nils-herrmann/clean_scirepeval huggingface dataset:
A statistical analysis of state-space approaches to signal modeling

This paper addresses the problem of analyzing the perturbation of state-space approaches to signal modeling. Principal component techniques have been used in many algorithms involving frequency estimation and system identification. It is shown that in many instances the performance of these principal component based approaches is far better than that of classical methods. However, most of the performance evaluations of these methods are based on simulation results only. In this paper, the perturbation of the parameter estimate for a single sinusoid signal corrupted with noise is studied analytically using a power method.
Effects of Gaussian perturbations on parameter estimators derived from an estimated signal subspace

The authors present theoretical analyses that are appropriate for both high and low signal-to-noise ratio (SNR) of signal subspace or SVD-based signal-processing algorithms. For the low-SNR case, the probability of obtaining an outlier is calculated and is used to determine the threshold SNR at which the variance of parameter estimation errors departs from Cramer-Rao bound behavior. At high-SNR, the perturbation of the parameter estimates from SVD-based linear prediction and Prony-Lanczos algorithms is considered using matrix approximation and Taylor series approximation.<<ETX>>
Face spoofing detection with image quality regression

Face spoofing detection nowadays has attracted attentions regarding the biometrics authentication issue. Inspired by the observation that face spoofing detection is highly relevant with the inherent image quality which also strongly depends on the properties of the capturing devices and conditions, in this paper, we tackle the spoofing detection problem based on a two-stage learning approach. Firstly, we manually cluster the training samples based on the prior knowledge of face sample quality (e.g. camera model), and multiple quality-guided classifiers are trained based on each cluster with extracted image quality assessment (IQA) feature. Subsequently, a regression function is learned by mapping from the IQA scores to the corresponding classifier's parameters, which can be further used for classification. As such, given a new face input for verification, we can predict its classifier's coefficients based on the pre-learned regression model, with which spoofing detection can be effectively achieved. Experimental results show that we achieve significantly better classification performance compared with the strategy that directly applies the IQA features with single classifier.
A comparison between transfer-function and state-space approaches to signal modeling

There exist well known algorithms for estimating either a transfer function or a state-space description corresponding to a given noise-free impulse response. When the data consists of impulse response plus noise, the algorithms have to be modified in order to give good performance. In both the transfer function and state-space approaches, the key modification involves the use of the singular value decomposition (SVD). However, there are fundamental differences in the way in which the SVD is used to improve performances in the noisy data case. IN this paper, the differences between the two approaches are studied, as well as the corresponding performance of each method.
Data hiding for bi-level documents using smoothing technique

In this paper, a method for watermarking on binary images to authenticate the owner or authorized user is proposed. The proposed method makes use of noise suppression patterns which takes into account the smoothness in 5 /spl times/ 5 neighborhood. It has a moderate capacity of the watermark while preserving very good invisibility to human eyes. The resulted watermarked binary image is an enhancement to the original image in terms of smoothness. The fast computational speed and time complexity makes the method suitable for real-time authentication. Retrieval of the watermark doesn't require the presence of the original binary document image. However the secret key and the location map information are needed for the retrieval. The watermark used in the algorithm is the gold-like sequence. Discussions on issues in binary image watermarking have also been presented. Experimental results validate the arguments made in the paper.
Binary Image Authentication With Tampering Localization by Embedding Cryptographic Signature and Block Identifier

This letter proposes a novel two-layer blind binary image authentication scheme, in which the first layer is targeted at the overall authentication and the second layer is targeted at identifying the tampering locations. The "flippability" of a pixel is determined by the "connectivity-preserving" transition criterion. The image is partitioned into multiple macro-blocks that are subsequently classified into eight categories. The block identifier is defined adaptively for each class and embedded in those "qualified" and "self-detecting" macro-blocks in order to identify the tampered locations. Experimental results validate the arguments made
Dynamic witness selection for trustworthy distributed cooperative sensing in cognitive radio networks

