nils-herrmann/clean_scirepeval huggingface dataset:
Seam-carving based anonymization against image & video source attribution

As image source attribution techniques have become significantly sophisticated and are now becoming commonplace, there is a growing need for capabilities to anonymize images and videos. Focusing on the photo response non-uniformity noise pattern based sensor fingerprinting technique, this work evaluates the effectiveness of well-established seam carving method to defend against sensor fingerprint matching. We consider ways in which seam-carving based anonymization can be countered and propose enhancements over conventional seam carving method. Our results show that applying geometrical distortion in addition to seam carving will make counter attack very ineffective both in terms of computational complexity and accuracy.
Classification of audio codecs based on coding characteristics

We propose a fast, simple and new method for identification of audio codecs that does not require decoding of coded audio data. The method uses chaotic and randomness features of coded audio to build models associated with different codecs. The most important features of the method are operating on just a few kilobytes of data sampled randomly from audio file and assuming no knowledge on encoding structure of a codec. These two features make it fast and simple. Experiments are performed on both singly coded and transcoded audio samples to measure accuracy of the technique.
Audio codec identification through payload sampling

W0065 present a new method for audio codec identification that does not require decoding of coded audio data. The method utilizes randomness and chaotic characteristics of coded audio to build statistical models that represent encoding process associated with different codecs. The method is simple, as it does not assume knowledge on encoding structure of a codec. It is also fast, since it operates on a block of data, which is as small as a few kilobytes, selected randomly from the coded audio. Tests are performed to evaluate the effectiveness of the technique in identification of the codec used in encoding on both singly coded and transcoded audio samples
Improvements on data hiding for lossy compression

Compression is the most common application that an forms of multimedia data undergo. Lossy nature of the compression due to the use of quantizers should be taken into account by the watermarking technique employed. In this paper, we present a data hiding scheme that incorporates the embedding with the quantization of lossy compression. We show that embedder-detector sets making use of compression scheme's quantization characteristics have better payload and lower compression bit rates. We also optimized the system parameters that maximize the hiding rate at different compression levels.
Cross-View Action Recognition Based on a Statistical Translation Framework

Actions captured under view changes pose serious challenges to modern action recognition methods. In this paper, we propose an effective approach for cross-view action recognition based on a statistical translation framework, which boils down to estimation of visual word transfer probabilities across views. Specifically, local features are extracted from action video frames and form bags of words based on k-means clustering. Though the appearance of an action may vary due to view changes, the underlying transfer tendency between visual words across views can be exploited. We propose two methods to measure the visual-word-based transfer relationship that are eventually based on frequency counts of word pairs. In the first method, word transfer probabilities are estimated by maximizing the likelihood of a shared action set with the EM algorithm. In the second method, word transfer probabilities are estimated by using likelihood-ratio tests. The two methods achieve comparable results and perform better when they are combined. For cross-view action classification, we compute action transfer probabilities based on the estimated word transfer probabilities and then implement a K-NN-like classification based on action video transfer probabilities. We verified our method on the public multiview IXMAS dataset and the WVU dataset. Promising results are obtained compared with state-of-the-art methods.
Robust visual tracking using local salient coding and PCA subspace modeling

Recently sparse coding has been successfully used in robust visual tracking. However, sparse coding is very computationally demanding as it needs to solve the L1 norm minimization problem. In this paper we propose a visual tracking algorithm based on both holistic and local appearance modeling. For the local appearance model, we use salient coding instead of the usual sparse coding to encode image patches sampled from each frame. Salient coding method exploits K closest codes to obtain a salient representation, which can be implemented efficiently. In our tracker, we combined the strength of global and local models to form a robust and effective tracking approach. Moreover, we propose a simple yet effective update strategy adapted to our collaborative model to deal with appearance change and reduce the drifting problem. We tested our algorithm on several challenging image sequences involving partial occlusion, drastic illumination change, pose change, and fast motion. Experimental results show that the proposed algorithm performs well against several state-of-the-art methods.
Pedestrian Counting Based on Crowd Density Estimation and Lucas-Kanade Optical Flow

