Software Impacts最新文献

License Plate Recognition (LPR) sensors often fail to detect vehicles or to identify all plate numbers correctly. This noise results in missing digits or an incomplete route of a vehicle, for example, missing one node (LPR camera) in the route. Addressing these issues, RouteRecoverer creates the route followed by a vehicle while efficiently recovering absent LPR plate digits, and filling gaps in routes. For example, when a vehicle is detected by LPR A and C, with the only route between them being B, our tool seamlessly retrieves the missing information, improving the data output.

SPHMPS 1.0, developed within a Lagrangian framework, offers a robust solution for modeling multi-structure collision problems involving large plastic deformation and inherent thermal effects. Utilizing its innovative algorithm, SPHMPS 1.0 emerges as a versatile tool for researchers in the field of fluid-rigid-elastic structure interactions. By providing a comprehensive framework tailored to address these complex phenomena, SPHMPS 1.0 facilitates reproducible, extendable, and efficient research endeavors. Implemented in Fortran, its flexible algorithm ensures adaptability to a wide range of applications requiring solutions for fluid-rigid-elastic structure interaction problems.

LATTIN is a Python-based tool for Lagrangian atmospheric moisture and heat tracking. It can read input data from the Lagrangian FLEXPART and FLEXPART-WRF models. Features include parallel reading of atmospheric parcel trajectories and user custom threshold criteria. It complements and improves existing tools by including several tracking approaches and also by its non-dependence on the horizontal resolution of the input or output grid. LATTIN provides a compact tool for Lagrangian atmospheric moisture and heat tracking, which will support a wide range of research to understand future changes in the hydrological cycle and extreme temperature events.

This paper presents a user-friendly version of Persistent Homology (PH) graph code to model financial market structures and changes. By leveraging Topological Data Analysis (TDA), the code offers an effective approach for analyzing high-dimensional stock data, enabling the identification of persistent topological features indicative of market changes. The code’s potential applications in financial stability prediction, investment strategy development, and educational advancement are discussed. This contribution aims to facilitate the adoption of PH techniques in finance, promising significant implications for academic research and practical market analysis.

This paper presents user-friendly code for the implementation of a loss function for neural network time series models that exploits the topological structures of financial data. By leveraging the recently-discovered presence of topological features present in financial time series data, the code offers a more effective approach for creating forecasting models for such data given the fact that it allows neural network models to not only learn temporal patterns of the data, but also topological patterns. This paper aims to facilitate the adoption of the loss function proposed by Souto and Moradi (2024a) in financial time series by practitioners and researchers.

Many graph mining algorithms process large graphs with several passes and suffers from huge I/O cost. GraphIdx, an open-source C library, facilitates a memory-efficient indexing of large graphs to reduce that I/O cost. GraphIdx indexes a block of graph data for a set of nodes based on the empirical evaluation of edges. Due to the indexed graph, graph mining algorithms can access and process only the related nodes and their edges instead of scanning entire graph. As a result, the number of I/Os is significantly reduced. Moreover, GraphIdx accredited algorithms can process graphs in parallel due to the indexed data.

We present a computer program called Evolutionary Cellular Automaton (ECA) in Python, which simulates in silico, in the simplest form found, all the known processes and mechanisms underlying natural selection. Mathematical and statistical functions condition the dynamics of real populations, through variables that in each habitat and in each organism acquire a specific parameter. In ECA, we have simplified these variables by working with mean and standard values and by simplifying the interactions between species in such a way that the mechanisms underlying natural selection also work in ECA, but in a digital environment under controlled and reproducible conditions.

The PyMLDA-Machine Learning for Damage Assessment is an open-source software developed for damage pattern recognition, detection, and quantification that uses the system’s vibration signatures as input. The software automatically evaluates the structure or system integrity by detecting and assessing structural damage by combining supervised, unsupervised, and regression Machine Learning (ML) algorithms. It employs different damage index techniques based on the system’s dynamic response, such as natural or frequency response frequency, to normalise the dataset input of the software. The classification ML route effectively identifies and categorises the damage, even when the integrity condition of the structure is unknown. The regression algorithm quantifies the damage levels, considering the uncertainty quantification in the estimation. The PyMLDA employs a range of validation and cross-validation metrics to evaluate the effectiveness and accuracy of these ML algorithms in detecting and diagnosing structural damage.

Governify is a service governance framework designed to enhance service operation by providing automated audit capabilities. It enables the creation of customized microservice architectures to fit various domains. This framework has been applied in real scenarios in both Industry and Academy where it has served researchers and practitioners in service governance as both a visual analytic tool and a test bed for experiments. Governify has proved its ability to gather insights into potential risks tied to noncompliance and to design and monitor best practices in forms of agreements.

Dental Loop Signals (DLS) offers a unique approach to biomedical signal-processing, employing deep learning to convert archived images of mandibular muscle activity during dynamic functions into signal data. DLS, processed through unsupervised learning, introduces a cluster-centric signal processing method, enhancing data normalisation for broad applicability. The modular design of the software facilitates customisable use in Temporomandibular Joint (TMJ) and orthopaedic clinics for long-term patient follow-ups and retrospective research. The software’s robustness increases with a larger dataset of electromyographic muscle activities, promising versatility across devices, clinics, and timeframes.