77,485 research outputs found
Metadata Augmentation for Semantic- and Context- Based Retrieval of Digital Cultural Objects
Cultural objects are increasingly stored and generated in digital form, yet effective methods for their indexing and retrieval still remain an open area of research. The main problem arises from the disconnection between the content-based indexing approach used by computer scientists and the description-based approach used by information scientists. There is also a lack of representational schemes that allow the alignment of the semantics and context with keywords and low-level features that can be automatically extracted from the content of these cultural objects. This paper presents an integrated approach to address these problems, taking advantage of both computer science and information science approaches. The focus is on the rationale and conceptual design of the system and its various components. In particular, we discuss techniques for augmenting commonly used metadata with visual features and domain knowledge to generate high-level abstract metadata which in turn can be used for semantic and context-based indexing and retrieval. We use a sample collection of Vietnamese traditional woodcuts to demonstrate the usefulness of this approach
Getting one step closer to deduction: Introducing an alternative paradigm for transitive inference
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 Psychology Press.Transitive inference is claimed to be “deductive”. Yet every group/species ever reported apparently uses it. We asked 58 adults to solve five-term transitive tasks, requiring neither training nor premise learning. A computer-based procedure ensured all premises were continually visible. Response accuracy and RT (non-discriminative nRT) were measured as is typically done. We also measured RT confined to correct responses (cRT). Overall, very few typical transitive phenomena emerged. The symbolic distance effect never extended to premise recall and was not at all evident for nRT; suggesting the use of non-deductive end-anchor strategies. For overall performance, and particularly the critical B?D inference, our findings indicate that deductive transitive inference is far more intellectually challenging than previously thought. Contrasts of our present findings against previous findings suggest at least two distinct transitive inference modes, with most research and most computational models to date targeting an associative mode rather than their desired deductive mode. This conclusion fits well with the growing number of theories embracing a “dual process” conception of reasoning. Finally, our differing findings for nRT versus cRT suggest that researchers should give closer consideration to matching the RT measure they use to the particular conception of transitive inference they pre-held
Static and dynamic semantics of NoSQL languages
We present a calculus for processing semistructured data that spans
differences of application area among several novel query languages, broadly
categorized as "NoSQL". This calculus lets users define their own operators,
capturing a wider range of data processing capabilities, whilst providing a
typing precision so far typical only of primitive hard-coded operators. The
type inference algorithm is based on semantic type checking, resulting in type
information that is both precise, and flexible enough to handle structured and
semistructured data. We illustrate the use of this calculus by encoding a large
fragment of Jaql, including operations and iterators over JSON, embedded SQL
expressions, and co-grouping, and show how the encoding directly yields a
typing discipline for Jaql as it is, namely without the addition of any type
definition or type annotation in the code
How Random is a Coin Toss? Bayesian Inference and the Symbolic Dynamics of Deterministic Chaos
Symbolic dynamics has proven to be an invaluable tool in analyzing the
mechanisms that lead to unpredictability and random behavior in nonlinear
dynamical systems. Surprisingly, a discrete partition of continuous state space
can produce a coarse-grained description of the behavior that accurately
describes the invariant properties of an underlying chaotic attractor. In
particular, measures of the rate of information production--the topological and
metric entropy rates--can be estimated from the outputs of Markov or generating
partitions. Here we develop Bayesian inference for k-th order Markov chains as
a method to finding generating partitions and estimating entropy rates from
finite samples of discretized data produced by coarse-grained dynamical
systems.Comment: 8 pages, 1 figure; http://cse.ucdavis.edu/~cmg/compmech/pubs/hrct.ht
Inferring Concise Specifications of APIs
Modern software relies on libraries and uses them via application programming
interfaces (APIs). Correct API usage as well as many software engineering tasks
are enabled when APIs have formal specifications. In this work, we analyze the
implementation of each method in an API to infer a formal postcondition.
Conventional wisdom is that, if one has preconditions, then one can use the
strongest postcondition predicate transformer (SP) to infer postconditions.
However, SP yields postconditions that are exponentially large, which makes
them difficult to use, either by humans or by tools. Our key idea is an
algorithm that converts such exponentially large specifications into a form
that is more concise and thus more usable. This is done by leveraging the
structure of the specifications that result from the use of SP. We applied our
technique to infer postconditions for over 2,300 methods in seven popular Java
libraries. Our technique was able to infer specifications for 75.7% of these
methods, each of which was verified using an Extended Static Checker. We also
found that 84.6% of resulting specifications were less than 1/4 page (20 lines)
in length. Our technique was able to reduce the length of SMT proofs needed for
verifying implementations by 76.7% and reduced prover execution time by 26.7%
The Use of Ontologies in Contextually Aware Environments
In this paper we outline work in progress related to the construction of contextually aware pervasive computing environments, through the use of semantic and knowledge technologies. Key to this activity is modelling both where and what a user is doing at any given time. We present a prototype application to illustrate this work and describe part of its implementation
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