nils-herrmann/clean_scirepeval huggingface dataset:
Instantly telling what happens in a video sequence using simple features

This paper presents an efficient method to tell what happens (e.g. recognize actions) in a video sequence from only a couple of frames in real time. For the sake of instantaneity, we employ two types of computationally efficient but perceptually important features, optical flow and edge, to capture motion and shape/structure information in video sequences. It is known that the two types of features are not sparse and can be unreliable or ambiguous at certain parts of a video. In order to endow them with strong discriminative power, we extend an efficient contrast set mining technique, the Emerging Pattern (EP) mining method, to learn joint features from videos to differentiate action classes. Experimental results show that the combination of the two types of features achieves superior performance in differentiating actions than that of using each single type of features alone. The learned features are discriminative, statistically significant (reliable) and display semantically meaningful shape-motion structures of human actions. Besides the instant action recognition, we also extend the proposed approach to anomaly detection and sequential event detection. The experiments demonstrate encouraging results.
Design and performance evaluation of differentiated services router for next generation Internet

The current Internet provides only "best effort" services, however, applications and users expect different QoS (Quality of Service). To meet these requirements, the differentiated services (DiffServ) architecture is introduced. Because DiffServ is regarded as the foundation of the next generation Internet QoS architecture, it is helpful to design a DiffServ Router (DSR) and evaluate its performance. We first analyze the specifications on DSR and then outline the modules required to construct a DSR based on IETF standards and drafts. In order to evaluate the performance of DSR, a prototype DSR is provided and implemented in the Network Simulator. Evaluations of the prototype DSR show that different structures and parameters affect the performance of DiffServ services. The results of prototype DSR simulation are useful for the design of future routers.
Sprite generation for frame-based video coding

Sprite coding is a very efficient method for the representation and compression of background video object, which has been accepted by MPEG-4 standard. However, how to automatically generate sprite image from raw video sequence is still an open issue, because the motion of foreground video objects not only disturbs the accuracy of motion estimation but also blurs the generated sprite image. This paper presents a novel technique for the generation of background sprite image with improved global motion estimation (GME) and automatic segmentation. Since GME plays an important role in sprite generation, a hybrid scheme jointing the short-term and long-term motion estimation techniques is first developed in this paper. Automatic video segmentation based on available motion information is incorporated into sprite generation, which can mask moving foreground objects and meanwhile protect background sprite from blur. Therefore, the proposed technique is used to construct high quality sprite directly from a raw video sequence. Furthermore, with this technique the existing sprite coding can be extended to the field of frame-based coding.
A Backward-Compatible Solution for Next Generation DVB-C System

In this paper, we propose hierarchical modulation as a backward compatible solution for next generation digital video broadcasting over cables (DVB-C2). Without modifying the architecture of legacy receivers, supplementary data can be transmitted via an added layer superimposed onto the legacy layer, and the resultant increase of transmission power is only a small fraction of that used by the legacy system. To obtain the upper bounds for the achievable throughputs, the modulation constrained capacities are derived for this layered transmission scheme. We formulate the hierarchical constellation design as a nonlinear optimization problem and then solve it through a simplified numerical method, which searches for the appropriate power allocation ratio in one-dimension space. Finally, to examine the inter-operability of channel encoder/decoder between DVB-S2 and DVB-C2, we investigate the error performance of the proposed scheme employing the LDPC codes given by DVB-S2 standard.
A closed-loop system with DC sensing method for vibratory gyroscopes

The paper presents a closed-loop system scheme with DC sensing method for vibratory gyroscopes. A DC sensing method is employed to convert the amplitude-modulated capacitive signal to voltage. The system realizes a closed-loop control in the driving mode. A high voltage (HV) drive signal which is compatible with the gyro is obtained by the on-chip level shifter. The ASIC occupies 2.5×2.0mm2 in a 0.35μm BCD process. The system requires a differential supply of ±12V and can work under a resonant frequency from 3 to 10 kHz.
A Single-Pass-Based Localized Adaptive Interpolation Filter for Video Coding

