nils-herrmann/clean_scirepeval huggingface dataset:
A finite-time convergence of acyclic generically persistent formation in 3-D using relative position measurements

This paper studies formation control problem of multi-agent system whose interaction graph is acyclic generically persistent. We design a distributed control law for each agent to maintain its inter-agent distance constraints toward its neighbours. The designed control law uses only local relative position measurements and is proved to guarantee successfully for the convergences of agents to their desired equilibria in finite time, at which the formation of overall group of agents is congruent with a given desired one.
Comparative study of dielectric loaded structures for suppressing gyro-BWO instabilities in gyrotron beam-ducts (BD) using HFSS

A comparative study of a variety of electromagnetic (EM) structures designed for suppressing a gyro-BWO instability in the W7-X gyrotron beam-duct is presented. The EM fields are calculated using the finite element code HFSS. It is shown that among dielectric-loaded ring-structures the one having random azimuthal indentations on the copper ring is efficient and so are conical dielectric-loaded structures without copper rings.
Convolutional neural networks on assembly code for predicting software defects

Software defect prediction is one of the most attractive research topics in the field of software engineering. The task is to predict whether or not a program contains semantic bugs. Previous studies apply conventional machine learning techniques on software metrics, or deep learning on source code's tree representations called abstract syntax trees. This paper formulates an approach for software defect prediction, in which source code firstly is compiled into assembly code and then a multi-view convolutional neural network is applied to automatically learn defect features from the assembly instruction sequences. The experimental results on four real-world datasets indicate that exploiting assembly code is beneficial to detecting semantic bugs. Our approach significantly outperforms baselines that are based on software metrics and abstract syntax trees.
Automatic gesture analysis using constant affine velocity

Hand human gesture recognition has been an important research topic widely studied around the world, as this field offers the ability to identify, recognize, and analyze human gestures in order to control devices or to interact with computer interfaces. In particular, in medical training, this approach is an important tool that can be used to obtain an objective evaluation of a procedure performance. In this paper, some obstetrical gestures, acquired by a forceps, were studied with the hypothesis that, as the scribbling and drawing movements, they obey the one-sixth power law, an empirical relationship which connects path curvature, torsion, and euclidean velocity. Our results show that obstetrical gestures have a constant affine velocity, which is different for each type of gesture and based on this idea this quantity is proposed as an appropriate classification feature in the hand human gesture recognition field.
Abstraction approach for developing and delivering cloud-based services

In this paper, we present a novel approach for developing services that can be deployed in different cloud infrastructures at the same time. The approach provides an instrument with emphasis on abstraction, inheritance and code reuse. Then, cloud-based services are developed easily by extending existing abstractions classes provided by the instrument or other developers. The interoperability between different clouds is solved by the basic abstraction classes of the instrument and all services are inherited and benefited from the advantage.
Compressive video sensing based on user attention model

We propose a compressive video sensing scheme based on user attention model (UAM) for real video sequences acquisition. In this work, for every group of consecutive video frames, we set the first frame as reference frame and build a UAM with visual rhythm analysis (VRA) to automatically determine region-of-interest (ROI) for non-reference frames. The determined ROI usually has significant movement and attracts more attention. Each frame of the video sequence is divided into non-overlapping blocks of 16×16 pixel size. Compressive video sampling is conducted in a block-by-block manner on each frame through a single operator and in a whole region manner on the ROIs through a different operator. Our video reconstruction algorithm involves alternating direction l1 — norm minimization algorithm (ADM) for the frame difference of non-ROI blocks and minimum total-variance (TV) method for the ROIs. Experimental results showed that our method could significantly enhance the quality of reconstructed video and reduce the errors accumulated during the reconstruction.
A Fast Uyghur Text Detector for Complex Background Images

Uyghur text localization in images with complex backgrounds is a challenging yet important task for many applications. Generally, Uyghur characters in images consist of strokes with uniform features, and they are distinct from backgrounds in color, intensity, and texture. Based on these differences, we propose a FASTroke keypoint extractor, which is fast and stroke-specific. Compared with the commonly used MSER detector, FASTroke produces less than twice the amount of components and recognizes at least 10% more characters. While the characters in a line usually have uniform features such as size, color, and stroke width, a component similarity based clustering is presented without component-level classification. It incurs no extra errors by incorporating a component-level classifier while the computing cost is drastically reduced. The experiments show that the proposed method can achieve the best performance on the UICBI-500 benchmark dataset.
Robust Spatial Matching for Object Retrieval and Its Parallel Implementation on GPU

Spatial matching for object retrieval is often time-consuming and susceptible to viewpoint changes. To address this problem, we propose a novel spatial matching method that is robust to viewpoint changes and implement it on modern graphics processing unit (GPU) in parallel for real-time applications. Unlike previous spatial matching methods used in object retrieval, in which the affine transformation estimation is based on the gravity vector assumption, our method abandons this strong assumption by matching the affine covariant neighbors (ACNs) of corresponding local regions and estimating affine transformation from each single pair of corresponding local regions. Taking into account real-time applications, we implement the method on modern GPU in parallel to speed up the process. Computations are distributed evenly to threads with load balancing, and device memory accesses are optimized with bitmap-based parallel scan. Experimental results demonstrate that our method is more robust and more efficient than previous methods especially when the viewpoints are changed, and the parallel implementation on GPU obtains ten times speedup.
Adaptive Selection of Motion Models for Panoramic Video Coding

A panoramic video is an image-based rendering (IBR) technique which provides users with a large field of view (e.g. 360 degree) on surrounding dynamic scenes. It includes not only the translational motions but also the non-translational motions, such as zooming, rotation and uneven stretching etc. This paper presents a motion compensated prediction scheme based on adaptive selection of motion models to predict the complex changes between successive frames efficiently in panoramic video coding. By performing the initial motion estimation phrase and the refined motion estimation phrase in the proposed scheme, simulated results show that the coding performance of the proposed scheme is much higher than the traditional motion compensated prediction scheme in panoramic video coding.
Local geometric consistency constraint for image retrieval

In state-of-the-art image retrieval systems, an image is represented by bag-of-features (BOF). As BOF representation discards geometric relationships among local features, exploiting geometric constraints as post-processing procedure has been shown to greatly improve retrieval precision. However, full geometric constraints are computationally expensive and weak geometric constraints have limited range of applications. To efficiently handle common transformations and deformations, we present a novel local geometric consistency constraint (LGC) method. It utilizes the local similarity characteristic of deformations, and measures the pairwise geometric similarity of matches between two sets of local features. Besides, we propose a new method to accurately calculate the transformation matrix between two matched features, with the information provided by their local neighbors. Experiments performed on famous datasets show the excellent performance of our method.
An Effective Global Motion Estimation Method Based on a Compact Motion Model

This paper proposes a global motion estimation (GME) method based on a compact motion model to predict the changes between the successive frames in panoramic video coding. Compared with other complex motion models, the compact motion model uses only three motion parameters to describe the translational motion and non-translational motion (i.e. zooming) accurately in GME. Experimental results show that the proposed method is a low complexity algorithm, whose coding performance is up to 1.36dB compared with the conventional method for compressing the panoramic video.
An efficient hierarchical disparity estimation algorithm for stereoscopic video coding

Disparity estimation is an important part of stereoscopic video coding. An efficient disparity estimation algorithm is proposed which is based on two successive steps. The former is performed by multiresolution block matching algorithm. The latter consists of a region merging process, which can merge different size blocks with similar disparity vectors into a larger region. The results indicate that the algorithm can overcome conventional algorithm's shortcoming and reduce the coding overhead for disparity information The method is particular suited for low-bit-rate stereoscopic video coding.
Accurate Estimation of Human Body Orientation From RGB-D Sensors

Accurate estimation of human body orientation can significantly enhance the analysis of human behavior, which is a fundamental task in the field of computer vision. However, existing orientation estimation methods cannot handle the various body poses and appearances. In this paper, we propose an innovative RGB-D-based orientation estimation method to address these challenges. By utilizing the RGB-D information, which can be real time acquired by RGB-D sensors, our method is robust to cluttered environment, illumination change and partial occlusions. Specifically, efficient static and motion cue extraction methods are proposed based on the RGB-D superpixels to reduce the noise of depth data. Since it is hard to discriminate all the 360 ° orientation using static cues or motion cues independently, we propose to utilize a dynamic Bayesian network system (DBNS) to effectively employ the complementary nature of both static and motion cues. In order to verify our proposed method, we build a RGB-D-based human body orientation dataset that covers a wide diversity of poses and appearances. Our intensive experimental evaluations on this dataset demonstrate the effectiveness and efficiency of the proposed method.
Compressive sensing based video scrambling for privacy protection

Surveillance video privacy protection has drawn significant attention recently. In this paper, we describe a privacy protected video surveillance system which utilizes the emerging compressive sensing (CS) theory. Privacy regions are scrambled through block based CS sampling on quantized coefficients during compression. Security is ensured by key controlled chaotic sequence which is used to construct CS measurement matrix. To prevent drift error caused by scrambling, a coding restricted scheme is exploited. Experimental results show that the proposed system effectively protects privacy with the scene intelligible. Compared with the existing ones, this system has high security and dramatic coding efficiency improvement.
Confusion network based Video OCR post-processing approach

The paper originally presents a confusion network based framework for Video OCR post-processing. The framework consists of four parts: selection of reference and hypotheses, construction of confusion network, decoding for final output, and a novel metric of quantitatively evaluating Video OCR post-processing approaches. By integrating both visual and textual information, we construct the character transition network to reduce the error rate for OCR outputs. The large-scale experimental results demonstrate that this approach can significantly improve the accuracy of Video OCR results with only little incremental time. Moreover, with comparison and the detailed analysis, we conclude that “Voting+2-gram” is the most applicable method for real application.
An Incremental Clustering based codebook construction in video copy detection

The quality of codebook is the determinant factor in BoW-based copy detection strategies. However, most of the adopted codebook construction algorithms are derived from image retrieval or object recognition, which neglect the robustness in partitioning original features and copy features (especially those with serious transformations) into the same group. To deal with this problem, we have developed an Incremental Clustering algorithm to construct a robust codebook. Unlike many existing algorithms which need loading the entire data into memory, our algorithm process data incrementally and improve the quality by transferring data from a global view. In addition, we propose a codebook evaluation scheme by simulating copy and non-copy pairs. Our experimental results show that our approach attains a high precision in copy pairs, which demonstrates the robustness of our codebook.
Tracking Web Video Topics: Discovery, Visualization, and Monitoring

Despite the massive growth of web-shared videos in Internet, efficient organization and monitoring of videos remains a practical challenge. While nowadays broadcasting channels are keen to monitor online events, identifying topics of interest from huge volume of user uploaded videos and giving recommendation to emerging topics are by no means easy. Specifically, such process involves discovering of new topic, visualization of the topic content, and incremental monitoring of topic evolution. This paper studies the problem from three aspects. First, given a large set of videos collected over months, an efficient algorithm based on salient trajectory extraction on a topic evolution link graph is proposed for topic discovery. Second, topic trajectory is visualized as a temporal graph in 2-D space, with one dimension as time and another as degree of hotness, for depicting the birth, growth, and decay of a topic. Finally, giving the previously discovered topics, an incremental monitoring algorithm is proposed to track newly uploaded videos, while discovering new topics and giving recommendation to potentially hot topics. We demonstrate the application on three months' videos crawled from YouTube during December 2008 to February 2009. Both objective and user studies are conducted to verify the performance.
Improvement on Rate-Distortion Performance of H.264 Rate Control in Low Bit Rate

This paper points out some defects in the techniques used in H.264 rate control and presents several new algorithms to improve them. The improved algorithm has the following main features: 1) the bits allocated to each P-frame is proportional to the local motion in it, i.e, more bits are allocated to a frame if the local motion in it is stronger; 2) the quantization parameter for I-frame is choosed based on a new bits allocation scheme for I-frame; 3)the quantization parameter calculation is based on a simple encoding complexity prediction scheme, which is more robust and of less complexity than the quadratic model used by H.264 in low bit rate video coding. Experimental results and analysis show that the improved rate-control scheme has significantly increased the average peak signal-to-noise ratio up to 1.53 dB, reduced the variation of buffer level, improved the perpetual quality of the reconstructed video and reduced the computation complexity.
Representative local features mining for large-scale near-duplicates retrieval

Local features have been widely used in many computer vision related researches, such as near-duplicate image and video retrieval. However, the storage and query cost of local features become prohibitive on large-scale database. In this paper, we propose a representative local features mining method to generate a compact but more effective feature subset. First, we do an unsupervised annotation for all similar images(or frames in video) in the database. Second, we compute a comprehensive score for every local feature. The score function combines the robustness and discrimination. Finally, we sort all the local features in an image by their scores and the low-score local features can be removed. The selected local features are robust and discriminative, which can guarantee the better retrieval quality than using full of the original feature set. By our method, the number of local features can be significantly reduced and a large amount of storage and computational cost can be saved. The experimental results show that we can use 30% of the features to get a better query performance than that of full feature set.
Object retrieval based on spatially frequent items with informative patches

Spatial relation of local image patches plays an important role in object-based image retrieval. An approach called spatial frequent items is proposed as an extension of Bag-of-Words method by introducing spatial relations between patches. Spatial frequent items are defined as frequent pairs of adjacent local image patches in polar coordinates, and exploited using data mining. Based on these frequent configurations, we develop a method to encode patches and their spatial relations for image indexing and retrieval. Besides, to avoid the interference of background patches, informative patches are filtrated based on their local entropy and self-similarity in the preprocess stage. Experimental results demonstrate that our method can be 8.6% more effective than the state-of-art object retrieval methods.
Contextual Query Expansion for Image Retrieval

In this paper, we study the problem of image retrieval by introducing contextual query expansion to address the shortcomings of bag-of-words based frameworks: semantic gap of visual word quantization, and the efficiency and storage loss due to query expansion. Our method is built on common visual patterns (CVPs), which are the distinctive visual structures between two images and have rich contextual information. With CVPs, two contextual query expansions on visual word-level and image-level are explored, respectively. For visual word-level expansion, we find contextual synonymous visual words (CSVWs) and expand a word in the query image with its CSVWs to boost retrieval accuracy. CSVWs are the words that appear in the same CVPs and have same contextual meaning, i.e. similar spatial layout and geometric transformations. For image-level expansion, the database images that have the same CVPs are organized by linked list and the images that have the same CVPs as the query image, but not included in the results are automatically expanded. The main computation of these two expansions is carried out offline, and they can be integrated into the inverted file and efficiently applied to all images in the dataset. Experiments conducted on three reference datasets and a dataset of one million images demonstrate the effectiveness and efficiency of our method.
Image compressed sensing reconstruction with 3D transform domain collaborative filtering

Compressed Sensing (CS) has drawn quite an amount of attention as novel digital signal sampling theory in recent years when the signal is sparse in some domain. However, signal reconstruction from undersampled data has always been challenging due to its implicit ill-posed nature. This paper proposes an image compressed sensing reconstruction algorithm for image CS application, which consists of iteratively collaborative filtering of non local similar image patches in 3D transform domain and solving the least squares problems. In addition, the linearization technique is exploited to reduce the computation complexity. The results of various experiments on natural images and MRI images consistently demonstrate that the proposed algorithm can efficiently reconstruct images and gain more 2dB as compared to the current leading CS image reconstruction algorithm.
Format-Independent Motion Content Description based on Spatiotemporal Visual Sensitivity

With the extensive application of digital video technology, developing format-independent motion describing method is of great significance for retrieving and searching video content in different formats. In this paper, a format-independent motion describing method for video content is proposed. The features based on visual sensitivity are extracted from spatiotemporal slice. Since the same video content gives the same visual stimuli to visual perception, the method based on this kind of visual sensitivity related feature is format-independent. The experiments show the feature proposed is sensitive to the variation of video content and robust to the variation of video format. The motion describing method proposed is format-independent.
Efficient Video Coding Optimization Using a Novel Perceptual Distortion Model

With the latest H.264/AVC standard, coding performance has made a tremendous progress. However, coding strategy is as important as coding standard which helps further improve the coding efficiency. People are eager to achieve less distortion with less bandwidth consumption. In most cases, fixed coding parameters are allocated to each unit regardless of the difference of the perceptual characteristics. Since videos are always received by human eyes, observation characteristics should be considered in coding strategies. Distortions should be reasonably arranged to fit the subjective sense. In this paper, firstly a new model between perceptual distortion and traditional distortion (like MSE) is established. Then the new model is used to optimize two critical modules in video coding framework, the rate-distortion optimization module and the quantization control module, in order to make the overall coding result suit for human observation. The simulation result shows that our proposed strategy is fit well for the H.264/AVC. The quality can be improved in both subjective and objective tests.