nils-herrmann/clean_scirepeval huggingface dataset:
Blind estimation for PN code of LFM-PRBC signal based on DPT and spectrum shifting

A new approach is proposed to blindly estimate the pseudo noise (PN) code of compound signal combined linear frequency modulation (LFM) and pseudo-random binary phase code (PRBC), which is based on methods of discrete polynomial phase transform (DPT) and spectrum shifting. Firstly, the square method is adopted to eliminate phase mutation. Then, apply DPT method to estimate the chirp rate, which is used to reconstruct second order exponent. The original compound signal is multiplied by the conjugated second order exponent to get a new compound signal. And then, the original PN code can be restored from the real part of the new compound signal by the way of spectrum shifting. Simulation results show that the approach can correctly estimate PN code when the signal-to-noise ratio (SNR) is 3 dB, and performance will be improved with the increase of sub-pulse number. It also has a better performance compared with the method of FM-AM time-frequency analysis.
Pose-Driven Deep Convolutional Model for Person Re-identification

Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts. To match the features from global human body and local body parts, a pose driven feature weighting sub-network is further designed to learn adaptive feature fusions. Extensive experimental analyses and results on three popular datasets demonstrate significant performance improvements of our model over all published state-of-the-art methods.
A Mid-level Visual Concept Generation Framework for Sports Analysis

The development of mid-level concepts helps to bridge the gap between low-level feature and high-level semantics in video analysis. Most existing work combines the customized mid-level concepts and statistical models to detect particular events. Based on broadcast sports video production knowledge, we extend our previous work to present a unified framework for mid-level concept generation in this paper. A video segment is characterized via three essential aspects: camera shot size, an object appearing in a scene, and video production technology. These three aspects clearly summarize the primary concerns in terms of a generic concept generation. Within this framework, we can flexibly and clearly define meaningful mid-level concepts towards comprehensive video content analysis, such as replay classification and the detection of events (e.g. goal, shoot, attack, foul, offside, and out of bound, etc.
PIDNN decoupling control of boiler combustion system based on MCS

Aimed at the problem that traditional decoupling control method is not ideal for the decoupling effect of nonlinear, multi variable and strong coupling systems, we put forward a control method combining the cuckoo search algorithm (MCS) which use learning evolution as search guide with PID neural network, it eliminate the strong coupling effect between the loops through the PIDNN network training and use the improved cuckoo algorithm to optimize the connection weights. Compared with the reverse BP neural network, the MCS algorithm overcomes the characteristic that is easy to fall into local convergence of PIDNN, and comparing with basic cuckoo algorithm, it has better ability in search precision and convergence speed. By the combination of learning evolving and Gauss distribution and the new search mechanism formed by mixing it with Levy flight search mechanism based on probability of selection, it accelerate the optimization of PIDNN weights. Through the simulation of boiler combustion system of strong coupling fire power plant, it appears that this method has better control characteristics such as good decoupling performance, strong robustness and high control accuracy, which provides an effective way to improve the actual industrial production process of strong coupling control system.
DOA Estimation of Coherent NLFM Signals

As the prevalence of broadband coherent sources, a novel algorithm for the direction of arrival (DOA) estimation of coherent signals based on DPT and virtual array is introduced. And one of the algorithms is to be done a comparison, analysis and improvement. It can settle the problem of not finding coherent signals in routine algorithm. This approach can estimate accurately and do not decrease effective array aperture and improve the utilization of array. Simulation results verify the correctness and efficiency of the new algorithm.
Video Object Boundary Reconstruction by 2-Pass Voting

In this paper we propose a voting-based object boundary reconstruction approach. Tensor voting has been studied by many people recently, and it can be used for boundary estimation on curves or irregular trajectories. However, the complexity of saliency tensor creation limits its applications in real-time systems. In order to have an efficient solution, we introduce an alternative voting approach. Rather than creating saliency tensors, we use a "2-pass" method for orientation estimation. For the first pass, Sobel detector is applied on a coarse boundary image to get the gradient map, then the orientation information is updated by accumulating votes on the corresponding direction. In the second pass, edge linking is performed based on the pixels orientation map, and extra lines are eliminated by detecting intersections. The approach has been applied to various video clips under different conditions, and the experimental results demonstrate significant improvement on the final extracted objects accuracy
An Asset-Based Architecture Design Methodology for Rapid Telecom Service Delivery Platform Development