Recently, an efficient coding tool named adaptive interpolation filtering (AIF) has been proposed to hybrid video coding scheme. By introducing Wiener filter into the fractional-pixel interpolation procedure, AIF can reduce the inter-prediction error and improve coding efficiency significantly. However, the training-based Wiener filter mechanism brings AIF an inherent multi-pass encoding structure, which imposes big burdens on the encoder in terms of huge computational complexity and memory access. In this paper, we propose a single-pass-based localized adaptive interpolation filtering (SPL-AIF) algorithm for video coding, which can reduce the complexity of AIF dramatically without sacrifice of its outstanding coding performance. The proposed SPL-AIF algorithm is based on the observation that there is a high correlation among optimal interpolation filters of consecutive frames, and different regions in a frame often possess different statistical characteristics. Accordingly, the proposed algorithm can be designed including two major parts. First, a competitive filter set which includes the optimal interpolation filters of several previous frames as well as the fixed H.264/AVC interpolation filters is built up for the coding of the current frame. Then a rate-distortion optimization criterion is used to select the best one at macroblock (MB) level. In order to reduce overhead, a predictive coding method is used to compress the filter signaling flag for each MB. Experimental results show that, by using the proposed algorithm, the encoding complexity can be reduced significantly while the average coding gain in Bjöntegaard distortion bit-rate reduction can be improved about 1% compared with the multi-pass AIF. The proposed method has been adopted into the Video Coding Expert Group Key Technology Area software.
Development of a through-stack-via integrated SRAM module

In this paper, a through-stack-via integration process for SRAM module was developed using wafer level pre-patterned BCB bonding. A SRAM module with a built-in decoder has been designed according to this integration process. TSVs passed through all stacked SRAM chips and common signals, including address bus, data bus, power, write and read control, were connected to the same TSV using RDL. The chip select signals are individually connected to the built-in decoder. RDL was fabricated using lift-off process prior to wafer bonding and via filling. Double-layer spin coating technology was employed to prevent photoresist residues left in TSVs. With pre-patterned BCB adhesive bonding, a bottom-up TSV filling features as the last step, which eliminates the traditional solder bumping, flip chip bonding and underfill filling processes. Preliminary results have shown that this process is promising for integration of memory chips with similar layout.
Pedestrian detection via part-based topology model

In this paper, we propose a part-based topology model and a pedestrian detection method, which obviously improve the detection accuracy. In Our method, pedestrian is divided into several parts. Firstly, histogram of oriented gradients (HOG) features and linear support vector machine (SVM) classifier are used to detect pedestrian parts. Secondly, a novel binary descriptor called log-polar pattern (LPP) is proposed to represent the spatial relation of a part pair. Then multiple LPPs are combined as a log-polar topology pattern (LTP) to model the global topology of a pedestrian. Finally, we put the LTP into One-Class SVM (OC-SVM) to determine whether the detected parts indicate a pedestrian or not. Experiments in INRIA dataset show that our method is robust to occlusion and multi-postures, which obviously reduces the miss rate.
Safe Reconfiguring Data Plane via Supervision over Resource and Flow States

In the Software-defined networking (SDN), when multiple control domains are used in control services, the transient state problem can occur causing the service flow interruption when border switches are updated asynchronously. We analyze the uniqueness of this problem for SDN, and propose a light-weight protocol for safe reconfiguration of the border switches in order to improve the availability of the services running in SDN. Our solution is designed as a generic supervision layer added in the control plane to support different types of services. To demonstrate the benefits, we implement a prototype of Informationcentric networks (ICN) with the protocol, and conduct experiments using PlanetLab. The results show the ICN service can continuously serve high volume requests for contents despite the congestions built by the heavy background traffic. The performance gains in terms of the mean and standard deviation of the content retrieve delay are 40.5% and 21.56%.
High efficient sprite coding with directional spatial prediction

Sprite is an efficient and concise method for the representation of a background video object, which is typically compressed with the MPEG-4 object-based coding technique. Due to the property of a background video object, a sprite image often comprises many flat and texture-correlation regions. In order to fully exploit spatial redundancy, the paper proposes an idea of utilizing directional spatial prediction to improve sprite coding. In general, the generated sprite is not a rectangular image. Since traditional spatial prediction techniques have difficultly dealing with those contour blocks, a padding technique is first proposed to fill transparent regions in contour blocks by considering both the correlation of pixels within a block and the correlation among neighboring blocks. Then every block is encoded by the INTRA coding technique developed in H.26L. Experimental results show that the propose sprite coding scheme outperforms the MPEG-4 object-based coding by up to 3.0 dB at low bit rates. Furthermore, the proposed scheme essentially extends directional spatial prediction from frame-based coding to arbitrary shape object coding.
Macroblock-based progressive fine granularity scalable (PFGS) video coding with flexible temporal-SNR scalablilities

