nils-herrmann/clean_scirepeval huggingface dataset:
Performance analysis of a W-CDMA based land mobile satellite communication system with diversity combining over fading channels

We derive the average bit error rate (BER) of a wideband DS-CDMA, land mobile LEO satellite communication system with an equal-gain RAKE receiver. We consider an urban channel model, which is a combination of a narrowband model and a wideband tapped delay line model. A computational simple and efficient approach is presented in this paper for obtaining approximated average BER, which takes advantage of moment parameters of path gain random variables with Rayleigh distributions. The analytical results with BPSK modulation, validated by simulation, are provided to illustrate the achievable performance with both diversity and no diversity reception for various satellite elevation angles.
Complementary feature extraction for branded handbag recognition

Fine-grained object recognition aims at recognizing objects belonging to the same basic-level class such as dog, bird or fish, which is a challenging problem in computer vision. In this paper, we consider the problem of recognizing handbags that belong to a specific brand. In order to identify the subtle differences among handbags, we propose to enhance the handbag local structure pattern by using the Hölder exponent, and extract the feature from the enhanced handbag image to complement the feature extracted directly from the original handbag image. We term such two types of features as the complementary and original features. These features will then be fused by using Multiple Kernel Learning (MKL) for branded handbag recognition. We conduct the experiments on a newly built branded handbag dataset, the results of which demonstrate the effectiveness of the proposed complementary feature in recognizing the handbags.
Accurate Detection of Demosaicing Regularity for Digital Image Forensics

In this paper, we propose a novel accurate detection framework of demosaicing regularity from different source images. The proposed framework first reversely classifies the demosaiced samples into several categories and then estimates the underlying demosaicing formulas for each category based on partial second-order derivative correlation models, which detect both the intrachannel and the cross-channel demosaicing correlation. An expectation-maximization reverse classification scheme is used to iteratively resolve the ambiguous demosaicing axes in order to best reveal the implicit grouping adopted by the underlying demosaicing algorithm. Comparison results based on syntactic images show that our proposed formulation significantly improves the accuracy of the regenerated demosaiced samples from the sensor samples for a large number of diversified demosaicing algorithms. By running sequential forward feature selection, our reduced feature sets used in conjunction with the probabilistic support vector machine classifier achieve superior performance in identifying 16 demosaicing algorithms in the presence of common camera post demosaicing processing. When applied to real applications, including camera model and RAW-tool identification, our selected features achieve nearly perfect classification performances based on large sets of cropped image blocks.
Performance study of a maximum-likelihood receiver for FFH/BFSK systems with multitone jamming

We derive the optimum structure of a maximum-likelihood (ML) receiver for a fast frequency-hopped binary frequency-shift-keying (FFH/BFSK) spread-spectrum (SS) communication system operating in the presence of multitone jamming (MTJ) and additive white Gaussian noise (AWGN). It is shown that the side information of noise variance, signal tone amplitude, and multiple interfering tone amplitude at each hop, as well as the computation of nonlinear modified Bessel function are required to implement the optimum ML receiver. We have also derived and analyzed two suboptimum receivers-namely, the ML-I and ML-II receivers-for large and small signal-to-noise ratio (SNR), respectively. Performance comparisons among various receivers show that the ML receiver gives the best performance, while the ML-I and ML-II receivers also outperform the other existing methods under both high and low SNR conditions.
Sparse Representation-Based Image Quality Index With Adaptive Sub-Dictionaries

Distortions cause structural changes in digital images, leading to degraded visual quality. Dictionary-based sparse representation has been widely studied recently due to its ability to extract inherent image structures. Meantime, it can extract image features with slightly higher level semantics. Intuitively, sparse representation can be used for image quality assessment, because visible distortions can cause significant changes to the sparse features. In this paper, a new sparse representation-based image quality assessment model is proposed based on the construction of adaptive sub-dictionaries. An over-complete dictionary trained from natural images is employed to capture the structure changes between the reference and distorted images by sparse feature extraction via adaptive sub-dictionary selection. Based on the observation that image sparse features are invariant to weak degradations and the perceived image quality is generally influenced by diverse issues, three auxiliary quality features are added, including gradient, color, and luminance information. The proposed method is not sensitive to training images, so a universal dictionary can be adopted for quality evaluation. Extensive experiments on five public image quality databases demonstrate that the proposed method produces the state-of-the-art results, and it delivers consistently well performances when tested in different image quality databases.
Orthogonal Data Embedding for Binary Images in Morphological Transform Domain- A High-Capacity Approach

