nils-herrmann/clean_scirepeval huggingface dataset:
Refining image retrieval using one-class classification

Can we take advantage of the huge number of online images to improve image search quality? Motivated by this question, we propose a novel model to re-rank Google image search results by exploring the latent characteristic of massive unrelated images as a clue to filter them in the reranking. Inspired by the characteristic of the intrinsic diversity and the unwanted availability of the unrelated images, in our model, we adopt one-class classification to build a hyper-sphere for the target objects, unrelated images, and construct a robust boundary to distinguish them from the related images effectively. Then the Google results can be easily re-ranked by filtering the unrelated images with the built-up model. Extensive experiments demonstrate our approach outperforms Google image search engine's results, even if its baseline is high.
A novel customer segmentation framework for china's supermarkets

With the increasingly intense competition, China' s supermarket retailers call for a complete, stable and practical customer segmentation approach to utilize consumer data so as to effectively customize the firm's marketing efforts and targets. We propose a novel three-step segmentation framework to reveal the consumer's demographics, behavioral, psychological characteristics in supermarket shopping, which is theoretically validated and verified through a real China supermarket case. This approach is also applicable to other retail sectors and even other consumer-oriented industries.
Efficient discovery of unknown ads for audio podcast content

Audio podcasting has been widely used by many online sites such as newspapers, web portals, journal, etc., to deliver audio content to users through download or subscription. Within 1 to 30 minutes long of one podcast story, it is often that multiple audio advertisements (ads) are inserted into and repeated, with each of a length of 5 to 30 seconds, at different locations. Based on knowledge of typical structures of podcast contents, this paper proposes a novel efficient advertisement discovery approach to identify and locate unknown ads from a large collection of audio podcasting. Two techniques: candidate region segmentation and sampling technique are employed to speed up the search. The approach has been tested over a variety of podcast contents collected from MIT Technology Review,  Experimental results show that the proposed approach achieves detection rate of 97.5% with a significant computation saving as compared to existing state-of-the art methods.
Scalable Person Re-identification on Supervised Smoothed Manifold

Most existing person re-identification algorithms either extract robust visual features or learn discriminative metrics for person images. However, the underlying manifold which those images reside on is rarely investigated. That arises a problem that the learned metric is not smooth with respect to the local geometry structure of the data manifold. In this paper, we study person re-identification with manifold-based affinity learning, which did not receive enough attention from this area. An unconventional manifold-preserving algorithm is proposed, which can 1) make best use of supervision from training data, whose label information is given as pairwise constraints, 2) scale up to large repositories with low on-line time complexity, and 3) be plunged into most existing algorithms, serving as a generic postprocessing procedure to further boost the identification accuracies. Extensive experimental results on five popular person re-identification benchmarks consistently demonstrate the effectiveness of our method. Especially, on the largest CUHK03 and Market-1501, our method outperforms the state-of-the-art alternatives by a large margin with high efficiency, which is more appropriate for practical applications.
Large-scale person re-identification as retrieval

This paper targets to bring together the research efforts on two fields that are growing actively in the past few years: multicamera person Re-Identification (ReID) and large-scale image retrieval. We demonstrate that the essentials of image retrieval and person ReID are the same, i.e., measuring the similarity between images. However, person ReID requires more discriminative and robust features to identify the subtle differences of different persons and overcome the large variance among images of the same person. Specifically, we propose a coarse-to-fine (C2F) framework and a Convolutional Neural Network structure named as Conv-Net to tackle the large-scale person ReID as an image retrieval task. Given a query person image, the C2F firstly employ Conv-Net to extract a compact descriptor and perform the coarse-level search. A robust descriptor conveying more spatial cues is hence extracted to perform the fine-level search. Extensive experimental results show that the proposed method outperforms existing methods on two public datasets. Further, the evaluation on a large-scale Person-520K dataset demonstrates that our work is significantly more efficient than existing works, e.g., only needs 180ms to identify a query person from 520K images.
Coloring image search with coupled multi-index

The precision of visual matching and the trade-off between accuracy and time efficiency have long been bottlenecks of image search systems. This work addresses the two problem simultaneously by introducing the coupled Multi-Index (cMI) structure. First, by combining SIFT and color features on the indexing-level, the discriminative power of visual words is greatly enhanced. Second, by reducing the number of inverted entries to be traversed, c-MI brings about significant improvement in time efficiency. Experiments are performed on two widely used benchmark datasets. We demonstrate both state-of-the-art image search accuracy and cut-by-half query time.
Voice activity detection using higher-order statistics in the teager energy domain

In this paper, an effective algorithm employing higher-order statistics (HOS) with teager energy operator (TEO) for voice activity detection (VAD) in noisy environments is proposed. The presented VAD utilizes the kurtosis of a speech signal in the teager energy domain and is shown to be efficient and robust in detecting speech in low signal-to-noise ratio (SNR) conditions without being adversely affected by types of colored noises. The use of TEO significantly enhances the discriminability between speech and noise via a nonlinear operator, and overcomes the inability of the HOS in classifying speech and non-Gaussian noise. The results of computer simulations prove that the proposed approach has an overall better performance than the standard ITU-T G.729B VAD, and other recently reported VADs.
A hybrid integration method of frequency agile radar for uniform moving target

Frequency agility (FA) is one of the important techniques in radar systems for the superiority in radar range, glint reduction, electronic counter-countermeasures and so on. However, frequency agility makes the signal processing complex and degrades the detection performance. For uniform moving targets, a hybrid integration method is proposed in group-group frequency agile radar (FAR). The Keystone transform is applied in each group for coherent integration. Due to the FA, a scale transformation based on linear interpolation is proposed, and then the trajectory feature of target is used to achieve the hybrid integration based on Hough transform. The experimental results validate the effectiveness and advantages of the proposed method.
Fundamental Limits on Uncertain Delays: When Is a Delay System Stabilizable by LTI Controllers?

This paper concerns the stabilization of linear time-invariant (LTI) systems subject to uncertain, possibly time-varying delays. The fundamental issue under investigation, referred to as the delay margin problem, addresses the question: What is the largest range of delay such that there exists a single LTI feedback controller capable of stabilizing all the plants for delays within that range? Drawing upon analytic interpolation and rational approximation techniques, we derive fundamental bounds on the delay margin, within which the delay plant is guaranteed to be stabilizable by a certain LTI output feedback controller. Our contribution is threefold. First, for single-input single-output (SISO) systems 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 (MIMO) systems, we show that estimates on the variation ranges of multiple delays can be obtained by solving LMI problems, and further, by finding bounds on the radius of delay variations. Third, we show that 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 but possibly multiple nonminimum phase zeros, our results give rise to analytical expressions exhibiting explicit dependence of the bounds and estimates on the pole and zeros, thus demonstrating how fundamentally unstable poles and nonminimum phase zeros may limit the range of delays over which a plant can be stabilized by a LTI controller.
Traffic incident Duration analysis using Hazard duration model

Duration analysis is one of the most important steps of the overall traffic incident management process. Based on the survey data on traffic incident information, this paper developed the Hazard duration model, and analyzed the statistical indicators and key factors of incident duration by using the theory of Hazard function. The results indicate that the Hazard duration model can be used for traffic incident duration analysis. The results of this paper can help the government to analyze traffic incident duration and provide decision method for the government to make real-time decisions and conduct incident management.
InterActive: Inter-Layer Activeness Propagation

An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spatial context contained in the higher layers. In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. Activeness is propagated through a neural network in a top-down manner, carrying highlevel context and improving the descriptive power of lowlevel and mid-level neurons. Visualization indicates that neuron activeness can be interpreted as spatial-weighted neuron responses. We achieve state-of-the-art classification performance on a wide range of image datasets.
Similarity Gaussian Process Latent Variable Model for Multi-modal Data Analysis

Data from real applications involve multiple modalities representing content with the same semantics and deliver rich information from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized cross-modal mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy of multi-modal data. In this paper, we build our work based on Gaussian process latent variable model (GPLVM) to learn the non-linear non-parametric mapping functions and transform heterogeneous data into a shared latent space. We propose multi-modal Similarity Gaussian Process latent variable model (m-SimGP), which learns the nonlinear mapping functions between the intra-modal similarities and latent representation. We further propose multi-modal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the output space. The overall objective functions are solved by simple and scalable gradient decent techniques. The proposed models are robust to content divergence and high-dimensionality in multi-modal representation. They can be applied to various tasks to discover the non-linear correlations and obtain the comparable low-dimensional representation for heterogeneous modalities. On two widely used real-world datasets, we outperform previous approaches for cross-modal content retrieval and cross-modal classification.
Two Degree of Freedom Controller Design for Leader-to-Formation

