Open Source Ecosystem Research Articles

Python-Based Urban Land Suitability Evaluation for Effective Urban Planning

Abstract: One of the global environmental problems is that the population is growing rapidly around the world. unfortunately, land is a limited resource. Land suitability analysis merges as the most efficient and commonly used method that represents an effective informative tool to support policymakers and planners in developing optimal master plans, maintaining sustainability, and ensuring social equity among residents. Python's free and open-source ecosystem of geospatial libraries like Geo Pandas makes it a cost-effective alternative to proprietary GIS software for this purpose. Furthermore, Python excels at automation, allowing you to write scripts that save time and ensure consistency for repetitive tasks in land suitability analysis. At first, users can choose the project type based on the classification of land use and input raster and vector data. After that, each layer is reclassified, and a weighted overlay is applied at the end. A raster would be the final land suitability output file. Urban planning benefits greatly from Python's capabilities. Land suitability models can assess the viability of development projects by considering factors like flood risk, slope stability, and proximity to infrastructure. The applications extend far beyond these initial examples. Python's land suitability models are instrumental in environmental management as well. Habitat suitability modeling, for instance, considers factors like vegetation cover, water availability, and connectivity to existing habitats to identify areas suitable for specific wildlife species. This information is crucial for conservation planning and restoration efforts. This paper is designed to explain the beneficial aspects of a Python-based approach to land suitability modelling and how it operates.

A Web Semantic Mining Method for Fake Cybersecurity Threat Intelligence in Open Source Communities

In order to overcome the challenges of inadequate classification accuracy in existing fake cybersecurity threat intelligence mining methods and the lack of high-quality public datasets for training classification models, we propose a novel approach that significantly advances the field. We improved the attention mechanism and designed a generative adversarial network based on the improved attention mechanism to generate fake cybersecurity threat intelligence. Additionally, we refine text tokenization techniques and design a detection model to detect fake cybersecurity threats intelligence. Using our STIX-CTIs dataset, our method achieves a remarkable accuracy of 96.1%, outperforming current text classification models. Through the utilization of our generated fake cybersecurity threat intelligence, we successfully mimic data poisoning attacks within open-source communities. When paired with our detection model, this research not only improves detection accuracy but also provides a powerful tool for enhancing the security and integrity of open-source ecosystems.

Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking.

Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

Beyond the Repository

Best practices for open source ecosystems researchers.

PyMC: a modern, and comprehensive probabilistic programming framework in Python.

PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU. Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs). We demonstrate PyMC's versatility and ease of use with examples spanning a range of common statistical models. Additionally, we discuss the positive role of PyMC in the development of the open-source ecosystem for probabilistic programming.

(Invited) Opportunities for Lab-to-Market and Innovation Support at the National Science Foundation

The National Science Foundation offers support for translation of research into the market via a number of innovation programs, now housed in the new Technology, Innovation, and Partnerships (TIP) Directorate. These programs will be discussed at a high level, with a focus toward understanding how stakeholders from academia, industry, and government can engage with these programs. Programs covered will include Small Business Innovation Research, Small Business Technology Transfer, Innovation Corps, Pathways to Enable Open-Source Ecosystems, Partnerships for Innovation, and other NSF opportunities. The talk will also briefly touch on the societal and economic impacts of these programs over the past decades, with some examples of success stories that have benefitted from multiple of these opportunities. Finally, the talk will discuss the motivations for the creation of the TIP Directorate as well as potential concepts that might inform the future expansion of TIP.

Software Architectural Practices: Influences on the Open Source Ecosystem Health

The health state of a software ecosystem has been determined by its capacity of growth and longevity. Three health indicators represent a healthy software ecosystem: robustness, productivity, and niche creation. Studies focusing on understanding the causes and processes of the state of health of ecosystems have used these indicators largely. Researchers have intensified studies to understand how to achieve a good health state. Despite the growing number of studies, there is little knowledge about influences and actions to achieve health and, more specifically, that consider the effects of the software architecture on the ecosystem. This article presents a study exploring seven open source ecosystems within different domains to describe the influence of architectural practices on the software ecosystem health in terms of their motivations and effects. Our main goal was to understand how the software architecture and related practices can contribute to a healthy ecosystem. We conducted a netnography-based study to gather practices used to create and maintain the software architecture of these ecosystems. Our study brings evidence that architectural practices play a critical role in the achievement of ecosystems’ health. We found fifty practices that have influenced different aspects of health indicators. We highlight the importance of five influential factors – business goals, experience, requirements, resources, and time-to-market – for motivating the adoption of such practices. These factors may also contribute to understanding different strategies used to achieve a good health state. Moreover, we proposed a novel health indicator, trustworthiness, that accounts for the normal operation of a healthy software ecosystem.

Using the uniqueness of global identifiers to determine the provenance of Python software source code

We consider the problem of identifying the provenance of free/open source software (FOSS) and specifically the need of identifying where reused source code has been copied from. We propose a lightweight approach to solve the problem based on software identifiers-such as the names of variables, classes, and functions chosen by programmers. The proposed approach is able to efficiently narrow down to a small set of candidate origin products, to be further analyzed with more expensive techniques to make a final provenance determination.By analyzing the PyPI (Python Packaging Index) open source ecosystem we find that globally defined identifiers are very distinct. Across PyPI's 244 K packages we found 11.2 M different global identifiers (classes and method/function names-with only 0.6% of identifiers shared among the two types of entities); 76% of identifiers were used only in one package, and 93% in at most 3. Randomly selecting 3 non-frequent global identifiers from an input product is enough to narrow down its origins to a maximum of 3 products within 89% of the cases.We validate the proposed approach by mapping Debian source packages implemented in Python to the corresponding PyPI packages; this approach uses at most five trials, where each trial uses three randomly chosen global identifiers from a randomly chosen python file of the subject software package, then ranks results using a popularity index and requires to inspect only the top result. In our experiments, this method is effective at finding the true origin of a project with a recall of 0.9 and precision of 0.77.

Beyond the Repository

Much of the existing research about open source elects to study software repositories instead of ecosystems. An open source repository most often refers to the artifacts recorded in a version control system and occasionally includes interactions around the repository itself. An open source ecosystem refers to a collection of repositories, the community, their interactions, incentives, behavioral norms, and culture. The decentralized nature of open source makes holistic analysis of the ecosystem an arduous task, with communities and identities intersecting in organic and evolving ways. Despite these complexities, the increased scrutiny on software security and supply chains makes it of the utmost importance to take an ecosystem-based approach when performing research about open source. This article provides guidelines and best practices for research using data collected from open source ecosystems, encouraging research teams to work with communities in respectful ways.

OpenSpyrit: an ecosystem for open single-pixel hyperspectral imaging.

This paper describes OpenSpyrit, an open access and open source ecosystem for reproducible research in hyperspectral single-pixel imaging, composed of SPAS (a Python single-pixel acquisition software), SPYRIT (a Python single-pixel reconstruction toolkit) and SPIHIM (a single-pixel hyperspectral image collection). The proposed OpenSpyrit ecosystem responds to the need for reproducibility and benchmarking in single-pixel imaging by providing open data and open software. The SPIHIM collection, which is the first open-access FAIR dataset for hyperspectral single-pixel imaging, currently includes 140 raw measurements acquired using SPAS and the corresponding hypercubes reconstructed using SPYRIT. The hypercubes are reconstructed by both inverse Hadamard transformation of the raw data and using the denoised completion network (DC-Net), a data-driven reconstruction algorithm. The hypercubes obtained by inverse Hadamard transformation have a native size of 64 × 64 × 2048 for a spectral resolution of 2.3 nm and a spatial resolution that is comprised between 182.4 µm and 15.2 µm depending on the digital zoom. The hypercubes obtained using the DC-Net are reconstructed at an increased resolution of 128 × 128 × 2048. The OpenSpyrit ecosystem should constitute a reference to support benchmarking for future developments in single-pixel imaging.

The OpenCDA Open-Source Ecosystem for Cooperative Driving Automation Research

Advances in Single-vehicle intelligence of automated driving has encountered great challenges because of limited capabilities in perception and interaction with complex trafic environments. Cooperative Driving Automation (CDA) has been considered a pivotal solution to next-generation automated driv-ing and smart transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world trafic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem (OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete devel-opment toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation.