This paper proposes a data-hiding technique for binary images in morphological transform domain for authentication purpose. To achieve blind watermark extraction, it is difficult to use the detail coefficients directly as a location map to determine the data-hiding locations. Hence, we view flipping an edge pixel in binary images as shifting the edge location one pixel horizontally and vertically. Based on this observation, we propose an interlaced morphological binary wavelet transform to track the shifted edges, which thus facilitates blind watermark extraction and incorporation of cryptographic signature. Unlike existing block-based approach, in which the block size is constrained by 3times3 pixels or larger, we process an image in 2times2 pixel blocks. This allows flexibility in tracking the edges and also achieves low computational complexity. The two processing cases that flipping the candidates of one does not affect the flippability conditions of another are employed for orthogonal embedding, which renders more suitable candidates can be identified such that a larger capacity can be achieved. A novel effective Backward-Forward Minimization method is proposed, which considers both backwardly those neighboring processed embeddable candidates and forwardly those unprocessed flippable candidates that may be affected by flipping the current pixel. In this way, the total visual distortion can be minimized. Experimental results demonstrate the validity of our arguments.
Lossless Data Embedding in Electronic Inks

This paper presents a novel lossless data embedding algorithm for electronic inks. The proposed algorithm first computes the analytical ink-curve for each stroke as a set of smoothly concatenated cubic Bezier curves. During embedding, a set of data carrier points on the ink-curve are evaluated, perturbed, and then inserted into the original point array. Our proposed smooth ink-curve generation method iteratively refines the parameterization based on the local curve geometry. We demonstrate experimentally that the inserted points incur the least perceptional distortion on the marked ink-curves even after large mount of secret message is embedded. On extraction, the perturbed data carriers are identified and the original point array can be recovered. By subtracting these perturbed data carriers from their recomputed reference locations, we derive a sequence of perturbation vectors, from which the embedded secret message can be decoded. Based on the proposed embedding technique and the public-key cryptography, a hybrid secure ink authentication system is designed to secure electronic writings for tampering detection.
Partial-band jammer suppression in FFH spread-spectrum system using FFT

This paper presents the performance evaluation of partial-band jammer suppression using fast Fourier transform (FFT) as an alternative in a fast frequency-hopped spread-spectrum (FFH/SS) communication system. The FFH/SS system employs a clipper to restrict the effect of a partial-band jammer before diversity combining. No additional side information of the jammer is required, and the analytical expression for the bit error rate (BER) is derived. It is shown that the performance of the FFT-based clipper receiver is better than that of the square-law linear combining receiver and comparable to that of the square-law clipper receiver.
Data hiding for text document image authentication by connectivity-preserving

A novel blind data hiding method for text document images is proposed; it aims to preserve the connectivity in a local neighborhood. The "flippability" of a pixel is determined by imposing three transition criteria in a 3/spl times/3 moving window which is centered at the pixel. The "embeddability" of a block is invariant in the watermark embedding process, while the "flipped" pixels can be located by imposing a constraint. The "uneven embeddability" of the host image is considered by embedding the watermark only in those "embeddable" blocks. The location is chosen in such a way that the visual quality of the watermarked image is guaranteed. Different types of blocks are employed and their abilities to increase the capacity are compared. A hard authenticator watermark is also generated to ensure the integrity and authenticity of the document.
Fashion analysis with a subordinate attribute classification network

In this paper we deal with two image-based object search tasks in the fashion domain, clothing attribute prediction and cross-domain shoe retrieval. Clothing attribute prediction is about describing the appearances of clothes via semantic attributes and cross-domain shoe retrieval aims at retrieving the same shoe items from online stores given a daily life shoe photo. We jointly solve these two problems by a novel Subordinate Attribute Convolutional Neural Network (SA-CNN), with the newly designed loss function that systematically merges semantic attributes of closer visual appearance to prevent images with obvious visual differences being confused with each other. A three-level feature representation is further developed based on SA-CNN for shoes from different domains. The experimental results demonstrate that the clothing attribute prediction using the proposed SA-CNN achieves better performance than that using traditional features and fine-tuned conventional CNN. Moreover, for the task of cross-domain shoe retrieval, the top-20 retrieval accuracy with deep features extracted from SA-CNN has a significant improvement of 43% compared to that with the pretrained CNN features.
Global Context-Aware Attention LSTM Networks for 3D Action Recognition

