Set Of Application Programming Interfaces Research Articles

API Recommendation For Mashup Creation: A Comprehensive Survey

Abstract Mashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners.

Computational complexity when constructing rational plans for program execution in a given field of parallel computers

Objectives. The construction of rational plans (schedules) for parallel program execution (PPE) represents a challenging problem due to its ambiguity. The aim of this work is to create methods for developing such plans and specialized software for implementing these methods, which are based on the internal properties of algorithms, primarily on the property of internal (hidden) parallelism.Methods. The main method for developing PPE plans was the construction, analysis, and purposeful transformation of the stacked-parallel form (SPF) of information graphs of algorithms (IGA). The SPF was transformed by transferring operators from tier to tier of the SPF (this event was taken as an elementary step in determining the computational complexity of scenario execution). As a transformation tool, a method for developing transformation scenarios in the scripting programming language Lua was used. Scenarios were created by a heuristic approach using a set of Application Programming Interface (API) functions of the developed software system. These functions formed the basis for a comprehensive study of the parameters of the IGA and its SPF representation for the subsequent construction of a PPE plan applying to a given field of parallel computers.Results. Features of the internal properties of the algorithms that affect the efficiency of SPF transformations were identified during the course of computational experiments. Comparative indices of the computational complexity of obtaining PPE plans and other parameters (including code density, etc.) were obtained for various SPF transformation scenarios. An iterative approach to improving heuristic methods favors developing optimal schemes for solving the objective problem.Conclusions. The developed software system confirmed its efficiency for studying the parameters of hidden parallelism in arbitrary algorithms and rational use in data processing. The approach of using a scripting language to develop heuristic methods (scenarios) for the purposeful transformation of IGA forms showed great flexibility and transparency for the researcher. The target consumers of the developed methods for generating schedules for parallel execution of programs are, first of all, developers of translators and virtual machines, and researchers of the properties of algorithms (for identifying and exploiting the potential of their hidden parallelism). The developed software and methods have been successfully used for a number of years for increasing student competence in data processing parallelization at Russian universities.

A parallel programming assessment for stream processing applications on multi-core systems

Multi-core systems are any computing device nowadays and stream processing applications are becoming recurrent workloads, demanding parallelism to achieve the desired quality of service. As soon as data, tasks, or requests arrive, they must be computed, analyzed, or processed. Since building such applications is not a trivial task, the software industry must adopt parallel APIs (Application Programming Interfaces) that simplify the exploitation of parallelism in hardware for accelerating time-to-market. In the last years, research efforts in academia and industry provided a set of parallel APIs, increasing productivity to software developers. However, a few studies are seeking to prove the usability of these interfaces. In this work, we aim to present a parallel programming assessment regarding the usability of parallel API for expressing parallelism on the stream processing application domain and multi-core systems. To this end, we conducted an empirical study with beginners in parallel application development. The study covered three parallel APIs, reporting several quantitative and qualitative indicators involving developers. Our contribution also comprises a parallel programming assessment methodology, which can be replicated in future assessments. This study revealed important insights such as recurrent compile-time and programming logic errors performed by beginners in parallel programming, as well as the programming effort, challenges, and learning curve. Moreover, we collected the participants’ opinions about their experience in this study to understand deeply the results achieved.

Building Infrastructure to Exploit Evidence from Patient Preference Information (PPI) Studies: A Conceptual Blueprint

Patients are the most important actors in clinical research. Therefore, patient preference information (PPI) could support the decision-making process, being indisputable for research value, quality, and integrity. However, there is a lack of clear guidance or consensus on the search for preference studies. In this blueprint, an openly available and regularly updated patient preference management system for an integrated database (PPMSDB) that contains the minimal set of data sufficient to provide detailed information for each study (the so-called evidence tables in systematic reviews) and a high-level overview of the findings of a review (summary tables) is described. These tables could help determine which studies, if any, are eligible for quantitative synthesis. Finally, a web platform would provide a graphical and user-friendly interface. On the other hand, a set of APIs (application programming interfaces) would also be developed and provided. The PPMSDB, aims to collect preference measures, characteristics, and meta-data, and allow researchers to obtain a quick overview of a research field, use the latest evidence, and identify research gaps. In conjunction with proper statistical analysis of quantitative preference measures, these aspects can facilitate formal evidence-based decisions and adequate consideration when conducting a structured decision-making process. Our objective is to outline the conceptual infrastructure necessary to build and maintain a successful network that can monitor the currentness and validity of evidence.