Cooperative spectrum sensing by secondary user (SU) nodes in cognitive radio networks (CRNs) is a promising approach to increase the spectrum access efficiency and overall network performance. However, unreliable sensing results or malicious behaviors from cooperator SU nodes can be very disruptive and reduce the network performance. Trust and reputation modeling has been identified as one of the potential solutions to address this problem, but the current centralized trust evaluation approach in CRN lacks scalability. Although some decentralized trust models have been proposed in CRN, without proper protection mechanisms, they are vulnerable to collusive behaviors by the witness SU nodes when they share testimonies about the trustworthiness of neighboring SU nodes. In this paper, we propose a clustering based witness selection method to address this problem. By dividing the witness SU nodes testimonies about the trustworthiness of neighboring SU nodes into clusters, the proposed method helps SU nodes to select which witness's opinion to trust mode in the future. The proposed method has been studied using extensive computer simulation and has demonstrated good robustness against common collusive attacks.
Cross-Domain Shoe Retrieval With a Semantic Hierarchy of Attribute Classification Network

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the semantic hierarchy of attribute convolutional neural network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe data set composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.
An intelligent pressure sensor using neural networks

In this paper, we propose a scheme of an intelligent capacitive pressure sensor (CPS) using an artificial neural network (ANN). A switched-capacitor circuit (SCC) converts the change in capacitance of the pressure-sensor into an equivalent voltage. The effect of change in environmental conditions on the CPS and subsequently upon the output of the SCC is nonlinear in nature. Especially, change in ambient temperature causes response characteristics of the CPS to become highly nonlinear, and complex signal processing may be required to obtain correct readout. The proposed ANN-based scheme incorporates intelligence into the sensor. It is revealed from the simulation studies that this CPS model can provide correct pressure readout within /spl plusmn/1% error (full scale) over a range of temperature variations from -20/spl deg/C to 70/spl deg/C. Two ANN schemes, direct modeling and inverse modeling of a CPS, are reported. The former modeling technique enables an estimate of the nonlinear sensor characteristics, whereas the latter technique estimates the applied pressure which is used for direct digital readout. When there is a change in ambient temperature, the ANN automatically compensates for this change based on the distributive information stored in its weights.
Effect of other-cell MAI on linear and nonlinear multiuser detectors

This paper investigates the effect of other-cell multiple-access interference (MAI) on some suboptimal multiuser detectors such as decorrelating multiuser detector, parallel interference canceller and adaptive minimum mean-squared error (MMSE) multiuser detector. Based on the Gaussian approximation for other-cell MAI, the bit-error rate (BER) of each detector is derived. Both analytical results and simulations indicate that the performance degradation due to other-cell MAI is significant. It is also observed that the other-cell MAI introduces more errors to multiuser detectors that require prior information on users' signature waveforms.
On decision-feedback detection of differential space-time modulation in continuous fading

We show that linear prediction (LP)-based decision-feedback detection (DFD) for nondiagonal differential space-time modulation (DSTM) may suffer from a severe performance degradation in continuously fading channels. DSTM constellations that incur no degradation in LP-DFD are identified as those with a diagonal generator. To cater to other constellations, we propose a low-complexity DFD scheme by inserting decision-feedback symbols into the metric of multiple-symbol differential detection.
A subspace method for signature waveform estimation and detection in dispersive CDMA channels

We consider the problem of signature waveform estimation and detection in a dispersive CDMA channel in the presence of multiple access interference (MAI). A subspace training method for signature waveform estimation is proposed. By using this method the signature waveform can be estimated given only a small number of training symbols of the desired user. Simulation results show that the proposed method offers significant performance gain over the conventional matched filter receiver and it is near-far resistant.
Depth of field guided reflection removal

Reflection removal aims at separating the mixture of the desired scene and the undesired reflections. Locating reflection and background edges is a key step for reflection removal. In this paper, we present a visual depth guided method to remove reflections. Our idea is to use Depth of Field (DoF) to label the background and reflection edges. We propose a DoF confidence map where pixels with higher DoF values are assumed to belong to the desired background components. Moreover, we observe that images with different resolutions show different properties in the DoF map. Thus, we introduce a multi-scale DoF computing strategy to classify edge pixels more efficiently. Based on the results of edge classification, the background and reflection layers can be separated. Experimental results validate the effectiveness of our method using real-world photos.
Dominant SIFT: A novel compact descriptor

