Need For Software Development Research Articles

Design of English viewing, listening, and speaking mobile teaching software based on an interactive digital media algorithm

China has now embraced the information era, which has had a significant impact on everyday life, employment, and educational practices. Information technology has also had a significant impact on the growth of the education sector, resulting in a fast-paced and resource-rich setting for student interaction. Through the network platform, various interactive software can improve students’ learning methods, especially language teaching software. English audio-visual speaking is software for training English language listening and speaking, which can carry out relevant oral activities and topic discussions according to the imported materials. As a result, you can assist pupils in using the vocabulary and knowledge associated with the subject, which will increase their interest in learning. English teachers can fully prepare for speaking and listening tasks in the classroom by using audio-visual speaking. At the same time, through the learning of TV and movie trailers, English audio-visual speaking can provide readers with background knowledge, which is ready for readers to fully understand the language and content in the video materials. Based on information technology, this paper constructs English audio-visual and oral mobile teaching software, which depends on interactive digital media algorithms. Through the mobile teaching software for English audio-visual speaking, students can form good English listening and reading habits, which will provide important help for English language learning.First, this essay examines the value and benefits of mobile applications for providing English instruction orally and visually, which might help to illustrate the need for software development. The research then suggests various algorithms for English that are related to audio, visual, and oral input that can detect, assess, and correct students’ learning mistakes. Finally, this work develops the fundamental methodology of the audio-visual and verbal mobile software for instruction in English.

How Freelance Developers Embrace Software Reuse Practice? A perspective analysis using clustering techniques

The advancement of ICT technologies has modernized software development practices, thus presenting opportunities for developers to reuse software components in developing new software. These practices allow developers to use reliable reusable software components to quickly develop software. However, not much research has reported how freelance developers capitalize on these practices, despite the rapid rise of freelancing development. This research investigates how freelance developers embrace software reuse practices to achieve project success. A survey that gathers 351 responses from freelancers was analyzed using clustering techniques, revealing high levels of self-efficacy and satisfaction towards deploying software reuse practices and highlighting the need for software development firms to embrace this method.

Secured file transmission in knowledge management-cloud

The victory of an organisation is primarily depending on continuous investment learning as well as obtaining some or more new knowledge which creates new business and improve and enhances new performance and techniques. So, this knowledge management should be resulting to more succeeding, or even greater than that of organisations objectives. As per modern methodologies and also paradigms came and emerged, new techniques and efforts should be made in businesses to clearly in line with them, specifically in the area of knowledge management. Nowadays, more popular and efficient one is cloud computing paradigm, because of its less cost, time and efforts to fulfil the need for software development. Also, it gives an excellent measures and ways to collect and reallocate knowledge. Let us discuss the risk factors in utilising cloud computing in the field of knowledge management systems and their solutions.

Impact of individualism and collectivism cultural profiles on the behaviour of software developers: A study of stack overflow

Literature is scarce on culture and its impact on the behavioural patterns within software development communities. However, globalisation in software development has intensified the need for software development teams to navigate culture issues to ensure the successful implementation of projects. Therefore, the current study examines whether the effects of culture on software developers conform to Hofstede’s individualism cultural dimension. Individualism is studied because of its established negative impacts on teamwork, which is central to software development. Data comprised artefacts from Stack Overflow, a popular online programming community. Developers were from the United States (US), China, and Russia, three countries that differ in terms of their individualistic or collectivistic cultures. Data mining techniques, as well as statistical, linguistic, and content analysis were used to compare the orientation, attitudes, interaction, and knowledge sharing patterns of the three groups of developers. Differences revealed among the three groups were consistent with their cultural backgrounds. US developers, who are from a more individualistic culture, had higher average reputations, used the pronoun “I” more frequently, and were more task-focused. Conversely, Chinese developers, who are from a more collectivistic culture, used the pronouns “we” and “you” more frequently, and were more likely to engage in information exchange. Russian developers had been using Stack Overflow the longest and were the most reflective. The cultural patterns identified in this study have implications for enhancing in-group interactions and team behaviour management during software development, especially when global teams assemble.

Building a Distributed Computing System for LDMX

Particle physics experiments rely extensively on computing and data services, making e-infrastructure an integral part of the research collaboration. Constructing and operating distributed computing can however be challenging for a smaller-scale collaboration. The Light Dark Matter eXperiment (LDMX) is a planned small-scale accelerator-based experiment to search for dark matter in the sub-GeV mass region. Finalizing the design of the detector relies on Monte-Carlo simulation of expected physics processes. A distributed computing pilot project was proposed to better utilize available resources at the collaborating institutes, and to improve scalability and reproducibility. This paper outlines the chosen lightweight distributed solution, presenting requirements, the component integration steps, and the experiences using a pilot system for tests with large-scale simulations. The system leverages existing technologies wherever possible, minimizing the need for software development, and deploys only non-intrusive components at the participating sites. The pilot proved that integrating existing components can dramatically reduce the effort needed to build and operate a distributed e-infrastructure, making it attainable even for smaller research collaborations.

Designing Software Application Decision With Multi Criteria Android-based Analytic Network Process Algorithm

Multi-criteria decision-making is one of the techniques in determining a choice of several alternative options. There are several methods in making decisions, one of which is ANP (Analytic Network Process). ANP is a development of AHP (Analytic Hierarchy Process) method. AHP is a hierarchical method in which any existing criteria will be compared first which will produce an ordinary matrix. While ANP is a network method that compares all things including criteria and alternatives that exist so as to obtain a super matrix is very complex. Currently ANP method is not only calculated but already there is software support one of them is Super Decisions. The software can be used to generate a decision by the ANP method. Every software must have advantages and disadvantages. That's why there is a need for software development that has been there before to appear new software better.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy.

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

Streamlining GC-MS metabolomic analysis using the M-IOLITE software suite

Abstract: Metabolomics, as a rapidly growing omic analysis, has being used extensively to explore the dynamic response of biological systems in several diseases/disorders and contexts. Therefore, it has become commonplace in a wide variety of disciplines and there is an intense need for development of software suites that provide the user with a less complicated and invalid analysis. These suites must integrate meta-analysis, a standardized data normalization method and a safe repository for all types of biological samples. In the case of the Gas Chromatography-Mass Spectrometry (GC-MS) metabolomics, due to the complexity of the analysis, multiple procedures that are essential for the metabolite identification require special manipulation. Moreover, metabolomic analysis produces a vast amount of unidentified compound data, so there is a need for unknown peak identification methods. While a number of tools offer access to datasets, constantly providing new releases for data processing and the fact that considerable progress has been made in that area, there is no computational platform that emerges as a standardized approach which includes specialized normalization methods for GC-MS metabolomic analysis and incorporates the metabolic network analysis into data interpretation and unknown peak identification. To address these issues, as the datasets obtained from metabolomics experiments still remain extremely large and dense, we have implemented M-IOLITE, a computational suite for the efficient and automatic analysis of high-throughput metabolomic experiments. The aim of the suite is to streamline GC-MS metabolomic data analysis and to reduce complexity enabling the use of a friendly interface for processing, validating and annotating data. It integrates specialized normalization methods, a safe data repository and a peak library providing through its pipeline a useful tool which enables rapid and accurate analysis of the metabolomic profiles into an interactive system.