nils-herrmann/clean_scirepeval huggingface dataset:
Key factors for large scale visual vocabulary

The visual vocabulary, which is the key component of Bag-of-Words(BoW) model, plays an important role for representing visual content in both effectiveness and efficiency. Although various construction methods have been proposed in previous work, less effects have been paid on discovering the key factors that impacts the performance of visual vocabulary, especially in the case of building large scale visual vocabulary. In this paper, we systematically investigate the performance change in descriptor matching level when adapting different visual vocabulary schemes. Then, we will deduce some useful observations according to performance analysis, which can be used to design more effective and fast image search engine. To certify the correctness of these observations, we develop a BoW-based image search engine by following the observations. The comprehensive experiments show the search performance in both effectiveness and efficiency.
IPA Derivatives for Make-to-Stock Production-Inventory Systems With Lost Sales

This note applies the stochastic fluid model (SFM) paradigm to a class of single-stage, single-product make-to-stock (MTS) production-inventory systems with stochastic demand and random production capacity, where the finished-goods inventory is controlled by a continuous-time base-stock policy and unsatisfied demand is lost. This note derives formulas for infinitesimal perturbation analysis (IPA) derivatives of the sample-path time averages of the inventory level and lost sales with respect to the base-stock level and a parameter of the production rate process. These formulas are comprehensive in that they are exhibited for any initial inventory state, and include right and left derivatives (when they differ). The formulas are obtained via sample path analysis under very mild regularity assumptions, and are inherently nonparametric in the sense that no specific probability law need be postulated. It is further shown that all IPA derivatives under study are unbiased and fast to compute, thereby providing the theoretical basis for online adaptive control of MTS production-inventory systems.
De-biased dart ensemble model for personalized recommendation

Personalized recommendation aims to use the historical behavior of users to recommend new items that are likely to be of interest to them. Due to a tiny improvement of it can lead to a huge profits, lots of giant e-commerce companies, such as Amazon, Alibaba and eBay, have put their great effort on this field. In this paper, we formulate the problem of personalized recommendation as a tree based regression task and present a de-biased DART ensemble model. To generate compact personalized profile of user, a decision tree based sparse encoding approach is first proposed to refine those raw behaviors, like clicks, views, and purchases from user-item interaction. Considering the problem of class imbalance in training the predictive models, resampling based de-biased DART model is utilized to form multiple weak predictors for recommendation. Thus, based on these learnt weak predictors, a stronger predictor can be built. Experimental results on real commercial dataset demonstrate the effectiveness of the proposed personalized recommendation model.
Spread Spectrum-Based Image Watermarking Resistant to Rotation and Scaling Using Radon Transform

In this paper, a novel multibit image watermarking scheme resistant to rotation and scaling attacks is presented. The Radon transform is used to correct the orientation of an image. Then the spread spectrum-based watermarking scheme which is scaling invariant is used to embed and extract the watermark message in the DCT domain of the corrected image. Experimental results show that the scheme possesses good robustness against rotation, scaling attacks and considerable robustness against typical image processing.
Distributed Bragg reflector fibre laser-based sensor array for multi-parameter detection

A sensor array employing the distributed Bragg reflector (DBR) fibre laser is presented. This array could be utilised as a multi-channel multi-functional detector for bending radius, transverse load and axial strain by simultaneously monitoring the lasing wavelength and the output beat note frequency. The measurement theory of the DBR fibre laser is explained, and it is then experimentally verified with a three-probe array. The system's detection sensitivities for curvature, transverse load and axial strain are found to be 600 MHz/cm−2, 1.3 MHz/g and 1.263 pm/μe, respectively.
Improving the similarity estimation via score distribution

Generally distance-based similarity estimation between two images is not always reliable due to the limitations in both image understanding techniques and distance measure methods. This paper presents a novel approach for improving the similarity estimation through introducing the distribution information of similarity scores. The key idea is based on an underlying assumption that the distributions of similarity scores are similar for true-relevant images when they query an independent database. By representing each distribution with the area under the corresponding similarity score curve, the difference between different distributions can be easily calculated and employed to update the original distance measure. Experiments on three public datasets with various feature representations show that the enhanced similarity estimation remarkably outperforms the original distance measure and the proposed approach also keeps a good generalization ability on various datasets and feature representations.
Study on convergent architecture Of Web 2.0 and IMS services

