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arXiv:2405.19433v1 Announce Type: new Abstract: While current automated essay scoring (AES) methods show high agreement with human raters, their scoring mechanisms are not fully explored. Our proposed method, using counterfactual intervention assisted by Large Language Models (LLMs), reveals that when scoring essays, BERT-like models primarily focus on sentence-level features, while LLMs are attuned to conventions, language complexity, as well as organization, indicating a more comprehensive alignment with scoring rubrics. Moreover, LLMs can discern counterfactual interventions during feedback. Our approach improves understanding of neural AES methods and can also apply to other domains seeking transparency in model-driven decisions. The codes and…

Data quality monitoring. Data testing. Data observability. Say that five times fast.  Are they different words for the same thing? Unique approaches to the same problem? Something else entirely? And more importantly-do you really need all three? Like everything in data engineering, data quality management is evolving at lightning speed. The meteoric rise of data and AI in the enterprise has made data quality a zero day risk for modern businesses-and THE problem to solve for data teams. With so much overlapping terminology, it's not always clear how it all fits together-or if it fits together.  But contrary to what…

We are excited to announce the general availability of Row Filters and Column Masks in Unity Catalog on AWS, Azure and GCP! Managing fine-grained access controls on rows and columns in tables is critical to ensure data security and meet compliance. With Unity Catalog, you can use standard SQL functions to define row filters and column masks, allowing fine-grained access controls on rows and columns. Row Filters let you control which subsets of your tables' rows are visible to hierarchies of groups and users within your organization. Column Masks let you redact your table values based on the same dimensions.…

[Submitted on 29 May 2024] View a PDF of the paper titled Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning, by Alexander Politowicz and 2 other authors View PDF HTML (experimental) Abstract:Designing Reinforcement Learning (RL) solutions for real-life problems remains a significant challenge. A major area of concern is safety. "Shielding" is a popular technique to enforce safety in RL by turning user-defined safety specifications into safe agent behavior. However, these methods either suffer from extreme learning delays, demand extensive human effort in designing models and safe domains in the problem, or require pre-computation. In this paper,…

arXiv:2405.19413v1 Announce Type: new Abstract: Thermal cameras are an important tool for agricultural research because they allow for non-invasive measurement of plant temperature, which relates to important photochemical, hydraulic, and agronomic traits. Utilizing low-cost thermal cameras can lower the barrier to introducing thermal imaging in agricultural research and production. This paper presents an approach to improve the temperature accuracy and image quality of low-cost thermal imaging cameras for agricultural applications. Leveraging advancements in computer vision techniques, particularly deep learning networks, we propose a method, called $textbf{VisTA-SR}$ ($textbf{Vis}$ual & $textbf{T}$hermal $textbf{A}$lignment and $textbf{S}$uper-$textbf{R}$esolution Enhancement) that combines RGB and thermal images to…

arXiv:2405.19426v1 Announce Type: new Abstract: Reading fluency assessment is a critical component of literacy programmes, serving to guide and monitor early education interventions. Given the resource intensive nature of the exercise when conducted by teachers, the development of automatic tools that can operate on audio recordings of oral reading is attractive as an objective and highly scalable solution. Multiple complex aspects such as accuracy, rate and expressiveness underlie human judgements of reading fluency. In this work, we investigate end-to-end modeling on a training dataset of children's audio recordings of story texts labeled by human experts. The pre-trained wav2vec2.0 model is…

In the fast-evolving FinTech sector, data quality and accuracy are non-negotiable. High-quality data is fundamental to informed decision-making, regulatory compliance, and customer satisfaction. This article delves into essential strategies for maintaining data quality and accuracy in FinTech, ensuring firms can thrive in a competitive landscape.   Define Data Quality Standards To begin with, FinTech companies must establish explicit criteria for data accuracy, completeness, consistency, and timeliness. Leveraging industry standards such as DAMA DMBOK (Data Management Body of Knowledge) and ISO 8000 ensures a robust framework for evaluating and maintaining data quality. These standards provide comprehensive guidelines that help organizations define…

arXiv:2405.19376v1 Announce Type: new Abstract: Data poisoning attacks pose a significant threat to the integrity of machine learning models by leading to misclassification of target distribution test data by injecting adversarial examples during training. Existing state-of-the-art (SoTA) defense methods suffer from a variety of limitations, such as significantly reduced generalization performance, specificity to particular attack types and classifiers, and significant overhead during training, making them impractical or limited for real-world applications. In response to this challenge, we introduce a universal data purification method that defends naturally trained classifiers from malicious white-, gray-, and black-box image poisons by applying a universal…

arXiv:2405.19387v1 Announce Type: new Abstract: Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring. While numerous surveys focus on conventional VAD methods, they often lack depth in exploring specific approaches and emerging trends. This survey explores deep learning-based VAD, expanding beyond traditional supervised training paradigms to encompass emerging weakly supervised, self-supervised, and unsupervised approaches. A prominent feature of this review is the investigation of core challenges within the VAD paradigms including large-scale datasets, features extraction, learning methods, loss functions, regularization, and anomaly score prediction. Moreover, this review also investigates the vision language…

arXiv:2405.19425v1 Announce Type: new Abstract: Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particular application remains an art. It is thus intriguing to answer a critical question: Given a task, how can we build a team of LLM agents to solve it effectively? Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. It dynamically forms and manages teams for each step of a task-solving process, utilizing nested group conversations and reflection to…