nils-herrmann/clean_scirepeval huggingface dataset:
Application of pulse compression with a Doppler shift using neural network

Common biphase pulse compression techniques were not very robustness against Doppler distortions, and the peak signal-to-noise ratio of these methods was not very high. Kwan and Lee (1993) presented a pulse compression approach by using a neural network. A 40 dB peak signal-to-noise ratio can be achieved. Based on Kwan and Lee's method, we present an improved BP (backpropagation) algorithm. The robustness against Doppler distortions and the output peak signal-to-noise ratio of the improved algorithm are examined. The improved method get a 50 dB output peak signal-to-noise ratio, about 10 dB higher than Kwan and Lee's method. The improved method also has a better Doppler resistance ability.
Path and countermeasure of large-scale wind power development in African countries

Africa has abundant wind energy resources, and consumption of conventional energy will continue to grow with economic and social development, an urgent need for abundant renewable energy, and particularly abundant wind power resources as a new energy. Africa is copying the model and path of Western countries for wind power development, which will certainly increase the price of wind power equipment and wind power. By relying on government subsidy, it also cannot play a strategic role in guiding or promoting Africa's economic development and adjustment of energy structure, but it achieves better social benefits. Therefore, this article has found that large-scale wind power development could adapt to Africa's actual circumstances to achieve unity of social benefits, environmental benefits and economic benefits.
A semi-automatic deshredding method based on curve matching

We present a semi-automatic method to reconstruct shredded documents. The novelty of the method lies in the way it performs pairwise matching of chads. The technique divides chad contours into curves using corner detection and introduces a procedure to assess the match of two curves. The proposed curve matching technique is robust to translation and rotation and can cope with shape deformations due to shredding by allowing overlapping of chads during matching. The alignment of text lines, crossing characters and color information on the chads is also utilized to improve matching performance. Visual interfaces are designed to allow for user input in identifying correctly matching chad pairs and reconstructing the document. The effectiveness of the method is demonstrated by solving the first and second puzzles of the DARPA shredder challenge.
Sparse Feature Preservation for Relative Attribute Learning

Relative attributes learning provides a way to capture the strength of the attributes under consideration and it can provide a more specific and accurate information to describe images. But for computers, extracting low-level features is the foundation of understanding images. Thus there is no doubt that the features will have an significant influence on relative attribute models learning. %For example, local features don't have much positive effects on learning global attributes. In this paper, we propose a sparse feature preservation (SFP) method to preserve the most important features on the learning of each attribute model. SFP is formulated through using rearrangement inequality according to relative attribute models learning. We first train the relative attribute models according to the supervision information of attribute pairs. Then the sorting results are used to train the key feature preservation factors and the sparse features are utilized to retrain the relative attribute models. We demonstrate the approach on five datasets and show its significant improvement on the accuracy of relative attribute learning.
Information Security Problem Research Based on Game Theory

The purpose of this paper is to resolve information security problem in the mobile electronic commerce industry chain. We analyze information security based on evolutionary game theory. In this paper, we set up the information security game model with penalty parameter, calculate replicator dynamics, and analyze the evolutionary stable strategy of the game model. The result reveals that reducing the investment cost is the key factor to promote information security investment. If this condition can not be satisfied, the regulation of penalty parameter will help to promote the investment. The research method in this paper provides a new thought for the solution of information security in the mobile electronic commerce chain.
Research on the development policy of non-carbon energy-consuming industry using large-scale non-grid-connected wind power

This paper proposes that the inevitable direction for wind power development in China is large-scale non-grid-connected wind power system because of its importance for speeding up the development of non-carbon energy-consuming industries. The challenges and urgency for constituting the wind power development policy in China are expounded after summarizing domestic and foreign policy experience in developing wind power. Furthermore, a basic policy framework for developing non-carbon energy-consuming industries using large-scale non-grid-connected wind power is built.
Robust Principal Component Analysis Based on Maximum Correntropy Criterion

Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced to a quadratic problem that can be efficiently solved by a standard optimization method. The proposed method exhibits the following benefits: 1) it is robust to outliers through the mechanism of MCC which can be more theoretically solid than a heuristic rule based on MSE; 2) it requires no assumption about the zero-mean of data for processing and can estimate data mean during optimization; and 3) its optimal solution consists of principal eigenvectors of a robust covariance matrix corresponding to the largest eigenvalues. In addition, kernel techniques are further introduced in the proposed method to deal with nonlinearly distributed data. Numerical results demonstrate that the proposed method can outperform robust rotational-invariant PCAs based on L1 norm when outliers occur.
Detecting Image Tampering Using Feature Fusion

