nils-herrmann/clean_scirepeval huggingface dataset:
Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition

This paper proposes a novel nonnegative sparse representation approach, called two-stage sparse representation (TSR), for robust face recognition on a large-scale database. Based on the divide and conquer strategy, TSR decomposes the procedure of robust face recognition into outlier detection stage and recognition stage. In the first stage, we propose a general multisubspace framework to learn a robust metric in which noise and outliers in image pixels are detected. Potential loss functions, including L1 , L2,1, and correntropy are studied. In the second stage, based on the learned metric and collaborative representation, we propose an efficient nonnegative sparse representation algorithm to find an approximation solution of sparse representation. According to the L1 ball theory in sparse representation, the approximated solution is unique and can be optimized efficiently. Then a filtering strategy is developed to avoid the computation of the sparse representation on the whole large-scale dataset. Moreover, theoretical analysis also gives the necessary condition for nonnegative least squares technique to find a sparse solution. Extensive experiments on several public databases have demonstrated that the proposed TSR approach, in general, achieves better classification accuracy than the state-of-the-art sparse representation methods. More importantly, a significant reduction of computational costs is reached in comparison with sparse representation classifier; this enables the TSR to be more suitable for robust face recognition on a large-scale dataset.
Printer forensics based on page document's geometric distortion

A printed document can provide intrinsic features of the printer so as to distinguish which printer it comes from. But how to extract the intrinsic features is critical in printer forensics. In this paper, the page document's geometric distortion is extracted as the intrinsic features, and a printer forensics method based on the distortion is proposed. Firstly projective transformation model is used to model the geometric distortion. After the feature point set of the model is extracted, the model's parameters considered as the geometric distortion features can be estimated, and then the model's error pattern can be obtained. During the process, the least squares method is used to estimate the model's parameters, and SVM technique is used for classification. The effectiveness of the model's parameters in the printer forensics is demonstrated by experimental results.
A DCT-based image steganographic method resisting statistical attacks

The aim of steganography is to conceal the very existence of hidden communication, so its demand on security is serious. The security includes both imperceptibility and undetectability. However, most steganographic methods do not pay enough attention to the undetectability. We propose a novel DCT-based steganographic method for images. The method takes advantage of the similarities of DCT coefficients between the adjacent image blocks and makes the embedding distortion spread to the adjacent image blocks. Experimental results demonstrate that not only can the proposed method preserve good image quality, but it can also resist some typical statistical attacks.
Security analysis of optimal multi-carrier spread-spectrum embedding

This paper considers optimal multi-carrier (multiple messages) spread-spectrum (SS) data embedding on linearly-transformed host. We present information-theoretic security analysis for the optimal SS embedding. The security is quantified by both the Kullback-Leibler distance and Bhat-tacharyya distance between the cover and stego probability distributions. The main results of this paper permit to establish fundamental security limits for the optimal SS embedding. Theoretical analysis and experimental results show the impact of the number of embedding messages, the embedding distortion, and the host transformation in the security level.
Content based color image adaptive watermarking scheme

A color image adaptive watermarking scheme is proposed. To increase the robustness and perceptual invisibility, a model called ICPM (image complexity and perceptibility model) is proposed to analyze the different sub-blocks' properties. A novel technique is proposed for the watermark casting and retrieval by utilizing the properties of the image itself. Experimental results demonstrate the robustness of the algorithm to many attacks, such as JPEG, JPEG2000 and A/D conversion.
Discrete Cross-Modal Hashing for Efficient Multimedia Retrieval

Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxation step also brings quantization errors, resulting in poor retrieval performances. Other cross-modal hashing methods try to directly optimize the challenging objective function with discrete binary constraints. Inspired by , we propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the discrete binary codes without relaxing them. DCMH is formulated through reconstructing the semantic similarity matrix and learning binary codes as ideal features for classification. Furthermore, DCMH alternately updates binary codes of each modality, and iteratively learns the discrete hashing codes bit by bit efficiently, which is quite promising for large-scale datasets. Extensive empirical results on three real-world datasets show that DCMH outperforms the baseline approaches significantly.
Statistic-based color image watermarking scheme in DWT domain

