Orion: A menu-driveri source code generator for end users

Low software development productivity is a major concern for corporations. This paper describes one approach to improve productivity by allowing end users to build their own business data processing systems. The approach uses Orion, a menu-driven software development tool. Orion generates source level COBOL programs that compile and run without modification. The tool does not require end users to know COBOL or high-level query languages. Orion was used for nearly two years to build real business systems. The approach resulted in a significant improvement in software development productivity

Forward-Mode Automatic Differentiation in Julia

We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently developed AD tools in other popular high-level languages such as Python and MATLAB, ForwardDiff takes advantage of just-in-time (JIT) compilation to transparently recompile AD-unaware user code, enabling efficient support for higher-order differentiation and differentiation using custom number types (including complex numbers). For gradient and Jacobian calculations, ForwardDiff provides a variant of vector-forward mode that avoids expensive heap allocation and makes better use of memory bandwidth than traditional vector mode. In our numerical experiments, we demonstrate that for nontrivially large dimensions, ForwardDiff's gradient computations can be faster than a reverse-mode implementation from the Python-based autograd package. We also illustrate how ForwardDiff is used effectively within JuMP, a modeling language for optimization. According to our usage statistics, 41 unique repositories on GitHub depend on ForwardDiff, with users from diverse fields such as astronomy, optimization, finite element analysis, and statistics. This document is an extended abstract that has been accepted for presentation at the AD2016 7th International Conference on Algorithmic Differentiation.Comment: 4 page

NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on Services Computing) on July 1

Students’ English Proficiency: The Case of One Madrasah in Jambi City

This present study aimed to find out the senior high school students’ English proficiency. The study employed descriptive quantitative design. It took place at Madrasah Aliyah Labor Kota Jambi. There were 104 students of Madrasah Aliyah Labor purposefully selected in the study. The data were collected through English proficiency test for basic users namely Key English Test (KET). Descriptive statistics was employed to analyze the data. The result of this study revealed that four students (3.8%) were categorized as beginners (level A1) while the rest (96.2%) were not included in the entry level of Common European of Reference for Languages (CEFR) as their scores were lower than those of the beginner level. Moreover, based on the result of reading and writing test in KET analyzed by using the criteria used at school, the students scored low in both sessions

Simplifying syntactic and semantic parsing of NL-based queries in advanced application domains

The paper presents a high level query language (MDDQL) for databases, which relies on an ontology driven automaton. This is simulated by the human-computer interaction mode for the query construction process, which is driven by an inference engine operating upon a frames based ontology description. Therefore, given that the query construction process implicitly leads to the contemporary construction of high level query trees prior to submission of the query for transformation and execution to a semantic middle-ware, syntactic and semantic parsing of a query with conventional techniques, i.e., after completion of its formulation, becomes obsolete. To this extent, only, as meaningful as possible, queries can be constructed at a low typing, learning, syntactic and semantic parsing effort and regardless the preferred natural (sub)language. From a linguistics point o view, it turns out that the query construction mechanism can easily be adapted and work with families of natural languages, which underlie another type order such as Subject-Object-Verb as opposed to the typical Subject-Verb-Object type order, which underlie most European languages. The query construction mechanism has been proved as practical in advanced application domains, such as those provided by medical applications, with an advanced and hardly understood terminology for naive users and the public

Open-Source Skull Reconstruction with MONAI

We present a deep learning-based approach for skull reconstruction for MONAI, which has been pre-trained on the MUG500+ skull dataset. The implementation follows the MONAI contribution guidelines, hence, it can be easily tried out and used, and extended by MONAI users. The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework. Nowadays, open-sourcing software, especially (pre-trained) deep learning models, has become increasingly important. Over the years, medical image analysis experienced a tremendous transformation. Over a decade ago, algorithms had to be implemented and optimized with low-level programming languages, like C or C++, to run in a reasonable time on a desktop PC, which was not as powerful as today's computers. Nowadays, users have high-level scripting languages like Python, and frameworks like PyTorch and TensorFlow, along with a sea of public code repositories at hand. As a result, implementations that had thousands of lines of C or C++ code in the past, can now be scripted with a few lines and in addition executed in a fraction of the time. To put this even on a higher level, the Medical Open Network for Artificial Intelligence (MONAI) framework tailors medical imaging research to an even more convenient process, which can boost and push the whole field. The MONAI framework is a freely available, community-supported, open-source and PyTorch-based framework, that also enables to provide research contributions with pre-trained models to others. Codes and pre-trained weights for skull reconstruction are publicly available at: https://github.com/Project-MONAI/research-contributions/tree/master/SkullRe

PyXNAT: XNAT in Python

As neuroimaging databases grow in size and complexity, the time researchers spend investigating and managing the data increases to the expense of data analysis. As a result, investigators rely more and more heavily on scripting using high-level languages to automate data management and processing tasks. For this, a structured and programmatic access to the data store is necessary. Web services are a first step toward this goal. They however lack in functionality and ease of use because they provide only low-level interfaces to databases. We introduce here PyXNAT, a Python module that interacts with The Extensible Neuroimaging Archive Toolkit (XNAT) through native Python calls across multiple operating systems. The choice of Python enables PyXNAT to expose the XNAT Web Services and unify their features with a higher level and more expressive language. PyXNAT provides XNAT users direct access to all the scientific packages in Python. Finally PyXNAT aims to be efficient and easy to use, both as a back-end library to build XNAT clients and as an alternative front-end from the command line

Design and analysis of visual programming language for microcontroller systems

Conventional methods of programming microcontrollers using textual languages are hard to learn and are daunting to novice programmers seeking to learn microcontroller programming. Visual languages have always been regarded as a useful tool in helping non-programmers and novice-programmers to write programs. However there have been limited attempts at creating a visual language for microcontroller systems and there isn't a visual language for microcontrollers that is flexible and easy to use. This thesis presents a way of addressing the issue by creating a low level visual programming language for microcontroller systems. The low level visual programming language aims to alleviate the problem by using a fine grained language to improved flexibility and providing an integrated visual language environment in which users can focus on writing programs and solving problems. A visual language environment called CoreChart was developed for this purpose. CoreChart aims to simplify the process of programming microcontrollers by providing users with a tool to construct assembly programs visually. The visual language will utilize flow chart diagramming techniques to present users with a more meaningful view of the program. This allows users to focus on writing programs to solve problems, rather than on the rules and syntax of the language. The procedure of programming micro controllers is further simplified by automating the task of compiling the program and downloading the program into the microcontroller. A survey was conducted on university and high school students to evaluate the effectiveness of CoreChart.Thesis (M.Eng.Sc.) -- University of Adelaide, School of Electrical and Electronic Engineering, 200