nils-herrmann/clean_scirepeval huggingface dataset:
Multiple Description Video Coding Based on Human Visual System Characteristics

In this paper, a novel multiple description video coding scheme is proposed based on the characteristics of the human visual system (HVS). Due to the underlying spatial-temporal masking properties, human eyes cannot sense any changes below the just noticeable difference (JND) threshold. Therefore, at an encoder, only the visual information that cannot be predicted well within the JND tolerance needs to be encoded as redundant information, which leads to more effective redundancy allocation according to the HVS characteristics. Compared with the relevant existing schemes, the experimental results exhibit better performance of the proposed scheme at same bit rates, in terms of perceptual evaluation and subjective viewing.
Distributed Video Coding Based on Multiple Description

In our paper, we propose a novel distributed video coding (DVC) scheme using the theory of multiple description (MD), in which key frame is encoded by MD codec and transmitted over the corresponding channel. This scheme combines the advantage of DVC as well as robustness of MD, and exploits three different methods to generate multiple descriptions for the key frames that are essential to side information. Experiments demonstrate that it can get better performance than some general DVC methods. Besides, it demonstrates higher robustness in packet-loss channel than general DVC due to the MD algorithm.
Hybrid time-domain adaptive integration method for simulating transient electromagnetic responses of an arbitrary structure with large scale

An effective hybrid method, based on time domain adaptive integral method - marching on in degree scheme (TD-AEM-MOD), is presented for studying on transient scattering characteristics of large-scale perfectly conductive structures. 4-D FFT, including 3-D spatial FFT and blocked 1-D “temporal” (order) FFT, is used to accelerate the convolution during the MOD iteration. The technique of singular value decomposition (SVD) compression is also adopted to relief the memory requirement for the near field correction data. Numerical results are presented to demonstrate both accuracy and efficiency of the hybrid method for fast capturing transient electromagnetic responses of some typical PEC structures.
Error Resilient Scalable Video Transmission System Using Multiple Description Coding

In this paper a practical real-time video trans-mission system is proposed by combining SVC (Scalable Video Coding) and MDC (Multiple Description Coding). Each description of MDC can be designed as scalable bit stream which is helpful for UDP/RTP based transmission over practical packet loss network, such as Internet. To provide different spatial resolution / fidelity videos and flexible transmission over error-prone network, a new coding architecture needs to be revised. In the proposed coding scheme, each MDC description includes independent copy of raw video with lower quality, and each copy can be compressed using SVC. The proposed system achieved better experimental results when compared to schemes without MDC, especially when packet loss occurs.
Multiple Description Wavelet Based Image Coding with Classification

In this paper, a multiple description image coding scheme with classified method is presented, in which the wavelet coefficients in each class can be processed separately according to its own source distribution. All the classes will be divided into two larger classes again. One of them is quantized by MDSQ and the other is sub sampled by quad tree and quantized subsequently. The two parts are combined to form two descriptions. It is the number of class quantized by the above two ways that determines the tradeoff between side and central performance. The whole scheme is applied to natural images and simulation demonstrates that the system outperforms other related works. Most important of all, by employing the classified scheme, the central performance can get improved even when the side performance is close to its corresponding single description one.
A novel approach using mutual information for critical nodes detecting in virtual networking environment

Finding importance of nodes in the virtual network is an important research problem. In this study, our team analyzed the small-world network, and proposed a network modeling method for the virtual networking. Firstly, we analyze two key nodes detecting algorithms, one is based on depth-first search, while the other concentricity analysis. Then we proposed an evaluation method for node importance based on mutual information. This method precisely reveals topology characteristic of the network, reflecting the relative importance of nodes. The experiment is carry on and compared with other methods. Results shows that our method based on mutual information is simple and effective, it is suitable for evaluating the node importance in large virtual networking.
A Unified System for Web Personal Image Retrieval

Although Web search engines have developed so fast, Web image retrieval is still a challenging task which needs more efforts to be made. In this paper, a system for personal image retrieval on the WWW is proposed. Particularly, a powerful text-based search engine (for instance, Google and Baidu, etc.) is first borrowed to start the initial retrieval with a given personal name. Then, the visual confidence with respect to somebody to be searched is explored (facial visual information in this case). By the analysis of the retrieved Web pages, the text-based semantic information is also extracted. With the fusion of visual confidence and textual information, the well known semantic gap can be narrowed down to some extent. The experiment results show that the proposed system achieved much better performance than the initial Web image retrieval via search engine used popularly.
Autofocus of sparse microwave imaging radar based on phase recovery

Sparse microwave imaging radar is a newly developed concept of microwave imaging, which introduces the sparse signal processing theory to traditional microwave imaging. As a combination of sparse signal processing theory and microwave imaging, the sparse microwave imaging is attracting people's interest for its several potential advantages including better imaging performance and lower system complexity requirement. Airborne platform is an important application platform of sparse microwave imaging radar. On the airborne platform, the effect of motion phase error must be considered. Autofocus is an important technology to deal with the phase error in radar imaging, while traditional autofocus algorithms could not be directly applied under the sparse microwave imaging framework. In this paper, we introduce the phase recovery problem in the sparse signal processing theory to sparse microwave imaging, develop a novel autofocus algorithm under the sparse microwave imaging framework based on phase recovery. The phase recovery problem is solved via a greedy algorithm. A simulation is provided to demonstrate the effectiveness of the suggested algorithm.
Detection of image sharpening based on histogram aberration and ringing artifacts

With the wide use of sophisticated photo manipulation, capability of digital images for recording authentic scene information has been addressed with public suspicion. So it is urgent to develop forensics techniques for verifying photographs' originality and authenticity. In this paper, a blind forensic algorithm is proposed to detect sharpening manipulation in digital images. Gradient aberration of the gray histogram generated from unsaturated luminance regions of an image, is measured and employed to capture trails of sharpening operation. Ringing artifacts around step edges are exploited to provide another complementary clue, especially useful when the histogram-based features are not available. Tests on plenty of photo images show the effecttiveness of our proposed sharpening detection scheme.
CNN: Single-label to Multi-label

Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) no explicit hypothesis label is required; 4) the shared CNN may be well pre-trained with a large-scale single-label image dataset, e.g. ImageNet; and 5) it may naturally output multi-label prediction results. Experimental results on Pascal VOC2007 and VOC2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 84.2% by HCP only and 90.3% after the fusion with our complementary result in  based on hand-crafted features on the VOC2012 dataset, which significantly outperforms the state-of-the-arts with a large margin of more than 7%.
Mining Service Tags with Enriched Information from the Internet

Recently it receives extensive concerns that mining tags from WSDL documents, since service tags are widely used for better utilization of web services. However, most WSDL documents lack contents, which lead to great limitation for service tag mining. In this paper, we propose a novel approach to retrieve and extract service tags by leveraging information from the Internet. Our approach retrieves service related web pages by searching online for the URL of the services, then executes a Vector Space Model (VSM) based procedure to fulfill structure analysis and content extraction of the pages, finally mines service related tags together with WSDL documents. Experiments on real web services demonstrate the effectiveness of our approach, which is able to extract more number of abundant and meaningful tags.
Modified hybrid modulation scheme with even switch thermal distribution for H-bridge hybrid cascaded inverters

Hybrid cascaded multilevel inverters have been recognised as an important alternative in the medium-voltage inverter market due to their high-quality output and outstanding availability. To optimise the performance of voltage-source converters, modulation technologies have attracted more and more attention. With conventional modulation technologies, the switches within a cell are not utilised at a uniform switching frequency, which is not benefit for reliability and modularity. To solve this problem, a modified hybrid modulation strategy based on unipolar frequency doubling is proposed for H-bridge hybrid cascaded inverters. The equivalence of different multilevel carrier-based modulation strategies is revealed and corresponding implement methods are provided. Based on the equivalent hybrid modulation strategy, a modified hybrid modulation strategy is presented by using unipolar frequency doubling. Compared with the conventional modulation strategies, the proposed strategy makes a more uniform use of the switches within a cell. The proposed strategy keeps the output quality and efficiency while the maximum switching frequency is decreased by half. The strategy has been successfully substantiated by the simulation and experimental results of a seven-level hybrid cascaded inverter.
Multilevel Indexing Structure for Object Based Image Retrieval