In the telecom industry, it becomes increasingly important for operators to provide a service delivery platform (SDP) quickly to improve time-to-revenue cycle of new innovative value-added services at lower cost. A rapid architecture solution design method based on SDP capability model and SDP architecture blueprint is proposed to deal with this problem in this paper. SDP capability model and the corresponding SDP architecture blueprint are designed as two reusable assets for business requirement analysis and IT architecture design in telecom service delivery problem domain to reduce cost and improve quality. Furthermore, the linkage between SDP capability model and SDP architecture blueprint is built as the corner stone to speed up the solution design process. The approach on how to use capability model, architecture blueprint, and their linkage to perform customer requirement analysis and quick solution design throughout SDP engagements is introduced. This approach can facilitate developers to adopt asset-based service model in the rapidly growing telecom industry.
Distance Learning for Similarity Estimation

In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance functions. A new set of distance measures are derived from the harmonic distance, the geometric distance, and their generalized variants according to the maximum likelihood theory. These measures can provide a more accurate feature model than the classical euclidean and Manhattan distances. We also find that the feature elements are often from heterogeneous sources that may have different influence on similarity estimation. Therefore, the assumption of single isotropic distribution model is often inappropriate. To alleviate this problem, we use a boosted distance measure framework that finds multiple distance measures, which fit the distribution of selected feature elements best for accurate similarity estimation. The new distance measures for similarity estimation are tested on two applications: stereo matching and motion tracking in video sequences. The performance of boosted distance measure is further evaluated on several benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.
Multi-level trajectory modeling for video copy detection

The main issue of video copy detection is to estimate a constant spatial-temporal transformation in object level between the original video and the copies. In this paper, we propose a multi-level trajectory modeling approach for video copy detection. It includes a rich trajectory description and a robust trajectory-to-trajectory matching to preserve and explore the trajectory characteristics in both spatial-temporal space and feature space. In summary, we will describe the trajectories in three levels: feature-level descriptor, spatial-temporal coordinates and high-level dynamic behaviors. After extracting the trajectories of videos, we apply a two-stage trajectory-to-trajectory based parametric matching technique to achieve an optimal spatial-temporal transformation between query video and the database videos. To speed up the detection process, we use Locality Sensitive Hashing (LSH) to index and query trajectories with the dynamic behavior and features. Extensive experiments on 100 hours of videos from the TRECVID 2008 demonstrate the effectiveness of our approach.
A Novel Dynamic Priority Scheduling Algorithm of Process Engine in SOA

EAI and SOA are widely adopted in enterprise information systems with business processes being orchestrated by a process engine. An algorithm which schedules tasks of process instances to maximize overall customer satisfaction is proposed in this paper. This new algorithm maximizes the total value of all process instances by dynamically assigning different priority to each task based on the business value and time-left of the corresponding process instances. The value of each kind of process instance is modeled as a utility function of response time which reflects the inverse proportional relationship between the customer satisfaction and response time. Experiments show that the total value of utility function with the proposed algorithm is promoted a lot than traditional methods.
Locality-sensitive support vector machine by exploring local correlation and global regularization

Local classifiers have obtained great success in classification task due to its powerful discriminating ability on local regions. However, most of them still have restricted generalization in twofold: (1) each local classifier is sensitive to noise in local regions which leads to overfitting phenomenon in local classifiers; (2) the local classifiers also ignore the local correlation determined by the sample distribution in each local region. To overcome the above two problems, we present a novel locality-sensitive support vector machine (LSSVM) in this paper for image retrieval problem. This classifier applies locality-sensitive hashing (LSH) to divide the whole feature space into a number of local regions, on each of them a local model can be better constructed due to smaller within-class variation on it. To avoid these local models from overfitting into locality-sensitive structures, it imposes a global regularizer across local regions so that local classifiers are smoothly glued together to form a regularized overall classifier. local correlation is modeled to capture the sample distribution that determines the locality structure of each local region, which can increase the discriminating ability of the algorithm. To evaluate the performance, we apply the proposed algorithm into image retrieval task and competitive results are obtained on the real-world web image data set.
Fast Image Retrieval: Query Pruning and Early Termination