Long Short-Term Memory (LSTM) networks have shown superior performance in 3D human action recognition due to their power in modeling the dynamics and dependencies in sequential data. Since not all joints are informative for action analysis and the irrelevant joints often bring a lot of noise, we need to pay more attention to the informative ones. However, original LSTM does not have strong attention capability. Hence we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information. In order to achieve a reliable attention representation for the action sequence, we further propose a recurrent attention mechanism for our GCA-LSTM network, in which the attention performance is improved iteratively. Experiments show that our end-to-end network can reliably focus on the most informative joints in each frame of the skeleton sequence. Moreover, our network yields state-of-the-art performance on three challenging datasets for 3D action recognition.
Error probabilities and performance comparisons of FFH/BFSK receivers with multitone jamming and AWGN

This paper studies the bit-error rate (BER) performance of a fast frequency-hopped (FFH) binary frequency-shift-keying (BFSK) clipper receiver in the presence of multitone jamming (MTJ) and additive white Gaussian noise (AWGN). By using the Taylor-series expansion and the quantization approach, the BER expressions for higher diversity levels can be obtained without much extra computational complexity. The analytical BER results, validated by simulations, show that there is an optimum diversity level for the clipper receiver. Performance comparisons among various receivers demonstrate that the BER performance of the clipper receiver is significantly better than that of the linear-combining receiver. In addition, the clipper receiver also outperforms the product-combining receiver and the self-normalizing receiver provided that the clipping threshold is set at the desired signal power level.
Estimating EXIF Parameters Based on Noise Features for Image Manipulation Detection

We propose in this paper a novel technique to correlate statistical image noise features with three EXchangeable Image File format (EXIF) header features for manipulation detection. By formulating each EXIF feature as a weighted sum of selected statistical image noise features using sequential floating forward selection, the weights are then solved as a least squares solution for modeling the correlation between the intact image and the corresponding EXIF header. Image manipulations like brightness and contrast adjustment can affect these noise features and lead to enlarged numerical difference between each actual and its estimated EXIF feature from the noise features. By using the numerical difference as a manipulation indicator, we achieve excellent performance in detecting common brightness and contrast adjustment. Based on cameras of different brands, our manipulation detection is also demonstrated to work well in a blind mode, where the camera brand/model source is unavailable. Several detection examples suggest that our model can be applied in detecting real-world forgeries.
Steganalysis of Binary Cartoon Image using Distortion Measure

We present a steganalysis technique for data hiding in binary cartoon images. Due to the perturbation from the embedding, the contours of a stego binary cartoon image are distorted. When calculating the distortion of the image based on a distortion measure, the distortion score between a stego image and its de-noised version should be different from that between an original image and its de-noised version. Different binary image distortion measures are used to calculate the distortion scores, which are used as the features for classification. The sequential floating forward search (SFFS) method is used to search for the combination of the features that yields the best classification results.
Performance study of an FFH/BFSK clipper receiver with multitone jamming over a Rician-fading channel

We study the bit-error rate performance of a fast frequency-hopped binary frequency-shift-keying (FFH/BFSK) spread spectrum (SS) communication system over fading channels with the worst-case band multitone jamming (MTJ) and additive white Gaussian noise (AWGN). The FFH system employing a clipper receiver is investigated and the corresponding bit-error expressions are obtained using the quantization approach. The desired signal and MTJ are assumed to undergo independent fading and our analysis, validated with simulation results, shows that the performance of the system is not sensitive to a particular type of MTJ fading model.
Accurate detection of demosaicing regularity from output images

Demosaicing regularity is an important processing regularity associated with the internal camera processing and its detection from output photos is useful for non-intrusive forensic engineering. In this paper, we propose a reverse grouping technique to improve the detection accuracy of our earlier proposed detection model based on second-order image derivatives. Comparison results based on syntactic images shows that the proposed technique significantly reduces the reprediction errors for some commonly used demosaicing algorithms. When applied to a real application, i.e. camera model identification, our demosaicing features in conjunction with probabilistic support vector machine classifier achieve excellent classification performance.
A novel approach to FM interference suppression in DS spread-spectrum communication systems

A new recursive technique for the excision of FM interference in DS spread-spectrum communication system is introduced. The proposed algorithm tabulates the jammer-corrupted received samples as a matrix to model a time-varying process. Input vectors are sent into the matrix to produce upon multiplication output vectors from which the frequency information in the matrix is deduced. The scheme resembles the familiar closed-loop frequency response estimation procedures in classical control theories. The estimated frequency then tunes an adaptive notch filter for interference suppression. The method provides more accurate IF estimates than given by the Wigner-Ville distribution and a performance close to notch filtering at the exact IF at a significantly lower computational requirement.
A novel transform domain fully digital PN code tracking loop

