nils-herrmann/clean_scirepeval huggingface dataset:
Data collection for distributed surveillance sensor networks in disaster-hit regions

The objective of many applications with the surveillance missions in wireless sensor networks is to provide long-term monitoring of the specific environments, such as disaster-hit regions. These applications usually perform continuous monitoring without any maintenance, even if some sensor nodes fail. A significant challenge when designing the data collection approaches for such systems is that the conventional communication protocols for wireless sensor networks would present low efficiency, since the network topology changes rapidly due to the node failure. Thus the sensor nodes in such systems should use an automatic transmission approach to disseminate their sensed data to the sink in a distributed manner. In this paper, we propose a novel Coding-based Probabilistic Routing (CPR) to address this specific problem of data collection for distributed surveillance sensor networks in disaster-hit regions. CPR dynamically adapts to node failure to collect the maximum data in any given time and chooses an optimal probabilistic routing to decrease the transmission consumption. The extensive simulation results are presented to show that CPR outperforms other strategies.
Image Watermarking Robust to Print and Generation Copy

In general, after a watermarked image is printed, copied several times and scanned, the watermark usually cannot be extracted rightly, as well as distorted by noise. To solve this problem, in this paper, two watermarks were embedded into the image. The first one is a periodic watermark. The second one is the real watermark message. These two watermarks are embedded into the image by modifying the gray value of each pixel. The cover image is not needed when the watermark is detected. Both of these two watermarks are estimated exploiting the properties of HVS (human visual system) and extracted through the same key-dependent 2D sequences. Experiments results show that the scheme can resist print, generation copy and scan process
An adaptive total variation regularization method for SAR image despeckling

In this paper, we introduce a total variation (TV) regularization model for SAR image despeckling. A dual formulation based adaptive total variation (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV regularization based image restoration model has a good performance in preserving image sharpness and edges while removing noises and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. An evaluation index is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of a pixel with different sizes. Experimental results show that the proposed method can effectively suppress SAR image speckles without compromise the edge sharpness of image features according to both subjective visual examination and objective evaluation indices of image quality.
Learning a mid-level feature space for cross-media regularization

In this paper, we propose a cross-media regularization framework to enhance image understanding which can benefit image retrieval, classification and so on. The goal of cross-media regularization is to find regularization projections by exploiting the correlations between visual features and textual features. Thus, the original noisy distribution of visual features can be refined by leveraging the discriminative distribution of the corresponding textual features. Within the proposed cross-media regularization framework, a mid-level representation is built by jointly projecting both visual and textual features into a shared feature subspace, which leads to transferring of the discriminative semantic characteristic embedded in the textual modality into the corresponding visual modality. Meanwhile, the discriminative characteristic of textual features can also be boosted simultaneously. The experimental results demonstrate that the proposed mid-level space learning process can remarkably improve the search quality and outperform the existing semantic regularization methods.
Multiple description video coding based on forward error correction within expanding windows

In this paper, an MDC scheme based on forward error correction(FEC) within expanding windows is proposed. Firstly, the video sequence will be coded into source packets with/without slice group enabled. Secondly, the appropriate FEC packets are inserted according to the packet loss rate. Since the previous frames in a GOP is generally more important than the following frames in the GOP, an expanding window is exploited so that the FEC packets for the current frame will also protect the previous frames in the window. After this, the source packets with the inserted FEC packets will be divided into two descriptions and transmitted into two independent channels. When some packets in one description are lost, FEC decoding will try to recover the lost packets. Through this scheme, the source packets can get appropriate protection while the compression efficiency will not be degraded too much. The experimental results show that the proposed scheme outperforms the compared schemes up to 3 dB.
Time-axis based video watermarking resisting to geometrical attacks

In this paper a video watermarking scheme that can resist to geometrical attacks is proposed. The message is modulated by the key-dependent pseudo-random sequences to produce a CDMA signal. This signal is embedded into the cover video by modifying the variance of every frame of the sequence. In watermark detection process variance of every frame of stegovideo sequence is calculated. The CDMA signal is estimated from the preprocessed variance sequence and the message is extracted by correlation detection. Experimental results show the scheme is very robust to geometrical attacks.
A comparative evaluation: Different methods for simplifying the deep compositional features

