657 research outputs found
Agreement graphs and data dependencies
The problem of deciding whether a join dependency [R] and a set F of functional dependencies logically imply an embedded join dependency [S] is known to be NP-complete. It is shown that if the set F of functional dependencies is required to be embedded in R, the problem can be decided in polynomial time. The problem is approached by introducing agreement graphs, a type of graph structure which helps expose the combinatorial structure of dependency implication problems. Agreement graphs provide an alternative formalism to tableaus and extend the application of graph and hypergraph theory in relational database research;Agreement graphs are also given a more abstract definition and are used to define agreement graph dependencies (AGDs). It is shown that AGDs are equivalent to Fagin\u27s (unirelational) embedded implicational dependencies. A decision method is given for the AGD implication problem. Although the implication problem for AGDs is undecidable, the decision method works in many cases and lends insight into dependency implication. A number of properties of agreement graph dependencies are given and directions for future research are suggested
Design and optimisation of scientific programs in a categorical language
This thesis presents an investigation into the use of advanced computer languages for scientific computing, an examination of performance issues that arise from using such languages for such a task, and a step toward achieving portable performance from compilers by attacking these problems in a way that compensates for the complexity of and differences between modern computer architectures. The language employed is Aldor, a functional language from computer algebra, and the scientific computing area is a subset of the family of iterative linear equation solvers applied to sparse systems. The linear equation solvers that are considered have much common structure, and this is factored out and represented explicitly in the lan-guage as a framework, by means of categories and domains. The flexibility introduced by decomposing the algorithms and the objects they act on into separate modules has a strong performance impact due to its negative effect on temporal locality. This necessi-tates breaking the barriers between modules to perform cross-component optimisation. In this instance the task reduces to one of collective loop fusion and array contrac
Estimating Gene Interactions Using Information Theoretic Functionals
With an abundance of data resulting from high-throughput technologies, like DNA microarrays,
a race has been on the last few years, to determine the structures and functions of genes and
their products, the proteins. Inference of gene interactions, lies in the core of these efforts.
In all this activity, three important research issues have emerged. First, in much of the current
literature on gene regulatory networks, dependencies among variables in our case genes - are
assumed to be linear in nature, when in fact, in real-life scenarios this is seldom the case.
This disagreement leads to systematic deviation and biased evaluation. Secondly, although
the problem of undersampling, features in every piece of work as one of the major causes for
poor results, in practice it is overlooked and rarely addressed explicitly. Finally, inference
of network structures, although based on rigid mathematical foundations and computational
optimizations, often displays poor fitness values and biologically unrealistic link structures, due
- to a large extend - to the discovery of pairwise only interactions.
In our search for robust, nonlinear measures of dependency, we advocate that mutual information
and related information theoretic functionals (conditional mutual information, total
correlation) are possibly the most suitable candidates to capture both linear and nonlinear
interactions between variables, and resolve higher order dependencies.
To address these issues, we researched and implemented under a common framework, a selection
nonparametric estimators of mutual information for continuous variables. The focus of their
assessment was, their robustness to the limited sample sizes and their expansibility to higher
dimensions - important for the detection of more complex interaction structures. Two different
assessment scenaria were performed, one with simulated data and one with bootstrapping the
estimators in state-of-the-art network inference algorithms and monitor their predictive power
and sensitivity. The tests revealed that, in small sample size regimes, there is a significant difference
in the performance of different estimators, and naive methods such as uniform binning,
gave consistently poor results compared with more sophisticated methods.
Finally, a custom, modular mechanism is proposed, for the inference of gene interactions,
targeting the identi cation of some of the most common substructures in genetic networks,
that we believe will help improve accuracy and predictability scores
Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design
The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface
BNAIC 2008:Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference
First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)
International audienceThe development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques
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COMPUTATIONAL COMMUNICATION INTELLIGENCE: EXPLORING LINGUISTIC MANIFESTATION AND SOCIAL DYNAMICS IN ONLINE COMMUNICATION
We now live in an age of online communication. As social media becomes an integral part of our life, online communication becomes an essential life skill. In this dissertation, we aim to understand how people effectively communicate online. We research components of success in online communication and present scientific methods to study the skill of effective communication. This research advances the state of art in machine learning and communication studies.
For communication studies, we pioneer the study of a communication phenomenon we call Communication Intelligence in online interactions. We create a theory about communication intelligence that measures participantsâ ten high-order communication skills, including restraint, self-reflection, perspective taking, and balance. We present a multi-perspective analysis for understanding communication intelligence, including its diverse language, shared linguistic characteristics across people, social dynamics, and the effects of communication modality on communication intelligence.
For machine learning, we contribute new computational models and formulations for addressing multi-label and multi-task machine learning problems. We develop a new hierarchical probabilistic model for simultaneously identifying multiple intelligence-embodied communication skills from natural language. The model learns the topic assignment for each sentence and provides a practical and simple way to determine document labels without relying on a threshold function. The model performance increases as the number of labels grows, which makes it a promising approach for large-scale data analysis. We also develop a new multi-task formulation for simultaneously identifying multiple intelligence-embodied communication skills from lexical, discourse, and interaction features. The key merit of this model is that it is a general multi-task formulation that unifies many widely used regularization techniques, including Lasso, group Lasso, sparse-group Lasso, and the Dirty model. This model expands the applicability of multi-task learning by allowing analyzing real-world problems where the degree of task relatedness is uncertain and the true structure of the groups in data is not clear ahead of time. Moreover, it can be applied to streaming data to perform large-scale analysis in real time. Beyond the application of studying communication intelligence, the developed models and formulations can also benefit research in other areas where the problems of simultaneously predicting multiple categories are abundant
Combining SOA and BPM Technologies for Cross-System Process Automation
This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation
Parameter dependencies for reusable performance specifications of software components
To avoid design-related perÂforÂmance problems, model-driven performance prediction methods analyse the response times, throughputs, and reÂsource utilizations of software architectures before and during implementation. This thesis proposes new modeling languages and according model transformations, which allow a reusable description of usage profile dependencies to the performance of software components. Predictions based on this new methods can support performance-related design decisions
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