Efficiency is of great importance for image retrieval systems. For this pragmatic issue, this paper proposes a fast image retrieval framework to speed up the online retrieval process. To this end, an impact score for local features is proposed in the first place, which considers multiple properties of a local feature, including TF-IDF, scale, saliency, and ambiguity. Then, to decrease memory consumption, the impact score is quantized to an integer, which leads to a novel inverted index organization, called Q-Index. Importantly, based on the impact score, two closely complementary strategies are introduced: query pruning and early termination. On one hand, query pruning discards less important features in the query. On the other hand, early termination visits indexed features only with high impact scores, resulting in the partial traversing of the inverted index. Our approach is tested on two benchmark datasets populated with an additional 1 million images to account as negative examples. Compared with full traversal of the inverted index, we show that our system is capable of visiting less than 10% of the “should-visit” postings, thus achieving a significant speed-up in query time while providing competitive retrieval accuracy.
Neighborhood issue in single-frame image super-resolution

Super-resolution is the problem of generating one or a set of high-resolution images from one or a sequence of low-resolution frames. Most methods have been proposed for super-resolution based on multiple low resolution images of the same scene, which is called multiple-frame super-resolution. Only a few approaches produce a high-resolution image from a single low-resolution image, with the help of one or a set of training images from scenes of the same or different types. It is referred to as single-frame super-resolution. This article reviews a variety of single-frame super-resolution methods proposed in the recent years. In the paper, a new manifold learning method: locally linear embedding (LLE) and its relation with single-frame super-resolution is introduced. Detailed study of a critical issue: "neighborhood issue" is presented with related experimental results and analysis and possible future research is given.
A robust object-based video authentication system

We propose an object-based video authentication system, which employs a combination of watermarking, error correction coding (ECC) and digital signature techniques, to protect the authenticity between video objects and their associated backgrounds. In this system, a set of angular radial transformation (ART) coefficients is selected as the feature to represent the video object and the background respectively. ECC and cryptographic hashing are applied to those selected coefficients to generate the robust watermark. This semifragile watermark is then embedded into the objects frame by frame before MPEG4 coding. The experimental results demonstrate that our system is robust against MPEG4 compression, object segmentation errors, and some common object-based video processing such as object translation, rotation, scaling.
Retrieval Oriented Deep Feature Learning With Complementary Supervision Mining

Deep convolutional neural networks (CNNs) have been widely and successfully applied in many computer vision tasks, such as classification, detection, semantic segmentation, and so on. As for image retrieval, while off-the-shelf CNN features from models trained for classification task are demonstrated promising, it remains a challenge to learn specific features oriented for instance retrieval. Witnessing the great success of low-level SIFT feature in image retrieval and its complementary nature to the semantic-aware CNN feature, in this paper, we propose to embed the SIFT feature into the CNN feature with a Siamese structure in a learning-based paradigm. The learning objective consists of two kinds of loss, i.e., similarity loss and fidelity loss. The first loss embeds the image-level nearest neighborhood structure with the SIFT feature into CNN feature learning, while the second loss imposes that the CNN feature with the updated CNN model preserves the fidelity of that from the original CNN model solely trained for classification. After the learning, the generated CNN feature inherits the property of the SIFT feature, which is well oriented for image retrieval. We evaluate our approach on the public data sets, and comprehensive experiments demonstrate the effectiveness of the proposed method.
Blind recognition of RS codes based on Galois field columns Gaussian elimination

