In search of meta-knowledge

Development of an Intelligent Information System (IIS) involves application of numerous artificial intelligence (AI) paradigms and advanced technologies. The National Aeronautics and Space Administration (NASA) is interested in an IIS that can automatically collect, classify, store and retrieve data, as well as develop, manipulate and restructure knowledge regarding the data and its application (Campbell et al., 1987, p.3). This interest stems in part from a NASA initiative in support of the interagency Global Change Research program. NASA's space data problems are so large and varied that scientific researchers will find it almost impossible to access the most suitable information from a software system if meta-information (metadata and meta-knowledge) is not embedded in that system. Even if more, faster, larger hardware is used, new innovative software systems will be required to organize, link, maintain, and properly archive the Earth Observing System (EOS) data that is to be stored and distributed by the EOS Data and Information System (EOSDIS) (Dozier, 1990). Although efforts are being made to specify the metadata that will be used in EOSDIS, meta-knowledge specification issues are not clear. With the expectation that EOSDIS might evolve into an IIS, this paper presents certain ideas on the concept of meta-knowledge and demonstrates how meta-knowledge might be represented in a pixel classification problem

Diverging fluctuations of the Lyapunov exponents

D. P. acknowledges support by MINECO (Spain) under a Ramón y Cajal fellowship. We acknowledge support by MINECO (Spain) under Project No. FIS2014-59462-P.Peer reviewedPublisher PD

Logic programming and metadata specifications

Artificial intelligence (AI) ideas and techniques are critical to the development of intelligent information systems that will be used to collect, manipulate, and retrieve the vast amounts of space data produced by 'Missions to Planet Earth.' Natural language processing, inference, and expert systems are at the core of this space application of AI. This paper presents logic programming as an AI tool that can support inference (the ability to draw conclusions from a set of complicated and interrelated facts). It reports on the use of logic programming in the study of metadata specifications for a small problem domain of airborne sensors, and the dataset characteristics and pointers that are needed for data access

Towards a Model for Public Private Partnership (P3) Success: Understanding the Critical Success Factors of Public Private Partnerships (P3s) for Local Government Services and Infrastructure Delivery

This study focuses on the identification of factors that influence the success of Public Private Partnerships (P3) for local government service and infrastructure delivery. A framework is presented integrating economic, relationship, and project management P3 critical success factors (CSF) identified from previous literature together with public agency entrepreneurial orientation introduced as a potential critical success factor which has been absent in previous P3 CSF literature. Also, the framework examines how external stakeholder influence from the government, private sector, and the end user moderates these success factors. Public administrators from municipalities and counties in Florida provided their perceptions of these critical success factors to empirically assess their effect on P3 success. After analysis, the results show that the P3 relationship, project management and public agency entrepreneurial orientation are all critical to the success of the project. Moreover, government stakeholder influence has a significant impact on these factors and their effect on P3 success. Private sector stakeholder influence has an impact specifically on project management and public agency entrepreneurial orientation’s effect on P3 success. When applied in a practical context, these findings provide a framework of factors that can be built upon and assessed by public agencies to help improve their P3 success rates, encourage P3 growth, and help with solving the infrastructure and service delivery crises facing the US today. Furthermore, the results are integrated into success building strategies for managerial application. Overall, this study contributes to the extant literature and theory by supporting public agency entrepreneurial orientation as a P3 critical success factor, confirming that stakeholders influence P3 success factors, and providing a framework of constructs comprised of P3 CSFs for future study and managerial application

Public-Private Partnership (P3) Success: Critical Success Factors for Local Government Services and Infrastructure Delivery

The Public-Private Partnership (P3) approach is a viable option to address the slow growth and burgeoning need to deliver infrastructure projects and services by state and local governments. This study focuses on identifying critical success factors (CSF) that influence the success of P3s for local government service and infrastructure delivery. A framework is presented for integrating relationship and project management CSFs identified from previous literature into P3s. In addition, public agency entrepreneurial orientation is introduced as a potential CSF – a focus that has been absent in previous P3 CSF literature. To empirically assess the influence of these CSFs on P3 success, we surveyed public administrators from municipalities and counties in Florida, asking about their perceptions of these success factors. The results show that the P3 relationship, project management, and public agency entrepreneurial orientation are critical to a project’s success. Moreover, government stakeholder influence significantly affects these factors. Private sector stakeholder influence also affects project management and public agency entrepreneurial orientation’s effect on P3 success. When applied in a managerial context, these findings can help public agencies to improve their P3 success rates and growth and help to solve the infrastructure and service delivery challenges facing local governments in the US today

Co-training for On-board Deep Object Detection

Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e.objects) within the training images.Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this paper, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation