Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.

Hazard and early warning analysis based on domain specific modeling technologies

An aim of proactive risk management strategies is the timely identification of safety related risks. One way to achieve this is by deploying early warning systems. Early warning systems aim to provide useful information on the presence of potential threats to the system, the level of vulnerability of a system, or both of these, in a timely manner. This information can then be used to take proactive safety measures. The United Nation’s has recommended that any early warning system need to have four essential elements, which are the risk knowledge element, a monitoring and warning service, dissemination and communication and a response capability. This research deals with the risk knowledge element of an early warning system. The risk knowledge element of an early warning system contains models of possible accident scenarios. These accident scenarios are created by using hazard analysis techniques, which are categorised as traditional and contemporary. The assumption in traditional hazard analysis techniques is that accidents are occurred due to a sequence of events, whereas, the assumption of contemporary hazard analysis techniques is that safety is an emergent property of complex systems. The problem is that there is no availability of a software editor which can be used by analysts to create models of accident scenarios based on contemporary hazard analysis techniques and generate computer code that represent the models at the same time. This research aims to enhance the process of generating computer code based on graphical models that associate early warning signs and causal factors to a hazard, based on contemporary hazard analyses techniques. For this purpose, the thesis investigates the use of Domain Specific Modeling (DSM) technologies. The contributions of this thesis is the design and development of a set of three graphical Domain Specific Modeling languages (DSML)s, that when combined together, provide all of the necessary constructs that will enable safety experts and practitioners to conduct hazard and early warning analysis based on a contemporary hazard analysis approach. The languages represent those elements and relations necessary to define accident scenarios and their associated early warning signs. The three DSMLs were incorporated in to a prototype software editor that enables safety scientists and practitioners to create and edit hazard and early warning analysis models in a usable manner and as a result to generate executable code automatically. This research proves that the DSM technologies can be used to develop a set of three DSMLs which can allow user to conduct hazard and early warning analysis in more usable manner. Furthermore, the three DSMLs and their dedicated editor, which are presented in this thesis, may provide a significant enhancement to the process of creating the risk knowledge element of computer based early warning systems.

Combining and choosing case base maintenance algorithms

Case-Based Reasoning (CBR) uses past experiences to solve new problems. The quality of the past experiences, which are stored as cases in a case base, is a big factor in the performance of a CBR system. The system's competence may be improved by adding problems to the case base after they have been solved and their solutions verified to be correct. However, from time to time, the case base may have to be refined to reduce redundancy and to get rid of any noisy cases that may have been introduced. Many case base maintenance algorithms have been developed to delete noisy and redundant cases. However, different algorithms work well in different situations and it may be difficult for a knowledge engineer to know which one is the best to use for a particular case base. In this thesis, we investigate ways to combine algorithms to produce better deletion decisions than the decisions made by individual algorithms, and ways to choose which algorithm is best for a given case base at a given time. We analyse five of the most commonly-used maintenance algorithms in detail and show how the different algorithms perform better on different datasets. This motivates us to develop a new approach: maintenance by a committee of experts (MACE). MACE allows us to combine maintenance algorithms to produce a composite algorithm which exploits the merits of each of the algorithms that it contains. By combining different algorithms in different ways we can also define algorithms that have different trade-offs between accuracy and deletion. While MACE allows us to define an infinite number of new composite algorithms, we still face the problem of choosing which algorithm to use. To make this choice, we need to be able to identify properties of a case base that are predictive of which maintenance algorithm is best. We examine a number of measures of dataset complexity for this purpose. These provide a numerical way to describe a case base at a given time. We use the numerical description to develop a meta-case-based classification system. This system uses previous experience about which maintenance algorithm was best to use for other case bases to predict which algorithm to use for a new case base. Finally, we give the knowledge engineer more control over the deletion process by creating incremental versions of the maintenance algorithms. These incremental algorithms suggest one case at a time for deletion rather than a group of cases, which allows the knowledge engineer to decide whether or not each case in turn should be deleted or kept. We also develop incremental versions of the complexity measures, allowing us to create an incremental version of our meta-case-based classification system. Since the case base changes after each deletion, the best algorithm to use may also change. The incremental system allows us to choose which algorithm is the best to use at each point in the deletion process.

