nils-herrmann/clean_scirepeval huggingface dataset:
Supervised Distributed Hashing for Large-Scale Multimedia Retrieval

Recent years have witnessed the growing popularity of hashing for large-scale multimedia retrieval. Extensive hashing methods have been designed for data stored in a single machine, that is, centralized hashing . In many real-world applications, however, the large-scale data are often distributed across different locations, servers, or sites. Although hashing for distributed data can be implemented by assembling all distributed data together as a whole dataset in theory, it usually leads to prohibitive computation, communication, and storage costs in practice. Up to now, only a few methods were tailored for distributed hashing, which are all unsupervised approaches. In this paper, we propose an efficient and effective method called supervised distributed hashing (SupDisH), which learns discriminative hash functions by leveraging the semantic label information in a distributed manner. Specifically, we cast the distributed hashing problem into the framework of classification, where the learned binary codes are expected to be distinct enough for semantic retrieval. By introducing auxiliary variables, the distributed model is then separated into a set of decentralized subproblems with consistency constraints, which can be solved in parallel on each vertex of the distributed network. As such, we can obtain high-quality distinctive unbiased binary codes and consistent hash functions with low computational complexity, which facilitate tackling large-scale multimedia retrieval tasks involving distributed datasets. Experimental evaluations on three large-scale datasets show that SupDisH is competitive to centralized hashing methods and outperforms the state-of-the-art unsupervised distributed method significantly.
A novel pupil localization method based on GaborEye model and radial symmetry operator

The eyes are the most important facial landmarks on the human face. The accuracy of face normalization, which is critical to the performance of the following face analysis steps, depends on the locations of the two eyes, as well as their relatively constant interocular distance. In this paper, we propose a novel GaborEye model for eye localization. Based on the special gray distribution in the eye-and-brow region, a proper Gabor kernel is adaptively chosen to convolute with the face image to highlight the eye-and-brow region, which can be exploited to segment the two pupil regions efficiently. After getting the region of the pupil, a fast radial symmetry operator is used to locate the center of the pupil. Extensive experiments show that the method can accurately locate the pupils, and it is robust to the variations of face poses, expressions, accessories and illuminations.
A new power control strategy for hybrid fuel cell vehicles

A new power control strategy is proposed to improve fuel economy for hybrid fuel cell vehicles (FCVs) through the consideration of fuel cell system efficiency and battery energy management. In order to show power flows and the improvement of fuel economy due to the proposed power control strategy, an overall vehicle simulation is performed and analyzed. The simulation results are compared with the results of two fuel cell vehicles with different configurations and control strategies, namely, a semihybrid FCV and a nonhybrid FCV. The results demonstrate that the new control strategy can significantly improve fuel economy. In particular, for FUDS cycle, the fuel economy of the hybrid FCV with the new control strategy is increased by 7.36% with respect to the semihybrid FCV and 14.69% with respect to the nonhybrid FCV; for US06 cycle, the fuel economy is increased by 5.9% with respect to the semihybrid FCV and 7.27% with respect to the nonhybrid FCV.
Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding

The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.
A Universal Rate Control Scheme for Video Transcoding

Video transcoding is proposed for the bitrate adaption, spatial and/or temporal resolutions adaption, and video format conversion. In video streaming application, it converts videos at server to the compatible versions demanded by networks or clients' devices, so that the videos can be delivered over networks and displayed in the clients' devices successfully. This paper provides a universal rate control scheme for various video transcoding purposes. First, a new rate-distortion (R-D) model is established theoretically for better representing the real R-D feature of transcoding. Second, a window-level rate control algorithm is proposed for providing smooth visual quality with compliant buffer constraint by utilizing the two-pass R-D model and a new proposed sliding window buffer control strategy. Finally, a universal rate control scheme for transcoding is developed based on the established R-D model and the proposed window-level rate control algorithm. The extensive experimental results demonstrate that as compared to other state-of-the-art rate control algorithms for transcoding, the proposed scheme can achieve more bit control accuracy with the average mismatch below 0.2%, and much more consistent visual quality with 0.1 dB-0.3 dB peak-to-signal noise ratio improvement in average, while with low computational complexity.
Block-Wise Adaptive Motion Accuracy Based B-Picture Coding With Low-Complexity Motion Compensation

This paper presents a novel B-picture coding based on block-wise adaptive motion accuracy (BAMA) with low-complexity motion compensation (MC). BAMA is able to adaptively select the motion accuracy for each inter-block in a B-picture depending on whether it is of bidirectional prediction, in which compared to the unidirectionally predicted block, lower motion accuracy is applied to the bidirectionally predicted block to reduce MC complexity. To further reduce MC complexity for the bidirectionally predicted luminance block with subpixel motion vectors in both directions, the forward and backward MC interpolations are merged according to the linear operation property of MC interpolation. In addition, a simplified MC interpolation method is also introduced for chrominance components for the bidirectionally predicted block. When integrating the proposed methods into H.264/AVC reference software, the experimental results demonstrate that they can significantly reduce MC complexity for a B-picture while yielding the comparable rate distortion performance in comparison with H.264/AVC although they no longer generate H.264/AVC compliant bitstream.
Parallelized scheduling algorithm for input queued switches using local search technique

Input queued switches have been well studied in the recent past. A scheduling algorithm is required to schedule the transfer of packets through the switch fabric (e.g. crossbar) at every time slot. The maximum weight matching (MWM) algorithm is known to deliver 100% throughput under any admissible traffic. However, MWM is not practical for its high computational complexity O(N/sup 3/). In this paper, we study a class of approximation algorithms to MWM from the point of view of local search. Notably we observe that: (a) Local search is well suitable for parallel computation. (b) Each line card of high performance router has at least one processor. Based on the two important observations, a parallelized greedy scheduling algorithm is proposed. The proposed algorithm is proved to be rate stable under any admissible traffic. Simulation results show that our algorithm outperforms other randomized approximations to MWM.
Overview of IEEE 1857 video coding standard

1857 is a multi-part standard for multimedia data compression and transmission in a highly efficient way under constraints, which include limited complexity and bandwidth.  1857 video part is organized in an application-oriented way that suitable tools are chosen to get a high compression ratio. This paper will give an overview of its six profiles (Main, Broadcasting, Enhanced, Portable, Surveillance Baseline, and Surveillance) which are designed respectively for normal video, high-definition video broadcasting, digital cinema, video communication among portable devices and surveillance video. In the paper, the background of application requirement and the key coding tools of IEEE 1857 video coding standards are introduced, and the performance comparison between IEEE 1857 and the state-of-the art coding standards are provided.
Resampling for face detection by self-adaptive genetic algorithm

Over the past ten years, face detection has been thoroughly studied in computer vision research for its interesting applications. However, all of the state-of-the-art statistical methods suffer from the data collection for training a classifier. This paper presents a self-adaptive genetic algorithm (GA)-based method to swell face database through re-sampling from the existing faces. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. To verify the generalization capability of the proposed method, we also use the expanded database to train an Adaboost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods.
A sort-based improved real-code genetic algorithm

As an adaptive global optimize method by probabilistic search, genetic algorithm had been comprehensively used in many engineering realms. But some disadvantages of this method such as slow convergence speed and local optimization confined further applications. Improved genetic algorithm in speed of convergence and the rate of obtain the optimal solution improved significantly by combine and sort parent-child generations, applying improved proportional selection, anticipative crossover, additive gauss-mutation and so on. Improved genetic algorithm has excellent performance, good universality, suitable for promotion and application.
A spatial inter-view auto-regressive super-resolution scheme for multi-view image via scene matching algorithm

Binocular suppression theory states that the stereo vision quality is not much influenced by asymmetric degradation of the individual views. Based on these findings, mixed resolution (MR) multi-view framework jointly utilizes the lower and full resolution images to reduce the data amount, while maintaining good stereo vision quality. To enhance the resolution of the lower resolution image, a novel superresolution scheme for the MR multi-view framework is presented in the paper. It is based on the assumption that the image is modeled as a 2D piecewise auto-regressive process. In the scheme, each pixel to be interpolated is estimated as the linear weighted summation of the pixels, which are consisted of the spatial neighboring ones from lower resolution image in the current view and the ones from the full resolution image in the neighboring views. To get the corresponding pixels in the neighboring views that match the scene to be reconstructed, a window based scene-matching approach is used. Through exploiting the spatial correlation and the inter-view correlation, the proposed scheme achieves a significant gain in PSNR and visual quality for the test sequences.
Deeply pipelined DSP solution to deblocking filter for H.264/AVC

The in-loop deblocking filter in H.264/AVC contains highly adaptive processing on both sample level and block edge level, which inevitably appears in the loop kernel of the algorithm. Therefore it is quite difficult to efficiently implement the filter on digital signal processor (DSP) platform. In this paper, deeply pipelined DSP solutions to both edge filter and boundary strength decision are presented. To avoid conditional jumping when performing edge filter, DSP first calculate all possible filtered outputs, then combine them with corresponding masks to get single output for each sample, and last conditionally store the combined output based on the final content activity check result. Moreover, on the basis of the symmetry of filtering on both sides of the edge, two symmetrical samples are packed into one register for "pair processing" to increase the parallelism of the pipeline. While for boundary strength decision, two-pass pipelines are designed. In the first pass, strengths are created by using the inter-relative information on block level, and then in the second pass, these strengths are refined based on the intra mode checks on macroblock level. To cooperate with global filter control and also to keep pipelining, a two-level internal memory organization is presented as well. The simulated results indicate that this efficient implementation can support real-time filtering for high resolution videos
Principal component analysis based on drive cycles for hybrid electric vehicle

This paper presents the method of principal component analysis (PCA) to study the characteristics of different drive cycles. The vehicular performance of hybrid electric bus is simulated and then the relationship between different hybridization factors and vehicular fuel economy and dynamic performance is analyzed. The simulation results of fuel economy over different cycles have been obtained in ADVISOR. Using principal component analysis, it can be concluded that three most important principal components can be used as inputs to design an intelligent vehicular control strategy.
Fast simulation approaches for power fluctuation model of wind farm based on frequency domain

This paper discusses one model developed by Riso, DTU, which is capable of simulating the power fluctuation of large wind farms in frequency domain. In the original design, the “frequency-time” transformations are time-consuming and might limit the computation speed for a wind farm of large size. Under this background, this paper proposes four efficient approaches to accelerate the simulation speed. Two of them are based on physical model simplifications, and the other two improve the numerical computation. The case study demonstrates the efficiency of these approaches. The acceleration ratio is more than 300 times if all these approaches are adopted, in any low, medium and high wind speed test scenarios.
An auto-regressive model for checkboard splitting based Wyner-Ziv coding

An auto-regressive (AR) based side information (SI) generation is proposed in this paper for block based chessboard pattern Wyner-Ziv (WZ) coding, where each WZ frame is split into two sets at encoder and then encoded separately. At the decoder, one set of the WZ frame will be firstly reconstructed, and then proposed AR model is used to generate the SI of the other set, where each pixel is generated as a linear weighted summation of pixels within two square windows in the previous and following reconstructed WZ/key frames along the motion trajectory. To obtain high quality SI for the second set, reconstructed pixels in the four neighboring blocks of the first set are employed to derive accurate AR coefficients. Several experimental results demonstrate that the proposed AR model is able to improve the quality of the SI for the second set of the WZ frame, which leads to the improvement of the rate-distortion performance of the WZ coding.
Mining Compact Bag-of-Patterns for Low Bit Rate Mobile Visual Search

Visual patterns, i.e., high-order combinations of visual words, contributes to a discriminative abstraction of the high-dimensional bag-of-words image representation. However, the existing visual patterns are built upon the 2D photographic concurrences of visual words, which is ill-posed comparing with their real-world 3D concurrences, since the words from different objects or different depth might be incorrectly bound into an identical pattern. On the other hand, designing compact descriptors from the mined patterns is left open. To address both issues, in this paper, we propose a novel compact bag-of-patterns (CBoPs) descriptor with an application to low bit rate mobile landmark search. First, to overcome the ill-posed 2D photographic configuration, we build up a 3D point cloud from the reference images of each landmark, therefore more accurate pattern candidates can be extracted from the 3D concurrences of visual words. A novel gravity distance metric is then proposed to mine discriminative visual patterns. Second, we come up with compact image description by introducing a CBoPs descriptor. CBoP is figured out by sparse coding over the mined visual patterns, which maximally reconstructs the original bag-of-words histogram with a minimum coding length. We developed a low bit rate mobile landmark search prototype, in which CBoP descriptor is directly extracted and sent from the mobile end to reduce the query delivery latency. The CBoP performance is quantized in several large-scale benchmarks with comparisons to the state-of-the-art compact descriptors, topic features, and hashing descriptors. We have reported comparable accuracy to the million-scale bag-of-words histogram over the million scale visual words, with high descriptor compression rate (approximately 100-bits) than the state-of-the-art bag-of-words compression scheme.
People re-detection using Adaboost with sift and color correlogram

People re-detection aims at performing re-identification of people who leave the scene and reappear after some time. This is an important problem especially in video surveillance scenarios. In this paper, we present a method of people re-detection within the context of visual sequence in single-camera setup. We consider re-detection as a binary classification problem, where both global and local descriptors are employed for training strong classifier on-line with adaboost to distinguish a newly detected people as tracked or new occurrence. The strong classifier will be updated while match is ascertained. A predetermined classifier with well-chosen threshold is employed as assistant of training examples collection. We test the performance of our approach on 4 different scenes including 51 video sequences taken from the CAVIAR database and 4 video sequences shot by ourselves. The results show that our re-detection algorithm can robustly handle variations in illumination, pose, scale, and camera-view.
Recovering Missing Contours for Occluded Object Detection

One difficult problem in practical applications is the corrupted or missing data frequently encountered in digital images. It introduces great challenges to the tasks such as object detection. This letter provides new methods for recovering missing object contours and detecting occluded objects. First, we propose an efficient contour reconstruction approach according to the Bayesian rule, utilizing global shape prior knowledge. Second, the contour reconstruction is applied to a robust detection framework for occluded objects. Based on the observed broken curves we iteratively recover object contours and propose object candidates. The experimental results demonstrate the high detection performance, localization accuracy and great advantages of our method for severe occlusion cases.
Position dependent linear intra prediction for image coding

Intra prediction has been efficiently employed in block based image/video coding to remove the spatial redundancy. In this paper, we propose a position dependent linear intra prediction (PDLIP) approach to further improve the accuracy of prediction. Specifically, for each prediction direction, the prediction of each sample within a target block is calculated as a linear weighted summation of the surrounding spatial neighboring samples in a fixed window. And the prediction coefficients are adaptively derived in the encoding process using the least square estimation method. The derivation process utilizes the original data of the previous image with the same prediction direction after rate-distortion selection to adaptively adjust to the video contents and local context within one block. To alleviate the computation burden of decoding, these prediction coefficients are sent to decoder as side information. Experimental results demonstrate that the proposed PDLIP can achieve a significant coding gain with a slight increase in computational complexity at encoder.
Globally Variance-Constrained Sparse Representation for Rate-Distortion Optimized Image Representation

Sparse representation is efficient to approximately recover signals by a linear composition of a few bases from an over-complete dictionary. However, in the scenario of data compression, its efficiency and popularity are hindered due to the extra overhead for encoding the sparse coefficients. Therefore, how to establish an accurate rate model in sparse coding and dictionary learning becomes meaningful, which has been not fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of data source can bound its entropy, thus can reflect the actual coding bits. Therefore, a Globally Variance-Constrained Sparse Representation (GVCSR) model is proposed, where a variance-constrained rate term is introduced to the conventional sparse representation. To solve the non-convex optimization problem, we employ the Alternating Direction Method of Multipliers (ADMM) for sparse coding and dictionary learning, both of which have shown state-of-the-art rate-distortion performance in image representation.
Influences of aggregative state on electrical trees growth characteristics under cryogenic condition

The electrical trees aging processes in different kinds of polymer directly determine the insulation property; therefore the aggregative state in polymer insulation under cryogenic condition is specially focused in this paper. The results indicate that polymer brittleness plays an important role for electrical ageing under cryogenic condition as well as the mechanical destruction which is induced by PD. Thus the influence of aggregative state on aging processes is less obvious under cryogenic than that at room temperature. The crystallinity and average size of crystallite can both be decreased with the increase of cooling-down speed. The trees morphological properties imply that trees grow directly to the crystal regions under cryogenic condition and large branch with big space intervals can be formed with small size of crystals, which are also proofed in the samples after many times hot and cold circles treating.