Open Source Products Research Articles

Hidden code vulnerability detection: A study of the Graph-BiLSTM algorithm

Context:The accelerated growth of the Internet and the advent of artificial intelligence have led to a heightened interdependence of open source products, which has in turn resulted in a rise in the frequency of security incidents. Consequently, the cost-effective, fast and efficient detection of hidden code vulnerabilities in open source software products has become an urgent challenge for both academic and engineering communities. Objectives:In response to this pressing need, a novel and efficient code vulnerability detection model has been proposed: the Graph-Bi-Directional Long Short-Term Memory Network Algorithm (Graph-BiLSTM). The algorithm is designed to enable the detection of vulnerabilities in Github’s code commit records on a large scale, at low cost and in an efficient manner. Methods:In order to extract the most effective code vulnerability features, state-of-the-art vulnerability datasets were compared in order to identify the optimal training dataset. Initially, the Joern tool was employed to transform function-level code blocks into Code Property Graphs (CPGs). Thereafter, structural features (degree centrality, Katz centrality, and closeness centrality) of these CPGs were computed and combined with the embedding features of the node sequences to form a two-dimensional feature vector space for the function-level code blocks. Subsequently, the BiLSTM network algorithm was employed for the automated extraction and iterative model training of a substantial number of vulnerability code samples. Finally, the trained algorithmic model was applied to code commit records of open-source software products on GitHub, achieving effective detection of hidden code vulnerabilities. Conclusion:Experimental results indicate that the PrimeVul dataset represents the most optimal resource for vulnerability detection. Moreover, the Graph-BiLSTM model demonstrated superior performance in terms of accuracy, training cost, and inference time when compared to state-of-the-art algorithms for the detection of vulnerabilities in open-source software code on GitHub. This highlights the significant value of the model for engineering applications.

A Model for Total Cost Determination in Open-Source Software Ownership: Case of Kenyan Universities’ Learning Management System

The adoption of open-source products is slowly increasing; the increase, however, is slower than expected, considering that most open-source products are freely available. Researchers and scholars have attributed the adoption levels to, among other things, a lack of know-how of the total cost of ownership of the open-source software. Thus, it is crucial for the cost of owning the software to be developed. While an ongoing endeavor to develop a model to determine the total cost of ownership of open-source software, the models have proved to be less accurate and do not capture essential elements. Moreover, there has been a rising call for organizations to adopt open-source software to lower the software costs incurred on proprietary software. If the cost of owning open-source software were known, it would be beneficial as several organizations and institutions could adopt it readily. The data was collected from Universities in Kiambu and Embu Counties. Linear regression analysis was done to help develop the model, and a mathematical model was developed. The proposed model was: total cost of open-source software ownership = direct + +indirect + hidden costs. To validate the model, it was subjected to expert validation. The model will be an outstanding contribution to information technology as it will make it possible to come up with the total cost of owning open-source software.

Occupational Therapy and Assistive Technology: Experience in Using 3D Printing and Open Source Platforms

Three-dimensional printing technology has evolved rapidly, being widely spread throughout the world and used in various fields such as design, engineering, medicine, and dentistry, among others. However, it is still little used in research and clinical practice in occupational therapy. A report is presented on the use of three-dimensional printing and open source platforms in clinical practice, based on experience in research and extension activity developed by the Laboratory for Research in Neuropediatrics, Technology and Inclusion (LINTI), which aims to provide an assistive product made in 3D printing to patients treated in the Supervised Internship in Occupational Therapy services that take place at the Specialized Rehabilitation Center (CER II). The actions are developed in four stages: 1- identification of the need; 2 - problem analysis and specification of the open source product;3 – 3D printing production; 4- user assistive product validation tests. This project has highlighted the possibility of creating functional, accessible assistive products, with desired aesthetics, and above all, with modification alternatives and adjustments for the user. 3D printing technology and open source platforms can be applicable and innovative tools in the practice of occupational therapists working in assistive technology.

