19 citations · 51 across the 7 of their papers we have counts for
9 papers
: Rigorous and Efficient Directed Greybox Fuzzing
Abhishek Shah, Dongdong She, Samanway Sadhu +3
Directed greybox fuzzing is a popular technique for targeted software testing that seeks to find inputs that reach a set of target sites in a program. Most existing directed greybo…
Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis
Dongdong She, Abhishek Shah, Suman Jana
Seed scheduling, the order in which seeds are selected, can greatly affect the performance of a fuzzer. Existing approaches schedule seeds based on their historical mutation data,…
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations
Haode Qi, Lin Pan, Atin Sood +4
Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users' text input. There are three primary challe…
Multilingual BERT Post-Pretraining Alignment
Lin Pan, Chung-Wei Hang, Haode Qi +3
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models. Usi…
Iterative Data Programming for Expanding Text Classification Corpora
Neil Mallinar, Abhishek Shah, Tin Kam Ho +2
Real-world text classification tasks often require many labeled training examples that are expensive to obtain. Recent advancements in machine teaching, specifically the data progr…
Fine Grained Dataflow Tracking with Proximal Gradients
Gabriel Ryan, Abhishek Shah, Dongdong She +2
Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel…
Neutaint: Efficient Dynamic Taint Analysis with Neural Networks
Dongdong She, Yizheng Chen, Abhishek Shah +2
Dynamic taint analysis (DTA) is widely used by various applications to track information flow during runtime execution. Existing DTA techniques use rule-based taint-propagation, wh…
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Tahira Naseem, Abhishek Shah, Hui Wan +3
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score…
Bootstrapping Conversational Agents With Weak Supervision
Neil Mallinar, Abhishek Shah, Rajendra Ugrani +9
Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a…