An interaction paradigm for impact analysis

The Aerospace industry is concerned with huge software projects. Software development is an evolving process resulting in larger and larger software systems. As systems grow in size, they become more complex and hence harder to maintain. Thus it appears that the maintenance of software systems is the most expensive part of the software life-cycle, often consuming 50-90% of a project total budget. Yet while there has been much research carried out on the problems of program and system development very little work has been done on the problem of maintaining developed programs. Thus it will be essential to improve the software maintenance process and the environment for maintenance. Historically, the term Software Maintenance has been applied to the process of modifying a software program after it has been delivered and during its life time. The high cost of software during its life cycle can be attributed largely to software maintenance activities, and a major part of these activities is to deal with the modifications of the software. These modifications may involve changes at any level of abstraction of a software system (i.e design, specification, code,...). Software Maintenance has to deal with modifications which can have severe Ripple Effects at other points in the software system. Impact Analysis addresses the problem and attempts to localize these Ripple Effects. In this thesis the Software Maintenance process and more specifically the Impact Analysis process is examined. The different parts of the implementation for the Impact Analysis System are explained. The main results of the thesis are the dependencies generation and the graph tool used to visualize these dependencies as well as the impacts on general dependency graph for impact analysis purpose

From skills and competencies to outcome-based collaborative work : tracking a decade's development of personal knowledge management (PKM) models

2011-2012 > Academic research: refereed > Publication in refereed journalAuthor’s OriginalPublishe

Towards Collaborative Scientific Workflow Management System

The big data explosion phenomenon has impacted several domains, starting from research areas to divergent of business models in recent years. As this intensive amount of data opens up the possibilities of several interesting knowledge discoveries, over the past few years divergent of research domains have undergone the shift of trend towards analyzing those massive amount data. Scientific Workflow Management System (SWfMS) has gained much popularity in recent years in accelerating those data-intensive analyses, visualization, and discoveries of important information. Data-intensive tasks are often significantly time-consuming and complex in nature and hence SWfMSs are designed to efficiently support the specification, modification, execution, failure handling, and monitoring of the tasks in a scientific workflow. As far as the complexity, dimension, and volume of data are concerned, their effective analysis or management often become challenging for an individual and requires collaboration of multiple scientists instead. Hence, the notion of 'Collaborative SWfMS' was coined - which gained significant interest among researchers in recent years as none of the existing SWfMSs directly support real-time collaboration among scientists. In terms of collaborative SWfMSs, consistency management in the face of conflicting concurrent operations of the collaborators is a major challenge for its highly interconnected document structure among the computational modules - where any minor change in a part of the workflow can highly impact the other part of the collaborative workflow for the datalink relation among them. In addition to the consistency management, studies show several other challenges that need to be addressed towards a successful design of collaborative SWfMSs, such as sub-workflow composition and execution by different sub-groups, relationship between scientific workflows and collaboration models, sub-workflow monitoring, seamless integration and access control of the workflow components among collaborators and so on. In this thesis, we propose a locking scheme to facilitate consistency management in collaborative SWfMSs. The proposed method works by locking workflow components at a granular attribute level in addition to supporting locks on a targeted part of the collaborative workflow. We conducted several experiments to analyze the performance of the proposed method in comparison to related existing methods. Our studies show that the proposed method can reduce the average waiting time of a collaborator by up to 36% while increasing the average workflow update rate by up to 15% in comparison to existing descendent modular level locking techniques for collaborative SWfMSs. We also propose a role-based access control technique for the management of collaborative SWfMSs. We leverage the Collaborative Interactive Application Methodology (CIAM) for the investigation of role-based access control in the context of collaborative SWfMSs. We present our proposed method with a use-case of Plant Phenotyping and Genotyping research domain. Recent study shows that the collaborative SWfMSs often different sets of opportunities and challenges. From our investigations on existing research works towards collaborative SWfMSs and findings of our prior two studies, we propose an architecture of collaborative SWfMSs. We propose - SciWorCS - a Collaborative Scientific Workflow Management System as a proof of concept of the proposed architecture; which is the first of its kind to the best of our knowledge. We present several real-world use-cases of scientific workflows using SciWorCS. Finally, we conduct several user studies using SciWorCS comprising different real-world scientific workflows (i.e., from myExperiment) to understand the user behavior and styles of work in the context of collaborative SWfMSs. In addition to evaluating SciWorCS, the user studies reveal several interesting facts which can significantly contribute in the research domain, as none of the existing methods considered such empirical studies, and rather relied only on computer generated simulated studies for evaluation

