Software Impacts最新文献

FlowTransformer is a software framework tailored for building Machine Learning based Network Intrusion Detection Systems (NIDSs) leveraging transformer architectures known for their effectiveness in both NLP and more broadly for handling sequences of data. FlowTransformer is a flexible pipeline composed of a definable dataset definition, efficient preprocessing, and a flexible model construction, supporting different input-encodings, transformer models and classification heads. Furthermore, users can extend the framework by defining their own components. FlowTransformer’s contribution lies in its easy customisation, and ability to leverage transformers to enable enhanced long-term pattern detection, offering cybersecurity researchers and practitioners a valuable tool.

The increasing prevalence of high-speed trains necessitates robust analysis tools to ensure the safety and reliability of railway bridges. This paper presents a user-friendly software application designed for the dynamic analysis of railway bridges subjected to high-speed train loadings. Leveraging the semi-analytical modal method, the software offers a balanced approach that combines computational efficiency with high accuracy. Key features include an intuitive interface, rapid analysis capabilities, and reliable prediction of bridge responses, facilitating design optimization and maintenance planning. This software is poised to become an indispensable tool for structural engineers, researchers, and infrastructure planners.

A quantum field lens coding simulator (QF-LCS) is presented on a high-level end-user application software run by CLI $⟷$ GUI with custom commands input by the user to process, analyze, validate QF-LC algorithm (QF-LCA) datasets in a QF-LC Python game. On the low-level system software, measurement data are acquired from quantum computers. The datasets contain these measurement data, processed and classified according to QF-LCA circuit design and steps determining system states and their prediction. This software, impacts advances made in applied sciences, statistics, law and physics, where data validation of samples including system simulation projecting and predicting events are achieved.

Despite its age, Fortran remains essential in many scientific fields. Ensuring code quality in long-term projects with evolving standards is critical, but few tools analyse Fortran, and they are not free. We present FortranAnalyser, a multi-platform, static analysis tool designed to enhance Fortran code quality. This paper outlines its development, features, and comparison with other tools. Additionally, we demonstrate its effectiveness through real-world applications, such as improving the Fortran code in a major global climate model.

In scientific imaging, deep learning has become a pivotal tool for image analytics. However, handling large volumetric datasets, which often exceed the memory capacity of standard GPUs, require special attention when subjected to deep learning efforts. This paper introduces qlty, a toolkit designed to address these challenges through tensor management techniques. qlty offers robust methods for subsampling, cleaning, and stitching of large-scale spatial data, enabling effective training and inference even in resource-limited environments.

“SpectroChat”, a user-friendly, windows-based graphical user interface (GUI) for chemometric analysis, is designed to avoid the complexity of high-level programming and expensive software subscriptions. Developed in Python, this software offers versatile data partitioning, spectral pre-processing, and an optimizable genetic algorithm (GA) for feature selection for spectroscopic data analysis. SpectroChat enables the execution of multivariate regression analyses with options for hyperparameter adjustments and saving model diagnostics. This open-source software, designed to alleviate resource constraints, streamlines chemometric studies without requiring advanced programming platforms.

PixSim is a flexible, open-source forest growth simulator designed to operate at the pixel level of high-resolution, wall-to-wall forest resource maps generated through remote sensing approaches. PixSim addresses the need to adapt forest growth simulators to the data produced by modern remote sensing-based forest inventories, rather than relying on stand-level averages from traditional field-based inventories. By operating at the pixel level, PixSim captures intra-stand variability in high-resolution forest resource maps, which is often overlooked by stand-level simulators. This capability aligns with the current focus on precision forestry, aimed at improving management decisions with localized data and small-scale management. Implemented in the R programming language, PixSim features minimal package dependencies, provides flexibility and scalability, and has been optimized for high-resolution, large-scale simulations, ensuring efficient computation. The simulator’s flexibility and open-source nature support the incorporation of management modules and the inclusion of climate change scenarios in simulations.