Model-based sequential organization in cochannel speech
A human listener has the ability to follow a speaker's voice while others are speaking simultaneously; in particular, the listener can organize the time-frequency energy of the same speaker across time into a single stream. In this paper, we focus on sequential organization in cochannel speech, or mixtures of two voices. We extract minimally corrupted segments, or usable speech, in cochannel speech using a robust multipitch tracking algorithm. The extracted usable speech is shown to capture speaker characteristics and improves speaker identification (SID) performance across various target-to-interferer ratios. To utilize speaker characteristics for sequential organization, we extend the traditional SID framework to cochannel speech and derive a joint objective for sequential grouping and SID, leading to a problem of search for the optimum hypothesis. Subsequently we propose a hypothesis pruning algorithm based on speaker models in order to make the search computationally efficient. Evaluation results show that the proposed system approaches the ceiling SID performance obtained with prior pitch information and yields significant improvement over alternative approaches to sequential organization.

title: On Connectedness: A Solution Based on Oscillatory Correlation
abstract: A long-standing problem in neural computation has been the problem of connectedness, first identified by Minsky and Papert (1969). This problem served as the cornerstone for them to establish analytically that perceptrons are fundamentally limited in computing geometrical (topological) properties. A solution to this problem is offered by a different class of neural networks: oscillator networks. To solve the problem, the representation of oscillatory correlation is employed, whereby one pattern is represented as a synchronized block of oscillators and different patterns are represented by distinct blocks that desynchronize from each other. Oscillatory correlation emerges from LEGION (locally excitatory globally inhibitory oscillator network), whose architecture consists of local excitation and global inhibition among neural oscillators. It is further shown that these oscillator networks exhibit sensitivity to topological structure, which may lay a neurocomputational foundation for explaining the psychophysical phenomenon of topological perception.

title: Intrinsic generalization analysis of low dimensional representations
abstract: Low dimensional representations of images impose equivalence relations in the image space; the induced equivalence class of an image is named as its intrinsic generalization. The intrinsic generalization of a representation provides a novel way to measure its generalization and leads to more fundamental insights than the commonly used recognition performance, which is heavily influenced by the choice of training and test data. We demonstrate the limitations of linear subspace representations by sampling their intrinsic generalization, and propose a nonlinear representation that overcomes these limitations. The proposed representation projects images nonlinearly into the marginal densities of their filter responses, followed by linear projections of the marginals. We use experiments on large datasets to show that the representations that have better intrinsic generalization also lead to better recognition performance.

title: Image Segmentation Based on Oscillatory Correlation
abstract: We study image segmentation on the basis of locally excitatory, globally inhibitory oscillator networks (LEGION), whereby the phases of oscillators encode the binding of pixels. We introduce a lateral potential for each oscillator so that only oscillators with strong connections from their neighborhood can develop high potentials. Based on the concept of the lateral potential, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions but without affecting those corresponding to major regions. We show that the resulting oscillator network separates an image into several major regions, plus a background consisting of all noisy regions, and we illustrate network properties by computer simulation. The network exhibits a natural capacity in segmenting images. The oscillatory dynamics leads to a computer algorithm, which is applied successfully to segmenting real gray-level images. A number of issues regarding biological plausibility and perceptual organization are discussed. We argue that LEGION provides a novel and effective framework for image segmentation and figure-ground segregation.

title: Texture classification using spectral histograms
abstract: Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage. The distance between two spectral histograms is measured using chi(2)-statistic. The spectral histogram with the associated distance measure exhibits several properties that are necessary for texture classification. A filter selection algorithm is proposed to maximize classification performance of a given dataset. Our classification experiments using natural texture images reveal that the spectral histogram representation provides a robust feature statistic for textures and generalizes well. Comparisons show that our method produces a marked improvement in classification performance. Finally we point out the relationships between existing texture features and the spectral histogram, suggesting that the latter may provide a unified texture feature.