Design and Implementation of a Domain Specific Language for Deep Learning

\textit {Deep Learning} (DL) has found great success in well-diversified areas such as machine vision, speech recognition, big data analysis, and multimedia understanding recently. However, the existing state-of-the-art DL frameworks, e.g. Caffe2, Theano, TensorFlow, MxNet, Torch7, and CNTK, are programming libraries with fixed user interfaces, internal representations, and execution environments. Modifying the code of DL layers or data structure is very challenging without in-depth understanding of the underlying implementation. The optimization of the code and execution in these tools is often limited and relies on the specific DL computation graph manipulation and scheduling that lack systematic and universal strategies. Furthermore, most of these tools demand many dependencies beside the tool itself and require to be built to some specific platforms for DL training or inference. \\\\ \noindent This dissertation presents {\it DeepDSL}, a \textit {domain specific language} (DSL) embedded in Scala, that compiles DL networks encoded with DeepDSL to efficient, compact, and portable Java source programs for DL training and inference. DeepDSL represents DL networks as abstract tensor functions, performs symbolic gradient derivations to generate the Intermediate Representation (IR), optimizes the IR expressions, and compiles the optimized IR expressions to cross-platform Java code that is easily modifiable and debuggable. Also, the code directly runs on GPU without additional dependencies except a small set of \textit{JNI} (Java Native Interface) wrappers for invoking the underneath GPU libraries. Moreover, DeepDSL provides static analysis for memory consumption and error detection. \\\\ \noindent DeepDSL\footnote{Our previous results are reported in~\cite{zhao2017}; design and implementation details are summarized in~\cite{Zhao2018}.} has been evaluated with many current state-of-the-art DL networks (e.g. Alexnet, GoogleNet, VGG, Overfeat, and Deep Residual Network). While the DSL code is highly compact with less than 100 lines for each of the network, the Java source code generated by the DeepDSL compiler is highly efficient. Our experiments show that the output java source has very competitive runtime performance and memory efficiency compared to the existing DL frameworks

NeuroProv - A visualisation system to enhance the utility of provenance Data for neuroimaging analysis

E-Science platforms such as myGRID and NeuGRID for Users are growing at an amazing rate. One of the key barriers to their widespread use in practice is the lack of provenance data to support the reasoning and verification of experimental or analysis results. Clinical researchers use workflows to orchestrate the data present in e-science platforms in order to facilitate processing. Even though most systems capture provenance data and store it, systems rarely make use of it, thus limiting the exploitation of the true potential of such provenance. This thesis investigates mechanisms to visualise provenance data for neuroimaging analysis and to provide means to exploit the true potential of provenance data. In order to achieve this, a visualisation system has been implemented based on the use-cases that have been designed following requirements elicited for neuroimaging analysis. In this research a technique has been used to address the requirements of provenance visualisation for neuroimaging analysis. The prototype system has been tested against the provenance generated by NeuGRID for Users (N4U) as a proof of concept for our research. Different workflows have been visualised to study the efficacy of the proposed solution. Furthermore, evaluation metrics have been defined to determine whether the proposed solution is suitable for the purpose of the research conducted. The results show that the proposed visualisation system enhances the utility of provenance data for neuroimaging analysis and therefore the proposed research can be used to provide value to provenance data for neuroimaging analyses

Web based public participation in visual impact assessment of urban landscape.