Watermarking is a new technique for copyright protection. The robustness of watermarking is desirable in most practical application. In this paper, we propose a statistic-based watermarking scheme for color images in discrete wavelet domain. Unlike the traditional methods, the watermark is embedded into the statistic of the wavelet coefficients instead of an individual coefficient. We also present a channel adaptive watermarking (CAW) and a strength control function (SCF) to balance the robustness and the invisibility of the watermark. By experiments our algorithm shows a high performance on surviving multiple attacks from Stirmark Benchmark 4.0. The algorithm extends to blind watermarking easily, although the original watermark is needed in this paper.
Wind energy exploitation and hydrogen production bases construction applying non-grid-connected wind power in Jiangsu coastal areas

Energy consumption is increasing dramatically nowadays and the gap in the power supply is enlarging. Current energy consumption and environmental pollution call for improving the layout of energy consumption, protecting the environment, and large-scale application of renewable energies. Wind energy is abundant in Jiangsu (especially in Jiangsu’s coastal areas), which provides large space for wind power development. Application of large-scale, non-grid-connected wind power is one of the ways to develop wind power. In this paper, author analyzes distribution and exploitation of wind resources in Jiangsu’s coastal areas, studies the industrial chain of “applying non-grid-connected wind power to large scale hydrogen production” based on non-grid-connected wind power theory, and puts forward some suggestions for constructing hydrogen production bases in Jiangsu’s coastal areas.
A Task-Role Based Access Control Model with Multi-Constraints

Although RBAC models have received broad support as a generalized approach to access control, the administration of roles in large organizations can become quite cumbersome. In this paper, we develop a new paradigm for access control and authorization management, called task-role based access control (TRBAC) with multi-constraint. The basic idea of this model different from traditional RBAC is that roles and permissions are not connected directly but are put together by tasks. It is a dynamic authorization model with fine-grained partition on users, roles, tasks and sessions. The unit of task becomes the permission granularity. It is more convenient for enterprise privilege management such as distributed application, C/S access control and workflow management. It can reduce the administrator's burden and avoid some potential safety hazards because of adopted dynamic authorization.
Class-Aware Analysis Dictionary Learning for Pattern Classification

Dictionary learning (DL) plays an important role in pattern classification. However, learning a discriminative dictionary has not been well addressed in analysis dictionary learning (ADL). This letter proposes a Class-aware Analysis Dictionary Learning (CADL) model to improve the classification performance of conventional ADL. The objective function of CADL mainly includes two parts to promote the discriminability. The first part aims to learn a discriminative analysis subdictionary for each class instead of a global dictionary for all classes. The learned analysis dictionary is class-aware, generating a block-diagonal coding coefficient matrix. The second part aims to enhance the discrimination of coding coefficients by integrating a max-margin regularization term into our proposed framework. This term ensures the coefficients of different classes to be separated by a max-margin, which boosts the confidence of classification. A theoretical analysis is also given to support the max-margin regularization term from the perspective of preserving the pairwise relations of samples in coding space. We employ an alternating minimization algorithm to iteratively find the convergent solution. By evaluating our method on four pattern classification datasets, we demonstrate the superiority of our CADL method to the state-of-the-art DL methods.
Nonnegative sparse coding for discriminative semi-supervised learning

An informative and discriminative graph plays an important role in the graph-based semi-supervised learning methods. This paper introduces a nonnegative sparse algorithm and its approximated algorithm based on the l0–l1 equivalence theory to compute the nonnegative sparse weights of a graph. Hence, the sparse probability graph (SPG) is termed for representing the proposed method. The nonnegative sparse weights in the graph naturally serve as clustering indicators, benefiting for semi-supervised learning. More important, our approximation algorithm speeds up the computation of the nonnegative sparse coding, which is still a bottle-neck for any previous attempts of sparse non-negative graph learning. And it is much more efficient than using l1-norm sparsity technique for learning large scale sparse graph. Finally, for discriminative semi-supervised learning, an adaptive label propagation algorithm is also proposed to iteratively predict the labels of data on the SPG. Promising experimental results show that the nonnegative sparse coding is efficient and effective for discriminative semi-supervised learning.
Nonlinear Discrete Cross-Modal Hashing for Visual-Textual Data

Hashing techniques have been widely adopted for cross-modal retrieval due to their low storage cost and fast query speed. Recently, some unimodal hashing methods have tried to directly optimize the objective function with discrete binary constraints. Inspired by these methods, the authors propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the binary codes without relaxing them. DCMH is formulated through semantic similarity reconstruction, and it learns binary codes for use as ideal features for classification. Furthermore, DCMH alternately updates binary codes for each modality, and its discrete hashing codes are learned efficiently, bit by bit, which is quite promising for large-scale datasets. To evaluate the effectiveness of the proposed discrete optimization, the authors optimize their objective function in a relax-and-threshold manner. Extensive empirical results on both image-text and image-tag datasets demonstrate that DCMH is a significant improvement over previous approaches in terms of training time and retrieval performance.
PN-sequence masked spread-spectrum data embedding

Conventional additive spread-spectrum (SS) data embedding has a dangerous security flaw that unauthorized receivers can blindly extract hidden information without the knowledge of carrier(s). In this paper, pseudo-noise (PN) masking technique is adopted as an efficient security measure against illegitimate data extraction. The proposed PN-sequence masked SS embedding can offer efficient security against current SS embedding analysis without inducing any additional distortion to host nor notable recovery performance loss. To further improve recovery performance, optimal carrier design for PN-masked SS embedding is also developed. With any given host distortion budget, we aim at designing a carrier to maximize the output signal-to-interference-plus-noise ratio (SINR) of the corresponding maximum-SINR linear filter. Then, we present jointly optimal carrier and linear processor designs for PN-masked SS embedding in linearly modified transform domain host data. The extensive experimental studies confirm our analytical performance predictions and illustrate the benefits of the designed PN masked optimal SS embedding.
Silhouette coefficient based approach on cell-phone classification for unknown source images

Cell-phones have become a necessary communication accessory in daily life. MMS (Multimedia Messaging Service) used by smart phones has caused higher requirement on mobile image manipulation. Classifying image source cell-phones has become a major issue in the cell-phone communication forensics. There are two ways usually used for tracing and identifying the source device: image characteristics and equipment fingerprint. Both of the above schemes require a set of images captured by known source cell-phones for training a classification model. To avoid using any prior knowledge in practical scenarios, a graph based approach was proposed to classify the source cell-phones. Though an acceptable result has been obtained, a problem of incomplete classification appears in the case that one image is classified wrong into a single subset. In this paper, a silhouette coefficient based algorithm is proposed for source cell-phone classification. The spectral clustering algorithm is adopted in graph partitioning and the silhouette coefficient is used to extract the optimal classification from all the possibilities of classification. Experimental results show the validity of the proposed method.
Steganalysis of Palette Images: Attack Optimal Parity Assignment Algorithm

Steganography is the art of communicating a message by embedding it into multimedia data without drawing any suspicions. Optimal parity assignment (OPA) algorithm is one of palette image steganographic methods. But there is still no effective steganalytic method that can attack it reliably. In this paper, we present a statistical steganalysis for detecting the secret messages embedded by using OPA algorithm. Based on the analysis of RTS (replacement-transfer structure) of index replacement in GIF-based steganography, we explore the singular statistic of core elements in OPA stego-images via convergent continuous replacement (CCR) operation. On the other hand, we can estimate the statistic of cover image from stego-image by a special filtering operation. With the estimation of cover image statistic and the singular statistic of the stego-image, the length of secret messages embedded in GIF images with OPA algorithm can be estimated. The experimental results indicate that our algorithm can estimate the length of hidden messages accurately
Contribution-based feature transfer for JPEG mismatched steganalysis

In realistic steganalysis applications, the mismatched problem can lead to the degradation of performance in steganalysis. The main reason is the discrepancy of feature distributions between training set and testing set. In this paper, we present a Contribution-based Feature Transfer (CFT) algorithm for JPEG mismatched steganalysis. CFT tries to learn two transformations to transfer training set features by evaluating both the sample feature and dimensional feature contributions. We can obtain new feature representations so as to approach the feature distribution of the testing samples. The comparison to prior arts reveals the superiority of CFT on the experiments for the mismatched JPEG steganalysis in the heterogeneous cover source scenario.
Generalized transfer component analysis for mismatched JPEG steganalysis

Most universal JPEG steganalysis approaches rely on the assumption that training and testing samples come from the same distribution. They fail when training set and testing set are mismatched. In this paper, we propose generalized transfer component analysis for mismatched JPEG steganalysis to derive new representations from original features for training and testing samples to correct the mismatches. We first apply domain alignment to transform source domain (training set) to an intermediate domain closer to target domain (testing set). Then a set of common transfer components are learnt across two domains by minimizing the distribution distance between them. In the space spanned by these transfer components, two domains manifest similar characteristics and preserve enough discrimination to different categories. Extensive experiments demonstrate our method performs well in mismatched JPEG steganalysis.
RST-invariant sketch retrieval based on circular description

