Semantic Component Composition

Building complex software systems necessitates the use of component-based architectures. In theory, of the set of components needed for a design, only some small portion of them are "custom"; the rest are reused or refactored existing pieces of software. Unfortunately, this is an idealized situation. Just because two components should work together does not mean that they will work together. The "glue" that holds components together is not just technology. The contracts that bind complex systems together implicitly define more than their explicit type. These "conceptual contracts" describe essential aspects of extra-system semantics: e.g., object models, type systems, data representation, interface action semantics, legal and contractual obligations, and more. Designers and developers spend inordinate amounts of time technologically duct-taping systems to fulfill these conceptual contracts because system-wide semantics have not been rigorously characterized or codified. This paper describes a formal characterization of the problem and discusses an initial implementation of the resulting theoretical system.Comment: 9 pages, submitted to GCSE/SAIG '0

A Fuzzy Approach to Text Segmentation in Web Images Based on Human Colour Perception

This chapter describes a new approach for the segmentation of text in images on Web pages. In the same spirit as the authors’ previous work on this subject, this approach attempts to model the ability of humans to differentiate between colours. In this case, pixels of similar colour are first grouped using a colour distance defined in a perceptually uniform colour space (as opposed to the commonly used RGB). The resulting colour connected components are then grouped to form larger (character-like) regions with the aid of a propinquity measure, which is the output of a fuzzy inference system. This measure expresses the likelihood for merging two components based on two features. The first feature is the colour distance between the components, in the L*a*b* colour space. The second feature expresses the topological relationship of two components. The results of the method indicate a better performance than previous methods devised by the authors and possibly better (a direct comparison is not really possible due to the differences in application domain characteristics between this and previous methods) performance to other existing methods

Transitioning Applications to Semantic Web Services: An Automated Formal Approach

Semantic Web Services have been recognized as a promising technology that exhibits huge commercial potential, and attract significant attention from both industry and the research community. Despite expectations being high, the industrial take-up of Semantic Web Service technologies has been slower than expected. One of the main reasons is that many systems have been developed without considering the potential of the web in integrating services and sharing resources. Without a systematic methodology and proper tool support, the migration from legacy systems to Semantic Web Service-based systems can be a very tedious and expensive process, which carries a definite risk of failure. There is an urgent need to provide strategies which allow the migration of legacy systems to Semantic Web Services platforms, and also tools to support such a strategy. In this paper we propose a methodology for transitioning these applications to Semantic Web Services by taking the advantage of rigorous mathematical methods. Our methodology allows users to migrate their applications to Semantic Web Services platform automatically or semi-automatically

Joint Video and Text Parsing for Understanding Events and Answering Queries

We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results

Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks

Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one. It was shown that the performance improves rapidly when the context is taken into account. We propose an utterance-level attention-based bidirectional recurrent neural network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances to classify the current one. In our setup, the BiRNN is given the input set of current and preceding utterances. Our model outperforms previous models that use only preceding utterances as context on the used corpus. Another contribution of the article is to discover the amount of information in each utterance to classify the subsequent one and to show that context-based learning not only improves the performance but also achieves higher confidence in the classification. We use character- and word-level features to represent the utterances. The results are presented for character and word feature representations and as an ensemble model of both representations. We found that when classifying short utterances, the closest preceding utterances contributes to a higher degree.Comment: Proceedings of INTERSPEECH 201

Liveness of Randomised Parameterised Systems under Arbitrary Schedulers (Technical Report)

We consider the problem of verifying liveness for systems with a finite, but unbounded, number of processes, commonly known as parameterised systems. Typical examples of such systems include distributed protocols (e.g. for the dining philosopher problem). Unlike the case of verifying safety, proving liveness is still considered extremely challenging, especially in the presence of randomness in the system. In this paper we consider liveness under arbitrary (including unfair) schedulers, which is often considered a desirable property in the literature of self-stabilising systems. We introduce an automatic method of proving liveness for randomised parameterised systems under arbitrary schedulers. Viewing liveness as a two-player reachability game (between Scheduler and Process), our method is a CEGAR approach that synthesises a progress relation for Process that can be symbolically represented as a finite-state automaton. The method is incremental and exploits both Angluin-style L*-learning and SAT-solvers. Our experiments show that our algorithm is able to prove liveness automatically for well-known randomised distributed protocols, including Lehmann-Rabin Randomised Dining Philosopher Protocol and randomised self-stabilising protocols (such as the Israeli-Jalfon Protocol). To the best of our knowledge, this is the first fully-automatic method that can prove liveness for randomised protocols.Comment: Full version of CAV'16 pape