This paper discusses feedback control design problem for leader-to-formation systems which could include one leader and one follower. The performance of the leader is measured by energy of the error between its output and reference while the performance of the follower is measured by the energy of the error between the distance from the leader to the follower and the desired distance. In this paper, a sub-optimal two degree of freedom (2DOF) control design method is presented by applying H2 and Hinfin control design. Upper bounds of the cost functions for both of leader and follower are minimized. Numerical example illustrates that the presented controllers have good performance in disturbance rejection and formation control.
Decision support services for retail site location using a two-stage guiding optimization approach

How best to locate new outlets among alternative position offerings is a critical operational decision that is faced by all retailers. Thus they are eager to have a quantitative method and tool to help them derive more scientific strategic suggestions. In this paper, the retail site location (RSL) problem is described at the beginning. Then we propose a two-stage guiding optimization procedure to guide users finding the best location strategy gradually, with both of the stages formulated subsequently. The first stage relies on finding “ideal” optimal solutions without considering the competition from opponents. The second one offers the risk of every solution which is caused by opponents' activities. Finally, a RSL decision support system based on the new approach and GIS platform is designed and implemented.
Self-supervised learning for gene classification on microarray data

With the development of microarray technology, it provides massive amounts of high dimensional gene expression data simultaneously and most of their functions are unknown. Computational methods that can effectively resolve high dimensionality and small sample size problems for the high throughput data are valuable in systems biology. Self- supervised learning techniques, which take a hybrid of labeled and unlabeled data to train classifiers, can solve the problem efficiently. Discriminant-EM (DEM) proposes a framework for such tasks by applying self-supervised learning in an optimal discriminating subspace of the original feature space. In this paper, the linear algorithm is extended to a nonlinear kernel algorithm to capture the non-linearity in the data distribution. Extensive experiments on the Plasmodium falciparum dataset show the promising performance of the approach.
Robust ImageGraph: Rank-Level Feature Fusion for Image Search

Recently, feature fusion has demonstrated its effectiveness in image search. However, bad features and inappropriate parameters usually bring about false positive images, i.e., outliers, leading to inferior performance. Therefore, a major challenge of fusion scheme is how to be robust to outliers. Towards this goal, this paper proposes a rank-level framework for robust feature fusion. First, we define Rank Distance to measure the relevance of images at rank level. Based on it, Bayes similarity is introduced to evaluate the retrieval quality of individual features, through which true matches tend to obtain higher weight than outliers. Then, we construct the directed ImageGraph to encode the relationship of images. Each image is connected to its K nearest neighbors with an edge, and the edge is weighted by Bayes similarity. Multiple rank lists resulted from different methods are merged via ImageGraph. Furthermore, on the fused ImageGraph, local ranking is performed to re-order the initial rank lists. It aims at local optimization, and thus is more robust to global outliers. Extensive experiments on four benchmark data sets validate the effectiveness of our method. Besides, the proposed method outperforms two popular fusion schemes, and the results are competitive to the state-of-the-art.
Novel Visual and Statistical Image Features for Microblogs News Verification

Microblog has been a popular media platform for reporting and propagating news. However, fake news spreading on microblogs would severely jeopardize its public credibility. To identify the truthfulness of news on microblogs, images are very crucial content. In this paper, we explore the key role of image content in the task of automatic news verification on microblogs. Existing approaches to news verification depend on features extracted mainly from the text content of news tweets, while image features for news verification are often ignored. According to our study, however, images are very popular and have a great influence on microblogs news propagation. In addition, fake and real news events have different image distribution patterns. Therefore, we propose several visual and statistical features to characterize these patterns visually and statistically for detecting fake news. Experiments on a real-world multimedia dataset collected from Sina Weibo validate the effectiveness of our proposed image features. The news verification performance of our method outperforms baseline methods. To the best of our knowledge, this is the first attempt that systematically explores image features on news verification task.
Action Recognition Using Spatial-Temporal Context

The spatial-temporal local features and the bag of words representation have been widely used in the action recognition field. However, this framework usually neglects the internal spatial-temporal relations between video-words, resulting in ambiguity in action recognition task, especially for videos “in the wild”. In this paper, we solve this problem by utilizing the volumetric context around a video-word. Here, a local histogram of video-words distribution is calculated, which is referred as the “context” and further clustered into contextual words. To effectively use the contextual information, the descriptive video-phrases (ST-DVPs) and the descriptive video-cliques (ST-DVCs) are proposed. A general framework for ST-DVP and ST-DVC generation is described, and then action recognition can be done based on all these representations and their combinations. The proposed method is evaluated on two challenging human action datasets: the KTH dataset and the YouTube dataset. Experiment results confirm the validity of our approach.
An integrated multiple neural network architecture for reading alphanumeric characters in complex scenes

An integrated multiple neural network (NN) architecture is proposed to handle the automatic extraction and recognition of machine-printed alphanumeric characters in a complex scene. The computational framework is illustrated by our implementation of a multi-expert, multi-level reading system comprising of five NNs for both segmentation and recognition. A window-based alphanumeric NN is trained as a first-pass character-level primary recognizer. A window-based alphabet NN and a window-based digit NN are then used as group-level secondary recognizers. To further boost the recognition accuracy, a Fourier-descriptor-based probabilistic neural network is trained on characters which were misrecognized by the above three networks. In the event of mis-segmentation of merged or damaged characters, a special character horizontal position neural network is used to detect the horizontal positional occurrence of characters. Our system has been applied to the recognition and verification of the 11 machine-printed alphanumeric ID on containers.<<ETX>>
Beyond Explicit Codebook Generation: Visual Representation Using Implicitly Transferred Codebooks

The bag-of-visual-words model plays a very important role for visual applications. Local features are first extracted and then encoded to get the histogram-based image representation. To encode local features, a proper codebook is needed. Usually, the codebook has to be generated for each data set which means the codebook is data set dependent. Besides, the codebook may be biased when we only have a limited number of training images. Moreover, the codebook has to be pre-learned which cannot be updated quickly, especially when applied for online visual applications. To solve the problems mentioned above, in this paper, we propose a novel implicit codebook transfer method for visual representation. Instead of explicitly generating the codebook for the new data set, we try to make use of pre-learned codebooks using non-linear transfer. This is achieved by transferring the pre-learned codebooks with non-linear transformation and use them to reconstruct local features with sparsity constraints. The codebook does not need to be explicitly generated but can be implicitly transferred. In this way, we are able to make use of pre-learned codebooks for new visual applications by implicitly learning the codebook and the corresponding encoding parameters for image representation. We apply the proposed method for image classification and evaluate the performance on several public image data sets. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
Query-adaptive late fusion for image search and person re-identification

Feature fusion has been proven effective [35, 36] in image search. Typically, it is assumed that the to-be-fused heterogeneous features work well by themselves for the query. However, in a more realistic situation, one does not know in advance whether a feature is effective or not for a given query. As a result, it is of great importance to identify feature effectiveness in a query-adaptive manner.
A method of natural language understanding on SPARQL ontology query

In this paper, the development of a SPARQL ontology query based on Natural Language Understanding is presented. To obtain ontology knowledge conveniently, Stanford Parser for user's natural language inquiring is utilized, and query triple according to the grammar is constructed. The method greatly reduces the number of combinations compared with the key word method. Combined with user dictionary, the terms of query triple can be more accurately mapped to the ontology entities. Meanwhile scores calculation is not only considered the similarity of words' form and semantic, but also considered the ambiguity of concept, for returning to the specific concept as far as possible. Experimental results are presented to demonstrate the performance and validity of method.
Mean-square stabilizability for output feedback control under quantization constraints

This paper studies the stability problem of linear time-invariant (LTI), single-input single-output (SISO) discrete-time network control systems with quantization constraints on control signal. The logarithmic quantizer is considered and we want to design an output-feedback controller to make the closed-loop system be mean-square stability. In this process, the output-feedback controller is realized by Youla parameterization. A sufficient and necessary condition is receive to guarantee the mean-square stability of closed-loop system which is determined by the unstable poles of given systems and the unique solutions to two algebraic Riccati equations. Specially, this sufficient and necessary condition is only depended on the plant unstable poles for minimum phase plant case. These results also provide an output feedback controller design method to stabilize a linear system with multiplicative noise.