13 citations · 60 across the 14 of their papers we have counts for
7 papers · 1 filter
Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science
Huichen Yang, Carlos A. Aguirre, Maria F. De La Torre +10
This paper describes a machine learning and data science pipeline for structured information extraction from documents, implemented as a suite of open-source tools and extensions t…
A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams
Avishek Bose, Vahid Behzadan, Carlos Aguirre +1
We present a new machine learning and text information extraction approach to detection of cyber threat events in Twitter that are novel (previously non-extant) and developing (mar…
Hybridized Threshold Clustering for Massive Data
Jianmei Luo, ChandraVyas Annakula, Aruna Sai Kannamareddy +3
As the size of datasets become massive, many commonly-used clustering algorithms (for example, -means or hierarchical agglomerative clustering (HAC) require prohibitive comp…
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies
Vahid Behzadan, William Hsu
This paper proposes a novel scheme for the watermarking of Deep Reinforcement Learning (DRL) policies. This scheme provides a mechanism for the integration of a unique identifier w…
Adversarial Exploitation of Policy Imitation
Vahid Behzadan, William Hsu
This paper investigates a class of attacks targeting the confidentiality aspect of security in Deep Reinforcement Learning (DRL) policies. Recent research have established the vuln…
Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)
Vahid Behzadan, William Hsu
This paper investigates the effectiveness of adversarial training in enhancing the robustness of Deep Q-Network (DQN) policies to state-space perturbations. We first present a form…
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies
Vahid Behzadan, William Hsu
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach fo…