一种恶意模型下高效的两方安全计算协议

为了提高恶意参与者模型下两方安全计算协议的效率,文中协议使用了简单的轮换映射,这样不仅可以检测恶意参与方输入的一致性,而且避免了检测一致性时由于图的全连接性带来的复杂度,从而与经典协议相比效率提高了近50%。此外,为了使协议的安全性得到更好的保证,在理想/现实对模型下,本文采用回退的方法,在OT12协议完全可模拟和知识证明等性质的基础上,用现实模型模拟理想模型的方法,给出了协议完整、严格的形式化证明和失败率分析。     

Robust parallel computation in floating-point and SLI arithmetic

In this paper we consider the parallel computation of vector norms and inner products in floating-point and a proposed new form of computer arithmetic, the symmetric level-index system. The vector norms provide an illuminating example of the contrast between the two arithmetic systems under discussion in terms of the ability to program for (complete) robustness and parallelizability. The conflict between robustness of the computation—in the sense of the dual requirements of accuracy and freedom from overflow and underflow—and easy parallelization of the algorithms within a floating-point environment is made plain. It is seen that this conflict disappears if the symmetric level-index system of arithmetic is used. The freedom from overflow and underflow offered by this system allows the programming of the straightforward definitions in a way which is simple, robust and immediately parallelizable. Numerical results are given to illustrate the fact that the symmetric level-index system yields results of comparable accuracy to those of floating-point in cases where the latter system works and still yields results of high accuracy when the floating-point system fails altogether.     

Secure linear system computation in the presence of malicious adversaries

In this paper,we study the system of linear equation problems in the two-party computation setting.Consider that P1 holds an m×m matrix M1 and an m-dimensional column vector B1.Similarly,P2holds M2 and B2.Via executing a secure linear system computation,P1 gets the output x(or⊥)conditioned on(M1+M2)x=(B1+B2),and the rank of matrix M1+M2,while P2 gets nothing.This also can be used to settle other cooperative linear system problems.We firstly design an efficient protocol to solve this problem in the presence of malicious adversaries,then propose a simple way to modify our protocol for having a precise functionality,in which the rank of matrix M1+M2 is not necessary.We note that our protocol is more practical than these existing malicious secure protocols.We also give comparisons with other protocols and extensions to similar functions.     

恶意模型下计算欧几里德距离的协议

安全多方计算（MPC）是一个允许多个参与方在保持各自输入隐私的前提下联合计算一个函数。Yao和Goldreich等人（STOC’87）开创性的工作表明,存在陷门置换的前提下,任何一个函数都存在安全多方计算协议,并给出了安全多方计算的一个通用解决方案,但是该方案由于效率问题而不实用。因此,Goldreich同时指出需要针对特定问题提出特定的安全多方计算协议。提出了一个新的基于分布式EI Gamal加密的计算两个向量欧几里德距离的安全协议,并在混合模型下给出了协议的安全性证明。与原来的方案比较,该协议的计算和通信复杂度都较低,适用于计算和通信能力都有限的应用环境。     

Ergodicity of hidden Markov models

In this paper we study ergodic properties of hidden Markov models with a generalized observation structure. In particular sufficient conditions for the existence of a unique invariant measure for the pair filter-observation are given. Furthermore, necessary and sufficient conditions for the existence of a unique invariant measure of the triple state-observation-filter are provided in terms of asymptotic stability in probability of incorrectly initialized filters. We also study the asymptotic properties of the filter and of the state estimator based on the observations as well as on the knowledge of the initial state. Their connection with minimal and maximal invariant measures is also studied. Work partially supported by grants MIUR-PRIN 2001, PBZ KBN 016/P03/99 and IMPAN-BC Centre of Excellence     

Adaptation of hidden Markov models using maximum model distance algorithm

This paper presents a new approach that uses the maximum model distance (MMD) method for the adaptation of hidden Markov models (HMMs). This method has the same framework as it is used for constructing speech recognizers with abundant data, and work effectively with any amount of adaptation data. All parameters of the HMMs with or without the adaptation data could be adapted. If the adaptation data is sufficient, then the adapted models will gradually become a speaker-dependent one. Both the dialect and the speaker adaptation experiments were conducted to investigate the effectiveness of the proposed algorithm. In the speaker adaptation experiments, up to 65.55% phoneme error reduction was achieved, and the MMD could reduce the phoneme error by 16.91% even only one adaptation utterance is available.     

Evolving the structure of hidden Markov models

A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C. jejuni. The resulting HMM has a superior discrimination ability to a handcrafted model that has been published in the literature.     

