Introducing Dynamic Behavior in Amalgamated Knowledge Bases

The problem of integrating knowledge from multiple and heterogeneous sources is a fundamental issue in current information systems. In order to cope with this problem, the concept of mediator has been introduced as a software component providing intermediate services, linking data resources and application programs, and making transparent the heterogeneity of the underlying systems. In designing a mediator architecture, we believe that an important aspect is the definition of a formal framework by which one is able to model integration according to a declarative style. To this purpose, the use of a logical approach seems very promising. Another important aspect is the ability to model both static integration aspects, concerning query execution, and dynamic ones, concerning data updates and their propagation among the various data sources. Unfortunately, as far as we know, no formal proposals for logically modeling mediator architectures both from a static and dynamic point of view have already been developed. In this paper, we extend the framework for amalgamated knowledge bases, presented by Subrahmanian, to deal with dynamic aspects. The language we propose is based on the Active U-Datalog language, and extends it with annotated logic and amalgamation concepts. We model the sources of information and the mediator (also called supervisor) as Active U-Datalog deductive databases, thus modeling queries, transactions, and active rules, interpreted according to the PARK semantics. By using active rules, the system can efficiently perform update propagation among different databases. The result is a logical environment, integrating active and deductive rules, to perform queries and update propagation in an heterogeneous mediated framework.Comment: Other Keywords: Deductive databases; Heterogeneous databases; Active rules; Update

General Dynamic Scene Reconstruction from Multiple View Video

This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination. Existing techniques for dynamic scene reconstruction from multiple wide-baseline camera views primarily focus on accurate reconstruction in controlled environments, where the cameras are fixed and calibrated and background is known. These approaches are not robust for general dynamic scenes captured with sparse moving cameras. Previous approaches for outdoor dynamic scene reconstruction assume prior knowledge of the static background appearance and structure. The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras. Evaluation is performed on a variety of indoor and outdoor scenes with cluttered backgrounds and multiple dynamic non-rigid objects such as people. Comparison with state-of-the-art approaches demonstrates improved accuracy in both multiple view segmentation and dense reconstruction. The proposed approach also eliminates the requirement for prior knowledge of scene structure and appearance

NOUS: Construction and Querying of Dynamic Knowledge Graphs

The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic institutions. We propose an end-to-end framework for developing custom knowledge graph driven analytics for arbitrary application domains. The uniqueness of our system lies A) in its combination of curated KGs along with knowledge extracted from unstructured text, B) support for advanced trending and explanatory questions on a dynamic KG, and C) the ability to answer queries where the answer is embedded across multiple data sources.Comment: Codebase: https://github.com/streaming-graphs/NOU

Dynamic Key-Value Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW), 201

Building dynamic capabilities in product development: the role of knowledge management

This paper contributes to the clarification of the connections between knowledge management and dynamic capabilities in the context of product development to see how they explain product development competences. Building on the knowledge management and dynamic capabilities literatures, the paper argues that the social side of knowledge management has a role to play as enabler of dynamic capabilities in the context of product development. Further, dynamic capabilities shape product development competences. Empirical evidence is provided by performing survey research with data collected from 80 product development projects developed in Spain.Capabilities , Knowledge management, Organizational knowledge

Dynamic Pricing and Imperfect Common Knowledge

This paper introduces private information into the dynamic pricing decision of firms in an otherwise standard new Keynesian model by adding an idiosyncratic component to firms’ marginal costs. The model can then replicate two stylised facts about price changes: aggregate inflation responds gradually and with inertia to shocks, while at the same time price changes of individual goods can be quite large. The inertial behaviour of inflation is driven by privately informed firms strategically ‘herding’ on the public information contained in the observations of lagged aggregate variables. The model also matches the average duration between price changes found in the data and it nests the standard new Keynesian Phillips curve as a special case. To solve the model, the paper derives an algorithm for solving a class of dynamic models with higher-order expectations.higher-order expectations; idiosyncratic marginal cost; price dynamics; new Keynesian Phillips curve

A Survey of Languages for Specifying Dynamics: A Knowledge Engineering Perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machine

