More Like this

1. Optimistic Hybrid Analysis: Accelerating Dynamic Analysis through Predicated Static Analysis

   https://doi.org/10.1145/3173162.3177153  

   Devecsery, David; Chen, Peter M.; Flinn, Jason; Narayanasamy, Satish (January 2018, Architectural Support for Programming Languages and Operating Systems)
2. Program Analysis for Adaptive Data Analysis

   https://doi.org/10.1145/3656414  

   Liu, Jiawen; Qu, Weihao; Gaboardi, Marco; Garg, Deepak; Ullman, Jonathan (June 2024, Proceedings of the ACM on Programming Languages)

   Data analyses are usually designed to identify some property of the population from which the data are drawn, generalizing beyond the specific data sample. For this reason, data analyses are often designed in a way that guarantees that they produce a low generalization error. That is, they are designed so that the result of a data analysis run on a sample data does not differ too much from the result one would achieve by running the analysis over the entire population. An adaptive data analysis can be seen as a process composed by multiple queries interrogating some data, where the choice of which query to run next may rely on the results of previous queries. The generalization error of each individual query/analysis can be controlled by using an array of well-established statistical techniques. However, when queries are arbitrarily composed, the different errors can propagate through the chain of different queries and bring to a high generalization error. To address this issue, data analysts are designing several techniques that not only guarantee bounds on the generalization errors of single queries, but that also guarantee bounds on the generalization error of the composed analyses. The choice of which of these techniques to use, often depends on the chain of queries that an adaptive data analysis can generate. In this work, we consider adaptive data analyses implemented as while-like programs and we design a program analysis which can help with identifying which technique to use to control their generalization errors. More specifically, we formalize the intuitive notion ofadaptivityas a quantitative property of programs. We do this because the adaptivity level of a data analysis is a key measure to choose the right technique. Based on this definition, we design a program analysis for soundly approximating this quantity. The program analysis generates a representation of the data analysis as a weighted dependency graph, where the weight is an upper bound on the number of times each variable can be reached, and uses a path search strategy to guarantee an upper bound on the adaptivity. We implement our program analysis and show that it can help to analyze the adaptivity of several concrete data analyses with different adaptivity structures. 

   more » « less
3. Traffic Analysis Resistant Network (TARN) Anonymity Analysis

   https://doi.org/10.1109/ICNP.2019.8888134  

   Tusing, Nathan; Oakley, Jonathan; Barrineau, Geddings; Yu, Lu; Wang, Kuang-Ching; Brooks, Richard R. (October 2019, Proceedings of 2019 IEEE 27th International Conference on Network Protocols (ICNP))

   null (Ed.)
4. Traffic Analysis Resistant Network (TARN) Anonymity Analysis

   Nathan Tusing, Jonathan Oakley (October 2019, Proceedings for the 2019 Midscale Education and Research Infrastructure Tools (MERIT) Workshop)
5. Incorporating Sentiment Analysis with Epistemic Network Analysis to Enhance Discourse Analysis of Twitter Data

   https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_26  

   Misiejuk, Kamila; Scianna, Jennifer; Kaliisa, Rogers; Vachuska, Karl; Shaffer, David W. (January 2021, Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings)

   Ruis, Andrew R.; Lee, Seung B. (Ed.)

   While there has been much growth in the use of microblogging platforms (e.g., Twitter) to share information on a range of topics, researchers struggle to analyze the large volumes of data produced on such platforms. Established methods such as Sentiment Analysis (SA) have been criticized over their inaccuracy and limited analytical depth. In this exploratory methodological paper, we propose a combination of SA with Epistemic Network Analysis (ENA) as an alternative approach for providing richer qualitative and quantitative insights into Twitter discourse. We illustrate the application and potential use of these approaches by visualizing the differences between tweets directed or discussing Democrats and Republicans after the COVID-19 Stimulus Package announcement in the US. SA was integrated into ENA models in two ways: as a part of the blocking variable and as a set of codes. Our results suggest that incorporating SA into ENA allowed for a better understanding of how groups viewed the components of the stimulus issue by splitting them by sentiment and enabled a meaningful inclusion of data with singular subject focus into the ENA models. 

   more » « less