Although deep compositional features have achieved an amazing performance in many application scenarios, it is not easy for them to directly tackle big data case due to their high computing time and memory usage. This paper presents a comparative evaluation for the simplification of deep compositional features by exploring the existing vector quantization and binarization techniques. Different techniques display different capabilities in similarity-preserving or memory-saving when projecting the original deep compositional features into more compact visual words or binary codes. We propose a dedicated image searching framework to evaluate all the techniques in terms of computational cost, memory usage and discrimination preserving. Extensive experiments demonstrate that it is feasible to greatly reduce computational cost and memory usage of the deep compositional features while preserving enough discriminative power. In addition, some useful conclusions are derived to guide the design of better simplifying schemes.
Solutions to cold-start problems for latent factor models

In the data age, the “information overload” problem severely impacts the precise of people to choose what they prefer. However, recommendation systems are able to provide people related information from huge amounts of data, and effectively solve the “information overload” problem. Currently, Latent Factor Model(LFM) has become dominant in the recommendation field. For example, Matrix Factorization performs excellently on rating prediction problem. By optimizing a ranking criterion, LFM also has an outstanding performance on top-N recommendation problem, such as Bayesian Personalized Ranking. But LFM can't solve the cold-start problem. Aiming at solving the cold-start problem, we obtain the mapping concept to construct a hybrid model, in which we map new entities' (e.g. user or item) attributes to their latent features vector. Experiments on the cold-start problem show that the hybrid model provides much better recommendation precision.
Forensics of image blurring and sharpening history based on NSCT domain

Detection of multi-manipulated image has always been a more realistic direction for digital image forensic technologies, which extremely attracts interests of researchers. However, mutual affects of manipulations make it difficult to identify the process using existing single-manipulated detection methods. In this paper, a novel algorithm for detecting image manipulation history of blurring and sharpening is proposed based on non-subsampled contourlet transform (NSCT) domain. Two main sets of features are extracted from the NSCT domain: extremum feature and local directional similarity vector. Extremum feature includes multiple maximums and minimums of NSCT coefficients through every scale. Under the influence of blurring or sharpening manipulation, the extremum feature tends to gain ideal discrimination. Directional similarity feature represents the correlation of a pixel and its neighbors, which can also be altered by blurring or sharpening. For one pixel, the directional vector is composed of the coefficients from every directional subband at a certain scale. Local directional similarity vector is obtained through similarity calculation between the directional vector of one random selected pixel and the directional vectors of its 8-neighborhood pixels. With the proposed features, we are able to detect two particular operations and determine the processing order at the same time. Experiment results manifest that the proposed algorithm is effective and accurate.
Design of representaiton scheme towards better image search

Recently, Bag-of-Words(BoW) model has been widely used for feature representation in multimedia search area due to its simplification and effectiveness. While various variants of BoW models have been developed to improve the discriminative cabability, less efforts have been paid on discovering the working mechanism underlying them. In this paper, we systematically investigate the impact of various factors in a quantitive manner, and discover how to improve the image search by jointly optimizing these factors. Subsequently, a rational, which states the rule of factor optimization, is proposed on the basis of the experiments on descriptor matching and used to guide the design of better image search systems. To validate the correctness of the proposed rational, a BoW-based image search system, which consists of image database indexing and searching components, is developed for testing. The comprehensive experiments demonstrate that it is beneficial to employ the proposed rational to develop a better image search system.
Generalized Gradient Vector Flow for Snakes: New Observations, Analysis, and Improvement

Snakes, or active contours, have been widely used in image processing applications. An external force for snakes called gradient vector flow (GVF) attempts to address traditional snake problems of initialization sensitivity and poor convergence to concavities, while generalized GVF (GGVF) aims to improve GVF snake convergence to long and thin indentations (LTIs). In this paper, we find and show that both GVF and GGVF snakes essentially yield the same performance in capturing LTIs of odd widths, and generally neither can converge to even-width LTIs. Based on a thorough investigation of the GVF and GGVF fields within the LTI during their iterative processes, we identify the crux of the convergence problem, and accordingly propose a novel external force termed as component-normalized GGVF (CN-GGVF) to eliminate the problem. CN-GGVF is obtained by normalizing each component of initial GGVF vectors with respect to its own magnitude. Experimental results and comparisons against GGVF snakes show that the proposed CN-GGVF snakes can capture LTIs regardless of odd or even widths with a remarkably faster convergence speed, while preserving other desirable properties of GGVF snakes with lower computational complexity in vector normalization.
Multiple description image coding based on fast fractal coding

