Tootfinder

Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries
Yihao Hu, Amir Gilad, Kristin Stephens-Martinez, Sudeepa Roy, Jun Yang
https://arxiv.org/abs/2404.04352

Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries
We describe a system called Qr-Hint that, given a (correct) target query Q* and a (wrong) working query Q, both expressed in SQL, provides actionable hints for the user to fix the working query so that it becomes semantically equivalent to the target. It is particularly useful in an educational setting, where novices can receive help from Qr-Hint without requiring extensive personal tutoring. Since there are many different ways to write a correct query, we do not want to base our hints complete…

Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries
Yihao Hu, Amir Gilad, Kristin Stephens-Martinez, Sudeepa Roy, Jun Yang
https://arxiv.org/abs/2404.04352

Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries
We describe a system called Qr-Hint that, given a (correct) target query Q* and a (wrong) working query Q, both expressed in SQL, provides actionable hints for the user to fix the working query so that it becomes semantically equivalent to the target. It is particularly useful in an educational setting, where novices can receive help from Qr-Hint without requiring extensive personal tutoring. Since there are many different ways to write a correct query, we do not want to base our hints complete…

Semantically Aligned Question and Code Generation for Automated Insight Generation
Ananya Singha, Bhavya Chopra, Anirudh Khatry, Sumit Gulwani, Austin Z. Henley, Vu Le, Chris Parnin, Mukul Singh, Gust Verbruggen
https://arxiv.org/abs/2405.01556

Semantically Aligned Question and Code Generation for Automated Insight Generation
Automated insight generation is a common tactic for helping knowledge workers, such as data scientists, to quickly understand the potential value of new and unfamiliar data. Unfortunately, automated insights produced by large-language models can generate code that does not correctly correspond (or align) to the insight. In this paper, we leverage the semantic knowledge of large language models to generate targeted and insightful questions about data and the corresponding code to answer those qu…

This https://arxiv.org/abs/2405.01461 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

SATO: Stable Text-to-Motion Framework
Is the Text to Motion model robust? Recent advancements in Text to Motion models primarily stem from more accurate predictions of specific actions. However, the text modality typically relies solely on pre-trained Contrastive Language-Image Pretraining (CLIP) models. Our research has uncovered a significant issue with the text-to-motion model: its predictions often exhibit inconsistent outputs, resulting in vastly different or even incorrect poses when presented with semantically similar or ide…

Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration
Shivam Singh, Karthik Swaminathan, Raghav Arora, Ramandeep Singh, Ahana Datta, Dipanjan Das, Snehasis Banerjee, Mohan Sridharan, Madhava Krishna
https://arxiv.org/abs/2404.03587…

Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration
An agent assisting humans in daily living activities can collaborate more effectively by anticipating upcoming tasks. Data-driven methods represent the state of the art in task anticipation, planning, and related problems, but these methods are resource-hungry and opaque. Our prior work introduced a proof of concept framework that used an LLM to anticipate 3 high-level tasks that served as goals for a classical planning system that computed a sequence of low-level actions for the agent to achie…

5th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2024) is co-organized by my colleagues from @…, co-located with SIGIR 2024.
paper deadline: April 25, 2024
website: http://

Bias Mitigation via Compensation: A Reinforcement Learning Perspective
Nandhini Swaminathan, David Danks
https://arxiv.org/abs/2404.19256 https://

Bias Mitigation via Compensation: A Reinforcement Learning Perspective
As AI increasingly integrates with human decision-making, we must carefully consider interactions between the two. In particular, current approaches focus on optimizing individual agent actions but often overlook the nuances of collective intelligence. Group dynamics might require that one agent (e.g., the AI system) compensate for biases and errors in another agent (e.g., the human), but this compensation should be carefully developed. We provide a theoretical framework for algorithmic compens…

This https://arxiv.org/abs/2309.14262 has been replaced.
initial toot: https://mastoxiv.page/@a…

Chiral Meissner effect in time-reversal invariant Weyl superconductors
Weyl semimetals have nodes in their electronic structure at which electrons attain a definite chirality. Due to the chiral anomaly, the non-conservation of charges with given chirality, the axion term appears in their effective electromagnetic action. We determine how this affects the properties of time-reversal invariant Weyl {\it superconductors} (SCs) in the London regime. For type II SCs the axion coupling generates magnetic $B$-fields transverse to vortices, which become unstable at a crit…

Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration
Shivam Singh, Karthik Swaminathan, Raghav Arora, Ramandeep Singh, Ahana Datta, Dipanjan Das, Snehasis Banerjee, Mohan Sridharan, Madhava Krishna
https://arxiv.org/abs/2404.03587…

Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration
An agent assisting humans in daily living activities can collaborate more effectively by anticipating upcoming tasks. Data-driven methods represent the state of the art in task anticipation, planning, and related problems, but these methods are resource-hungry and opaque. Our prior work introduced a proof of concept framework that used an LLM to anticipate 3 high-level tasks that served as goals for a classical planning system that computed a sequence of low-level actions for the agent to achie…

This https://arxiv.org/abs/2404.13078 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Empowering Interdisciplinary Research with BERT-Based Models: An Approach Through SciBERT-CNN with Topic Modeling
Researchers must stay current in their fields by regularly reviewing academic literature, a task complicated by the daily publication of thousands of papers. Traditional multi-label text classification methods often ignore semantic relationships and fail to address the inherent class imbalances. This paper introduces a novel approach using the SciBERT model and CNNs to systematically categorize academic abstracts from the Elsevier OA CC-BY corpus. We use a multi-segment input strategy that proc…