Based on the investigation on current convergent methods for IMS services and Web 2.0 services, a Component Based Convergent Services Architecture (CBCSA) is proposed. The architecture and the work mechanism of CBCSA are discussed in detail. By fully making use of COM characteristic, the Telecom services can be composited freely and the Web 2.0 services and Telecom services can cooperate efficiently to provide convergent services in CBCSA.
Multiple Description Shifted Lattice Vector Quantization for Progressive Wavelet Image Coding

Multiple description (MD) coding is a promising alternative for robust transmission of information over non-prioritized and unpredictable networks. Furthermore, practical variable-bandwidth channels also require fine grain scalability of the descriptions (bit streams). In this paper, according to the geometrical structure and the special relationship of lattice vector quantizers, a MD quantizer called shifted lattice vector quantization (SLVQ) is employed in MD image coding to realize progressive transmission over unreliable channels. In view of the characteristics of wavelet coefficients in different frequency subbands, besides an appropriate construction of wavelet coefficient vectors, the algorithm of modified zerotree coding is also applied to improve compression performance. Experimental results validate the effectiveness of the proposed scheme with better performance than the other schemes based on MD scalar quantization for progressive transmission.
Using the fractal code to watermark images

We present a novel technique for the digital watermarking of still images based on fractal encoding and decoding. In general, a fractal coder exploits the spatial redundancy within the image by establishing a relationship between its different parts. We use this relationship to embed a watermark and use the invariability of the fractal parameters to retrieve the signature. The original unsigned image is not required for watermark extraction. Simulation results show the superior performance of our methods and demonstrate the robustness to such signal distortions as JPEG compression, low pass filtering, additive noise and scaling.
Rake: Semantics assisted network-based tracing framework

The ability to trace request execution paths is critical for diagnosing performance faults in large-scale distributed systems. Previous black-box and white-box approaches are either inaccurate or invasive. We present a novel semantics-assisted gray-box tracing approach, called Rake, which can accurately trace individual request by observing network traffic. Rake infers the causality between messages by identifying polymorphic IDs in messages according to application semantics. To make Rake universally applicable, we design a Rake language so that users can easily describe necessary semantics of their applications while reusing the core Rake component. We evaluate Rake using a few popular distributed applications, including web search, distributed computing cluster, content provider network, and online chatting. Our results demonstrate Rake is much more accurate than the black-box approaches while requiring no modification to OS/applications. In the CoralCDN (a content distributed network) experiments, Rake links messages with much higher accuracy than WAP5, a state-of-the-art black-box approach. In the Hadoop (a distributed computing cluster platform) experiments, Rake helps reveal several previously unknown issues that may lead to performance degradation, including a RPC (Remote Procedure Call) abusing problem.
Dual-Streams Edge Driven Encoder-Decoder Network for Image Super-Resolution

Single image super-resolution (SR) aims at reconstructing high-resolution (HR) images from low-resolution (LR) ones. One of the most key issues is to recover finer image details of LR images. In this paper, considering the importance of edge prior for image SR, we propose a dual-streams edge driven encoder-decoder network, which combines edge stream-based encoder-decoder network (edge-EDN) and color stream based encoder-decoder network (color-EDN) to reconstruct HR images with more image details. Instead of utilizing two sub-networks to learn edge information and color image contents respectively, a multitask learning framework is developed to jointly train edge-EDN and color-EDN. Therefore, as the structure prior, the reconstructed HR edge maps are fused with learned features of color stream to refine the HR color images. To reconstruct HR images with better visual quality, a total loss function combining edge loss and color loss is designed to make an optimal tradeoff between the image fidelity and texture details. Our extensive benchmark evaluations demonstrate that our method for SR performs better both on high objective quality and human visual perception compared with several state-of-the-art SR methods.
Real-time forward error correction for video transmission

When the video streams are transmitted over the unreliable networks, forward error correction (FEC) codes are usually used to protect them. Reed-Solomon codes are block-based FEC codes. On one hand, enlarging the block size can enhance the performance of the Reed-Solomon codes. On the other hand, large Reed-Solomon block size leads to long delay which is not tolerable for real-time video applications. In this paper a novel approach is proposed to improve the performance of Reed-Solomon codes. With the proposed approach, more than one video frame are encompassed in the Reed-Solomon coding block yet no delay is introduced. Experimental results show that the proposed approach outperforms other real-time error resilient video coding technologies.
MIMU/GPS information fusion: Normal cloud model based fuzzy adaptive filtering

