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SpaceRL — A reinforcement learning-based knowledge graph driver

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-12 DOI: 10.1016/j.softx.2025.102078

Miguel Bermudo, Daniel Ayala, Inma Hernández, David Ruiz, Miguel Toro

Knowledge Graphs are powerful data structures used by large IT companies and the scientific community alike. They aid in the representation of related information by means of nodes connected through links indicating types of relations. These graphs are used as the basis for several smart applications, such as question answering or product recommendation. However, they are built in an automated unsupervised way, which leads to gaps in information, usually in the form of missing links between related entities in the original data source, which have to be added later by completion techniques.

SpaceRL is an end-to-end Python framework designed for the generation of reinforcement learning (RL) agents, which can be used to complete knowledge graphs through link discovery. The purpose of the generated agents is to help identify missing links in a knowledge graph by finding paths that implicitly connect two nodes, incidentally providing a reasoned explanation for the inferred new link. The generation of such agents is a complex task, even more so for a non-expert user.

SpaceRL is meant to overcome these limitations by providing a flexible set of tools designed with a wide variety of customization options, in order to adapt to different users’ needs, while also including a variety of state-of-the-art RL algorithms and several embedding models that can be combined to optimize the agents performance. Furthermore, SpaceRL offers different interfaces to make it available either locally (programmatically or via a GUI), or through an OpenAPI-compliant REST API.

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引用次数: 0

VisualCodeMOOC: A course platform for algorithms and data structures integrating a conversational agent for enhanced learning through dynamic visualizations

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-11 DOI: 10.1016/j.softx.2025.102072

Mingyuan Li , Duan Wang , Erick Purwanto , Thomas Selig , Qing Zhang , Hai-Ning Liang

The abstract nature of algorithms and data structures poses challenges for students, and the integration of visualization into comprehensive learning systems remains underexplored. This article presents VisualCodeMOOC, incorporating VisualCodeChat, a conversational agent that enhances algorithm and data structure learning through dynamic visualizations and personalized feedback. The platform effectively addresses these challenges, improving student engagement and comprehension. With instructions structuring, novel response-based algorithm visualization, exercise design, VisualCodeMOOC provides a cohesive and supportive learning environment that promotes active learning. Evaluation results demonstrate its usability, responsiveness, and educational value, confirming its potential as a promising tool for advancing computer science education.

引用次数: 0

AgriSenAI: Automating UAV thermal and multispectral image processing for precision agriculture

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-11 DOI: 10.1016/j.softx.2025.102083

Boaz B. Tulu , Fitsum Teshome , Yiannis Ampatzidis , Niguss Solomon Hailegnaw , Haimanote K Bayabil

Unmanned Aerial Vehicles (UAVs) equipped with thermal and multispectral imaging capabilities, which offer high spatial and temporal resolutions, are becoming increasingly valuable for timely crop monitoring and informed decision-making in precision agriculture. However, processing and extracting useful information from UAV images is often complex, time-consuming, and requires specialized software, which limits its broader adoption for practical field implementations. To address these challenges, the Agriculture Sensing and Artificial Intelligence (AgriSenAI), a user-friendly Python-based desktop application, was developed to automate processing and information extraction from UAV-acquired thermal and multispectral imagery. AgriSenAI was developed by integrating advanced image processing with geospatial analysis to streamline field and plot extraction, plant canopy detection, noise removal, and extraction of information at pixel, plot, and field scales. The application was designed and tested using UAV-based thermal and multispectral imagery collected daily for three years from a research field at the University of Florida's Tropical Research and Education Center in Homestead, Florida. The research field consisted of 12 plots of green beans and 12 plots of sweet corn. The processing time and accuracy of AgriSenAI were evaluated. Results showed that AgriSenAI had a very high level of accuracy in extracting pixel values and significantly reduced processing time and costs compared with traditional approaches involving commercial software. The streamlined AgriSenAI workflow produced reliable canopy temperature information and vegetation indices, demonstrating the capacity to handle large-scale datasets and enhance precision agriculture through improved efficiency and accuracy in remote sensing data processing and information extraction, which could potentially be used to inform timely and data-driven crop management decisions.

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引用次数: 0

ProcessM: Intelligent Process Mining software

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-10 DOI: 10.1016/j.softx.2025.102079

Tomasz P. Pawlak, Jędrzej Potoniec

Contemporary software guides and executes various business processes, such as production, delivery of goods, sales, and official procedures. However, due to software flexibility, unforeseen circumstances, and exceptional conditions, processes may not always be executed as intended. Without proper monitoring, these deviations can go unnoticed. ProcessM is an open-source software designed for users with limited technical expertise specializing in business process analysis. It integrates with multiple database management systems to extract and transform data into event logs, and monitor databases for new events. ProcessM generates process models in standard representations like BPMN and tracks changes in processes. It also calculates Key Performance Indicators (KPIs), breaking them down by individual activities and relationships. ProcessM is designed for auditing, decision-making, and operations management across industries.

引用次数: 0

iRheoFoam: A package for simulating complex planar interfaces

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-10 DOI: 10.1016/j.softx.2025.102068

Adolfo Esteban , Julio Hernández , Javier Tajuelo , Miguel Ángel Rubio

The numerical simulation of the flow around a rigid object, such as a probe, placed at a complex interface to reproduce certain experimental conditions is a useful tool for the analysis and design of interfacial stress rheometers. To perform such simulations, we have developed iRheoFoam, an open-source package implemented in the OpenFOAM framework. It is based on the Boussinesq–Scriven constitutive law and the Navier–Stokes equations, includes various solvers for both time-dependent and steady problems, and allows easy incorporation of new interface models and solvers, making it a versatile and powerful tool.

引用次数: 0

MitoSeg: Mitochondria segmentation tool

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-09 DOI: 10.1016/j.softx.2025.102081

Faris Serdar Taşel , Efe Çi̇ftci̇

Recent studies suggest a potential link between the physical structure of mitochondria and neurodegenerative diseases. With advances in Electron Microscopy techniques, it has become possible to visualize the boundary and cristae structures of mitochondria in detail. Segmenting mitochondria from microscopy images remains challenging due to image quality and complex morphology of mitochondria, including cristae and the other subcellular structures. It is crucial to automatically segment mitochondria from images exhibiting different mitochondrial boundary and crista characteristics to investigate the relationship between mitochondria and diseases. In this paper, we present a software solution for mitochondrial segmentation using an automatic validation scheme based on the general physical properties of mitochondria, highlighting boundaries in electron microscopy tomography images and generating corresponding 3D meshes. These capabilities help researchers conduct further investigations into mitochondrial morphology and explore its role in the mechanisms of neurodegenerative diseases.

引用次数: 0

MALVADA: A framework for generating datasets of malware execution traces

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-08 DOI: 10.1016/j.softx.2025.102082

Razvan Raducu, Alain Villagrasa-Labrador, Ricardo J. Rodríguez, Pedro Álvarez

Malware attacks have been growing steadily in recent years, making more sophisticated detection methods necessary. These approaches typically rely on analyzing the behavior of malicious applications, for example by examining execution traces that capture their runtime behavior. However, many existing execution trace datasets are simplified, often resulting in the omission of relevant contextual information, which is essential to capture the full scope of a malware sample’s behavior. This paper introduces MALVADA, a flexible framework designed to generate extensive datasets of execution traces from Windows malware. These traces provide detailed insights into program behaviors and help malware analysts to classify a malware sample. MALVADA facilitates the creation of large datasets with minimal user effort, as demonstrated by the WinMET dataset, which includes execution traces from approximately 10,000 Windows malware samples.

引用次数: 0

ConfSync: Confirmation of mutual synchronization of the TPMs in Python

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-01 DOI: 10.1016/j.softx.2025.102053

Ivan Jiron Araya , Michel Campos , Freddy I. Chan-Puc , Rafael Martínez-Peláez , Carlos Pon Soto , Homero Toral-Cruz

ConfSync is a specialized open-source tool for simulating synchronization of Tree Parity Machines (TPMs). This new tool introduces advanced verification models, including hash-based, matrix-based and polynomial function methods for synaptic weight comparison. With these enhancements, researchers and students can observe how different parameters and learning rules (Hebbian, Anti-Hebbian, Random-Walk) affect TPM synchronization, providing a greater understanding of neural synchronization and key exchange mechanisms. ConfSync automates stimulus and weight generation, output computation, and synaptic updates while providing comprehensive data export for thorough analysis and educational exploration of secure communication systems.

引用次数: 0

action-rules: GPU-accelerated Python package for counterfactual explanations and recommendations

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-01 DOI: 10.1016/j.softx.2024.102000

Lukáš Sýkora, Tomáš Kliegr

The action-rules package provides an efficient method for mining action rules using the Action-Apriori algorithm, a modification of the traditional Apriori algorithm tailored specifically for action rule mining. Designed to generate counterfactual explanations, this Python package enables researchers and practitioners to discover actionable insights by integrating user-defined parameters directly into the rule generation process, reducing computational overhead. The action-rules package supports optional GPU acceleration to further speed up processing on large datasets. The package provides a user-friendly API, as well as a command-line interface for versatile use. The package supports the customization of stable and flexible attributes, as well as separate minimum support and confidence thresholds for both the desired and undesired components of the rules. Comprehensive documentation, including a Jupyter Notebook example, is provided to facilitate ease of use for both novice and expert users.

引用次数: 0

pyCLAD: The universal framework for continual lifelong anomaly detection

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-02-01 DOI: 10.1016/j.softx.2024.101994

Kamil Faber , Bartlomiej Sniezynski , Nathalie Japkowicz , Roberto Corizzo

Anomaly detection is a recognized problem with high significance and impact in many real-world settings. Continual anomaly detection is an emerging paradigm that allows for the design of anomaly detection methods capable of adapting to new challenges in dynamic environments while retaining past knowledge. In this paper, we propose pyCLAD, the first software framework providing foundations for the design of new continual anomaly detection scenarios, strategies, and evaluation protocols, while streamlining the execution of experimental workflows with high reproducibility standards.

引用次数: 0

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