Extracting Formal Models from Normative Texts

We are concerned with the analysis of normative texts - documents based on the deontic notions of obligation, permission, and prohibition. Our goal is to make queries about these notions and verify that a text satisfies certain properties concerning causality of actions and timing constraints. This requires taking the original text and building a representation (model) of it in a formal language, in our case the C-O Diagram formalism. We present an experimental, semi-automatic aid that helps to bridge the gap between a normative text in natural language and its C-O Diagram representation. Our approach consists of using dependency structures obtained from the state-of-the-art Stanford Parser, and applying our own rules and heuristics in order to extract the relevant components. The result is a tabular data structure where each sentence is split into suitable fields, which can then be converted into a C-O Diagram. The process is not fully automatic however, and some post-editing is generally required of the user. We apply our tool and perform experiments on documents from different domains, and report an initial evaluation of the accuracy and feasibility of our approach.Comment: Extended version of conference paper at the 21st International Conference on Applications of Natural Language to Information Systems (NLDB 2016). arXiv admin note: substantial text overlap with arXiv:1607.0148

Scene extraction in motion pictures

This paper addresses the challenge of bridging the semantic gap between the rich meaning users desire when they query to locate and browse media and the shallowness of media descriptions that can be computed in today\u27s content management systems. To facilitate high-level semantics-based content annotation and interpretation, we tackle the problem of automatic decomposition of motion pictures into meaningful story units, namely scenes. Since a scene is a complicated and subjective concept, we first propose guidelines from fill production to determine when a scene change occurs. We then investigate different rules and conventions followed as part of Fill Grammar that would guide and shape an algorithmic solution for determining a scene. Two different techniques using intershot analysis are proposed as solutions in this paper. In addition, we present different refinement mechanisms, such as film-punctuation detection founded on Film Grammar, to further improve the results. These refinement techniques demonstrate significant improvements in overall performance. Furthermore, we analyze errors in the context of film-production techniques, which offer useful insights into the limitations of our method

Recursive Program Optimization Through Inductive Synthesis Proof Transformation

The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality

Safe abstractions of data encodings in formal security protocol models

When using formal methods, security protocols are usually modeled at a high level of abstraction. In particular, data encoding and decoding transformations are often abstracted away. However, if no assumptions at all are made on the behavior of such transformations, they could trivially lead to security faults, for example leaking secrets or breaking freshness by collapsing nonces into constants. In order to address this issue, this paper formally states sufficient conditions, checkable on sequential code, such that if an abstract protocol model is secure under a Dolev-Yao adversary, then a refined model, which takes into account a wide class of possible implementations of the encoding/decoding operations, is implied to be secure too under the same adversary model. The paper also indicates possible exploitations of this result in the context of methods based on formal model extraction from implementation code and of methods based on automated code generation from formally verified model

Requirements for Information Extraction for Knowledge Management

Knowledge Management (KM) systems inherently suffer from the knowledge acquisition bottleneck - the difficulty of modeling and formalizing knowledge relevant for specific domains. A potential solution to this problem is Information Extraction (IE) technology. However, IE was originally developed for database population and there is a mismatch between what is required to successfully perform KM and what current IE technology provides. In this paper we begin to address this issue by outlining requirements for IE based KM