Open-source API Research Articles

IoT-based system to measure thermal insulation efficiency

The main purpose of thermal insulation is to reduce the effect of weather conditions, which can reduce energy consumption and help preserve the natural environment. Due to increasing demand for thermal insulation in modern buildings, it is necessary to monitor the effects of weather condition on the efficiency of thermal insulation. To measure this effect, Internet of Things (IoT)-based sensor networks play an important role. This paper proposes an IoT-based monitoring system that measures and analyses the effect of weather conditions on thermal insulation efficiency. The proposed system includes an Arduino as a central unit that interfaces at the input with temperature and relatively humidity sensors, and at the output with a WiFi module that transmit the collected data via the Internet to an IoT analytics platform to aggregate, analyze, and visualize the data. The proposed system is low cost and provides insight into the design and implementation of a complete application with important IoT features, e.g., sensing and transmitting data to the cloud, data processing, and data retrieval through the ThingSpeak open-source API. In a proof of concept, the proposed system was implemented and tested in different environments and at different altitudes, and the results were analyzed and compared to standard weather data. The sensed data can be further analyzed and leveraged by end users to mitigate the effects of weather conditions on thermal insulation.

Comparison of state-of-the-art deep learning APIs for image multi-label classification using semantic metrics

Image understanding heavily relies on accurate multi-label classification. In recent years, deep learning algorithms have become very successful for such tasks, and various commercial and open-source APIs have been released for public use. However, these APIs are often trained on different datasets, which, besides affecting their performance, might pose a challenge to their performance evaluation. This challenge concerns the different object-class dictionaries of the APIs’ training dataset and the benchmark dataset, in which the predicted labels are semantically similar to the benchmark labels but considered different simply because they have different wording in the dictionaries. To face this challenge, we propose semantic similarity metrics to obtain richer understating of the APIs predicted labels and thus their performance. In this study, we evaluate and compare the performance of 13 of the most prominent commercial and open-source APIs in a best-of-breed challenge on the Visual Genome and Open Images benchmark datasets. Our findings demonstrate that, while using traditional metrics, the Microsoft Computer Vision, Imagga, and IBM APIs performed better than others. However, applying semantic metrics also unveil the InceptionResNet-v2, Inception-v3, and ResNet50 APIs, which are trained only with the simple ImageNet dataset, as challengers for top semantic performers.

Deep learning seismic substructure detection using the Frozen Gaussian approximation

We propose a deep learning algorithm for seismic interface and pocket detection with neural networks trained by synthetic high-frequency displacement data efficiently generated by the frozen Gaussian approximation (FGA). In seismic imaging high-frequency data is advantageous since it can provide high resolution of substructures. However, generation of sufficient synthetic high-frequency data sets for training neural networks is computationally challenging. This bottleneck is overcome by a highly scalable computational platform built upon the FGA, which comes from the semiclassical theory and approximates the wavefields by a sum of fixed-width (frozen) Gaussian wave packets.Training data for deep neural networks is generated from a forward simulation of the elastic wave equation using the FGA. This data contains accurate traveltime information (from the ray path) but not exact amplitude information (with asymptotic errors not shrinking to zero even at extremely fine numerical resolution). Using this data we build convolutional neural network models using an open source API, GeoSeg, developed using Keras and Tensorflow. On a simple model, networks, despite only being trained on data generated by the FGA, can detect an interface with a high success rate from displacement data generated by the spectral element method. Benchmark tests are done for P-waves (acoustic) and P- and S-waves (elastic) generated using the FGA and a spectral element method. Further, results with a high accuracy are shown for more complicated geometries including a three-layered model, a sine interface, and a 2D-pocket model where the neural networks are trained by both clean and noisy data.

OpenViBE2Unity: Unity3D 기반 뇌-컴퓨터 인터페이스를 위한 오픈소스 API

뇌-컴퓨터 인터페이스는 많은 관심을 받으며 사람의 의도, 인지, 느낌 등을 정량화하여 활용하고자 하는 연구 및 개발에 사용되고 있다. 하지만 뇌파를 측정하고 분석하여 결과를 활용하는 애플리케이션을 제작하는 것은 상당한 노력이 요구되며, 실제로 연구 및 개발보다는 상대적으로 플랫폼간의 연동에 적지 않은 시간을 투자하게 된다. 이에 따라, 뇌-컴퓨터 인터페이스 어플리케이션 개발을 용이하게 하고자 대표적인 게임 개발 플랫폼인 Unity3D와 뇌 신호의 실시간 수집, 처리, 시각화 기능을 제공하는 OpenViBE를 연결하는 Application Programming Interface(API), OpenViBE2Unity(O2U)를 개발하였다. 개발된 API는 두 오픈소스 플랫폼간의 연동을 쉽게 할 수 있도록 몇 가지 함수들을 제공하며 주요 코드 및 사용설명서는 Github를 통해 공개되어 있다. 본 논문에서는 개발한 OpenViBE2Unity의 구조 및 사용법 그리고 OpenViBE2Unity를 활용하여 개발 된 애플리케이션을 소개한다.

ЦИФРОВА ТРАНСФОРМАЦІЯ ГЛОБАЛЬНОГО БАНКІВНИЦТВА: ПРОБЛЕМИ I ПЕРСПЕКТИВИ

На основі аналізу тенденцій і загроз цифрової трансформації глобального банківництва прийшли до висновку про нерівномірність цифровізації банківського бізнесу в різних регіонах. Розвиток цифрових технології чинить вплив на глобальний банківський ринок, його структуру і послуги. Хмарні технології, блокчейн та Big Data, штучний інтелект, біометрика та відкриті програмні інтерфейси (API) використовуються фінансовими установами як у рутинній роботі з клієнтами (оптичне розпізнавання символів, розпізнавання голосу, мови, зображення та обличчя клієнтів тощо), так і у складному аналізі неструктурованих наборів даних (наприклад, розпізнавання шаблонів у трансакціях стосовно відмивання грошей, боротьба із фінансуванням тероризму). Їхнє використання, з одного боку, підвищує конкуренцію та ефективність ринку, а з другого — створює нові системні ризики для фінансової стабільності та цілісності.Основними перепонами трансформації є не технологічні, а різність в організаційній культурі традиційних банків та фінтех-компаній, різне стратегічне бачення топ-менеджменту, брак кваліфікованих кадрів, у результаті чого банкам важко трансформуватись для співробітництва. Використання цифрових технологій посилює системні ризики, які пов’язані з кібербезпекою, шахрайством та етичними проблеми. Для зменшення шахрайства необхідно удосконалити процедури підтвердження ідентичності: інтеграція офлайн- та онлайн-режимів, використання неявних факторів (геолокація IP-адреси) та біометричну поведінку споживачів.З огляду на ймовірність спаду в найближчі роки геополітичні проблеми, торгові та валютні війни традиційні банки повинні трансформувати свій бізнес відповідно до нових тенденцій. У сучасному глобальному світі конкурентні переваги отримують не найбільші банки, а те, які найбільш пристосовані до змін.

Open source software security vulnerability detection based on dynamic behavior features.

Open source software has been widely used in various industries due to its openness and flexibility, but it also brings potential security problems. Therefore, security analysis is required before using open source software. The current mainstream open source software vulnerability analysis technology is based on source code, and there are problems such as false positives, false negatives and restatements. In order to solve the problems, based on the further study of behavior feature extraction and vulnerability detection technology, a method of using dynamic behavior features to detect open source software vulnerabilities is proposed. Firstly, the relationship between open source software vulnerability and API call sequence is studied. Then, the behavioral risk vulnerability database of open source software is proposed as a support for vulnerability detection. In addition, the CNN-IndRNN classification model is constructed by improving the Independently Recurrent Neural Net-work (IndRNN) algorithm and applies to open source software security vulnerability detection. The experimental results verify the effectiveness of the proposed open source software security vulnerability detection method based on dynamic behavior features.

WEBGIS Application for Planning the Tsunami Evacuation Route

The tsunami that occurred on December 26, 2004 has resulted in massive casualties in Banda Aceh City. As one of the steps to avoid the huge casualty if the tsunami repeats itself, a number of Escape Buildings have been built in the city. In spite of the availability of the Tsunami Escape Buildings, the number of casualties can be reduced only if everyone in the tsunami-prone areas knows the nearest Escape Building from his or her position and the best route to reach the building. The purpose of this research was to build a GIS web application that can help determine the nearest Escape Building from a user-selected location and the best route to reach it. This application utilizes a web service that implements the Dijkstra algorithm to find the closest facility and the best route to the facility. The web GIS application was built using the open source API. The web GIS application developed in this research helps users find the nearest Escape Building and the best route to reach it without the need of any GIS software. The application is easy to use because it provides a user-friendly interface via a web browser.

Towards Prioritizing Documentation Effort

Programmers need documentation to comprehend software, but they often lack the time to write it. Thus, programmers must prioritize their documentation effort to ensure that sections of code important to program comprehension are thoroughly explained. In this paper, we explore the possibility of automatically prioritizing documentation effort. We performed two user studies to evaluate the effectiveness of static source code attributes and textual analysis of source code towards prioritizing documentation effort. The first study used open-source API Libraries while the second study was conducted using closed-source industrial software from ABB. Our findings suggest that static source code attributes are poor predictors of documentation effort priority, whereas textual analysis of source code consistently performed well as a predictor of documentation effort priority.