Usability-enhanced coordination design of geovisualisations to communicate coastal flood risk information

For at least two millennia and probably much longer, the traditional vehicle for communicating geographical information to end-users has been the map. With the advent of computers, the means of both producing and consuming maps have radically been transformed, while the inherent nature of the information product has also expanded and diversified rapidly. This has given rise in recent years to the new concept of geovisualisation (GVIS), which draws on the skills of the traditional cartographer, but extends them into three spatial dimensions and may also add temporality, photorealistic representations and/or interactivity. Demand for GVIS technologies and their applications has increased significantly in recent years, driven by the need to study complex geographical events and in particular their associated consequences and to communicate the results of these studies to a diversity of audiences and stakeholder groups. GVIS has data integration, multi-dimensional spatial display advanced modelling techniques, dynamic design and development environments and field-specific application needs. To meet with these needs, GVIS tools should be both powerful and inherently usable, in order to facilitate their role in helping interpret and communicate geographic problems. However no framework currently exists for ensuring this usability. The research presented here seeks to fill this gap, by addressing the challenges of incorporating user requirements in GVIS tool design. It starts from the premise that usability in GVIS should be incorporated and implemented throughout the whole design and development process. To facilitate this, Subject Technology Matching (STM) is proposed as a new approach to assessing and interpreting user requirements. Based on STM, a new design framework called Usability Enhanced Coordination Design (UECD) is ten presented with the purpose of leveraging overall usability of the design outputs. UECD places GVIS experts in a new key role in the design process, to form a more coordinated and integrated workflow and a more focused and interactive usability testing. To prove the concept, these theoretical elements of the framework have been implemented in two test projects: one is the creation of a coastal inundation simulation for Whitegate, Cork, Ireland; the other is a flooding mapping tool for Zhushan Town, Jiangsu, China. The two case studies successfully demonstrated the potential merits of the UECD approach when GVIS techniques are applied to geographic problem solving and decision making. The thesis delivers a comprehensive understanding of the development and challenges of GVIS technology, its usability concerns, usability and associated UCD; it explores the possibility of putting UCD framework in GVIS design; it constructs a new theoretical design framework called UECD which aims to make the whole design process usability driven; it develops the key concept of STM into a template set to improve the performance of a GVIS design. These key conceptual and procedural foundations can be built on future research, aimed at further refining and developing UECD as a useful design methodology for GVIS scholars and practitioners.

Self-neglect: development and evaluation of a self-neglect (SN-37) measurement instrument

Aim: To develop and evaluate the psychometric properties of an instrument for the measurement of self-neglect (SN).Conceptual Framework: An elder self-neglect (ESN) conceptual framework guided the literature review and scale development. The framework has two key dimensions physical/psycho-social and environmental and seven sub dimensions which are representative of the factors that can contribute to intentional and unintentional SN. Methods: A descriptive cross-sectional design was adopted to achieve the research aim. The study was conducted in two phases. Phase 1 involved the development of the questionnaire content and structure. Phase 2 focused on establishing the psychometric properties of the instrument. Content validity was established by a panel of 8 experts and piloted with 9 health and social care professionals. The instrument was subsequently posted with a stamped addressed envelope to 566 health and social care professionals who met specific eligibility criteria across the four HSE areas. A total of 341 questionnaires were returned, a response rate of 60% and 305 (50%) completed responses were included in exploratory factor analysis (EFA). Item and factor analyses were performed to elicit the instruments underlying factor structure and establish preliminary construct validity. Findings: Item and factor analyses resulted in a logically coherent, 37 items, five factor solution, explaining 55.6% of the cumulative variance. The factors were labelled: ‘Environment’, ‘Social Networks’, ‘Emotional and Behavioural Liability’, ‘Health Avoidance’ and ‘Self-Determinism’. The factor loadings were >0.40 for all items on each of the five subscales. Preliminary construct validity was supported by findings. Conclusion: The main outcome of this research is a 37 item Self-Neglect (SN-37) measurement instrument that was developed by EFA and underpinned by an ESN conceptual framework. Preliminary psychometric evaluation of the instrument is promising. Future work should be directed at establishing the construct and criterion related validity of the instrument.

Human interfaces to structured documents

This research investigates some of the reasons for the reported difficulties experienced by writers when using editing software designed for structured documents. The overall objective was to determine if there are aspects of the software interfaces which militate against optimal document construction by writers who are not computer experts, and to suggest possible remedies. Studies were undertaken to explore the nature and extent of the difficulties, and to identify which components of the software interfaces are involved. A model of a revised user interface was tested, and some possible adaptations to the interface are proposed which may help overcome the difficulties. The methodology comprised: 1. identification and description of the nature of a ‘structured document’ and what distinguishes it from other types of document used on computers; 2. isolation of the requirements of users of such documents, and the construction a set of personas which describe them; 3. evaluation of other work on the interaction between humans and computers, specifically in software for creating and editing structured documents; 4. estimation of the levels of adoption of the available software for editing structured documents and the reactions of existing users to it, with specific reference to difficulties encountered in using it; 5. examination of the software and identification of any mismatches between the expectations of users and the facilities provided by the software; 6. assessment of any physical or psychological factors in the reported difficulties experienced, and to determine what (if any) changes to the software might affect these. The conclusions are that seven of the twelve modifications tested could contribute to an improvement in usability, effectiveness, and efficiency when writing structured text (new document selection; adding new sections and new lists; identifying key information typographically; the creation of cross-references and bibliographic references; and the inclusion of parts of other documents). The remaining five were seen as more applicable to editing existing material than authoring new text (adding new elements; splitting and joining elements [before and after]; and moving block text).

