nils-herrmann/clean_scirepeval huggingface dataset:
AutoBD: Automated Bi-Level Description for Scalable Fine-Grained Visual Categorization

Compared with traditional image classification, fine-grained visual categorization is a more challenging task, because it targets to classify objects belonging to the same species, e.g., classify hundreds of birds or cars. In the past several years, researchers have made many achievements on this topic. However, most of them are heavily dependent on the artificial annotations, e.g., bounding boxes, part annotations, and so on. The requirement of artificial annotations largely hinders the scalability and application. Motivated to release such dependence, this paper proposes a robust and discriminative visual description named Automated Bi-level Description (AutoBD). “Bi-level” denotes two complementary part-level and object-level visual descriptions, respectively. AutoBD is “automated,” because it only requires the image-level labels of training images and does not need any annotations for testing images. Compared with the part annotations labeled by the human, the image-level labels can be easily acquired, which thus makes AutoBD suitable for large-scale visual categorization. Specifically, the part-level description is extracted by identifying the local region saliently representing the visual distinctiveness. The object-level description is extracted from object bounding boxes generated with a co-localization algorithm. Although only using the image-level labels, AutoBD outperforms the recent studies on two public benchmark, i.e., classification accuracy achieves 81.6% on CUB-200–2011 and 88.9% on Car-196, respectively. On the large-scale Birdsnap data set, AutoBD achieves the accuracy of 68%, which is currently the best performance to the best of our knowledge.
An Efficient Identity-Based Short Signature Scheme from Bilinear Pairings

In this paper, we present an ID-based signature scheme that is proved to be secure in the random oracle model under the hardness assumption of k-CAA problem. The proposed scheme upholds all desirable properties of previous IBS schemes, and requires general cryptographic hash functions instead of MapToPoint hash function that is inefficient and probabilistic. Furthermore, our scheme requires less computation cost and is significantly more efficient than all known IBS schemes, and the size of signatures generated by our scheme is approximate 160 bits, which is the shortest ID-based signatures so far.
A look at the development and countermeasures of medium-sized and small property rights transfer agencies

The Chinese property rights market has undergone more than 20-year exploration and practice. It has now become the foundational capital market and is playing a vital role in China's market economy construction. But the agencies - serving as the functional transfers in this market - are varying significantly and out of balance, especially for the numerous medium-sized and small property rights transfer agencies. This article firstly deals with the classification into the present development situation of the property rights market nationwide; and then emphatically analyzes the main problems existing in the development of the medium-sized and small property rights transfer agencies; finally some countermeasures are put forward for its development by exemplifying some functions that can be provided for them.
Incremental Codebook Adaptation for Visual Representation and Categorization

The bag-of-visual-words model is widely used for visual content analysis. For visual data, the codebook plays an important role for efficient representation. However, the codebook has to be relearned with the changes of training images. Once the codebook is changed, the encoding parameters of local features have to be recomputed. To alleviate this problem, in this paper, we propose an incremental codebook adaptation method for efficient visual representation. Instead of learning a new codebook, we gradually adapt a prelearned codebook using new images in an incremental way. To make use of the prelearned codebook, we try to make changes to the prelearned codebook with sparsity constraint and low-rank correlation. Besides, we also encode visually similar local features within a neighborhood to take advantage of locality information and ensure the encoded parameters are consistent. To evaluate the effectiveness of the proposed method, we apply the proposed method for categorization tasks on several public image datasets. Experimental results prove the effectiveness and usefulness of the proposed method over other codebook-based methods.
An Efficient Sequential Approach to Tracking Multiple Objects Through Crowds for Real-Time Intelligent CCTV Systems

Efficiency and robustness are the two most important issues for multiobject tracking algorithms in real-time intelligent video surveillance systems. We propose a novel 2.5-D approach to real-time multiobject tracking in crowds, which is formulated as a maximum a posteriori estimation problem and is approximated through an assignment step and a location step. Observing that the occluding object is usually less affected by the occluded objects, sequential solutions for the assignment and the location are derived. A novel dominant color histogram (DCH) is proposed as an efficient object model. The DCH can be regarded as a generalized color histogram, where dominant colors are selected based on a given distance measure. Comparing with conventional color histograms, the DCH only requires a few color components (31 on average). Furthermore, our theoretical analysis and evaluation on real data have shown that DCHs are robust to illumination changes. Using the DCH, efficient implementations of sequential solutions for the assignment and location steps are proposed. The assignment step includes the estimation of the depth order for the objects in a dispersing group, one-by-one assignment, and feature exclusion from the group representation. The location step includes the depth-order estimation for the objects in a new group, the two-phase mean-shift location, and the exclusion of tracked objects from the new position in the group. Multiobject tracking results and evaluation from public data sets are presented. Experiments on image sequences captured from crowded public environments have shown good tracking results, where about 90% of the objects have been successfully tracked with the correct identification numbers by the proposed method. Our results and evaluation have indicated that the method is efficient and robust for tracking multiple objects ( ges 3) in complex occlusion for real-world surveillance scenarios.
Real-time parameter extraction of the frequency agile PAR based on FPGA

According to the characteristic of a frequency agile radar, a fast method for parameter estimation of the direct-path signals is presented in this paper. In this method, the pulse width (PW) is roughly estimated based on the method in the frequency domain, and accurate PW parameter is estimated by matching the PW template. Second, accurate bandwidth (BW) is estimated and carrier frequency (CF) is roughly estimated based on de-chirp. Accurate CF is estimated by matching the CF template. Finally, parameter extraction algorithm of the paper is implemented on the FPGA hardware system. The test results of real data show that this method is highly accurate and computationally efficient. It has been successfully applied to an electronic reconnaissance system.
Obstacle detection research based on four-line laser radar in vehicle

In order to complete the task of vehicle's autonomous driving in structural road, it's necessary to detect obstacles. This paper proposed a method of obstacle detection, taking use of the four-line laser radar. This algorithm combined the improved DBSCAN with K-Means and overcame DBSCAN's defect that it couldn't divide obstacles with similar density. At the same time, this method can eliminate noise points effectively. Based on clustering, obstacle's information can be acquired, such as angle, distance and size, to achieve the task of obstacle detection. The algorithm is applied to obstacle detection in intelligent vehicle. The test proved that it is consistent and reliable, which complies with the requirement of intelligent vehicle's autonomous driving.
Trajectory-Based Ball Detection and Tracking in Broadcast Soccer Video

This paper presents a novel trajectory-based detection and tracking algorithm for locating the ball in broadcast soccer video (BSV). The problem of ball detection and tracking in BSV is well known to be very challenging because of the wide variation in the appearance of the ball over frames. Direct detection algorithms do not work well because the image of the ball may be distorted due to the high speed of the ball, occlusion, or merging with other objects in the frame. To overcome these challenges, we propose a two-phase trajectory-based algorithm in which we first generate a set of ball-candidates for each frame, and then use them to compute the set of ball trajectories. Informally, the two key ideas behind our strategy are 1) while it is very challenging to achieve high accuracy in locating the precise location of the ball, it is relatively easy to achieve very high accuracy in locating the ball among a set of ball-like candidates and 2) it is much better to study the trajectory information of the ball since the ball is the "most active" object in the BSV. Once the ball trajectories are computed, the ball locations can be reliably recovered from them. One important advantage of our algorithm is that it is able to reliably detect partially occluded or merged balls in the sequence. Two videos from the 2002 FIFA World Cup were used to evaluate our algorithm. It achieves a high accuracy of about 81% for ball location
Orientational Pyramid Matching for Recognizing Indoor Scenes

Scene recognition is a basic task towards image understanding. Spatial Pyramid Matching (SPM) has been shown to be an efficient solution for spatial context modeling. In this paper, we introduce an alternative approach, Orientational Pyramid Matching (OPM), for orientational context modeling. Our approach is motivated by the observation that the 3D orientations of objects are a crucial factor to discriminate indoor scenes. The novelty lies in that OPM uses the 3D orientations to form the pyramid and produce the pooling regions, which is unlike SPM that uses the spatial positions to form the pyramid. Experimental results on challenging scene classification tasks show that OPM achieves the performance comparable with SPM and that OPM and SPM make complementary contributions so that their combination gives the state-of-the-art performance.
${\rm S}^{3}{\rm MKL}$: Scalable Semi-Supervised Multiple Kernel Learning for Real-World Image Applications

We study the visual learning models that could work efficiently with little ground-truth annotation and a mass of noisy unlabeled data for large scale Web image applications, following the subroutine of semi-supervised learning (SSL) that has been deeply investigated in various visual classification tasks. However, most previous SSL approaches are not able to incorporate multiple descriptions for enhancing the model capacity. Furthermore, sample selection on unlabeled data was not advocated in previous studies, which may lead to unpredictable risk brought by real-world noisy data corpse. We propose a learning strategy for solving these two problems. As a core contribution, we propose a scalable semi-supervised multiple kernel learning method (S3MKL) to deal with the first problem. The aim is to minimize an overall objective function composed of log-likelihood empirical loss, conditional expectation consensus (CEC) on the unlabeled data and group LASSO regularization on model coefficients. We further adapt CEC into a group-wise formulation so as to better deal with the intrinsic visual property of real-world images. We propose a fast block coordinate gradient descent method with several acceleration techniques for model solution. Compared with previous approaches, our model better makes use of large scale unlabeled images with multiple feature representation with lower time complexity. Moreover, to address the issue of reducing the risk of using unlabeled data, we design a multiple kernel hashing scheme to identify the “informative” and “compact” unlabeled training data subset. Comprehensive experiments are conducted and the results show that the proposed learning framework provides promising power for real-world image applications, such as image categorization and personalized Web image re-ranking with very little user interaction.
Control Under Stochastic Multiplicative Uncertainties: Part II, Optimal Design for Performance