REHUNT: a reliable and open source package for restriction enzyme hunting

BackgroundRestriction enzymes are used frequently in biotechnology. However, manual mining of restriction enzymes is challenging. Furthermore, integrating available restriction enzymes into different bioinformatics systems is necessary for many biotechnological applications, such as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Thus, in the present study, we developed the package REHUNT (Restriction Enzymes HUNTing), which mines restriction enzymes from the public database REBASE using a series of search operations.ResultsREHUNT is a reliable and open source package implemented in JAVA. It provides useful methods and manipulations for biological sequence analysis centered around restriction enzymes contained in REBASE. All available restriction enzymes for the imported biological sequences can be identified by REHUNT. Different genotypes can be identified using PCR-RFLP based on REHUNT for single nucleotide polymorphism (SNP), mutations, and the other variations. REHUNT robustly recognizes multiple inputs with different formats, e.g. regular DNA sequences, variation-in-sequence indicated by IUPAC code, as well as variation-in-sequence indicated by dNTPs format. Variations including di-, tri-, and tetra-allelic types and indel formats are also acceptable. Furthermore, REHUNT provides classified restriction enzymes output, including IUPAC and general sequence types, as well as commercial and non-commercial availabilities. REHUNT also enables analysis for high throughput screening (HTS) technologies.ConclusionsREHUNT is open source software with GPL v3 license and can be run on all platforms. Its features include: 1) Quick restriction enzymes search throughout a sequence based on the Boyer-Moore algorithm; 2) all available restriction enzymes provided and regularly updated from REBASE; 3) an open source API available of integrating all types of bioinformatics systems and applications; 4) SNP genotyping available for plant and animal marker-assisted breeding, and for human genetics; and 5) high throughput analysis available for Next Generation Sequencing (NGS). REHUNT not only to effectively looks for restriction enzymes in a sequence, but also available for SNP genotyping. Furthermore, it can be integrated into other biological and medical applications. REHUNT offers a convenient and flexible package for powerful restriction enzymes analyses in association studies, and supports high throughput analysis. The source codes and complete API documents are available at SourceForge: https://sourceforge.net/projects/rehunt/, GitHub: https://github.com/yuhuei/rehunt, and at: https://sites.google.com/site/yhcheng1981/rehunt.

Fix Risk-Cybernetics Protocol

A decentralized and indestructible risk-reduction protocol integrated on the plethora of qualified Blockchains, implementing Genetic-Algorithm Neural-Network (GANN) Risk-Cybernetics powered by Atomic Portfolio Selection (APS) and MVSK Utility Optimization, for autonomous and instantaneous liquidity provision in assets including FX, cryptocurrencies, etc. on an open-source API high-frequency options trading exchange for volatility-arbitrage, delta-neutral and delta-hedged portfolios operated by institutions or individuals.

Patterns affecting structural properties of social networking site 'Twitter'

Online social networking platforms are kind of complex networks where users are treated as nodes for interactions among them. Understanding such complex network is critical to enhance their existing frameworks and important to incorporate new future applications. Thus, a mixing network pattern is possible due to the diversified geographic locations on user. The present study is focused on measuring assortativity coefficient of network complexity and its effect on the structural properties of the network. We examined crawled users data (group wise) gathered from 'Twitter' by using open source API. Among the group, all the users are ranked according to their followers count. As part of algorithmic process, the assortativity coefficient is calculated in various steps by removing few nodes randomly from the existing network group. It is found that network is resilient to the deletion of highest degree nodes and assortativity is indeed present in network.

GIFT-Grab: Real-time C++ and Python multi-channel video capture, processing and encoding API

GIFT-Grab is an open-source API for acquiring, processing and encoding video streams in real time. GIFT-Grab supports video acquisition using various frame-grabber hardware as well as from standard-compliant network streams and video files. The current GIFT-Grab release allows for multi-channel video acquisition and encoding at the maximum frame rate of supported hardware – 60 frames per second (fps). GIFT-Grab builds on well-established highly configurable multimedia libraries including FFmpeg and OpenCV. GIFT-Grab exposes a simplified high-level API, aimed at facilitating integration into client applications with minimal coding effort. The core implementation of GIFT-Grab is in C++11. GIFT-Grab also features a Python API compatible with the widely used scientific computing packages NumPy and SciPy. GIFT-Grab was developed for capturing multiple simultaneous intra-operative video streams from medical imaging devices. Yet due to the ubiquity of video processing in research, GIFT-Grab can be used in many other areas. GIFT-Grab is hosted and managed on the software repository of the Centre for Medical Image Computing (CMIC) at University College London, and is also mirrored on GitHub. In addition it is available for installation from the Python Package Index (PyPI) via the pip installation tool.

A Novel Automatic Source Code Defects Detection Framework and Evaluation on Popular Java Open Source APIs

Abstract— The unmatched growth in the automation and application of software code segments for automation is the main reactive reason for improvements in industrial, education, and healthcare and security sectors. The deployed code segments or the complete application used for the purpose is developed extensively with ample amount of features. The number of lines of code and number of man-hours deployed to build the applications are gigantic. In addition to that, the testing of the applications is the added cost for the development cycle. However, in spite of the best practice efforts, the applications can fail in real-time due to undetected errors resulting in fault and failure. Hence, the demand of the modern code development industry to the current research trend is to automate the testing process and derive a framework for enhanced defects detection. This work proposes a novel code defect detection technique to deep scan the code and report all possible bugs and defects and errors. To justify the thoughts, the framework tests the most popular java open source APIs and demonstrates the results. Another novel outcome of this work is to build a generic defect metric for all classes of source code.

Linking Human Health and Neighborhood Using Big Data: A Case Study in Torino, Italy

Background Assessing and mapping public health within neighborhoods has long been a focus area for researchers. The major weaknesses of previous research have been the use of untimely and aggregated data. The advent of Big Data, however, has opened up new level of analysis and type of information which can be advantageous for assessing mental health. Geosocial media (GSM), such as Twitter, contains three attributes which sets it apart from other data: a timestamp, location, and human discourse. These three pieces of information collectively reveal previously unknown psychosocial relations to one’s environment. The purpose of the current research was to use GSM to examine where and why human sentiment among neighborhoods in Torino, Italy fluctuates spatially and temporally. Methods Twitter data was the source of all GSM data and collected through an open-source API. The database was filtered and used to derive a valance (i.e. sentiment) index. Compositional and contextual information for each statistical zone were then collected from government sources and then linked to the Twitter database using GIS. The zonal indicators included in this research consisted of: public health, infrastructure, travel safety, incivilities, accessibility, socioeconomic standing, and density. Exploratory spatial data analysis (ESDA), GIS, and regression modeling were implemented to investigate the spatial patterning of sentiments, and highlight important explanatory factors associated with increased or decreased values. Results The ESDA results showed that the spatial patterning of Tweets clustered near the central portion of the study area, a spatial clustering index supported this finding. Not surprisingly, it was also found that sentiment values differed depending on time-indicating that human sentiment varies temporally. A global model, although week (R 2 = .03) pointed to strong and statistically significant relations (p-value Conclusions We expect the results of this research to be valuable for practitioners and researchers on two fronts. First, though the models’ explanatory powers were weak, several noteworthy coefficients were observed. Second, we have shown that GSM data is a valid dataset for understanding the nuanced psychosocial relationships among mental health, time, and space.

An Impact of Digitalized Technologies Transformation in Healthcare Using Mobile Cloud Computing

Objectives: Smart Device with Open Source Platform, cloud tools and technologies are at the key point of healthcare variation. E-Devices replacing on manual basis or automated medical reports/graphs, private or secure clouds provide private access to automated healthcare records and device based device collaboration tools are reforming information share among medical experts. Methods/Analysis: In proposed Work, We are using Open Source API like Google API within system for automatic improvement of the entire system rather than deploy each time system after new version of implemented platform. According to below mentioned Proposed Work, We are encrypt word with current timestamp middle number using sampling method and every time system will auto decrypt private data with same TIMESATMP technique on receiver side panel. Findings: All the findings are mentioned in the form of statements given below: Statement 1: Existing System: MICROSOFT [Working on Only WINDOWS based platform] Statement 2: Existing System: PREBUILD ALGORITHM [Easy to Decrypt with predefined decryption methods]. Novelty/Improvement: Smart Devices based healthcare applications will more advance with Open Source Based API and Private data will more secure.

