Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Mean and variance of estimates of the bispectrum of a harmonic random process-an analysis including leakage effects

Analytical expressions are derived for the mean and variance, of estimates of the bispectrum of a real-time series assuming a cosinusoidal model. The effects of spectral leakage, inherent in discrete Fourier transform operation when the modes present in the signal have a nonintegral number of wavelengths in the record, are included in the analysis. A single phase-coupled triad of modes can cause the bispectrum to have a nonzero mean value over the entire region of computation owing to leakage. The variance of bispectral estimates in the presence of leakage has contributions from individual modes and from triads of phase-coupled modes. Time-domain windowing reduces the leakage. The theoretical expressions for the mean and variance of bispectral estimates are derived in terms of a function dependent on an arbitrary symmetric time-domain window applied to the record. the number of data, and the statistics of the phase coupling among triads of modes. The theoretical results are verified by numerical simulations for simple test cases and applied to laboratory data to examine phase coupling in a hypothesis testing framework

Order tracking for discrete-random separation in variable speed conditions

The transmission path from the excitation to the measured vibration on the surface of a mechanical system introduces a distortion both in amplitude and in phase. Moreover, in variable speed conditions, the amplification/attenuation and the phase shift, due to the transfer function of the mechanical system, varies in time. This phenomenon reduces the effectiveness of the traditionally tachometer based order tracking, compromising the results of a discrete-random separation performed by a synchronous averaging. In this paper, for the first time, the extent of the distortion is identified both in the time domain and in the order spectrum of the signal, highlighting the consequences for the diagnostics of rotating machinery. A particular focus is given to gears, providing some indications on how to take advantage of the quantification of the disturbance to better tune the techniques developed for the compensation of the distortion. The full theoretical analysis is presented and the results are applied to an experimental case.

Cryptanalysis of RC4-based hash function

RC4-Based Hash Function is a new proposed hash function based on RC4 stream cipher for ultra low power devices. In this paper, we analyse the security of the function against collision attack. It is shown that the attacker can find collision and multi-collision messages with complexity only 6 compress function operations and negligible memory with time complexity 2 13. In addition, we show the hashing algorithm can be distinguishable from a truly random sequence with probability close to one.

Key derivation function based on stream ciphers

A key derivation function (KDF) is a function that transforms secret non-uniformly random source material together with some public strings into one or more cryptographic keys. These cryptographic keys are used with a cryptographic algorithm for protecting electronic data during both transmission over insecure channels and storage. In this thesis, we propose a new method for constructing a generic stream cipher based key derivation function. We show that our proposed key derivation function based on stream ciphers is secure if the under-lying stream cipher is secure. We simulate instances of this stream cipher based key derivation function using three eStream nalist: Trivium, Sosemanuk and Rabbit. The simulation results show these stream cipher based key derivation functions offer efficiency advantages over the more commonly used key derivation functions based on block ciphers and hash functions.

Universal designated-verifier signatures

Motivated by privacy issues associated with dissemination of signed digital certificates, we define a new type of signature scheme called a ‘Universal Designated-Verifier Signature’ (UDVS). A UDVS scheme can function as a standard publicly-verifiable digital signature but has additional functionality which allows any holder of a signature (not necessarily the signer) to designate the signature to any desired designated-verifier (using the verifier’s public key). Given the designated-signature, the designated-verifier can verify that the message was signed by the signer, but is unable to convince anyone else of this fact. We propose an efficient deterministic UDVS scheme constructed using any bilinear group-pair. Our UDVS scheme functions as a standard Boneh-Lynn-Shacham (BLS) signature when no verifier-designation is performed, and is therefore compatible with the key-generation, signing and verifying algorithms of the BLS scheme. We prove that our UDVS scheme is secure in the sense of our unforgeability and privacy notions for UDVS schemes, under the Bilinear Diffie-Hellman (BDH) assumption for the underlying group-pair, in the random-oracle model. We also demonstrate a general constructive equivalence between a class of unforgeable and unconditionally-private UDVS schemes having unique signatures (which includes the deterministic UDVS schemes) and a class of ID-Based Encryption (IBE) schemes which contains the Boneh-Franklin IBE scheme but not the Cocks IBE scheme.

