nils-herrmann/clean_scirepeval huggingface dataset:
Practical Algorithms of Bandwidth Regulation for Rate-Based Switching

A rate-based switch fabric called smoothed buffer crossbar has been proposed in our recent work. It can provide 100% rate-guaranteed service with only a two-cell buffer at each crosspoint, and can also support best-effort service with an additional bandwidth regulator. However, the widely used max- flow model for bandwidth regulation is neither scalable nor of 100% throughput. In order to evaluate the performance of bandwidth regulator, we first introduce the 100% ideal throughput measurement in this paper. Then we prove that the total arrival average (TAA) algorithm has got this property, which does not happen in the max-flow allocation. Then an O(N) algorithm called proportion scaling allocation (PSA) is presented. Simulations reveal that PSA can deliver nearly 100% throughput even in a frequently changing traffic pattern. As a comparison, both theoretical and experimental evidences are given to show the inefficiency of the max-flow model.
On guaranteed smooth switching for buffered crossbar switches

Scalability considerations drive the evolution of switch design from output queueing to input queueing and further to combined input and crosspoint queueing (CICQ). However, CICQ switches with credit-based flow control face new challenges of scalability and predictability. In this paper, we propose a novel approach of rate-based smoothed switching, and design a CICQ switch called the smoothed buffered crossbar or sBUX. First, the concept of smoothness is developed from two complementary perspectives of covering and spacing, which, commonly known as fairness and jitter, are unified in the same model. Second, a smoothed multiplexer sMUX is designed that allocates bandwidth among competing flows sharing a link and guarantees almost ideal smoothness for each flow. Third, the buffered crossbar sBUX is designed that uses the scheduler sMUX at each input and output, and a two-cell buffer at each crosspoint. It is proved that sBUX guarantees 100% throughput for real-time services and almost ideal smoothness for each flow. Fourth, an on-line bandwidth regulator is designed that periodically estimates bandwidth demand and generates admissible allocations, which enables sBUX to support best-effort services. Simulation shows almost 100% throughput and multi-microsecond average delay. In particular, neither credit-based flow control nor speedup is used, and arbitrary fabric-internal latency is allowed between line cards and the switch core, simplifying the switch implementation.
The application of the Groebner Basis technique in power flow study

Power flow study is one of the most dominant subjects in the field of power system. This paper applies the Groebner Basis (GB) technique to solve power flow problem. The GB technique is a systematic mathematical tool to solve nonlinear polynomial algebraic equations. The technique has capabilities of uncoupling any given set of coupled equations. Uncoupling not only facilitates numerical work, but also provides the possibility of solving uncoupled equations of up to the fifth degree analytically in symbolic form. For higher degree, since the new set of equations is decoupled, the overall solution is simplified. The superior property of the technique brings an alternative method for power flow study. 3-bus and 5-bus power system are used to test and discuss the GB technique. The effectiveness of this technique is validated with solution from Newton-Raphson method.
Structure-driven Adaptive Non-local Filter for High Efficiency Video Coding (HEVC)

Deblocking filter (DF) Is High Efficiency Video Coding (HEVC) is Only Applied to all Samples Adjacent to prediction units (PU), or transform units (TU), which actually exists two issues. The first one is that DF in HEVC does not fully exploit nonlocal similarity structure information in video. The second one is that DF is HEVC does not consider the inside pixels, which often suffer from quantization distrotion. To alleviate these issues, in this paper, a structure-driven adaptive non-local filter (SANF) Is Proposed By Simultaneously Enforcing The Intrinsic Local Sparsity And The Non-Local Self-Similarity Of Each Frame. Not only SANF deals with the boundary pixels, but also the inside area, which is able to effectively reduce block artifacts while enhancing the quality of the deblocked frames. Applying SANF to luma and chroma components after DF, simulation results demonstrate that the proposed SANF can save BD-rate reduction up to 10.3% with ALF off. For luma component, SANF achieves 4.1%. 3.3%, 4.4% BD-rate saving for all intra, low delay B and random access configurations, respectively with ALF off. furthermore, the performance with ALF on is also discussed.
Optimizing Binary Fisher Codes for Visual Search

Fisher vectors (FV) aggregated from local invariant features (e.g., SIFT) is one of the state-of-the-art descriptors for visual search, due to high discriminability but small visual vocabulary. Nevertheless, a high-dimensional FV needs to be compressed into a compact descriptor for light storage and high matching eficiency. In this paper, we formulate the FV compression as a resource-constrained optimization problem. Our goal is to maximize search performance subject to the constraints of descriptor compactness, compression complexity in terms of memory usage and time cost. Accordingly, we present a selective binary Fisher codes (SBFC) to compress the raw FV. Firstly, to fulfill the constraint of compression complexity, we binarize the FV by a sign function, Secondly, we propose to select discriminative bits from the binarized FV (BFC) to maximize search performance, subject to the constraint of descriptor compactness. Extensive experiments over MPEG Compact Descriptor for Visual Search (CDVS) benchmark datasets have shown that S-BFC significantly improves search performance at a smaller descriptor size as well as much lower complexity, compared with the state-of-the-art FV compression algorithms like Hashing and Product Quantziation (PQ). A simplified version of SBFC, SBFC LS has been adopted by the MPEG CDVS standard. In the CDVS evaluation framework, SBFC LS has achieved promising performance mean Average Precision (mAP) 83% on average at much lower memory cost of 40KB.
Fea-Accu cascade for face detection

Aiming at unloading the high training time burden of the popular cascaded classifier, in this paper, a novel cascade structure called Fea-Accu cascade is proposed. In Fea-Accu cascade training, the times of feature selection are largely reduced by enhancing the correlation among different stage classifiers of the cascaded classifier. In detail, for each stage classifier, before selecting new features out, the features selected out by previous stage classifiers are reused through creating new corresponding weak classifiers. To verify the efficiency and effectiveness of the proposed method, experiment is designed on frontal face detection problem. The experimental results show that it can largely reduce the training time. A frontal face detector with state-of-the-art classification performance can be learned in less than 10 hours.
Adaptive Sign Language Recognition With Exemplar Extraction and MAP/IVFS

Sign language recognition systems suffer from the problem of signer dependence. In this letter, we propose a novel method that adapts the original model set to a specific signer with his/her small amount of training data. First, affinity propagation is used to extract the exemplars of signer independent hidden Markov models; then the adaptive training vocabulary can be automatically formed. Based on the collected sign gestures of the new vocabulary, the combination of maximum a posteriori and iterative vector field smoothing is utilized to generate signer-adapted models. Experimental results on six signers demonstrate that the proposed method can reduce the amount of the adaptation data and still can achieve high recognition performance.
Sigma Set: A small second order statistical region descriptor

Given an image region of pixels, second order statistics can be used to construct a descriptor for object representation. One example is the covariance matrix descriptor, which shows high discriminative power and good robustness in many computer vision applications. However, operations for the covariance matrix on Riemannian manifolds are usually computationally demanding. This paper proposes a novel second order statistics based region descriptor, named “Sigma Set”, in the form of a small set of vectors, which can be uniquely constructed through Cholesky decomposition on the covariance matrix. Sigma Set is of low dimension, powerful and robust. Moreover, compared with the covariance matrix, Sigma Set is not only more efficient in distance evaluation and average calculation, but also easier to be enriched with first order statistics. Experimental results in texture classification and object tracking verify the effectiveness and efficiency of this novel object descriptor.
Surveillance video coding with quadtree partition based ROI extraction

To reduce the surveillance video coding cost, it is intuitive to encode surveillance videos by dealing with the foreground objects and the background separately. One widely used method following this strategy is Region-of-Interest (ROI) based coding. To achieve significant improvement for the coding efficiency of ROI based methods, this paper presents a surveillance video coding method with High Efficiency Video Coding (HEVC) quadtree partition based ROI extraction. With automatically generated foreground mask and modeled background frame, a ROI extraction following the block partition in HEVC's quadtree structure is firstly performed. Afterwards, surveillance videos can be compressed by coding two-layer videos. One is the ROI-layer video generated by merging ROIs and background data in each frame together. The other is the background-layer video produced by subtracting the ROIs from the original input video. Results show our method can achieve remarkable total bit-rate saving and significant bit-rate cost reduction on ROIs.
A SRN/HMM system for signer-independent continuous sign language recognition

Sign language recognition is to provide an efficient and accurate mechanism to transcribe sign language into text or speech. State-of-the-art sign language recognition should be able to solve the signer-independent continuous problem for practical applications. A divide-and-conquer approach, which takes the problem of continuous Chinese Sign Language (CSL) recognition as subproblems of isolated CSL recognition, is presented for signer-independent continuous CSL recognition. In the proposed approach, the improved simple recurrent network (SRN) is used to segment the continuous CSL. The outputs of SRN are regarded as the states of hidden Markov models (HMM) in which the Lattice Viterbi algorithm is employed for searching for the best word sequence. Experimental results show that the SRN/HMM approach has a better performance than the standard HMM.
Inter-View Dependency-Based Rate Control for 3D-HEVC

We propose an inter-view dependency-based rate control (RC) algorithm for 3D extension of high efficiency video coding (HEVC). First, considering the rate-distortion (R-D) dependency between the synthesized views and the input views (including texture videos and depth maps), a synthesized view distortion model is derived. Second, a novel distortion model for dependent views is proposed by investigating the inter-view dependency between the base and dependent views. Based on these two distortion models, a joint optimal bit allocation strategy (including texture/depth level, view level, and frame level) is developed to allocate target bits for both texture videos and depth maps of different views. Furthermore, an effective initial quantization parameter decision scheme considering the characteristics of the input video content is presented. Extensive experimental results exhibit that the proposed scheme achieves a higher rate control accuracy and provides a better R-D performance than the state-of-the-art RC algorithms.
Open Bookmark service based on self-adaptive information agents