title: A schema-based model for phonemic restoration
abstract: Phonemic restoration is the perceptual synthesis of phonemes when masked by appropriate replacement sounds by utilizing linguistic context. Current models attempting to accomplish acoustic restoration of phonemes, however, use only temporal continuity and produce poor restoration of unvoiced phonemes, and are also limited in their ability to restore voiced phonemes. We present a schema-based model for phonemic restoration. The model employs a missing data speech recognition system to decode speech based on intact portions and activates word templates corresponding to the words containing the masked phonemes. An activated template is dynamically time warped to the noisy word and is then used to restore the speech frames corresponding to the masked phoneme, thereby synthesizing it. The model is able to restore both voiced and unvoiced phonemes with a high degree of naturalness. Systematic testing shows that this model outperforms a Kalman-filter based model.

title: Automated Abstraction Refinement for Model Checking Large State Spaces Using SAT Based Conflict Analysis
abstract: We introduce a SAT based automatic abstraction refinement framework for model checking systems with several thousand state variables in the cone of influence of the specification. The abstract model is constructed by designating a large number of state variables as invisible. In contrast to previous work where invisible variables were treated as free inputs we describe a computationally more advantageous approach in which the abstract transition relation is approximated by pre-quantifying invisible variables during image computation. The abstract counterexamples obtained from model-checking the abstract model are symbolically simulated on the concrete system using a state-of-the-art SAT checker. If no concrete counterexample is found, a subset of the invisible variables is reintroduced into the system and the process is repeated. The main contribution of this paper are two new algorithms for identifying the relevant variables to be reintroduced. These algorithms monitor the SAT checking phase in order to analyze the impact of individual variables. Our method is complete for safety properties (AGp) in the sense that - performance permitting - a property is either verified or disproved by a concrete counterexample. Experimental results are given to demonstrate the power of our method on real-world designs.

title: Protein Sequences Classification Using Modular RBF Neural Networks
abstract: A protein super-family consists of proteins which share amino acid sequence homology and which may therefore be functionally and structurally related. One of the benefits from this category grouping is that some hint of function may be deduced for individual members from information on other members of the family. Traditionally, two protein sequences are classified into the same class if they have high homology in terms of feature patterns extracted through sequence alignment algorithms. These algorithms compare an unseen protein sequence with all the identified protein sequences and returned the higher scored protein sequences. As the sizes of the protein sequence databases are very large, it is a very time consuming job to perform exhaustive comparison of existing protein sequence. Therefore, there is a need to build an improved classification system for effectively identifying protein sequences. This paper presents a modular neural classifier for protein sequences with improved classification criteria. The intelligent classification techniques described in this paper aims to enhance the performance of single neural classifiers based on a centralized information structure in terms of recognition rate, generalization and reliability. The architecture of the proposed model is a modular RBF neural network with a compensational combination at the transition output layer. The connection weights between the final output layer and the transition output layer are optimized by delta rule, which serve as an integrator of the local neural classifiers. To enhance the classification reliability, we present two heuristic rules to apply to decision-making. Two sets of protein sequences with ten classes of superfamilies downloaded from a public domain database, Protein Information Resources (PIR), are used in our simulation study. Experimental results with performance comparisons are carried out between single neural classifiers and the proposed modular neural classifier.

title: A Strategy for Speeding-up the Computation of Characteristic Sets
abstract: In this note a strategy is presented that speeds up the computation of characteristic sets several times in many cases. We test the possibility of success for this strategy in the quasi-sense by 50 examples, where the strategy succeeds in 58 and fails in 23 of the 81 completed cases. The speed-up of the strategy, while successful, ranges from a few to hundred times, whereas in the failure case the computation may be slightly slowed down (not less than 0.6 times).

title: IMDS: intelligent malware detection system
abstract: The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic and new, previously unseen malicious executables. In this paper, resting on the analysis of Windows API execution sequences called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of King-Soft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system out perform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques.

title: On avoidable and unavoidable claws
abstract: A claw of degree k is a directed tree consisting of k paths emerging from a common root. We prove that every claw of order n with degree less than 19 50 n appears in every n -vertex tournament. We also construct avoidable claws with degree approaching 11 23 n . Thus for large n , the maximum λ such that every claw with degree λn appears in every n -vertex tournament satisfies λ ⩽ 11 23 . This improves earlier bounds.