A design methodology for soft-core platforms on FPGA with SMP Linux, OpenMP support, and distributed hardware profiling system

In recent years, the use of multiprocessor systems has become increasingly common. Even in the embedded domain, the development of platforms based on multiprocessor systems or the porting of legacy single-core applications are frequent needs. However, such designs are often complicated, as embedded systems are characterized by numerous non-functional requirements and a tight hardware/software integration. This work proposes a methodology for the development and validation of an embedded multiprocessor system. Specifically, the proposed method assumes the use of a portable, open source API to support the parallelization and the possibility of prototyping the system on a field-programmable gate array. On this basis, the proposed flow allows an early exploration of the hardware configuration space, a preliminary estimate of performance, and the rapid development of a system able to satisfy the design specifications. An accurate assessment of the actual performance of the system is then enforced by the use of an hardware-based profiling subsystem. The proposed design flow is described, and a version specifically designed for LEON3 processor is presented and validated. The application of the proposed methodology in a real case of industrial study is then presented and analyzed.

РЕФАКТОРІНГ ПРОГРАМНОГО КОДУ ДІАЛОГОВОГО КОМПОНЕНТУ ПЛАТФОРМИ СИСТЕМИ ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕННЯ ДЛЯ МЕДИЧНОЇ ІНФОРМАЦІЙНОЇ СИСТЕМИ З ВІДКРИТИМ КОДОМ OpenMRS

The importance of Medical Information Systems (MIS) for medical practice is emphasized. The wide usage of the Electronic Medical Records (EMR) software is displayed. The importance and alternative approaches to implementation of the MIS in the Ukraine healthcare system are discussed. The benefits of the open-source MIS usage are shown. Effectiveness of the Clinical Decision Support System (CDSS) application in the medical decision making process is emphasized.<p>The open-source MIS OpenMRS developer tools and software API are reviewed. The results of code refactoring of the dialog subsystem of the CDSS platform which is made as module for the open-source MIS OpenMRS are presented. The structure of information model of database of the CDSS dialog subsystem was updated according with MIS OpenMRS requirements. The Model-View-Controller (MVC) based approach to the CDSS dialog subsystem architecture was re-implemented with Java programming language using Spring and Hibernate frameworks. The MIS OpenMRS Encounter portlet form for the CDSS dialog subsystem integration is developed as an extension. The administrative module of the CDSS platform is recreated. The data exchanging formats and methods for interaction of OpenMRS CDSS dialog subsystem module and DecisionTree GAE service are re-implemented with help of AJAX technology via jQuery library</p>

Constructing a Computer-Aided Differential Diagnosis Engine from Open-Source APIs.

This paper describes the design and implementation of an application that parses and analyzes radiology report text to provide a radiologic differential diagnosis. The system was constructed using a combination of freely available web-based APIs and originally developed during the Society for Imaging Informatics in Medicine (SIIM) 2014 Hackathon. Continued development has refined and increased the accuracy of the algorithm. This project demonstrates the power and possibilities of combining existing technologies to solve unique problems as well as the stimulus of the hackathon setting to spur innovation.

Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms

The cloud platform-as-a-service (PaaS) model provides developers with the ability to deploy and manage their applications remotely in the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS deployment and management; each PaaS provider [e.g., Google AppEngine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure] has its own proprietary APIs. This lack of standardisation adds a layer of complexity to application deployment and migration between heterogeneous PaaS platforms because of API incompatibility. A standard (generic) PaaS deployment API overcomes the previously mentioned PaaS API heterogeneity. A generic open-source API, namely the COAPS API, has been proposed to support deployment and management of applications on CF and OS PaaS platforms. This work implements COAPS deployment API to include the GAE PaaS. Whereas both CF and OS PaaS platforms use the same application packaging, deploying the same application on GAE requires application repackaging. We evaluated our work using a case study in which the same application is automatically deployed on CF and GAE.

Automatic accident detection with multi-modal alert system implementation for ITS

The rapid technological growth is now providing global opportunities to enable intelligent transportation system (ITS) to tackle road accidents which is considered one of the world's largest public injury prevention problem. For this purpose, eCall is an initiative by European Union (EU) with the purpose to bring rapid assistance to an accident location. This paper1 presents HDy Copilot, an application for automatic accident detection integrated with multimodal alert dissemination, via both eCall and IEEE 802.11p (ITS-G5). The proposed accident detection algorithm receives inputs from the vehicle, via ODB-II, and from the smartphone sensors, namely the accelerometer, the magnetometer and the gyroscope. An Android smartphone is used as human machine interface, so that the driver can configure the application, receive road hazard warnings issued by other vehicles in the vicinity and cancel countdown procedures upon false road vehicle crash detection. The HDy Copilot is developed for Android OS as it provides open source APIs that allow access to its hardware resources. The application is implemented, tested and connected to an IEEE 802.11p based prototype. The generated results show that the application successfully detects collisions, rollovers, performs the eCall along with sending Minimum Set of Data (MSD) and Decentralized Environmental Notification Message (DENM).