A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain

Expert assessments of retrofitting coal-fired power plants with carbon dioxide capture technologies

A set of 13 US based experts in post-combustion and oxy-fuel combustion CO2 capture systems responded to an extensive questionnaire asking their views on the present status and future expected performance and costs for amine-based, chilled ammonia, and oxy-combustion retrofits of coal-fired power plants. This paper presents the experts' responses for technology maturity, ideal plant characteristics for early adopters, and the extent to which R&D and deployment incentives will impact costs. It also presents the best estimates and 95% confidence limits of the energy penalties associated with amine-based systems. The results show a general consensus that amine-based systems are closer to commercial application, but potential for improving performance and lowering costs is limited; chilled ammonia and oxy-combustion offer greater potential for cost reductions, but not without greater uncertainty regarding scale and technical feasibility. © 2011 Elsevier Ltd.

Climate change, water resources, and the politics of adaptation in the Middle East and North Africa

Through an examination of global climate change models combined with hydrological data on deteriorating water quality in the Middle East and North Africa (MENA), we elucidate the ways in which the MENA countries are vulnerable to climate-induced impacts on water resources. Adaptive governance strategies, however, remain a low priority for political leaderships in the MENA region. To date, most MENA governments have concentrated the bulk of their resources on large-scale supply side projects such as desalination, dam construction, inter-basin water transfers, tapping fossil groundwater aquifers, and importing virtual water. Because managing water demand, improving the efficiency of water use, and promoting conservation will be key ingredients in responding to climate-induced impacts on the water sector, we analyze the political, economic, and institutional drivers that have shaped governance responses. While the scholarly literature emphasizes the importance of social capital to adaptive governance, we find that many political leaders and water experts in the MENA rarely engage societal actors in considering water risks. We conclude that the key capacities for adaptive governance to water scarcity in MENA are underdeveloped. © 2010 Springer Science+Business Media B.V.

An active learning approach for rapid characterization of endothelial cells in human tumors.

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

Ethics Standards (HRPP) and Public Partnership (PARTAKE) to Address Clinical Research Concerns in India: Moving Toward Ethical, Responsible, Culturally Sensitive, and Community-Engaging Clinical Research.

Like other emerging economies, India's quest for independent, evidence-based, and affordable healthcare has led to robust and promising growth in the clinical research sector, with a compound annual growth rate (CAGR) of 20.4% between 2005 and 2010. However, while the fundamental drivers and strengths are still strong, the past few years witnessed a declining trend (CAGR -16.7%) amid regulatory concerns, activist protests, and sponsor departure. And although India accounts for 17.5% of the world's population, it currently conducts only 1% of clinical trials. Indian and international experts and public stakeholders gathered for a 2-day conference in June 2013 in New Delhi to discuss the challenges facing clinical research in India and to explore solutions. The main themes discussed were ethical standards, regulatory oversight, and partnerships with public stakeholders. The meeting was a collaboration of AAHRPP (Association for the Accreditation of Human Research Protection Programs)-aimed at establishing responsible and ethical clinical research standards-and PARTAKE (Public Awareness of Research for Therapeutic Advancements through Knowledge and Empowerment)-aimed at informing and engaging the public in clinical research. The present article covers recent clinical research developments in India as well as associated expectations, challenges, and suggestions for future directions. AAHRPP and PARTAKE provide etiologically based solutions to protect, inform, and engage the public and medical research sponsors.

An exploration of future trends in environmental education research

This article describes future trends in environmental education (EE) research based on a mixed-methods study where data were collected through a content analysis of peer-reviewed articles published in EE journals between 2005 and 2010; interviews with experts engaged in EE research and sustainability-related fields; surveys with current EE researchers; and convenings with EE researchers and practitioners. We discuss four core thematic findings: (1) EE researchers are highlighting the importance of collective and community learning and action; (2) EE researchers are placing increased emphasis on the intersection of learning within the context of social-ecological communities (e.g. links between environmental quality and human well-being); (3) a pressing need exists for research conducted with urban and diverse populations; and (4) research around social media and other information technologies is of great interest, yet currently is sparse. © 2013 © 2013 Taylor & Francis.