Open-source Tool Research Articles

New interactive machine learning tool for marine image analysis.

Advancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter, to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua, was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter allow objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate M. lingua models were created using RootPainter, with an average dice score of 0.94 ± 0.06. Transfer learning aided the production of two of the models, increasing analysis efficiency from 6 to 16 times faster than manual annotation for time-lapse images. Surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. Moving forward, interactive machine learning tools and model sharing could dramatically increase image analysis speeds, collaborative research and our understanding of spatiotemporal patterns in biodiversity.

Using Facebook to Recruit Urban Participants for Smartphone-Based Travel Surveys

Social media has become an integral part of everyday life for many individuals, serving as a platform to express opinions, share memories and lifestyles, follow news, and adapt to social trends and norms. The wealth of user information and analytics on these platforms has facilitated the development and sale of tailored products and services, benefiting advertisers and researchers seeking survey participants. Social media advertising has demonstrated its effectiveness in reaching hard-to-reach populations. However, transport researchers have yet to capitalise on this potential fully. This paper presents our experience using social media to recruit participants for two smartphone travel surveys conducted in Australia. We demonstrate that social media recruitment and smartphone-based travel surveys are highly effective, adaptable, and can be rapidly deployed in response to research opportunities, such as during the early phase of the COVID-19 pandemic when traditional methods may be less suitable. This approach also holds great potential for travel surveys targeting the general population. This paper shares several lessons from this experiment, including our administrative approach and detailed technical instructions to utilise open-source software tools for conducting smartphone travel surveys like ours. This approach significantly reduces study costs compared to most commercial solutions.

Numerical Simulations of Josephson Traveling Wave Parametric Amplifiers (JTWPAs): Comparative Study of Open-Source Tools

Numerical Simulations of Josephson Traveling Wave Parametric Amplifiers (JTWPAs): Comparative Study of Open-Source Tools

MortalityMinder: Visualization and AI Interpretations of Social Determinants of Premature Mortality in the United States

MortalityMinder enables healthcare researchers, providers, payers, and policy makers to gain actionable insights into where and why premature mortality rates due to all causes, cancer, cardiovascular disease, and deaths of despair rose between 2000 and 2017 for adults aged 25–64. MortalityMinder is designed as an open-source web-based visualization tool that enables interactive analysis and exploration of social, economic, and geographic factors associated with mortality at the county level. We provide case studies to illustrate how MortalityMinder finds interesting relationships between health determinants and deaths of despair. We also demonstrate how GPT-4 can help translate statistical results from MortalityMinder into actionable insights to improve population health. When combined with MortalityMinder results, GPT-4 provides hypotheses on why socio-economic risk factors are associated with mortality, how they might be causal, and what actions could be taken related to the risk factors to improve outcomes with supporting citations. We find that GPT-4 provided plausible and insightful answers about the relationship between social determinants and mortality. Our work is a first step towards enabling public health stakeholders to automatically discover and visualize relationships between social determinants of health and mortality based on available data and explain and transform these into meaningful results using artificial intelligence.

An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score

Traditional views have long held that machine translation cannot achieve the quality and accuracy of human translators, especially in complex language pairs like Persian and English. This study challenges this perspective by demonstrating that ChatGPT-4, with access to vast amounts of multilingual data and leveraging advanced large language model algorithms, significantly outperforms widely utilized open-source machine translation tools and approaches the realm of human translation quality. This research aims to critically assess the translation accuracy of ChatGPT-4 against a traditional open-source machine translation tool from Persian to English, highlighting the advancements in artificial intelligence-driven translation technologies. Using Bilingual Evaluation Understudy scores for a comprehensive evaluation, this study compares the translation outputs from ChatGPT-4 with MateCat, providing a quantitative basis for comparing their accuracy and quality. ChatGPT-4 achieves a BLUE score of 0.88 and an accuracy of 0.68, demonstrating superior performance compared to MateCat, with a 0.82 BLUE score and 0.49 accuracy. The results indicate that the translations generated by ChatGPT-4 surpass those produced by MateCat and nearly mirror the quality of human translations. The evaluation demonstrates the effectiveness of OpenAI's large language model algorithms in improving translation accuracy.