Pathways to Enable Open‐Source Ecosystems (NSF)

Federal Grants & ContractsVolume 47, Issue 7 p. 2-2 Grants Alerts Pathways to Enable Open-Source Ecosystems (NSF) First published: 01 March 2023 https://doi.org/10.1002/fgc.32884Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue72 March 2023Pages 2-2 RelatedInformation

Application and technology of an open source AI large language model in the medical field

To explore the application prospects of an open source artificial intelligence (AI) large language model (LLM) in the medical field, we conducted an analysis from multiple dimensions, including the introduction of LLM, the classification of model types, and the status quo of the open source ecosystem development. The development of an open source LLM is currently in the rapid expansion phase, and there are many types of models and related tools. After analyzing the advantages and disadvantages of the models, we expounded feasible technical solutions for the application of an LLM in the medical field and made corresponding predictions. At present, LLMs in the medical field are still in the early stages, and there are still many problems related to ethics, technology, legal issues, and medical use.

A Theoretical Framework for Enhancing Open-Source Software Security

Cyber security is a rapidly developing field and open-source software (OSS) is being led by a collaborative community and a transparent nature. This research intends to make a theoretical framework highlighting the symbiosis of community engagement models, governance models, and the security of open-source ecosystems. The significant purposes aim to investigate how community engagement affects open source software development, analyze the deployment of the governance systems in widely used open source projects, and create the theoretical model that brings the cybersecurity processes in those practices. The literature survey covers existing research on open-source software development, community engagement, governance models, and cybersecurity practices within the OSS ecosystem. Through literature review the basic concepts are investigated using conceptual analysis to determine which principles and mechanisms the Community Model Engagement and Governance models operate, influencing the security of Open Source Software. Open-source project policy recommendations are the foundation for the security of communities where members fully contribute to better governance. This includes developing a fundamental conceptual analysis to lay out the framework's principles and operation (mechanisms). This contributes to the overall development of the theoretical framework. This research expects to develop a standardized theoretical framework that tackles the community engagement-governance models-security software link from new and insightful perspectives. Practical policy recommendations to protect OSS projects will be presented to solve the cybersecurity problem easily and immediately. This study aims to add to the academic and practical discussions that focus on human and organizational components' critical roles in securing open-source software. The discussion of theoretical aspects gives this platform a unique angle that fits well into the technical approach while improving the understanding of a legally open cybersecurity community

Co-producing industrial public goods on GitHub: Selective firm cooperation, volunteer-employee labour and participation inequality

The global economy’s digital infrastructure is based on free and open source software. To analyse how firms indirectly collaborate via employee contributions to developer-run projects, we propose a formal definition of ‘industrial public goods’ – inter-firm cooperation, volunteer and paid labour overlap, and participation inequality. We verify its empirical robustness by collecting networks of commits made by firm employees to active GitHub software repositories. Despite paid workers making more contributions, volunteers play a significant role. We find which firms contribute most, which projects benefit from firm investments, and identify distinct ‘contribution territories’ since the two central firms never co-contribute to top-20 repositories. We highlight the challenge posed by ‘Big Tech’ to the non-rival status of industrial public goods, thanks to cloud-based systems which resist sharing, and suggest there may be ‘contribution deserts’ neglected by large information technology firms, despite their importance for the open source ecosystem’s sustainability and diversity.

Flow Measurements Derived from Camera Footage Using an Open-Source Ecosystem

Sensors used for wastewater flow measurements need to be robust and are, consequently, expensive pieces of hardware that must be maintained regularly to function correctly in the hazardous environment of sewers. Remote sensing can remedy these issues, as the lack of direct contact between sensor and sewage reduces the hardware demands and need for maintenance. This paper utilizes off-the-shelf cameras and machine learning algorithms to estimate the discharge in open sewer channels. We use convolutional neural networks to extract the water level and surface velocity from camera images directly, without the need for artificial markers in the sewage stream. Under optimal conditions, our method estimates the water level with an accuracy of ±2.48% and the surface velocity with an accuracy of ±2.08% in a laboratory setting—a performance comparable to other state-of-the-art solutions (e.g., in situ measurements).