Compatibility-Aware Web API Recommendation for Mashup Creation via Textual Description Mining

With the ever-increasing prosperity of web Application Programming Interface (API) sharing platforms, it is becoming an economic and efficient way for software developers to design their interested mashups through web API re-use. Generally, a software developer can browse, evaluate, and select his or her preferred web APIs from the API's sharing platforms to create various mashups with rich functionality. The big volume of candidate APIs places a heavy burden on software developers’ API selection decisions. This, in turn, calls for the support of intelligent API recommender systems. However, existing API recommender systems often face two challenges. First, they focus more on the functional accuracy of APIs while neglecting the APIs’ actual compatibility. This then creates incompatible mashups. Second, they often require software developers to input a set of keywords that can accurately describe the expected functions of the mashup to be developed. This second challenge tests partial developers who have little background knowledge in the fields. To tackle the above-mentioned challenges, in this article we propose a compatibility-aware and text description-driven web API recommendation approach (named WAR text ). WAR text guarantees the compatibility among the recommended APIs by utilizing the APIs’ composition records produced by historical mashup creations. Besides, WAR text entitles a software developer to type a simple text document that describes the expected mashup functions as input. Then through textual description mining, WAR text can precisely capture the developers’ functional requirements and then return a set of APIs with the highest compatibility. Finally, through a real-world mashup dataset ProgrammableWeb, we validate the feasibility of our novel approach.

G 3

This paper demonstrates G 3 , a framework for <u>G</u>raph Neural Network (GNN) training, tailored from <u>G</u>raph processing systems on <u>G</u>raphics processing units (GPUs). G 3 aims at improving the efficiency of GNN training by supporting graph-structured operations using parallel graph processing systems. G 3 enables users to leverage the massive parallelism and other architectural features of GPUs in the following two ways: building GNN layers by writing sequential C/C++ code with a set of flexible APIs (Application Programming Interfaces); creating GNN models with essential GNN operations and layers provided in G 3 . The runtime system of G 3 automatically executes the user-defined GNNs on the GPU, with a series of graph-centric optimizations enabled. We demonstrate the steps of developing some popular GNN models with G 3 , and the superior performance of G 3 against existing GNN training systems, i.e., PyTorch and TensorFlow.

FRNAkenseq: a fully powered-by-CyVerse cloud integrated RNA-sequencing analysis tool.

BackgroundDecreasing costs make RNA sequencing technologies increasingly affordable for biologists. However, many researchers who can now afford sequencing lack access to resources necessary for downstream analysis. This means that even as algorithms to process RNA-Seq data improve, many biologists still struggle to manage the sheer volume of data produced by next generation sequencing (NGS) technologies. Scalable bioinformatics tools that exploit multiple platforms are needed to democratize bioinformatics resources in the sequencing era. This is essential for equipping many research groups in the life sciences with the tools to process the increasingly unwieldy datasets they produce.MethodsOne strategy to address this challenge is to develop a modern generation of sequence analysis tools capable of seamless data sharing and communication. Such tools will provide interoperability through offerings of interlinked resources. Systems of interlinked, scalable resources, which often incorporate cloud data storage, are broadly referred to as cyberinfrastructure. Cyberinfrastructure integrated tools will help researchers to robustly analyze large scale datasets by efficiently sharing data burdens across a distributed architecture. Additionally, interoperability will allow emerging tools to cross-adapt features of existing tools. It is important that these tools are designed to be easy to use for biologists.ResultsWe introduce fRNAkenseq, a powered-by-CyVerse RNA sequencing analysis tool that exhibits interoperability with other resources and meets the needs of biologists for comprehensive, easy to use RNA sequencing analysis. fRNAkenseq leverages a complex set of Application Programming Interfaces (APIs) associated with the NSF-funded cyberinfrastructure project, CyVerse, to execute FASTQ-to-differential expression RNA-Seq analyses. Integrating across bioinformatics platforms, fRNAkenseq also exploits cloud integration and cross-talk with another CyVerse associated tool, CoGe. fRNAkenseq offers novel features for the biologist such as more robust and comprehensive pipelines for enrichment than those currently available by default in a single tool, whether they are cloud-based or local installation. Importantly, cross-talk with CoGe allows fRNAkenseq users to execute RNA-Seq pipelines on an inventory of 47,000 archived genomes stored in CoGe or upload their own draft genome.