The suffix-free-prefix-free hash function construction and its indifferentiability security analysis

In this paper, we observe that in the seminal work on indifferentiability analysis of iterated hash functions by Coron et al. and in subsequent works, the initial value (IV) of hash functions is fixed. In addition, these indifferentiability results do not depend on the Merkle–Damgård (MD) strengthening in the padding functionality of the hash functions. We propose a generic n -bit-iterated hash function framework based on an n -bit compression function called suffix-free-prefix-free (SFPF) that works for arbitrary IV s and does not possess MD strengthening. We formally prove that SFPF is indifferentiable from a random oracle (RO) when the compression function is viewed as a fixed input-length random oracle (FIL-RO). We show that some hash function constructions proposed in the literature fit in the SFPF framework while others that do not fit in this framework are not indifferentiable from a RO. We also show that the SFPF hash function framework with the provision of MD strengthening generalizes any n -bit-iterated hash function based on an n -bit compression function and with an n -bit chaining value that is proven indifferentiable from a RO.

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.

A directed continuous time random walk model with jump length depending on waiting time

In continuum one-dimensional space, a coupled directed continuous time random walk model is proposed, where the random walker jumps toward one direction and the waiting time between jumps affects the subsequent jump. In the proposed model, the Laplace-Laplace transform of the probability density function P(x,t) of finding the walker at position at time is completely determined by the Laplace transform of the probability density function φ(t) of the waiting time. In terms of the probability density function of the waiting time in the Laplace domain, the limit distribution of the random process and the corresponding evolving equations are derived.

Memory of past random wave conditions in submarine groundwater discharge

Submarine groundwater discharge (SGD) is an integral part of the hydrological cycle and represents an important aspect of land-ocean interactions. We used a numerical model to simulate flow and salt transport in a nearshore groundwater aquifer under varying wave conditions based on yearlong random wave data sets, including storm surge events. The results showed significant flow asymmetry with rapid response of influxes and retarded response of effluxes across the seabed to the irregular wave conditions. While a storm surge immediately intensified seawater influx to the aquifer, the subsequent return of intruded seawater to the sea, as part of an increased SGD, was gradual. Using functional data analysis, we revealed and quantified retarded, cumulative effects of past wave conditions on SGD including the fresh groundwater and recirculating seawater discharge components. The retardation was characterized well by a gamma distribution function regardless of wave conditions. The relationships between discharge rates and wave parameters were quantifiable by a regression model in a functional form independent of the actual irregular wave conditions. This statistical model provides a useful method for analyzing and predicting SGD from nearshore unconfined aquifers affected by random waves

Generation of optimum random pinhole arrays for multiple imaging

Through an analysis using the transfer function of a pinhole camera, the multiple imaging characteristics of photographic diffusers described by Grover and Tremblay [Appl. Opt.21,4500(1982)] is studied. It is found that only one pinhole diameter satisfies the optimum imaging condition for best contrast transfer at any desired spatial frequency. A simple method of generating random pinhole arrays with a controlled pinhole diameter is described. These pinhole arrays are later used to generate high frequency sinusoidal gratings from a coarse grid. The contrast in the final gratings is found to be reasonably high.

Non-linear resonance in strings under narrow band random excitation, part I: Planar response and stability

Non-linear planar response of a string to planar narrow band random excitation is investigated in this paper. A response equation for the mean square deflection σ2 is obtained under a single mode approximation by using the equivalent linearization technique. It is shown that the response is triple valued, as in the case of harmonic excitation, if the centre frequency of excitation Ω lies in a certain specified range. The triple valued response occurs only if the excitation bandwidth β is smaller than a critical value βcrit which is a monotonically increasing function of the intensity of excitation. An approximate method of investigating the almost sure asymptotic stability of the solution is presented and regions of instability in the Ω-σ2 plane have been charted. It is shown that planar response can become unstable either due to an unbounded growth of the in-plane component of motion or due to a spontaneous appearance of an out-of-plane component.

Nonstationary Random Critical Seismic Excitations

A method is presented to find nonstationary random seismic excitations with a constraint on mean square value such that the response variance of a given linear system is maximized. It is also possible to incorporate the dominant input frequency into the analysis. The excitation is taken to be the product of a deterministic enveloping function and a zero mean Gaussian stationary random process. The power spectral density function of this process is determined such that the response variance is maximized. Numerical results are presented for a single-degree system and an earth embankment modeled as shear beam.

Distribution of the angle of rotation plane random walks

We study the probability distribution of the angle by which the tangent to the trajectory rotates in the course of a plane random walk. It is shown that the determination of this distribution function can be reduced to an integral equation, which can be rigorously transformed into a differential equation of Hill's type. We derive the asymptotic distribution for very long walks.