This paper studies the optimal control design problem for linear discrete-time systems with stochastic multiplicative uncertainties. These uncertainties are assumed to be present in the control inputs and modeled as independent and identically distributed (i.i.d.) random processes. The optimal performance under study is defined in the mean-square sense, referred to as the mean-square optimal <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_{2}$</tex-math></inline-formula> performance. It is shown that the mean-square optimal <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_{2}$</tex-math></inline-formula> control problem via state feedback can be solved using a mean-square stabilizing solution to a modified algebraic Riccati equation (MARE). A necessary and sufficient condition for the existence of this solution is presented, which constitutes a generalization of the solution to the classic optimal <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_{2}$</tex-math></inline-formula> state feedback design problem, whereas the latter can be obtained by solving an algebraic Riccati equation (ARE). It is also proven that the optimal control design problem can be cast as an eigenvalue problem (EVP). For the output feedback case with possible input delays, we show that the mean-square optimal <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_{2}$</tex-math></inline-formula> control problem also amounts to solving an MARE, when the plant has no nonminimum phase zeros from the inputs to the measurement outputs. That is, the global optimal solution is obtained by solving an MARE incorporating the delays. The implication is that in this case a separation principle still holds.
A novel method to design Wilkinson kind power divider

In this paper a novel way to design Wilkinson power divider is proposed. Traditional Wilkinson power divider design methods always propose a specific circuit structure, and then explicit deductive equations, equivalent circuit models and parameters will be given, the method how the structure is discovered hardly represent. In this paper, the general structure of Wilkinson power divider is analyzed, a novel design method is proposed, make use of this method kinds of power divider structure can be designed.
Coherent Semantic-Visual Indexing for Large-Scale Image Retrieval in the Cloud

The rapidly increasing number of images on the internet has further increased the need for efficient indexing for digital image searching of large databases. The design of a cloud service that provides high efficiency but compact image indexing remains challenging, partly due to the well-known semantic gap between user queries and the rich semantics of large-scale data sets. In this paper, we construct a novel joint semantic-visual space by leveraging visual descriptors and semantic attributes, which narrows the semantic gap by combining both attributes and indexing into a single framework. Such a joint space embraces the flexibility of coherent semantic-visual indexing, which employs binary codes to boost retrieval speed while maintaining accuracy. To solve the proposed model, we make the following contributions. First, we propose an interactive optimization method to find the joint semantic and visual descriptor space. Second, we prove convergence of our optimization algorithm, which guarantees a good solution after a certain number of iterations. Third, we integrate the semantic-visual joint space system with spectral hashing, which finds an efficient solution to search up to billion-scale data sets. Finally, we design an online cloud service to provide a more efficient online multimedia service. Experiments on two standard retrieval datasets (i.e., Holidays1M, Oxford5K) show that the proposed method is promising compared with the current state-of-the-art and that the cloud system significantly improves performance.
Parametric modeling in food package defect imaging

A novel approach in food package defect detection is proposed based on system identification theory, in which the channel defect detection problem can be regarded as the conventional system identification problem, i.e., estimation of the system impulse response based on the input-output sequence using parametric and nonparametric models. The well-known parametric model ARX has been investigated in this paper. The data are collected with a focused ultrasound transducer (17.3 MHz, 6.35-mm diameter, f/2, 173 /spl mu/m -6 dB pulse-echo lateral beam width at the focus) scanned over a rectangular grid, keeping the packages in the focus. Performance is measured in terms of detection rate, image contrast, and contrast-to-noise ratio. The results using the ARX model are compared with previous image formation techniques and also compared with the non-parametric method, i.e., spectral analysis. The results show that the ARX model has the comparable detection rate as RFCS and higher detection rate than BAI and RFS (except 6-/spl mu/m air-filled channel in plastic trilaminate film) for channel in plastic trilaminate film. The ARX model has achieved the moderate contrast enhancement and ranks second in contrast-to-noise ratio enhancement among the compared techniques. The ARX model has a low detection rate for channel defects in aluminum trilaminate film, which shows that its performance is material-dependent. Finally, the parametric method, ARX model demonstrates better performance than the non-parametric method, spectral analysis for food package defect detection.
An improved pitch detection of speech combined with speech enhancement

