nils-herrmann/clean_scirepeval huggingface dataset:
Scalable transcoding for video transmission over space-time OFDM systems

A new scheme combining a scalable transcoder with space time block codes (STBC) for an orthogonal frequency division multiplexing (OFDM) system is proposed for robust video transmission in dispersive fading channels. The target application for such a scalable transcoder is to provide successful access to the pre-encoded high quality video from mobile wireless terminals. In the scalable transcoder, besides outputting the fine granular scalability (FGS) bitstream, both size of video frames and the bit rate are reduced. And an array processing algorithm of layer interference suppression is used at the receiver which makes the system structure provide different levels of protection to different layers. Furthermore, by considering the important level of scalable bitstream, the different bitstreams can be given different level protection by the system structure and channel coding. With the proposed system, the concurrent large diversity gain characteristic of STBC and alleviating the frequency-selective fading effect of OFDM can be achieved. The simulation results suggest that the proposed schemes integrating scalable transcoding can provide a basic quality of video transmission and outperform the convention single layer transcoding transmitted under the random and bursty error channel conditions.
An adaptive edge-preserving artifacts removal filter for video post-processing

We propose an adaptive edge-preserving artifacts removal filter (EPARF) for post-processing video compressed using block-based motion compensation so as to improve visual quality. The proposed filter is based on half-quadratic regularization, and it can adaptively remove both blocking and ringing artifacts while preserving object edges. The proposed algorithm has been applied to video sequences compressed by the MSRA scalable video coding (SVC) codec with block-based motion-compensated temporal filtering (MCTF). Experimental results demonstrate that EPARF can significantly improve both subjective and objective quality.
Cross-scene crowd counting via deep convolutional neural networks

Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to fine-tune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.
Exploiting neural models for no-reference image quality assessment

We propose an improved algorithm for no-reference image quality assessment (NR-IQA) using the convolutional neural network (CNN) and neural theory based saliency detection. Firstly, we extract non-overlapping patches from the input image. For each patch, we obtain the quality score by CNN network, which consists of seven layers and integrates feature learning and regression into image patch quality estimation. Considering that the patches attracting much attention take significant role in visual perception, an efficient technique based on free energy based neural model is used to detect the saliency map. This saliency map is then applied as a weighting mask to output the quality score of the whole image. Results of experiments show that our algorithm achieves state-of-the-art performance, as compared with the prevailing IQA methods.
An Improved Motion-Compensated 3-D LLMMSE Filter With Spatio–Temporal Adaptive Filtering Support

In this paper, we propose a motion-compensated spatio-temporal Locally adaptive linear minimum mean squared-error (LLMMSE) filter for noisy image sequences with both temporal and spatially adaptive filtering support. Motion compensation and an adaptive temporal filtering support (TFS) structure guarantee the uniformity of the TFS. An intelligent pixel aggregation algorithm is proposed to include homogeneous neighboring pixels and exclude the outlier pixels, resulting in uniform spatial filtering support. By using the proposed spatio-temporal LLMMSE filter with uniform spatio-temporal support, we can reduce noise efficiently without introducing visually disturbing blurring artifacts. Furthermore, we employ an adaptive weighted local mean and variance estimation algorithm to improve the accuracy of estimation. The weights provide an implicit mechanism for deemphasizing the contribution of the outlier pixels which are wrongly kept within the support to avoid blurring. The performance of the proposed filter is quantitatively evaluated and compared with state-of-the-art methods. The results demonstrate that the proposed filter can achieve superior filtering performance, both subjectively and objectively.
Joint Prediction of Rating and Popularity for Cold-Start Item by Sentinel User Selection

New item or topic profiling and recommendation are useful yet challenging, especially in face of a “cold-start” situation with sparse user-item ratings for the new arrivals. In this paper, a method of acquiring review opinions of the “sentinel” users on the cold-start items is proposed to elicit those items’ latent profiles, and thus both user-specific ratings and future popularity of the items can be predicted simultaneously. Specifically, such a joint prediction task is formulated as a two-stage optimization problem, and a sentinel user selection algorithm is devised to facilitate effective latent profiles extraction for both item ratings and popularity predictions. Experiments with microblogging and movie data sets corroborate that the proposed method is capable of mitigating the cold-start problem and it outperforms several competitive peer methods.
A Reconfigurable Tangram Model for Scene Representation and Categorization.

This paper presents a hierarchical and compositional scene layout (i.e., spatial configuration) representation and a method of learning reconfigurable model for scene categorization. Three types of shape primitives (i.e., triangle, parallelogram, and trapezoid), called tans, are used to tile scene image lattice in a hierarchical and compositional way, and a directed acyclic AND-OR graph (AOG) is proposed to organize the overcomplete dictionary of tan instances placed in image lattice, exploring a very large number of scene layouts. With certain off-the-shelf appearance features used for grounding terminal-nodes (i.e., tan instances) in the AOG, a scene layout is represented by the globally optimal parse tree learned via a dynamic programming algorithm from the AOG, which we call tangram model. Then, a scene category is represented by a mixture of tangram models discovered with an exemplar-based clustering method. On basis of the tangram model, we address scene categorization in two aspects: 1) building a tangram bank representation for linear classifiers, which utilizes a collection of tangram models learned from all categories and 2) building a tangram matching kernel for kernel-based classification, which accounts for all hidden spatial configurations in the AOG. In experiments, our methods are evaluated on three scene data sets for both the configuration-level and semantic-level scene categorization, and outperform the spatial pyramid model consistently.
Buffer-constrained R-D model-based rate control for H.264/AVC

Most existing R-D model based rate control schemes are not applicable in H.264/AVC due to the unique features of H.264/AVC, for example, the QP-dependent rate distortion optimization (RDO); the large number of bits (header bits) used for encoding the header information (except DCT coefficients) of a macroblock (MB); the huge number of zero-coefficient MBs. We present an R-D model based rate control scheme for H.264/AVC. A pre-analysis is conducted for R-D estimation. A rate model is developed to estimate the coefficient bits and the header bits separately. A frame-level bit allocation scheme is proposed. In comparison with JM6.1e, our scheme not only evidently improves the PSNR of decoded video (an average 0.54 dB PSNR gain is achieved for 7 sequences tested) but also meets the target bit rates accurately (within 2%).
Development of multimegawatt gyrotrons for fusion plasma heating and current drive

High frequency gyrotrons with high output power are mainly used for microwave heating and current drive in plasmas for thermonuclear fusion experiments. Electron cyclotron resonance heating (ECRH) has proven to be an important tool for plasma devices, especially for stellarators, as it provides both net current free plasma start up from the neutral gas and efficient plasma heating. The development of high power gyrotrons (118 GHz, 140 GHz and 170 GHz) in continuous wave operation (CW) has been in progress for several years in a joint collaboration between different European research institutes and industrial partners. This paper describes the work of the Forschungszentrum Karlsruhe for the development of conventional-cavity 1-MW CW gyrotrons, coaxial cavity 2-MW short-pulse gyrotrons and a frequency step-tunable gyrotron in the frequency range between 105-140 GHz.
Cost-Effective User Monitoring for Popularity Prediction of Online User-Generated Content

In this paper, we study on the popularity prediction of online user-generated contents, where high quality predictions give us much more flexibility and preparing time in deploying limited resources (such as advertising budget, monitoring capacity) into more popular contents. However the high retrieval cost of data used in prediction is a big challenge due to the large amount of users and contents involved. We propose a notion that higher popularity user-generated contents can be predicted by concentrating on fewer but informative users, as we notice the fact that contents generated by those users tend to become popular while that which are generated by the rest users do not. We develop a cost-effective popularity prediction framework to fulfil online prediction. It contains 3 modules: (a) online data retrieving, (b) informative users selection and (c) popularity prediction. A hybrid user selection algorithm and several popularity prediction algorithms/improvements are presented, and their performance are evaluated and compared using (a) the selected users' generated data and (b) all users' generated data, retrieved from Sina Weibo Micro blogger. The best prediction algorithm reaches a 78% accuracy at the time of 24 hours after publishing time when level width Nl equals 500. And the best combination of prediction and selection algorithms performs only about 7% worse on dataset of 2000 users than on dataset of all users (about 4.46 million).
A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting

This paper addresses the problem of matching N weighted graphs referring to an identical object or category. More specifically, matching the common node correspondences among graphs. This multi-graph matching problem involves two ingredients affecting the overall accuracy: i) the local pairwise matching affinity score among graphs; ii) the global matching consistency that measures the uniqueness of the pairwise matching results by different chaining orders. Previous studies typically either enforce the matching consistency constraints in the beginning of iterative optimization, which may propagate matching error both over iterations and across graph pairs; or separate affinity optimizing and consistency regularization in two steps. This paper is motivated by the observation that matching consistency can serve as a regularizer in the affinity objective function when the function is biased due to noises or inappropriate modeling. We propose multigraph matching methods to incorporate the two aspects by boosting the affinity score, meanwhile gradually infusing the consistency as a regularizer. Furthermore, we propose a node-wise consistency/affinitydriven mechanism to elicit the common inlier nodes out of the irrelevant outliers. Extensive results on both synthetic and public image datasets demonstrate the competency of the proposed algorithms.
Multilevel Framework to Detect and Handle Vehicle Occlusion

This paper presents a multilevel framework to detect and handle vehicle occlusion. The proposed framework consists of the intraframe, interframe, and tracking levels. On the intraframe level, occlusion is detected by evaluating the compactness ratio and interior distance ratio of vehicles, and the detected occlusion is handled by removing a ldquocutting regionrdquo of the occluded vehicles. On the interframe level, occlusion is detected by performing subtractive clustering on the motion vectors of vehicles, and the occluded vehicles are separated according to the binary classification of motion vectors. On the tracking level, occlusion layer images are adaptively constructed and maintained, and the detected vehicles are tracked in both the captured images and the occlusion layer images by performing a bidirectional occlusion reasoning algorithm. The proposed intraframe, interframe, and tracking levels are sequentially implemented in our framework. Experiments on various typical scenes exhibit the effectiveness of the proposed framework. Quantitative evaluation and comparison demonstrate that the proposed method outperforms state-of-the-art methods.
Possibilities for multifrequency operation of a gyrotron at FZK

We investigate the possibility of multifrequency operation of a 140-GHz gyrotron, which is designed to operate in the TE/sub 22,8/ mode at 140 GHz and the TE/sub 19,6/ mode at 111 GHz or the TE/sub 17,6/ mode at 105 GHz, for which existing equipment can be used. The present calculations compute beam properties for a given set of coil currents, accelerating voltage and current, and then use these beam properties to compute the output power and efficiency. These calculations are performed separately for each mode.
Joint Optimization for Consistent Multiple Graph Matching

The problem of graph matching in general is NP-hard and approaches have been proposed for its sub optimal solution, most focusing on finding the one-to-one node mapping between two graphs. A more general and challenging problem arises when one aims to find consistent mappings across a number of graphs more than two. Conventional graph pair matching methods often result in mapping inconsistency since the mapping between two graphs can either be determined by pair mapping or by an additional anchor graph. To address this issue, a novel formulation is derived which is maximized via alternating optimization. Our method enjoys several advantages: 1) the mappings are jointly optimized rather than sequentially performed by applying pair matching, allowing the global affinity information across graphs can be propagated and explored, 2) the number of concerned variables to optimize is in linear with the number of graphs, being superior to local pair matching resulting in O(n2) variables, 3) the mapping consistency constraints are analytically satisfied during optimization, and 4) off-the-shelf graph pair matching solvers can be reused under the proposed framework in an `out-of-the-box' fashion. Competitive results on both the synthesized data and the real data are reported, by varying the level of deformation, outliers and edge densities.
Progress toward optimization of phase-correcting mirrors for a multifrequency 1-MW gyrotron

Due to the requirement of a newly designed ultra-broadband chemical vapour deposition (CVD)-diamond window for a multifrequency 1-MW short-pulse gyrotron at Forschungszentrum Karlsruhe (FZK), the existing beam-forming mirror system inside the gyrotron tube has to be modified. This paper describes the design scheme for shaping the output beams from the launching antenna waveguide into the desired fundamental Gaussian profile by using adapted phase-correcting mirrors with nonquadratic surface contour function. Simulation results show these phase-correcting mirrors can be used for broadband operation of a frequency step-tunable gyrotron, which operates in nine modes from the TE17,6 mode at the frequency of 105 GHz to the TE23,8  mode at 143 GHz. Further calculations predict that efficiencies of more than 94% can be obtained for converting the high-order cylindrical cavity modes into the usable fundamental Gaussian mode
Retransmission-based error spreading for layered video streaming over wireless LANs

Robust video streaming over wireless LANs is very challenging due to the burst-error behavior of wireless channels. In this paper, a novel error-resilience scheme, called retransmission-based error spreading, is proposed for layered video streaming over wireless LANs. Considering the unequal importance of different video partitions under the real-time and bandwidth constraints, the proposed scheme uses unequal retransmission attempts at the medium access control (MAC) layer to spread out burst errors. Consequently, burst errors are separated into those relatively unimportant partitions in a picture, and graceful quality degradation can be achieved. Simulation results show that the proposed scheme can be aware of time-varying channel conditions, and thus significantly improve the quality of received video
Performance analysis of half bridge three-level full bridge bi-directional DC-DC converters

A novel topology of half bridge three-level full bridge bidirectional DC-DC converter is proposed. The configuration and operational principles of the novel converters with the dual-phase-shifting control were analyzed. The ZVS soft-switching range of the left three-level half bridge and the right H-bridge under different voltage conversion ratio were discussed. In order to improve the whole system efficiency, Based on the mathematical models of transmission power and backflow power, the equation describing the minimum backflow power under the given transmission power was formulated. The dual-phase-shifting optimized control strategy with the minimum backflow power for different given transmission power under ZVS soft- switching conditions were proposed. At last, the experimental results were presented to validate the theoretical analysis.
Robust object tracking with bidirectional corner matching and trajectory smoothness algorithm

This paper proposes a novel method for robust object tracking. The method consists of three different components: a short term tracker, an object detector, and an online object model. For the short term tracker, we use an advanced Lucas Kanade tracker with bidirectional corner matching to capture object frame by frame. Meanwhile, statistical filtering and matching algorithm combined with haar-like feature random fern play as a detector to extract all possible object candidates in the current frame. Making use of trajectory information, the online object model decides the best target match among the candidates. And the model also trains the random fern feature adaptively online to better guide consecutive tracking. We demonstrate our method is robust to track an object in a long term and under large variations of view angle and lighting conditions. Moreover, our method is efficient to re-detect the object and keep tracking even after it's out of view or recover from heavy occlusion. To achieve state-of-the-art performance, it is highlighted that our method can be extended to multiple objects tracking application. Finally, comparisons with other state-of-the-art trackers are presented to show the robustness of our tracker.
H//sub/spl infin//-optimal model for VBR video traffic prediction

Traffic prediction generated by multimedia sources is needed for effective dynamic bandwidth allocation and for multimedia quality of services (QoS) control strategies implemented at the network edges. In this paper, an Hinfin-optimal mode is proposed for video traffic prediction, which not only minimizes the worst possible effect in the estimation error but also does not require a priori knowledge of the noise source statistics. The proposed method has been applied to modeling and prediction MPEG-4 variable bit rate (VBR) video traffic. Simulation results show that the proposed model is more robust compared with the Kalman prediction model and is able to predict the original traffic more accurately, thus more suitable for real time video traffic prediction and resource reservation
Temporal Action Localization with Pyramid of Score Distribution Features

We investigate the feature design and classification architectures in temporal action localization. This application focuses on detecting and labeling actions in untrimmed videos, which brings more challenge than classifying presegmented videos. The major difficulty for action localization is the uncertainty of action occurrence and utilization of information from different scales. Two innovations are proposed to address this issue. First, we propose a Pyramid of Score Distribution Feature (PSDF) to capture the motion information at multiple resolutions centered at each detection window. This novel feature mitigates the influence of unknown action position and duration, and shows significant performance gain over previous detection approaches. Second, inter-frame consistency is further explored by incorporating PSDF into the state-of-the-art Recurrent Neural Networks, which gives additional performance gain in detecting actions in temporally untrimmed videos. We tested our action localization framework on the THUMOS'15 and MPII Cooking Activities Dataset, both of which show a large performance improvement over previous attempts.
A high-efficiency quasi-optical mode converter for a 140-GHz 1-MW CW gyrotron

A highly efficient quasi-optical mode converter system with several novel features has been designed and tested at Forschungszentrum Karlsruhe (FZK). The converter consists of a dimpled-wall waveguide launcher, one quasi-elliptical mirror and two toroidal mirrors. The coupled-mode theory has been used to analyze the operation of the prebunching waveguide launcher; the radiated fields from the cut of the launcher have been calculated by the scalar diffraction integral. Simulation results show that the advanced dimpled-wall launcher generates a well-focused Gaussian radiation pattern with low diffraction losses. In this case, toroidal mirrors are sufficient to obtain a desired output beam pattern. An efficiency of more than 98% has been achieved to convert the rotating TE/sub 28,8/ cavity mode at 140 GHz into a fundamental Gaussian beam. Experimental measurements show close agreement with theoretical predictions.