Exploitation of unlabeled sequences in hidden Markov models

This paper presents a method for effectively using unlabeled sequential data in the learning of hidden Markov models (HMMs). With the conventional approach, class labels for unlabeled data are assigned deterministically by HMMs learned from labeled data. Such labeling often becomes unreliable when the number of labeled data is small. We propose an extended Baum-Welch (EBW) algorithm in which the labeling is undertaken probabilistically and iteratively so that the labeled and unlabeled data likelihoods are improved. Unlike the conventional approach, the EBW algorithm guarantees convergence to a local maximum of the likelihood. Experimental results on gesture data and speech data show that when labeled training data are scarce, by using unlabeled data, the EBW algorithm improves the classification performance of HMMs more robustly than the conventional naive labeling (NL) approach.     

Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees

Hidden Markov models have been found very useful for a wide range of applications in machine learning and pattern recognition. The wavelet transform has emerged as a new tool for signal and image analysis. Learning models for wavelet coefficients have been mainly based on fixed-length sequences, but real applications often require to model variable-length, very long or real-time sequences. In this paper, we propose a new learning architecture for sequences analyzed on short-term basis, but not assuming stationarity within each frame. Long-term dependencies will be modeled with a hidden Markov model which, in each internal state, will deal with the local dynamics in the wavelet domain, using a hidden Markov tree. The training algorithms for all the parameters in the composite model are developed using the expectation-maximization framework. This novel learning architecture could be useful for a wide range of applications. We detail two experiments with artificial and real data: model-based denoising and speech recognition. Denoising results indicate that the proposed model and learning algorithm are more effective than previous approaches based on isolated hidden Markov trees. In the case of the ‘Doppler’ benchmark sequence, with 1024 samples and additive white noise, the new method reduced the mean squared error from 1.0 to 0.0842. The proposed methods for feature extraction, modeling and learning, increased the phoneme recognition rates in 28.13%, with better convergence than models based on Gaussian mixtures.     

Asymptotical statistics of misspecified hidden Markov models

This paper deals with the problem of modeling data generated by an ergodic stochastic process as the output of a hidden Markov model (HMM). More specifically, we consider the problem of fitting a parametric family of HMM with continuous output to an ergodic stochastic process with continuous values, which does not necessarily belong to the family. In this context, we derive the main asymptotic results: almost sure consistency of the maximum likelihood estimator, asymptotic normality of the estimation error and the exact rates of almost sure convergence.     

Minimax a posteriori estimation in the hidden Markov models

Consideration was given to the minimax estimation in the observation system including a hidden Markov model for continuous and counting observations. The dynamic and observation equations depend on a random finite-dimensional parameter having an unknown distribution with the given support. The conditional expectation of the available observation of some generalized quadratic loss function was used as the risk function. Existence of the saddle point in the formulated minimax problem was proved, and the worst distribution and the minimax estimate as the solution of a simpler dual problem were characterized.     

Factorial hidden Markov models and the generalized backfitting algorithm

Previous researchers developed new learning architectures for sequential data by extending conventional hidden Markov models through the use of distributed state representations. Although exact inference and parameter estimation in these architectures is computationally intractable, Ghahramani and Jordan (1997) showed that approximate inference and parameter estimation in one such architecture, factorial hidden Markov models (FHMMs), is feasible in certain circumstances. However, the learning algorithm proposed by these investigators, based on variational techniques, is difficult to understand and implement and is limited to the study of real-valued data sets. This chapter proposes an alternative method for approximate inference and parameter estimation in FHMMs based on the perspective that FHMMs are a generalization of a well-known class of statistical models known as generalized additive models (GAMs; Hastie & Tibshirani, 1990). Using existing statistical techniques for GAMs as a guide, we have developed the generalized backfitting algorithm. This algorithm computes customized error signals for each hidden Markov chain of an FHMM and then trains each chain one at a time using conventional techniques from the hidden Markov models literature. Relative to previous perspectives on FHMMs, we believe that the viewpoint taken here has a number of advantages. First, it places FHMMs on firm statistical foundations by relating them to a class of models that are well studied in the statistics community, yet it generalizes this class of models in an interesting way. Second, it leads to an understanding of how FHMMs can be applied to many different types of time-series data, including Bernoulli and multinomial data, not just data that are real valued. Finally, it leads to an effective learning procedure for FHMMs that is easier to understand and easier to implement than existing learning procedures. Simulation results suggest that FHMMs trained with the generalized backfitting algorithm are a practical and powerful tool for analyzing sequential data.     

Profile hidden Markov models and metamorphic virus detection

Metamorphic computer viruses “mutate” by changing their internal structure and, consequently, different instances of the same virus may not exhibit a common signature. With the advent of construction kits, it is easy to generate metamorphic strains of a given virus. In contrast to standard hidden Markov models (HMMs), profile hidden Markov models (PHMMs) explicitly account for positional information. In principle, this positional information could yield stronger models for virus detection. However, there are many practical difficulties that arise when using PHMMs, as compared to standard HMMs. PHMMs are widely used in bioinformatics. For example, PHMMs are the most effective tool yet developed for finding family related DNA sequences. In this paper, we consider the utility of PHMMs for detecting metamorphic virus variants generated from virus construction kits. PHMMs are generated for each construction kit under consideration and the resulting models are used to score virus and non-virus files. Our results are encouraging, but several problems must be resolved for the technique to be truly practical.     

