arXiv subjects

Jung

Publications and source records attributed to Jung.

3 recordsLinked to original sources

arXiv2024

Beyond Beats: A Recipe to Song Popularity? A machine learning approach

Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics and their impact on popularity. Results: Ordinary Least Squares (OLS) regression analysis reveals genre as the primary influencer of popularity, with notable trends over time. MARS modelling highlights the complex relationship between variables, particularly with features like instrumentalness and duration. Random Forest and XGBoost models underscore the importance of genre, especially EDM, in predicting popularity. Despite variations in performance, Random Forest emerges as the most effective model, improving prediction accuracy by 7.1% compared to average scores. Despite the importance of genre, predicting song popularity remains challenging, as observed variations in music-related features suggest complex interactions between genre and other factors. Consequently, while certain characteristics like loudness and song duration may impact popularity scores, accurately predicting song success remains elusive.

cs.IR↗

arXiv2023

A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks

Ordinary and partial differential equations (DE) are used extensively in scientific and mathematical domains to model physical systems. Current literature has focused primarily on deep neural network (DNN) based methods for solving a specific DE or a family of DEs. Research communities with a history of using DE models may view DNN-based differential equation solvers (DNN-DEs) as a faster and transferable alternative to current numerical methods. However, there is a lack of systematic surveys detailing the use of DNN-DE methods across physical application domains and a generalized taxonomy to guide future research. This paper surveys and classifies previous works and provides an educational tutorial for senior practitioners, professionals, and graduate students in engineering and computer science. First, we propose a taxonomy to navigate domains of DE systems studied under the umbrella of DNN-DE. Second, we examine the theory and performance of the Physics Informed Neural Network (PINN) to demonstrate how the influential DNN-DE architecture mathematically solves a system of equations. Third, to reinforce the key ideas of solving and discovery of DEs using DNN, we provide a tutorial using DeepXDE, a Python package for developing PINNs, to develop DNN-DEs for solving and discovering a classic DE, the linear transport equation.

cs.NE↗

arXiv2018

Rationality-proof consensus: extended abstract

Blockchain systems benefit from lessons in prior art such as fault tolerance, distributed systems, peer-to-peer systems, and game theory. In this paper we argue that blockchain algorithms should tolerate both rational (self-interested) users and Byzantine (malicious) ones, rather than assuming all non-Byzantine users are altruistic and follow the protocols blindly. Such algorithms are called BAR-tolerant [1]. To design a BAR-tolerant system, one can follow these three steps: clearly define the utility function for the rational users, prove the algorithm is such that there is no benefit from unilaterally deviating (that is, it's a Byzantine Nash Equilibrium), then prove the algorithm correct assuming the rational actors follow the protocol. We present an example attack by rational users: the gatekeeping attack, where members of a system selfishly decide to prevent newcomers from joining. This attack may affect any stake-based system where the existing members prevent newcomers from making a stake, and essentially form a cartel. We then sketch a BAR-tolerant consensus protocol for blockchain that can defend against this attack. It relies on a strict order to decide who gets to propose a new block (so there's no need to race to solve a crypto puzzle) and it relies on hardware ID tokens to make sure every computer is only represented at most once as a block proposer to mitigate Sybil attacks. It also defends against the gatekeeper attack. The BAR-tolerant approach is naturally also applicable to other blockchain algorithms.

cs.DC↗