As a consequence of the statistical characteristics of noise changes with time or surrounding environment, typical MEMS IMUs/ GPS can hardly provide the ideal navigation accuracy as expected. A normal cloud model based fuzzy adaptive filtering (NCMFAF) is proposed for optimal state estimates, and the essential part of this derived filtering consists in its positive effect on updating the covariance difference degree between theoretical residuals and actual ones even with the large external variation of navigation process. Specifically, Normal cloud model module in cases is adopted as an adaptive controller, fulfilling the coefficient adjustment of measurement noise variance involved in typical Kalman filter, which, in turn, improves the performance of Kalman filter in dealing with the time/ environment -varying noise. With the numerical simulation being carried out, the results indicate the NCMFAF process can respond to this sudden statistical characteristics change of noise in real time, and the estimates accuracy of velocity/position is proved to be dramatically enhanced by reference to the dynamic environment statistics.
Parameterization of all robust tracking internal model controllers and its comparison with conventional IMC

By using the transfer-function-based factorization approach in the frequency domain, the parameterized representation of all feedforward controllers and feedback filters is derived for internal model control (IMC) systems, which can eliminate steady-state tracking error under the model mismatches and for any given reference input and disturbance. The obtained results can be applied to both continuous-time and discrete-time systems and afford a foundation for further performance optimizations in the frequency domain. An example is given to demonstrate the design process of controller parameterization and optimization. A comparison with conventional IMC and simulation results are also given.
Seeking User&#146;s Query Concept Dynamically Based on Region in Relevance Feedback

Relevance feedback (RF) and object-oriented description for image content are efficient improvements for narrowing down the gap between low-level feature and high-level semantic concept in content-based image retrieval (CBIR). In this paper, a dynamic scheme for seeking user's query concept is put forth in order to provide an accurate description for the query image content. To mine more semantic information accurately, an over-completed description space of image content based on region is first built. Furthermore, the non-parametric estimation is applied to modeling the over-completed description space with the premise of not knowing any prior knowledge about it. At the same time, the mean-shift optimization technique is adopted to grasp the semantic intent online in rounds of feedback. The experimental results on the Corel image database show that the proposed approach improves the retrieval performance largely
Detection of Seam Carving and Contrast Enhancement Operation Chain

With the wide use of digital image processing software, it is easier to edit images. Digital image forensic technology is becoming vitally necessary to identify the content authenticity. Although numbers of detection algorithms have been proposed in the past years, there is not a perfect solution for detecting multi-operation problems. However, multi-operation in image editing process is more practical, so detection for multi-operation has more significance. In this paper, we focus on forensics of operations involving contrast enhancement and seam carving. In order to solve the two-manipulation forensic problem, we propose to calculate Markov one-step transition probability matrix on the second order difference array, and use the high dimensional feature of DCT coefficients for further classification. Both contrast enhancement followed by seam carving and seam carving followed by contrast enhancement can be detected correctly through the classification. A series of experiments are conducted to prove the accuracy of the proposed algorithm.
Detection of interpolation using correlation coefficients

Resampling detection is a very useful tool in digital image forensic. In this paper, we propose a new algorithm for detecting interpolation by using the periodicity characteristics hidden among the rows(or columns) of the image. The core of our algorithm is the use of correlation coefficients based on the observation that the correlation coefficients of the row(or column) vectors vary periodically in resampled image. In some images, the image contents themselves correlate periodically. To reduce this kind of interference and enhance the detection efficiency, we use the first derivatives signals and detect the image both horizontally and vertically under some condition. As experiments show, the proposed algorithm works well, and most of the resampling factors can be correctly determined with high accuracy.
Mining Semantically Consistent Patterns for Cross-View Data

In some real world applications, like information retrieval and data classification, we often are confronted with the situation that the same semantic concept can be expressed using different views with similar information. Thus, how to obtain a certain Semantically Consistent Patterns (SCP) for cross-view data, which embeds the complementary information from different views, is of great importance for those applications. However, the heterogeneity among cross-view representations brings a significant challenge on mining the SCP. In this paper, we propose a general framework to discover the SCP for cross-view data. Specifically, aiming at building a feature-isomorphic space among different views, a novel Isomorphic Relevant Redundant Transformation (IRRT) is first proposed. The IRRT linearly maps multiple heterogeneous low-level feature spaces to a high-dimensional redundant feature-isomorphic one, which we name as mid-level space. Thus, much more complementary information from different views can be captured. Furthermore, to mine the semantic consistency among the isomorphic representations in the mid-level space, we propose a new Correlation-based Joint Feature Learning (CJFL) model to extract a unique high-level semantic subspace shared across the feature-isomorphic data. Consequently, the SCP for cross-view data can be obtained. Comprehensive experiments on three data sets demonstrate the advantages of our framework in classification and retrieval.
Depth Map Down-Sampling and Coding Based on Synthesized View Distortion

