4,526 research outputs found
The determinants of value addition: a crtitical analysis of global software engineering industry in Sri Lanka
It was evident through the literature that the perceived value delivery of the global software
engineering industry is low due to various facts. Therefore, this research concerns global
software product companies in Sri Lanka to explore the software engineering methods and
practices in increasing the value addition. The overall aim of the study is to identify the key
determinants for value addition in the global software engineering industry and critically
evaluate the impact of them for the software product companies to help maximise the value
addition to ultimately assure the sustainability of the industry.
An exploratory research approach was used initially since findings would emerge while the
study unfolds. Mixed method was employed as the literature itself was inadequate to
investigate the problem effectively to formulate the research framework. Twenty-three face-to-face online interviews were conducted with the subject matter experts covering all the
disciplines from the targeted organisations which was combined with the literature findings as
well as the outcomes of the market research outcomes conducted by both government and nongovernment institutes. Data from the interviews were analysed using NVivo 12. The findings
of the existing literature were verified through the exploratory study and the outcomes were
used to formulate the questionnaire for the public survey. 371 responses were considered after
cleansing the total responses received for the data analysis through SPSS 21 with alpha level
0.05. Internal consistency test was done before the descriptive analysis. After assuring the
reliability of the dataset, the correlation test, multiple regression test and analysis of variance
(ANOVA) test were carried out to fulfil the requirements of meeting the research objectives.
Five determinants for value addition were identified along with the key themes for each area.
They are staffing, delivery process, use of tools, governance, and technology infrastructure.
The cross-functional and self-organised teams built around the value streams, employing a
properly interconnected software delivery process with the right governance in the delivery
pipelines, selection of tools and providing the right infrastructure increases the value delivery.
Moreover, the constraints for value addition are poor interconnection in the internal processes,
rigid functional hierarchies, inaccurate selections and uses of tools, inflexible team
arrangements and inadequate focus for the technology infrastructure. The findings add to the
existing body of knowledge on increasing the value addition by employing effective processes,
practices and tools and the impacts of inaccurate applications the same in the global software
engineering industry
AIUCD 2022 - Proceedings
L’undicesima edizione del Convegno Nazionale dell’AIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo è presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sull’interrelazione tra tecnologie digitali, scienze dell’informazione, discipline filosofiche, mondo delle arti e cultural studies
Hunting Wildlife in the Tropics and Subtropics
The hunting of wild animals for their meat has been a crucial activity in the evolution of humans. It continues to be an essential source of food and a generator of income for millions of Indigenous and rural communities worldwide. Conservationists rightly fear that excessive hunting of many animal species will cause their demise, as has already happened throughout the Anthropocene. Many species of large mammals and birds have been decimated or annihilated due to overhunting by humans. If such pressures continue, many other species will meet the same fate. Equally, if the use of wildlife resources is to continue by those who depend on it, sustainable practices must be implemented. These communities need to remain or become custodians of the wildlife resources within their lands, for their own well-being as well as for biodiversity in general. This title is also available via Open Access on Cambridge Core
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UTILIZATION AND EFFECT OF MULTIPLE CONTENT MODALITIES IN ONLINE HIGHER EDUCATION: SHIFTING TRAJECTORIES TOWARD SUCCESS THROUGH UNIVERSAL DESIGN FOR LEARNING
The idea that offering multiple means of representing course content will assist students of all abilities constitutes one pillar of Universal Design for Learning (UDL), a framework intended to address needs of students with disabilities while also holding relevance for all students. The efficacy of this UDL guideline lacks a verified empirical basis and therefore merits rigorous examination. My dissertation investigates the effect on learning outcomes of students using multiple modalities while learning course content (e.g., text, video, audio, interactive, or mixed content), targeting improving educational success for non-traditional online students.
I investigate this effect for older undergraduates from a women’s institution who are predominantly low income and working mothers returning to school, many of whom are racial/ethnic minorities. Notably, challenges resulting from a lack of disability diagnosis and accommodation may be prevalent but hidden among these students. Traditional higher education typically does not serve such students well. Use of multiple modalities in class activities holds potential for improving their outcomes.
Results show positive effects of using multiple modalities for learning content in courses across the curriculum presented in an adaptive learning system. Using a within-subjects study design, I found a medium-large positive effect size for knowledge gained across adaptive activities. Using an instrumental variables approach, I found a very large positive effect size for weekly assignment and quiz grades, and results suggest a large positive effect on course grade as well. I illustrate how combining knowledge of this effect with other information from the adaptive learning system and online tutoring in a Bayesian network analysis can predict where students may benefit from tutoring. This can inform potential support recommendations that would be particularly relevant when implementation of UDL-based design does not yet fully address students’ learning needs.
These results provide the first evidence confirming an effect of UDL’s multiple modalities guideline on collegiate learning outcomes and illustrate how this information could be used to provide recommendations to students using a learning analytics perspective. Results have implications for researchers, faculty, course developers, instructional designers, analytics professionals, and institutions aiming to improve learning outcomes through a design-based approach
A Syntactical Reverse Engineering Approach to Fourth Generation Programming Languages Using Formal Methods
Fourth-generation programming languages (4GLs) feature rapid development with minimum configuration required by developers. However, 4GLs can suffer from limitations such as high maintenance cost and legacy software practices.
Reverse engineering an existing large legacy 4GL system into a currently maintainable programming language can be a cheaper and more effective solution than rewriting from scratch. Tools do not exist so far, for reverse engineering proprietary XML-like and model-driven 4GLs where the full language specification is not in the public domain.
This research has developed a novel method of reverse engineering some of the syntax of such 4GLs (with Uniface as an exemplar) derived from a particular system, with a view to providing a reliable method to translate/transpile that system's code and data structures into a modern object-oriented language (such as C\#).
The method was also applied, although only to a limited extent, to some other 4GLs, Informix and Apex, to show that it was in principle more broadly applicable. A novel testing method that the syntax had been successfully translated was provided using 'abstract syntax trees'.
The novel method took manually crafted grammar rules, together with Encapsulated Document Object Model based data from the source language and then used parsers to produce syntactically valid and equivalent code in the target/output language.
This proof of concept research has provided a methodology plus sample code to automate part of the process. The methodology comprised a set of manual or semi-automated steps. Further automation is left for future research.
In principle, the author's method could be extended to allow the reverse engineering recovery of the syntax of systems developed in other proprietary 4GLs. This would reduce time and cost for the ongoing maintenance of such systems by enabling their software engineers to work using modern object-oriented languages, methodologies, tools and techniques
Optimización del rendimiento y la eficiencia energética en sistemas masivamente paralelos
RESUMEN Los sistemas heterogéneos son cada vez más relevantes, debido a sus capacidades de rendimiento y eficiencia energética, estando presentes en todo tipo de plataformas de cómputo, desde dispositivos embebidos y servidores, hasta nodos HPC de grandes centros de datos. Su complejidad hace que sean habitualmente usados bajo el paradigma de tareas y el modelo de programación host-device. Esto penaliza fuertemente el aprovechamiento de los aceleradores y el consumo energético del sistema, además de dificultar la adaptación de las aplicaciones.
La co-ejecución permite que todos los dispositivos cooperen para computar el mismo problema, consumiendo menos tiempo y energÃa. No obstante, los programadores deben encargarse de toda la gestión de los dispositivos, la distribución de la carga y la portabilidad del código entre sistemas, complicando notablemente su programación.
Esta tesis ofrece contribuciones para mejorar el rendimiento y la eficiencia energética en estos sistemas masivamente paralelos. Se realizan propuestas que abordan objetivos generalmente contrapuestos: se mejora la usabilidad y la programabilidad, a la vez que se garantiza una mayor abstracción y extensibilidad del sistema, y al mismo tiempo se aumenta el rendimiento, la escalabilidad y la eficiencia energética. Para ello, se proponen dos motores de ejecución con enfoques completamente distintos.
EngineCL, centrado en OpenCL y con una API de alto nivel, favorece la máxima compatibilidad entre todo tipo de dispositivos y proporciona un sistema modular extensible. Su versatilidad permite adaptarlo a entornos para los que no fue concebido, como aplicaciones con ejecuciones restringidas por tiempo o simuladores HPC de dinámica molecular, como el utilizado en un centro de investigación internacional.