Zhang Zongyu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 101-108).Abstracts in English and Chinese.ABSTRACT IN ENGLISH --- p.i-iiABSTRACT IN CHINESE --- p.iiiACKNOWLEDGEMENTS --- p.iv-vTABLE OF CONTENTS --- p.vi-viiiLIST OF TABLES --- p.ixLIST OF FIGURES --- p.x-xiChapter CHAPTER ONE --- INTRODUCTIONChapter 1.1 --- Landscape and landscape Assessment --- p.1Chapter 1.1.1 --- The descriptive inventory approach --- p.2Chapter 1.1.2 --- Public preference models --- p.4Chapter 1.2 --- Urban Landscape --- p.5Chapter 1.3 --- Relationship between professional and public --- p.8Chapter 1.3.1 . --- Inherent conflicts --- p.9Chapter 1.3.2. --- Roles of both sides --- p.9Chapter 1.3.3 --- Collaboration between professionals and the public --- p.10Chapter CHAPTER TWO --- VISUAL IMPACT ASSESSMENTChapter 2.1 --- The needs for visual impact assessment --- p.13Chapter 2.2 --- The visual impact assessment process --- p.16Chapter 2.3 --- The information inventory in the visual impact assessment --- p.19Chapter 2.3.1 --- Landscape simulation --- p.20Chapter 2.3.2 --- Visual impacts identification --- p.22Chapter 2.4 --- Public participation --- p.23Chapter 2.4.1 --- Public preference in the urban landscape --- p.24Chapter 2.4.2 --- Public accessibility to the urban landscape planning process --- p.28Chapter CHAPTER THREE --- CAPTURING THE SYSTEM SPECIFICATIONSChapter 3.1 --- General considerations --- p.30Chapter 3.1.1 --- Function requirements --- p.30Chapter 3.1.2 --- Project management --- p.32Chapter 3.1.3 --- User interface --- p.33Chapter 3.1.4 --- Web access --- p.34Chapter 3.1.5 --- Qualification of public participation in urban planning --- p.35Chapter 3.2 --- Envisioning the proposed web based system --- p.37Chapter 3.2.1 --- Proposed virtual collaboration --- p.38Chapter 3.2.1.1 --- Improving participants' access to the web based visual impact assessment --- p.39Chapter 3.2.1.2 --- Capturing the public appreciation --- p.41Chapter 3.2.2 --- Collaboration between planners and public --- p.43Chapter CHAPTER FOUR --- SYSTEM DESIGNChapter 4.1 --- Main software or tools for developing the proposed web based system --- p.45Chapter 4.1.1 --- Arcview 3.1 or Arc/Info with 3D analyst and Internet mapping server extensions --- p.46Chapter 4:1.2 --- VRML 2.0 and Java --- p.49Chapter 4.1.3 --- Java3D API --- p.52Chapter 4.2 --- System configuration --- p.55Chapter 4.2.1. --- System architecture --- p.55Chapter 4.2.2. --- Data management --- p.57Chapter 4.2.2.1 --- Urban landscape information management --- p.57Chapter 4.2.2.2 --- Public participation --- p.64Chapter 4.2.3. --- User interface design --- p.69Chapter CHAPTER FIVE --- PROTOTYPE SYSTME AND PILOT STUDYChapter 5.1 --- General description --- p.74Chapter 5.2 --- Implementation --- p.75Chapter 5.2.1 --- Connecting the two-dimensional world with a three-dimensional virtual urban environment --- p.75Chapter 5.2.2 --- Data flow of the system for interactions between the GIS and the VRML browser --- p.77Chapter 5.3 --- Data preparation --- p.81Chapter 5.3.1 --- Constructing the terrain model --- p.81Chapter 5.3.2 --- Retrieving the landscape themes --- p.87Chapter 5.4 --- Public oriented user interface design --- p.88Chapter 5.5 --- Participation log --- p.96Chapter CHAPTER SIX --- CONCLUSIONAPPENDI

Third Conference on Artificial Intelligence for Space Applications, part 1

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed