nils-herrmann/clean_scirepeval huggingface dataset:
Attribute-based multi-dimension scalable access control for social media sharing

Social media sharing is one of the most popular social interactions in online social networks (OSNs). Due to the diverse networking conditions and various privacy requirements of OSN users, scalable media sharing has become a promising paradigm. It allows a media data distributor to share a media content of different qualities with different data consumers. To guarantee user privacy in scalable media sharing, it is essential to design an effective scalable media access control (SMAC) mechanism. However, all existing schemes cannot support scalable media streams with more than two dimensions, which significantly limits the flexibility of OSN services. In this paper, we present the first multi-dimension SMAC (MD-SMAC) system for social media sharing, based on the proposed scalable ciphertext policy attribute-based encryption (SCP-ABE) algorithm. In the MD-SMAC system, secure and reliable access control can be performed on multidimension scalable media streams according to data consumers' attributes. Through the security analysis, we prove the security and reliability of the MD-SMAC system. We also conduct experiments on mobile devices to demonstrate the computation efficiency of the proposed system.
Blind Channel Equalization for Fast Moving Terminals in Prioritized Spatial Multiplexing MIMO Systems

A prioritized spatial multiplexing scheme has recently been developed for the transmission of scalable video coded (SVC) video over MIMO systems. One unique feature of this scheme is its capability to channelize the sub-channels according to the importance of the individually transmitted data streams. We consider in this research a challenging task of providing high quality multimedia services over MIMO systems with fast moving terminals. With fast moving terminals many existing approaches for MIMO channel estimation and equalization cannot be applied as most of them rely on the help of periodic pilot signals. When the terminals are travelling in high speed, the undesired Doppler effects prevent the existing approaches from efficient use of pilot signals for channel estimation and equalization. Although existing blind channel estimation and equalization schemes are able to avoid the problems associated with pilot signals, these schemes cannot be directly adopted for the prioritized spatial multiplexing MIMO system for video streaming in which data streams under different modulations need to be transmitted through sub-channels. In this paper, we develop a blind constant modulus algorithm (CMA)-based channel equalization (CMACE) scheme to track the fast time-varying channel without the requirement of pilot signals. We show that the transmitted signal in each sub-channel can be modulated with different symbol constellation, and delivered at different rate according to their importance. Simulation results demonstrate that the proposed scheme is effective for channel equalization with fast moving terminal in the prioritized spatial multiplexing MIMO systems
Fairness and QOS guaranteed user scheduling for multi-user MIMO broadcasting channel

This paper investigates fairness and quality of service (QoS) guaranteed user scheduling scheme in multi-user MIMO broadcast channel. The main contribution of this paper is to consider characteristic of video source in user scheduling process. As a result, the proposed scheme can provide fair and satisfactory services to regular data users, while QoS of multimedia consumers is guaranteed. Since video source is delay-sensitive, in our framework, first we will estimate the transmitting time of each video packet. The estimation of transmission time serves dual purpose: one is to guarantee the transmission of any given video packet before it expires, and another one is to allow other unscheduled regular data users to access the channel when there is no pressing need to transmit any video packet for the video consumer. Simulation results show that the proposed user scheduling framework achieves high QoS performance for multimedia consumers.
Receiver rate adaptation for MIMO system

This paper presents a novel design of linear random space time coding (LRSTC) scheme. This scheme is able to achieve the dual benefits for MIMO receiver rate adaptation: (1) rateless coding and modulation and (2) approximately universal tradeoff between diversity gain and multiplexing gain. In this scheme, an LRSTC symbol is the superposition of a block of randomly rotated QPSK symbols. Such superposition generates a dense constellation that ensures a high saturation rate. The random rotation guarantees the pseudo-orthogonality between QPSK symbols. Through utilizing the orthogonality of symbols, a power gain can be generated at receiver end. The power gain can be increased by transmitting more LRSTC symbols to the receiver in order to resist stronger noise of the MIMO system. Therefore, the rate can be seamlessly adapted through varying the number of transmitted symbols rather than the adopting conventional strategies in terms of modulation, coding and antenna selection. Moreover, the transmission of LRSTC symbols will span over all of the transmit antennas which allows the spatial diversity to be fully exploited. When the rank of MIMO matrix is greater than 1, more linear independent symbols can be received in each time slot. Hence, multiplexing gain can also be achieved. We have carried out both analysis and simulations to verify this LRSTC design. The simulation results show that LRSTC not only achieves the throughput comparable with the ideal conventional rate adaptation, but also accomplishes a seamless switching between diversity and multiplexing gain.
Joint facial landmark detection and action estimation based on deep probabilistic random forest

Random forest is effective and efficient for detecting facial landmark from visual images, and has achieved the state-of-the-art performance, both in accuracy and speed, by regressing local binary features (LBF). This paper aims to increase the detection accuracy of random forest for facial landmarks and extends it to facial action estimation. First, probabilistic features are designed to overcome the weaknesses of LBF, e.g., feature sparseness and tracking jitter. Second, a deep architecture is introduced to random forest for enhancing the capacity of representation learning. Third, the initial detected facial landmarks are refined and 3D facial actions are estimated jointly by registering a deformable facial model to images based on an optimized iterative closest point framework. Experiments show that the proposed methods significantly outperform the state-of-the-art ones in terms of accuracy, as well as achieve the excellent tracking stability and real-time ability at about 60 fps on an ordinary PC.
A novel prioritized spatial multiplexing for MIMO wireless system with application to H.264 SVC video

Spatial multiplexing MIMO wireless system is an excellent choice for next generation broadband multimedia communications because of its parallel transmission capability. A major challenge for such MIMO system is that it usually requires appropriate decomposition of both wireless channels and multimedia data bitstreams. Existing approaches to MIMO channel decomposition have been passively based on simple channel feedback. To facilitate proper match between decomposed channels with compressed video that exhibit variable priority for different layer of bitstreams, it is necessary to proactively prioritize the decomposed MIMO channels. We present in this paper a novel pre-coding scheme capable of integrating both channel and source characteristics in order to achieve the desired prioritized spatial multiplexing. This pre-coding scheme is applied to the transmission of video data com-pressed with H.264 Scalable Video Coding (SVC) standard. Based on the desired biterror-rate (BER) and signal-to-noise-ratio (SNR) for each SVC video layer, the base layer video is proactively assigned the highest priority sub-channel to guarantee minimum required quality while enhancement layers are mapped to other lower priority sub-channels. Comparing with existing schemes, the proposed pre-coder has low computational complexity and is suitable for rapid hardware implementation. The experimental results demonstrate that the proposed prioritized spatial multiplexing approach can efficiently enhance the quality of SVC video streaming over MIMO systems.
Double Compression Detection Based on Markov Model of the First Digits of DCT Coefficients

Double compression usually occurs after the image has gone through some kinds of tampering, so double compression detection is a basic mean to assess the authenticity of a given image. In this paper, we propose to model the distribution of the mode based first digits of DCT (Discrete Cosine Transform) coefficients using Markov transition probability matrix and utilize its stationary distribution as features for double compression detection. Experiment results show the effectiveness of the proposed method and comparison has been made to show the improvement by using this second order statistical model.
Research and application of large-scale non-grid-connected wind power coupled with high-energy consumption system

For the development bottleneck of wind power, based on research from the national “973” plan wind power project, this paper clarifies the content, basic research and validates the results of large-scale non-grid-connected wind power coupled with high energy consumption system. Building a Chinese characteristic large-scale non-grid-connected wind power high-energy consumption system for consuming large-scale wind power will increase the utilization efficiency of wind power, and achieve good social, environmental and economic benefits. The application of this new system will not only have an important strategic significance on accelerating the transformation of China's economic development, but also have theoretical significance and a practical role in promoting the development of wind power industry in the world.
Binary Code Reranking Method Based on Bit Importance

Due to its compact binary codes and efficient search scheme, image hashing method is suitable for large-scale image retrieval. In image hashing methods, Hamming distance is used to measure similarity between two points. For K-bit binary codes, the Hamming distance is an into and bounded by K. Therefore, there are many returned images share the same Hamming distances with the query. In this paper, we propose an efficient image ranking method based on bit importance of binary code. Compared with the returned images of Hamming distance, important bits of query image are detected. Then, large weights are assigned to important bits and small weights are assigned to minor bits. The advantage of this proposed method is calculation efficiency. Evaluations on two large-scale image data sets demonstrate the efficacy of our binary code ranking method based on bit importance.
Semi-supervised learning based on group sparse for relative attributes

Relative attributes provide accurate information for image processing to describe which image is more natural, more open, etc. Robustness of relative attribute learning depends on the labeled comparative image pairs. However, manually labeling is a labor intensive and time-consuming task. In this paper, a semi-supervised learning approach based on group sparse is proposed to discover pairwise comparisons automatically. We generate an initial level division of the labeled training images for the basic of new constraints. Then, group sparse representation for the unlabeled images is introduced by embedding the level information into the dictionary. The semi-supervised process is conducted by selecting samples which have minimum reconstruction errors and adding new constraints to the model by comparing the selected ones with the samples in dictionary. Experiments on three public datasets demonstrate the effectiveness of our proposed method.
Topology preserving dictionary learning for pattern classification

In recent years, dictionary learning (DL) has shown significant potential in various classification tasks. However, most of previous works aim to learn a synthesis dictionary. The other major category of DL-analysis dictionary learning has not been fully exploited yet. This paper proposes a novel DL method, named Topology Preserving Dictionary Learning (TPDL). First, we propose a triplet-constraint-based topology preserving loss function to capture the underlying local topological structures of data in a supervised manner. Second, a sparse-label-matrix-based function is integrated into the basic analysis model to improve discriminative ability. Third, Huber M-estimator is employed as a robust metric to handle the errors (e.g., outliers and noise) that possibly exist in data. Then, an alternating optimization algorithm is developed based on half-quadratic minimization and alternate search strategy. Closed-form solutions in each alternating optimization stage speed up the minimization process. Experiments on four commonly used datasets show that our proposed TPDL achieves competitive performance in contrast to state-of-the-art DL methods.
SIFT-Based Image Retrieval Combining the Distance Measure of Global Image and Sub-Image

This paper presents a similarity match method based on global image and local sub-image using the SIFT features of digital images, and applies our algorithm to Content-Based Image Retrieval. In order to make the SIFT-based image retrieval results better, the most fundamental improvement comes in two areas. One is the introduction of the distance between the matched keypoints, and the shorter the distance between the matched keypoints, the lower the similarity measure. The other is that the image is partitioned off into sub-images, which reduces the mismatched keypoints. Experiments demonstrate effectiveness of the proposed approach compared with the traditional SIFT-based image retrieval and reveal it as a good option to image retrieval.
Saliency detection via local single Gaussian model

Saliency detection has been long researched. However, most existing algorithms can not uniformly highlight salient objects. To approach this problem, we propose a novel saliency detection algorithm based on the Local Single Gaussian Model (LSGM). First, we utilize a bottom-up model to generate an initial saliency map and construct a background dictionary and a foreground dictionary based on the initial saliency map, respectively. Then, a LSGM is used to obtain a LSGM-based map. Note that we construct a corresponding LSGM for each superpixel region and thus the LSGM is a dynamic model with geometric structure information. Finally, we integrate the LSGM-based saliency map and the initial bottom-up map with global information as the final saliency map. Extensive experiments on four public datasets show that our algorithm outperforms state-of-the-art methods.
A Clustering-Based Location Privacy Protection Scheme for Pervasive Computing

In pervasive computing environments, Location-Based Services (LBSs) are becoming increasingly important due to continuous advances in mobile networks and positioning technologies. Nevertheless, the wide deployment of LBSs can jeopardize the location privacy of mobile users. Consequently, providing safeguards for location privacy of mobile users against being attacked is an important research issue. In this paper a new scheme for safeguarding location privacy is proposed. Our approach supports location K-anonymity for a wide range of mobile users with their own desired anonymity levels by clustering. The whole area of all users is divided into clusters recursively in order to get the Minimum Bounding Rectangle (MBR). The exact location information of a user is replaced by his MBR. Privacy analysis shows that our approach can achieve high resilience to location privacy threats and provide more privacy than users expect. Complexity analysis shows clusters can be adjusted in real time as mobile users join or leave. Moreover, the clustering algorithms possess strong robustness.
Information Security Game Analysis with Penalty Parameter

This paper analyzes information security in the E-commerce based on game theory, and this game analysis method is applied to information security for the first time. We set up the information security game model of the defender and the attacker, make the equilibrium analysis of this game model, and get the ideal strategy combination. Then we introduce the penalty parameter of the defender and the penalty parameter of the attacker to solve the problem that the restriction condition is not satisfied. The introduction of the penalty parameter of the defender promotes the defender to invest in information security, and the introduction of the penalty parameter of the attacker promotes the attacker to take no attack strategy. This paper provides good reference for information security in the E-commerce.
Information Security Investment Game with Penalty Parameter

The purpose of this paper is to analyze the strategy to promote the information security investment based on game theory. We use game theory to make the analysis and put forward the fruitful strategy suggestions for the defender organization to invest in information security. We set up the information security game model, and make the Nash Equilibrium analysis. The Nash Equilibrium analysis results of pure strategy and mixed strategy are consistent. By Nash Equilibrium analysis, we get the cost demand for information security investment. If this demand condition is not satisfied, we introduce the penalty parameter to the investment game. By regulating the value of the penalty parameter, we solve the hard problem of information security investment when it is difficult to reduce the investment cost. It is the first time to analyze information security investment by game theory. Our results provide fruitful strategy suggestions to promote information security investment.
Design of heart and lung auscultation training system based on BF518