Along with the development of sophisticated image processing software, it is getting easier forging a digital image but harder to detect it. It is already a problem for us to distinguish tampered photos from authentic ones. In this paper, we propose an approach based on feature fusion to detect digital image tampering. First, we extract the feature statistics that can represent the property of a camera from the images taken by that camera. These feature statistics are used for training a one-class classifier in order to get the feature pattern of the given camera. Then, we do sliding segmentation to testing images. Finally, feature statistics extracted from image blocks are fed into the trained one-class classifier to match the feature pattern of the given camera. The images with low percentage of matched blocks are classified as tampered ones. Our method could achieve a high accuracy in detecting the tampered images that undergone post-processing such as JPEG compression, re-sampling and retouching.
Adaptive feature extraction based on Stacked Denoising Auto-encoders for asynchronous motor fault diagnosis

As the important power equipment in the mechanical system, fault diagnosis for asynchronous motor is helpful to monitor working status and prevent failure causing unnecessary loss. In the fault diagnosis domain, feature extraction is the key step which is related to the performance of diagnosis results. For the asynchronous motor, the motor current signature analysis (MCSA) is one of the most powerful diagnosis method with stator-current signals. However, MCSA has some shortcomings, which degrade performance and accuracy of a motor-diagnosis system. Therefore, advanced feature extraction algorithm of current signal using Stacked Denoising Auto-encoders (SDAE) is proposed in this paper. The method of SDAE and application in motor are discussed in detail. Then, the features learned from the SDAE is displayed and a softmax regression model is used to verify the discriminability of the features. The experiments show that SDAE is an effective feature extraction technique for asynchronous motor fault diagnosis.
Amplitude-adaptive spread-spectrum data embedding

In this study, the authors consider additive spread-spectrum (SS) data embedding in transform-domain host data. Conventional additive SS embedding schemes use an equal-amplitude modulated carrier to deposit one information symbol across a group of host data coefficients which act as interference to SS signal of interest. If there is a flexibility of assigning different amplitudes across symbol bits, the probability of error can be further reduced by adaptively allocating amplitude to each symbol bit based on its own host/interference. In this study, they present a novel amplitude-adaptive SS embedding scheme. Particularly, symbol-by-symbol adaptive amplitude allocation algorithms are developed to compensate for the impact from the known interference. They aim at designing the SS embedding amplitude for each symbol adaptively in order to minimise the receiver bit-error-rate (BER) at any given distortion level. Then, optimised amplitude allocation for multi-carrier/multi-message embedding in the same host data is studied as well. Finally, they consider the problem of amplitude optimisation for an ideal scenario where no external noise is introduced during embedding and transmission. Extensive experimental results illustrate that the proposed amplitude-adaptive SS embedding scheme can provide order-of-magnitude performance improvement over several other state-of-the-art SS embedding schemes.
Source Camera Identification Forensics Based on Wavelet Features

Source camera identification forensics aims at determining and authenticating the original sources of digital images to support forensics and get the trace of digital images. This paper introduces a new wavelet features based passive forensic method for the identification of the image source camera. We consider the intrinsic defects and processing of imaging pipeline within digital cameras can be used to formulate the source camera identification problem. Based on this idea, we extract higher-order wavelet features and wavelet coefficient co-occurrence features from taken images, and then apply Sequential Forward Feature Selection (SFFS) method to reduce the redundancy and correlation of features and finally use multi-class Support Vector Machine (multi-class SVM) as classifier to identify source cameras. The effectiveness of the proposed approach, also in comparison with other approaches, is experimentally proved on images of six digital cameras.
Unsupervised feature selection with ordinal locality

Unsupervised feature selection has shown significant potential in distance-based clustering tasks. This paper proposes a novel triplet induced method. Firstly, a triplet-based loss function is introduced to enforce the selected feature groups to preserve ordinal locality of original data, which contributes to distance-based clustering tasks. Secondly, we simplify the orthogonal basis clustering by imposing an orthogonal constraint on the feature projection matrix. Consequently, a general framework for simultaneous feature selection and clustering is discussed. Thirdly, an alternating minimization algorithm is employed to efficiently optimize the proposed model together with rapid convergence. Extensive comparison experiments on several benchmark datasets well validate the encouraging gain in clustering from our proposed method.
Exposing the double compression in MP3 audio by frequency vibration

A novel approach is presented to detect double compressed MP3 audio by frequency vibration. With the analysis of double compression effect on MDCT (Modified Discrete Cosine Transform) coefficients in MP3 audio, we propose a simple feature called FVV (frequency vibration value) to measure the vibration caused by double compression. The experimental results on challenging dataset show that our method outperforms most of the existing methods in double MP3 compression detection, especially with a second bitrate higher than the first one. Besides, we can also estimate the original bit-rate for a double compressed MP3 by this technique.
Model Semantic Relations with Extended Attributes

Attribute based image retrieval has offered a powerful way to bridge the gap between low level features and high level semantic concepts. However, existing methods rely on manually pre-labeled queries, limiting their scalability and discriminative power. Moreover, such retrieval systems restrict the users to use only the exact pre-defined query words when describing the intended search targets, and thus fail to offer good user experience. In this paper, we propose a principled approach to automatically enrich the attribute representation by leveraging additional linguistic knowledge. To this end, an external semantic pool is introduced into the learning paradigm. In addition to modelling the relations between attributes and low level features, we also model the join interdependencies of pre-labeled attributes and semantically extended attributes, which is more expressive and flexible. We further propose a novel semantic relation measure for extended attribute learning in order to take user preference into account, which we see as a step towards practical systems. Extensive experiments on several attribute benchmarks show that our approach outperforms several state-of-the-art methods and achieves promising results in improving user experience.
Patch-based sensor pattern noise for camera source identification

Sensor pattern noise (SPN) has been proved to be an inherent fingerprint of a camera, and it has been broadly used in the fields of image authentication and camera source identification. However, the SPN extracted using current denoising algorithm always contains image content residual, which would significatively influence the accuracy of camera source identification. In this paper, a novel patch-based (PB) sensor pattern noise algorithm for camera source identification is proposed to solve this problem. Low-complexity patches of images are selected to construct local reference SPN, which contains least image content residual. The global reference SPN is constituted with the block-wised local SPN. Similarly for the test image, SPN is extracted from low-complexity region, and making correlation with corresponding local reference SPN. Our experiments on the Dresden database demonstrate that the proposed approach outperforms two sensor pattern noise estimation methods on the literatures as baseline.
Multi-View Analysis Dictionary Learning for Image Classification

A great number of studies show that considering information from multiple views results in better performance than their single-view counterparts. However, many previous works aiming to improve classification performance fail to effectively tackle the inter-view correlation or the intra-class variability. Analysis dictionary learning (ADL) has theoretic significance and practical potential in classification tasks. Based on the ADL, this paper proposes a new method, namely, multi-view analysis dictionary learning (MvADL) for image classification. Specifically, multi-view analysis dictionaries are designed to reduce the intra-class variability in the transformed space in the multi-view scenario. Then, a marginalized classification term is incorporated to integrate the semantic information into the basic dictionary learning model. In the marginalized classification term, a marginalized target learning strategy is applied to improve the flexibility and discriminability of the whole model. Besides, an iteratively optimizing algorithm is designed to solve the proposed MvADL. Experiments on benchmark data sets demonstrate the superiority of our proposed method.
Visual Search Based on Contour Salient

Online shopping is becoming more and more popular for a number of reasons; prices are often lower online, you don’t have to queue up in busy shops and you can buy almost any product imaginable with just a few clicks of your mouse. But the general problems of shopping website is that, most of the existing online shops list products based on keywords. As the inherent limitation, keyword browsing makes it difficult to find the exact products that human being desire. In this paper, we propose a visual search algorithm based on contour salient. The proposed approach extracts the object edge using Canny edge detector, and then chooses the salient point from the contour based on the points’ contour flexibility. We perform Fourier transformation to these salient points and a shape normalization procedure to generate the descriptor representing the shape feature. Finally, SVM and dynamic time warping method are used to train the database images and compute the distance between query image and test image. Experimental result shows our method is effective to search the similar product images with query.
A balanced semi-supervised hashing method for CBIR

Hashing methods have attracted much attention in large scale image research in recent years, because they are not only fast, but also needing a little memory. This paper proposed a balanced semi-supervised hashing method by dividing image into several blocks. With the help of improved semi-supervised hashing, we obtain a short hash code of each block, which jointed together forms a hash code of an integrated image. In the improved semi-supervised hashing, the supervised information is completed by combining the similarity of image pairs and label information. Extensive experiments demonstrate that our method can get more balanced result between retrieval speed, saving storage of original data and retrieval accuracy in CBIR than the state-of-the-art hashing methods.
Source separation based on nonnegative matrix factorization and independent component correlation algorithm

