A Reference Implementation of the API for Media Resources

2011 ◽  
pp. 192-193
Author(s):  
Florian Stegmaier ◽  
Werner Bailer ◽  
Martin Höffernig ◽  
Tobias Bürger ◽  
Ludwig Bachmaier ◽  
...  
Keyword(s):  
Reference Implementation

Model Kurikulum Pendidikan Pra Nikah Untuk MembentukKeluarga Sakinah: Studi Impelementasi Surat Edaran Dirjen Bimas Islam No. DJ.II/491 Tahun 2009 Tentang Kursus Calon Pengantin di Kantor Urusan Agama Karawang

2016 ◽  
Vol 5 (1) ◽  
pp. 84
Author(s):  
Munir Huda ◽  
Ulil Amri Syafri ◽  
Didin Hafidhuddin ◽  
Irfan Syauqi Beik
Keyword(s):  
Director General ◽  
Premarital Education ◽  
Domestic Life ◽  
Model Curriculum ◽  
District Office ◽  
Reference Implementation ◽  
The Republic ◽  
Stock Of Knowledge ◽  
Harmonious Family

<p>Marriage is one of Allah's command execution. and the Sunnah of Muhammad SAW. However, very few people know about it. For the Ministry of Religious Affairs of the Republic of Indonesia through the District Office of Religious Affairs has been providing services courses bride is the provision of a stock of knowledge, understanding and skills of the domestic life / family. The bride is the basis of the convening of the course; Regulation of the Director General of Islamic Community Guidance No. DJ.II / 491 in 2009. The conclusion that the implementation of the course curriculum bride in Karawang Religious Affairs Office has not carried out systematically. This is due to the lack of a model or guide that can be used as a reference implementation. Therefore, to address this problem researchers offer a model curriculum-based lessons bride harmonious family to KUA in Karawang. Through the Model Curriculum bid is expected to be used as reference or guidance in implementing the premarital education in the Office of Religious Affairs Karawang.</p><p><strong>Keyword</strong>: sakinah family, curriculum, prewedding</p>

A Lightweight Formalism for Reference Lifetimes and Borrowing in Rust

2021 ◽  
Vol 43 (1) ◽  
pp. 1-73
Author(s):  
David J. Pearce
Keyword(s):  
Memory Management ◽  
Programming Model ◽  
Type System ◽  
Control Flow ◽  
Two Phase ◽  
Type Checking ◽  
Strong Focus ◽  
Reference Implementation ◽  
Two Phases ◽  
Complex Features

Rust is a relatively new programming language that has gained significant traction since its v1.0 release in 2015. Rust aims to be a systems language that competes with C/C++. A claimed advantage of Rust is a strong focus on memory safety without garbage collection. This is primarily achieved through two concepts, namely, reference lifetimes and borrowing . Both of these are well-known ideas stemming from the literature on region-based memory management and linearity / uniqueness . Rust brings both of these ideas together to form a coherent programming model. Furthermore, Rust has a strong focus on stack-allocated data and, like C/C++ but unlike Java, permits references to local variables. Type checking in Rust can be viewed as a two-phase process: First, a traditional type checker operates in a flow-insensitive fashion; second, a borrow checker enforces an ownership invariant using a flow-sensitive analysis. In this article, we present a lightweight formalism that captures these two phases using a flow-sensitive type system that enforces “ type and borrow safety .” In particular, programs that are type and borrow safe will not attempt to dereference dangling pointers. Our calculus core captures many aspects of Rust, including copy- and move-semantics, mutable borrowing, reborrowing, partial moves, and lifetimes. In particular, it remains sufficiently lightweight to be easily digested and understood and, we argue, still captures the salient aspects of reference lifetimes and borrowing. Furthermore, extensions to the core can easily add more complex features (e.g., control-flow, tuples, method invocation). We provide a soundness proof to verify our key claims of the calculus. We also provide a reference implementation in Java with which we have model checked our calculus using over 500B input programs. We have also fuzz tested the Rust compiler using our calculus against 2B programs and, to date, found one confirmed compiler bug and several other possible issues.

scholarly journals Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation

2014 ◽  
Author(s):  
Sameer Pradhan ◽  
Xiaoqiang Luo ◽  
Marta Recasens ◽  
Eduard Hovy ◽  
Vincent Ng ◽  
...  
Keyword(s):  
Reference Implementation

A Reference Implementation of a Learning Design Engine

2006 ◽  
pp. 91-108 ◽  
Author(s):  
Harrie Martens ◽  
Hubert Vogten
Keyword(s):  
Learning Design ◽  
Reference Implementation

scholarly journals Design and Implementation of a Gesture-Aided E-Learning Platform

