相关论文: A tale of two toolkits, report the first: benchmar…

Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Training a deep network is usually a very time-consuming process.…

This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive -- the state of the art repository of time series data.…

Time series classification is an important analytical task across diverse domains. However, its practical application is often hindered by the scarcity of labeled data and the requirement for substantial computational resources. To address…

This manuscript describes the software package SCOUT, which analyzes, characterizes, and corrects one-dimensional signals. Specifically, it allows to check and correct for stationarity, detect spurious samples, check for normality, check…

In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early and risk paying the…

Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as…

Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised learning problem. However, it suffers from significant out-of-distribution generalization challenges. We reveal three limitations of…

Over the past years, the industrial sector has seen many innovations brought about by automation. Inherent in this automation is the installation of sensor networks for status monitoring and data collection. One of the major challenges in…

Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking models in this regime remains underexplored. We formalize dense benchmark ranking under test-time scaling and introduce Scorio, a library that…

The Scrum framework has gained widespread adoption in the industry for its emphasis on collaboration and continuous improvement. However, it has not reached a similar relevance in Software Engineering (SE) curricula. This work reports the…

Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine learning tasks to standardize the setup, execution, and…

Three controlled experiments testing the benefits that Java programmers gain from using the Two-Tier Programming Toolkit have recently been concluded. The first experiment offers statistically significant evidence (p-value: 0.02) that…

Generative models have demonstrated remarkable potential in time series analysis tasks, like synthesis, forecasting, imputation, etc. However, offering limited coverage for generative models, existing time series libraries are mainly…

Time serves as the foundation of modern society and will continue to grow in value in the future world. Unlike previous research papers, authors delve into various time sources, ranging from atomic time and GPS time to quartz time.…

This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classification prowess of…

Time awareness is critical to a broad range of emerging applications -- in Cyber-Physical Systems and Internet of Things -- running on commodity platforms and operating systems. Traditionally, time is synchronized across devices through a…

Test Suite Minimization (TSM) reduces the size of test suites while preserving their fault detection capability. In black-box TSM, reduction is performed without relying on production-code instrumentation. While several black-box TSM…

Published studies on agile effort estimation predominantly focus on comparisons of the accuracy of different estimation methods, while efficiency comparisons, i.e. how much time the estimation methods consume was not in the forefront.…

Runtime verification is concerned with monitoring program traces. In particular, stream runtime verification (SRV) takes the program trace as input streams and incrementally derives output streams. SRV can check logical properties and…

There exist several data-driven approaches that enable us model time series data including traditional regression-based modeling approaches (i.e., ARIMA). Recently, deep learning techniques have been introduced and explored in the context…