nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Photograph: A Compositional Perspective

In this paper, we present an intelligent photography system which can recommend the most user-favored view rectangle for arbitrary camera input, from a photographic compositional perspective. Automating this process is difficult, due to the subjectivity of human's aesthetics judgement and large variations of image contents, where heuristic compositional rules lack generality. Motivated by the recent prevalence of photo-sharing websites, e.g., Flickr.com, we develop a learning-based framework which discovers the underlying aesthetic photographic compositional structures from a large set of user-favored online sharing photographs and utilizes the implicitly shared knowledge among the professional photographers for aesthetically optimal view recommendation. In particular, we propose an Omni-Range Context method which explicitly encodes the spatial and geometric distributions of various visual elements in the photograph as well as cooccurrence characteristics of visual element pairs by using generative mixture models. Searching the optimal view rectangle is then formulated as maximum a posterior by imposing the trained prior distributions along with additional photographic constraints. The proposed system has the potential to operate in near real-time. Comprehensive user studies well demonstrate the effectiveness of the proposed framework for aesthetically optimal view recommendation.
Contextual Exemplar Classifier-Based Image Representation for Classification

The use of local features for image representation has become popular in recent years. Local features are often used in the bag-of-visual-words scheme. Although proven effective, this method still has two drawbacks. First, local regions from which local features are extracted are not discriminative enough for visual tasks. Hence, the combination of local features is necessary. Second, the semantic gap between visual features and human perception also hinders the performance. To address these two problems, in this paper, we propose a novel contextual exemplar classifier-based method for image representation and apply it for classification tasks. Each exemplar classifier is trained to separate one training image from the other images of different classes. We partition each image into a number of regions and use the responses of these exemplar classifiers as the image region’s representation. The contextual relationship is then modeled using mixture Dirichlet distributions. A bilayer model is used to predict image classes with  $L_{2}$  constraints. Experimental results on the Natural Scene, Caltech-101/256, Flower-17/102, and SUN-397 data sets show that the proposed method is able to outperform the state-of-the-art local feature-based methods for image classification.
Blind estimation of the PN sequence for weak DSSS signals in dynamic environments

This paper presents a method of singular value decomposition (SVD) plus digital phase lock loop (DPLL) to solve the difficult problem of blind pseudo-noise (PN) sequence estimation for low signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals in dynamic environments. Of course, the method needs to know the parameters of DS signal, such as the period and code rate of PN sequence. Firstly, the received signal is sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by the signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of this autocorrelation matrix. Further more, a DPLL is used to deal with the estimated PN sequence with residual carrier in dynamic environments, it estimates and tracks the residual carrier, removes the residual carrier in the end. Theory analysis and computer simulation results show that this method can realize the PN sequence blind estimation from lower SNR input DS signals in dynamic environments effectively.
A novel light load performance enhanced variable-switching-frequency and hybrid single-dual-phase-shift control for single-stage dual-active-bridge based AC/DC converter

Full-bridge power-factor-correction (PFC) front-end + dual-active-bridge (DAB) AC/DC topology is widely used in industry, e.g., electrical vehicle on-board charger. Such two-stage topology limits the system efficiency, and the bulky DC link bus capacitor makes the system power density relatively low. Compared to the two-stage design, the single-stage design, unfolding bridge + DAB, eliminates the bulky DC link bus capacitor and operates the front-end with only 60Hz switching frequency, thereby has the potential to increase the system power density and efficiency. A novel variable-switching-frequency and hybrid single-dual-phase-shift (VSF-SDPS) control strategy is proposed and analyzed for the DAB based single-stage topology. The proposed VSF-SDPF control consists of two phase shifts to guarantee Zero-Voltage-Switching (ZVS) over the full range of the AC line voltage, and frequency modulation to achieve boost PFC. The conventional front-end PFC is simplified to an unfolding bridge by changing DAB control strategy to achieve PFC and ZVS at the same time. Besides, a special ZVS boundary is utilized to solve the grid current distortion problem when the switching frequency saturated, which is especially severe at light load condition. Simulation results and experimental validation are presented under 50Vrms AC line voltage and 200V DC battery voltage test condition.
Color indexing using wavelet-based salient points

Color is an important attribute for image matching and retrieval. Most of the attention from the research literature has been focused on color indexing techniques based on global color distributions. However, these global distributions have limited discriminating power because they are unable to capture local color information. We present a wavelet-based salient point extraction algorithm. We show that extracting the color information in the locations given by these points provides significantly improved retrieval results as compared to the global color feature approaches.
Multimodal Similarity Gaussian Process Latent Variable Model

Data from real applications involve multiple modalities representing content with the same semantics from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized modality specific mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy in multimodal data. In this paper, we build our work based on the Gaussian process latent variable model (GPLVM) to learn the non-parametric mapping functions and transform heterogeneous modalities into a shared latent space. We propose multimodal Similarity Gaussian Process latent variable model (m-SimGP), which learns the mapping functions between the intra-modal similarities and latent representation. We further propose multimodal distance-preserved similarity GPLVM (m-DSimGP) to preserve the intra-modal global similarity structure, and multimodal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the latent space. We propose m-DRSimGP, which combines the distance preservation in m-DSimGP and semantic preservation in m-RSimGP to learn the latent representation. The overall objective functions of the four models are solved by simple and scalable gradient decent techniques. They can be applied to various tasks to discover the nonlinear correlations and to obtain the comparable low-dimensional representation for heterogeneous modalities. On five widely used real-world data sets, our approaches outperform existing models on cross-modal content retrieval and multimodal classification.
Wind Power Prediction Considering the Layout of the Wind Turbines and Wind Direction

It Points out that when the wind farms are undertaking the short-term forecast, wind power will be all crew for a machine to equivalent the whole prediction of the deficiencies, and through the forecast of the contrast, it proves the influence of the overall prediction power according to the units in different layouts .Considering the wind power prediction, the established prediction model of wind farms super short term is be tested by the example, which improves that wind speed has greater influence on the overall power output and it is important to predict the wind direction when predict the speed in order to improve the prediction precision of power.
An adaptive enhanced focusing technique for whole slide imaging using contextual information

Optical imaging systems especially microscopy systems under higher magnifications often have relatively limited depth of field. This constraint results in partial focusing in the image. To overcome this partial focusing problem, enhanced focusing (EF) techniques are introduced to generate all-well-focused images. A sequence of images for each field is acquired at the same image field and along the same optical axis at different acquisition depths. The images are partitioned into tiles where the best focused sub-images for each tile among the sequence is found. The final image is the mosaicking of the found sub-images. Whole slide imaging (WSI) is an emerging imaging technology since the mid-1990s. It automatically acquires digital images from a biomedical slide field by field using robotic microscopic devices at high resolution and then stitches them together to form a large-dimensional high- resolution montage. Each field is just a small window of the large scene of the slide. In the context of WSI, all fields are to be focused and such an EF-WSI procedure could be very slow. In this work, we have proposed a new adaptive focusing method using contextual information in the slide for EF-WSI. We can reduce about 1/3 of computation time while maintaining good image quality.
Semi-supervised Relational Topic Model for Weakly Annotated Image Recognition in Social Media

In this paper, we address the problem of recognizing images with weakly annotated text tags. Most previous work either cannot be applied to the scenarios where the tags are loosely related to the images, or simply take a pre-fusion at the feature level or a post-fusion at the decision level to combine the visual and textual content. Instead, we first encode the text tags as the relations among the images, and then propose a semi-supervised relational topic model (ss-RTM) to explicitly model the image content and their relations. In such way, we can efficiently leverage the loosely related tags, and build an intermediate level representation for a collection of weakly annotated images. The intermediate level representation can be regarded as a mid-level fusion of the visual and textual content, which is able to explicitly model their intrinsic relationships. Moreover, image category labels are also modeled in the ss-RTM, and recognition can be conducted without training an additional discriminative classifier. Our extensive experiments on social multimedia datasets (images+tags) demonstrated the advantages of the proposed model.
Category sensitive codebook construction for object category recognition

Recently, the bag of visual words based image representation is getting popular in object category recognition. Since the codebook of the bag-of-words (BOW) based image representation approach is typically constructed by only measuring the visual similarity of local image features (e.g., k-means), the resulting codebooks may not capture the desired information for object category recognition. This paper proposes a novel optimization method for discriminative codebook construction that considers the category information of local image features as an additional term in traditional visual-similarity-only based codebook construction methods. The category sensitive codebook is constructed through solving an optimization problem. Therefore, the category sensitive codebook construction method goes one step beyond visual-similarity-only methods. Besides, the proposed category sensitive codebook construction method can be implemented with k-means clustering very efficiently and effectively. Experimental results on PASCAL VOC Challenge 2006 data set demonstrate the effectiveness of our method.
Constructing Descriptive and Discriminant Features for Face Classification

Linear discriminant analysis (LDA) has been widely applied in the field of face classification because of its simplicity and efficiency in capturing the most discriminant features. However LDA often fails when facing the small sample set and change in illumination, pose or expression. To overcome those difficulties, principal component analysis (PCA), which recovers the most descriptive/informative features in the dimension-reduced feature space, is often used in the preprocessing stage. Although there is a trend of preferring LDA to PCA in classification, it has been found that PCA may perform better than LDA in some cases, especially when the size of the training set is small. In this paper we propose a parametric framework that can unify PCA and LDA to find both discriminant and descriptive features. To avoid the exhaustive parameter searching, we incorporate a non-linear boosting process to enhance a pool of hybrid classifiers and adaptively combine them into a more accurate one. To evaluate the performance of our boosted hybrid method, we compare it to state-of-the-art LDA variants and the other PCA-LDA techniques on three widely used face image benchmark databases. The experiment results show the superior performance of our novel boosted hybrid discriminant analysis
An interleaving approach of enhancing the performance of RS codes in two dimensional space

This paper proposed a modified Block Successive Packing(BSP) interleaving algorithm according to the properties of Reed Solomon code, which improves robustness of digital watermark on burst errors when combined with RS code. Firstly, construct a low-dimensional SP interleaving matrix and elementary matrices respectively, and put RS coded bits orderly into each elementary matrices, the number of bits in each elementary matrix equals to that in each RS codeword component. Then put all elementary matrices into a new matrix according to the order of bits in low-dimensional SP interleaving matrix, the new matrix is BSP interleaving matrix, which improves the error correcting capability of RS code via dispersing RS codeword symbol components, instead of bit components. Simulations show that the combination of BSP interleaving with RS code is more robust than that of SP interleaving with BCH code on burst errors in two dimensional space.
Full-Space Local Topology Extraction for Cross-Modal Retrieval

With the ever increasing availability of various kinds of multimedia data, cross-modal retrieval, which enables information retrieval from various types of data given various types of query, has become a research hotspot. Hashing-based techniques have been developed to solve this problem, however, most previous works cannot capture the shared underlying structure of real-world multimodal data, which degrades their retrieval performances. In this paper, we propose a novel hashing method based on the extraction of the common manifold structure shared among different feature spaces. To faithfully represent the common structure, two kinds of local topology information are exploited in our method. Local angles are incorporated within the extraction of local topology of each feature space, which is then used to learn a common intermediate subspace. After heterogeneous features being embedded into this subspace, local similarities are exploited to extract the local topology between different feature spaces, and learn compact Hamming embeddings to facilitate cross-modal retrieval. The proposed method is referred to as full-space local topology extraction for hashing. Extensive comparisons with other state-of-the-art methods on three benchmark multimedia data sets demonstrate the superiority of our proposed method in terms of retrieval recall and search accuracy.
Interactive Web Video Advertising with Context Analysis and Search

Online media services and electronic commerce are booming recently. Previous studies have been devoted to contextual advertising, but few work deals with interactive web advertising. In this paper, we propose to put users in the loop of collecting contextual ad information with an interaction process, establishing semantic ad links across media platforms. Given an ad video, the key frames with explicit product information are located, which allow users to click favorite key frames for searching ads interactively. A three-stage contextual search is applied to find relevant products or services from web pages, i.e., searching visually similar product images on shopping websites, ranking product tags by text aggregation, and re-search textual items consisting of semantic meaningful tags to make a recommendation. In addition, users can choose automatically suggested keywords to reflect their intentions. Subjective evaluation has demonstrated the effectiveness of the proposed approach to interactive video advertising over the Web.
A new way to express the multi-infeed HVDC system and interaction factor

In multi-infeed HVDC system, the interactions and influences between DC systems and AC systems are complex as the electrical distances among DC converter stations are relatively short. Multi-infeed interaction factor (MIIF) can effectively reflect the interaction among DC systems. However, present methods to obtain the interaction factors are through simulations which are unclear in physical meaning. The paper theoretically analyzes recent analytical expressions based on impedance matrix to calculate the MIIF and proposes a new way to express the MIIF based on the admittance matrix. From both expressions, it is clear that how the equivalent impedance of AC system and coupling impedance can affect MIIF. PSCAD simulations validate the effectiveness and the correctness of the proposed expressions and some useful conclusions are drawn.
Embedding Multi-Order Spatial Clues for Scalable Visual Matching and Retrieval

