Issues about the Adoption of Formal Methods for Dependable Composition of Web Services

Web Services provide interoperable mechanisms for describing, locating and invoking services over the Internet; composition further enables to build complex services out of simpler ones for complex B2B applications. While current studies on these topics are mostly focused - from the technical viewpoint - on standards and protocols, this paper investigates the adoption of formal methods, especially for composition. We logically classify and analyze three different (but interconnected) kinds of important issues towards this goal, namely foundations, verification and extensions. The aim of this work is to individuate the proper questions on the adoption of formal methods for dependable composition of Web Services, not necessarily to find the optimal answers. Nevertheless, we still try to propose some tentative answers based on our proposal for a composition calculus, which we hope can animate a proper discussion

Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies

This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game

Enhancements to the frame virtual machine

The Enhanced Frame Package is a tool to build Expert Systems. It is a frame based system, that initially was developed in C-Prolog by LaMora S. Hiss at Rochester Institute of Technology in 1987 for her master\u27s thesis. It was enhanced in the current thsis to provide much larger expressive power andgreater ease of use. Several operators were modified/enhanced, and several new operators were added, while providing the user a balance of computational tractability, expressive power and consistency. Major concepts provided in the Enhanced Frame Package include - local consistency checking as opposed to global consistency checking and how the user can have the best of both options; the flexibility of loading a knowledge base file as a consistent system or as an inconsistent system; operations that work on working memory and operations that work on the original file in the working directory; the concept of a knowledge analyzer; the way one sees the human mind, knowledge and learning and its parallel in knowledge representation and the surrounding issues of consistency, expressive power and computational tractability

Expanding the capabilities of normalizing flows in deep generative models and variational inference

Deep generative models and variational Bayesian inference are two frameworks for reasoning about observed high-dimensional data, which may even be combined. A fundamental requirement of either approach is the parametrization of an expressive family of density models. Normalizing flows, sometimes also referred to as invertible neural networks, are one class of models providing this: they are formulated to be bijective and differentiable, and thus produce a tractable density model via the change-of-variable formula. Beyond just deep generative modelling and variational inference, normalizing flows have shown promise as a plug-in density model in other settings such as approximate Bayesian computation and lossless compression. However, the bijectivity constraint can pose quite a restriction on the expressiveness of these approaches, and forces the learned distribution to have full support over the ambient space which is not well-aligned with the common assumption that low-dimensional manifold structure is embedded within high-dimensional data. In this thesis, we challenge this requirement of strict bijectivity over the space of interest to modify normalizing flow models. The first work focuses on the setting of variational inference, defining a normalizing flow based on a discretized time-inhomogeneous Hamiltonian dynamics over an extended position-momentum space. This enables the flow to be guided by the true posterior unlike baseline flow-based models, thus requiring fewer parameters in the inference model to achieve comparable improvements in inference. The next chapter proposes a new deep generative model which relaxes the bijectivity requirement of normalizing flows by injecting learned noise at each layer, sacrificing easy evaluation of the density for expressiveness. We show, theoretically and empirically, the benefits of these models in density estimation over baseline flows. We then demonstrate in the next chapter that the benefits of this model class extend to the setting of variational inference, relying on auxiliary methods to train our models. Finally, the last paper in this thesis addresses the issue of full support in the ambient space and proposes injective flow models directly embedding low-dimensional structure into high dimensions. Our method is the first to optimize the injective change-of-variable term and produces promising results on out-of-distribution detection, which had previous eluded deep generative models. We conclude with some directions for future work and a broader perspective on the field

Space station automation of common module power management and distribution, volume 2

The new Space Station Module Power Management and Distribution System (SSM/PMAD) testbed automation system is described. The subjects discussed include testbed 120 volt dc star bus configuration and operation, SSM/PMAD automation system architecture, fault recovery and management expert system (FRAMES) rules english representation, the SSM/PMAD user interface, and the SSM/PMAD future direction. Several appendices are presented and include the following: SSM/PMAD interface user manual version 1.0, SSM/PMAD lowest level processor (LLP) reference, SSM/PMAD technical reference version 1.0, SSM/PMAD LLP visual control logic representation's (VCLR's), SSM/PMAD LLP/FRAMES interface control document (ICD) , and SSM/PMAD LLP switchgear interface controller (SIC) ICD

QNRs: toward language for intelligent machines

Impoverished syntax and nondifferentiable vocabularies make natural language a poor medium for neural representation learning and applications. Learned, quasilinguistic neural representations (QNRs) can upgrade words to embeddings and syntax to graphs to provide a more expressive and computationally tractable medium. Graph-structured, embedding-based quasilinguistic representations can support formal and informal reasoning, human and inter-agent communication, and the development of scalable quasilinguistic corpora with characteristics of both literatures and associative memory. To achieve human-like intellectual competence, machines must be fully literate, able not only to read and learn, but to write things worth retaining as contributions to collective knowledge. In support of this goal, QNR-based systems could translate and process natural language corpora to support the aggregation, refinement, integration, extension, and application of knowledge at scale. Incremental development of QNRbased models can build on current methods in neural machine learning, and as systems mature, could potentially complement or replace today’s opaque, error-prone “foundation models” with systems that are more capable, interpretable, and epistemically reliable. Potential applications and implications are broad