This paper proposes a novel method to estimate crowd densities in regions and count pedestrians passing through a virtual line. Firstly, the crowd density estimation for regions is based on regional feature analysis and support vector regression (SVR). We extract the following features from each segmented region: the pixel ratio and block-size histogram of the foreground, the pixel ratio and the Minkowski dimension of the edge image, and the gray-level co-occurrence matrix (GLCM) features of the gray image. SVR is used to train and estimate the crowd densities of regions with the extracted features. Secondly, we use the estimated crowd densities and the Lucas-Kanade (LK) optical flow to count pedestrians passing through the line. In each region, we divide the estimated crowd density by the number of foreground pixels and get the density per foreground pixel. Then, people moving speeds based on the LK optical flow are used to compute the number of foreground pixels crossing the line. After that, we can estimate the number of people passing through the line by multiplying the density per foreground pixel by the number of foreground pixels crossing the line. Each region is segmented into several small blocks for higher accuracy to compute the people moving speeds. The experimental results show that the proposed method has a good performance on both crowd density estimation and pedestrian counting.
Person Re-identification by Multi-resolution Saliency-Weighted Color Histograms and Local Structural Sparse Coding

Person re-identification plays an important role in computer vision, aiming to identify the same person viewed by disjoint cameras at different time instants and locations. In this paper we present a novel appearance-based method by multi-resolution saliency-weighted color histograms and local structural sparse coding for re-identification work. The former descriptor captures global chromatic content while the latter exploits both partial and spatial information of individuals. Specifically, visual saliency is considered as weighting operators to increase the discriminative power of features. Finally a combinational matching strategy is employed to measure the similarity between individuals. Experimental results over two challenging benchmark datasets (VIPeR, ETHZ) demonstrate that our method obtains competitive performance.
Vehicle logo recognition based on a weighted spatial pyramid framework

This paper proposes an effective vehicle logo recognition framework, which is robust when the logos are only roughly located but not well segmented. Regions of interest (ROI) are first detected by using an AdaBoost-based detector. The detector is tuned to have a low false negative rate so as to guarantee coverage of the vehicle logo as much as possible. A weighted spatial pyramid framework is introduced to extract feature vectors from these ROIs. In this framework, we consider the union of ROIs instead of processing the ROIs individually for robustness and efficiency. Dense SIFT descriptors are extracted from the ROIs for robust description of the image. The scale-invariant feature transform (SIFT) descriptors are weighted based on the corresponding ROIs, highlighting locations with high confidence. The spatial pyramid scheme is then implemented to exploit the spatial distribution of local features. Finally, we apply a linear support vector machine (SVM) classifier to classify the logos based on max pooling of local descriptors. Experiments show that the proposed method attains high recognition accuracies in decent time on logo images captured by surveillance cameras in the real-world scenario, which verifies the robustness and effectiveness of the proposed framework.
New discrete-time ZNN models and numerical algorithms derived from a new Zhang function for time-varying linear equations solving

Different from the Zhang function (i.e., a type of error function) defined in the authors' previous work, a new Zhang function is proposed, designed and exploited to construct new Zhang neural network (ZNN) models in this paper. Moreover, two discrete-time ZNN models are developed and investigated to solve the problem of time-varying linear equations (TVLE). Such discrete-time ZNN models can exploit methodologically the time derivatives of time-varying coefficients and the theoretical inverse of the time-varying coefficient matrix. When the time-varying coefficient matrix is positive-definite and symmetric, the BFGS quasi-Newton method is introduced to eliminate the explicit matrix-inversion operation. Thus, two other discrete-time ZNN models combined with the BFGS quasi-Newton method (i.e., ZNN-BFGS) are proposed and investigated for TVLE solving. In addition, according to the criterion whether the time-derivative information of time-varying coefficients is explicitly known/used or not, these proposed discrete-time models are investigated in two categories: 1) the models with time-derivative information known (i.e., ZNN-K and ZNN-BFGS-K models); and 2) the models with time-derivative information unknown (i.e., ZNN-U and ZNN-BFGS-U models). Two illustrated examples verify the efficacy of these proposed models for TVLE solving.
Blocking objectionable images: adult images and harmful symbols

This paper describes a practical objectionable image filtering system, aimed at children's safer Web access. It includes two image filters: adult image filter and harmful symbol filter. In the adult image filter, we adopt a statistical model for skin detection and a neural network for adult image classification. The performance of the skin detection of our model outperforms that of the baseline model. Its elapsed time is about 0.18 second per image, which compares very well against previous systems. In the harmful symbol filter, we present an edge based Zernike moments method, which can capture the shape feature of a symbol object effectively. Its elapsed time is about 0.13 second per image. Experimental results on a large image database show that both of our filters can give promising performances
View-robust action recognition based on temporal self-similarities and dynamic time warping