Considerando las tendencias industriales y enfatizando la aplicabilidad profesional, CoexecutorRuntime proporciona un sistema flexible centrado en C++/SYCL que dota de soporte a la co-ejecución a la tecnologÃa oneAPI. Este runtime acerca a los programadores al dominio del problema, posibilitando la explotación de estrategias dinámicas adaptativas que mejoran la eficiencia en todo tipo de aplicaciones.ABSTRACT Heterogeneous systems are becoming increasingly relevant, due to their performance and energy efficiency capabilities, being present in all types of computing platforms, from embedded devices and servers to HPC nodes in large data centers. Their complexity implies that they are usually used under the task paradigm and the host-device programming model. This strongly penalizes accelerator utilization and system energy consumption, as well as making it difficult to adapt applications.
Co-execution allows all devices to simultaneously compute the same problem, cooperating to consume less time and energy. However, programmers must handle all device management, workload distribution and code portability between systems, significantly complicating their programming.
This thesis offers contributions to improve performance and energy efficiency in these massively parallel systems. The proposals address the following generally conflicting objectives: usability and programmability are improved, while ensuring enhanced system abstraction and extensibility, and at the same time performance, scalability and energy efficiency are increased. To achieve this, two runtime systems with completely different approaches are proposed.
EngineCL, focused on OpenCL and with a high-level API, provides an extensible modular system and favors maximum compatibility between all types of devices. Its versatility allows it to be adapted to environments for which it was not originally designed, including applications with time-constrained executions or molecular dynamics HPC simulators, such as the one used in an international research center.
Considering industrial trends and emphasizing professional applicability, CoexecutorRuntime provides a flexible C++/SYCL-based system that provides co-execution support for oneAPI technology. This runtime brings programmers closer to the problem domain, enabling the exploitation of dynamic adaptive strategies that improve efficiency in all types of applications.Funding: This PhD has been supported by the Spanish Ministry of Education (FPU16/03299 grant),
the Spanish Science and Technology Commission under contracts TIN2016-76635-C2-2-R
and PID2019-105660RB-C22.
This work has also been partially supported by the Mont-Blanc 3: European Scalable and
Power Efficient HPC Platform based on Low-Power Embedded Technology project (G.A. No.
671697) from the European Union’s Horizon 2020 Research and Innovation Programme
(H2020 Programme). Some activities have also been funded by the Spanish Science and Technology
Commission under contract TIN2016-81840-REDT (CAPAP-H6 network).
The Integration II: Hybrid programming models of Chapter 4 has been partially performed
under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC
Research Innovation Action under the H2020 Programme. In particular, the author gratefully
acknowledges the support of the SPMT Department of the High Performance Computing
Center Stuttgart (HLRS)
Behavioral Economics & Machine Learning Expanding the Field Through a New Lens
In this thesis, I investigate central questions in behavioral economics as well as law and economics. I examine well-studied problems through a new methodological lens. The aim is to generate new insights and thus point behavioral scientists to novel analytical tools. To this end, I show how machine learning may be used to build new theories by reducing complexity in experimental economic data. Moreover, I use natural language processing to show how supervised learning can enable the scientific community to expand limited datasets. I also investigate the normative impact of the use of such tools in social science research or decision-making as well as their deficiencies
Improving the domain generalization and robustness of neural networks for medical imaging
Deep neural networks are powerful tools to process medical images, with great potential to accelerate clinical workflows and facilitate large-scale studies. However, in order to achieve satisfactory performance at deployment, these networks generally require massive labeled data collected from various domains (e.g., hospitals, scanners), which is rarely available in practice. The main goal of this work is to improve the domain generalization and robustness of neural networks for medical imaging when labeled data is limited.