The explosive growth in touchscreen smartphones and tablets need simpler and intelligent image retrieval method to offer the convenience for users. Sketch based image retrieval (SBIR) which is based on a free hand sketch has recently attracted more attention, but current methods are sensitive to rotation, scaling and translation (RST). In this paper, we introduce an efficient SBIR method called RST-Invariant Circular Description (RICD). The proposed method utilizes saliency map and boundary information to detect salient contours for each image, then uses patch hashing to eliminate the deformations and redundancies of sketches. To achieve rotational invariance, we describe salient contours using circular description. The experiment results on two public datasets demonstrated that the proposed method outperforms the state-of-the-arts in both natural and product images, and handles rotation, scaling and translation transformations.
Printer identification using page geometric distortion on text lines

In this paper, we propose novel printer identification algorithms based on page geometric distortion features on text lines. The features include Page Text Line Slope (PTLS) sequence and Page Text Line Interval (PTLI) sequence which are applied to describe the page geometric distortion in horizontal and vertical directions, respectively. The similarity measure of two feature sequences with different length can be presented by the Matching Euclidean Distance. Four printer identification algorithms are proposed to identify the printer source. The proposed algorithms do not need to restore the reference document manually, and maintain high performance even when the document has partial content.
Study on integration of production, education and research for non-grid-connected wind power

Wind power industry is an explosively growing industry, and wind power utilization is a sunrise industry in the new century. The application of large-scale non-grid-connected wind power throws the convention to the grid, so that the field, which could use non-grid-connected wind power, is extended. In this paper, the method of integration of production, education and research for non-grid-connected wind power is studied. The approach of integration of production, education and research is discussed from the viewpoint of promoting cohesion for technological achievements. The reasons for choosing this topic, the method of developing integration and the mode of promoting integration are investigated.
An improved shape-based arbitrary shape ROI coding method with SA-DWT in JPEG2000

The JPEG2000 image coding standard supports two region-of-interest (ROI) coding methods: the maximum shift (Maxshift) method and the general scaling based method. This paper extends the general scaling based method of JPEG2000, and presents an effective ROI coding method based on shape coding with shape-adaptive discrete wavelet transforms (SA-DWT) applied to the arbitrary shape ROI and background respectively. The proposed method not only allows the coding of multiple arbitrary shape ROI, but also the image quality of ROI boundary will not decrease for the wavelet coefficients shifting of background. Coding results illustrate the performance of the proposed coding method.
Forward/backward adaptive context selection with applications to motion vector field encoding

In low rate motion-compensated video coding, the rate required to encode the motion field can become a significant portion of the overall rate budget. This motivates us to investigate methods to efficiently, and losslessly, encode the motion field. Reductions in the required motion field bit rate allow increasing the rate for the residue images or may permit a higher density motion field to be used. We consider adaptive context modeling techniques, such as those proposed in image coding applications, and explore their effectiveness for coding the motion data. We rely on various forward/backward context selection algorithms, and demonstrate how forward adaptation, based on alphabet partitioning approaches, can result in performance improvements over purely backward adaptive methods. We observe substantial reductions in rate, especially for dense motion fields, when comparing with popular differential coding schemes using VLC tables, such as those in the H.263 standard.
Wavelet-based redundant representation for efficient random access of volumetric images

Random access for remote visualization of an arbitrary 2-D planar cut from a large volumetric dataset poses significant challenges in terms bandwidth, storage and memory requirements. In our previous work, we have proposed a fast random image 3D retrieval system, which use overlapping rotated tiles to represent the dataset. This approach achieves a significant reduction in average transmission rate as compared to traditional cubic tiling. Additionally, this approach leads to improved random access, with less storage and runtime memory required at the client. In this paper, we first describe the details of our 3D compression technique. Then, we propose a new wavelet based approach, which can achieve 25% reduction in the average transmission rate at the cost of a factor of five in storage overhead (half of what was required for our previously proposed method).
Local two-channel critically sampled filter-banks on graphs

In this paper, we propose two-channel filter-bank designs for signals defined on arbitrary graphs. These filter-banks are local, invertible and critically sampled. Depending on the chosen downsampling method, we obtain two design techniques. We propose general 2-channel transforms, where output signal is downsampled to guarantee invertibility. We also propose a lifting-based approach, where signals are downsampled before applying the transforms. Our proposed transforms are polynomials of the graph Laplacian matrix and have a simple spectral interpretation.