Employability skills among work ready professionals in higher education: mapping the field through bibliometric analysis with R studio

PurposeThe current study intends to synthesise the prior studies on employability skills among work-ready professionals in higher education through bibliometric and network visualisation tools. This study also identifies the gaps in the existing literature that still need to be filled and maps the course for future investigation.Design/methodology/approachThe current study examined research papers on employability skills among work-ready professionals in higher education to evaluate global patterns and the top authors, institutions, journals, and nations contributing to this field. The authors analysed eight hundred eighty-five papers from the Scopus database between 1982 and 2023 for the study. Open-source bibliometric tools like Biblioshiny were used in the study to examine the body of literature and to spot new directions for future research.FindingsThe study’s findings indicate that since 2008, researchers, academics, and policymakers have paid more attention to employability skills among work-ready professionals, and 2019 is the year seeing the highest no. of publications. Across the globe, 2077 authors contributed to the articles, papers, and journals published in the particular domain. Regarding location, the United Kingdom is the world’s most productive nation, with 342 articles produced. Similarly, Australia, India, and Malaysia are the leading nations that have contributed to the field. Both keywords significantly contribute to scientific knowledge.Practical implicationsThe gaps in this study will serve as a reference point for researchers conducting future studies in this field. Additionally, the result of this analysis offers a roadmap for higher education to enhance graduate employability by embedding necessary skills into them.Originality/valueThere has not been a bibliometric analysis concerning employability skills among Work-ready professionals in the existing literature; hence, this article is innovative. This study is the first attempt at integrating the concept. The study also gives an overview of potential directions for future research in this discipline.

The Genesis of AIbyAI Integrated Circuit: Where AI Creates AI

The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AIbyAI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AIbyAI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs.

PROACT 2.0: A new open-source tool to improve patient-doctor communication in clinical trials.

The use of Digital Healthcare Products is leading to significant improvements in clinical practice. Herein, we discuss the development of PROACT 2.0 (Patient Reported Opinions About Clinical Tolerability v2.0), a novel open-source mobile and web application developed at Fondazione IRCCS Istituto Nazionale Tumori in Milan. It was developed in collaboration with The Christie, Manchester, in the context of work package 2 of the UpSMART Accelerator project, involving a consortium of referral cancer centers from the UK, Spain and Italy. PROACT 2.0 enhances communication between patients and healthcare providers in cancer clinical trials, allowing patients to report adverse events and side effects, and healthcare teams to collect valuable patient-reported outcome measures for treatment management. PROACT 2.0 supports text, audio, and video messaging, offering a secure, non-urgent communication channel that integrates with, or replaces, traditional methods. Its user-friendly and multilingual interface provides a new route for patient engagement and streamlines the handling of logistical information. Positive feedback from initial testing warrants future enhancements for broader applicability in cancer research and treatment.

CFIEE: An Open-Source Critical Metadata Extraction Tool for RISC-V Hardware-Based CFI Schemes

Control flow critical metadata play a key role in hardware-based control flow integrity (CFI) mechanisms that effectively monitor and secure program control flow based on pre-extracted metadata. The existing control flow analysis tools exhibit some deficiencies, including inadequate compatibility with the RISC-V architecture, a steep learning curve, limited automation capabilities, and restricted data output formats. CFIEE is an open-source tool with a graphical interface for the automated extraction of control flow critical metadata. The tool possesses the capability to analyze RISC-V binary executables, transforming the binary into an intermediate representation (IR) in the form of the disassembled code, and extracting the critical metadata required for studying hardware-based CFI mechanism through a designed control flow transfer relationship analysis algorithm. The extracted metadata include program basic blocks and their corresponding hash values, control flow graphs, function call relationships, distribution of forward transfer instructions, etc. We selected 15 embedded system programs with processor adaptation for functional verification. The results demonstrate the CFIEE’s capability to automatically analyze programs within the supported RISC-V instruction set and generate comprehensive and precise metadata files. This tool can significantly enhance the efficiency of control flow metadata extraction and furnish configurable metadata for the hardware-based security mechanisms.

Evaluating the knowledge of diarrhoea disease prevention measures among caregivers in Nyalenda, Kisumu County

This study sought to evaluate knowledge of diarrhoea disease prevention measures among caregivers. Diarrhoea disease is a prominent public health concern, with children aged 6-24 months more at risk. Caregiver education is crucial for eliminating child diarrhoea. The study employed a quasi-experimental design using mixed-method analysis. The study included a sample size of 302 households of caregivers of children aged 6-24 months living in Nyalenda informal settlements. The study used an open-source web-based statistical tool. Data was collected, entered into SPSS, and analysed. Descriptive analysis determined the mean and proportions. Multivariate logistic regression analysis assessed the connection between independent and dependent variables. This study found that the mean knowledge score for diarrhoea disease prevention from the 12 items calculated was 6.3 ± 2.6 at baseline for all the recruited caregivers. It was also established that the caregivers from Nyalenda A (intervention group) had a higher mean knowledge score of 7.9 ± 2.7 than those from Nyalenda B (control group), which had a mean knowledge score of 6.4 ± 2.6. In conclusion, the study proved that training caregivers using visual charts played a role in enhancing the knowledge of the caregivers. This study recommends focusing on implementing visual charts to increase caregivers’ knowledge of diarrhoea prevention measures.

