nils-herrmann/clean_scirepeval huggingface dataset:
Scalable Similarity Search With Topology Preserving Hashing

Hashing-based similarity search techniques is becoming increasingly popular in large data sets. To capture meaningful neighbors, the topology of a data set, which represents the neighborhood relationships between its subregions and the relative proximities between the neighbors of each subregion, e.g., the relative neighborhood ranking of each subregion, should be exploited. However, most existing hashing methods are developed to preserve neighborhood relationships while ignoring the relative neighborhood proximities. Moreover, most hashing methods lack in providing a good result ranking, since there are often lots of results sharing the same Hamming distance to a query. In this paper, we propose a novel hashing method to solve these two issues jointly. The proposed method is referred to as topology preserving hashing (TPH). TPH is distinct from prior works by also preserving the neighborhood ranking. Based on this framework, we present three different TPH methods, including linear unsupervised TPH, semisupervised TPH, and kernelized TPH. Particularly, our unsupervised TPH is capable of mining semantic relationship between unlabeled data without supervised information. Extensive experiments on four large data sets demonstrate the superior performances of the proposed methods over several state-of-the-art unsupervised and semisupervised hashing techniques.
Scalable Feature Matching by Dual Cascaded Scalar Quantization for Image Retrieval

In this paper, we investigate the problem of scalable visual feature matching in large-scale image search and propose a novel cascaded scalar quantization scheme in dual resolution. We formulate the visual feature matching as a range-based neighbor search problem and approach it by identifying hyper-cubes with a dual-resolution scalar quantization strategy. Specifically, for each dimension of the PCA-transformed feature, scalar quantization is performed at both coarse and fine resolutions. The scalar quantization results at the coarse resolution are cascaded over multiple dimensions to index an image database. The scalar quantization results over multiple dimensions at the fine resolution are concatenated into a binary super-vector and stored into the index list for efficient verification. The proposed cascaded scalar quantization (CSQ) method is free of the costly visual codebook training and thus is independent of any image descriptor training set. The index structure of the CSQ is flexible enough to accommodate new image features and scalable to index large-scale image database. We evaluate our approach on the public benchmark datasets for large-scale image retrieval. Experimental results demonstrate the competitive retrieval performance of the proposed method compared with several recent retrieval algorithms on feature quantization.
The united detection of weak MSK signal using Duffing oscillator and Stochastic resonance

Concerning the problem that the traditional method is not valid to demodulate the MSK signal, this article is the first time to use Duffing oscillator and Stochastic Resonance to demodulate the MSK signal, a new demodulation method was proposed in this article. First, we put the MSK's frequency compressed into a low range; then, we put the MSK signal into the Stochastic Resonance system to increase its SNR; later, we use the STFT to detect every code element's signal's frequency; finally, the decision device recovers the binary data according to this frequency. In this article, the power spectral entropy and the symbol demarcation point searching device are also the first time be introduced. The MATLAB simulation results show that this method can successfully demodulate the MSK signal, its minimum SNR is in −26dB.
Fusing feature and similarity for multimodal search

It is well known that multiple information fusion can enhance the retrieval performance of multimedia systems. However, what to fuse and how to fuse them are still open issues for multimodal correlation learning. In this paper, we address the problem of combining multiple resources to enhance the multimodal correlation learning ability. We propose two fusion strategies: multi-feature fusion and multi-similarity fusion. For multi-feature fusion, feature concatenation is used to integrate various features. For multi-similarity fusion, three fusion rules are investigated: MIN, MAX, and weighted AVG fusion. The effectiveness of the fusion strategies is evaluated on several state-of-the-art multimodal correlation learning models for cross-modal retrieval tasks. Results suggest that with proper fusion strategy selection, the multimodal retrieval performance can be significantly enhanced.
Effective image representation based on bi-layer visual codebook

Recently, the Bag-of-visual Words (BoW) based image representation has drawn much attention in image categorization and retrieval applications. It is known that the visual codebook construction and the related quantization methods play the important roles in BoW model. Traditionally, visual codebook is generated by clustering local features into groups, and the original feature is hard quantized to its nearest centers. It is known that the quantization error may degrade the effectiveness of the BoW representation. To address this problem, several soft quantization based methods have been proposed in literature. However, the effectiveness of these methods is still unsatisfactory. In this paper, we propose a novel and effective image representation method based on a bi-layer codebook. In this method, we first construct the bi-layer codebook to explicitly reduce the quantization error. And then, inspired by the locality-constrained linear coding method[18], we propose a ridge regression based quantization to assign multiple visual words to the local feature. Furthermore, the k nearest neighbor strategy is integrated to improve the efficiency of quantization. To evaluate the proposed image representation, we compare it with the existing image representations on two benchmark datasets in the image classification experiments. The experimental results demonstrate the superiority over the state-of-the-art techniques.
Affective MTV analysis based on arousal and valence features

Nowadays, MTV has become an important favorite pastime to modern people because of its conciseness, convenience to play and the characteristic that can bring both audio and visual experiences to audiences. In this paper, we propose an affective MTV analysis framework, which realizes MTV affective state extraction, representation and clustering. Firstly, affective features are extracted from both audio and visual signals. Then, the affective state of each MTV is modeled with 2D dimensional affective model and visualized in the Arousal-Valence space. Finally the MTVs having similar affective states are clustered into same categories. The validity of proposed framework is proved by subjective user study. The comparisons between our selected features and those in related work prove that our features improve the performance by a significant margin.
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval

In the Bag-of-Words (BoW) model, the vocabulary is of key importance. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the intersection set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the intersection set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the intersection set. We evaluate our method on three benchmark datasets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance with the state-of-the-art methods.
Geometric Hypergraph Learning for Visual Tracking

Graph-based representation is widely used in visual tracking field by finding correct correspondences between target parts in different frames. However, most graph-based trackers consider pairwise geometric relations between local parts. They do not make full use of the target’s intrinsic structure, thereby making the representation easily disturbed by errors in pairwise affinities when large deformation or occlusion occurs. In this paper, we propose a geometric hypergraph learning-based tracking method, which fully exploits high-order geometric relations among multiple correspondences of parts in different frames. Then visual tracking is formulated as the mode-seeking problem on the hypergraph in which vertices represent correspondence hypotheses and hyperedges describe high-order geometric relations among correspondences. Besides, a confidence-aware sampling method is developed to select representative vertices and hyperedges to construct the geometric hypergraph for more robustness and scalability. The experiments are carried out on three challenging datasets (VOT2014, OTB100, and Deform-SOT) to demonstrate that our method performs favorably against other existing trackers.
Utilizing affective analysis for efficient movie browsing

Because of the fast increasing number of movies and long time span each movie lasts, novel methods should be developed to help users browse movies and find their desired clips effectively. Affective information in movies is closely related with users' experiences and preferences. Therefore, in this paper, we analyze the affective states of movies and propose affective information based movie browsing. Affective movie content analysis is challenging due to the great variety of movie contents and styles. To address this challenge, we first extract rich audio-visual features. Then, feature selection and affective modeling are carried out to select and map effective features into corresponding affective states. Finally, we propose novel Affective Visualization techniques which intuitively visualize affective states to achieve efficient and user-friendly movie browsing. Experiments on representative movie dataset demonstrate the effectiveness of our proposed methods.
Fundamental bounds on delay margin: When is a delay system stabilizable?

This paper concerns the stabilization of linear systems subject to unknown, possibly time-varying delays. Drawing upon analytic interpolation and rational approximation techniques, we develop fundamental bounds on the delay margin, with which the delay plant is guaranteed to be stabilizable by a controller. Our contribution is threefold. First, for a single-input single-output system with an arbitrary number of plant unstable poles and nonminimum phase zeros, we provide an explicit, computationally efficient bound on the delay margin, which requires computing only the largest real eigenvalue of a constant matrix. Second, for multi-input multi-output systems, we show that estimates on the variation ranges of multiple delays can be obtained by solving LMI problems, and further, by computing the radius of delay variations. Third, we show that with appropriate care, these bounds and estimates can be extended to systems subject to time-varying delays. When specialized to more specific cases, e.g., to plants with one unstable pole and one nonminimum phase zero, our results give rise to analytical expressions exhibiting explicit dependence of the bounds and estimates on the pole and zero, thus demonstrating how fundamentally unstable poles and nonminimum phase zeros may limit the range of delays over which a plant can be stabilized.
Asset-based requirement analysis in telecom Service Delivery Platform domain

Software product line engineering is an emerging software engineering paradigm which develops products or solutions based on domain assets. Domain requirements are core domain asset in software product line engineering, and requirement analysis for domain products or solutions based on domain requirements is a key step. Previous domain requirement models are suitable to model tens of variabilities in a domain which contains only a limited number of products. But in a solution domain such as SDP (service delivery platform) domain of telecom, there are thousands of variabilities among the solutions. In this paper, a new domain requirement model for solution domains is proposed. In this model, domain requirements are organized with three kinds of aggregation relationships and three kinds of generalization relationships. Based on these relationships, a configuration model is proposed to analyze the commonalities and variabilities among domain solutions. A solution requirement analysis method is also described based on the new domain requirement model. A case study is conducted on SDP domain which demonstrates how the proposed domain requirement model and solution requirement analysis method is used.
Fast algorithm and application of Hough transform in iris segmentation

Hough transform is a common algorithm used to detect geometry shape of objects in computer image processing, some points satisfied lines, circles and other curves can be detected easily by using Hough transform in image. In iris recognition system, both the inner boundary and the outer boundary of a typical iris can approximately be taken as circles, so the two circles of iris can be obtained by using Hough transform. In this paper, Hough transform algorithm is introduced and is adopted in iris segmentation. A modified fast algorithm is presented to solving low speed of Hough transform. Simulations and research results show that Hough transforms are satisfied in iris segmentation.
i.Boosting for Image Classification

Traditional boosting method like adaboost, boosts a weak learning algorithm by updating the sample weights (the relative importance of the training samples) iteratively. In this paper, we propose to integrate feature re-weighting into boosting scheme, which not only weights the samples but also weights the feature elements iteratively. To avoid overfitting problem caused by feature re-weighting on a small training data set, we also incorporate relevance feedback into boosting and propose an interactive boosting called i.Boosting. It merges adaboost, feature re-weighting and relevance feedback into one framework and exploits the favorable attributes of these methods. In this paper, i.Boosting is implemented using Adaptive Discriminant Analysis (ADA) as base classifiers. It not only enhances but also combines a set of ADA classifiers into a more powerful one. A specific feature re-weighting method for ADA is also proposed and integrated in i.Boosting. Extensive experiments show the superior performance of i.Boosting over AdaBoost and other state-of-the-art projection-based classifiers.
Cross-Modal Retrieval Using Multiordered Discriminative Structured Subspace Learning

This paper proposes a novel method for cross-modal retrieval. In addition to the traditional vector (text)-to-vector (image) framework, we adopt a matrix (text)-to-matrix (image) framework to faithfully characterize the structures of different feature spaces. Moreover, we propose a novel metric learning framework to learn a discriminative structured subspace, in which the underlying data distribution is preserved for ensuring a desirable metric. Concretely, there are three steps for the proposed method. First, the multiorder statistics are used to represent images and texts for enriching the feature information. We jointly use the covariance (second-order), mean (first-order), and bags of visual (textual) features (zeroth-order) to characterize each image and text. Second, considering that the heterogeneous covariance matrices lie on the different Riemannian manifolds and the other features on the different Euclidean spaces, respectively, we propose a unified metric learning framework integrating multiple distance metrics, one for each order statistical feature. This framework preserves the underlying data distribution and exploits complementary information for better matching heterogeneous data. Finally, the similarity between the different modalities can be measured by transforming the multiorder statistical features to the common subspace. The performance of the proposed method over the previous methods has been demonstrated through the experiments on two public datasets.
Orientational Spatial Part Modeling for Fine-Grained Visual Categorization

Although significant success has been achieved in fine-grained visual categorization, most of existing methods require bounding boxes or part annotations for training and test, resulting in limited usability and flexibility. To conquer these limitations, we aim to automatically detect the bounding box and parts for fine-grained object classification. The bounding boxes are acquired by a transferring strategy which infers the locations of objects from a set of annotated training images. Based on the generated bounding box, we propose a multiple-layer Orientational Spatial Part (OSP) model to generate a refined description for the object. Finally, we employ the output of deep Convolutional Neural Network (dCNN) as the feature and train a linear SVM as object classifier. Extensive experiments on public benchmark datasets manifest the impressive performance of our method, i.e., Classification accuracy achieves 63.9% on CUB-200-2011 and 75.6% on Aircraft, which are actually higher than many existing methods using manual annotations.
Highly efficient chip scale package (CSP) LED based on surface patterning

Surface patterning, in terms of microstructure, as a precise surface roughness method for chip scale package (CSP) LED, will enormously improve the light extraction efficiency (LEE) with much less total internal reflection loss. In this paper, several kinds of microstructures and layouts have been designed and optimized by using the Monte Carlo ray-tracing simulation method. During the simulation, an accurate CSP-LED model is built Shape, size, separation and arrangement of the microstructures are taken into consideration to find out how much the above parameters affect the LEE. LEE Enhancement of more than 20% is achieved by several structures compared to CSP-LED without surface patterning. Among which, enhancement of 20.90% is reached utilizing 5×5 pyramid arrays with a gradient of 54.7 degrees. Furthermore, each designed microstructure is also fabricated through the nano-imprint technology. Experimental results prove the feasibility of former designs and 20.31% enhancement of LEE is achieved.
High-Order Local Spatial Context Modeling by Spatialized Random Forest

In this paper, we propose a novel method for spatial context modeling toward boosting visual discriminating power. We are particularly interested in how to model high-order local spatial contexts instead of the intensively studied second-order spatial contexts, i.e., co-occurrence relations. Motivated by the recent success of random forest in learning discriminative visual codebook, we present a spatialized random forest (SRF) approach, which can encode an unlimited length of high-order local spatial contexts. By spatially random neighbor selection and random histogram-bin partition during the tree construction, the SRF can explore much more complicated and informative local spatial patterns in a randomized manner. Owing to the discriminative capability test for the random partition in each tree node's split process, a set of informative high-order local spatial patterns are derived, and new images are then encoded by counting the occurrences of such discriminative local spatial patterns. Extensive comparison experiments on face recognition and object/scene classification clearly demonstrate the superiority of the proposed spatial context modeling method over other state-of-the-art approaches for this purpose.
Statistical modeling of complex backgrounds for foreground object detection

This paper addresses the problem of background modeling for foreground object detection in complex environments. A Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance is proposed. Under this framework, the background is represented by the most significant and frequent features, i.e., the principal features , at each pixel. A Bayes decision rule is derived for background and foreground classification based on the statistics of principal features. Principal feature representation for both the static and dynamic background pixels is investigated. A novel learning method is proposed to adapt to both gradual and sudden "once-off" background changes. The convergence of the learning process is analyzed and a formula to select a proper learning rate is derived. Under the proposed framework, a novel algorithm for detecting foreground objects from complex environments is then established. It consists of change detection, change classification, foreground segmentation, and background maintenance. Experiments were conducted on image sequences containing targets of interest in a variety of environments, e.g., offices, public buildings, subway stations, campuses, parking lots, airports, and sidewalks. Good results of foreground detection were obtained. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results.
Blind estimation of PN sequence period for DS-UWB signal received in multi-path environment

This paper proposes a method to estimate the pseudonoise (PN) sequence period for direct sequence ultra-wideband (DS-UWB) signal in multi-path channel. The power spectrum of received signals is firstly estimated, then the estimated power spectrum is taken as input signals and its power spectrum is computed again. Finally, it will appear a series of peak pulses with interval, which equates to the PN sequence period of DS-UWB signal. The expression of reprocessed power spectrum can reveal the feasibility of this method. Meanwhile, to enhance estimation performance, average accumulative method is used. The simulation results show that the proposed approach is very effective at lower signal-to-noise ratio (SNR).
Event detection based on non-broadcast sports video

Most recent research work on sports video analysis focuses on broadcast video. The broadcast video is a post-produced video and has much additional editing information inserted. In this paper, we propose a novel event detection framework in sports game only based on the video taken by single (main) camera. Compared with event detection from broadcast video, the proposed framework is more challenging as interesting events needs to be automatically detected. The results of the research could be used for automatic replay generation in sports video. In this paper, a mid-level representation is first created based on low-level audio/visual feature and events related to the replay are then detected from this representation. We experiment our framework and some promising results are obtained.