I Can’t Get No Satis-Searching: Reassessing Discovery Layers in Academic Libraries Journal of Web Librarianship

Much has been written about discovery layer strengths and weaknesses in academic libraries. Many academic libraries have a discovery layer, using products, such as those from OCLC, EBSCO, ExLibris, Summon, or open-source products, such as VuFind. But after over 10 years of academic libraries adopting discovery layers as part of their discovery ecosystem, it may be time to take a step back and revisit the question posed by the speakers at the American Libraries Live “Making the Discovery Decision” January 2014 online discussion, namely: What is a discovery layer and its purpose? Are discovery layers accomplishing what they purport to accomplish for the end user? Through examining three particular discovery layers—OCLC WorldCat Discovery, ExLibris Primo VE, and EBSCO EDS—in regards to their conformance to the National Information Standards Organization (NISO)'s Open Discovery Initiative (ODI) “Checklist for Discovery Service Providers,” relevancy ranking algorithms, and mapping tables with facets, this article argues that the goals of discovery layers need to be reconsidered. Specifically, rather than the discovery layers’ goal to be Google-like and provide a self-sufficient user experience, discovery layers might be better promoted by libraries as a tool to be used most effectively with assistance from librarians. Discovery Layer vendors could focus their design efforts more on accurate faceted searching, citation tools, and saved search features, and less on esthetics and patron self-sufficiency.

DriveLEDs: software for synchronous control and video acquisition of fluorescent signals

Current research uses various fluorescent biosensors to measure ion concentrations, neuronal activity, or cellular components in biological preparations. Several free solutions are available to researchers for recording or analysing fluorescent signals. However, when using different software packages, there are great difficulties in converting data between them. Problems also arise with linking and coordination of individual hardware devices into a single measurement system. Our paper presents useful software that allows to avoid most of these problems. It enables the recording, online visualization and preliminary analysis of fluorescent signals in brain cells and other experimental models. We describe and test software optimized for ratiometric measurements. The program selects device operating modes that allow reliable changes in illumination wavelength, camera image capture, and online graphical visualization of fluorescent signal amplitudes or ratiometric data. The performance of the software was successfully tested on mouse brain using two experimental models; i) simultaneous monitoring of intracellular chloride and hydrogen in transgenic mice expressing genetically encoded biosensor; ii) recording changes in reactive oxygen species during synaptic stimulation of neurons in mouse hippocampal slices. This software allows to overcome the incompatibility of the devices used and reduce the cost of experimental measurements. The software is completely original, easy to use and may be of interest to many scientists involved in the analysis of light-controlled signals in a variety of experimental models, including drug screening, epilepsy models, and other applications. The software is open-source product and can be obtained via GitHub: https://github.com/AndreyZakharovExp/DriveLEDs.

Simplified System Integration of Robust Mobile Robot for Initial Pose Estimation for the Nakanoshima Robot Challenge

This paper describes a study of the simple system integration of a mobile robot in the Nakanoshima Robot Challenge 2022. To improve the operability of the robot at the start of its journey, we studied the solution to the problem of initial localization during the experimental run and the setting of virtual obstacles on the map to be used by the mobile robot. This method reduces the amount of time and manual operation required to estimate the initial position and orientation of a mobile robot in mobile robot experiments. In this study, a mobile robot is implemented using open-source products such as robot operating system (ROS) and i-Cart mini. Experimental runs in the Extra Challenge of the Nakanoshima Robot Challenge 2022 demonstrate the feasibility of the method.

Towards Evaluating the Robustness of the Open-Source Product Community under Multiple Attack Strategies

As an emerging product innovation model, open-source innovation has undergone rapid development in recent years. The sustainability and stability of the open-source product community (OSPC) is crucial for product innovation, and the effect that users have on the OSPC’s robustness is an important and closely scrutinized topic. This paper explores the robustness of the OSPC from the aspect of user knowledge contribution. We first construct an OSPC network and analyze its characteristics. An improved node evaluation method is then proposed to identify different types of knowledge contribution nodes. Further, seven node- and edge-based attack strategies are designed to simulate network robustness changes, with evaluation indicators being proposed for structural robustness and knowledge robustness. The results reveal that our proposed node evaluation method can effectively identify nodes of different knowledge contribution types. Additionally, the network is found to have different robustness performance when facing multiple deliberate attacks on three important knowledge contribution node types. Moreover, the network shows different robustness characteristics when facing deliberate attacks on betweenness and weight edges. Our findings can benefit product innovation and OSPC managers by enhancing the robustness of the OSPC network.

Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software

Recently, many open-source products have been used under the situations of general software development, because the cost saving and standardization. Therefore, many open-source products are gathering attention from many software development companies. Then, the reliability/quality of open-source products becomes very important factor for the software development. This paper focuses on the reliability/quality evaluation of open-source products. In particular, the large quantity fault data sets recorded on Bugzilla of open-source products is used in many open-source development projects. Then, the large amount of data sets of software faults is recorded on the Bugzilla. This paper proposes the reliability/quality evaluation approach based on the deep machine learning by using the large quantity fault data on the Bugzilla. Moreover, the large quantity fault data sets are analyzed by the deep machine learning based on the fine-tuning.

ECOSTRESS Reveals the Importance of Topography and Forest Structure for Evapotranspiration from a Tropical Forest Region of the Andes

Tropical forests are major sources of global terrestrial evapotranspiration (ET), but these heterogeneous landscapes pose a challenge for continuous estimates of ET, so few studies are conducted, and observation gaps persist. New spaceborne products such as ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) are promising tools for closing such observation gaps in understudied tropical areas. Using ECOSTRESS ET data across a large, protected tropical forest region (2250 km2) situated on the western slope of the Andes, we predicted ET for different days. ET was modeled using a random forest approach, following best practice workflows for spatial predictions. We used a set of topographic, meteorological, and forest structure variables from open-source products such as GEDI, PROBA-V, and ERA5, thereby avoiding any variables included in the ECOSTRESS L3 algorithm. The models indicated a high level of accuracy in the spatially explicit prediction of ET across different locations, with an r2 of 0.61 to 0.74. Across all models, no single predictor was dominant, and five variables explained 60% of the models’ results, thus highlighting the complex relationships among predictor variables and their influence on ET spatial predictions in tropical mountain forests. The leaf area index, a forest structure variable, was among the three variables with the highest individual contributions to the prediction of ET on all days studied, along with the topographic variables of elevation and aspect. We conclude that ET can be predicted well with a random forest approach, which could potentially contribute to closing the observation gaps in tropical regions, and that a combination of topography and forest structure variables plays a key role in predicting ET in a forest on the western slope of the Andes.

VanityX: An Agile 3D Rendering Platform Supporting Mixed Reality

VanityX is a prototype, low-level, real-time 3D rendering and computing platform. Unlike most XR solutions, which integrate several commercial and/or open-source products, such as game engines, XR libraries, runtime, and services, VanityX is a platform ready to adapt to any business domain including anthropology and medicine. The design, architecture, and implementation are presented, which are based on CPU and GPU asymmetric multiprocessing with explicit synchronization and collaboration of parallel tasks and a predictable transfer of pipeline resources between processors. The VanityX API is based on DirectX 12 and native programming languages C++20 and HLSL 6, which, in conjunction with explicit parallel processing, the asynchronous loading and explicit managing of graphic resources, and effective algorithms, results in great performance and resource utilization close to metal. Surface-based rendering, direct volume rendering (DVR), and mixed reality (MR) on the HoloLens 2 immersive headset are currently supported. Our MR applications are directly compiled and deployed to HoloLens 2 allowing for better programming experiences and software engineering practices such as testing, debugging, and profiling. The VanityX server provides various computational and rendering services to its clients running on HoloLens 2. The use and test cases are in many business domains including anthropology and medicine. Our future research challenges will primarily, via the MetaverseMed project, focus on opening new opportunities for implementing innovative MR-based scenarios in medical procedures, especially in education, diagnostics, and surgical operations.