“Small is beautiful” – Examining reliable determination of low-abundant therapeutic antibody glycovariants

Glycans associated with biopharmaceutical drugs play crucial roles in drug safety and efficacy, and therefore, their reliable detection and quantification is essential. Our study introduces a multi-level quantification approach for glycosylation analysis in monoclonal antibodies (mAbs), focusing on minor abundant glycovariants. Mass spectrometric data is evaluated mainly employing open-source software tools. Released N-glycan and glycopeptide data form the basis for integrating information across different structural levels up to intact glycoproteins. Comprehensive comparison showed that indeed, variations across structural levels were observed especially for minor abundant species. Utilizing modification finder (MoFi), a tool for annotating mass spectra of intact proteins, we quantify isobaric glycosylation variants at the intact protein level. Our workflow's utility is demonstrated on NISTmAb, rituximab and adalimumab, profiling their minor abundant variants for the first time across diverse structural levels. This study enhances understanding and accessibility in glycosylation analysis, spotlighting minor abundant glycovariants in therapeutic antibodies.

PyLandslide: A Python tool for landslide susceptibility mapping and uncertainty analysis

Mitigating the impacts of landslides and planning resilient infrastructure necessitates assessing the exposure to this hazard through, for example, susceptibility mapping involving the spatial integration of various contributing factors. Here, we introduce PyLandslide, an open-source Python tool that leverages machine learning and sensitivity analysis to quantify the weights of various contributing factors, estimate the associated uncertainties, and generate susceptibility maps. We apply PyLandslide to the case of rainfall-triggered landslides in Italy driven by historical precipitation data (1981–2023) and nine climate projections for the mid-century (2041–2050). Results highlight distance to roads as the most influential factor in determining landslide susceptibility in Italy, followed by slope. Our findings reveal an overall reduction in susceptibility in the mid-century compared to the historical period; however, the directional changes vary spatially. Uncertainty analysis should play a central role in decision-making on landslides, where weights are intricately linked to investments.

Simulation model of tumor-treating fields

Tumor-treating fields (TTFields) is a novel treatment modality for malignant solid tumors, often employing electric field simulations to analyze the distribution of electric fields on the tumor under different parameters of TTFields. Due to the present difficulties and high costs associated with reproducing or implementing the simulation model construction techniques, this study used readily available open-source software tools to construct a highly accurate, easily implementable finite element simulation model for TTFields. The accuracy of the model is at a level of 1 mm 3. Using this simulation model, the study carried out analyses of different factors, such as tissue electrical parameters and electrode configurations. The results show that factors influncing the distribution of the internal electric field of the tumor include changes in scalp and skull conductivity (with a maximum variation of 21.0% in the treatment field of the tumor), changes in tumor conductivity (with a maximum variation of 157.8% in the treatment field of the tumor), and different electrode positions and combinations (with a maximum variation of 74.2% in the treatment field of the tumor). In summary, the results of this study validate the feasibility and effectiveness of the proposed modeling method, which can provide an important reference for future simulation analyses of TTFields and clinical applications.

Gravity current analyzer for lock-release experiments

Analyzing gravity currents and stratified flows thoroughly is a time-consuming task, particularly in engineering and educational contexts. Despite the availability of numerous tools for studying stratified flows, there is currently no software dedicated to analyzing gravity currents, integrating flow characterization with theoretical and empirical analyses. This study introduces an open-source tool (Dyenamic), specifically designed for conducting in-depth analyses of gravity currents using a single recorded video. The software incorporates a particle image velocimetry routine to examine the velocity field of the gravity current and the sediment particle transport, offering essential physical indices and categorization based on existing literature. Data from modeling results and laboratory experiments are used to perform calibration, validation, and sensitivity analyzes. The findings demonstrate strong alignment with the existing gravity current literature, highlighting the effectiveness of the software in characterizing gravity currents. Dyenamic emerges as a robust, user-friendly, and versatile tool suitable for educational and research endeavors.

Deep Learning for Iris Recognition: A Survey

In this survey, we provide a comprehensive review of more than 200 articles, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition.

WEB VISUALIZATION AND SPATIAL ANALYSIS OF TREE SPECIES DISTRIBUTION AT UNIVERSITY OF THE PHILIPPINES DILIMAN ACADEMIC OVAL

Abstract. Understanding the distribution of tree species is crucial for ecological comprehension and effective conservation strategies. This research presents a web-based platform aimed at visually representing the trees found at the University of the Philippines Diliman campus. Open-source geospatial tools such as OpenStreetMap, KoboToolbox, Pl@ntNet, JOSM, uMap, and QGIS were employed for data visualization, collection, analysis, validation, and spatial pattern assessment. Through these tools, a total of 11 tree species were identified, with acacia emerging as the dominant species, especially concentrated along the academic oval. The application of Kernel Density Estimation (HeatMap) in QGIS revealed these spatial distribution patterns. The findings contribute to the effective management, conservation, and restoration of the campus' green spaces, shedding light on the significance of trees in urban ecosystems. The online interactive map developed serves as a valuable tool for improving management and conservation strategies. Importantly, the study assesses the effectiveness of open-source tools, showcasing their versatility and comparable capabilities to proprietary alternatives in geospatial applications.

Modeling Radioactive Decay Series Phenomenon Involving First-Order Differential Equation and Visualizing Solutions Using Open-Source Software XCOS

Numerous physical and chemical processes can be accurately described by first-order differential equations and can be solved through analytical methods. The solutions to these equations necessitate the specification of initial or boundary conditions. In the realm of physics and chemistry education, the ability to visualize the solutions of differential equations and comprehend their implications for physical/chemical changes holds significant importance. XCOS, an open-source software tool, serves as a valuable resource for both solving differential equations and graphically representing their solutions. Furthermore, XCOS aids in tackling nonlinear differential equations for which analytical solutions are often elusive. This paper delves into radioactive decay and decay series reactions by first-order differential equations. The paper explores the successful resolution and visualization of the phenomenon involving differential equations through XCOS. This approach extends to the manipulation of various parameters within the equations, further fostering inquiry-based learning.

Explainable Anomaly Detection of Synthetic Medical IoT Traffic Using Machine Learning

In the context of the Internet of Things (IoT), particularly within medical facilities, the detection and categorization of Internet traffic remain significant challenges. While conventional methods for IoT traffic analysis can be applied, obtaining suitable medical traffic data is challenging due to the stringent privacy constraints associated with the health domain. To address this, this study proposes a network traffic simulation approach using an open-source tool called IoT Flock, which supports both CoAP and MQTT protocols. The tool is used to create a synthetic dataset, to simulate IoT traffic originating from various smart devices in different hospital rooms. The study shows a complete anomaly detection analysis of IoT-Flock-generated traffic, both normal and malicious, by leveraging and comparing traditional machine learning techniques, deep learning models with multiple hidden layers, and explainable artificial intelligence techniques. The results are very promising. For the binary classification, for example, the obtained accuracy is close to 100%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$100\\%$$\\end{document} in the case of the CoAP protocol. Good results are also obtained when the multinomial classification is performed, observing that CoAP packets are classified better than MQTT packets, even if the identification of the different MQTT packets reaches very high metrics for the most of the considered algorithms. Moreover, the obtained classification rules are also meaningful in the considered IoT context. The results indicate that IoT-Flock synthetic data can effectively be used to train and test machine and deep learning models for detecting abnormal IoT traffic in medical scenarios. This research attempts also to bridge the gap between IoT security and healthcare, providing useful insights into securing medical IoT networks in general.

DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox. In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms. DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

EMB3Rs: A game-changer tool to support waste heat recovery and reuse

At a time when European countries try to cope with escalating energy prices while decarbonizing their economies, waste heat recovery and reuse arises as part of the solution for sustainable energy transitions. The lack of appropriate assessment tools has been pointed out as one of the main barriers to the wider deployment of waste heat recovery projects and as a reason why its potential remains largely untapped.The EMB3Rs platform emerges as an online, open-source, comprehensive and novel tool that provides an integrated assessment of different types of waste heat recovery solutions, (e.g. internal or external) and comprises several analysis dimensions (e.g. physical, geographical, technical, market, and business models). It has been developed together with stakeholders, and tested in a number of representative contexts, covering both industrial and heat network applications. This has demonstrated the enormous potential of the tool in dealing with complex simulations, while delivering accurate results within a significantly lower time-frame than traditional analysis.The EMB3Rs tool removes important barriers such as analysis costs, time and complexity for the user, and aims at supporting a wider investment in waste heat recovery and reuse by providing an integrated estimation of the costs and benefits of such projects. This paper describes the tool and illustrates how it can be applied to help unlock the potential of waste heat recovery across European countries.