Short Window Sizes Research Articles

The complex dynamics of correlations within chaotic systems

It is widely reported that the functional connectivity estimated by statistical correlations is often varying within nonlinear systems. Generally, these varying correlations between time series are detected by sliding windows. Still, it is unclear how these correlations evolve within a chaotic system. This work intends to give a quantitative framework to identify the dynamics of correlations within chaotic systems. To this end, we embed the pairwise statistical correlations (from time series within a system) into a correlation-based system by sliding windows. This allows for detecting the dynamics of correlations within a complex system through the embedded correlation-based system. Three chaotic systems (i.e., the Lorenz, the Rossler, and the Chen systems) are employed as benchmark examples. We find that both linear and nonlinear correlations within three chaotic systems show chaotic behaviors on some short window sizes, then transit to non-chaotic states with window size increasing. Moreover, the chaotic dynamics of nonlinear correlations exhibit higher uncertainty than the linear one and the original chaotic systems. The chaotic behaviors of correlations within chaotic systems give another evidence of the difficulty of prediction for chaotic systems. Meanwhile, the identified state transitions (concerning the window size) of correlations may provide a quantitative rule to select an appropriate window size for sliding windows.

STFF-SM: Steganalysis Model Based on Spatial and Temporal Feature Fusion for Speech Streams

The real-time detection of speech steganography in Voice-over-Internet-Protocol (VoIP) scenarios remains an open problem, as it requires steganalysis methods to perform for low-intensity embeddings and short-sample inputs, as well as provide rapid detection results. To address these challenges, this paper presents a novel steganalysis model based on spatial and temporal feature fusion (STFF-SM). Differing from the existing methods, we take both the integer and fractional pitch delays as input, and design subframe-stitch module to organically integrate subframe-wise integer delays and frame-wise fractional pitch delays. Further, we design a spatial fusion module based on pre-activation residual convolution to extract the pitch spatial features and gradually increase their dimensions to discover finer steganographic distortions to enhance the detection effect, where a Group-Squeeze-Weighting block is introduced to alleviate the information loss in the process of increasing the feature dimension. In addition, we design a temporal fusion module to extract pitch temporal features using the stacked LSTM, where a Gated Feed-Forward Network is introduced to learn the interaction between different feature maps while suppressing the features that are not useful for detection. We evaluated the performance of STFF-SM through comprehensive experiments and comparisons with the state-of-the-art solutions. The experimental results demonstrate that STFF-SM can well meet the needs of real-time detection of speech steganography in VoIP streams, and outperforms the existing methods in detection performance, especially with low embedding strengths and short window sizes.

LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold's purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.

Fraudulent Detection Model Using Machine Learning Techniques for Unstructured Supplementary Service Data

The increase in mobile phones accessibility and technological advancement in almost every corner of the world has shaped how banks offer financial service. Such services were extended to low-end customers without a smartphone providing Alternative Banking Channels (ABCs) service, rendering regular financial service same as those on smartphones. One of the services of this ABC’s is Unstructured Supplementary Service Data (USSD), two-way communication between mobile phones and applications, which is used to render financial services all from the bank accounts linked for this USSD service. Fraudsters have taken advantage of innocent customers on this channel to carry out fraudulent activities with high impart of fraudulent there is still not an implemented fraud detection model to detect this fraud activities. This paper is an investigation into fraud detection model using machine learning techniques for Unstructured Supplementary Service Data based on short-term memory. Statistical features were derived by aggregating customers activities using a short window size to improve the model performance on selected machine learning classifiers, employing the best set of features to improve the model performance. Based on the results obtained, the proposed Fraudulent detection model demonstrated that with the appropriate machine learning techniques for USSD, best performance was achieved with Random forest having the best result of 100% across all its performance measure, KNeighbors was second in performance measure having an average of 99% across all its performance measure while Gradient boosting was third in its performance measure, its achieved accuracy is 91.94%, precession is 86%, recall is 100% and f1 score is 92.54%. Result obtained shows two of the selected machine learning random forest and decision tree are best fit for the fraud detection in this model. With the right features derived and an appropriate machine learning algorithm, the proposed model offers the best fraud detection accuracy.

A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples

BackgroundSplice sites prediction has been a long-standing problem in bioinformatics. Although many computational approaches developed for splice site prediction have achieved satisfactory accuracy, further improvement in predictive accuracy is significant, for it is contributing to predict gene structure more accurately. Determining a proper window size before prediction is necessary. Overly long window size may introduce some irrelevant features, which would reduce predictive accuracy, while the use of short window size with maximum information may performs better in terms of predictive accuracy and time cost. Furthermore, the number of false splice sites following the GT–AG rule far exceeds that of true splice sites, accurate and rapid prediction of splice sites using imbalanced large samples has always been a challenge. Therefore, based on the short window size and imbalanced large samples, we developed a new computational method named chi-square decision table (χ2-DT) for donor splice site prediction.ResultsUsing a short window size of 11 bp, χ2-DT extracts the improved positional features and compositional features based on chi-square test, then introduces features one by one based on information gain, and constructs a balanced decision table aimed at implementing imbalanced pattern classification. With a 2000:271,132 (true sites:false sites) training set, χ2-DT achieves the highest independent test accuracy (93.34%) when compared with three classifiers (random forest, artificial neural network, and relaxed variable kernel density estimator) and takes a short computation time (89 s). χ2-DT also exhibits good independent test accuracy (92.40%), when validated with BG-570 mutated sequences with frameshift errors (nucleotide insertions and deletions). Moreover, χ2-DT is compared with the long-window size-based methods and the short-window size-based methods, and is found to perform better than all of them in terms of predictive accuracy.ConclusionsBased on short window size and imbalanced large samples, the proposed method not only achieves higher predictive accuracy than some existing methods, but also has high computational speed and good robustness against nucleotide insertions and deletions.ReviewersThis article was reviewed by Ryan McGinty, Ph.D. and Dirk Walther.

