scholarly journals Bitcoin’s APIs in Open-Source Projects: Security Usability Evaluation

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1077
Author(s):  
Philipp Tschannen ◽  
Ali Ahmed
Keyword(s):  
Open Source ◽  
Programming Languages ◽  
Application Programming Interface ◽  
Usable Security ◽  
Software Developers ◽  
Security Vulnerabilities ◽  
Software Applications ◽  
Current State ◽  
Digital Money ◽  
Application Programming

Given the current state of software development, it does not seem that we are nowhere near vulnerability-free software applications, due to many reasons, and software developers are one of them. Insecure coding practices, the complexity of the task in hand, and usability issues, amongst other reasons, make it hard on software developers to maintain secure code. When it comes to cryptographic currencies, the need for assuring security is inevitable. For example, Bitcoin is a peer-to-peer software system that is primarily used as digital money. There exist many software libraries supporting various programming languages that allow access to the Bitcoin system via an Application Programming Interface (API). APIs that are inappropriately used would lead to security vulnerabilities, which are hard to discover, resulting in many zero-day exploits. Making APIs usable is, therefore, an essential aspect related to the quality and robustness of the software. This paper surveys the general academic literature concerning API usability and usable security. Furthermore, it evaluates the API usability of Libbitcoin, a well-known C++ implementation of the Bitcoin system, and assesses how the findings of this evaluation could affect the applications that use Libbitcoin. For that purpose, the paper proposes two static analysis tools to further investigate the use of Libbitcoin APIs in open-source projects from a security usability perspective. The findings of this research have improved Libbitcoin in many places, as will be shown in this paper.

scholarly journals System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

2015 ◽  
Vol 8 (2) ◽  
pp. 2271-2312 ◽  
Author(s):  
O. Conrad ◽  
B. Bechtel ◽  
M. Bock ◽  
H. Dietrich ◽  
E. Fischer ◽  
...  
Keyword(s):  
Programming Languages ◽  
Wide Spectrum ◽  
Application Programming Interface ◽  
Terrain Analysis ◽  
Digital Terrain Analysis ◽  
Object Oriented Design ◽  
Digital Terrain ◽  
Current State ◽  
Application Programming ◽  
Functional Features

Abstract. The System for Automated Geoscientific Analyses (SAGA) is an open-source Geographic Information System (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular organized software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, an easily approachable graphical user interface with many visualization options, a command line interpreter, and interfaces to scripting and low level programming languages like R and Python. The current version 2.1.4 offers more than 700 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Further, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.

scholarly journals A SEMI-AUTOMATIC PROCEDURE FOR A DEMOGRAPHIC ANALYSIS OF THE FOSS4G DEVELOPERS’ COMMUNITY

2018 ◽  
Vol XLII-4/W8 ◽  
pp. 171-174
Author(s):  
D. Oxoli ◽  
H.-K. Kang ◽  
M. A. Brovelli
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Source Code ◽  
Application Programming Interface ◽  
Demographic Analysis ◽  
Software Applications ◽  
Individual Project ◽  
Application Programming ◽  
Programming Interface ◽  
The Web

<p><strong>Abstract.</strong> The open and direct collaboration at the creation, improvement, and documentation of source code and software applications &amp;ndash; enabled by the web &amp;ndash; is recognized as a peculiarity of the Free and Open Source Software for Geospatial (FOSS4G) projects representing, at the same time, one of their main strengths. With this in mind, it turns out to be interesting to perform an extensive monitoring of both the evolution and the geographical arrangement of the developers’ communities in order to investigate their actual extension, evolution and degree of activity. In this work, a semi-automatic procedure to perform this particular analysis is described. The procedure is mainly based on the use of the GitHub Search Application Programming Interface by means of JavaScript custom modules to perform a census of the users registered with a collaborator role to the repositories of the most popular FOSS4G projects, hosted on the GitHub platform. The collected data is processed and analysed using Python and QGIS. The results &amp;ndash; presented through tables, charts, and thematic maps &amp;ndash; allow describing both dimensions as well as the geographical heterogeneity of the contributing community of each individual project, while enabling to identify the most active countries &amp;ndash; in terms of the number of contributors &amp;ndash; in the development of the most popular FOSS4G. The limits of the analysis, including technical constraints and considerations on the significance of the developers' census, are finally highlighted and discussed.</p>

Python Earth Engine API as a new open-source ecosphere for characterizing offshore hydrocarbon seeps and spills

2021 ◽  
Vol 40 (1) ◽  
pp. 35-44
Author(s):  
Whitney Trainor-Guitton ◽  
Leo Turon ◽  
Dominique Dubucq
Keyword(s):  
Open Source ◽  
Application Programming Interface ◽  
Google Earth ◽  
Hydrocarbon Detection ◽  
Detection Algorithms ◽  
Hydrocarbon Seeps ◽  
Oil Seeps ◽  
Application Programming ◽  
Calculated Probability ◽  
Programming Interface