We proposed a flexible and efficient architecture for scalable video coding, namely, the macroblock (MB)-based progressive fine granularity scalable video coding with temporal-SNR scalabilities (PFGST). The proposed architecture can provide not only much improved coding efficiency but also simultaneous SNR scalability and temporal scalability. Building upon the original frame-based progressive fine granularity scalable (PFGS) coding approach, the MB-based PFGS scheme is first proposed. Three INTER modes and the corresponding mode selection mechanism are presented for coding the SNR enhancement MBs in order to make a good trade-off between low drifting errors and high compression efficiency. Furthermore, temporal scalability is introduced into the MB-based PFGS, which forms the MB-based PFGST scheme. Two coding modes are proposed for coding the temporal enhancement MBs. Since it would not cause any error propagation if using the high quality reference in the temporal enhancement MB coding, the coding efficiency of the PFGST is highly improved by always choosing the most suitable reference for the temporal scalable coding. Experimental results show that the MB-based PFGST video coding scheme can significantly improve the coding efficiency up to 2.8 dB compared with the FGST scheme adopted in MPEG-4, while supporting full SNR, full temporal, and hybrid SNR-temporal scalabilities according to the different requirements from the channels, the clients or the servers.
Large vocabulary sign language recognition based on fuzzy decision trees

The major difficulty for large vocabulary sign recognition lies in the huge search space due to a variety of recognized classes. How to reduce the recognition time without loss of accuracy is a challenging issue. In this paper, a fuzzy decision tree with heterogeneous classifiers is proposed for large vocabulary sign language recognition. As each sign feature has the different discrimination to gestures, the corresponding classifiers are presented for the hierarchical decision to sign language attributes. A one- or two- handed classifier and a hand-shaped classifier with little computational cost are first used to progressively eliminate many impossible candidates, and then, a self-organizing feature maps/hidden Markov model (SOFM/HMM) classifier in which SOFM being as an implicit different signers' feature extractor for continuous HMM, is proposed as a special component of a fuzzy decision tree to get the final results at the last nonleaf nodes that only include a few candidates. Experimental results on a large vocabulary of 5113-signs show that the proposed method dramatically reduces the recognition time by 11 times and also improves the recognition rate about 0.95% over single SOFM/HMM.
The application of fuzzy mathematics to transformer diagnosis expert system

Qualitative diagnosis method is used to analyze test data in transformer fault diagnosis expert system basically. But this usually cause misjudge fault that near threshold. In this paper, fuzzy mathematics theory is used to process DGA data and electrical tests data. IEC three-ratio fuzzy codes are calculated and the fuzzy membership degrees of electrical tests data are determined. A Comprehensive reasoning mechanism is built based on fuzzy mathematics. Finally, an example is given to prove the accuracy of diagnosis.
Blind watermarking method based on DWT middle frequency pair

In this paper a novel watermarking method based on the discrete wavelet transform (DWT) is proposed. Different from previous watermarking methods, the watermark is embedded into the middle frequency coefficients by quantizing the middle frequency coefficient pair, which is defined as a pair of coefficients that are at the same location in the LH and HL bands of the DWT coefficients. This watermarking technique can successfully resist severe transformation of gray levels while it is still robust to normal attacks such as blurring, sharpening and JPEG compression. In detection, it does not need the original image to recover the watermark.
Modeling A320 EIS based on UML

With avionics system getting more sophisticated, Electric Instrument System simulation has become an important component of the aero plane simulator that it can enhance the simulators' authenticity and effectiveness. Based on line maintenance, the architecture of A320 Electric Instrument System is established. A UML modeling methodology is applied while object-oriented analysis to the system is done. The Airbus A320 EIS' analysis of functional requirements is met by Use Case Diagram. The design with class and package is used to establish a static structure model of EIS. Both Statechart Diagram and Sequence Diagram are used to establish a dynamic model of EIS. According to these above, a concise, clear and feasible technical solution is provided for implementation of the system function.
Robust Head Pose Estimation Using LGBP

In this paper, we introduce a novel discriminative feature which is efficient for pose estimation. The multi-view face representation is based on local Gabor binary patterns (LGBP) and encodes the local facial characteristics in to a compact feature histogram. In LGBP, Gabor filters can extract the feature of the orientation of head and local binary pattern (LBP) can extract the features official local orientation. To keep the spatial information of the multi-view face images, LGBP is operated on many sub-regions of the images. The combination of them can represent well and truly the multi-view face images. Considering the derived feature space, a radial basis function (RBF) kernel SVM classifier is trained to estimate pose. Extensive experiments demonstrate that the facial representation can be effective for pose estimation
Mahalanobis distance based Polynomial Segment Model for Chinese Sign Language Recogniton

Sign Language Recognition (SLR) systems are mostly based on Hidden Markov Model (HMM) and have achieved excellent results. However, the assumption of frame independence in HMM makes it inconsistent with the characteristic of strong temporal correlation in sign language signals. Polynomial Segment Model (PSM) explicitly represents the temporal evolution of sign language features as a Gaussian process with time-varying parameters. In this paper PSM is first introduced to SLR framework to solve the temporal correlation problem. Considering the correlation among the coefficients of polynomial trajectorypsilas different orders, Mahalanobis distance is used as the classification criterion to evaluate the likelihood of test data. Experimental results show that our method outperform the conventional HMM methods by 6.81% in recognition accuracy.
Eigen-harmonics faces: face recognition under generic lighting