Realistic models of apparent dynamic changes in resting-state connectivity in somatosensory cortex.

Variations over time in resting-state correlations in blood oxygenation level-dependent (BOLD) signals from different cortical areas may indicate changes in brain functional connectivity. However, apparent variations over time may also arise from stationary signals when the sample duration is finite. Recently, a vector autoregressive (VAR) null model has been proposed to simulate real functional magnetic resonance imaging (fMRI) data, which provides a robust stationary model for identifying possible temporal dynamic changes in functional connectivity. In this work, we propose a simpler model that uses a filtered stationary dataset. The filtered stationary model generates statistically stationary time series from random data with a single prescribed correlation coefficient that is calculated as the average over the entire time series. In addition, we propose a dynamic model, which is better able to replicate real fMRI connectivity, estimated from monkey brain studies, than the two stationary models. We compare simulated results using these three models with the behavior of primary somatosensory cortex (S1) networks in anesthetized squirrel monkeys at high field (9.4 T), using a sliding window correlation analysis. We found that at short window sizes, both stationary models reproduced the distribution of correlations of real signals well, but at longer window sizes, a dynamic model reproduced the distribution of correlations of real signals better than the stationary models. While stationary models replicate several features of real data, a close representation of the behavior of resting-state data acquired from somatosensory cortex of non-human primates is obtained only when a dynamic correlation is introduced, suggesting dynamic variations in connectivity are real. Hum Brain Mapp 37:3897-3910, 2016. © 2016 Wiley Periodicals, Inc.

Signatures of chaotic and stochastic dynamics uncovered with ε -recurrence networks

An old and important problem in the field of nonlinear time-series analysis entails the distinction between chaotic and stochastic dynamics. Recently, ε -recurrence networks have been proposed as a tool to analyse the structural properties of a time series. In this paper, we propose the applicability of local and global ε -recurrence network measures to distinguish between chaotic and stochastic dynamics using paradigmatic model systems such as the Lorenz system, and the chaotic and hyper-chaotic Rössler system. We also demonstrate the effect of increasing levels of noise on these network measures and provide a real-world application of analysing electroencephalographic data comprising epileptic seizures. Our results show that both local and global ε -recurrence network measures are sensitive to the presence of unstable periodic orbits and other structural features associated with chaotic dynamics that are otherwise absent in stochastic dynamics. These network measures are still robust at high noise levels and short data lengths. Furthermore, ε -recurrence network analysis of the real-world epileptic data revealed the capability of these network measures in capturing dynamical transitions using short window sizes. ε -recurrence network analysis is a powerful method in uncovering the signatures of chaotic and stochastic dynamics based on the geometrical properties of time series.

Impulse Noise Suppression Method by Median Filtering with Parity Extraction

A new impulse noise suppression method by median filtering with parity extraction was proposed in this paper. The window size of the median filter has important effect on the performance of the filtering result, larger window size can suppress impulse noise effectively but often at cost of loss of the detail information of the signal, while smaller window size can protect the detail information better but results in degrading of the noise suppression. Parity extraction is done to the signal at first and median filtering carries on the odd and even part respectively, and then a new method of median filtering with short window size to suppress the impulse noise is obtained. Simulation and experiment data of telemetry process results show the effectiveness of the proposed method.

Fast optimization method: the window size and hurst parameter estimator on self-similar network traffic

This paper describes a version of the Fast Optimization Method (FOM) used to estimate the Hurst parameter (H) with appropriate window sizes in self-similar network traffic. Large or short window sizes, for example, may cause the results to become unreliable. Estimating window sizes requires that the estimation process be repeated, such as in Iterative Optimization Method (IOM) and will result in the high consumption of CPU time. This paper empirically compares the CPU time and the estimation results using both the FOM and IOM methods. Finally, it shows that the FOM outperforms the IOM in that it significantly reduces total CPU time and maintains the reliability of the estimation results.

Orthogonal decision-feedback detector for asynchronous multiuser CDMA systems

This paper introduces an asynchronous orthogonal decision-feedback detector (AODFD) for asynchronous CDMA multiuser detection. The AODFD based on entire message-length detection is studied first. A realizable scheme, sliding-window AODFD, is then proposed and its performance is analyzed. In spite of its simple structure, the sliding-window AODFD can perform as desirably as ADDFD, which has a much higher complexity. The reduced complexity of the sliding-window AODFD is due to the use of orthogonal matched-filtering and a short window size. Different from ADDFD that requires computational intensive z-transformed matrix inversion and spectral factorization, AODFD uses the agile Gram-Schmidt procedure. It is possible for AODFD to adopt a simple updating algorithm and parameter updating is no longer always necessary when users leave the system. The comparisons are also made with other orthogonal-based detectors and the bit error probability results show that the AODFD is an attractive multiuser detector.