In this paper, we design a system that it is used for simulating heart and lung auscultation for medical students. The single chip BF518 with low power assumption and higher performance is used in the system as the core processor. And it can play 16 audio files at the same time. So the system becomes more realistic in the operation of the real clinical environment. In the class, the reproduction of clinical scenarios for learners could provide a risk-free learning environment of the clinical knowledge and skills. Additionally, for added security of the audio of case, they are all encrypted and stored in the SD card.
Hybrid dictionary learning for JPEG steganalysis

In order to distinguish cover images and stego images, JPEG steganalysis technology has growing ties with machine learning in recent years. As an important research field in machine learning, dictionary learning (DL) has been successfully applied to various tasks, but its application in steganalysis is insufficient. In this paper, we propose a hybrid dictionary learning framework for JPEG steganalysis based on the fact that features of stego images have a close connection with image content but not helpful for steganalysis. We learn class-specific dictionaries for both cover and stego images to obtain their different particularities simultaneously. Besides, we learn a shared dictionary which can represent the common content of both sides. In such a way, class-specific dictionaries are used for classification while the shared dictionary contributes to reconstructing data. In addition, the proposed method also learns a synthesis dictionary for representation, and an analysis dictionary to achieve good classification. Compared with previous methods, our method dose not need ℓ0-norm or ℓ1-norm constraint and employs linear projection for obtaining the discriminative codes to make our method more efficient. So the hybrid dictionary is a phrase with double meaning, it is not only the combination of the class-specific dictionary and the shared dictionary, but also the incorporation of the synthesis dictionary and the analysis dictionary. The experimental results indicate that our hybrid dictionary learning based steganalysis method could achieve good performance.
A New Approach to Improve Signal Classification in Low SNR Environment in Spectrum Sensing

The performance of cognitive radio is sensitive to the accuracy of signal classification. The proposed method can increase the accuracy of existing methods on the certain degree at SNR=0 dB and below. In simulation, we classify five types of signals which are AM, BPSK, FSK, MSK and QPSK. The experiments show that above 99.9% received signals are correctly classified at SNR=-12 dB and above.
A printer forensics method using halftone dot arrangement model

In this paper, we present a printed image forensics method based on detection of halftone dots arrangement. The proposed method can be applied to identify printers and expose image forgery. The printed image is composed of halftone dots and these dots have regular arrangement. For a halftone dot, its adjacent halftone dots can be seen as the vertexes of a hexagon, we model the printer by 6 distance and 6 angles based on the halftone dot positions in the hexagon. In examination step, we apply Euclidean distance and K-means to identify printer and expose the forgeries, respectively. The experimental results have demonstrated the performance of proposed method both on printer identification and forgery detection.
Coupled Dictionary Learning for Target Recognition in SAR Images

In this letter, we propose a novel classification strategy called the coupled dictionary learning for target recognition in synthetic aperture radar (SAR) images. First, we train structured synthesis dictionaries to reflect the difference among each category. Second, we introduce a shared dictionary to reduce the effect of common features, such as the high similarity caused by specular reflection. Finally, we use the analysis dictionary to improve the efficiency of recognition by eliminating the constraint of the <inline-formula> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula>-norm or <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm of sparse code. Experimental results on Moving and Stationary Target Acquisition and Recognition data set indicate that our method can achieve better performance in SAR target recognition than the state-of-the-art methods, such as tritask joint sparse representation and CKLR. Especially, this method can be more robust when the depressions have obvious changes.
CRF with locality-consistent dictionary learning for semantic segmentation

The use of top-down categorization information in bottom-up semantic segmentation can significantly improve its performance. The basic Conditional Random Field (CR-F) model can capture the local contexture information, while the locality-consistent sparse representation can obtain the category-level priors and the relationship infeature space. In this paper, we propose a novel semantic segmentation method based on an innovative CRF with locality-consistent dictionary learning. The framework aims to model the local structure in both location and feature space as well as encourage the discrimination of dictionary. Moreover, an adapted algorithm for the proposed model is described. Extensive experimental results on Graz-02, PASCAL VOC 2010 and MSRC-21 databases demonstrate that our method is comparable to or outperforms state-of-the-art Bag-of-Features (BoF) based segmentation methods.