The Python Earth Engine application programming interface (API) provides a new open-source ecosphere for testing hydrocarbon detection algorithms on large volumes of images curated with the Google Earth Engine. We specifically demonstrate the Python Earth Engine API by calculating three hydrocarbon indices: fluorescence, rotation absorption, and normalized fluorescence. The Python Earth Engine API provides an ideal environment for testing these indices with varied oil seeps and spills by (1) removing barriers of proprietary software formats and (2) providing an extensive library of data analysis tools (e.g., Pandas and Seaborn) and classification algorithms (e.g., Scikit-learn and TensorFlow). Our results demonstrate end-member cases in which fluorescence and normalized fluorescence indices of seawater and oil are statistically similar and different. As expected, predictive classification is more effective and the calculated probability of oil is more accurate for scenarios in which seawater and oil are well separated in the fluorescence space.

scholarly journals Pengembangan Sistem Informasi Registrasi Mahasiswa Baru dengan Metode Analisis Gugus Kendali Mutu

2021 ◽  
Vol 10 (3) ◽  
pp. 191
Author(s):  
Santo Wijaya ◽  
Marta H.R.S.R. Sari ◽  
Adian Wihariono Putera
Keyword(s):  
Open Source ◽  
Application Programming Interface ◽  
Web Based ◽  
Application Programming ◽  
Programming Interface

Pendidikan sebagai industri produk dan jasa berbasis ilmu pengetahuan dan keterampilan menghadapi persaingan yang semakin kompetitif dengan banyaknya institusi baik dalam dan luar negeri yang operasional di Indonesia. Untuk meningkatkan daya saing, maka utilisasi teknologi informasi khususnya di era revolusi industri 4.0 menjadi kunci penting. Penelitian ini bertujuan untuk mengembangkan Sistem Informasi Registrasi Mahasiswa Baru (SIRMB) menggunakan kerangka open-source web-based application serta integrasinya dengan teknologi Application Programming Interface (API) Bank BNI menjadikan layanan administrasi yang terotomasi. Proses identifikasi masalah sampai perancangan solusi SIRMB menggunakan analisis gugus kendali mutu (QCC) dengan pendekatan metode Plan-Do-Check-Action (PDCA) sehingga menjamin perbaikan yang berkesinambungan. Penelitian ini berkontribusi terhadap perbaikan 76.9% terhadap proses kerja dengan eliminasi proses kerja manual registrasi mahasiswa baru, sehingga memberikan peningkatan kualitas layanan dan peningkatan produktivitas secara keseluruhan.

scholarly journals Arangopipe, a tool for machine learning meta-data management

Data Science ◽  
2021 ◽  
pp. 1-15
Author(s):  
Jörg Schad ◽  
Rajiv Sambasivan ◽  
Christopher Woodward
Keyword(s):  
Machine Learning ◽  
Life Cycle ◽  
Open Source ◽  
Data Model ◽  
Application Programming Interface ◽  
Learning Models ◽  
Essential Components ◽  
Application Programming ◽  
Programming Interface ◽  
Machine Learning Models

Experimenting with different models, documenting results and findings, and repeating these tasks are day-to-day activities for machine learning engineers and data scientists. There is a need to keep control of the machine-learning pipeline and its metadata. This allows users to iterate quickly through experiments and retrieve key findings and observations from historical activity. This is the need that Arangopipe serves. Arangopipe is an open-source tool that provides a data model that captures the essential components of any machine learning life cycle. Arangopipe provides an application programming interface that permits machine-learning engineers to record the details of the salient steps in building their machine learning models. The components of the data model and an overview of the application programming interface is provided. Illustrative examples of basic and advanced machine learning workflows are provided. Arangopipe is not only useful for users involved in developing machine learning models but also useful for users deploying and maintaining them.

scholarly journals APIfix: output-oriented program synthesis for combating breaking changes in libraries

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-27
Author(s):  
Xiang Gao ◽  
Arjun Radhakrishna ◽  
Gustavo Soares ◽  
Ridwan Shariffdeen ◽  
Sumit Gulwani ◽  
...  
Keyword(s):  
Program Synthesis ◽  
Application Programming Interface ◽  
Third Party ◽  
Security Vulnerabilities ◽  
Synthesis Algorithm ◽  
Transformation Rules ◽  
Art Program ◽  
Application Programming ◽  
Api Usage ◽  
Programming Interface

Use of third-party libraries is extremely common in application software. The libraries evolve to accommodate new features or mitigate security vulnerabilities, thereby breaking the Application Programming Interface(API) used by the software. Such breaking changes in the libraries may discourage client code from using the new library versions thereby keeping the application vulnerable and not up-to-date. We propose a novel output-oriented program synthesis algorithm to automate API usage adaptations via program transformation. Our aim is not only to rely on the few example human adaptations of the clients from the old library version to the new library version, since this can lead to over-fitting transformation rules. Instead, we also rely on example usages of the new updated library in clients, which provide valuable context for synthesizing and applying the transformation rules. Our tool APIFix provides an automated mechanism to transform application code using the old library versions to code using the new library versions - thereby achieving automated API usage adaptation to fix the effect of breaking changes. Our evaluation shows that the transformation rules inferred by APIFix achieve 98.7% precision and 91.5% recall. By comparing our approach to state-of-the-art program synthesis approaches, we show that our approach significantly reduces over-fitting while synthesizing transformation rules for API usage adaptations.