In this paper, we propose an approach for human action recognition based on self-similarities of actions and dynamic-time warping method. To recognize actions under arbitrary views, we use a recent self-similarity matrix (SSM) method. Through analyzing the essence of SSMs we find that the SSMs capture a wealth of global time information useful for action recognition robust to viewpoints. The dynamic-time warping (DTW) algorithm is applied to make full use of the time information contained in SSMs. After performing DTW, we compute a collection of distances corresponding to mapped set of descriptors between the test sequence and all training sequences. Then the k-nearest neighbor classifier (KNNC) is implemented to classify the test action. We validated our method on the public multi-view IXMAS dataset and obtained promising results compared to the state-of-the-art bag-offeature-based method.
Nonlinear auto disturbance rejection control for vibration displacement system of continuous cast mold driven by servo motor

Aiming at the problem about the nonlinear function and the mechanical zero offset in the vibration system of continuous casting mold driven by servo motor, a discrete auto disturbance rejection control (ADRC) with nonlinear function processing algorithm is proposed. Firstly, for the nonlinear multi solutions problem existing between rotation angle and displacement of mold, a nonlinear algorithm is proposed to get the practical phase difference of eccentric shaft. The algorithm is achieved by calculating the time difference between ideal time of arrival and actual time of arrival at the same position. Secondly, the mathematical model of vibration system is discretized by using the Euler method, and an ADRC is designed to estimate the eccentric shaft mechanical zero offset and load disturbance, and then compensate and weaken the position error. Finally, the simulation results demonstrate the feasibility of the proposed method.
Different Zhang functions leading to various ZNN models illustrated via solving the time-varying overdetermined system of linear equations

Since 2001, a special class of recurrent neural network (RNN), termed Zhang neural network (ZNN), has been proposed, generalized and investigated for solving time-varying problems by following Zhang et al.'s design method. In the procedure of constructing ZNN models, designing a suitable error function [i.e., the so-called Zhang function (ZF) used in the methodology] plays an important role, as different ZFs lead to various ZNN models. Besides, differing from other error functions, e.g., the nonnegative energy function associated with the conventional gradient-based neural network (GNN), ZF is indefinite which means that such a ZF can be positive, zero, negative, bounded, or unbounded, even lower-unbounded. In this paper, two different main ZNN models are designed, constructed and investigated to solve the problem of time-varying overdetermined system of linear equations (TVOSLE) by exploiting different ZFs. Computer-simulation results substantiate the effectiveness of the two main ZNN models for solving such a time-varying problem.
Discrete-time Zhang neural network and numerical algorithm for time-varying linear equations solving

Since 2001, a special class of recurrent neural network (RNN), termed Zhang neural network (ZNN), has been proposed, generalized and exploited for online solution of time-varying problems by following Zhang et al's design method. In this paper, for possible digital hardware realization, discrete-time ZNN models are proposed and investigated for time-varying linear equations solving. Such discrete-time ZNN models make use of time-derivative information of the time-varying coefficients. For comparison purposes, a discrete-time gradient-based neural network (GNN) model is also presented to solve the same problem. Simulative and numerical results illustrate the efficacy and superiority of these discrete-time ZNN models used for time-varying linear equations solving, as compared to the GNN model.
Statistical Models for Skin Detection

We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model is well known from practitioners. Pixels are considered as independent. The second model is a Hidden Markov Model. It includes constraints that force smoothness of the solution. The third model is a first order model. The full color gradient is included. Parameter estimation as well as optimization cannot be tackled without approximations. We use thoroughly Bethe tree approximation of the pixel lattice. Within it , parameter estimation is eradicated and the belief propagation algorithm permits to obtain exact and fast solution for skin probability at pixel locations. We then assess the performance on the Compaq database.
Modeling Nonlinear Manifolds with Mixtures of Localized Principal Subspaces under a Self-Organizing Framework

This paper presents a neural network which models nonlinear manifolds with mixtures of localized principal subspaces under an online self-organizing framework. Each neuron in our network approximately learns a principal subspace of the local data distribution. Compared to other similar networks, the local principal subspaces learned at neurons of our network have better local property due to a new cost function, which helps to avoid confusion of different local sub-models. It is proved that there is no local extremum for each local model. Experiments show that the new model is better adapted to nonlinear manifolds for various data distributions than other models in comparison. The online-learning property of this model makes it feasible for sequentially acquired data and large data sets. Potential applications of this model include deployment of directional sensor networks, mapping high-dimensional signal space to physical space in wireless sensor networks, or simply nonlinear dimension reduction for data mining in sensor networks.
Fast-Learning Adaptive-Subspace Self-Organizing Map: An Application to Saliency-Based Invariant Image Feature Construction