Creating a call center test bench for load balancing Asterisk servers in a cluster

The article deals with the issues of increasing throughput in call centers. The current solution is to cluster call servers and evaluate their characteristics to ensure efficient operation and the necessary fault tolerance. It is shown that one of the main aspects of the quality functioning of the call center is load balancing of servers in the cluster.
 The features of the call-center modeling process are considered. The organization scheme of the call center of the company and the network model of the call center have been created. Virtualization technology was used to create a network model of a call center. The VMWare ESXI 6.7 hypervisor and the vCenter client were used as a network configuration platform.
 An analysis of load balancing was carried out using different algorithms and strategies.
 Asterisk PBX was configured and a server cluster was created. A test bench was developed and configured using the Zabbix open source product to explore a cluster of call servers. A bandwidth characteristic for the Asterisk _1 server and a network map were obtained, which actually represents a simulated structure of the call center network.
 The process of load testing on three Asterisk servers and the implementation of Zabbix load balancing capabilities are shown. A custom SIPp session script has been created for accepting calls and load testing. The number of calls that the call center is able to handle is calculated. It has been determined that a single Asterisk server with its current settings can handle a maximum of 915 concurrent calls. The process of load balancing on a cluster of SIP servers has been launched. A cluster of Asterisk servers has been found to be capable of handling 2550 simultaneous calls.

An algorithm for steganographic embedding of encrypted files in raster images

The aim of this paper is proposing a method for the application of algorithms for hiding an encrypted file inside of a digital image for cybersecurity purposes. The originality of the method is expressed in the possibility of studying the mathematical algorithms for creating computer graphics. The development of a comprehensive algorithm should contribute to the practical realization of the method proposed. It emphasizes on built-in algorithms in software systems for creating computer graphics and the advantages of steganographic embedding by opensource products that hides any archive file in a digital image. Its application contribution is supported by practical examples of software hiding and archiving an encrypted file in a graphic image.

Robustness Evaluation of the Open Source Product Community Network Considering Different Influential Nodes

With the rapid development of Internet technology, the innovative value and importance of the open source product community (OSPC) is becoming increasingly significant. Ensuring high robustness is essential to the stable development of OSPC with open characteristics. In robustness analysis, degree and betweenness are traditionally used to evaluate the importance of nodes. However, these two indexes are disabled to comprehensively evaluate the influential nodes in the community network. Furthermore, influential users have many followers. The effect of irrational following behavior on network robustness is also worth investigating. To solve these problems, we built a typical OSPC network using a complex network modeling method, analyzed its structural characteristics and proposed an improved method to identify influential nodes by integrating the network topology characteristics indexes. We then proposed a model containing a variety of relevant node loss strategies to simulate the changes in robustness of the OSPC network. The results showed that the proposed method can better distinguish the influential nodes in the network. Furthermore, the network’s robustness will be greatly damaged under the node loss strategies considering the influential node loss (i.e., structural hole node loss and opinion leader node loss), and the following effect can greatly change the network robustness. The results verified the feasibility and effectiveness of the proposed robustness analysis model and indexes.

Leveraging Intellectual Property to Prevent Nuclear War

Although international law forbids nuclear attacks, only nine states have mutually assured destruction available to prevent direct attacks against themselves, while non-nuclear states have few substantive options to deter a nuclear attack. This study analyzes the economic impacts of a theoretical international agreement that eliminates patent rights for any nuclear aggressor (i.e., free global compulsory licensing of all intellectual property (IP) for a nuclear aggressor). The results found that all but one of the nuclear states would have a significant economic disincentive to start a nuclear attack if the proposal was put into force. Payback times ranged from 1.2 to 40 years, where the entire GDP of a nuclear aggressor would be needed to offset the loss for aggression, indicate such a mechanism as a whole would be an effective nuclear deterrent. This method would not be universally effective without ensuring all nuclear states are members of the international economy and IP processes. With the growth of open-source products and reduced value of patents, this mechanism does have a limited effectiveness time. Currently it appears to be a policy trajectory worthy of future work that can enhance safety from nuclear threat without causing harm to countries of goodwill.

