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Found 11 papers, 3 papers with code
Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts
This paper explores the transformative potential of computer-assisted textual analysis in enhancing instructional quality through in-depth insights from educational artifacts.
From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews
Obtaining stakeholders' diverse experiences and opinions about current policy in a timely manner is crucial for policymakers to identify strengths and gaps in resource allocation, thereby supporting effective policy design and implementation.
Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference
In this talk, we introduce Merlin HugeCTR.
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure.
Measuring and Clustering Network Attackers using Medium-Interaction Honeypots
Network honeypots are often used by information security teams to measure the threat landscape in order to secure their networks.
Sparse deep computer-generated holography for optical microscopy
Computer-generated holography (CGH) has broad applications such as direct-view display, virtual and augmented reality, as well as optical microscopy.
Secret Sharing based Secure Regressions with Applications
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Specifically, (i) we develop a new attack model that jointly optimizes adversarial inputs and poisoned models; (ii) with both analytical and empirical evidence, we reveal that there exist intriguing "mutual reinforcement" effects between the two attack vectors -- leveraging one vector significantly amplifies the effectiveness of the other; (iii) we demonstrate that such effects enable a large design spectrum for the adversary to enhance the existing attacks that exploit both vectors (e. g., backdoor attacks), such as maximizing the attack evasiveness with respect to various detection methods; (iv) finally, we discuss potential countermeasures against such optimized attacks and their technical challenges, pointing to several promising research directions.
Transfer Learning for Entity Recognition of Novel Classes
Our results empirically demonstrate when each of the published approaches tends to do well.
Entity Extraction using GAN
Named Entity Recognition (NER)
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