Natural mapping between voice commands and APIs

2021 ◽  
Vol 11 (1) ◽  
pp. 135-145
Author(s):  
Matúš Sulír ◽  
Jaroslav Porubän
Keyword(s):  
Control System ◽  
Natural Language ◽  
Source Code ◽  
Application Programming Interface ◽  
Third Party ◽  
Natural Mapping ◽  
Voice Control ◽  
Mapping Process ◽  
Application Programming ◽  
Programming Interface

Abstract After a voice control system transforms audio input into a natural language sentence, its main purpose is to map this sentence to a specific action in the API (application programming interface) that should be performed. This mapping is usually specified after the API is already designed. In this paper, we show how an API can be designed with voice control in mind, which makes this mapping natural. The classes, methods, and parameters in the source code are named and typed according to the terms expected in the natural language commands. When this is insufficient, annotations (attribute-oriented programming) are used to define synonyms, string-to-object maps, or other properties. We also describe the mapping process and present a preliminary implementation called VCMapper. In its evaluation on a third-party dataset, it was successfully used to map all the sentences, while a large portion of the mapping was performed using only naming and typing conventions.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

Recommending Relevant Tutorial Fragments for API-Related Natural Language Questions

2021 ◽  
Vol 31 (09) ◽  
pp. 1251-1275
Author(s):  
Di Wu ◽  
Xiao-Yuan Jing ◽  
Haowen Chen ◽  
Xiaohui Kong ◽  
Jifeng Xuan
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Metric Learning ◽  
Application Programming Interface ◽  
Manual Annotation ◽  
Candidate List ◽  
Novel Approach ◽  
Application Programming ◽  
Programming Interface ◽  
Reciprocal Rank

Application Programming Interface (API) tutorial is an important API learning resource. To help developers learn APIs, an API tutorial is often split into a number of consecutive units that describe the same topic (i.e. tutorial fragment). We regard a tutorial fragment explaining an API as a relevant fragment of the API. Automatically recommending relevant tutorial fragments can help developers learn how to use an API. However, existing approaches often employ supervised or unsupervised manner to recommend relevant fragments, which suffers from much manual annotation effort or inaccurate recommended results. Furthermore, these approaches only support developers to input exact API names. In practice, developers often do not know which APIs to use so that they are more likely to use natural language to describe API-related questions. In this paper, we propose a novel approach, called Tutorial Fragment Recommendation (TuFraRec), to effectively recommend relevant tutorial fragments for API-related natural language questions, without much manual annotation effort. For an API tutorial, we split it into fragments and extract APIs from each fragment to build API-fragment pairs. Given a question, TuFraRec first generates several clarification APIs that are related to the question. We use clarification APIs and API-fragment pairs to construct candidate API-fragment pairs. Then, we design a semi-supervised metric learning (SML)-based model to find relevant API-fragment pairs from the candidate list, which can work well with a few labeled API-fragment pairs and a large number of unlabeled API-fragment pairs. In this way, the manual effort for labeling the relevance of API-fragment pairs can be reduced. Finally, we sort and recommend relevant API-fragment pairs based on the recommended strategy. We evaluate TuFraRec on 200 API-related natural language questions and two public tutorial datasets (Java and Android). The results demonstrate that on average TuFraRec improves NDCG@5 by 0.06 and 0.09, and improves Mean Reciprocal Rank (MRR) by 0.07 and 0.09 on two tutorial datasets as compared with the state-of-the-art approach.

Mapping Discord’s darkside: Distributed hate networks on Disboard

2021 ◽  
pp. 146144482110625
Author(s):  
Daniel G Heslep ◽  
PS Berge
Keyword(s):  
Discourse Analysis ◽  
Deleterious Effect ◽  
Application Programming Interface ◽  
Third Party ◽  
White Supremacist ◽  
Public Commitment ◽  
Application Programming ◽  
Programming Interface