For poor robustness issues of pitch detection of noisy speech, the improved pitch detection method combined with speech enhancement is proposed in this paper. Firstly, in order to reduce background noise and receive the clean speech relatively, we use the multi-band spectral subtraction and the masking properties of human auditory system to work on the noisy speech, and next use the energy and zero-crossing rate's product, quotient to adjudge the voiced part. Finally, the method of improved average magnitude difference function (AMDF) weighted autocorrelation function (ACF) is used for accurate pitch detection of the voiced. Theoretical and experimental simulations show that, the method can detect pitch accurately in low SNR, and the robustness improved significantly.
Infrared imaging simulation for small target under complex background

In this paper we proposed a simple and effective simulation method for small IR targets under complex background in order to offer abundant test images for the performance evaluation of IR target detection algorithms. This method contains five main steps to acquire a simulation image. The first step is to model the small target according to a real target. The second step is to compute the temperature field. In the third step the irradiance image is computed based on irradiance model with kinds of imaging conditions. Moreover, the effect of IR imaging system is simulated in fourth step. Finally in the fifth step the real background image and the IR target simulation image which is achieved by above four steps are synthesized to achieve the final simulation image. Experimental results show that the simulation images are similar to real images and thus the proposed method has rationality.
One step beyond bags of features: Visual categorization using components

The bag-of-visual-words (BoW) representation has received wide application and public acceptance for visual categorization. However, the histogram based image representation ignores the spatial information and correlations among visual words. To tackle these problems, in this paper, we propose to use some image regions called ‘components’, as the higher-level visual elements to represent an image associating with the lower-level elements of ‘visual words’. Then we formulate the task of visual categorization into two progressive relationships among a given concept and the two-level visual elements of images, i.e., visual-words-to-components and components-to-concept. Firstly, component level linear SVM classifiers are learned to model the relationship between visual words and components, then the output of these SVM classifiers are linearly combined to model the relationships between components and concept. Experiments on the Scene-15 dataset and the Oxford Flowers dataset demonstrate the effectiveness of the proposed method.
Simple Techniques Make Sense: Feature Pooling and Normalization for Image Classification

Image classification is a fundamental task in computer vision, implying a wide range of challenging problems, such as object recognition, scene understanding, and image tagging. One of the most popular approaches to image classification, the bag-of-features (BoF) model, represents an image with a long feature vector and adopts machine learning algorithms for training and testing. Owing to its simplicity and scalability, the BoF model is widely used in both academic research studies and industrial applications. This paper discusses the feature summarization stage, including pooling and normalization, in the BoF model. We show that these two modules, although devalued sometimes, have important impacts on image classification performance. We present two algorithms, i.e., generalized regular spatial pooling for constructing a better group of spatial bins and hierarchical feature normalization for assigning proper weights for regional feature normalization. Both algorithms are independent of the descriptor extraction and feature encoding stages, and therefore, they could be freely transplanted onto many other classification frameworks based on local feature statistics. We further provide insightful discussions for the nature of designing efficient image classification models. Experiments verify that the proposed algorithm achieves state-of-the-art results on a wide range of image classification data sets.
Small-gain stability conditions for linear systems with time-varying delays

Sufficient stability conditions for linear systems subject to time-varying delays are developed in this paper, which are in the form of scaled small gain conditions and can be checked readily by solving corresponding linear matrix inequality problems. From a robust control perspective, our development seeks to cast the stability problem as one of robust stability analysis, and the resulting stability conditions are also reminiscent of robust stability bounds typically found in robust control theory. Examples show significant improvement of our results over the existing criteria.
An Improved Backward SBR-PO/PTD Hybrid Method for the Backward Scattering Prediction of an Electrically Large Target

An improved hybrid method of backward shooting and bouncing ray (SBR) physical optics (PO)/physical theory of diffraction (PTD) is proposed to investigate the backward scattering from a three-dimensional electrically large target. The basic idea is to combine the forward-ray-tracing technique to accelerate the original backward-ray-tracing one. The proposed method significantly reduces the computational complexity while ensuring the same computational accuracy with the traditional backward SBR method. The accuracy and performance of our method are validated and evaluated by comparison with the exact computational electromagnetic method (FEKO-MLFMM) and experimental data for electrically small targets. The last case for the warship model illustrates that our method can enable good speedup for electrically large complex objects.