An Approach to Facebook Post Analytics Using Python and Advance Open Source Tools

2018 ◽  
Vol 6 (6) ◽  
pp. 78
Author(s):  
Amit Sharma
Keyword(s):  
Open Source ◽  
Long Range ◽  
Data Extraction ◽  
Data Gathering ◽  
Application Programming Interface ◽  
Informal Communication ◽  
Standard Document ◽  
Kinship Networks ◽  
Application Programming ◽  
Programming Interface

The paper portrays the utilization of tools for data gathering and extraction that permits researchers to fare data in standard document groups from various areas of the facebook long range informal communication benefit. Kinship networks, gatherings, and pages can subsequently be breaking down quantitatively and subjectively with respect to demographical, post-demographical, and social qualities. The paper gives a review over expository headings opened up by the data made accessible, talks about stage particular parts of data extraction through the official Application Programming Interface, and quickly connects with the troublesome moral contemplations connected to this sort of research.

An API in JAVA Which Render Ease at Programming for Developers

2021 ◽  
Vol 1 (4) ◽  
pp. 27-31
Author(s):  
Bhuvan Agarwal ◽  
Soumyajeet Bhattacharjee ◽  
Sima Kar ◽  
Madhurima Saha ◽  
Vijay Kumar ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Open Source ◽  
Application Programming Interface ◽  
Development Environment ◽  
Application Programming ◽  
The Right ◽  
Almost All ◽  
Time And Space Complexity ◽  
Tree Traversals ◽  
Programming Interface

Abstract – Based on the concept of Application programming interface (API).This project comprises of a package named "algokit" which contains several algorithms based on the category of searching, sorting, dynamic programming, tree traversals and swapping. Keeping in mind that different algorithms from the same category have its own benefit in time and space complexity, This project covers almost all the algorithms known and available from each category. This would give the user several options to choose the right algorithm for its code.An user just requires to import the package named AlgoKit and call the functions inside it for a smooth programming experience. One of the prime objectives of this project is to build a kit that serves the purpose of reducing the number of lines of code and also reduce the time taken to run the same code elsewhere. It is platform independent and can be used in any open source Java development environment.

A Comprehensive Survey of Deep Learning Models Based on Keras Framework

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Bahzad Taha Chicho ◽  
◽  
Amira Bibo Sallow ◽  
Keyword(s):  
Deep Learning ◽  
Programming Languages ◽  
Application Programming Interface ◽  
Learning Models ◽  
Multiple Gpus ◽  
Learning Framework ◽  
Current Survey ◽  
Comprehensive Survey ◽  
Application Programming ◽  
Graph Neural Networks

Python is one of the most widely adopted programming languages, having replaced a number of those in the field. Python is popular with developers for a variety of reasons, one of which is because it has an incredibly diverse collection of libraries that users can run. The most compelling reasons for adopting Keras come from its guiding principles, particularly those related to usability. Aside from the simplicity of learning and model construction, Keras has a wide variety of production deployment options and robust support for multiple GPUs and distributed training. A strong and easy-to-use free, open-source Python library is the most important tool for developing and evaluating deep learning models. The aim of this paper is to provide the most current survey of Keras in different aspects, which is a Python-based deep learning Application Programming Interface (API) that runs on top of the machine learning framework, TensorFlow. The mentioned library is used in conjunction with TensorFlow, PyTorch, CODEEPNEATM, and Pygame to allow integration of deep learning models such as cardiovascular disease diagnostics, graph neural networks, identifying health issues, COVID-19 recognition, skin tumors, image detection, and so on, in the applied area. Furthermore, the author used Keras's details, goals, challenges, significant outcomes, and the findings obtained using this method.

scholarly journals KOFFI and Anabel 2.0—a new binding kinetics database and its integration in an open-source binding analysis software

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Leo William Norval ◽  
Stefan Daniel Krämer ◽  
Mingjie Gao ◽  
Tobias Herz ◽  
Jianyu Li ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Application Programming Interface ◽  
Extraction Methods ◽  
Binding Kinetics ◽  
Binding Data ◽  
Meta Information ◽  
Application Programming ◽  
Kinetics Of ◽  
Programming Interface

Abstract The kinetics of featured interactions (KOFFI) database is a novel tool and resource for binding kinetics data from biomolecular interactions. While binding kinetics data are abundant in literature, finding valuable information is a laborious task. We used text extraction methods to store binding rates (association, dissociation) as well as corresponding meta-information (e.g. methods, devices) in a novel database. To date, over 270 articles were manually curated and binding data on over 1705 interactions was collected and stored in the (KOFFI) database. Moreover, the KOFFI database application programming interface was implemented in Anabel (open-source software for the analysis of binding interactions), enabling users to directly compare their own binding data analyses with related experiments described in the database.

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