Because the initialization of Nonnegative Matrix Factorization (NMF) has a great impact to the final result, a new method that combines Independent Component Analysis (ICA) with NMF is put forward. Firstly, ICA is used to process the raw data and the initial value of NMF can be obtatined. Secondly, according to the characters of machine faults, the Hierarchical Alternating Least Squares (HALS-CR) is adopted to extract fault feature from complex system. Finally, simulations and application data show that the proposed approach is effective in improving the separation of traditional NMF.
Disrupting MIMO Communications With Optimal Jamming Signal Design

This paper considers the problem of intelligent jamming attack on a MIMO wireless communication link with a transmitter, a receiver, and an adversarial jammer, each equipped with multiple antennas. We present an optimal jamming signal design, which can maximally disrupt the MIMO transmission when the transceiver adopts an anti-jamming mechanism. In particular, signal-to-jamming-plus-noise ratio (SJNR) at the receiver is used as the anti-jamming reliability metric of the legitimate MIMO transmission. The jamming signal design is developed under the most crucial scenario for the jammer where the legitimate transceiver adopt jointly designed maximum-SJNR transmit beamforming and receive filter to suppress/mitigate the disturbance from the jammer. Under this best anti-jamming scheme, we aim to optimize the jamming signal to minimize the receiver's maximum-SJNR under a given jamming power budget. The optimal jamming signal designs are developed in different cases with accordance to the availability of channel state information (CSI) at the jammer. The analytical approximations of the jamming performance in terms of average maximum-SJNR are also provided. Extensive simulation studies confirm our analytical predictions and illustrate the efficiency of the designed optimal jamming signal on disrupting MIMO communications.
Unsupervised Support Vector Machines with Perturbations

Support vector machines (SVMs) have been dominant learning techniques for almost ten years, and mostly applied to supervised learning problems. Recently nice results are obtained by two-class unsupervised and semi-supervised classification algorithms where the optimization problems based on bounded C-SVMs, bounded v-SVMs and Lagrangian SVMs respectively are relaxed to semi-definite programming (SDP). These support vector methods implicitly assume that training data in the optimization problems are known exactly. But in practice, the training data are usually subjected to measurement noise. Zhao et al proposed robust version to unsupervised and semi-supervised classification problems based on Bounded C-SVMs, which need to find the dual problem twice. In this paper we propose unsupervised classification algorithm based on primal problem of standard SVMs with perturbations, which directly relaxes it with label variables to a semi-definite programming. Numerical results confirm the robustness of the proposed method.
Feature extraction via multi-view non-negative matrix factorization with local graph regularization

Feature extraction is a crucial and difficult issue in pattern recognition tasks with the high-dimensional and multiple features. To extract the latent structure of multiple features without label information, multi-view learning algorithms have been developed. In this paper, motivated by manifold learning and multi-view Non-negative Matrix Factorization (NM-F), we introduce a novel feature extraction method via multi-view NMF with local graph regularization, where the inner-view relatedness between data is taken into consideration. We propose the matrix factorization objective function by constructing a nearest neighbor graph to integrate local geometrical information of each view and apply two iterative updating rules to effectively solve the optimization problem. In the experiment, we use the extracted feature to cluster several realistic datasets. The experimental results demonstrate the effectiveness of our proposed feature extraction approach.
A new steganographic method resisting statistical attack

In this paper we present a new steganographic method for JPEG images that can resist both chi-square attack and Fridrich's "Breaking the F5" statistical attack. A key element in our method is a compensatory strategy that keeps the first-order statistical property of DCT coefficients in the stego-image almost identical with that in the cover-image, thus the statistical attacks are defeated. Unlike the correcting transform adopted in OutGuess that modifies steganographically usable coefficients to correct the statistical deviations caused by the embedding process leading to a relatively small steganographic capacity, the compensatory scheme in our method uses steganographically unusable coefficients (coefficients with absolute value equal to zero) to compensate the distortion of the first-order statistical property of the stego-image induced by the embedding process. Therefore both the steganographic capacity and the quality of the stego-image remain high. Experimental results based on a large set of test images demonstrate the performance of our method in the aspects of both security and capacity issue.