Definition and extraction of local features play a very important role in image retrieval (IR), pattern recognition and computer vision. Fast growth of technology today calls for local features to be as compact as possible toward real-time and limited bandwidth applications. In this paper, we study the problem of representing images in a compact way to achieve low bit-rate transmission while maintaining good performance. To be more specific, we propose a novel compact descriptor, dominant SIFT, which only uses 48 bits to describe local features. Importantly, our descriptor is training-free, vocabulary-free and suitable for real-time and mobile applications. We show the effectiveness of the proposed compact descriptor in image retrieval.
Decision-feedback multiple-symbol differential detection of differential space-time modulation in continuously fading channels

Linear prediction (LP)-based decision-feedback differential detection (DFDD) only works for diagonal differential space-time modulation (DSTM) when fading is changing fast and continuously. For other constellations, it suffers bad performance. We propose DFDD based on multiple-symbol differential detection (MSDD) for DSTM to cope with continuous fading. A key observation is that the correlation matrix of the received signal can be expressed in terms of DSTM matrices corresponding to the sent information symbols. In this way, decision feedback can be inserted into the MSDD metric, yielding a DF-MSDD receiver while maintaining almost the same performance as MSDD.
Intersymbol decorrelating detector for asynchronous CDMA systems

Estimated channel information, especially multipath length and multipath channel of the desired user, is necessary for most previously reported linear blind multiuser detectors in code division multiple access (CDMA) systems. This paper presents a new blind intersymbol decorrelating detector in asynchronous CDMA systems, which uses the cross correlation matrix of the consecutive symbols. The proposed detector is attractive for its simplicity because no channel estimation is required except the synchronization of the desired user. Compared with other reported multiuser detectors, simulation results show that the proposed detector provides a good performance when the active users have significant intersymbol interference and the multipath length is short.
Modified Kohonen learning network and application in Chinese character recognition

Normal multilayer neural network is rarely used to solve pattern match problem of large scale without grouping classes and creating subnetworks. In this paper, a modified single-layer Kohonen learning network structure based on generalized learning vector quantization (GLVQ) theory is proposed. By cascading two of the proposed learning networks in handwritten Chinese character recognition, training, preclassification and final recognition processes are easily integrated. Experiments conducted with off-line handwritten samples show the efficiency of the network.
New method of performance analysis for diversity reception with correlated Rayleigh-fading signals

This paper proposes a novel method for performance analysis of diversity reception with two correlated Rayleigh-fading signals. Based upon a transformation technique, two correlated Rayleigh-fading signals can be converted into two independent Rayleigh-fading signals. The performance of the diversity reception can be easily evaluated by analyzing the two independent Rayleigh-fading signals. The presented examples, performance analysis of maximal ratio combining (MRC), selection combining (SC), and equal gain combining (EGC) diversity reception with correlated Rayleigh-fading signals, show that the proposed approach is simple and effective.
Objective distortion measure for binary images

This paper describes an approach to evaluate the distortions introduced by changing individual pixels in the binary document image. The approach calculates the distortion by combining the change of smoothness with the change of connectivity. It is different from traditional distortion measures, such as PSNR and SNR, which do not correlate with human visual perception for binary image. PSNR and SNR only concern about how many pixels have been changed. The new approach considers not only how many pixels have been changed but also where the pixels have been changed. Simulation results show that the new distortion measure correlates well with human visual perception and its application in watermarking can be effective.
2D Finite Rate of Innovation Reconstruction Method for Step Edge and Polygon Signals in the Presence of Noise

The finite rate of innovation (FRI) principle is developed for sampling a class of non-bandlimited signals that have a finite number of degrees of freedom per unit of time, i.e., signals with FRI. This sampling scheme is later extended to three classes of sampling kernels with compact support and applied to the step edge reconstruction problem by treating the image row by row. In this paper, we regard step edges as 2D FRI signals and reconstruct them block by block. The step edge parameters are obtained from the 2D moments of a given image block. Experimentally, our technique can reconstruct the edge more precisely and track the Cramér-Rao bounds (CRBs) closely with a signal-to-noise ratio (SNR) larger than 4 dB on synthetic step edge images. Experiments on real images show that our proposed method can reconstruct the step edges under practical conditions, i.e., in the presence of various types of noise and using a real sampling kernel. The results on locating the corners of data matrix barcodes using our method also outperform some state-of-the-art barcode decoders.
DeepBag: Recognizing Handbag Models