As the Web grows, the massive increase in information is placing severe burdens on information retrieval. Automated search engines and information directories with small editorial staff are unable to keep up with the increasing submissions of Web sites. To address the problem, this article introduces the Open Bookmark service based on self-adaptive information agents. One can record favorite URLs and easily manage them in the Web site. All the records are stored in a directory-tree, with different types of records in different directories. To perform information filtering, self-adaptive information agents are established to extract the user's profile from his bookmark records that represent his interests. This makes it possible for a personal and active information service. The agents are trained through interaction with the user so as to represent his interests more exactly. Open Bookmark service is also an open framework, in which information agents communicate with each other and exchange useful information. Thus the quality of information service can be greatly improved. Our application experiences show that Open Bookmark service is much more powerful than a conventional one.
Scanned beam synthesis technique with projection matrix algorithm for phased-array fed offset paraboloid antennas

A novel phased-array fed offset paraboloid antenna synthesis technique based on projection matrix algorithm is presented. Far-field of the array fed offset paraboloid antenna is calculated based on Physical Optics theory. The excitation coefficients of the feed array are determined under phase only control constraint. Antenna patterns are synthesized efficiently using PMA. Namely the beam direction, half-power beam width and side-lobe level are well controlled. Numerical simulation results are given to demonstrate the feasibility of the method.
A deterministic parallel scheduling algorithm for input queued crossbar switches

(IQ) switch architectures have become predominant in high-speed switching for the past decade. Maximum weight matching (MWM) algorithms are known to achieve 100% throughput under any admissible traffic. Unfortunately, MWM is impractical for its high computational complexity O(N/sup 3/). In this paper, we study a new type of approximate algorithm to MWM using local search technique. Instead of using randomized technique which is mainly used by the existing approximations, our algorithm runs in a deterministic way which is easy to be implemented by hardware. It provides 100% throughput under any admissible traffic and simulation results show that the proposed algorithm with only 3 iterations outperforms the existing approximations to MWM.
Advanced spatial and Temporal Direct Mode for B picture coding

The direct mode in H.264/AVC can efficiently improve the coding performance of B pictures, since it exploits the spatial or temporal correlation by deriving its motion vector from previously encoded information. Therefore, it does not require any additional motion information and could save many bits. Considering the spatial and temporal correlation has not been fully exploited in the current direct mode, in this paper, we propose an advanced Spatial and Temporal Direct Mode (STDM) for B picture coding. The motion vector is selected from a set of spatial-temporal neighboring motion vectors, and the selection criterion is to minimize a spatial-temporal cost function. The framework of Decoder-side Motion Vector Derivation (DMVD) is utilized, where encoder and decoder use the same derivation process to obtain motion vectors, thus no index for the chosen motion vectors need to be coded and transmitted. Simulation results show that the proposed method significantly outperforms the current SDM and TDM in H.264/AVC.
Novel example-based shape learning for fast face alignment

In this paper, a novel example-based shape learning (ESL) strategy has been proposed for facial feature alignment. The method is motivated by an intuitive and experimental observation that there exists an approximate linearity relationship between the image difference and the shape difference, that is, similar face images imply similar face shapes. Therefore, given a learning set of face images with their corresponding face landmarks labeled, the shape of any novel face image can be learned by estimating its similarities to the training images in the learning set and applying these similarities to the shape reconstruction of a novel face image. Concretely, if the novel face image is expressed by an optimal linear combination of the training images, the same linear combination coefficients can be directly applied to the linear combination of the training shapes to construct the optimal shape for the novel face image. Our experiments have convincingly shown the effectiveness and efficiency of the proposed approach in both speed and accuracy performance compared with other methods.
A simple sign language recognition system based on data glove

Human beings usually interact with each other either by via a natural language channel such as speech and writing, or by body languag, e.g. hand gestures, head gestures, facial expression, lip motion and so on. As a part of natural language understanding, sign language recognition is very important. On one hand, it is one of the main methods of human-computer interaction in virtual reality; on the other hand, it is an auxiliary tool for a deaf-mute to communicate with ordinary people through computer. In this paper the process of building a simple word-level sign language recognition system is presented, and the method for recognizing sign language words is also proposed.
A novel compressed domain shot segmentation algorithm on H.264/AVC

This paper presents a novel shot segmentation algorithm on the H.264/AVC video, which operates in the compressed domain. First, the algorithm exploits the intra prediction mode histogram to locate those potential GOPs, where shot transitions occur with great probability. Secondly, to further find shot boundaries at the frame level, we count the number of macroblocks with different inter prediction modes as the features and exploit HMMs to automatically model different cases in which shot transitions can occur among I, P and B frames. Since H.264/AVC provides more motion compensation modes, using HMMs can avoid the tediousness of manually tuning multiple thresholds simultaneously. The experimental results show that the algorithm is efficient and robust and it can not only locate cuts, but also work for gradual shot transitions.
Multiple layer parallel motion estimation on GPU for High Efficiency Video Coding (HEVC)

This paper provides a multiple-layer parallel motion estimation (ME) scheme implemented on GPU for High Efficiency Video Coding (HEVC). The scheme is hierarchically structured, including four layers: coding tree unit (CTU), prediction unit (PU), motion vector (MV) selection and instruction optimization. In PU-layer, costs of various PU sizes were obtained through a SAD (sum of absolute differences) look-up table instead of progressive cost merging. And during MV selection, GPU's comparison instruction was used to avoid branches. At the same time, concurrent CTUs processing and SIMD (Single Instruction, Multiple Data) optimization also improve the performance significantly. Experimental results show that the proposed scheme can take full advantage of GPU and achieves over 90 times speedup compared with the HM10.0 using fast ME.
Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing

The compact descriptors for visual search (CDVS) standard from ISO/IEC moving pictures experts group has succeeded in enabling the interoperability for efficient and effective image retrieval by standardizing the bitstream syntax of compact feature descriptors. However, the intensive computation of a CDVS encoder unfortunately hinders its widely deployment in industry for large-scale visual search. In this paper, we revisit the merits of low complexity design of CDVS core techniques and present a very fast CDVS encoder by leveraging the massive parallel execution resources of graphics processing unit (GPU). We elegantly shift the computation-intensive and parallel-friendly modules to the state-of-the-arts GPU platforms, in which the thread block allocation as well as the memory access mechanism are jointly optimized to eliminate performance loss. In addition, those operations with heavy data dependence are allocated to CPU for resolving the extra but non-necessary computation burden for GPU. Furthermore, we have demonstrated the proposed fast CDVS encoder can work well with those convolution neural network approaches which enables to leverage the advantages of GPU platforms harmoniously, and yield significant performance improvements. Comprehensive experimental results over benchmarks are evaluated, which has shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising for scalable visual search.
In-loop deblocking filter for block based video coding

Blocking artifacts are inherent and inevitable phenomena at low bit rates in current block-based video coding schemes. The paper proposes a deblocking technique to reduce blocking artifacts and improve visual quality. Different from the post-processing technique adopted in MPEG-4, the proposed technique is directly applied to the reference image for effective motion prediction instead of just for display purposes. In other words, the deblocking filter is introduced into the motion compensation loop in the video coding. Because of the negative effects of the deblocking filter, such as blurring the details of the image, not every block boundary needs to be filtered. It has to be determined whether the boundaries between two blocks should be filtered or not. Therefore, we also propose a decision-making criterion based on the available motion information (vector and mode). Experimental results show that the proposed deblocking method can improve image quality both in subjective and objective aspects. It also outperforms the post-processing technique in MPEG-4.
Quantization bin matching for cloud storage of JPEG images

Social media sites like Facebook are obligated to store all photos uploaded by an ever growing user base-which translates to an increasingly expensive storage cost-but only a fraction of uploaded images are revisited thereafter. In this paper, we propose a cloud storage system that trades off computation of a small fraction of requested images with storage of all photos. The key idea is to re-encode uploaded JPEG photos with coarser quantization parameters (QP) for permanent storage, then exploit a signal sparsity prior during inverse mapping to recover fine quantization bin indices via a maximum a posteriori (MAP) formulation. Because by design the system guarantees recovery of an original compressed image (either with exactly the same input fine quantization bin indices or has visual quality indistinguishable by human eyes), from the user's viewpoint it is a normal cloud storage, while from the operator's viewpoint there is pure compression gain and hence lower storage cost. Experimental results show that our storage system can reap significant storage savings (up to 20%) at roughly the same image PSNR (within 0.13dB).
Spectrum Sensing Using Cyclostationary Properties and Application to IEEE 802.22 WRAN

Spectrum sensing in a very low SNR environment (less than -20 dB) is considered in this paper. We make use of the noise rejection property of the cyclostationary spectrum. The sensing algorithms are based on measurement of the cyclic spectrum of the received signals. The statistics of the cyclic spectrum of the stationary white Gaussian process are fully analyzed for three measurement methods of the cyclic spectrum. The application to IEEE 802.22 WRAN is presented and the probability of false alarm is analytically derived. The operating characteristic curves for the sensing algorithms are determined from computer simulations using ATSC A/74 DTV signal captures as a test database.