Dynamic reasoning in a knowledge-based system

Any space based system, whether it is a robot arm assembling parts in space or an onboard system monitoring the space station, has to react to changes which cannot be foreseen. As a result, apart from having domain-specific knowledge as in current expert systems, a space based AI system should also have general principles of change. This paper presents a modal logic which can not only represent change but also reason with it. Three primitive operations, expansion, contraction and revision are introduced and axioms which specify how the knowledge base should change when the external world changes are also specified. Accordingly the notion of dynamic reasoning is introduced, which unlike the existing forms of reasoning, provide general principles of change. Dynamic reasoning is based on two main principles, namely minimize change and maximize coherence. A possible-world semantics which incorporates the above two principles is also discussed. The paper concludes by discussing how the dynamic reasoning system can be used to specify actions and hence form an integral part of an autonomous reasoning and planning system

Improving the reconstruction of dynamic processes by including prior knowledge

Visualizing and analyzing dynamic processes in 3 dimensions is an increasingly important topic. High-resolution CT-scanning is a suitable technique for this, as it is non-destructive and therefore does not hinder the dynamic process while it is advancing. However, CT reconstruction algorithms, which reconstruct a 3D volume from a series of projection images, assume a static sample. Motion artefacts are introduced when this assumption is invalid. This is usually solved by dividing the set of projection images in smaller subsets, each representing a time frame in which the change to the sample is assumed to be sufficiently small. Each subset can be reconstructed separately. However, due to the small size of the subsets and/or the high speed (and therefore lower statistics and higher noise) at which is scanned, the reconstruction quality is reduced. One method to improve reconstruction quality is using a priori knowledge. Of the two most used reconstruction algorithms, the iterative reconstruction scheme is best suited for this. The simultaneous algebraic reconstruction technique or SART starts from a (typically empty) volume and improves this gradually by back projecting the difference between a simulated projection from this volume and the measured projection. The resulting volume is used for the next iteration step. After a number of iterations, the solution converges to the final volume which represents the sample. In this research, this algorithm is used and adapted to take prior knowledge into account. Prior knowledge can take various forms. Using an initial volume (to start the reconstruction algorithm with) that resembles the sample is the most well-known and already presents a great improvement. This can be a volume that is reconstructed from a previous scan of the same sample, before the dynamic process is initiated, or one from after the process has finished. It is also possible to incorporate information in the algorithm about the regions in the volume where the changes are most likely to occur. The voxels in these regions are assigned a higher contribution from the back projection in comparison with their 'static' neighboring voxels which are assumed to be valid in the initial volume. This reduces the number of projections needed significantly. These forms of prior knowledge already pose a great improvement to the reconstruction quality, as is shown by the preliminary results. There are however numerous other possibilities to improve the reconstruction of dynamic processes. Other forms of prior knowledge, e.g. the continuity of changes or external measurements, can be included. Spatio-temporal correlations present another way to improve 4D-reconstruction. The projections will no longer be divided into completely separate subsets. Instead, the correlations between different projections will be used. This means that projections 'far' away from the time point that is being reconstructed will also (partially) be included. In this way the limitation of a small subset is (partially) removed, since much larger sets of projections are considered. The reconstructions that lie some time away from the reconstruction point cannot be straightforwardly included, since this would include exactly the artefacts that made the scanning of dynamic processes hard in the first place. This is a subject of further and current research. REFERENCES [1] M. Beister, D. Kolditz, W. A. Kalender, “Iterative reconstruction methods in X-ray CT,” Physica Medica, vol. 28, no. 2, pp. 94-108, Apr. 2012. [2] S. Berg, H. Ott, S. A. Klapp, A. Schwing, R. Neiteler, N. Brussee, A. Makurat, L. Leu, F. Enzmann, J.-O. Schwarz, “Real-time 3D imaging of Haines jumps in porous media flow,” Proc Natl Acad Sci U S A, vol. 110(10), pp. 3755–3759, Mar. 2013. [3] T. Bultreys, M. A. Boone, M. N. Boone, T. De Schryver, B. Masschaele, L. Van Hoorebeke, V. Cnudde, “Fast laboratory-based micro-computed tomography for pore-scale research: illustrative experiments and perspectives on the future,” Adv. Wat. Res., In Press. Available online May 2015. [4] V. Cnudde, M. N. Boone, “High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications,” Earth-Science Reviews, vol. 123, pp. 1-17, Aug. 2013. [5] G. Van Eyndhoven, K. J. Batenburg, J. Sijbers, “Region-based iterative reconstruction of structurally changing objects in CT”, IEEE Trans. Image Processing, vol. 23, no. 2, pp. 909-919, Feb. 2014. [6] L. Brabant, “Latest developments in the improvement and quantification of high resolution X-ray tomography data,” Ph.D. dissertation, Dep. Phys. and Astr., Fac. Sciences, Ghent Univ., Ghent, Belgium, 2013