In this paper, we address the problem of branded handbag recognition. It is a challenging problem due to the non-rigid deformation, illumination changes, and inter-class similarity. We propose a novel framework based on deep convolutional neural network (CNN). Concretely, we propose a new CNN model, called feature selective joint classification - regression CNN (FSCR-CNN). Its advantages lie in two folds: 1) it alleviates the illumination changes by a feature selection strategy to focus on the color- nondiscriminative features in the network learning, and 2) rather than only targeting on the hard label (i.e., the handbag model), it also incorporates a soft label (i.e., a distribution measuring the similarity between the ground truth model and all the models to be trained) to construct the loss function for training CNN, which leads to a better classifier for handbags with large inter-class similarity. We evaluate the performance of our framework on a newly built branded handbag dataset. The results show that it performs favorably for recognizing handbags with 94.48% in accuracy. We also apply the proposed FSCR-CNN model in recognizing other fine-grained objects with state-of-the-art CNN architectures, which is able to achieve over 5% improvement in accuracy.
On Establishing Edge Adaptive Grid for Bilevel Image Data Hiding

We propose, in this paper, a novel edge-adaptive data hiding method for authenticating binary host images. Through establishing a dense edge-adaptive grid (EAG) along the object contours, we use a simple binary image to show that EAG more efficiently selects good data carrying pixel locations (DCPL) associated with “ l-shaped” patterns than block-based methods. Our method employs a dynamic system structure with the redesigned fundamental content adaptive processes (CAP) switch to iteratively trace new contour segments and to search for new DCPLs. By maintaining and updating a location status map, a protective mechanism is proposed to preserve the context of each CAP and their corresponding outcomes. We prove that our method is robust against the interferences caused by close-by contours, image noises, and invariantly selects the same sequence of DCPLs for an arbitrary binary host image and its various marked versions. Comparison shows that our method achieves a good tradeoff between large payload and minimal visual distortion as compared with several classic prior arts for diverse types of binary host images. Moreover, our method well supports state-of-the-art hybrid authentication that integrates data hiding and modern cryptographic techniques.
Mobile camera identification using demosaicing features

Mobile cameras are typically low-end cameras equipped on handheld devices such as personal digital assistants and cellular phones. The fast proliferation of these mobile cameras has brought up concerns on the origin and integrity of their output images. In this paper, we identify blindly the source mobile cameras by combining 3 types of demosaicing features extracted from a test image. Through Eigenfeature regularization and feature reduction, comparison results show our Eigen demosaicing features perform significantly better than several conventional features in distinguishing 9 mobile cameras of dissimilar models based on cropped image blocks. By including cameras of the same and very similar models, in 15-cam identification, our Eigen demosaicing features achieves excellent classification accuracies in distinguishing cameras from dissimilar models and the classification accuracies expectedly tend to confuse among cameras of the same or very similar models.
Bias and variance calculation of a principal component based frequency estimator

The authors address the performance evaluation of the state variable algorithm based on singular value decomposition in frequency estimation. The bias and the variance expressions of the estimated frequency are obtained analytically to study the characteristics and the performance of the algorithm with respect to the free parameters. The calculations are based on the second-order Taylor series approximation of the principal singular vectors and values of the data matrix and its related derivatives with respect to its elements. The derived bias and variance expression are verified to be valid via simulation results. The estimated bias and variance are compared for variation in the dimensions of the data matrix and for wide range of signal-to-noise ratio.
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates

Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously. Based on the pictorial structure of Kinect's skeletal data, an effective tree-structure based traversal framework is also proposed. In order to deal with the noise in the skeletal data, a new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell. Moreover, we introduce a novel multi-modal feature fusion strategy within the LSTM unit in this paper. The comprehensive experimental results on seven challenging benchmark datasets for human action recognition demonstrate the effectiveness of the proposed method.