Object based technique is a good way to grasp the user's query intentions felicitously and at the same time to make retrieval procedure flexible with user to avoid pseudo retrieval in content-based image retrieval (CBIR). But the main problem for such technique is its high computation complexity for searching objects among image dataset. Aiming at the problem mentioned above, a multilevel indexing structure (MIS) for object-based image retrieval is proposed in this paper. A three-level tree structure in the image domain is first constructed to reduce the object searching complexity. In order to further fasten the speed of scanning objects, i.e., to eliminate those unrelated images in the dataset rapidly, the clustering in the dataset domain is also conducted. The final experimental results show that the proposed MIS for image retrieval is much more efficient
A Real-Valued Quantum Genetic Niching Clustering Algorithm and its Application to Color Image Segmentation

This paper proposes a novel genetic clustering algorithm, called a real-valued quantum genetic niching clustering algorithm (RQGN), which is based on the concept and principles of quantum computing, such as the qubits and superposition of states. Our algorithm can automatically clustering a data set into clusters without the need to know the number of clusters in advance. A dynamic identification of the niches is performed at each generation to automatically evolve the optimal number of clusters as well as the cluster centers of the data set. After getting the niches of the population, a Q-gate with adaptive selection of the angle for every niches is introduced as a variation operator to drive individuals toward better solutions. The experimental results show that RQGN algorithm has high performance, effectiveness and flexibility.
Enhanced performance of large-area vertical light-emitting diodes treated by laser irradiation

In this work, a comparative analysis of vertical light-emitting diodes (VLEDs) examining the device properties is performed with different surface treatment schemes. The VLEDs on graphite substrate are fabricated by the laser lift-off and the wafer bonding processes. The significance of KrF laser irradiation to form protrusions on the surfaces of undoped gallium nitride (u-GaN) and n-GaN is analysed in detail. The light output power of the fabricated VLEDs is significantly increased through the formation of micro-sized protrusions by the laser irradiation. For VLED irradiated with an energy density of 600 mJ/cm2 on n-GaN surface with a thin u-GaN layer, the light output power is improved by 24.8% at 350 mA without degradation of electrical properties compared with that of the conventional VLED.
Multiple description coding with randomly offset quantizers

A multiple description coding scheme based on prediction-induced randomly offset quantizers is proposed, where each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. Due to the prediction, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped with each others. The optimal reconstruction is obtained by finding the intersection of all received quantization bins. Using the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained. The proposed scheme is then applied to lapped transform-based multiple-description image coding, and an iterative optimization scheme is developed to find the optimal lapped transform. Experimental results show that the proposed scheme achieves better performance than other methods in this category.
Forensic detection of median filtering in digital images

In digital image forensics, prior works are prone to the detection of malicious tampering. However, there is also a need for developing techniques to identify general content-preserved manipulations, which are employed to conceal tampering trails frequently. In this paper, we propose a blind forensic algorithm to detect median filtering (MF), which is applied extensively for signal denoising and digital image enhancement. The probability of zero values on the first order difference map in texture regions can serve as MF statistical fingerprint, which distinguishes MF from other operations. Since anti-forensic techniques enjoy utilizing MF to attack the linearity assumption of existing forensics algorithms, blind detection of the non-linear MF becomes especially significant. Both theoretically reasoning and experimental results verify the effectiveness of our proposed MF forensics scheme.
Neighborhood reversibility verifying for image search

The neighborhood structure can significantly impact the effectiveness of image search, and fulfilling the reversibility of neighborhood may improve the image search quality. This paper proposes an effective and efficient scheme for reconstructing the symmetry relationship of k-nearest neighborhood (KNN). In particular, we design a verifying function to learn the prior knowledge of neighborhood reversibility among images. By exploiting the prior knowledge, the image search system will give higher rank to those images that satisfy the reversibility of KNN relationship with the query. In addition, we systematically investigate the sensitivity of neighborhood size on image search quality and propose an adaptive selection scheme for improving robustness of neighborhood reversibility learning methods. The extensive experimental results show that the proposed scheme remarkably improves the image search quality and give a comparable but more stable performance to the state-of-the-art method for various image datasets.
Detection of various image operations based on CNN

Over the past years, a number of effective digital image forensic techniques have been proposed. However, most of them design features focused on specific image operation and do binary classification, which are not very reasonable in practice and don't work for detecting other operations. To detect various image operations, in this paper, we propose a carefully crafted CNN model to learn features from the magnified images and do multi-classification automatically. Firstly, the images will be magnified by nearest neighbor interpolation in the preprocessing layer. The property of image operations can be well preserved by the nearest up-sampling. Then, hierarchical representations of different operations are learned via two multi scale convolutional layers. After that, the well-known mlpconv layers are used to enhance the whole architecture's nonlinear modeling ability and finally derive the feature map. Further more, shortcut connections between mlpconv layers allow for increasing the depth of the network while reducing information loss. We present comprehensive experiments on 6 typical image operations. The results show that the proposed method have a good performance both in binary and multi-class detection.
Video copy detection based on median of key frames

Video copy detection is an important technology with many applications, especially since detecting copies becomes an alternative to watermarking. In this paper, an effective and efficient content-based video copy detection method is proposed, which is based on median features of key frames. Firstly, we extract the key frames of a video. Secondly, each key frame is divided into several blocks and the feature of the key frame is extracted based on the median of each block. Thirdly, we use the consecutive frame features as the fingerprint of a video. Finally, features of the query video and the reference video are compared to determine whether they are copies. The experimental results show the proposed scheme is effective in both copy detection and beginning localization.
Multimodal Fusion for Video Search Reranking

Analysis on click-through data from a very large search engine log shows that users are usually interested in the top-ranked portion of returned search results. Therefore, it is crucial for search engines to achieve high accuracy on the top-ranked documents. While many methods exist for boosting video search performance, they either pay less attention to the above factor or encounter difficulties in practical applications. In this paper, we present a flexible and effective reranking method, called CR-Reranking, to improve the retrieval effectiveness. To offer high accuracy on the top-ranked results, CR-Reranking employs a cross-reference (CR) strategy to fuse multimodal cues. Specifically, multimodal features are first utilized separately to rerank the initial returned results at the cluster level, and then all the ranked clusters from different modalities are cooperatively used to infer the shots with high relevance. Experimental results show that the search quality, especially on the top-ranked results, is improved significantly.
A predictive guidance algorithm simulation analysis of aerocraft based on neural network

Neural network is a kind of machine learning algorithm, applied in many ways. The traditional predictive guidance of aerocraft is hard to resolve the contradiction among robustness, real-time and the guidance of precision. The paper provides a predictive guidance algorithm for aerocraft, by combining neural network with predictive guidance to solve this problem. This research about the new style guidance of aerocraft is exploring, applying neural network in the design of guidance algorithm of aerocraft. Through simulation analysis, the results show that the neural network predictive guidance method has nice real-time and strong robustness with expansive application future.
Learning a General Assignment Model for Video Analytics

Video analytics, also known as video content analysis, involves a variety of pivotal tasks, such as video segmentation and recognition. In essence, performing most of these tasks can be viewed as learning an assignment model. Here, assignment model refers to what each element in a target set is assigned to the element in an opposite source set under some kind of constraint. Existing assignment models generally suffer from some limitations. For instance, imperfect results can be obtained when the source set is artificially provided with less representativeness, or part of the target set has expected assignments, thus significantly limiting the application of assignment model. To alleviate these issues, we develop, in this paper, a general assignment model to dynamically learn a source set by minimizing structural dissimilarity in a low-dimensional space. Furthermore, by associating the expected assignment solution with empirical assignment indicator via a consistency boosting strategy, the proposed assignment model is provided with a potential powerful generalization ability to deal flexibly with the unsupervised, semi-supervised, and fully supervised scenarios in assignment model learning. Considering the separability of both objective and constraints to be solved, an alternating direction method of multipliers solver is presented with rigorous theoretical analysis on its convergence. Experimental results on video analysis, including motion segmentation, activities recognition, and scene categorization, demonstrate that the proposed general assignment model is considerable superior to the state-of-the-art methods.
Reversible Watermarking Based on Invariability and Adjustment on Pixel Pairs

A novel reversible data hiding scheme based on invariability of the sum of pixel pairs and pairwise difference adjustment (PDA) is presented in this letter. For each pixel pair, if a certain value is added to one pixel while the same value is subtracted from the other, then the sum of these two pixels will remain unchanged. How to properly select this value is the key issue for the balance between reversibility and distortion. In this letter, half the difference of a pixel pair plus 1-bit watermark has been elaborately selected to satisfy this purpose. In addition, PDA is proposed to significantly reduce the capacity consumed by overhead information. A series of experiments is conducted to verify the effectiveness and advantages of the proposed approach.