RS (Reed Solomon) codes have strong error correction capability. The reliability of information transmission can be improved by RS codes. It is widely used in modern communication. The blind recognition of RS codes is particularly important in the field of information interception and intelligent communication. In order to solve the blind recognition problem of the high rate RS codes, this paper presents a blind recognition method of RS codes based on Galois field columns Gaussian elimination. Firstly, code length and symbolic number are recognized by using the difference function of the matrix rank. Then the primitive proved the polynomials corresponding to the number of symbol at this time are traversed. And the matrix is eliminated with columns in Galois field. By using the difference of entropy, the primitive polynomial is identified. Finally, as the code word polynomial roots are found, the continuous roots are the roots of the generator polynomial. Code length, the primitive polynomial and the generator polynomial are identified by the method. The tedious process of Galois field Fourier transform is avoided. Simulation results show that the recognition probability is higher than 90% at an error code rate of 3×10-3.
Robust Co-Optimization Scheduling of Electricity and Natural Gas Systems via ADMM

The significant growth of gas-fired power plants and emerging power-to-gas (PtG) technology has intensified the interdependency between electricity and natural gas systems. This paper proposes a robust co-optimization scheduling model to study the coordinated optimal operation of the two energy systems. The proposed model minimizes the total costs of the two systems, while considering power system key uncertainties and natural gas system dynamics. Because of the limitation on exchanging private data and the challenge in managing complex models, the proposed co-optimization model is tackled via alternating direction method of multipliers (ADMM) by iteratively solving a power system subproblem and a gas system subproblem. The power system subproblem is solved by column-and-constraint generation (C&CG) and outer approximation (OA), and the nonlinear gas system subproblem is solved by converting into a mixed-integer linear programming model. To overcome nonconvexity of the original problem with binary variables, a tailored ADMM with a relax-round-polish process is developed to obtain high-quality solutions. Numerical case studies illustrate the effectiveness of the proposed model for optimally coordinating electricity and natural gas systems with uncertainties.
Coupled Binary Embedding for Large-Scale Image Retrieval

Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.
Automatic density clustering with multiple kernels for high-dimension bioinformatics data

Clustering is an effective method for data analysis and can be exploited to unknown features of data samples, its applications range from data mining to bioinformatics analysis. Several clustering approaches have been proposed in order to obtain a better trade-off between accuracy and efficiency of the clustering process. It is well-known that no existing clustering algorithm completely satisfies both accuracy and efficiency requirements, thus we propose a clustering algorithm called ADCMK (for Automatic Density Clustering with Multiple Kernels) exhibiting higher quality than the density ones proposed so far, while allowing users to cluster efficiently without determining parameters manually. The algorithm consists of learning optimal combined kernel, reducing dimensionality with the optimal kernel, automatically detecting cluster centroids with outliers test, assigning clusters and visualizing results. The proposed clustering algorithm is extensively tested on several well-known high-dimensional dataset in biomedical and bioinformatics field. The results show that the new algorithm tends to automatically produce clusters of better quality than other density clustering algorithms.
Two-Dimensional Adaptive Discriminant Analysis

In this paper, we develop a new feature extraction and dimension reduction technique: 2-dimensional adaptive discriminant analysis (2DADA) based on 2DLDA and our proposed 2DBDA. It effectively exploits favorable attributes of both 2DBDA and 2DLDA and avoids their unfavorable ones. 2DADA can easily find an optimal discriminative subspace with adaptation to different sample distributions. It not only alleviates the problem of high dimensionality, but also enhances the classification performance in the subspace with KNN classifier. Experimental results on hand-written digit database and face databases show an improvement of 2DADA over other traditional dimension reduction techniques.
Image Class Prediction by Joint Object, Context, and Background Modeling

State-of-the-art image classification methods often use spatial pyramid matching or its variants to make use of the spatial layout of visual features. However, objects may appear at various places with different scales and orientations. Besides, traditionally object-centric-based methods only consider objects and the background without fully exploring the context information. To solve these problems, in this paper we propose a novel image classification method by jointly modeling the object, context, and background information (OCB). OCB consists of three components: 1) locate the positions of objects; 2) determine the context areas of objects; and 3) treat the other areas as the background. We use objectness proposal techniques to select candidate bounding boxes. Boxes with high confidence scores are combined to determine objects’ positions. To select the context areas, we use candidate boxes that have relatively lower confidence scores compared with boxes for object location selection. The other areas are viewed as the background. We jointly combine the object, context, and background for image representation and classification. Experiments on six data sets well demonstrate the superiority of the proposed OCB method over other spatial partition methods.