Morphometric Analysis of Saz-Çayırova Drainage Basin using Geographic Information Systems and Different Digital Elevation Models

Drainage basin/watershed analysis based on morphometric parameters has an essential role in watershed management and planning. Reliable delineation of watersheds and drainage networks is critical for hydrological and geomorphological studies. Since access to high-resolution digital elevation models (DEMs) and digital surface models (DSMs) is costly, many researchers need to evaluate low-resolution open-source products. Several data sources produced from different surveying techniques are used in the morphometric analysis. In this study, five different datasets such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM, Shuttle Radar Topography Mission (SRTM) DEM, Advanced Land Observing Satellite (ALOS) DSM, National Aeronautics and Space Administration (NASA) DEM, and a DEM from topographic maps (TOPO DEM), were investigated based on morphometric parameters. The tests was carried out in the Saz-Çayırova Basin, which is one of the critical urbanization and industrialization regions of Kocaeli, Turkey. In this study, the TOPO DEM, whose horizontal resolution is 30 m, was produced from 1:25K scaled digitized topographical maps. It was used for comparative analysis, as in all DEMs sources. The morphometric parameters' result of the TOPO DEM was used as the reference data for comparing the results of the other DEMs sources. In addition, the mean absolute percentage error (MAPE) was used to compute the accuracy between the freely available DEMs and the TOPO DEM for each morphometric parameter. The outcomes of this study reveal that the most consistent results with the TOPO DEM are provided by SRTM DEM, following the NASA DEM.

Big data architecture for connected vehicles: Feedback and application examples from an automotive group

Nowadays, using their onboard built-in sensors and communication devices, connected vehicles (CVs) can perform numerous measurements (speed, temperature, fuel consumption, etc.) and transmit them, in a real-time fashion, to dedicated infrastructure, usually via 4G/5G wireless communications. This raises many opportunities to develop new innovative telematics services, including, among others, driver safety, customer experience, location-based services and infotainment. Indeed, it is expected that there will be roughly 2 billion connected cars by the end of 2025 on the world’s roadways, where each of which can produce up to 30 terabytes of data per day. Managing this big automotive data, in real and batch modes, imposes tight constraints on the underlying data management platform. To contribute to this research area, in this paper, we report on a real, in-production automotive big data platform; specifically, the one deployed by Groupe PSA (a French car manufacturer known also as Peugeot-Citroën). In particular, we present the technologies and open-source products used within the different components of this CV platform to gather, store, process, and leverage big automotive data. The proposed architecture is then assessed through realistic experiments, and the obtained results are reported and analyzed. Finally, we also provide examples of deployed automotive applications and reveal the implementation details of one of them (an eco-driving service).

Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.

Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and devices, a longstanding culture of open-source products in other industries, and the beginnings of machine learning–friendly regulatory pathways together allow for the development and deployment of open-source machine learning models. Such tools have distinct advantages including enhanced product integrity, customizability, and lower cost, leading to increased access. However, significant questions regarding engineering concerns about implementation infrastructure and model safety, a lack of incentives from intellectual property protection, and nebulous liability rules significantly complicate the ability to develop such open-source models. Ultimately, the reconciliation of open-source machine learning and the proprietary information–driven health care environment requires that policymakers, regulators, and health care organizations actively craft a conducive market in which innovative developers will continue to both work and collaborate.

Open-source software product line extraction processes: the ArgoUML-SPL and Phaser cases

Software Product Lines (SPLs) are rarely developed from scratch. Commonly, they emerge from one product when there is a need to create tailored variants, or from existing variants created in an ad-hoc way once their separated maintenance and evolution become challenging. Despite the vast literature about re-engineering systems into SPLs and related technical approaches, there is a lack of detailed analysis of the process itself and the effort involved. In this paper, we provide and analyze empirical data of the extraction processes of two open-source case studies, namely ArgoUML and Phaser. Both cases emerged from the transition of a monolithic system into an SPL. The analysis relies on information mined from the version control history of their respective source-code repositories and the discussion with developers that took part in the process. Unlike previous works that focused mostly on the structural results of the final SPL, the contribution of this study is an in-depth characterization of the processes. With this work, we aimed at providing a deeper understanding of the strategies for SPL extraction and their implications. Our results indicate that the source code changes can range from almost a fourth to over half of the total lines of code. Developers may or may not use branching strategies for feature extraction. Additionally, the problems faced during the extraction process may be due to lack of tool support, complexity on managing feature dependencies and issues with feature constraints. We made publicly available the datasets and the analysis scripts of both case studies to be used as a baseline for extractive SPL adoption research and practice.