In this paper, we propose a depth map down-sampling and coding scheme that minimizes the view synthesis distortion. Moreover, a solution for the optimal depth map down-sampling problem that minimizes the depth-caused distortion in the virtual view by exploiting the depth map and the associated texture information along with the up-sampling method to be used in the decoder side is derived. Furthermore, to enhance compression performance, the synthesized view distortion, which is evaluated by emulating the interpolation and the virtual view synthesis process, is used in the optimization objective function for coding mode selection in the video encoder. Experimental results show that both the proposed depth map down-sampling and encoding methods lead to good performance, and the average bit rate reduction is 2.62% compared with 3D-AVC.
Recaptured Image Detection Based on Texture Features

With the development of digital image processing technology, image capture and image tampering are easy to obtain with the help of portable devices and software tools. Subsequently, digital image forensics has become increasingly important, in which recaptured image detection is one branch. In this paper, a set of features based on image texture are used to identify the recaptured images. Because the recapture process generally accompanies with some image quality losses, which can be reflected from the texture features, we study the effectiveness of LBPV and the proposed Relative-Contrast. Then, these two kinds of features are combined to make a distinction between real-scene images and the corresponding recaptured ones. With a support vector machine classifier, the experimental results show that the proposed features perform well.
Efficient hop ID based routing for sparse ad hoc networks

Routing in mobile ad hoc networks remains as a challenging problem given the limited wireless bandwidth, users' mobility and potentially large scale. Recently, there has been a thrust of research to address these problems, including on-demand routing, geographical routing, virtual coordinates, etc. In this paper, we focus on geographical routing, which was shown to achieve good scalability without flooding, but it usually requires location information and can suffer from the severe dead end problem especially in sparse networks. Specifically, we propose a new hop ID based routing protocol, which does not require any location information, yet achieves comparable performance with the shortest path routing. In addition, we design efficient algorithms for setting up the system and adapt to the node mobility quickly, and can effectively route out of dead ends. The extensive analysis and simulation show that the hop ID based routing achieves efficient routing for mobile ad hoc networks with various density, irregular topologies and obstacles
Segmentation-based Image Authentication and Recovery Scheme Using Reference Sharing Mechanism

In this paper, we propose an efficient watermarking scheme for digital image tamper detection and recovery. To enhance the capability of resisting VQ attacks, the image is partitioned into interrelated regions with irregular shapes. Authentication watermark, which can detect and locate the tampered region, is generated by a feedback-based chaotic system. Recovery watermark is constructed by applying a reference sharing mechanism on the encoded DCT coefficients and embedded into each region dispersedly. Experimental results demonstrate that the proposed scheme can successfully identify and recover the tampered regions.
Simulation research on fault ride through for permanent magnet synchronous generator wind power system

This paper analysis the characteristics of the high permeability of wind power in the end of the major power grids and the characteristics of the wind turbines operation. Study the key technologies of the High Voltage Ride Through of the direct-driven wind turbines and the control strategies. Build the simulation model of the High Voltage Ride Through of the direct-driven wind turbines generator and the double-fed wind generator by using the PSCAD/EMTDC software. Also study the ability of the High Voltage Ride Through of the direct-driven wind turbines in detail and the structure of the DC bus braking circuit(chopper) and the control strategies. The simulation results verify the effectiveness of the model and the control strategies, show the feasibility of High Voltage Ride Through ability of the direct-driven wind turbines in the end of the major power grids and the chopper circuit can inhibit the rise of the DC bus effectively during the process of the High Voltage Ride Through.
Multiple Description Image Coding Based on DSC and Pixel Interleaving

This paper presents a novel multiple description image coding (MDC) using the theory of distributed source coding (DSC). The scheme is based on pixel interleaving MD image framework. A so-called Slepian-Wolf set partitioning in hierarchical tree (SW-SPIHT) is presented to efficiently and flexibly insert redundancy in each description and help to estimate the lost description. Experiments demonstrate that it can get better performance than some general MDC methods. Besides, it demonstrates higher robustness in packet-loss channel than pixel interleaving MDC method due to the error-correcting decoding adopted by DSC.