The performances of face recognition systems are heavily subject to the variations in lighting. We propose a novel approach for face recognition under generic illumination conditions, named as Eigen-harmonics faces in this work. First, using bootstrap set consisting of 3D face models with texture, we render the spherical harmonic images for every face and train the PCA harmonics faces model. During registration, given a novel face image under arbitrary illumination, we estimate the lighting of the image and recover the PCA coefficients of the spherical harmonics images for this face. During testing, we recognize the face using the PCA coefficients. The experimental results on the images under a wide range of illumination conditions in the public CMU-PIE database are promising.
A virtual view genetation method for free-viewpoint video system

Generating virtual views is one of the crucial problems for a free-viewpoint video system. In this paper, we solve the problem by using a method based on the technique of view morphing, and construct a multi-camera based experimental free-viewpoint video platform to validate its feasibility. This method requires no setting up of dense cameras, therefore the system is easy to be established and controlled in practice, and the mount of data for processing and transmitting can also be reduced. Experimental results show that the effect of this method can satisfy the requirement of view generation for the system.
Classifiability-based Optimal Discriminatory Projection Pursuit

Linear discriminant analysis (LDA) might be the most widely used linear feature extraction method in pattern recognition. Based on the analysis on the several limitations of traditional LDA, this paper makes an effort to propose a new computational paradigm named optimal discriminatory projection pursuit (ODPP), which is totally different from the traditional LDA and its variants. Only two simple steps are involved in the proposed ODPP: one is the construction of candidate projection set; the other is the optimal discriminatory projection pursuit. For the former step, candidate projections are generated as the difference vectors between nearest between-class boundary samples with redundancy well-controlled, while the latter is efficiently achieved by classifiability-based AdaBoost learning from the large candidate projection set. We show that the new ldquoprojection pursuitrdquo paradigm not only does not suffer from the limitations of the traditional LDA but also inherits good generalizability from the boundary attribute of candidate projections. Extensive experimental comparisons with LDA and its variants on synthetic and real data sets show that the proposed method consistently has better performances.
A Joint Compression Scheme of Video Feature Descriptors and Visual Content.

High-efficiency compression of visual feature descriptors has recently emerged as an active topic due to the rapidly increasing demand in mobile visual retrieval over bandwidth-limited networks. However, transmitting only those feature descriptors may largely restrict its application scale due to the lack of necessary visual content. To facilitate the wide spread of feature descriptors, a hybrid framework of jointly compressing the feature descriptors and visual content is highly desirable. In this paper, such a content-plus-feature coding scheme is investigated, aiming to shape the next generation of video compression system toward visual retrieval, where the high-efficiency coding of both feature descriptors and visual content can be achieved by exploiting the interactions between each other. On the one hand, visual feature descriptors can achieve compact and efficient representation by taking advantages of the structure and motion information in the compressed video stream. To optimize the retrieval performance, a novel rate-accuracy optimization technique is proposed to accurately estimate the retrieval performance degradation in feature coding. On the other hand, the already compressed feature data can be utilized to further improve the video coding efficiency by applying feature matching-based affine motion compensation. Extensive simulations have shown that the proposed joint compression framework can offer significant bitrate reduction in representing both feature descriptors and video frames, while simultaneously maintaining the state-of-the-art visual retrieval performance.
A Spatio-Temporal Auto Regressive Model for Frame Rate Upconversion

This paper proposes a spatio-temporal auto regressive (STAR) model for frame rate upconversion. In the STAR model, each pixel in the interpolated frame is approximated as the weighted combination of a sample space including the pixels within its two temporal neighborhoods from the previous and following original frames as well as the available interpolated pixels within its spatial neighborhood in the current to-be-interpolated frame. To derive accurate STAR weights, an iterative self-feedback weight training algorithm is proposed. In each iteration, first the pixels of each training window in the interpolated frames are approximated by the sample space from the previous and following original frames and the to-be-interpolated frame. And then the actual pixels of each training window in the original frame are approximated by the sample space from the previous and following interpolated frames and the current original frame with the same weights. The weights of each training window are calculated by jointly minimizing the distortion between the interpolated frames in the current and previous iterations as well as the distortion between the original frame and its interpolated one. Extensive simulation results demonstrate that the proposed STAR model is able to yield the interpolated frames with high performance in terms of both subjective and objective qualities.