The Impact of (DEM) Accuracy and (LC/LU) Resolution on the Watersheds Areas

Land Cover/Land Use (LC/LU) and Digital Elevation Model (DEM) are the main inputs for watershed modelling. Recently, (DEM) and (LC/LU) are freely available as online open-source products in varied accuracies, spatial and spectral resolutions as result of several remote sensing platforms. Therefore, it is very important to determine which one is the optimum for modeling the watersheds in the selected study area, which is represented by five valley watersheds of diverse characteristics, located on east and west sides of the Mosul dam reservoir, Nineveh province, Iraq. In this research, the different accuracy of 30m resolution DEMs with Satellites (Copernicus (GLO-30), ASTER, and SRTM), besides to supervised classification (Support vector machines (SVM) classifier) results (LC/LU) main layers (green land, bare soil, urban areas, and water) of different spatial and spectral resolutions images with Satellites (10m Sentinel-2, 30m Landsat-8, and resampled 15m from 30m Landsat-8) are examined by using the techniques of Remote Sensing (RS) and Geographic Information System (GIS). Analysis of the results led to the finding that Copernicus DEM (GLO-30) 30 m spatial resolution is the most accurate and optimum DEM in the research study area with 1.1615 m vertical accuracy and 2.276 m at 95% confidence level. The optimum most accurate image for (LC/LU) thematic map production in the selected area is (Sentinel-2 l0 m) satellite at overall Classification accuracy (97%) and the overall kappa statistics (95%). The optimum remote sensing data (sentinel-2 image and Copernicus DEM) are mapped, 3D simulated and analyzed by GIS to calculate the areas of (LC/LU) main layers and to find that the considerable part of the selected watersheds is the area of green lands 404.32676 km2 was about 54.396% of the total area in the study, and these areas are vary depending on many factors such as remote sensing data, precipitation, cultivation, season, and human activities.

De Novo Powered Air-Purifying Respirator Design and Fabrication for Pandemic Response.

The rapid spread of COVID-19 and disruption of normal supply chains has resulted in severe shortages of personal protective equipment (PPE), particularly devices with few suppliers such as powered air-purifying respirators (PAPRs). A scarcity of information describing design and performance criteria for PAPRs represents a substantial barrier to mitigating shortages. We sought to apply open-source product development (OSPD) to PAPRs to enable alternative sources of supply and further innovation. We describe the design, prototyping, validation, and user testing of locally manufactured, modular, PAPR components, including filter cartridges and blower units, developed by the Greater Boston Pandemic Fabrication Team (PanFab). Two designs, one with a fully custom-made filter and blower unit housing, and the other with commercially available variants (the “Custom” and “Commercial” designs, respectively) were developed; the components in the Custom design are interchangeable with those in Commercial design, although the form factor differs. The engineering performance of the prototypes was measured and safety validated using National Institutes for Occupational Safety and Health (NIOSH)-equivalent tests on apparatus available under pandemic conditions at university laboratories. Feedback was obtained from four individuals; two clinicians working in ambulatory clinical care and two research technical staff for whom PAPR use is standard occupational PPE; these individuals were asked to compare PanFab prototypes to commercial PAPRs from the perspective of usability and suggest areas for improvement. Respondents rated the PanFab Custom PAPR a 4 to 5 on a 5 Likert-scale 1) as compared to current PPE options, 2) for the sense of security with use in a clinical setting, and 3) for comfort compared to standard, commercially available PAPRs. The three other versions of the designs (with a Commercial blower unit, filter, or both) performed favorably, with survey responses consisting of scores ranging from 3 to 5. Engineering testing and clinical feedback demonstrate that the PanFab designs represent favorable alternatives to traditional PAPRs in terms of user comfort, mobility, and sense of security. A nonrestrictive license promotes innovation in respiratory protection for current and future medical emergencies.