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A software debugging glossary
This glossary contains 291 definitions of terms dealing with the debugging of computer software. The list includes numerous synonyms, as well as the proper names of debugging systems described in the open literature. Terms and definitions have been obtained from various sources: the software-engineering literature, other software-engineering glossaries, and individual contributions.
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Translator Design to Support Run-time Debugging
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Some requirements for architectural support of software debugging
Architectural support of high-level, symbolic debugging is described at three levels of abstraction: the user's view of desired debugging functionality, the debugger implementor's view of architectural requirements that support the functionality, and the computer architect's view of architectural features or attributes that implement the requirements. References are made where possible to computing systems that meet the requirements. The paper is written from the viewpoint of debugger implementors, and is addressed primarily to computer architects.
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Steganalysis of recorded speech
Digital audio provides a suitable cover for high-throughput steganography. At 16 bits per sample and sampled at a rate of 44,100 Hz, digital audio has the bit-rate to support large messages. In addition, audio is often transient and unpredictable, facilitating the hiding of messages. Using an approach similar to our universal image steganalysis, we show that hidden messages alter the underlying statistics of audio signals. Our statistical model begins by building a linear basis that captures certain statistical properties of audio signals. A low-dimensional statistical feature vector is extracted from this basis representation and used by a non-linear support vector machine for classification. We show the efficacy of this approach on LSB embedding and Hide4PGP. While no explicit assumptions about the content of the audio are made, our technique has been developed and tested on high-quality recorded speech.
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Evolving visual routines
Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [10]. Recently, several researchers have criticized this model and proposed an alternative model that considers perception as a distributed collection of task-specific, context-driven visual routines [1, 12]. Some of these researchers have argued that in natural living systems these visual routines are the product of natural selection [11]. So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g., [3]). In this article we propose an alternative approach in which visual routines for simple tasks are created using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using genetic programming techniques [7]. The results obtained are promising: The evolved routines are able to process correctly up to 93% of the test images, which is better than any algorithm we were able to write by hand.
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Parsing with Discontinuous Constituents
By generalizing the notion of location of a constituent to allow discontinuous loctaions, one can describe the discontinuous constituents of non-configurational languages. These discontinuous constituents can be described by a variant of definite clause grammars, and these grammars can be used in conjunction with a proof procedure to create a parser for non-configurational languages.
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Contextual Dependencies in Unsupervised Word Segmentation
Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on sub-optimal search procedures.
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Finite-state Approximation of Constraint-based Grammars using Left-corner Grammar Transforms
This paper describes how to construct a finite-state machine (FSM) approximating a 'unification-based' grammar using a left-corner grammar transform. The approximation is presented as a series of grammar transforms, and is exact for left-linear and right-linear CFGs, and for trees up to a user-specified depth of center-embedding.