Thanuka L. Wickramarathne

Belief theoretic methods for soft and hard data fusion

In many contexts, one is confronted with the problem of extracting information from large amounts of different types soft data (e.g., text) and hard data (from e.g., physics-based sensing systems). In handling hard data, signal and data processing offers a wealth of methods related to modeling, estimation, tracking, and inference tasks. However, soft data present several challenges that necessitate the development of new data processing methods. For example, with suitable statistical natural language processing (NLP) methods, text can be converted into logic statements that are associated with various forms of associated uncertainty related to the credibility of the statement, the reliability of the text source, and so forth. In combining or fusing soft data with either soft or hard data, one must deploy methods that can suitably preserve and update the uncertainty associated with the data, thereby providing uncertainty bounds related to any inferences regarding semantics. Since standard Bayesian probabilistic approaches have problems with suitably handling uncertain logic statements, there is an emerging need for new methods for processing heterogeneous data. In this paper, we describe a framework for fusing soft and hard data based on the Dempster-Shafer (DS) belief theoretic approach which is well-suited to the task of capturing the types of models and uncertain rules that are more typical of soft data. Since the effectiveness of traditional DS methods has been hampered by high computational requirements, we base the processing framework on our new conditional approach to DS theoretic evidence updating and fusion. We address the issue of laying the foundation for a theoretically justifiable, and computationally efficient framework for fusing soft and hard data taking into account the inherent data uncertainty such as reliability and credibility. Moreover, we present an illustrative example that highlights the potential for the DS conditional approach for fusing heterogeneous data.

Improving management of aquatic invasions by integrating shipping network, ecological, and environmental data: data mining for social good

The unintentional transport of invasive species (i.e., non-native and harmful species that adversely affect habitats and native species) through the Global Shipping Network (GSN) causes substantial losses to social and economic welfare (e.g., annual losses due to ship-borne invasions in the Laurentian Great Lakes is estimated to be as high as USD 800 million). Despite the huge negative impacts, management of such invasions remains challenging because of the complex processes that lead to species transport and establishment. Numerous difficulties associated with quantitative risk assessments (e.g., inadequate characterizations of invasion processes, lack of crucial data, large uncertainties associated with available data, etc.) have hampered the usefulness of such estimates in the task of supporting the authorities who are battling to manage invasions with limited resources. We present here an approach for addressing the problem at hand via creative use of computational techniques and multiple data sources, thus illustrating how data mining can be used for solving crucial, yet very complex problems towards social good. By modeling implicit species exchanges as a network that we refer to as the Species Flow Network (SFN), large-scale species flow dynamics are studied via a graph clustering approach that decomposes the SFN into clusters of ports and inter-cluster connections. We then exploit this decomposition to discover crucial knowledge on how patterns in GSN affect aquatic invasions, and then illustrate how such knowledge can be used to devise effective and economical invasive species management strategies. By experimenting on actual GSN traffic data for years 1997-2006, we have discovered crucial knowledge that can significantly aid the management authorities.

Toward Efficient Computation of the Dempster–Shafer Belief Theoretic Conditionals

Dempster-Shafer (DS) belief theory provides a convenient framework for the development of powerful data fusion engines by allowing for a convenient representation of a wide variety of data imperfections. The recent work on the DS theoretic (DST) conditional approach, which is based on the Fagin-Halpern (FH) DST conditionals, appears to demonstrate the suitability of DS theory for incorporating both soft (generated by human-based sensors) and hard (generated by physics-based sources) evidence into the fusion process. However, the computation of the FH conditionals imposes a significant computational burden. One reason for this is the difficulty in identifying the FH conditional core, i.e., the set of propositions receiving nonzero support after conditioning. The conditional core theorem (CCT) in this paper redresses this shortcoming by explicitly identifying the conditional focal elements with no recourse to numerical computations, thereby providing a complete characterization of the conditional core. In addition, we derive explicit results to identify those conditioning propositions that may have generated a given conditional core. This “converse” to the CCT is of significant practical value for studying the sensitivity of the updated knowledge base with respect to the evidence received. Based on the CCT, we also develop an algorithm to efficiently compute the conditional masses (generated by FH conditionals), provide bounds on its computational complexity, and employ extensive simulations to analyze its behavior.