Matching duplicate visual contents among images serves as the basis of many vision tasks. Researchers have proposed different local descriptors for image matching, e.g., floating point descriptors like SIFT, SURF, and binary descriptors like ORB and BRIEF. These descriptors either suffer from relatively expensive computation or limited robustness due to the compact binary representation. This paper studies how to improve the matching efficiency and accuracy of floating points descriptors and the matching accuracy of binary descriptors. To achieve this goal, we embed the spatial clues among local descriptors to a novel local feature, i.e., multi-order visual phrase which contains two complementary clues: 1) the center visual clues extracted at each image keypoint and 2) the neighbor visual and spatial clues of multiple nearby keypoints. Different from existing visual phrase features, two multi-order visual phrases are flexibly matched by first matching their center visual clues, then estimating a match confidence by checking the spatial and visual consistency of their neighbor keypoints. Therefore, multi-order visual phrase does not scarify the repeatability of classic visual word and is more robust to the quantization error than existing visual phrase features. We extract multi-order visual phrases from both SIFT and ORB and test them in image matching and retrieval tasks on UKbench, Oxford5K, and 1 million distractor images collected from Flickr. Comparisons with recent retrieval approaches clearly demonstrate the competitive accuracy and significantly better efficiency of our approaches.
Optimal control strategy of vehicle-to-grid for modifying the load curve based on discrete particle swarm algorithm

The electric vehicle can curb the emission. Foremost, the batteries on electric vehicle not only can take energy from the grid, but also can provide energy back to the grid if necessary, which known as vehicle to grid (V2G) conception. Therefore, as energy storage unit, the vehicle has the potential ability to improve the efficiency and increase the reliability of the power grid. V2G will be the important role in the future's smart grid for the advantages above. However, it is in urgent need of a control strategy to figure out an appropriate charge and discharge times for fleets of vehicles. In the view of this, this paper presents an optimal control strategy based on discrete particle swarm algorithm, constraints in which are decided by the battery' own characteristic and set by the owner. In final, an example was displayed to support the feasibility of the algorithm.
Min-Max Hash for Jaccard Similarity

Min-wise hash is a widely-used hashing method for scalable similarity search in terms of Jaccard similarity, while in practice it is necessary to compute many such hash functions for certain precision, leading to expensive computational cost. In this paper, we introduce an effective method, i.e. the min-max hash method, which significantly reduces the hashing time by half, yet it has a provably slightly smaller variance in estimating pair wise Jaccard similarity. In addition, the estimator of min-max hash only contains pair wise equality checking, thus it is especially suitable for approximate nearest neighbor search. Since min-max hash is equally simple as min-wise hash, many extensions based on min-wise hash can be easily adapted to min-max hash, and we show how to combine it with b-bit minwise hash. Experiments show that with the same length of hash code, min-max hash reduces the hashing time to half as much as that of min-wise hash, while achieving smaller mean squared error (MSE) in estimating pair wise Jaccard similarity, and better best approximate ratio (BAR) in approximate nearest neighbor search.
USB: Ultrashort Binary Descriptor for Fast Visual Matching and Retrieval

Currently, many local descriptors have been proposed to tackle a basic issue in computer vision: duplicate visual content matching. These descriptors either are represented as high-dimensional vectors relatively expensive to extract and compare or are binary codes limited in robustness. Bag-of-visual words (BoWs) model compresses local features into a compact representation that allows for fast matching and scalable indexing. However, the codebook training, high-dimensional feature extraction, and quantization significantly degrade the flexibility and efficiency of BoWs model. In this paper, we study an alternative to current local descriptors and BoWs model by extracting the ultrashort binary descriptor (USB) and a compact auxiliary spatial feature from each keypoint detected in images. A typical USB is a 24-bit binary descriptor, hence it directly quantizes visual clues of image keypoints to about 16 million unique IDs. USB allows fast image matching and indexing and avoids the expensive codebook training and feature quantization in BoWs model. The spatial feature complementarily captures the spatial configuration in neighbor region of each keypoint, hence is used to filter mismatched USBs in a cascade verification. In image matching task, USB shows promising accuracy and nearly one-order faster speed than SIFT. We also test USB in retrieval tasks on UKbench, Oxford5K, and 1.2 million distractor images. Comparisons with recent retrieval methods manifest the competitive accuracy, memory consumption, and significantly better efficiency of our approach.