Automated Malware Detection in Mobile App Stores Based on Robust Feature Generation

Many Internet of Things (IoT) services are currently tracked and regulated via mobile devices, making them vulnerable to privacy attacks and exploitation by various malicious applications. Current solutions are unable to keep pace with the rapid growth of malware and are limited by low detection accuracy, long discovery time, complex implementation, and high computational costs associated with the processor speed, power, and memory. Therefore, an automated intelligence technique is necessary for detecting apps containing malware and effectively predicting cyberattacks in mobile marketplaces. In this study, a system for classifying mobile marketplaces applications using real-world datasets is proposed, which analyzes the source code to identify malicious apps. A rich feature set of application programming interface (API) calls is proposed to capture the regularities in apps containing malicious content. Two feature-selection methods—Chi-Square and ANOVA—were examined in conjunction with ten supervised machine-learning algorithms. The detection accuracy of each classifier was evaluated to identify the most reliable classifier for malware detection using various feature sets. Chi-Square was found to have a higher detection accuracy as compared to ANOVA. The proposed system achieved a detection accuracy of 98.1% with a classification time of 1.22 s. Furthermore, the proposed system required a reduced number of API calls (500 instead of 9000) to be incorporated as features.

Fine-Grained Isolation to Protect Data against In-Process Attacks on AArch64

In-process attacks are a new class of attacks that circumvent protection schemes centered around inter-process isolation. Against these attacks, researchers have proposed fine-grained data isolation schemes that can protect sensitive data from malicious accesses even during the same process. Their proposals based on salient hardware features, such as ARM® processor architecture’s domain protection, are quite successful, but it cannot be applied to a specific architecture, namely AArch64, as this does not provide the same hardware features. In this paper, therefore, we present Sealer, a fine-grained data isolation scheme applicable in AArch64. Sealer achieves its objective by brilliantly harmonizing two hardware features of AArch64: The eXecute-no-Read and the cryptographic extension. Sealer provides application developers with a set of application programming interface (API) so that the developers can enjoy the fine-grained data isolation in their own way.

Finding cultural heritage traces from modern social media

Finding cultural heritage traces from modern social media

Multi-agent cognitive architecture-enabled IoT applications of mobile edge computing

Mobile edge computing (MEC) offers cloud capabilities and service environment for Internet-of-Things (IoT) applications at the edge of the mobile network. New services for a specific set of IoT application programming interfaces (APIs) of diversified industry verticals can efficiently be deployed. As the set of APIs based on the IoT continues to thrive, reliance on the underlying cloud computing to deliver real-time and context-based services is also prominent in MEC. Due to the increased level of mutual interference of IoT APIs, the complexities are also multiplied. The set of IoT APIs competes for available resources within heterogeneous clouds (HCs). It is further aggravated by the unprecedented number of connected devices. We are presenting a cognitive agent (CA)-oriented approach to proactively subordinate IoT APIs with HCs. This paper contributes to establish an empirical multi-agent cognitive architecture framework (MCAF) for continuous transition of IoT APIs. We identified an adaptable composition method and a classification technique for CAs during this research effort. It introduces rationalization of CA creation, migration, and control. The approach achieves transparency in the heterogeneity and distribution of IoT APIs. It provides dynamism of cloud computing environments. The paper also illustrates the architecture impact analysis with the indicative capabilities of auto-scaling, predictability in HCs’ resource use, and responsiveness and attainment of IoT APIs.