Risk-sensitive filtering and smoothing for hidden Markov models

In this paper, we address the problem of risk-sensitive filtering and smoothing for discrete-time Hidden Markov Models (HMM) with finite-discrete states. The objective of risk-sensitive filtering is to minimise the expectation of the exponential of the squared estimation error weighted by a risk-sensitive parameter. We use the so-called Reference Probability Method in solving this problem. We achieve finite-dimensional linear recursions in the information state, and thereby the state estimate that minimises the risk-sensitive cost index. Also, fixed-interval smoothing results are derived. We show that L₂ or risk-neutral filtering for HMMs can be extracted as a limiting case of the risk-sensitive filtering problem when the risk-sensitive parameter approaches zero.     

Robust speech recognition based on joint model and feature spaceoptimization of hidden Markov models

The hidden Markov model (HMM) inversion algorithm, based on either the gradient search or the Baum-Welch reestimation of input speech features, is proposed and applied to the robust speech recognition tasks under general types of mismatch conditions. This algorithm stems from the gradient-based inversion algorithm of an artificial neural network (ANN) by viewing an HMM as a special type of ANN. Given input speech features s, the forward training of an HMM finds the model parameters lambda subject to an optimization criterion. On the other hand, the inversion of an HMM finds speech features, s, subject to an optimization criterion with given model parameters lambda. The gradient-based HMM inversion and the Baum-Welch HMM inversion algorithms can be successfully integrated with the model space optimization techniques, such as the robust MINIMAX technique, to compensate the mismatch in the joint model and feature space. The joint space mismatch compensation technique achieves better performance than the single space, i.e. either the model space or the feature space alone, mismatch compensation techniques. It is also demonstrated that approximately 10-dB signal-to-noise ratio (SNR) gain is obtained in the low SNR environments when the joint model and feature space mismatch compensation technique is used.     

Similarity-based classification of sequences using hidden Markov models

Hidden Markov models (HMM) are a widely used tool for sequence modelling. In the sequence classification case, the standard approach consists of training one HMM for each class and then using a standard Bayesian classification rule. In this paper, we introduce a novel classification scheme for sequences based on HMMs, which is obtained by extending the recently proposed similarity-based classification paradigm to HMM-based classification. In this approach, each object is described by the vector of its similarities with respect to a predetermined set of other objects, where these similarities are supported by HMMs. A central problem is the high dimensionality of resulting space, and, to deal with it, three alternatives are investigated. Synthetic and real experiments show that the similarity-based approach outperforms standard HMM classification schemes.     

Computational Bayesian analysis of hidden Markov mesh models

Versions of the Gibbs sampler are derived for the analysis of data from the hidden Markov mesh random fields sometimes used in image analysis. This provides a numerical approach to the otherwise intractable Bayesian analysis of these problems. Detailed formulation is provided for particular examples based on Devijver's Markov mesh model (1988), and the BUGS package is used to do the computations. Theoretical aspects are discussed and a numerical study, based on image analysis, is reported     

Efficient backward decoding of high-order hidden Markov models

The forward-backward search (FBS) algorithm [S. Austin, R. Schwartz, P. Placeway, The forward-backward search algorithm, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 1991, pp. 697-700] has resulted in increases in speed of up to 40 in expensive time-synchronous beam searches in hidden Markov model (HMM) based speech recognition [R. Schwartz, S. Austin, Efficient, high-performance algorithms for N-best search, in: Proceedings of the Workshop on Speech and Natural Language, 1990, pp. 6-11; L. Nguyen, R. Schwartz, F. Kubala, P. Placeway, Search algorithms for software-only real-time recognition with very large vocabularies, in: Proceedings of the Workshop on Human Language Technology, 1993, pp. 91-95; A. Sixtus, S. Ortmanns, High-quality word graphs using forward-backward pruning, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 1999, pp. 593-596]. This is typically achieved by using a simplified forward search to decrease computation in the following detailed backward search. FBS implicitly assumes that forward and backward searches of HMMs are computationally equivalent. In this paper we present experimental results, obtained on the CallFriend database, that show that this assumption is incorrect for conventional high-order HMMs. Therefore, any improvement in computational efficiency that is gained by using conventional low-order HMMs in the simplified backward search of FBS is lost.This problem is solved by presenting a new definition of HMMs termed a right-context HMM, which is equivalent to conventional HMMs. We show that the computational expense of backward Viterbi-beam decoding right-context HMMs is similar to that of forward decoding conventional HMMs. Though not the subject of this paper, this allows us to more efficiently decode high-order HMMs, by capitalising on the improvements in computational efficiency that is obtained by using the FBS algorithm.