First, we develop multi-task learning methods to exploit auxiliary data to enhance networks. We first present a multi-task U-net that performs image classification and MR atrial segmentation simultaneously. We then present a shape-aware multi-view autoencoder together with a multi-view U-net, which enables extracting useful shape priors from complementary long-axis views and short-axis views in order to assist the left ventricular myocardium segmentation task on the short-axis MR images. Experimental results show that the proposed networks successfully leverage complementary information from auxiliary tasks to improve model generalization on the main segmentation task.
Second, we consider utilizing unlabeled data. We first present an adversarial data augmentation method with bias fields to improve semi-supervised learning for general medical image segmentation tasks. We further explore a more challenging setting where the source and the target images are from different data distributions. We demonstrate that an unsupervised image style transfer method can bridge the domain gap, successfully transferring the knowledge learned from labeled balanced Steady-State Free Precession (bSSFP) images to unlabeled Late Gadolinium Enhancement (LGE) images, achieving state-of-the-art performance on a public multi-sequence cardiac MR segmentation challenge.
For scenarios with limited training data from a single domain, we first propose a general training and testing pipeline to improve cardiac image segmentation across various unseen domains. We then present a latent space data augmentation method with a cooperative training framework to further enhance model robustness against unseen domains and imaging artifacts.Open Acces
Understanding New Product Development and Value Creation for the Internet of Things
This thesis investigates IoT development processes and value creation from the perspective of the business. At the onset of the research there is a lack of existing research on how IoT products and services are designed and developed. IoT is distinctive to traditional product as it is the combination of physical components, smart components, and connectivity that allows for continuous value improvement. Consequently, New Product Development (NPD) process of IoT should reflect vital characteristics of networked artefacts and integrate the data science process. To achieve the research aim, the study is based on the literature review and an inductive approach, using a qualitative research methodology. Through a comprehensive literature review covering interdisciplinary subjects from economics, business, engineering, information systems, innovation, and design studies, a theoretical foundation of value creation activities, a NPD process and practice and design roles are developed. An exploratory multiple case study is adopted to gain a primary understanding of IoT design and development. Six cases are selected for the study from various sectors, including healthcare, smart home, drain maintenance, dairy, vertical farming, and tropical farming. Within the case study methodology, semi-structured interview, document reviews and graphic elicitation are adopted to capture each participant’s distinctive experience and design challenges within the context of the given project. Thematic analysis is used for a purely qualitative, rich, detailed yet complex, account of data analysis in IoT development. The transcribed interview script contents and obtained documents for all the cases are carefully analysed through within- and cross-case analysis strategies, using thematic analysis. To enhance the study trustworthiness, triangulation of multiple data sources, member checks, peer reviews and experts’ reviews are drawn upon. Through the discussion, the conceptual model of the IoT NPD process, the Mobius strip model, is developed, reflecting the attributes of complex development practice, challenges and value creation. The Mobius Strip Model implies three infinite loops of value creation and NPD activities each of which are a hardware centred, software centred, and data and algorithms centred IoT NPD. The hardware centred NPD cycle is hardware centred development which has stricter review gates compared to other two software centred and data/algorithms centred development cycles. The software centred NPD cycle is more flexible, efficient, and effective without major modification to the IoT system. The data and algorithms centred IoT NPD is slow and time-consuming, reflecting the challenges of the data science process. The IoT NPD process involves three different types of subject matter, hardware, software, and data/algorithms development. This research confirmed that value of IoT system can be created through a hardware centred, software centred, and data & algorithms centred which was reflected to a conceptual model. Service-Dominant Logic is applied as the fundamental theory that can explain IoT value creation, including delivering service and scaling up, value co-creation, and user-driven development. However, emerging theories, such as the value space framework, and data as critical resource for value creation, complement to comprehend IoT value creation. Design is not utilised to its full extent but limited as styling and a process within IoT development. Design as styling is mainly focused on designing, prototyping, and testing the product or user interface of web and app, and design as a process is utilised to identify user needs and develop solution ideation. This study provides businesses with an integrative understanding of the value creation, development process, and various challenges in IoT development. The proposed conceptual model of IoT NPD, 'The Mobius Strip Model’, contributes to a body of research by combining interdisciplinary knowledge within the process. The model provides a foundation for scholars to construct other knowledge upon, including business models, development risks, innovation, design, and product management studies
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