nils-herrmann/clean_scirepeval huggingface dataset:
Behavior Informatics: A New Perspective

This installment of Trends & Controversies provides an array of perspectives on the latest research in behavior informatics. Longbing Cao introduces the work in "Behavior Informatics: A New Perspective." Then, in "Behavior Computing," Longbing Cao and Thorsten Joachims provide a basic overview of the topic. Next is "Coupled Behavior Representation, Modeling, Analysis, and Reasoning" by Can Wang, Longbing Cao, Eric Gaussier, Jinjiu Li, Yuming Ou, and Dan Luo. The fourth article is "Behavior Analysis in Social Media," by Reza Zafarani and Huan Liu. The fifth article is "Group Recommendation and Behavior," by Guandong Xu and Zhiang Wu. Gabriella Pasi wrote the sixth article, "Web Search and Behavior." The seventh article, "Behaviors of IPTV Users," is by Ya Zhang, Xiaokang Yang, and Hongyuan Zha. Finally, "Should Behavioral Models of Terror Groups Be Disclosed?" is by Edoardo Serra and V.S. Subrahmanian.
2D Quaternion Fourier Transform: The Spectrum Properties and its Application in Color Image Registration

We first investigate 2D quaternion Fourier transform (QFT) spectrum relationship between an image and its geometrically transformed counterpart from the aspects of gray images and color images respectively, and then propose a 2D QFT-based color image registration approach, which is able to handle large translation, rotation and scaling. Fourier transform (FT) can be utilized in gray image registration but can not process color images naturally. As the extended version of FT in multidimensional signal processing, QFT is capable of processing three color components of color images together as pure quaternions and is able to deal with color image registration.
Gait identification by sparse representation

Gait recognition under variations of clothing and carrying condition is still a challenging task. In this paper, we present a gait identification method via sparse representation. We formulate the recognition problem as finding the coefficients of linear combination of the training samples plus an error term and discuss sparse signal representation theory that offers the solution to this problem. Based on the sparse representation computed by l1-minimization, we define a new distance metric to choose non-polluted area and propose a method for gait identification. Compared with the state-of-the-art methods on a large dataset, the proposed method achieves significant performance improvement in identification rates and it shows robustness to variations.
Deep hash learning for efficient image retrieval

Hashing method is a widely used method for content-based image retrieval. For more complicated semantic similarity of images, supervised hashing methods based on hand-crafted features show its limitations. Convolutional neural network (CNN) has powerful automatic feature learning ability. For this reason, CNN based deep hashing methods outperform previous methods. In this paper, we propose a new deep supervised hashing method for efficient image retrieval. We design a novel deep network with a hash layer as the output layer. An algorithm is proposed to generate optimal target hash code for training. We perform point-wise training for simultaneous feature extracting and hash function learning. Experiments on standard image retrieval benchmarks show that our method outperforms other state-of-the-art methods including unsupervised, supervised and deep hashing methods.
Recent results of the step-tunable 105-140 GHz, 1MW gyrotron development at Forschungszentrum Karisruhe

A multifrequency step-tunable 1MW-gyrotron for controlling plasma instabilities in fusion tokamaks has been designed. It operates in the TE/sub 22.8/ mode at 140 GHz and the TE/sub 17.6/ mode at 105 GHz and 7 other modes in between. In the final stage for long pulse operation, this gyrotron should be equipped with an advanced synthetic diamond Brewster window and an optimized beam forming system. The last design results of the Brewster window and the quasi-optical mode converter are presented.
A learning-based text detection method in camera images

This paper proposed a learning-based text detection method in camera images. First, we find 280 pictures of book covers, CD covers and movie posters shot with cameras on Internet. We manually label and extract text regions in them. Second, based on statistical analysis of the difference between text and non-text samples, we get three sets of features which are used to produce weak classifiers. Third, Ada-boost is utilized to select and combine these weak classifiers into two-stage attentional cascade. At last, this two-stage cascade can detect text area in images by classifying sub-regions of images as text and non-text. Compared with previous works, this method is robust in detecting single characters, skewed and even vertical lines.
Moving Cast Shadows Detection Using Ratio Edge

Moving objects segmentation plays a very important role in real-time image analysis. However, as one of the common parts in the natural scenes, shadows severely interfere with the accuracy of moving objects detection in video surveillance. In this paper, we present a novel method for moving cast shadows detection. Based on the analysis of the physical model of moving shadows, we prove that the ratio edge is illumination invariant. The distribution of the ratio edge is discussed and a significance test is performed to classify each moving pixel into foreground object or moving shadow. Intensity constraint and geometric heuristics are imposed to further improve the performance. Experiments on various typical scenes exhibit the robustness of the proposed method. Extensively quantitative evaluation and comparison demonstrate that the proposed method significantly outperforms state-of-the-art methods.
Self-example based super-resolution with fractal-based gradient enhancement

Recently, the example-based super-resolution method has been extensively studied due to its vivid perception. However, this kind of method directly transfers the high-frequency details of the examples to the low-resolution image, incurring false structures and over-sharpness around the texture regions. In this paper, the problem in the example-based method is investigated from an analytic discussion. Then we propose a super-resolution method that reconstructs sharp edges using the redundancy properties. The super-resolution problem is formulated as a unified regularization scheme which adaptively emphasizes the importance of high-frequency residuals in structural examples and scale invariant fractal property in textural regions. The experimental results show that the high-lights of our method exist in the enhanced visual quality with sharp edges, natural textures and few artifacts.
Building Artificial Identities in Social Network Using Semantic Information

as the popularity of social networking sites increase, so does their attractiveness for criminals. In this work, we show how an adversary can build artificial identities using semantic information in social network. Our method make the identities look more like real people, therefore can be used to support many kinds of attacks, such as ASE [1], profile cloning[2]. A prototype of this method is implemented, includes following stages: Firstly, categories of virtual identity are predefined, and each category has multiple properties, such as geographical region, hobby, education, age, interested topic/keywords, etc. Secondly, based on category information, each identity will foster its own "life" semantically, such as edit profile and update status, find hot related news/topic from Google then post to wall, find related groups/networks then request to add in, and find/like/create/comment pages/posts, etc. Thirdly, artificial identity will evolve to multiple stages according to its status (for example, number of friends of real people), single identity with different evolutionary stages is linked together to a group that will help to ensure the number of attack edges .
Event recognition with time varying Hidden Markov Model

Standard Hidden Markov Model (HMM) and the more general Dynamic Bayesian Network (DBN) models assume stationarity of state transition distribution. However, this assumption does not hold for many real life events of interest. In this paper, we propose a new time sequence model that extends HMM to time varying scenario. The time varying property is realized in our model by explicitly allowing the change of state transition density as the time spent in a particular state passes by. Rather than keeping transition densities at different time spots independent of each other, we exploit their temporal correlation by applying a hierarchical Dirichlet prior. This leads to a more robust time varying model, especially when training data are scarce. We also employ Markov Chain Monte Carlo (MCMC) sampling in learning the MAP estimate of time varying parameters, with a transition kernel incorporating linear optimization. The proposed model is applied to recognizing real video events, and is shown to outperform existing HMM-based methods.
Robust Video Region-of-Interest Coding Based on Leaky Prediction

A video region-of-interest (ROI) scalable coding scheme can ensure the priority of ROI. Error protection schemes can be used to guarantee the correct receipt of the ROI stream when transporting ROI scalable video over an error-prone network. However, we find that the correct receipt of ROI bitstreams cannot ensure the correct decoding of ROI due to the unique issue of the cross error propagation between ROI and background in ROI scalable coding. In this letter, we propose an ROI scalable coding framework based on leaky prediction (LP) for robustly transporting video over an error-prone network. Although several LP approaches have been proposed to improve layered coding, they cannot be applied to ROI scalable coding straightforwardly due to the cross error propagation issue. We deploy a leaky factor to weigh the two predictions: one from the constrained motion estimation (ME) within the ROI layer of the reference frame, and the other from the unrestricted ME in the overall reference frame. Simulation results show that the proposed scheme enhances the robustness of ROI scalability while maintaining coding efficiency.
Unequal Iterative Decoding for Power Efficient Video Transmission

We present an unequal iterative decoding (UID) approach for minimization of the receiver power consumption subject to a given quality of service, by exploiting data partitioning and turbo decoding. We assign unequal decoding iterations of forward error correction (FEC) to data partitions with different importance by jointly considering the source coding, channel coding and receiver power allocation. The proposed scheme has been applied to H.264 video over AWGN channel, and achieves excellent tradeoff between video delivery quality and power consumption, and yields significant power saving compared with the classical equal iterative decoding (EID) approach in wireless video transmission
Error robustness scheme for H.264 based on LDPC code

With the tremendous increase in the capabilities of wireless multimedia devices and services, the demand to improve quality of such systems within the limited bandwidth resources motivates the interest in robust video transmission. A new unequal error protect (UEP) scheme is proposed based on LDPC code and data partitioning, which can protect much more important bits and correct major error during the transmission over AWGN channel. Our proposed algorithm features assigning an unequal bits number of forward error correction (FEC) to each data partition for H.264, thus, efficiently makes use of available resources. Simulation results demonstrate that this proposed algorithm is more error robust compared to those obtained with the classical EEP technique and common coding mode, such as Turbo code
Speed up HEVC encoder by precoding with H.264

The new video coding standard, High Efficiency Video Coding (HEVC), was developed to replace H264/AVC and achieves high efficiency by introducing a new coding structure in adoption of coding unit (CU), prediction unit (PU) and transform unit (TU). However, it also imposes great computation burden on the mode decision of encoders. In this paper, a conceptual HEVC encoder architecture with cascaded H264/AVC encoder is proposed to speed up the CTU splitting process of HEVC encoder. First, the input frame is precoded with an efficient H264/AVC encoder to gather the partition and prediction information of H264/AVC macroblocks (MBs). Then based on the gathered information, CU and PU modes are carefully screened during HEVC encoding. Two schemes are proposed to reduce the CU and PU candidate modes to accelerate the Coding Tree Unit (CTU) splitting process. At last, the HEVC encoder tries all combinations of candidate CU and PU mode to determine the optimal CU, PU and TU mode by computing the rate-distortion (R-D) cost. The experimental results on highly-optimized open source codecs - x264 and x265 show that the proposed framework can effectively reduce encoding time with negligible loss in terms of PSNR and bitrate.
Learning super resolution with global and local constraints

In learning based single image super-resolution(SR) approach, the super-resolved image are usually found or combined from training database through patch matching. But because the representation ability of small patch is limited, it is difficult to guarantee that the super-resolved image is best under global view. To tackle this problem, we propose a statistical learning method for SR with both global and local constraints. Firstly, we use Maximum a Posteriori (MAP) estimation with learned image priors by Fields of Experts (FoE) model, and regularize SR globally guided by the image priors. Secondly, for each overlapped patch, the higher-order Markov random fields (MRFs) is used to model its local relationship with corresponding high-resolution candidates, then belief propagation is used to find high-resolution image. Compared with traditional patch based learning method without global constraint, our method could not only preserve the global image structure, but also restore the local details well. Experiments verify the idea of our global and local constraint SR method.
GPU accelerated high-quality video/image super-resolution

This paper presents several novel GPU optimization technologies to accelerate the SRCNN(Super-Resolution Convolutional Neural Network) - one of the best super-resolution algorithm. We first directly parallelize and implement the SRCNN, then accelerate the convolution by making use of the hierarchical feature of GPU memory. We explore different optimization methods on each convolution and select the fastest combination. Further acceleration can be achieved by fusing the convolution and ReLu(Rectified Linear units) operation to eliminate the memory access time of ReLu. Our experiments show that the overall execution time for 1080p to 4K upscaling is reduced from 300s/frame to 0.15s/frame, while the image quality is exactly the same as original SRCNN.
Channel-Aware Frame Dropping for Cellular Video Streaming

In the case of cellular video streaming over wireless channels, burst frame losses may be unavoidable. Considering the unequal importance of different frames in a group-of-pictures (GOP) and the burst-error characteristics of wireless channels, this paper proposes a channel-aware frame dropping scheme so as to shift burst losses into relatively unimportant frames in the same GOP. By using selective retransmission at the radio link layer, a base station can adaptively assign the unequal transmission attempts to different video frames. Simulation results show that the proposed scheme can be aware of the variation of wireless channel conditions, and thus significantly improve error resilience of cellular video streaming
Image restoration via efficient Gaussian mixture model learning

Expected Patch Log Likelihood (EPLL) framework using Gaussian Mixture Model (GMM) prior for image restoration was recently proposed with its performance comparable to the state-of-the-art algorithms. However, EPLL uses generic prior trained from offline image patches, which may not correctly represent statistics of the current image patches. In this paper, we extend the EPLL framework to an adaptive one, named A-EPLL, which not only concerns the likelihood of restored patches, but also trains the GMM to fit for the degraded image. To efficiently estimate GMM parameters in A-EPLL framework, we improve a recent Expectation-Maximization (EM) algorithm by exploiting specific structures of GMM from image patches, like Gaussian Scale Models. Experiment results show that A-EPLL outperforms the original EPLL significantly on several image restoration problems, like inpainting, denoising and deblurring.
A Novel Text Structure Feature Extractor for Chinese Scene Text Detection and Recognition

Scene text information extraction plays an important role in many computer vision applications. Most features in existing text extraction algorithms are only applicable to one text extraction stage (text detection or recognition), which significantly weakens the consistency in an end-to-end system, especially for the complex Chinese texts. To tackle this challenging problem, we propose a novel text structure feature extractor based on a text structure component detector (TSCD) layer and residual network for Chinese texts. Inspired by the three-layer Chinese text cognition model of a human, we combine the TSCD layer and the residual network to extract features suitable for both text extraction stages. The specialized modeling for Chinese characters in the TSCD layer simulates the key structure component cognition layer in the psychological model. And the residual mechanism in the residual network simulates the key bidirectional connection among the layers in the psychological model. Through the organic combination of the TSCD layer and the residual network, the extracted features are applicable to both text detection and recognition, as humans do. In evaluation, both text detection and recognition models based on our proposed text structure feature extractor achieve great improvements over baseline CNN models. And an end-to-end Chinese text information extraction system is experimentally designed and evaluated, showing the advantage of the proposed feature extractor as a unified feature extractor.
Image interpolation based on decomposition

This paper proposes a new approach for interpolating natural images. Unlike other conventional interpolation methods, we exploit the characteristics of an image and decompose it into a texture part and a non-texture (cartoon) part. In the non-texture part, we interpolate it with bicubic interpolation which performs well for smooth areas. We deal the texture part with peak transform based interpolation which resets the image pixels to capture the characters of the texture through peak transform. The experiment results demonstrate our new interpolation method has higher PSNR and better subjective quality of the interpolated image, especially in the texture part.
Real-time foreground detection based on tempo-spatial consistency validation and Gaussian Mixture Model

Robust foreground detection is a fundamental precursor of many video processing applications. Although various approaches were advanced, there still exist many factors making detection very challenging: 1) Dynamic background with gradual brightness changes, camera movement and large amount of noises. 2) Sharp illumination changes caused by shadows, light on-off, and so on. 3) Real-time requirement for practical systems. To overcome these problems, a new approach is proposed in this paper. It is based on the background of conventional Gaussian Mixed Model, incorporating tempo-spatial consistency validation to search genuine foreground seeds, so that foreground segments can be reliably acquired using region growth method. Experiments demonstrate that our approach achieves better performance than conventional GMM approach in detection accuracy, adaptability to sudden illumination changes and computation time.
Transform domain transcoding from MPEG-2 to H.264 with interpolation drift-error compensation

With the increasingly extensive applications of the new emerging video coding standard H.264, it inspires an urgent need to transcode the widely available MPEG-2 compressed video to H.264 format. In this paper, we investigate the issues on transcoding MPEG-2 into H.264 in transform domain with consideration of drift error due to the mismatch of motion compensation, and propose a transform domain solution to transcode MPEG-2 into H.264. We first analyze two major kinds of drifting error resulting from the mismatch of motion compensation: interpolation error and quantization error. The former is caused by the difference between the interpolation filters adopted in these two standards, thus very unique to the transform domain transcoding from MPEG-2 to H.264. Furthermore, it is identified as the dominant factor of the video quality degradation, especially in the case of small quantization parameter at high bit-rate, by extensive experimental results. As a major contribution of this paper, the close form of interpolation error is derived from transform domain. We then proposed the transcoding scheme based on quantization error drifting compensation and Interpolation error drifting compensation. Experimental results show that the proposed transform domain transcoding scheme achieves very promising performance in terms of low computational complexity and high transcoded video quality. Most importantly, its peak signal-to-noise ratio is very close to the cascaded transcoding architecture with time-consuming decoding and recoding process in pixel domain.
Progress towards optimization of phase-correcting mirrors for a multi-frequency gyrotron

The newly designed CVD-diamond Brewster window of the FZK multi-frequency gyrotron requires the existing mirror system to be modified to optimize the transmission through the window aperture. Intensive calculations have been done for this purpose; nine operating modes between 105 GHz (TE/sub 17.6/) and 143 GHz (TE/sub 23.8/) have a nearly fundamental Gaussian distribution at the window. Simulation results show this QO mode converter can be used for the broadband operation of a multi-frequency 1 MW gyrotron.
Local Quaternionic Gabor Binary Patterns for color face recognition

In this paper, a novel color face recognition method is proposed based on local binary patterns (LBP) of quaternionic Gabor features (QGF). By introducing quaternion Gabor analysis into image representation, we make full use of the interrelationship among different color channels to enhance the performance of the face recognition system. Moreover, the QGF are used to encode the positions and attributes of the face elements. Non-parametric transformation is then imposed on these QGF using LBP method to obtain the robustness against variations of pose, illumination and facial expressions. Compared with the monochromatic face recognition systems, which nowadays dominate the marketplace and research field, this approach materializes the strong potential use of color face recognition system by establishing invariant quaternion wavelet features of color images. The experimental results on the open face database testify the validity of the proposed method under severe noise corruption and distinct variations of scale, illumination and facial expressions.