Edge Workloads Monitoring and Failover: a StarlingX-Based Testbed Implementation and Measurement Study

With the ever-growing amount of time-critical, compute-intensive, and private IoT applications, the need for High Availability (HA) Edge Clouds becomes indispensable. Realizing HA Edge Clouds is inherently challenging due to the geographically-dispersed hierarchy of the Distributed Cloud Infrastructure (DCI). For example, frequent isolation between the central Cloud and Edge Clouds due to networking instability necessitates some autonomous operations at the Edge Clouds. Furthermore, because Edge Clouds have fewer resources than central Clouds, configuring the Edge functions (i.e., control, compute, and storage) in HA clusters will undoubtedly reduce downtime. However, it will limit the Edge scalability. To that end, StarlingX is developing an HA-protected and scalable DCI virtualization platform based on the open-source ecosystem, focusing on low-touch management of Edge Clouds. StarlingX provides a fault management service that realizes DCI-wide alarming and logging capabilities, allowing for rapid response to virtualized infrastructure events. Recently, the IETF Network Working Group proposed that monitoring both the DCI and the Edge workloads (software containers) is critical for an Edge Computing Platform to maintain HA IoT application deployment. Indeed, the possibility of the infrastructure remaining stable and healthy while the workloads suffer a fatal failure simultaneously necessitates failover functionality that monitors both the infrastructure and the Edge workloads. In this paper, we first propose a dynamic failover functionality that centrally monitors Edge workloads to recover from deployment or Edge node failures, motivated by the IETF direction. Second, we experimentally optimize the failover functionality for monitoring a microservice-architected IoT application deployed on a StarlingX-based DCI testbed to collect temperature sensor readings from Raspberry Pis. Regardless of how quickly the Edge workload health checks are collected, the recorded failover measurements reveal that the recovery time will not drop below a predetermined level controlled by Edge resources and network speed. Furthermore, reducing the statistics collection timeout reduces the recovery time of an Edge node failure. When the timeout value is less than the minimum achievable recovery time, false-positive failures (FPFs) can occur. Third, to supplement the StarlingX fault management service, we provide a modular implementation of the proposed failover functionality. Finally, we present the first-ever introduction of the StarlingX platform’s software stack to promote its use in academic research.

IEEE Solid-State Circuits Society Launches a Technical Committee to Support Open Source Ecosystem Ramp-Up [Society News

IEEE Solid-State Circuits Society Launches a Technical Committee to Support Open Source Ecosystem Ramp-Up [Society News

ATON: An Open-Source Framework for Creating Immersive, Collaborative and Liquid Web-Apps for Cultural Heritage

The web and its recent advancements represent a great opportunity to build universal, rich, multi-user and immersive Web3D/WebXR applications targeting Cultural Heritage field—including 3D presenters, inspection tools, applied VR games, collaborative teaching tools and much more. Such opportunity although, introduces additional challenges besides common issues and limitations typically encountered in this context. The “ideal” Web3D application should be able to reach every device, automatically adapting its interface, rendering and interaction models—resulting in a single, liquid product that can be consumed on mobile devices, PCs, Museum kiosks and immersive AR/VR devices, without any installation required for final users. The open-source ATON framework is the result of research and development activities carried out during the last 5 years through national and international projects: it is designed around modern and robust web standards, open specifications and large open-source ecosystems. This paper describes the framework architecture and its components, assessed and validated through different case studies. ATON offers institutions, researchers, professionals a scalable, flexible and modular solution to craft and deploy liquid web-applications, providing novel and advanced features targeting Cultural Heritage field in terms of 3D presentation, annotation, immersive interaction and real-time collaboration.

Strategizing with Hardware Rich Open Source Ecosystems

Companies are increasingly adopting open source strategies to develop and exploit complex infrastructures and platforms that combine software, hardware and standard interfaces. Such strategies require the development of a vibrant ecosystem of partners that combines the innovation capabilities of hundreds of companies from different industries. Our aim is to help decision makers assess the benefits and challenges associated with creating or joining such ecosystems. We use a case study approach on six major collaborative ecosystems that enable the development of complex, high cost infrastructures and platforms. We characterize their strategy, governance, and their degree of intellectual property (IP) openness. We offer a three-dimensional framework that helps managers characterize such ecosystems. Although all the ecosystems studied aim at scaling up innovative solutions, their strategy, governance and IP openness vary. An upstream strategy aimed at replacing supplier proprietary design with open substitutes requires a democratic governance and an intellectual property policy that maximize the attractiveness of the ecosystem. A downstream strategy aimed at carving a space in new markets requires an autocratic governance and an intellectual property policy that combine attractiveness and value capture opportunities.