Discord, a popular community chat application, has rhetorically distanced itself from its associations with white supremacist content through a public commitment to proactive moderation. However, Discord relies extensively on third-party services (like bots and server bulletins), which have been overlooked in their role in facilitating hateful networks. This study notes how Discord offloads searchability to server bulletin sites like Disboard, to deleterious effect. This study involves two parts: (1) we use critical technoculture discourse analysis to examine Discord’s blogs, policies, and application programming interface and (2) we present data scraped from 2741 Discord servers listed on Disboard, revealing networks of hateful and white supremacist communities that openly use “edgy,” raiding-oriented, and toxic messaging. These servers exploit Discord’s moderation tools and affordances to proliferate within Discord’s distributed ecology. We argue that Discord’s policies fail to address its reliance on unmoderated third-party services or the networked practices of its toxic communities.

A New Communication Method Using Natural Language as a Computer Communication Protocol

2003 ◽  
Vol 7 (2) ◽  
pp. 215-222 ◽  
Author(s):  
Ichiro Kobayashi ◽  
◽  
Toru Sugimoto ◽  
Shino Iwashita ◽  
Michiaki Iwazume ◽  
...  
Keyword(s):  
Natural Language ◽  
Communication Protocol ◽  
Application Programming Interface ◽  
Computer Communication ◽  
Communication Environment ◽  
Communication Method ◽  
Application Programming ◽  
Programming Interface ◽  
Communication Standard

We propose a computer communication protocol based on natural language called "language protocol", communication using the protocol, and an interface enabling connection any communication standard, called a "language application programming interface". We use simulation to confirm that the proposed methods provide a flexible communication environment for any communication object.

scholarly journals LTTng CLUST: A System-Wide Unified CPU and GPU Tracing Tool for OpenCL Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
David Couturier ◽  
Michel R. Dagenais
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Battery Life ◽  
Portable Devices ◽  
Processing Unit ◽  
Central Processing ◽  
Application Programming ◽  
Graphical Processing ◽  
Performance Analysis Tools ◽  
Programming Interface

As computation schemes evolve and many new tools become available to programmers to enhance the performance of their applications, many programmers started to look towards highly parallel platforms such as Graphical Processing Unit (GPU). Offloading computations that can take advantage of the architecture of the GPU is a technique that has proven fruitful in recent years. This technology enhances the speed and responsiveness of applications. Also, as a side effect, it reduces the power requirements for those applications and therefore extends portable devices battery life and helps computing clusters to run more power efficiently. Many performance analysis tools such as LTTng, strace and SystemTap already allow Central Processing Unit (CPU) tracing and help programmers to use CPU resources more efficiently. On the GPU side, different tools such as Nvidia’s Nsight, AMD’s CodeXL, and third party TAU and VampirTrace allow tracing Application Programming Interface (API) calls and OpenCL kernel execution. These tools are useful but are completely separate, and none of them allow a unified CPU-GPU tracing experience. We propose an extension to the existing scalable and highly efficient LTTng tracing platform to allow unified tracing of GPU along with CPU’s full tracing capabilities.

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.

scholarly journals Using Natural Language Preprocessing Architecture (NLPA) for Big Data Text Sources

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
María Novo-Lourés ◽  
Reyes Pavón ◽  
Rosalía Laza ◽  
David Ruano-Ordas ◽  
Jose R. Méndez
Keyword(s):  
Big Data ◽  
Natural Language ◽  
Semantic Information ◽  
Application Programming Interface ◽  
Unstructured Data ◽  
Application Programming ◽  
Text Preprocessing ◽  
Programming Interface ◽  
Proper Analysis

During the last years, big data analysis has become a popular means of taking advantage of multiple (initially valueless) sources to find relevant knowledge about real domains. However, a large number of big data sources provide textual unstructured data. A proper analysis requires tools able to adequately combine big data and text-analysing techniques. Keeping this in mind, we combined a pipelining framework (BDP4J (Big Data Pipelining For Java)) with the implementation of a set of text preprocessing techniques in order to create NLPA (Natural Language Preprocessing Architecture), an extendable open-source plugin implementing preprocessing steps that can be easily combined to create a pipeline. Additionally, NLPA incorporates the possibility of generating datasets using either a classical token-based representation of data or newer synset-based datasets that would be further processed using semantic information (i.e., using ontologies). This work presents a case study of NLPA operation covering the transformation of raw heterogeneous big data into different dataset representations (synsets and tokens) and using the Weka application programming interface (API) to launch two well-known classifiers.

