Open-source Development Framework Research Articles

Transporter Mobile Application

Transporter is a popular service within the IIUM community. It is an e-hailing service operated by the community itself. It is hosted on the Telegram app, which is available on Android, iOS, and desktop computers. However, there are various downsides related to handling user registration, transportation requests, and efficiency. To address these shortcomings, a transporter application was created as a mobile app. This project intends to create a mobile application employing the agile method and FlutterFlow, a low-code platform built on top of Flutter, an open-source development framework. At the same time, it improves on the current IIUM transporter system while retaining the features that make it so popular among the community.

Design and Implementation of Autonomous Underwater Vehicle Simulation System Based on MOOS and Unreal Engine

An effective autonomous underwater vehicle (AUV) simulation system can greatly improve development efficiency and reduce the cost and risk of actual equipment operation. In this paper, a comprehensive simulation system is developed using Mission Oriented Operating Suite (MOOS) and Unreal Engine 4 (UE4). The former provides an open-source development framework and application components, which are widely used in the field of underwater robots. The latter is a well-known game development engine that has realistic effects and various plugins. As far as we know, there are few simulation systems based on MOOS and UE4. Moreover, a modular architecture for the simulation system is designed and implemented in the paper. The developed system consists of four subsystems, including the simulation editing and controlling subsystem, shore-based operation subsystem, simulation engine subsystem, and three-dimensional (3D) visualization subsystem. These subsystems have modular characteristics and independent functions and communicate with each other through network sockets. Through the system, users can customize simulation scenarios, send missions and control simulation progress from the graphic user interface. And the system can be used in operation training, software testing, algorithm verification of AUV, etc.

IoT-based group size prediction and recommendation system using machine learning and deep learning techniques

In an open source software development environment, it is hard to decide the number of group members required for resolving software issues. Developers generally reply to issues based totally on their domain knowledge and interest, and there are no predetermined groups. The developers openly collaborate on resolving the issues based on many factors, such as their interest, domain expertise, and availability. This study compares eight different algorithms employing machine learning and deep learning, namely—Convolutional Neural Network, Multilayer Perceptron, Classification and Regression Trees, Generalized Linear Model, Bayesian Additive Regression Trees, Gaussian Process, Random Forest and Conditional Inference Tree for predicting group size in five open source software projects developed and managed using an open source development framework GitHub. The social information foraging model has also been extended to predict group size in software issues, and its results compared to those obtained using machine learning and deep learning algorithms. The prediction results suggest that deep learning and machine learning models predict better than the extended social information foraging model, while the best-ranked model is a deep multilayer perceptron((R.M.S.E. sequelize—1.21, opencv—1.17, bitcoin—1.05, aseprite—1.01, electron—1.16). Also it was observed that issue labels helped improve the prediction performance of the machine learning and deep learning models. The prediction results of these models have been used to build an Issue Group Recommendation System as an Internet of Things application that recommends and alerts additional developers to help resolve an open issue.

The eStation, an Earth Observation processing service in support to ecological monitoring

The eStation is a collecting and processing system designed to automatically deal with the reception, processing, analysis and dissemination of key environmental parameters derived from remotely sensed data. Developed mainly at the Joint Research Centre of the European Commission, the eStation has been distributed to 47 sub-Saharan countries in the frame of the AMESD (Africa n Monitoring of Environment for Sustainable Development) project to provide local institutions with the capacity to easily access a large range of remote sensing products on vegetation, precipitation, fires and oceans. These products, derived from the processing of images coming from various instruments including SPOT-Vegetation, MSG-SEVIRI and MODIS are developed to allow end-users to make local and regional assessments of the state of marine and terrestrial ecosystems. The products, dispatched to the users through the EUMETSAT data broadcasting system (EUMETCast) or provided by other Earth Observation (EO) data agencies (e.g. NASA), are further processed by the eStation to allow end-users to generate their own environmental, whether terrestrial or marine, assessments and reports. Initially designed as a stand-alone system using an open source development framework, the eStation has recently been further developed as a web processing service to allow a broader range of end-users to access the data and services over the Internet. It is the purpose of this paper to introduce the readers to the eStation and its products, to share the lessons learnt in deploying these services as well as to discuss its more recent use in chained environmental web based modeling services.

The Wikinomics of Saemaul Undong

Saemaul Undong is an integrated rural development program (IRDP) that was particularly active in the 1970s in South Korea. On the one hand, the IRDP literature generally refers to Saemaul Undong as a collection of know-hows and actions accumulated through the process of trial-and-error, i.e., an IRDP that lacks a coherent theoretical framework. On the other hand, while the social innovation literature considers some initiatives as being emblematic, great success stories in rural development are rare, particularly those initiated and led by the state. In this paper, I aim to interpret Saemaul Undong in a social innovation theoretical framework, with particular attention to its scaling methods through hybridization between top-down and bottom-up strategies and through the usage of key performance indicators (KPIs). In effect, I reinterpret Saemaul Undong as an ‘open-source rural development framework,’ drawing parallels with the open-source development framework, another successful contemporary social innovation paradigm in the information and communications technology (ICT) context.