award, the 2018 DARPA Director's Fellowship, and the 2019 USC Viterbi Research Award. His research is supported by DARPA, IARPA, Air Force, and Office of Naval Research.
Talk 2: The Network Science of COVID-19, by Kristina Lerman
Abstract: The COVID-19 pandemic is a social emergency, as much as a medical one. The novel virus that causes the disease is transmitted through social interactions, when individuals come in physical proximity to an infected individual, and since it can linger on surfaces for days, it can also be transmitted through shared public spaces. Halting the spread of the virus requires behavioral interventions that rapidly change how people interact and use shared spaces, as well as monitoring compliance---in real-time---and effectiveness of these behavioral interventions. An additional challenge is an accurate surveillance with incomplete data, and how to quantify policy implications of limited observation.
Social distancing has become a near-universal intervention to mitigate the spread of COVID-19. Social distancing measures implemented by various states and municipalities include school and business closures and prohibitions on large gatherings. However, the limit on crowds size has varied, ranging from 250 to 2. Does a safe crowd size exist for limiting the spread of the disease? We are creating social networks from mobility data at various levels of granularity. Our results suggest that the many interactions people have maintain the connectivity of the contact network, allowing infections to spread widely.
Bio: Kristina Lerman is a Principal Scientist at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Associate Professor in the USC Computer Science Department. Trained as a physicist, she now applies network analysis and machine learning to problems in computational social science, including crowdsourcing, social network and social media analysis. Her recent work on modeling and understanding cognitive biases in social networks has been covered by the Washington Post, Wall Street Journal, and MIT Tech Review.
Craig Knoblock, Executive Director, USC Information Sciences Institute
Bhaskar Krishnamachari, Director, USC Viterbi Center for CPS and IoT
Audiences: Everyone Is Invited
Contact: Craig Knoblock
Mon, Apr 27, 2020 @ 10:00 AM - 12:00 PM
Ph.D. Defense - Anandi Hira
Mon, April 27, 2020
10:00 am - 12:00 pm
Calibrating COCOMO(R) II for Functional Size Metrics
PhD Candidate: Anandi Hira
Date, Time, and Location: Monday, April 27, 2020 at 10am on https://usc.zoom.us/j/92966727414
Committee: Dr. Barry Boehm, Dr. Shang-hua Teng, Dr. Bherokh Khoshnevis
To date, a generalizable effort estimation model with functional size metrics does not exist. This dissertation provides a generalizable effort estimation model by calibrating the COCOMO II model (a generalizable model that uses lines of code as size input) to use either IFPUG (FPs) or COSMIC Function Points (CFPs) directly as size parameters. The calibrated COCOMO II model estimated within 25% of the actuals 68% of the time for FPs and 70% of the time for CFPs. In comparison, the best of the alternative solutions provided estimates within 25% of the actuals 36% of the time for FPs and 38% of the time for CFPs.
FPs and CFPs have been found to work well in different scenarios: FPs are well-suited for Management Information Systems (MIS) or data-driven applications, while CFPs are also well-suited for embedded, real-time, and web applications. No empirical studies have attempted to characterize software attributes and how FSMs behave differently with respect to them. Five types of software attributes were identified in the datasets used for this dissertation based on the number and complexity of operations and algorithms. The results show that the correlation between FPs/CFPs and effort depends on the amount of complexity operations required with respect to the functional processes.
WebCast Link: https://usc.zoom.us/j/92966727414
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
Thu, Apr 30, 2020 @ 10:00 AM - 01:00 PM
Title: Detecting SQL Antipatterns in Mobile Applications
PhD Candidate: Yingjun Lyu
William GJ Halfond (Chair)
Local databases underpin important features in many mobile applications. However, bad programming practices of using database operations, also called SQL antipatterns, can introduce high resource consumption, affect the responsiveness, and undermine the security of a mobile application.
In my dissertation, I designed and evaluated a framework, called SAND, to detect SQL antipatterns effectively and efficiently in mobile apps. The framework abstracts away the interactions between the application and the database. It provides a language that allows the framework users to query abstractions of application-database relationships and specify SQL antipattern detection tasks. To determine what kinds of application-database relationships should be abstracted, I first conducted a systematic literature review to collect a comprehensive list of SQL antipatterns and their detection approaches. I then analyzed the collected detection approaches and derived the abstractions from them. In order to extract the abstractions from the database access code, I developed a range of static analysis techniques that can analyze the database access code effectively and efficiently. Using experiments on the framework implementation for Android, I showed that SAND can be used to compactly (in 12-74 lines of code) specify SQL antipattern detection tasks previously reported in the literature. These detectors built on top of SAND precisely identified thousands of instances of SQL antipatterns with a precision of at least 99.4%. These detectors were also fast as applying eleven detectors only took an average of forty-one seconds per app. Overall, these results are positive and indicate that my framework can detect all kinds of SQL antipatterns effectively and efficiently in mobile apps.
WebCast Link: https://usc.zoom.us/j/94586333967
Audiences: Everyone Is Invited
Contact: Lizsl De Leon