Due to the nature of the code waveforms typically used in direct-sequence spread-spectrum (DS/SS) communication systems, the issue of pseudo-noise (PN) code synchronization is important. In this paper, fine synchronization (code tracking) is considered. A novel frequency domain approach to PN code tracking is proposed. The new method is based on the fuzzy technique. The code-timing is recovered by a tracking loop including the frequency domain fuzzy code phase estimator, the variable delay line and so on. The new tracking loop is fully digital and programmable, and hence is more stable to changes in environment parameters, which often influence the performance of analog loops. The structure and algorithm for the new tracking loop are presented and its performance is evaluated. Simulation results show that the frequency domain fuzzy tracking loop provides good performance even at low E/sub b//N/sub 0/ when the delay variation is slow.
Multitone jamming rejection of FFH/BFSK self-normalizing receiver

A computationally efficient bit-error rate (BER) expression for a fast frequency-hopped binary frequency shift-keying (FFH/BFSK) spread-spectrum (SS) communication system is derived based on Taylor-series expansion. The FFH system employs a soft-decision self-normalizing receiver against the worst-case multitone jamming (MTJ) and additive white Gaussian noise (AWGN). The analytical BER performance of the self-normalizing receiver, validated by the simulation results, is shown to be significantly better than that of the soft-decision linear-combining receiver under the conditions of the worst-case MTJ and AWGN.
Performance analysis of an FFH/BFSK linear-combining receiver against multitone jamming

A computationally efficient bit-error rate (BER) expression for a fast frequency-hopping binary frequency-shift-keying (FFH/BFSK) spread-spectrum system is derived based on Taylor-series expansion of the central differences. The FFH system employs a soft-decision linear-combining receiver against the worst-case band multitone jamming (MTJ) and additive white Gaussian noise. The analytical results are shown to match closely with the BER results based on simulation. This approach allows us to efficiently analyze the performance of the linear-combining receiver with higher diversity levels, which is otherwise mathematically intractable.
Performance analysis of an FFH/BFSK self-normalising receiver under multitone jamming

Analytical expressions for bit-error probability are derived for a fast frequency-hopping binary frequency-shift-keying (FFH/BFSK) spread-spectrum (SS) communication system based on Taylor-series expansion. The FFH system employs a soft-decision self-normalising receiver against the worst-case multitone jamming (MTJ) and additive white Gaussian noise (AWGN). The authors' analysis, validated by the simulation results, shows that the band MTJ is generally more harmful than the corresponding independent MTJ. Unlike the soft-decision linear-combining receiver, the worst-case partial-band jamming (PBJ) causes slightly poorer bit-error-rate (BER) performance than the corresponding worst-case band MTJ for the self-normalising receiver with diversity. The BER performance of the self-normalising receiver is shown to be significantly better than that of the soft-decision linear-combining receiver under the conditions of the worst-case band MTJ and AWGN.
Adaptive beamforming and power control for DS-CDMA mobile radio communications

Joint beamforming and power control is an efficient technique to increase the capacity of DS-CDMA systems. It is shown that the optimal solution of the uplink beamforming weights is independent of the combined path loss/shadowing effect. Based on this, a novel approach is proposed to implement adaptive beamforming and power control by converting the joint problem into a simple power control problem. It generates the optimal beamforming weights in each slot based on estimated channel responses. Computer simulations show that the new approach has much faster convergence for transmission power than the traditional LMS algorithm.
Data hiding in images based on tolerable error range

Previous proposed steganographic schemes based on tolerable error range (TER) can restrict the distortion caused by data hiding within an acceptable range, but both the stego-image and the original image are necessary for extracting the secret message. This paper presents a novel TER-based steganographic scheme, in which JPEG compression under a certain quality factor is regarded as TER, and the modification caused by data hiding on each pixel is less than that caused by JPEG compression. A verification mechanism is introduced to embed the quality factor which is used in JPEG compression. Furthermore, the JPEG version of the stego-image based on the novel scheme is the same as that of the corresponding original one while embedding secret bits. On the receiver side, the hidden message can be obtained by subtracting the stego-image from its JPEG version, and the original cover image is not needed. Experimental results show that the proposed scheme can produce a stego-image with acceptable quality and the capability of withstanding statistical detection.
Securing Online Reputation Systems Through Trust Modeling and Temporal Analysis

With the rapid development of reputation systems in various online social networks, manipulations against such systems are evolving quickly. In this paper, we propose scheme TATA, the abbreviation of joint Temporal And Trust Analysis, which protects reputation systems from a new angle: the combination of time domain anomaly detection and Dempster-Shafer theory-based trust computation. Real user attack data collected from a cyber competition is used to construct the testing data set. Compared with two representative reputation schemes and our previous scheme, TATA achieves a significantly better performance in terms of identifying items under attack, detecting malicious users who insert dishonest ratings, and recovering reputation scores.