Tootfinder

This https://arxiv.org/abs/2406.04995 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Data2Neo -- A Tool for Complex Neo4j Data Integration
This paper introduces Data2Neo, an open-source Python library for converting relational data into knowledge graphs stored in Neo4j databases. With extensive customization options and support for continuous online data integration from various data sources, Data2Neo is designed to be user-friendly, efficient, and scalable to large datasets. The tool significantly lowers the barrier to entry for creating and using knowledge graphs, making this increasingly popular form of data representation acce…

Predicting Heart Activity from Speech using Data-driven and Knowledge-based features
Gasser Elbanna, Zohreh Mostaani, Mathew Magimai. -Doss
https://arxiv.org/abs/2406.06341

Predicting Heart Activity from Speech using Data-driven and Knowledge-based features
Accurately predicting heart activity and other biological signals is crucial for diagnosis and monitoring. Given that speech is an outcome of multiple physiological systems, a significant body of work studied the acoustic correlates of heart activity. Recently, self-supervised models have excelled in speech-related tasks compared to traditional acoustic methods. However, the robustness of data-driven representations in predicting heart activity remained unexplored. In this study, we demonstrate…

This https://arxiv.org/abs/2312.13985 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
Pufferfish privacy is a flexible generalization of differential privacy that allows to model arbitrary secrets and adversary's prior knowledge about the data. Unfortunately, designing general and tractable Pufferfish mechanisms that do not compromise utility is challenging. Furthermore, this framework does not provide the composition guarantees needed for a direct use in iterative machine learning algorithms. To mitigate these issues, we introduce a Rényi divergence-based variant of Pufferfish…

Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge
Rui Liu, Zening Ma
https://arxiv.org/abs/2406.06646 https://

Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge
Speech Self-Supervised Learning (SSL) has demonstrated considerable efficacy in various downstream tasks. Nevertheless, prevailing self-supervised models often overlook the incorporation of emotion-related prior information, thereby neglecting the potential enhancement of emotion task comprehension through emotion prior knowledge in speech. In this paper, we propose an emotion-aware speech representation learning with intensity knowledge. Specifically, we extract frame-level emotion intensities…

This https://arxiv.org/abs/2406.04995 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Data2Neo -- A Tool for Complex Neo4j Data Integration
This paper introduces Data2Neo, an open-source Python library for converting relational data into knowledge graphs stored in Neo4j databases. With extensive customization options and support for continuous online data integration from various data sources, Data2Neo is designed to be user-friendly, efficient, and scalable to large datasets. The tool significantly lowers the barrier to entry for creating and using knowledge graphs, making this increasingly popular form of data representation acce…

This https://arxiv.org/abs/2309.04889 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A subspace constrained randomized Kaczmarz method for structure or external knowledge exploitation
We study a version of the randomized Kaczmarz algorithm for solving systems of linear equations where the iterates are confined to the solution space of a selected subsystem. We show that the subspace constraint leads to an accelerated convergence rate, especially when the system has approximately low-rank structure. On Gaussian-like random data, we show that it results in a form of dimension reduction that effectively increases the aspect ratio of the system. Furthermore, this method serves as…

Excited to share the insightful papers for the panel I organised on 'Epistemic Justice and the Knowledge Commons' with the incredible scholars Katherine Blouin, Girish Daswani, Blane Harvey, Julia Paulson, and humbly, me.
We presented at the 2024 CASID-ACEDI Conference on global development as part of the Congress of the Federation for the Humanities and Social Sciences.
It was great to be back in Montreal!
@…

SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models
Kehua Feng, Keyan Ding, Weijie Wang, Xiang Zhuang, Zeyuan Wang, Ming Qin, Yu Zhao, Jianhua Yao, Qiang Zhang, Huajun Chen
https://arxiv.org/abs/2406.09098

SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models
The burgeoning utilization of Large Language Models (LLMs) in scientific research necessitates advanced benchmarks capable of evaluating their understanding and application of scientific knowledge comprehensively. To address this need, we introduce the SciKnowEval benchmark, a novel framework that systematically evaluates LLMs across five progressive levels of scientific knowledge: studying extensively, inquiring earnestly, thinking profoundly, discerning clearly, and practicing assiduously. Th…

US College Net Price Prediction Comparing ML Regression Models
Zalak Patel, Ayushi Porwal, Kajal Bhandare, Jongwook Woo
https://arxiv.org/abs/2406.08071 ht…

US College Net Price Prediction Comparing ML Regression Models
This paper will illustrate the usage of Machine Learning algorithms on US College Scorecard datasets. For this paper, we will use our knowledge, research, and development of a predictive model to compare the results of all the models and predict the public and private net prices. This paper focuses on analyzing US College Scorecard data from data published on government websites. Our goal is to use four machine learning regression models to develop a predictive model to forecast the equitable…

Finished “Mr. Penumbra’s 24-Hour Bookstore” by Robin Sloan.
It’s escapism and without any real suspense or consequence. We just float along as the answers are revealed without much delay.
If anything, it’s practically an advertisement for Google circa 2014. We’re compelled to notice the friction of technology versus the “old knowledge” of books, typography, etc. Also a bit cumbersome & heavy-handed.
The good bits mostly outweigh the bad. 3 stars.

Mr. Penumbra's 24-Hour Bookstore (Mr. Penumbra's 24-Hou…
Global conspiracy, complex code-breaking, high-tech dat…