A two-step approach for pattern-based API-call constraint checking

An operating system publishes a set of application programming interface (API) functions along with a set of API-call constraints with which programs running on the operating system must comply. Any violation of these constraints may become a source of massive property damage or even human injury when such a program is used for safety-critical systems. A rigorous and targeted verification method is needed to identify such violations, which are frequently subtle and difficult to identify using conventional verification methods. As automated tool support for pre-checking constraint violations in the development process, this study presents a two-step approach for checking API-call constraints by using predefined patterns specifically designed for automotive operating systems. A lightweight checking method is designed for quick-and-easy checking of local API-call constraints, which utilizes constraint patterns and the C code model checker CBMC. The global constraint checking method is a heavyweight method, as it requires behavior models of the underlying operating system constructs as well as constraint patterns, but it produces more accurate verification results; it uses the symbolic model checker NuSMV as the backend verification engine. This two-step approach is effective in identifying constraint violations and efficient in reducing false alarms from infeasible execution paths. • A pattern-based two-step approach for API-call constraint checking is proposed. • Constraints are classified into local and global constraints for the OSEK/VDX OS. • Suggested a specialized constraint checking method for each classification. • A lightweight local constraint checking for C code is designed using CBMC. • Suggested more accurate global constraint checking method using formal OS models.

API vulnerabilities: current status and dependencies

Recently API (Application Programming Interface) is becoming more popular for developers. When software is designed, most of the time, developers need to use APIs to manage a specific task. Developers use various kinds of APIs. Some of them are built by themselves and some are used from public APIs. API is a set of functions and procedures that allows another program or application to get access to features or data. Public APIs are open in public networks; developers collect these APIs depending on their specific needs. Developers need to interact with other software, as a result, a developer can conduct specific task without authorization to access the entirety of the software. It definitely reduces our loads at the same time introduces risks. In the end every developer wants to ensure security to his/her application. Commonly used public APIs are not enough secure to provide security to confidential data. We focused on these public APIs that are commonly used by developers. We tested a set of public APIs in our security lab and we have found many vulnerabilities that are highly alarming for developers who are going to use these API. In this paper we have tried to introduce the current status of vulnerable APIs. Moreover, several relationships exist between API vulnerabilities. In this paper we have also discussed the dependencies and relationships between API vulnerabilities.

Generating API Call Rules from Version History and Stack Overflow Posts

Researchers have shown that related functions can be mined from groupings of functions found in the version history of a system. Our first contribution is to expand this approach to a community of applications and set of similar applications. Android developers use a set of application programming interface (API) calls when creating apps. These API calls are used in similar ways across multiple applications. By clustering co-changing API calls used by 230 Android apps across 12k versions, we are able to predict the API calls that individual app developers will use with an average precision of 75% and recall of 22%. When we make predictions from the same category of app, such as Finance, we attain precision and recall of 81% and 28%, respectively. Our second contribution can be characterized as “programmers who discussed these functions were also interested in these functions.” Informal discussions on Stack Overflow provide a rich source of information about related API calls as developers provide solutions to common problems. By grouping API calls contained in each positively voted answer posts, we are able to create rules that predict the calls that app developers will use in their own apps with an average precision of 66% and recall of 13%. For comparison purposes, we developed a baseline by clustering co-changing API calls for each individual app and generated association rules from them. The baseline predicts API calls used by app developers with a precision and recall of 36% and 23%, respectively.

Real Time Communications in the Web: Current Achievements and Future Perspectives

The articles in this special section focus on the creation of real-time communication between web browsers via communication standards. These initiatives involve both the Internet Engineering Task Force (IETF) and the World Wide Web Consortium (W3C). The IETF has focused its work on the architecture and on-the-wire protocols, identifying the state information and the events that need to be available to developers, while the W3C worked on application-level JavaScript APIs to be offered to developers in order to allow them to easily implement interoperable real-time multimedia applications in the web. Back in 2011, the IETF chartered the “Real-Time Communication in WEB-browsers” (RTCWEB) Working Group, with the aim of defining an architecture and a complete suite of protocols for the support of real-time multimedia communications directly between browsers. The RTCWEB WG has since worked on key aspects like the overall communication infrastructure, the protocols and API (Application Programming Interface) requirements, the security model, the media formats (and related media codecs), as well as advanced functionality like congestion/flow control and interworking with legacy VoIP equipment. In parallel, the W3C has developed a set of APIs exposing functions like exploration and access to device capabilities, capture of media from local devices, encoding/processing of “media streams,” establishment of peer-to-peer connections between browsers (and web-enabled devices in general), decoding/processing of incoming media streams and delivery of such streams to the end-user in an HTML5-compliant fashion.