Multiple description coding (MDC) is an effective method for robust transmission of image and video over unreliable networks. In this paper, a new multiple description image coding scheme based on fractal coding is proposed, which can satisfy the robust transmission in case of channel failure. In view of computation complexity of fractal image coding, Fine granularity successive elimination (FGSE) is applied to speed up the encoding process. Compared with the conventional scheme, the experimental results show that the proposed scheme can improve rate distortion performance efficiently and reduce the coding complexity simultaneously.
Computational intelligence design on vehicle axle of all terrain vehicle

Vehicle axle structural design and optimization is an important part of the vehicle development process. Its strength is directly related to vehicle safe under impact. The design should consider the integrity, achieve the same database of design and analysis of the structural data, The parameters model of ATV's axle is set up based on the seamless integration technique of all the module of Pro/E platform, and use the FEA module of Pro/E to analyze the stress and deformation of the ATV's axles,comparative analysis with other models and finally finish the vehicle axle design.
Packetization strategies for MVD-based 3D video transmission

In multi-view video plus depth (MVD) format, virtual views are synthesized by the compressed texture videos and their associated depth through depth-image-based rendering. In this paper, we consider the setup where both the encoded texture and depth bitstreams experience packet losses during transmission. Different packetization strategies are investigated and a novel strategy is developed to improve error resilience of MVD-based video transmission, where texture data and its corresponding depth are put into the same packet. The size of texture plus associated depth data included in each packet needs to be less than the Maximum Transfer Unit (MTU). Experimental results demonstrate that our proposed packetization scheme yields a significant improvement in terms of both texture views and synthesized virtual views quality when fit in H.264/AVC.
Depth down/up-sampling using hybrid correlation for depth coding

In this paper, the spatial correlation and temporal consistency are jointly employed in depth down/up-sampling to achieve efficient depth coding. Given that the temporal consistency of the depth maps, a direct down-sampling method that the pixels at different positions are sampled between the adjacent frames is applied. After down-sampling, High Efficiency Video Coding (HEVC) is adopted to encode and decode the down-sampled depth maps for its good compression efficiency. Then, the proposed up-sampling algorithm which considers the spatial correlation among the neighbor pixels in one depth map and the temporal consistency between the adjacent depth frames, as well as the correlation between the depth map and its corresponding texture image is utilized to get a good quality depth map with full resolution. Experimental results show that the proposed algorithm improves both depth map coding performance and synthesized quality.
Convolutional neural network-based depth image artifact removal

In 3D video coding and depth-based image rendering, the distortion of the compressed depth image often leads to wrong 3D warpping. In this paper, by generalizing the recent work of convolutional neural network (CNN)-based depth image up-sampling, we propose a CNN-based depth image artifact removal scheme, where both the compressed depth and color images are used to enhance the depth accuracy. The proposed CNN has two sub-networks: joint depth-color sub-network and joint depth sub-network. During the depth and color feature extraction, the gradient of the depth image is used as the input to color image, while the gradient of color image is used as the input of depth feature extraction. Such an exchange of gradient information improves the learned features. Experimental results in terms of both objective and subjective quality of the depth and color images verify the efficiency of the proposed method.
Kernel Canonical Correlation with Similarity Refinement for Automatic Image Tagging

Automatic image tagging (AIT) is an effective technology to facilitate the process of image retrieval without requiring user to provide a retrieval instance beforehand. In this paper, we propose an AIT method based on kernel canonical correlation analysis (KCCA) with similarity refinement (KCCSR). As a statistic correlation technique, the KCCA aims at extracting some kind of hidden information shared commonly by the two random variables. Different from the previous KCCA based tagging methods, the graph based similarity refinements are first implemented by an interactive way to obtain the enhanced visual and textual representations. Subsequently, the KCCA is applied to them to mine the unique intrinsic semantic representation space, in which the AIT can be completed. The final experimental results validate the effectiveness of the proposed KCCSR.
Polarimetric SAR tomography of forested areas based on compressive MUSIC

