FACILITATING AQUATIC INVASIVE SPECIES MANAGEMENT USING SATELLITE REMOTE SENSING AND MACHINE LEARNING FRAMEWORKS

The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and environmental data products in the form of new workflows and tools that facilitate data utilization across platforms. Timely risk assessments allow for the spatial prioritization of monitoring that could streamline invasive species management paradigms and invasive species’ ability to prevent irreversible damage, such that decision makers can focus surveillance and intervention efforts where they are likely to be most effective under budgetary and resource constraints. I present a workflow that generates rapid spatial risk assessments on aquatic invasive species by combining occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, I tested this workflow using extensive spatial and temporal occurrence data from Rainbow Trout (RBT; Oncorhynchus mykiss) invasion in the upper Flathead River system in northwestern Montana, USA. Due to this workflow’s high performance against cross-validated datasets (87% accuracy) and congruence with known drivers of RBT invasion, I developed a tool that generates agile risk assessments based on the above workflow and suggest that it can be generalized to broader spatial and taxonomic scales in order to provide data-driven management information for early detection of potential invaders. I then use this tool as technical input for a management framework that provides guidance for users to incorporate and synthesize the component features of the workflow and toolkit to derive actionable insight in an efficient manner

Towards an artificial intelligence (AI)-driven government in the United Arab Emirates (UAE): a framework for transforming and augmenting leadership capabilities

The UAE Government has recently launched a strategy for Artificial Intelligence (AI) that transitions the government to a new phase of becoming an AI-driven government. This strategy aimed to adopt AI-based technologies to boost the overall performance of the government. AI- based technologies have the capability to perform a wide range of human cognitive functions such as voice recognition, image recognition, and prediction. However, to achieve the vision of becoming an AI-driven government, the UAE has to prepare effectively for the transformation by anticipating the various challenges that accompany the adoption of AI-based technologies. Previous research indicated that infusing AI-based technologies will stimulate major shifts in organizations such as reshaping the nature of work, changing how work was previously done, and, more importantly, redefining the measurements of effective leadership. Hence, to particularly address the major shifts triggered by the adoption of AI-based technologies in terms of organizational leadership, this research study explored aspects of effective leadership in the UAE’s future AI-driven government. For this purpose, the study employed an explanatory sequential mixed methods design that incorporated the collection of both quantitative and qualitative data. The key findings of this study contributed to the development of a framework for transforming and augmenting leadership capabilities that could be implemented in UAE’s AI- driven government. This study found that public entities within the UAE government will need to redefine their organizational leadership structure by essentially incorporating roles that foster a culture of innovation and establish a data-driven organization as a major cornerstone for a successful AI-transformation. Equally important, AI-based technologies will enable leaders to become more efficient and productive through the concept of augmented intelligence. The findings of this study also indicated that agile mindset, AI-technology proficiency, data intelligence, and qualities associated with transformational leadership theory are the 4 main competencies which define an effective leader in UAE’s AI-driven government. Finally, this study highly recommends the implementation of more innovative development methods as a key step to build and prepare the leaders needed for the UAE’s AI-driven government

A Neuro Fuzzy Algorithm to Compute Software Effort Estimation

Software Effort Estimation is highly important and considered to be a primary activity in software project management The accurate estimates are conducted in the development of business case in the earlier stages of project management This accurate prediction helps the investors and customers to identify the total investment and schedule of the project The project developers define process to estimate the effort more accurately with the available mythologies using the attributes of the project The algorithmic estimation models are very simple and reliable but not so accurate The categorical datasets cannot be estimated using the existing techniques Also the attributes of effort estimation are measured in linguistic values which may leads to confusion This paper looks in to the accuracy and reliability of a non-algorithmic approach based on adaptive neuro fuzzy logic in the problem of effort estimation The performance of the proposed method demonstrates that there is a accurate substantiation of the outcomes with the dataset collected from various projects The results were compared for its accuracy using MRE and MMRE as the metrics The research idea in the proposed model for effort estimation is based on project domain and attribute which incorporates the model with more competence in augmenting the crux of neural network to exhibit the advances in software estimatio

The Importance of Accounting for Real-World Labelling When Predicting Software Vulnerabilities

Previous work on vulnerability prediction assume that predictive models are trained with respect to perfect labelling information (includes labels from future, as yet undiscovered vulnerabilities). In this paper we present results from a comprehensive empirical study of 1,898 real-world vulnerabilities reported in 74 releases of three security-critical open source systems (Linux Kernel, OpenSSL and Wiresark). Our study investigates the effectiveness of three previously proposed vulnerability prediction approaches, in two settings: with and without the unrealistic labelling assumption. The results reveal that the unrealistic labelling assumption can profoundly mis- lead the scientific conclusions drawn; suggesting highly effective and deployable prediction results vanish when we fully account for realistically available labelling in the experimental methodology. More precisely, MCC mean values of predictive effectiveness drop from 0.77, 0.65 and 0.43 to 0.08, 0.22, 0.10 for Linux Kernel, OpenSSL and Wiresark, respectively. Similar results are also obtained for precision, recall and other assessments of predictive efficacy. The community therefore needs to upgrade experimental and empirical methodology for vulnerability prediction evaluation and development to ensure robust and actionable scientific findings

ATM automation: guidance on human technology integration

© Civil Aviation Authority 2016Human interaction with technology and automation is a key area of interest to industry and safety regulators alike. In February 2014, a joint CAA/industry workshop considered perspectives on present and future implementation of advanced automated systems. The conclusion was that whilst no additional regulation was necessary, guidance material for industry and regulators was required. Development of this guidance document was completed in 2015 by a working group consisting of CAA, UK industry, academia and industry associations (see Appendix B). This enabled a collaborative approach to be taken, and for regulatory, industry, and workforce perspectives to be collectively considered and addressed. The processes used in developing this guidance included: review of the themes identified from the February 2014 CAA/industry workshop1; review of academic papers, textbooks on automation, incidents and accidents involving automation; identification of key safety issues associated with automated systems; analysis of current and emerging ATM regulatory requirements and guidance material; presentation of emerging findings for critical review at UK and European aviation safety conferences. In December 2015, a workshop of senior management from project partner organisations reviewed the findings and proposals. EASA were briefed on the project before its commencement, and Eurocontrol contributed through membership of the Working Group.Final Published versio

Service-oriented Context-aware Framework

Location- and context-aware services are emerging technologies in mobile and desktop environments, however, most of them are difficult to use and do not seem to be beneficial enough. Our research focuses on designing and creating a service-oriented framework that helps location- and context-aware, client-service type application development and use. Location information is combined with other contexts such as the users' history, preferences and disabilities. The framework also handles the spatial model of the environment (e.g. map of a room or a building) as a context. The framework is built on a semantic backend where the ontologies are represented using the OWL description language. The use of ontologies enables the framework to run inference tasks and to easily adapt to new context types. The framework contains a compatibility layer for positioning devices, which hides the technical differences of positioning technologies and enables the combination of location data of various sources

Towards the Design of Hybrid Intelligence Frontline Service Technologies – A Novel Human-in-the-Loop Configuration for Human-Machine Interactions

Rapid adoption of innovative technologies confront IT-Service-Management (ITSM) to incoming support requests of increasing complexity. As a consequence, job demands and turnover rates of ITSM support agents increase. Recent technological advances have introduced assistance systems that rely on hybrid intelligence to provide support agents with contextually suitable historical solutions to help them solve customer requests. Hybrid intelligence systems rely on human input to provide high-quality data to train their underlying AI models. Yet, most agents have little incentives to label their data, lowering data quality and leading to diminishing returns of AI systems due to concept drifts. Following a design science research approach, we provide a novel Human-in-the-Loop design and hybrid intelligence system for ITSM support ticket recommendations, which incentivize agents to provide high-quality labels. Specifically, we leverage agent’s need for instant gratification by simultaneously providing better results if they improve labeling automatically labeled support tickets