Rapid Cognitive Screen: Sensitivity and specificity for cognitive dysfunction and predictive validity for poor health outcomes

Background: The Alzheimer’s Disease Cooperative Study was founded in 1991 in response to a NIARFA. The primary aim of the ADCS is to ‘advance research in the development of interventions thatmight be useful for treating, delaying, or preventingAD’. Toward that goal theADCS has designed,managed, and analyzed 20 large, multi-site AD clinical trials. The psychometric, clinical, and biological data from these trials have been collected using a locally-developed, web-based electronic data capture (EDC) system. This system serves as a central hub used to coordinate the data management, quality assurance, and monitoring activities of thousands of staff across several continents. Methods: The management of medical images collected in the course of a clinical trial usually involves the use of a specialized repository which combines acquisition, de-identification, sharing, processing, and analysis capabilities. These monolithic repositories tend to be complex, inflexible, and resource-intensive. Furthermore, they contribute to the more general problem of “data sprawl”, a state of highly fragmented datasets that are difficult to manage and analyze. In the most extreme cases, the sprawl extends beyond institutional boundaries. As an alternative, we have chosen to extend the ADCS EDC to accommodate the collection, de-identification, and management of medical images while allowing analysis workflows to be handled flexibly via a standard set of APIs (application programming interfaces). This integrated approach ensures that timely access to a well structured and comprehensive trial data set can be easily provided. Results: This poster has the following aims:1. Provide a description of the ADCS EDC system’s image management capabilities.2. Discuss how these capabilities are used to support ongoing ADCS clinical trials.3. Outline opportunities for future improvement. Conclusions: The ADCS EDC system supports integrated image management that mitigates the need for specialized image management systems thereby avoiding the issues associated with dispersed datasets or “data sprawl”.

An efficient model-based methodology for developing device-independent mobile applications

Current methodologies for developing mobile applications are mostly based on the application programming interfaces (APIs) offered by the native platform. Hence, most solutions are characterized by a low portability and/or reusability. In this paper, we propose a novel methodology based on a declarative and device-independent approach for developing event-driven mobile applications. The methodology relies on: (i) an abstract mobile device based on the user interface markup language; (ii) a content adaptation mechanism based on user preferences; (iii) a context adaptation mechanism based on a standardized context of delivery; (iv) a uniform set of client-side APIs based on an interface object model; (v) an efficient transformational model. More specifically, in the design phase, the application is modeled as platform-independent on the abstract mobile device. In the execution phase, the application is automatically tailored to the specific platform on the basis of the content and context adaptation mechanisms. We describe the analysis, design and implementation of a framework, called MODIF, which supports the proposed methodology, and show its application to the development of both business and consumer real-world applications on Apple iPhone(TM) and Google Android(TM) mobile devices. Finally, we discuss how the experience of using MODIF highlights the quality of the methodology in terms of automation of the lifecycle, expressiveness and readability of the representation, efficiency of the compilation/interpretation, fast learning curve and predictability.

Streaming Media Portability with the Emerging Support Open MAX

In order to create multimedia products such as smart-phones, media players, gaming consoles among others, developers need to optimize the low-level code. Currently, this code is written mostly in assembly language and must be rewritten for each different hardware platform. For this reason, the development of media infrastructure is very expensive, time consuming for integration and programming. Therefore, the media infrastructure portability is a multi-level industry problem. The application programmers need a cross-platform portable API (Application Programming Interface) for controlling high-level media operations. The system integrators need cross-vendor standard for media component integration with sophisticated data routing and robust synchronization. The software component vendors and the silicon vendors need a reliable way to accelerate diverse codecs on diverse silicon. OpenMAX helps solve this industry problem. OpenMAX was founded to meet the need to accelerate the growth of multimedia platforms and therefore the creation of final products. The end result is a standardized set of open APIs for a variety of multimedia applications. OpenMAX define a royalty-free cross-platform API that standardizes access to multimedia processing primitives used extensively in video codecs such as MPEG-4, H.264/AVC, audio and image codecs and 2D and 3D graphics. OpenMAX defines three media as an open standard, designed holistically to provide complete media infrastructure portability. The authors present a description of the OpenMAX standard and the three layers that form the structure of multimedia portability. Some implementations in the market are also presented.

Atlas – a data warehouse for integrative bioinformatics

BackgroundWe present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development.DescriptionThe Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations.ConclusionThe Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: