API Endpoints Research Articles

RANCANG BANGUN APLIKASI MOBILE PEMBELAJARAN HANACARAKA BALI MENGGUNAKAN METODE CNN BERBASIS CLOUD COMPUTING

This research aims to develop a mobile application for learning Balinese Hanacaraka, designed to be engaging for students and to address the shortcomings of conventional learning methods, which are often less appealing. The application leverages Convolutional Neural Network (CNN) technology and cloud computing. TensorFlow is used for developing the CNN model, Android Studio for application development, and Google Cloud Platform (GCP) and Firebase for cloud services. The application offers four main features: writing practice, quizzes, statistics, and a dictionary. The CNN is divided into six models for Hanacaraka speech recognition, achieving an accuracy rate of 97-100% and an F1 score between 0.94-1. Testing results indicate that the application functions as expected, with a System Usability Scale (SUS) score of 70.57, suggesting user acceptance. API endpoints were tested using Postman and found to be reliable.

FastAPI vs. The Competition: A Security Feature Showdown with a Proposed Model for Enhanced Protection

API security is of paramount importance in modern web applications, as it protects sensitive data and ensures authorized access to resources. FastAPI, a Python-based web framework, offers various security features to developers for building secure APIs. This paper examines the security features provided as well as supported by FastAPI, The provided features consists of OAuth2 authentication, Dependency injection and Security Schemas and Scopes, whereas the supported security features includes Json Web Token (JWT) authentication, Cross Site Request Forgery (CSRF) token support and HTTPS support. The supported features can be implemented by using python libraries and middlewares. The proposed model combines the JWT token authentication, OAuth2 authentication, and Security Scopes and schema security features to enhance the security in the FastAPI application. The proposed model defines scopes for users and uses JWT to generate tokens and using OAuth2 authentication service to only allow a user who has the specific permissions and scopes to access and perform actions in the scope specific API endpoints. The model hence allows secure and safe working of applications by eliminating the threat of unauthorized users to corrupt the application code. The focus on performance and security makes FastAPI an excellent choice for developers seeking to build secure APIs. Overall, this paper highlights the importance of API security and showcases FastAPI's security features, demonstrating how developers can leverage FastAPI to build robust, performant, and secure APIs. Key Words: FastAPI, Flask, Django, OAuth2, JWT (JSON Web Token), CSRF (Cross Site Request Forgery)

Optimizing Online Shopping: Leveraging Llama for Product Review Summarization

This article highlights a unique "Customer Satisfaction Analysis" module and presents an innovative online commodity search system. The system's goal is to rapidly retrieve product data such as search results, product details, and reviews—through e-commerce API endpoints and display it in an intuitive user interface. The "Customer Satisfaction Analysis" section is particularly noteworthy as it utilizes sophisticated algorithms to examine customer feedback and produce detailed summaries that accentuate the salient features of user opinion. This methodology endows prospective purchasers with a more profound and perceptive comprehension of product attributes and client contentment. Sentiment analysis is integrated into the search process, allowing consumers to make well-informed buying selections. Our approach improves consumers' capacity to make decisions by synthesizing product features and user feedback. This allows consumers to make better educated choices and ultimately have a more fulfilling online shopping experience. Key Words: API, Large Language Model , Llama, Search System

Industrial IoT system for laser-wire direct energy deposition: data collection and visualization of manufacturing process signals

Industry 4.0, also known as the Fourth Industrial Revolution, is a term used to describe the current trend of automation and data exchange in manufacturing and other industries. The Internet of Things (IoT) plays a crucial role in Industry 4.0 by connecting devices, machines, and products to the Internet and enabling real-time data exchange. Moreover, additive manufacturing is a key developing manufacturing technology in Industry 4.0. New technologies such as data analysis with Artificial Intelligence and machine vision are widely used in optimization. However, in a lab environment, these technologies depend on the data collected from the process. For such work, the researchers should be able to focus on their core research rather than on the development of infrastructure to collect and analyse the data. This research presents an open software and hardware IoT solution to monitor a laser wire direct energy deposition system installed in a cartesian type 3-axis machine tool. The IoT solution adopts three open-source tools for core issues, such as 1) interoperability, flexibility, and availability; 2) data storage; and 3) data visualization of sensor data and manufacturing process signals. The system architecture is based on one or more edge devices connected to sensors and forwarding their data toward a local API endpoint. The endpoint is created with Node-RED, an open-source visual flow-based development tool for IoT data. Node-RED forwards the data to an open-source InfluxDB database. Finally, the data is visualized with an open-source Grafana application. The system is prototyped, designed, implemented, and tested in a lab environment to monitor a laser-wire direct energy deposition process. The significance of such a flexible IoT data collection system for research and development projects can be integral. Thus, providing savings in time and money can substantially speed up the development of new technologies where the value arises from the sensor data and its analysis.

Why are these publications missing? Uncovering the reasons behind the exclusion of documents in free‐access scholarly databases

AbstractThis study analyses the coverage of seven free‐access bibliographic databases (Crossref, Dimensions—non‐subscription version, Google Scholar, Lens, Microsoft Academic, Scilit, and Semantic Scholar) to identify the potential reasons that might cause the exclusion of scholarly documents and how they could influence coverage. To do this, 116 k randomly selected bibliographic records from Crossref were used as a baseline. API endpoints and web scraping were used to query each database. The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%–46%), and internal requirements that exclude secondary content (6.5%–11.6%). In the case of Dimensions, the only classic bibliographic database with the lowest coverage (7.6%), internal selection criteria such as the indexation of full books instead of book chapters (65%) and the exclusion of secondary content (15%) are the main motives of missing publications.

EnsemblQueryR: fast, flexible and high-throughput querying of Ensembl LD API endpoints in R.

We present ensemblQueryR, an Rpackage for querying Ensembl linkage disequilibrium (LD) endpoints. This package is flexible, fast and user-friendly, and optimised for high-throughput querying. ensemblQueryR uses functions that are intuitive and amenable to custom code integration, familiar R object types as inputs and outputs as well as providing parallelisation functionality. For each Ensembl LD endpoint, ensemblQueryR provides two functions, permitting both single- and multi-query modes of operation. The multi-query functions are optimised for large query sizes and provide optional parallelisation to leverage available computational resources and minimise processing time. We demonstrate improved computational performance of ensemblQueryR over an exisiting tool in terms of random access memory (RAM) usage and speed, delivering a 10-fold speed increase whilst using a third of the RAM. Finally, ensemblQueryR is near-agnostic to operating system and computational architecture through Docker and singularity images, making this tool widely accessible to the scientific community.

Development of a REST API for Human Resource Information System for Employee Referral Management Domain Using the Express JS Framework and Node.js

This paper presents the development process of a REST API for a Human Resource Information System (HRIS) focusing on employee referral management. The API was developed using the Express JS framework and Node.js, leveraging their powerful capabilities for efficient web application development. The HRIS serves as a centralized platform for managing employee referrals, tracking candidate progress, and optimizing talent acquisition. The article provides insights into the methodology, key features, and advantages of using the Express JS framework and Node.js for building such an HRIS. The results of the study indicate that the Express JS and Node.js frameworks have successfully developed a REST API for HRIS in the domain of employee referral management. This is evident from the black box testing results, which show valid outcomes for each tested request and response in every API endpoint.

AMAZON S3 STATIC WEBSITE HOSTING

Nowadays, especially in period of energetic crisis, it is very important to get reliable data storage and uninterrupted access to websites. Considering that the world’s mainstream is using the clouds services for educational, scientific, business purposes it is important to explore features of hosting websites in major cloud service providers. One of the leader’s vendors in cloud servicing is Amazon with its Web Services (AWS). AWS offers hosting solutions such as Lightsail, Amplify Console, Simple Storage Service (S3) and Elastic Cloud Computing, from simple static websites to complicated dynamic, The article deals with the method of hosting static websites. Simple Storage Service was used for this purpose. The method has many benefits: the service is simple, cheap, scales well, manages everything and has great integration with other AWS services. This method is the best for websites that do not contain server-side scripting, like PHP or ASP.NET. It is ideal for websites that change infrequently, such as personal, promo, startup websites, websites of small businesses and organizations. The article focuses on how set permissions, properties and policies of AWS S3 bucket for hosting a website. But usage of S3 bucket alone provides only http connection. That’s why to deploy secure connection and speed up delivery of the static content was proposed to use AWS CloudFront service. CloudFront is content delivery network service, that uses the latest version of Transport Layer Security Protocol and saves files in the cache for 24 hours. The article shows how to configure CloudFront distribution to serve https requests for an Amazon S3 bucket. Configuration S3 bucket REST API endpoint was used as the most secure case. As a result, latency was reduced, security was improved due to traffic encryption and the content of S3 bucket was kept private.

Exploration and comparison of molecular mechanisms across diseases using MINERVA Net.

Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease-related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open-access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens.

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.

Annotation of biologically relevant ligands in UniProtKB using ChEBI.

To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands. We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides. Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/). Supplementary data are available at Bioinformatics online.

GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies.

The GWAS Central resource gathers and curates extensive summary-level genome-wide association study (GWAS) data and puts a range of user-friendly but powerful website tools for the comparison and visualisation of GWAS data at the fingertips of researchers. Through our continued efforts to harmonise and import data received from GWAS authors and consortia, and data sets actively collected from public sources, the database now contains over 72.5 million P-values for over 5000 studies testing over 7.4 million unique genetic markers investigating over 1700 unique phenotypes. Here, we describe an update to integrate this extensive data collection with mouse disease model data to support insights into the functional impact of human genetic variation. GWAS Central has expanded to include mouse gene-phenotype associations observed during mouse gene knockout screens. To allow similar cross-species phenotypes to be compared, terms from mammalian and human phenotype ontologies have been mapped. New interactive interfaces to find, correlate and view human and mouse genotype-phenotype associations are included in the website toolkit. Additionally, the integrated browser for interrogating multiple association data sets has been updated and a GA4GH Beacon API endpoint has been added for discovering variants tested in GWAS. The GWAS Central resource is accessible at https://www.gwascentral.org/.

12. Expanding clinical actionability in individual patient profiles with the Molecular Oncology Almanac

<h3>Background</h3> MOAlmanac is an open source clinical interpretation algorithm and paired knowledge base for precision cancer medicine used to rapidly characterize and identify genomic features related to therapeutic sensitivity and resistance and of prognostic relevance. <h3>Methods</h3> MOAlmanac assesses tumor actionability by evaluating individual genomic features (e.g. somatic or germline variants, copy number alterations, or fusions), interactions between them, and secondary features (such as mutational burden, mutational signatures, and MSI status) relative to our knowledge base. In addition, it also performs profile-to-cell line matchmaking to nominate genomically similar cell lines for hypothesis expansion. All clinically relevant findings are summarized into a web-based report. The underlying knowledge base can be accessed through our API endpoints and web browser, and we developed a cloud-based web portal to increase accessibility of the algorithm. <h3>Results</h3> 32,108 samples from 66 cancer types received targeted sequencing with Oncopanel and were evaluated by MOAlmanac. Using Oncopanel's tier 1 and tier 2 criteria for clinical actionability, we observed that 8,285 samples harbored at least one alteration suggesting therapeutic sensitivity based on FDA approvals or clinical guidelines, increasing to 18,117 when considering clinical trials, clinical, preclinical, and inferential evidence. <h3>Conclusion</h3> Clinical actionability of molecular tumor data was increased in individual patients by expanding the set of evidence considered. Source code and a web portal for this project are available at moalmanac.org.

Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models

Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models

Conformance assessment of Architectural Design Decisions on API endpoint designs derived from domain models

Context:Domain-driven design (DDD) is commonly used to design microservices. A crucial aspect of microservice design is API design, which includes the design of API endpoints. Objective:Our objective is to automate the assessment of conformance to Architectural Design Decisions (ADDs) on the interrelation of DDD and APIs. In particular, we studied link mapping, API operation design, and resource segregation as API endpoint design issues that are linked to domain model design. We particularly aim to address conformance checking in the context of frequent release practices, as frequent manual conformance checking is difficult or infeasible. Methods:We suggest a new approach for the automated assessment of conformance to ADD options. The approach suggests automated detectors to detect ADD options selected in a given API endpoint design, as well as an assessment scoring scheme based on empirical results. For the evaluation of our approach, we first manually created a ground truth for 12 cases in a multi-case study, and then compared the results of our automated detectors to the ground truth for each of those cases. Results:With our approach, all ADD options in our multi-case study possibly can be automatically detected. Without further improvements, our approach identifies 83% of the decision points in the multi-case study correctly. A statistical analysis of our data shows only a negligible effect size for differences to the ground truth. Conclusion:Our new approach provides a pragmatic method for automated detection of conformance to ADDs on the interrelation of DDD and APIs. The approach can support the continuous analysis of API endpoint designs.

Abstract LB503: Using JBrowse 2 plugins to visualize cancer genomic data

Abstract Genome browsers are essential tools for integrating, exploring, and visualizing data from large cancer genomics datasets. JBrowse 2 is a JavaScript genome browser with novel features for visualizing structural variants, syntenic alignments, and multiple types of genomic data. Although JBrowse 2 is available as a web-based or desktop application, the capabilities and features are not fixed and can be expanded through a comprehensive plugin system. To help address users' needs, we launched the new JBrowse 2 Plugin Store (https://jbrowse.org/jb2/plugin_store/). The overarching goals of the Plugin Store are to showcase the crucial features plugins add to JBrowse 2, to enable researchers to search for plugins they need, and for developers to highlight their plugins. The plugin system spans all aspects of the JBrowse 2 application and enables new track types (e.g. Manhattan plots, Hi-C data, ideograms), data adapters (e.g. API endpoint adapters for NCI Genomic Data Commons and the International Cancer Genome Consortium), and views (e.g. dot plots and multiple sequence alignments). Here, we present the capabilities of JBrowse 2 plugins and describe usage scenarios for cancer biologists and bioinformaticians, and software developers. Citation Format: Robin Andrew Haw, Colin Diesh, Caroline Bridge, Rob Buels, Garrett Stevens, Peter Xie, Teresa Martinez, Elliot Hershberg, Junjun Zhang, Shihab Dider, Scott Cain, Lincoln Stein, Ian Holmes. Using JBrowse 2 plugins to visualize cancer genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB503.

Expanding clinical actionability in individual patient profiles with the Molecular Oncology Almanac.

3015 Background: The clinical care of oncology patients is routinely informed by tumor and inherited genetic profiles. This is accomplished by molecular pathologists synthesizing the growing body of clinical guidelines and scientific evidence that associates cancer genome alterations and therapeutic response, and applying that knowledge during case reviews. Many academic medical centers formalize this process in the form of molecular tumor boards. As the number of cases for review and literature continue to increase, there is opportunity to leverage clinical interpretation algorithms to computationally prioritize molecular features and both enhance and automate the sample contextualization process. Here, we present the Molecular Oncology Almanac (MOAlmanac) to enable the rapid assessment of tumor actionability. Methods: Molecular Oncology Almanac is an open source clinical interpretation algorithm and paired knowledge base for precision cancer medicine. It is used to rapidly characterize and identify genomic features related to therapeutic sensitivity and resistance and of prognostic relevance. This is performed by assessing not only individual genomic features (e.g. somatic variants, copy number alterations, germline variants, and fusions) but also interactions between these events as well as secondary features such as mutational burden, mutational signatures, MSI status, and aneuploidy. MOAlmanac summarizes all clinically relevant findings into a web-based actionability report. The underlying knowledge base can be accessed through our API endpoints and web browser, and entries may be recommended through either Github or our browser extension. In addition, we developed a cloud-based web portal on top of the Terra framework to increase accessibility. Results: A total of 32,108 samples from 30,607 patients across 66 cancer types received targeted sequencing to characterize somatic variants, copy number alterations, and fusions from PROFILE’s Oncopanel and were evaluated with MOAlmanac. Based on Oncopanel’s tier 1 and tier 2 criteria for clinical actionability, we observed that 8,285 samples (26%, 0 - 69% by cancer type) of patients harbored at least one alteration suggesting therapeutic sensitivity based on FDA approvals or clinical guidelines. Actionability increases to 18,117 samples (56%, 0 - 85% by cancer type) when considering an expanded set of evidence to include relationships captured from clinical trials, clinical, preclinical, and inferential evidence; consequently providing at least one therapeutic hypothesis to otherwise variant-negative patients. Conclusions: Clinical actionability of molecular tumor data was increased in individual patients by expanding the set of evidence considered. Source code and a web portal for this project are available at moalmanac.org .

BioExcel Building Blocks REST API (BioBB REST API), programmatic access to interoperable biomolecular simulation tools.

The BioExcel Building Blocks (BioBB) library offers a broad collection of wrappers on top of common biomolecular simulation and bioinformatics tools. The possibility to access the library remotely and programmatically increases its usability, allowing individual and sporadic executions and enabling remote workflows. BioBB REST API extends and complements the BioBB library offering programmatic access to the collection of biomolecular simulation tools included in the BioExcel Building Blocks library. Molecular Dynamics setup, docking, structure modeling, free energy simulations and flexibility analyses are examples of functionalities included in the endpoints collection. All functionalities are accessible through standard REST API calls, voiding the need for tool installation. All the information related to the BioBB REST API endpoints is accessible from https://mmb.irbbarcelona.org/biobb-api/. Links to extended documentation, including OpenAPI endpoints specification and examples, Read-The-Docs documentation and a complete workflow tutorial can be found in the Supplementary Table S1. Supplementary data are available at Bioinformatics online.

Back-End Design and Development on Rekaruang Application with Microservices Architecture

One of the basic human needs is a house. One aspect considered when building a home is interior design. However, today, many people do not care about the importance of interior design in human psychology, which may cause a problem for interior designers, especially in getting their service users. Therefore, this study will discuss solutions to overcome the issues of interior designers and interior designer service users through the development of the Rekaruang startup. This research focuses on planning and building the back-end, which will later be implemented in the Rekaruang Application. The back-end development will use a microservices architecture with the waterfall method as a guide for development. The services formed are user service, master data service, consultation service, transaction service, and chat service. The created services are represented in the form of API endpoints which are then tested with unit testing and load testing types to determine the performance of microservices. The test results using unit testing show that of the 34 test cases tested, all test cases can be declared victorious. Furthermore, this study only tested three endpoints for load testing because they were considered to have a long execution time. The total number of users that microservices can handle is 216 at the same time. The test results can be said to be good because the projection of Rekaruang users in the first year is only 40 users, consisting of 20 interior designers and 20 clients.

Auto{API} – A Web-Based Tool for Specification of an API Endpoint to Return JSON Data From an XML Source

auto{API} is a web-based tool for the specification and management of API endpoints to online XML data sources. The APIs generated return the data in JSON format. The tool allows users to specify the field names to be used in the JSON data and to control which elements of the original data set are returned. Options are provided for the returned data to skip null values and to flatten the structure that is retrieved. auto{API} is written in Python/Flask and is available under the MIT licence. The software is available from GitHub (https://github.com/aamoore/autoAPI) and can be seen deployed at https://autoapi-app.herokuapp.com/?http://apis.opendatani.gov.uk/translink/3042AA.xml.