nils-herrmann/clean_scirepeval huggingface dataset:
A high-performance YASS-like scheme using randomized big-blocks

Randomly selecting 8 × 8 host blocks in big-blocks for data embedding, YASS , a recently developed advanced stegano-graphic scheme makes these blocks not coincident with the 8×8 grids used in JPEG compression. As a result, it effectively invalidates the self-calibration technique used in modern steganaly-sis. However, the randomization is not sufficient enough, i.e., some positions in an image are possible to hold host blocks and some are definitely not. Based on this observation, the newly developed specific steganalyzer  can effectively defeat YASS. In this paper, a new steganographic scheme is presented. Through randomizing the size and position of each big-block, our improved steganographic method makes almost every position possible to hold a host block, which has been verified by our statistical analysis. Consequently, the proposed scheme can survive the attack made by the specific steganalyzer. Experimental results have demonstrated that the detection rate achieved by the specific steganalyzer on our proposed method is less than 58%, while that on YASS is about 95% and above.
A High-Frequency Accelerometer Based on Distributed Bragg Reflector Fiber Laser

We propose an optical accelerometer based on distributed Bragg reflector (DBR) fiber laser sensor. The transducer's vibration reduces the birefringence of the preloaded fiber laser and consequently changes the beat frequency of the two orthogonally polarized lasing modes, thus the frequency can be used to retrieve the acceleration information. The DBR probe is a segment of bare erbium fiber, which has a very high natural frequency of vibration. Experimental results show that sensitivity of 4.810 MHz/g with a dynamic range of 0.01g-5g (where g = 9.8 m/s2 is the gravity of the earth) and natural frequency of ~1.3 kHz can be obtained with this sensing system.
Improved Side-Information in Distributed Video Coding

In the distributed video coding system, side-information takes an important role in reconstructing the Wyner-Ziv frames. To generate good side-information, we propose a new interpolation strategy based on motion estimation and motion compensation which exploits the video temporal redundancy. Besides, we propose a new framework based on the above strategy. In this framework, multi-estimation mode and a new refinement to side-information are added. Experimental results show that the PSNR of the new framework is improved notably compared with normal DVC system
Cross-media hashing with Centroid Approaching

Cross-media retrieval has received increasing interest in recent years, which aims to addressing the semantic correlation issues within rich media. As two key aspects, cross-media representation and indexing have been studied for dealing with cross-media similarity measure and the scalability issue, respectively. In this paper, we propose a new cross-media hashing scheme, called Centroid Approaching Cross-Media Hashing (CAMH), to handle both cross-media representation and indexing simultaneously. Different from existing indexing methods, the proposed method introduces semantic category information into the learning procedure, leading to more exact hash codes of multiple media type instances. In addition, we present a comparative study of cross-media indexing methods under a unique evaluation framework. Extensive experiments on two commonly used datasets demonstrate the good performance in terms of search accuracy and time complexity.
Research on Suspension System Based on Genetic Algorithm and Neural Network Control

An active suspension system for vehicles using the genetic algorithms and neural network controls strategy is presented. A half car four degree of freedom suspension vibration model is described. Compared with the conventional passive suspension system, the analysis is done to the system control performance. The analysis of the system response is obtained through the change of the neural network training coefficients, genetic algorithms input functions and the change of velocity. The simulation results indicate that the vehicle vibration can be reduced and the ride comfort is improved by the proposed suspension systems.
Research of short-term load forecasting based on combined grey neural network and phase space reconstruction

According to the characteristics of grey theory, G-P algorithm of phase space reconstruction and artificial neural network (ANN), a combined algorithm (G-G-NN) is proposed. The original time series is transformed by accumulated generating of grey prediction and G-P algorithm of phase space reconstruction. When a regular time series phase space is generated, neural network is adopted to forecast. The practical example indicated that the algorithm is verified.
Efficient Wavelet Zero-Tree Video Coding Based on Wyner-Ziv Coding and Lattice Vector Quantization

In this paper, we present a new wavelet zero-tree video coding method based on lattice vector quantization (LVQ) and Wyner-Ziv coding. It uses dependences only at decoder and develops a new intra-encoding and inter-decoding framework. In this framework, the conventional entropy coding is replaced by a low-density parity-check (LDPC) Slepian-Wolf codec and the idea of Slepian-Wolf coding is used to set partitioning in hierarchical trees (SPIHT) and lattice vector quantization completely. Experiment results suggest that our scheme can get higher PSNR and better recovered video than intra-frame SPIHT and scalar Slepian-Wolf SPIHT
Error-free Authentication Watermarking Based on Prediction-Error-Expansion Reversible Technique

Watermarking technology is an efficient method to protect multimedia content. In this paper, an error-free authentication watermarking is proposed based on prediction-error-expansion reversible technique. A binary image is used as an au- thentication watermark, and embedded in the prediction errors block-wise. A location map is designed and encoded to promise accurate extraction and recovery. A retesting strategy utilizing the parity detection activates the capacity of the ambiguous pixels. In the authentication and recovery period, a watermarked image can be identified as authentic or tampered. If an image is authentic, it can be recovered without errors. The embedded information can be extracted correctly. If an image is a tampered one, the tampered positions can be labeled. The experimental results show the effectiveness and reliability of the proposed method.
Research on fault traveling wave ranging based on SVD theory

With the expansion of the grid scale, fault location for transmission lines put forward higher requirements. In this paper, the fault current as the research object, according to the singularity of the fault current, the iterative SVD decomposition method is adopted to reduce the noise of the fault current traveling wave, and the singular points of the fault current are detected by the dichotomy recursive SVD. Based on the analysis of the principle of the initial traveling wave and the reflected wave of the fault point, a method to eliminate the error caused by the uncertainty of the wave velocity is put forward. The correctness of the method is verified by PSCAD simulation.
QoS Prediction for the Cloud Service Marketplace: A Grassmann Manifold Approach

The emerging cloud computing technologies enable a cloud platform to provide diverse cloud services to its service subscribers. By federating different services within and across cloud boundaries, cloud service developers can provision new, composite cloud services in the marketplace of apps and services on a cloud platform. Complete and accurate service QoS information is important for service recommendation and service pricing. However, measuring the entire QoS matrix for all user-service pairs incurs a tremendous overhead and is practically infeasible. This work designs efficient algorithms for recovering the complete QoS matrix from partial measurements, exploiting the low rank feature of the matrix. Our solution applies tools from differential geometry for transforming rank-constrained matrix optimization in a flat space into an unconstrained geometric optimization in a smooth manifold. Besides QoS matrix completion, future QoS matrix prediction based on manifold algorithms is also studied in this work, for the first time in the literature.
Enhanced isomorphic semantic representation for cross-media retrieval

Nowadays cross-media retrieval is an useful technology that helps people find expected information from the huge amount of multimodal data more efficiently. A common cross-media retrieval framework is first to map features of different modalities into an isomorphic semantic space so that the similarity between heterogeneous data can be measured. For most of semantic space based methods, the mapping mechanism from original to semantic space of each modality is optimized independently, yet the more discriminative characteristic of a certain modality is not taken into account. In this paper, we propose a deep framework which introduces a latent embedding layer to learn joint parameters to obtain semantically meaningful representations of images and texts. Specifically, the discriminative characteristic embedded in the textual modality can be transferred to images through the latent embedding layer and joint parameters to enhance the consistency between semantic representations. Extensive experiments on the three popular publicly available datasets well demonstrate the superiority of the proposed method, which achieves the new state-of-the-arts.
Optimized Multiple Description Lattice Vector Quantization for Wavelet Image Coding

Multiple description (MD) coding is a promising alternative for robust transmission of information over non-prioritized and unpredictable networks. In this paper, an effective MD image coding scheme is introduced based on the MD lattice vector quantization (MDLVQ) for the wavelet transformed images. In view of the characteristics of wavelet coefficients in different frequency subbands, MDLVQ is applied in an optimized way, including an appropriate construction of wavelet coefficient vectors, the optimization of MDLVQ encoding parameters such as the choice of sublattice index values and the quantization accuracy for different subbands. More importantly, optimized side decoding is employed to predict lost information based on inter-vector correlation and an alternative transmission way for further reducing side distortion. Experimental results validate the effectiveness of the proposed scheme with better performance than some other tested MD image codecs including that based on optimized MD scalar quantization.
Sparsity Learning Formulations for Mining Time-Varying Data

Traditional clustering and feature selection methods consider the data matrix as static. However, the data matrices evolve smoothly over time in many applications. A simple approach to learn from these time-evolving data matrices is to analyze them separately. Such strategy ignores the time-dependent nature of the underlying data. In this paper, we propose two formulations for evolutionary co-clustering and feature selection based on the fused Lasso regularization. The evolutionary co-clustering formulation is able to identify smoothly varying hidden block structures embedded into the matrices along the temporal dimension. Our formulation is very flexible and allows for imposing smoothness constraints over only one dimension of the data matrices. The evolutionary feature selection formulation can uncover shared features in clustering from time-evolving data matrices. We show that the optimization problems involved are non-convex, non-smooth and non-separable. To compute the solutions efficiently, we develop a two-step procedure that optimizes the objective function iteratively. We evaluate the proposed formulations using the Allen Developing Mouse Brain Atlas data. Results show that our formulations consistently outperform prior methods.
Stereo video coding using distributed compressive sensing with joint dictionary

For many practical applications, stereo-paired video is an important special case of multiview video coding (MVC). This paper presents a novel framework of stereo video coding based on distributed compressive sensing, which also can be easily extended to MVC. According to distributed video coding (DVC) at the encoder the video sequences from each view can be compressed independently without any communications between the cameras while at the decoder the inter-view correlation can be exploited for quality enhancement. Furthermore, due to compressive sensing (CS) principles, low complexity at the encoder side can result in low power consumption in the cameras, which may be promising in wireless camera sensor network. Here, the joint dictionary is applied in compressive sensing, which can make good use of inter-view and temporal correlation for better reconstruction quality of convex optimization. The experimental results validate the effectiveness of the proposed scheme with better performance than other compared schemes.
Comparison of multiple 3D rotation methods

In this paper, we present an experimental comparison of 3D rotation techniques. In all techniques, the third degree of freedom is controlled by the mouse-wheel. The investigated techniques are Bell's Trackball, Shoemake's Arcball and the Two-axis Valuator method. The result from a pilot showed no performance or accuracy difference among these three. However, we did observe minor differences in an experiment with more participants and trials, though these differences were not significant. Also, from questionnaires, we found that most of the users considered the use of mouse wheel helpful for completing the tasks.
Simulation of wind turbine based on fuzzy-PI control

Aiming to improve the accuracy of the wind turbine model for wind power research in laboratory, this paper presents the fuzzy control to the imitation research with the Sepic topology. The fuzzy-PI control has the advantages of small overshoot and quick response, and then it makes the torque characteristic of DC motor much approaches to the wind turbine. The simulation results show that the system model and arithmetic are valid and feasible.
Security authentication of 3G-WLAN interworking

With the development of wireless Internet, the interworking of the third generation (3G) wide area wireless networks and wireless local area networks (WLAN) has become a focus of research. 3G systems have their advantages in charging management, roaming, and security facilities, and WLAN systems have the advantages of high bandwidth and low investment cost. Interworking of 3G-WLAN offers characteristics that complement each other perfectly. Several authentication protocols have been proposed to improve security. In this paper, we firstly introduce the architectures of 3G-WLAN interworking and discuss existing problems related to authentication and key agreement procedures. Then, we present two schemes by applying authentication based on EAP-TLS (extensible authentication protocol-transport layer security) and EAP-TTLS (tunneled TLS) into integrated 3G and WLAN to achieve high end-to-end security and authentication for the users. Finally, we conduct a series of performance evaluation on the energy consumption and latency of standard authentication protocols.
Super-Resolution Imaging Through Scattering Medium Based on Parallel Compressed Sensing

Recent studies show that compressed sensing (CS) can recover sparse signal with much fewer measurements than traditional Nyquist theorem. From another point of view, it provides a new idea for super-resolution imaging, like the emergence of single pixel camera. However, traditional methods implemented measurement matrix by digital mirror device (DMD) or spatial light modulator, which is a serial imaging process and makes the method inefficient. In this paper, we propose a super resolution imaging system based on parallel compressed sensing. The proposed method first measures the transmission matrix of the scattering sheet and then recover high resolution objects by “two-step phase shift” technology and CS reconstruction algorithm. Unlike traditional methods, the proposed method realizes parallel measurement matrix by a simple scattering sheet. Parallel means that charge-coupled device camera can obtain enough measurements at once instead of changing the patterns on the DMD repeatedly. Simulations and experimental results show the effectiveness of the proposed method.
Three-Channel Multiple Description Image Coding Based on Special Lattice Vector Quantization

In this paper, we use three-dimension lattice vector Z3 to design an index assignment for three-channel multiple description coding. In the labeling procedure, the side quantizers are based on the sublattice, but the central quantizer is not only based on the lattice but also combines some spheres to get a better tradeoff between the central distortion and side distortion. Then this scheme is used on wavelet image coding. The experiment results on gauss source and standard test image both show that this scheme is simple and effective.
Region-Based Multiple Description Coding for Multiview Video Plus Depth Video

Interframe and interview predictions are widely employed in multiview video coding. This technique improves the coding efficiency, but it also increases the vulnerability of the coded bitstream. Thus, one packet loss will affect many subsequent frames in the same view and probably in other referenced views. To address this problem, a region-based multiple description coding scheme is proposed for robust 3-D video communication in this paper, in which two descriptions are formed by setting the left and right view as dominant in the first and second description, respectively. This approach exploits the fact that most regions in the reference view could be synthesized from the base view. Hence, these regions could be skipped or only coarsely encoded. In our work, the disoccluded regions, illumination-affected regions, and remaining regions are first determined and extracted. By assigning different quantization parameters for these three different regions according to the network status, an efficient multiple description scheme is formed. Experimental results demonstrate that the proposed scheme achieves considerably better performance compared with the traditional approach.
Quantized dictionary for sparse representation

Dictionary learning for sparse representation has drawn considerable attention in recent years. In particular, the K-SVD algorithm is an efficient approach, and various modifications of the K-SVD have been developed for applications such as face recognition. However, the efficient storage of the dictionary has not been studied. Currently, the dictionary is simply normalized and saved as floating-point numbers, which could be quite large and lead to excessive cost and delay if the dictionary needs to be transmitted, e.g., to mobile users. In this paper, we develop a quantized K-SVD (Q-KSVD) to reduce the storage of the dictionary. We compress each basis image in the dictionary by the conventional image coding method. Moreover, we integrate the image compression step into various modified K-SVD optimization schemes, and develop an algorithm to find the optimal dictionary when there is a constraint on the total bits of the compressed dictionary. Our algorithm selects dictionary bases by ranking the contribution-rate slopes of all bases. This method also serves as an efficient approach to find the optimal number of bases of the dictionary at each rate constraint. Face recognition experiments using four K-SVD-based methods show that our method can achieve different tradeoffs between the dictionary storage space and the recognition accuracy. It can achieve comparable performance with as little as 3% of the original storage space. It can even yield higher accuracy than the uncompressed dictionary in some cases.
A Real-Time Error Resilient Video Streaming Scheme Exploiting the Late- and Early-Arrival Packets

For real-time video streaming systems, the video packets arriving after the display deadline of their frames are considered as late-arrival packets, and typically they are discarded. This will affect the current frame and the following ones due to error propagations. For this reason, in this paper, we propose an approach to exploit the late-arrival and out-of-order packets, which includes two mechanisms. The first mechanism will use these packets to update the reference frames to make them more consistent with the encoder side, and this will eventually reduce the error propagations. The second mechanism will use these packets to increase the chance of successfully decoding the Reed-Solomon (RS) code. In the proposed approach, a sub-GOP based systematic RS code is used and optimized to exploit these packets, where the size of each sub-GOP and the parity packet number for each sub-GOP are optimally tuned, taking into consideration the maximum end-to-end delay, the network conditions, and other system parameters, so as to make the best use of the late-arrival packets and to exploit the out-of-order packets. Finally, the experimental results show the advantage of the proposed approach over other approaches.
Parameters design and simulation of reversal six-connecting-rod in the Work Equipment

Apply computer-aided design and analysis software, combine with main design requirements of Loader work equipment,and investigate parametric design and simulation of reversal six-connecting-rod in the Work Equipment on Pro/E. On the basis of Work equipment loading characteristics,all hinge points of reverse the six bodies are looked as design variables, their job performance as the objective function. Considering to avoid the motion and structure interference as constraint conditions, establish multi-objective virtual prototype model of working bodies articulated positions, Completed working device parameters design. By kinematic simulation analysis,test and verify the feasibility of design.
Distributed joint iterative localization algorithm for WSN

The Distributed Iterative Localization algorithm (DILOC) is an attractive algorithm for wireless sensor network (WSN), which is used to locate sensors (with unknown locations) in m dimensional space. DILOC algorithm needs a minimal number of m +1 anchors (with known their exact locations). However, the convex hull test requires that sensors must be inside of the convex hull, so it will require relatively large communication radius. Considering this defect of DILOC, this paper proposes an improved algorithm that called distributed joint iterative localization (DJIL), through employing loop iteration and weighted average, which is capable of reducing communication radius and enhancing the precision of localization. The DJIL algorithm improves the convex hull test, and uses some sensors, which have known locations, participating in the second localization, and further improving centroid localization algorithm for reliability and practicality. Simulation results show that the communication radius is significantly reduced and error is also decreased for the DJIL algorithm. Compared with DILOC algorithm, if fewer anchors are distributed in region unequally, and the region contains more sensors, DJIL algorithm can reduce the communication radius of 30% and increase location accuracy of 21%. So DJIL algorithm is very flexible for scenarios of anchors uneven distribution.