Focal elements generated by the Dempster-Shafer theoretic conditionals: A complete characterization

Incorporation of soft evidence into the fusion process poses considerable challenges, including issues related to the material implications of propositional logic statements, contradictory evidence, and non-identical scopes of sources providing soft evidence. The conditional approach to Dempster-Shafer (DS) theoretic evidence updating and fusion provides a promising avenue for overcoming these challenges. However, the computation of the Fagin-Halpern (FH) conditionals utilized in the conditional evidence updating strategies is non-trivial because of the lack of a method to identify the conditional focal elements directly. The work in this paper presents a complete characterization of the conditional focal elements via a necessary and sufficient condition that identifies the explicit structure of a proposition that will remain a focal element after conditioning. We illustrate the resulting computational advantage via several experiments.

Consensus-Based Credibility Estimation of Soft Evidence for Robust Data Fusion

Due to its subjective naturewhich can otherwise compromise the integrity of the fusion process, it is critical that soft evidence (generated by human sources) be validated prior to its incorporation into the fusion engine. The strategy of discounting evidence based on source reliability may not be applicable when dealing with soft sources because their reliability (e.g., an eye witnesses account) is often unknown beforehand. In this paper, we propose a methodology based on the notion of consensus to estimate the credibility of (soft) evidence in the absence of a ‘ground truth.’ This estimated credibility can then be used for source reliability estimation, discounting or appropriately ‘weighting’ evidence for fusion. The consensus procedure is set up via Dempster-Shafer belief theoretic notions. Further, the proposed procedure allows one to constrain the consensus by an estimate of the ground truth if/when it is available. We illustrate several interesting and intuitively appealing properties of the consensus procedure via a numerical example.

Convergence Analysis of Iterated Belief Revision in Complex Fusion Environments

We study convergence of iterated belief revision in complex fusion environments, which may consist of a network of soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors and where agent communications may be asynchronous and the link structure may be dynamic. In particular, we study the problem in which network agents exchange and revise belief functions (which generalize probability mass functions) and are more geared towards handling the uncertainty pervasive in soft/hard fusion environments. We focus on belief revision in which agents utilize a generalized fusion rule that is capable of generating a rational consensus. It includes the widely used weighted average consensus as a special case. By establishing this fusion scheme as a pool of paracontracting operators, we derive general convergence criteria that are relevant for a wide range of applications. Furthermore, we analyze the conditions for consensus for various social networks by simulating several network topologies and communication patterns that are characteristic of such networks.

A belief theoretic approach for characterization of underwater munitions

Characterization, management and remediation of military munitions, especially in underwater environments, is a challenging task given all the technical and physical barriers. Optical cameras are better suited for identifying the physical shape of objects. But in underwater, low visibility almost prohibits the use of these cameras. Acoustic imaging is a good alternative to this, but the characteristics of imaging along with numerous artifacts of physical systems which are not easy to model, makes the object recognition task non-trivial. We explore here the possibility of exploiting the geometry of the object shadows for identification of objects itself. The inherited imperfections of the data and the numerous artifacts of sonar systems are counteracted via the use of a fusion algorithm which incorporates evidence from multiple perspectives. A Dempster-Shafer belief theoretic evidence updating scheme which is capable of modeling a wider variety of data imperfections is used for the fusion task. We illustrate the method via the use of real data obtained at a test site located in the Florida Atlantic University premises.

Exploiting multiple sensor modalities for classification of underwater munitions: A Dempster–Shafer theoretic approach

Detection and classification of underwater UXOs (UneXploded Ordnance) is a task that is receiving considerable attention from the DoD and related agencies that are involved in management of military munitions. Achieving a reasonable accuracy in detection and classification of these objects is extremely difficult mainly due to the harsh cluttered underwater environment. It is now an accepted fact that no single sensing technology can be both accurate and cost-effective. Low visibility in underwater exposes a significant limitation to optical cameras which are usually better suited for identifying the physical shape of objects. While acoustic imaging is a good alternative, the characteristics of imaging and physical system artifacts make the object recognition task non-trivial. Multi-sensory fusion provides an avenue to exploit the strengths of individual sensors and modalities while mitigating their weaknesses. We address the problem of fusion of multiple sensory information for the task of underwater UXO ...