This paper focus on the polarimetric synthetic aperture radar (SAR) tomography for forested areas based on compressive MUSIC. In the proposed method, full polarimetric SAR echo signal reflected from the imaging area is collected, the corresponding multiple measurement vector model is established according to the parameters of polarimetric channels, the wavelet basis is adopted for representing the sparse vertical structure of the imaging area, and finally, the backscattering coefficients of the area are reconstructed by compressive MUSIC algorithm. The necessary number of tracks for SAR tomography is reduced and the severity of spurious spikes is suppressed under the same measurement accuracy. Simulation results from the PolSARpro data validate the effectiveness.
Frame Fusion for Video Copy Detection

Content-based video copy detection is very important for copyright protection in view of the growing popularity of video sharing websites, which deals with not only whether a copy occurs in a query video stream but also where the copy is located and where the copy is originated from. While a lot of work has addressed the problem with good performance, less effort has been made to consider the copy detection problem in the case of a continuous query stream, for which precise temporal localization and some complex video transformations like frame insertion and video editing need to be handled. We attempt to attack the problem by presenting a frame fusion based copy detection approach, which converts video copy detection to frame similarity search and frame fusion under a temporal consistency assumption. Our work focuses mainly on the frame fusion stage due to its critical role in copy detection performance. The proposed frame fusion scheme is based on a Viterbi-like algorithm, comprising an online back-tracking strategy with three relaxed constraints. The experimental results show that the proposed approach achieves high localization accuracy in both the query stream and the reference database even when a query video stream undergoes some complex transformations, while achieving comparable performance compared with state-of-the-art copy detection methods.
Multi-View Missing Data Completion

A growing number of multi-view data arises naturally in many scenarios, including medical diagnosis, webpage classification, and multimedia analysis. A challenge in learning from multi-view data is that not all instances are fully represented in all views, resulting in missing view data. In this paper, we focus on feature-level completion for missing view of multi-view data. Aiming at capturing both semantic complementarity and identical distribution among different views, an Isomorphic Linear Correlation Analysis (ILCA) method is proposed to linearly map multi-view data to a feature-isomorphic subspace through learning a set of excellent isomorphic features, thereby unfolding the shared information from different views. Meanwhile, we assume that missing view obeys normal distribution. Then, the missing view data matrix can be modeled as a low-rank component plus a sparse contribution. Thus, to accomplish missing view completion, an Identical Distribution Pursuit Completion (IDPC) model based on the learned features is proposed, in which the identical distribution constraint of missing view to the other available one in the feature-isomorphic subspace is fully exploited. Comprehensive experiments on several multi-view datasets demonstrate that our proposed framework yields promising results.
Characterization of mutual coupling effects among multi-antennas mounted on a perfect electrically conducting (PEC) tower platform in the presence of intentional electromagnetic pulses (IEMPs)

An efficient hybrid method, based on time-domain integral equation (TDIE) and time-domain physical optics (TDPO), is proposed for analysing mutual coupling effects and transient responses of multi-antennas mounted on a PEC tower platform in the presence of single as well as multiple intentional electromagnetic pulses (IEMPs). The well-known RWG, the triangular, the wire/surface junction spatial basis functions and the high-order temporal basis functions are used to expand induced currents on the PEC bodies, the PEC wires, and the PEC wire/surface junctions, respectively. A set of hybrid TDIE-TDPO equations are derived and solved by using marching-on-in-time scheme (MOT). The developed numerical algorithm is verified in both time- and frequency-domains for predicting S11-and S21- parameters, directivities, induced currents together with backward scattered fields of several antennas mounted on the same PEC tower platform, respectively. In comparison with the commercial software FEKO and the conventional TDIE-MOT method, the proposed method is more efficient for evaluating various complex electromagnetic environment effects on some multi-antenna communication systems.
A Dynamic Load Balancing Algorithm for Distributed SLEE in Mobile Service Provisioning

Along with the growth of service type and user number due to network evolution and convergence, higher performance is required for service logic execution environment (SLEE) in service provisioning. In this paper, an accumulated k-subset algorithm (AK algorithm) is proposed to balance load in distributed SLEE. Based on a model of resource heterogeneity and load vector, the AK algorithm improves the k-subset algorithm by accumulating load information within every update interval. Experiments on different update intervals and request arrival rates suggest AK further reduces herd effect due to stale load information, and outperforms k-subset algorithm by 5%-10%