The adaptive-subspace self-organizing map (ASSOM) is useful for invariant feature generation and visualization. However, the learning procedure of the ASSOM is slow. In this paper, two fast implementations of the ASSOM are proposed to boost ASSOM learning based on insightful discussions of the basis rotation operator of ASSOM. We investigate the objective function approximately maximized by the classical rotation operator. We then explore a sequence of two schemes to apply the proposed ASSOM implementations to saliency-based invariant feature construction for image classification. In the first scheme, a cumulative activity map computed from a single ASSOM is used as descriptor of the input image. In the second scheme, we use one ASSOM for each image category and a joint cumulative activity map is calculated as the descriptor. Both schemes are evaluated on a subset of the Corel photo database with ten classes. The multi-ASSOM scheme is favored. It is also applied to adult image filtering and shows promising results.
Cross-View Action Recognition Based on Hierarchical View-Shared Dictionary Learning

Recognizing human actions across different views is challenging, since observations of the same action often vary greatly with viewpoints. To solve this problem, most existing methods explore the cross-view feature transfer relationship at video level only, ignoring the sequential composition of action segments therein. In this paper, we propose a novel hierarchical transfer framework, which is based on an action temporal-structure model that contains sequential relationship between action segments at multiple timescales. Thus, it can capture the view invariance of the sequential relationship of segment-level transfer. Additionally, we observe that the original feature distributions under different views differ greatly, leading to view-dependent representations irrelevant to the intrinsic structure of actions. Thus, at each level of the proposed framework, we transform the original feature spaces of different views to a view-shared low-dimensional feature space, and jointly learn a dictionary in this space for these views. This view-shared dictionary captures the common structure of action data across the views and can represent the action segments in a way robust to view changes. Moreover, the proposed method can be kernelized easily, and operate in both unsupervised and supervised cross-view scenarios. Extensive experimental results on the IXMAS and WVU datasets demonstrate superiority of the proposed method over state-of-the-art methods.
Extended state observer based nonsingular terminal sliding-mode control for non-sinusoidal oscillation of continuous casting mold driven by servo motor

A new driving mode that using servo motor to drive continuous casting mold oscillated following the expected non-sinusoidal (include sinusoidal) is introduced. For this system, the integral nonlinear mathematical model is built, which contains the nonlinear model of servo motor and the mechanical transmission parts of the mold. Because the servo motor rotates continuously in single direction while the oscillation waveform of the mold is nonlinear periodic function, firstly, establish the mapping between them to solve the irreversible of this system, and use a third-order tracking differentiator (TD) which has high-speed tracking ability and good tracking precision to track the result of the mapping function and obtain its derivative signal simultaneously. Then a new type of control scheme combined the extended state observer (ESO) with nonsingular terminal sliding-mode control is adopted in investigation. The controller is designed for the integral system by adopting a non-singular terminal sliding mode manifold and can make the singularity avoided by preventing differentiating terms deriving the control laws and applying a continuous control strategy to resolve the chattering. Meanwhile, the ESO is designed to estimate the states and the lumped uncertain disturbances accurately without requiring exact mathematical model and the sensors. The proposed controller can guarantee stabilization of the tracing system. Simulated results are provided to demonstrate the effectiveness of the proposed control scheme.
Discrete-time Zhang neural network and numerical algorithm for time-varying quadratic minimization

A special class of recurrent neural network (RNN), termed Zhang neural network (ZNN), has been proposed, generalized and investigated for online solution of time-varying problems by following Zhang et al's design method since 2001. For possible digital hardware realization, discrete-time ZNN models are proposed and investigated in this paper for time-varying quadratic minimization (QM). The proposed discrete-time ZNN models could utilize the time-derivative information of time-varying coefficients. For comparison, a discrete-time gradient neural network (GNN) model is also presented to solve the same time-varying QM problem. Simulative and numerical results demonstrate the efficacy and superiority of the proposed discrete-time ZNN models for time-varying QM, in comparison with the discrete-time GNN model.
Motion Analysis Based on Spatial-Temporal Visual Attention

Motion analysis can be applied in a number of fields including, e.g. video surveillance, human-computer interaction, smart homes, etc. Due to the large amounts of data associated with usual videos, it is essential for motion analysis algorithms to be computationally efficient. In view of the remarkable efficiency of biological vision systems in dealing with visual information, we propose a motion analysis framework based on spatial-temporal visual attention. More specifically, this paper has adopted a patch-correlation-based approach as the baseline for motion analysis. To achieve computational efficiency, a biologically plausible visual attention model has been adopted, which is based on spatial and temporal features. These features, including intensity, color and motion, are extracted and combined to form salient volumes, i.e. salient spatial-temporal regions in video. During the action matching procedure, only the salient patches or regions are correlated between the query video and the target video, which reduces dramatically the computational cost and improves the robustness of motion analysis. Experimental results of human action detection demonstrate the effectiveness of the proposed framework.