2560769
Invariance Properties of Random Sequences
We present invariance characterizations of different types of random sequences. We correct Schnorr’s original, incorrect characterization of Martin-Lof random sequences, compare it with Schnorr’s corresponding characterization of his own randomness concept, and give a similar, new chararacterization of Kurtz random sequences. That is, we show that an infinite sequence ξ is Kurtz random if and only if for every partial, computable, measure-invariant function Φ : Σ → Σ the sequence Φ(ξ) is not recursive.
5215725
Linear Complexity versus Pseudorandomness: On Beth and Dai's Result
Beth and Dai studied in their Eurocrypt paper [1] the relationship between linear complexity (that is, the length of the shortest Linear Feedback Shift Register that generates the given strings) of strings and the Kolmogorov complexity of strings. Though their results are correct, some of their proofs are incorrect. In this note, we demonstrate with a counterexample the reason why their proofs are incorrect and we prove a stronger result. We conclude our note with some comments on the use of the LIL test (the law of the iterated logarithm) for pseudorandom bits generated by pseudorandom generators.
8968053
Resource-Bounded Balanced Genericity, Stochasticity and Weak Randomness
We introduce balanced t(n)-genericity which is a refinement of the genericity concept of Ambos-Spies, Fleischhack and Huwig [2] and which in addition controls the frequency with which a condition is met. We show that this concept coincides with the resource-bounded version of Church's stochasticity [6]. By uniformly describing these concepts and weaker notions of stochasticity introduced by Wilber [19] and Ko [11] in terms of prediction functions, we clarify the relations among these resource-bounded stochasticity concepts. Moreover, we give descriptions of these concepts in the framework of Lutz's resource-bounded measure theory [13] based on martingales: We show that t(n)-stochasticity coincides with a weak notion of t(n)-randomness based on so-called simple martingales but that it is strictly weaker than t(n)-randomness in the sense of Lutz.
5782664
A Separation of Two Randomness Concepts
In this paper we give an affirmative answer to a conjecture by Lutz (1992). That is, we will show that there is a Schnorr random sequence which is not rec-random.
8204880
Privacy aware data generation for testing database applications
Testing of database applications is of great importance. A significant issue in database application testing consists in the availability of representative data. In this paper, we investigate the problem of generating a synthetic database based on a-priori knowledge about a production database. Our approach is to fit general location model using various characteristics (e.g., constraints, statistics, rules) extracted from the production database and then generate the synthetic data using model learnt. The generated data is valid and similar to real data in terms of statistical distribution, hence it can be used for functional and performance testing. As characteristics extracted may contain information which may be used by attacker to derive some confidential information about individuals, we present our disclosure analysis method which applies cell suppression technique for identity disclosure analysis and perturbation for value disclosure.
8247434
NP-Hard Sets are Superterse Unless NP is Small
Abstract We show that the class P- mc ( n ) of polynomial time n -membership comparable sets (defined by Ogihara, 1995) has p -measure 0. Using this result, we will show that the class of sets which are ⩽ tt P -reducible to some P-selective set has p -measure 0. Furthermore, we will show that if NP does not have p -measure 0 then no ⩽ tt p -hard set for NP is P-approximable.
125037919
Perceptual Scale Space and its Applications
In this paper, we study a perceptual scale space by constructing a so-called sketch pyramid which augments the Gaussian and Laplacian pyramid representations in traditional image scale space theory. Each level of this sketch pyramid is a generic attributed graph - called the primal sketch which is inferred from the corresponding image at the same level of the Gaussian pyramid. When images are viewed at increasing resolutions, more details are revealed. This corresponds to perceptual transitions which are represented by topological changes in the sketch graph in terms of a graph grammar. We compute the sketch or perceptual pyramid by Bayesian inference upwards-downwards the pyramid using Markov chain Monte Carlo reversible jumps. We show two example applications of this perceptual scale space: (1) motion tracking of objects over scales, and (2) adaptive image displays which can efficiently show a large high resolution image in a small screen (of a PDA for example) through a selective tour of its image pyramid. Other potential applications include super resolution and multiresolution object recognition.
14934450
Combining Statistical and Knowledge-Based Spoken Language Understanding in Conditional Models
Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive --- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. On the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. A generative HMM/CFG composite model, which integrates easy-to-obtain domain knowledge into a data-driven statistical learning framework, has previously been introduced to reduce data requirement. The major contribution of this paper is the investigation of integrating prior knowledge and statistical learning in a conditional model framework. We also study and compare conditional random fields (CRFs) with perceptron learning for SLU. Experimental results show that the conditional models achieve more than 20% relative reduction in slot error rate over the HMM/CFG model, which had already achieved an SLU accuracy at the same level as the best results reported on the ATIS data.
2059756
Design of a Cognitive Complexities Measurement and Analysis Tool
The complexities of software systems are more prominent now than ever, due to progression of simple software applications to large integrated systems, either centralized or distributed. The challenges to software engineer somehow to understand and manage such large software systems and deal with software cognitive complexities. Although there are research areas such as human machine interface or human factor engineering, there is still little research on how to quantitatively measure and analyze human cognitive complexities in comprehending and understanding complex software systems. This paper studies human interaction with software programs in the cognitive perspective on software complexity. Based on the findings, a pilot software cognitive complexity measurement and analyses tool (CCMAT) is presented
1564877
Analysis of a cone-based distributed topology control algorithm for wireless multi-hop networks
The topology of a wireless multi-hop network can be controlled by varying the transmission power at each node. In this paper, we give a detailed analysis of a cone-based distributed topology control algorithm. This algorithm, introduced in [16], does not assume that nodes have GPS information available; rather it depends only on directional information. Roughly speaking, the basic idea of the algorithm is that a node <i>u</i> transmits with the minimum power <i>p<subscrpt>u, α</subscrpt></i> required to ensure that in every cone of degree α around <i>u</i>, there is some node that <i>u</i> can reach with power <i>p<subscrpt>u, α</subscrpt></i>. We show that taking α = 5π/6 is a necessary and sufficient condition to guarantee that network connectivity is preserved. More precisely, if there is a path from <i>s</i> to <i>t</i> when every node communicates at maximum power then, if α ⪇ 5π/6, there is still a path in the smallest symmetric graph <i>G</i><subscrpt>α</subscrpt> containing all edges (<i>u, v</i>) such that <i>u</i> can communicate with <i>v</i> using power <i>p<subscrpt>u, α</subscrpt></i>. On the other hand, if α > 5π/6, connectivity is not necessarily preserved. We also propose a set of optimizations that further reduce power consumption and prove that they retain network connectivity. Dynamic reconfiguration in the presence of failures and mobility is also discussed. Simulation results are presented to demonstrate the effectiveness of the algorithm and the optimizations.
20180608
An Image Analysis System for Tongue Diagnosis in Traditional Chinese Medicine
We introduced a computer-aider tongue examination system, which can reduce the large variation between the diagnosis results of different doctors and quantize the tongue properties automatically in traditional Chinese medicine (TCM) diagnosis. Several key issues and algorithms in this system are discussed, involving some newly proposed image analysis techniques: i) a new tongue color calibration scheme is proposed to overcome the drawbacks of the previous method; ii) a gradient vector flow (GVF) snake based model integrating the chromatic information of the tongue image is used to extract the tongue body; iii) an unsupervised segmentation method of color-texture regions is adopted to quantitatively analyze the distribution of the substance and the coat on the tongue surface. The experimental results show that the presented system has a great deal of potential for the tongue diagnosis.