Vehicular System Management Architecture and Application Platform

2012 ◽  
pp. 426-444
Author(s):  
Teng-Wen Chang ◽  
Jiann-Liang Chen
Keyword(s):  
Service Providers ◽  
Application Programming Interface ◽  
Third Party ◽  
System Management ◽  
Service Initiative ◽  
Remote Management ◽  
Management Architecture ◽  
Application Programming ◽  
Equipment Manufacture ◽  
Programming Interface

Notably, not all telematics services can be used in telematics terminals as a result of the varied platform standards. The main issues are that most telematics technologies depend on vertical, proprietary and closed per-OEM Original Equipment Manufacture (OEM) platforms, forming islands of non-interoperable technology and preventing third-party service providers from creating valuable services. In this study, the Open Gateway Service Initiative Vehicle Expert Group (OSGi/VEG) was integrated into an Android platform to generate a vehicular Android/OSGi platform that has the advantages of both original platforms, such as remote management, rich class sharing, proprietary vehicular applications, security policies, easy management of application programming interface (APIs), and an environment with increased openness. Furthermore, this study integrates the cloud computing mechanism into the Android/OSGi platform, which allows service providers to upload their telematics bundles onto storage clouds via the provisioning server.

scholarly journals Automation of Data Consumption by Pluggable Module Software

2021 ◽  
Vol 23 (06) ◽  
pp. 1672-1681
Author(s):  
Vinay Balamurali ◽  
◽  
Prof. Venkatesh S ◽  
Keyword(s):  
Software Design ◽  
Data Science ◽  
Source Code ◽  
Application Programming Interface ◽  
Related Information ◽  
Server Systems ◽  
Data Consumption ◽  
Application Programming ◽  
Using Data ◽  
Programming Interface

Servers are required to monitor the health of the various I/O cards connected to it to alert the required personnel to service these cards. The Data Collection Unit (DCU) is responsible for detecting the I/O cards, sending their inventory as well as monitoring their health. Currently, the keys required to detect these I/O cards are manually coded into the source code. Such a task is highly laborious and time-consuming. To eliminate this manual work, a Software Pluggable Module was devised which would read the I/O card-related information from the I/O component list. This software design aims at using Data Science and OOPS concepts to automate certain tasks on server systems. The proposed methodology is implemented on a Linux system. The software design is modular in nature and extensible to accommodate future requirements. Such an automation framework can be used to track information maintained in Excel Spreadsheets and access them using an Application Programming Interface (API).

scholarly journals Tibanna: software for scalable execution of portable pipelines on the cloud

2018 ◽  
Author(s):  
Soohyun Lee ◽  
Jeremy Johnson ◽  
Carl Vitzthum ◽  
Koray Kırlı ◽  
Burak H. Alver ◽  
...  
Keyword(s):  
Web Services ◽  
Open Source ◽  
Open Source Software ◽  
Source Code ◽  
Software Tool ◽  
Application Programming Interface ◽  
Description Language ◽  
Amazon Web Services ◽  
Application Programming ◽  
Programming Interface

AbstractSummaryWe introduce Tibanna, an open-source software tool for automated execution of bioinformatics pipelines on Amazon Web Services (AWS). Tibanna accepts reproducible and portable pipeline standards including Common Workflow Language (CWL), Workflow Description Language (WDL) and Docker. It adopts a strategy of isolation and optimization of individual executions, combined with a serverless scheduling approach. Pipelines are executed and monitored using local commands or the Python Application Programming Interface (API) and cloud configuration is automatically handled. Tibanna is well suited for projects with a range of computational requirements, including those with large and widely fluctuating loads. Notably, it has been used to process terabytes of data for the 4D Nucleome (4DN) Network.AvailabilitySource code is available on GitHub at https://github.com/4dn-dcic/tibanna.

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