Software Impacts最新文献

We present RIGSS, software that can be used to run an Inverse Generative Social Science study in R. We give a brief overview of this research method and explain how our software can be used. We implement a Hawk Dove game as an executable example. We then discuss the potential that Inverse Generative Social Science has.

Among the methods for estimating fractal dimension, the Higuchi approach is limited to processing grayscale images. To improve this approach and estimate Higuchi fractal dimension values from color images, multidimensional and multiscale Higuchi software was developed for the pattern analysis present in images considering the RGB color model. The software can provide local/global fractal dimensions. Moreover, it was tested to quantify images from important datasets commonly explored to evaluate computer-aided diagnosis schemes (histological images of colorectal cancer, liver tissue, and oral epithelial dysplasia). The results were relevant and useful for the process of pattern recognition considering the investigated groups.

Ensuring software quality is critical aspect of the development process, with test beds playing a vital role in validating applications under several conditions. Traditional methods of test bed generation are time-consuming and often fail to cover wide range of testing scenarios. To address these challenges, we introduce a novel test bed generator software application BHRAMARI that automates the creation of test beds with high-quality code smells and samples. The integration of advanced technologies such as natural language processing and generative-AI paves the way for a new era in software testing, where automation and innovation ensure the highest standards of reliability.

ML-CCD is an open-source Python software based on a Machine-Learning model that was utilized to predict the premature failure of reinforced concrete (RC) beams strengthened with Fiber Reinforced Polymers (FRP). The model was trained using a database consisting of 70 experimentally tested beams that failed prematurely due to Concrete Cover Delamination (CCD). The significant beams parameters that influence the CCD failure were used in training the ML-CCD. This software predicts the ultimate strain in the FRP sheets at failure, thus finding its ultimate tensile strength and the effective strengthening ratio for design purposes.

Atomistic simulations are crucial for understanding material properties at the molecular level but are limited by high computational costs, especially for large, complex systems like bituminous materials. Our team developed a Force-matched United Atom (UA) Coarse Graining (CG) force field to enhance computational efficiency while retaining atomic detail. However, converting all-atom models to CG models is complex, requiring detailed atom-to-bead mapping and compatibility with molecular dynamics (MD) engines like LAMMPS. To address this, we introduce AA2UA, an open-source software that simplifies the conversion of PDB files into LAMMPS-readable structure topology files, facilitating broader use of the developed UA force field.

SNPgen is a web portal that simplifies primer design for the detection of single nucleotide polymorphisms (SNPs) and insertions/deletions (indels). It offers user-friendly tools for automating primer design, retrieve SNP details and generate primers for various genotyping methods such as ARMS-PCR and high-resolution melt (HRM) analysis. SNPgen considers factors such as GC content and melting temperature for optimal primers and allows visualization of amplicons and primers. This user-friendly portal can revolutionize genotyping workflows in various research areas.

Software Product Line Engineering (SPLE) involves developing multiple software variants with shared features, aiming for reuse. This reuse should not be limited to functional features but should also encompass managerial concerns. Among these concerns, implicit feedback is the process of collecting data on how and when software products are used to identify bugs, usability issues, and inform requirement prioritization. This paper introduces FEACKER, an extension to pure::variants, a variant management tool. FEACKER aims to shift feedback practices from individual products to the platform level, aligning with SPLE’s emphasis on systematic reuse.

WayWise is an innovative C++ and Qt-based rapid prototyping library designed to advance the development and analysis of connected, autonomous vehicles (CAVs) and Unmanned Arial Systems (UASs). It was deployed on model-sized cars and trucks as well as full-sized mobile machinery, tractors and UASs. It is actively being used in several European research projects. Developed by the RISE Dependable Transport Systems unit, the library facilitates exploration into safety and cybersecurity aspects inherent to various emerging vehicular applications within road traffic and offroad applications. This non-production library emphasizes rapid prototyping, leveraging commercial off-the-shelf hardware and the different protocols for vehicle-control communication, mainly focusing on MAVLINK. The utility of WayWise in rapidly evaluating complex vehicular behaviors is demonstrated through various research projects, thus contributing to the field of autonomous vehicular technology.

Advancements in artificial intelligence in the cybersecurity domain introduce significant security challenges. A critical concern is the exposure of deep learning techniques to adversarial attacks. Adversary users intentionally attempt to mislead the techniques by infiltrating adversarial samples to mislead the prediction of security devices. The study presents extensive experimentation of defense methods using Python-based open-source code with two benchmark datasets, and the outcomes are demonstrated using evaluation metrics. This code library can be easily utilized and reproduced for cybersecurity research on countering adversarial attacks. Exploring strategies for protecting against adversarial attacks is significant in enhancing the resilience of deep learning techniques.

Vendor lock-in has become a major concern in cloud computing. The term vendor lock-in describes situations where the subscriber cannot move data or services to another cloud vendor. This is due to heavy data volumes, high network bandwidth costs, dependencies, or unacceptable downtime. The proposed vendor lock-in dissolution practice migrates the database effectively in noticeably less time, regardless of database size and with a nominal network bandwidth requirement. Through this new practice, databases can be migrated to very remote regions, even across continents. A real-time implementation of the proposed method presented in this paper.