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8042
Author(s):  
Wolfgang Kremser ◽  
Stefan Kranzinger ◽  
Severin Bernhart
Keyword(s):  
Learning Outcomes ◽  
Field Test ◽  
Learning Experience ◽  
Learning Content ◽  
Learning Platform ◽  
System Usability Scale ◽  
E Learning ◽  
Learning Platforms ◽  
Reference Implementation ◽  
Generic System

In gesture-aided learning (GAL), learners perform specific body gestures while rehearsing the associated learning content. Although this form of embodiment has been shown to benefit learning outcomes, it has not yet been incorporated into e-learning. This work presents a generic system design for an online GAL platform. It is comprised of five modules for planning, administering, and monitoring remote GAL lessons. To validate the proposed design, a reference implementation for word learning was demonstrated in a field test. 19 participants independently took a predefined online GAL lesson and rated their experience on the System Usability Scale and a supplemental questionnaire. To monitor the correct gesture execution, the reference implementation recorded the participants’ webcam feeds and uploaded them to the instructor for review. The results from the field test show that the reference implementation is capable of delivering an e-learning experience with GAL elements. Designers of e-learning platforms may use the proposed design to include GAL in their applications. Beyond its original purpose in education, the platform is also useful to collect and annotate gesture data.

scholarly journals AT-DIFC + : Toward Adaptive and Trust-Aware Decentralized Information Flow Control

2020 ◽  
Vol 15 (4) ◽  
pp. 1-35
Author(s):  
Charilaos Skandylas ◽  
Narges Khakpour ◽  
Jesper Andersson
Keyword(s):  
Flow Control ◽  
Information Flow ◽  
Decentralized Control ◽  
Software Systems ◽  
Control Mechanisms ◽  
Information Flow Control ◽  
Decentralized System ◽  
Additional Information ◽  
Reference Implementation ◽  
And Performance

Modern software systems and their corresponding architectures are increasingly decentralized, distributed, and dynamic. As a consequence, decentralized mechanisms are required to ensure security in such architectures. Decentralized Information Flow Control (DIFC) is a mechanism to control information flow in distributed systems. This article presents and discusses several improvements to an adaptive decentralized information flow approach that incorporates trust for decentralized systems to provide security. Adaptive Trust-Aware Decentralized Information Flow (AT-DIFC + ) combines decentralized information flow control mechanisms, trust-based methods, and decentralized control architectures to control and enforce information flow in an open, decentralized system. We strengthen our approach against newly discovered attacks and provide additional information about its reconfiguration, decentralized control architectures, and reference implementation. We evaluate the effectiveness and performance of AT-DIFC + on two case studies and perform additional experiments and to gauge the mitigations’ effectiveness against the identified attacks.

scholarly journals The OO jDREW Reference Implementation of RuleML

2005 ◽  
pp. 218-223 ◽  
Author(s):  
Marcel Ball ◽  
Harold Boley ◽  
David Hirtle ◽  
Jing Mei ◽  
Bruce Spencer
Keyword(s):  
Reference Implementation

scholarly journals Curating GitHub for engineered software projects

2016 ◽  
Author(s):  
Nuthan Munaiah ◽  
Steven Kroh ◽  
Craig Cabrey ◽  
Meiyappan Nagappan
Keyword(s):  
Software Engineering ◽  
High Precision ◽  
Ground Truth ◽  
Software Project ◽  
Software Projects ◽  
Data Set ◽  
High Recall ◽  
Home Work ◽  
Reference Implementation ◽  
Engineering Practices

Software forges like GitHub host millions of repositories. Software engineering researchers have been able to take advantage of such a large corpora of potential study subjects with the help of tools like GHTorrent and Boa. However, the simplicity in querying comes with a caveat: there are limited means of separating the signal (e.g. repositories containing engineered software projects) from the noise (e.g. repositories containing home work assignments). The proportion of noise in a random sample of repositories could skew the study and may lead to researchers reaching unrealistic, potentially inaccurate, conclusions. We argue that it is imperative to have the ability to sieve out the noise in such large repository forges. We propose a framework, and present a reference implementation of the framework as a tool called reaper, to enable researchers to select GitHub repositories that contain evidence of an engineered software project. We identify software engineering practices (called dimensions) and propose means for validating their existence in a GitHub repository. We used reaper to measure the dimensions of 1,994,977 GitHub repositories. We then used the data set train classifiers capable of predicting if a given GitHub repository contains an engineered software project. The performance of the classifiers was evaluated using a set of 200 repositories with known ground truth classification. We also compared the performance of the classifiers to other approaches to classification (e.g. number of GitHub Stargazers) and found our classifiers to outperform existing approaches. We found stargazers-based classifier to exhibit high precision (96%) but an inversely proportional recall (27%). On the other hand, our best classifier exhibited a high precision (82%) and a high recall (83%). The stargazer-based criteria offers precision but fails to recall a significant potion of the population.

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