nils-herrmann/clean_scirepeval huggingface dataset:
Fast convolutional sparse coding with separable filters

Convolutional sparse representations (CSR) of images are receiving increasing attention as an alternative to the usual independent patch-wise application of standard sparse representations. For CSR the dictionary is a filter bank of non-separable 2D filters, and the representation itself can be viewed as the synthesis dual of the analysis representation provided by a single level of a convolutional neural network (CNN). The current state-of-the-art convolutional sparse coding (CSC) algorithms achieve their computational efficiency by applying the convolutions in the frequency domain. It has been shown that any given 2D non-separable filter bank can be approximated as a linear combination of a relatively small number of separable filters. This approximation has been exploited for computationally efficient CNN implementations, but has thus far not been considered for convolutional sparse coding. In this paper we propose a computationally efficient algorithm, that apply the convolution in the spatial domain, to solve the CSC problem when the corresponding dictionary filters are separable. Our algorithm, based on the ISTA framework, use a two-term penalty function to attain competitive results when compared to the state-of-the-art methods in terms of computational performance, sparsity and reconstruction quality.
A generalized vector-valued total variation algorithm

We propose a simple but flexible method for solving the generalized vector-valued TV (VTV) functional, which includes both the ¿2-VTV and ¿1-VTV regularizations as special cases, to address the problems of deconvolution and denoising of vector-valued (e.g. color) images with Gaussian or salt-and-pepper noise. This algorithm is the vectorial extension of the Iteratively Reweighted Norm (IRN) algorithm  originally developed for scalar (grayscale) images. This method offers competitive computational performance for denoising and deconvolving vector-valued images corrupted with Gaussian (¿2-VTV case) and salt-and-pepper noise (¿1-VTV case).
A radix-2 FFT algorithm for Modern Single Instruction Multiple Data (SIMD) architectures

Modern Single Instruction Multiple Data (SIMD) microprocessor architectures allow parallel floating point operations over four contiguous elements in memory. The radix-2 FFT algorithm is well suited for modern SIMD architectures after the second stage (decimation-in-time case). In this paper, a general radix-2 FFT algorithm is developed for the modern SIMD architectures. This algorithm (SIMD-FFT) is implemented on the Intel Pentium and Motorola PowerPC architecture for 1D and 2D. The results are compared against Intel's implementation of the split-radix FFT for the SIMD architecture  and the FFTW . Overall, the SIMDFFT was found to be faster than the other two implementations for complex 1D input data (ranging from 95.9% up to 372%), and for complex 2D input data (ranging from 68.8% up to 343%) as well.
Handgrip estimation based on total variation denoising filtering for control applications

In many biomechanical studies and control applications, such as ergonomics studies, control of upper limb prosthesis, and sports performance is required handgrip force estimation for both monitoring and control purposes. As it was proven in previous works, features extraction from the extensor carpi radialis longus (ecrl) sEMG had a linear relationship with the gripforce of the hand. However, most of the developed estimations have shown high variation, which are not quite suitable for control applications. Therefore we propose a methodology to estimate the grip force, which models the extrated features as the handgrip force signal with the presence gaussian noise. In order to estimate the force, these features are filtered with a regularized optimization problem based on total variation denoising (TVD). Furthermore, since TVD is not a trivial minimization problem it was used ADMM algorithm as a meant to implement the proposed methodology. The developed methodology yielded promising results (ρ > 0.94 NRMSE <; 0.07) between 30%-50% MVC.
iAVS2: A Fast Intra-Encoding Platform for IEEE 1857.4

The second generation of the audio video coding standard (AVS2), which has been issued as IEEE 1857.4, doubles the coding efficiency of AVS1 and H.264/AVC. However, the advanced techniques in AVS2 also dramatically increase the computational complexity. The research on a commercial encoder for AVS2 is still at an early stage. In this paper, we propose the first fast intra-encoding platform for AVS2, which we term iAVS2. The platform uses numerous speedup methods, including code optimization, single-instruction multiple-data acceleration, and fast algorithms for almost all the time-consuming modules. To meet different application scenarios, five different speed levels are designed in iAVS2 to provide flexible tradeoffs between compression efficiency and encoding speed. The experimental results show that the fastest speed level of iAVS2 can accelerate the AVS2 reference software (RD12.0) by <inline-formula> <tex-math notation="LaTeX">$144\times $ </tex-math></inline-formula> on average. Compared with the well-known HEVC encoder platform <inline-formula> <tex-math notation="LaTeX">$\times 265$ </tex-math></inline-formula>, iAVS2 can provide over 10% bit rate saving at a similar coding speed. For the UHD and 1080 sequences, iAVS2 outperforms <inline-formula> <tex-math notation="LaTeX">$\times 265$ </tex-math></inline-formula> at all speed levels.
Batch production planning method based on the order characteristics for cold mill

The cold rolling mill is the necessary process, in which the production planning of mill corresponds with one of the whole production flow. For solving the production scheduling problem of the whole process of cold rolling, according to the relation between the cold rolling mill and the whole production flow, the production planning of the mill is made. In this paper the problem is reduced to an asymmetric double traveling salesman problem with time windows and the multi-objective model and the multi-objective adaptive section researching ant colony algorithm are presented. Besides the constraints, such as the change in width and thick of entrance and exit, the continuous production is considered in the procedure for the solution and the relevant searching strategy is employed in the different computing stage. The simulation is conducted with the practical data from a cold rolling plant and the results indicate that the proposed method is effective.
Learning informative features for spatial histogram-based object detection

Feature extraction for object representation plays an important role in automatic object detection system. As the spatial histograms consist of marginal distribution of image over local patches, object texture and shape are simultaneously preserved by the spatial histogram representation. In this paper, we propose methods of learning informative features for spatial histogram-based object detection. We employ Fisher criterion to measure the discriminability of each spatial histogram feature and calculate features correlation using mutual information. In order to construct compact feature sets for efficient classification, we propose informative selection algorithm to select uncorrelated and discriminative spatial histogram features. The proposed approaches are tested on two different kinds of objects: car and video text. The experimental results show that the proposed approaches are efficient in object detection.
SVMs for few examples-based face recognition

We present an extensive study of the support vector machine (SVM) applied to the few examples-based face recognition problem. The few examples cannot express many conditions that the test data combine, such as changes of pose etc., so we use a simple method to generalize the examples to others. Then, principal component analysis (PCA) is applied to feature extraction from all the images; after that, we use SVM to train and test the data. In the ICT-YCNC face gallery, the proposed system obtains competitive results: a correct recognition rate of 91.59% for all 350 persons.
Low Complexity Rate Distortion Optimization for HEVC

The emerging High Efficiency Video Coding (HEVC) standard has improved the coding efficiency drastically, and can provide equivalent subjective quality with more than 50% bit rate reduction compared to its predecessor H.264/AVC. As expected, the improvement on coding efficiency is obtained at the expense of more intensive computation complexity. In this paper, based on an overall analysis of computation complexity in HEVC encoder, a low complexity rate distortion optimization (RDO) coding scheme is proposed by reducing the number of available candidates for evaluation in terms of the intra prediction mode decision, reference frame selection and CU splitting. With the proposed scheme, the RDO technique of HEVC can be implemented in a low-complexity way for complexity-constrained encoders. Experimental results demonstrate that, compared with the original HEVC reference encoder implementation, the proposed algorithms can achieve about 30% reduced encoding time on average with ignorable coding performance degradation (0.8%).
Improving QoS in SDN with lossless multi-domain reconfigurations

In this poster, we propose a novel approach of multidomain reconfigurations in SDN, termed as Lossless Reconfiguration (LR), to avoid packet loss and maintain the availability of services during the reconfiguration process. By leveraging the advantages of SDN, e.g., centralized control in one domain and feasible cooperation between the controllers of different domains, LR offers a better solution to the transient problem. First, we identify three categories of domains in the reconfiguration process. We then develop four steps to reconfigure the different categories of domains in order. By the synchronization of the involved controllers in each reconfiguration step, the transient problem can be resolved without any device modification requirement.
Spatio-temporal ssim index for video quality assessment

An ideal objective metric for video quality assessment (VQA) should achieve consistency between video distortion prediction and psychological perception of human visual system (HVS), and is important in many video processing applications. In general, both spatial distortion and temporal distortion should be carefully considered in the designing of VQA metrics. In this paper, we propose a novel spatio-temporal structural information based video quality metric. Motivated by the fact that pixels in natural videos are highly structured in both spatial domain and temporal domain, we propose to perform structural similarity evaluation in x-y, x-t and y-t dimensions respectively and pooled them adaptively based on local spatio-temporal activities. Experimental results on LIVE database show that such a conceptually simple and computationally efficient algorithm is competitive with state-of-the-art VQA metrics, and is very robust to various types of video distortions.
Hybrid of Job Sequencing and DVFS for Peak Temperature Reduction with Nondeterministic Applications

In this paper we propose a hybrid approach, which improves system thermal behavior with nondeterministic applications based on job sequencing as well as dynamic voltage and frequency selection (DVFS). The hybrid approach consists of two steps: 1) job sequencing and 2) lowering processor's speed by using dynamic voltage and frequency selection. The first step constructs a job sequence based on the average execution time of each job; and the constructed job sequence interleaves executions of hot jobs and cool jobs. While a hot job pushes up the temperature of a chip, a cool job cools the chip down. Accordingly the job sequencing step reduces the peak temperature of a chip. The second step further improves system-wide thermal behavior by dynamic voltage and frequency selection, which slows down the execution of hot jobs by utilizing job slacks. Hot jobs are the rooted cause for deteriorating the thermal behavior of a processor, so we only slow down the execution of hot jobs and job slacks are allocated among hot jobs. Hot jobs whose execution time deviates more above their average execution time get more slack; vice versa. Experimental results show that the proposed hybrid approach improves the system thermal behavior for various nondeterministic workloads by reducing peak temperature as well as decreasing thermal variations.
A fast intra optimization algorithm for HEVC

High Efficiency Video Coding (HEVC) has drastically improved the coding efficiency, and can provide more than 23% bit rate reduction compared to its predecessor H264/AVC for all intra (AI) configuration. However, the improvement on coding efficiency is obtained at the expense of much more intensive computation complexity. In this paper, a fast intra optimization scheme is proposed. Firstly, a shrinkage scheme for the number of mode prediction is proposed based on gradient variance. Then a novel estimation model for rate distortion cost is provided based on Hadamard transform and quantization to better determine the mode prediction for RQT. Experimental results demonstrate that, compared with the original HEVC reference encoder implementation in AI configuration, the proposed algorithms can achieve more than 26% coding complexity reduction on average with ignorable coding performance degradation.
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations

In this paper, we describe the acquisition and contents of a large-scale Chinese face database: the CAS-PEAL face database. The goals of creating the CAS-PEAL face database include the following: 1) providing the worldwide researchers of face recognition with different sources of variations, particularly pose, expression, accessories, and lighting (PEAL), and exhaustive ground-truth information in one uniform database; 2) advancing the state-of-the-art face recognition technologies aiming at practical applications by using off-the-shelf imaging equipment and by designing normal face variations in the database; and 3) providing a large-scale face database of Mongolian. Currently, the CAS-PEAL face database contains 99 594 images of 1040 individuals (595 males and 445 females). A total of nine cameras are mounted horizontally on an arc arm to simultaneously capture images across different poses. Each subject is asked to look straight ahead, up, and down to obtain 27 images in three shots. Five facial expressions, six accessories, and 15 lighting changes are also included in the database. A selected subset of the database (CAS-PEAL-R1, containing 30 863 images of the 1040 subjects) is available to other researchers now. We discuss the evaluation protocol based on the CAS-PEAL-R1 database and present the performance of four algorithms as a baseline to do the following: 1) elementarily assess the difficulty of the database for face recognition algorithms; 2) preference evaluation results for researchers using the database; and 3) identify the strengths and weaknesses of the commonly used algorithms.
Ant stigmergy shop floor control architecture for intelligent product oriented manufacturing system

In this paper we consider an alternative approach to the problem of infusing local shop floor control decisions with more global performance information. The approach proposed is that of using artificial ant colonies to decentralize and autonomize the shop floor routing. The proposed ant stigmergy shop floor control (ASSFC) has two functional levels: a virtual shop floor level in which ant agents explore optimal processing route stochastically, and a physical shop floor in which intelligent work pieces (IWPs) deterministically exploit the best paths that have been detected by their corresponding ant agents on virtual shop floor. The simple indirect communication and the robust adaptability to disturbances make the ASSFC more suitable for intelligent product oriented manufacturing system than tradition shop floor control.
Multiple-threshold based scheduling algorithm for high performance input queued switches

Input queued switching architectures has become attractive for implementing high performance switches. Some maximal matching algorithms, e.g. PIM, iSLIP, are easily implemented in hardware, hence attract more attention in designing of the practical router. However, they usually need multiple iterations to achieve satisfying performance. In this paper, we propose a multiple-threshold based round robin matching algorithm (MTRRM) for high performance input queued crossbar switch scheduling. In MTRRM, when an input and an output are matched, the matching might be kept for more than one time slot. Simulation results show that our scheme can strongly reduce the average cell delay and increase the throughput under non-uniform traffic. MTRRM achieves quite good performance with single iteration and this promises us that it is more suitable for switch with high line rate or large number of ports.
Economic dispatch for microgrid containing electric vehicles via probabilistic modelling

In this paper, an economic dispatch model with probabilistic modeling is developed for microgrid. Electric power supply in microgrid consists of conventional power plants and renewable energy power plants, such as wind and solar power plants. Due to the fluctuation of solar and wind plants' output, an empirical probabilistic model is developed to predict their hourly output. According to different characteristics of wind and solar plants, the parameters for probabilistic distribution are further adjusted individually for both power plants. On the other hand, with the growing trend of Plug-in Electric Vehicle (PHEV), an integrated microgrid system must also consider the impact of PHEVs. Not only the charging loads from PHEVs, but also the discharging output via Vehicle to Grid (V2G) method can greatly affect the economic dispatch for all the micro energy sources in microgrid. This paper presents an optimization method for economic dispatch in microgrid considering conventional, renewable power plants, and PHEVs. The simulation results reveal that PHEVs with V2G capability can be an indispensable supplement in modern microgrid.
Efficient background video coding with static sprite generation and arbitrary-shape spatial prediction techniques

An efficient static sprite-generation and compression scheme is proposed for background video coding. First, considering both short- and long-term motion influences, a hybrid technique is proposed in the sprite generation to search the background motion relative to the generated sprite image. In order to prevent the sprite image from being blurred due to the inaccurate segmentation, a reliability-based blending technique is developed. Both the background video at every frame and the sprite image are categorized into reliable, unreliable, and undefined regions. Each region uses a unique strategy in blending. This makes the proposed technique work well even in the case of coarse segmentation. Second, an arbitrary-shape spatial prediction technique is proposed to efficiently compress the sprite image. Due to the property of the background video object, the sprite image often contains many flat and texture-correlation regions. Therefore, utilizing directional spatial prediction can significantly improve the efficiency of sprite coding. In general, the generated sprite is not a rectangular image. Since traditional spatial-prediction methods have difficulty in handling those contour blocks, a padding technique derived from rate-distortion optimization is proposed. Experimental results show that the proposed scheme can generate the background sprite image with better visual quality. Compared with the MPEG-4 sprite-coding scheme, it can also significantly improve the coding efficiency up to 3.0 dB at low bit rates.
3D Haar-Like Features for Pedestrian Detection

One basic observation for pedestrian detection in video sequences is that both appearance and motion information are important to model the moving people. Based on this observation, we propose a new kind of features, 3D Haar-like (3DHaar) features. Motivated by the success of Haar-like features in image based face detection and differential-frame based pedestrian detection, we naturally extend this feature by defining seven types of volume filters in 3D space, instead of using rectangle filter in 2D space. The advantage is that it can not only represent pedestrian's appearance, but also capture the motion information. To validate the effectiveness of the proposed method, we combine the 3DHaar with support vector machine (SVM) for pedestrian detection. Our experiments demonstrate the 3DHaar are more effective for video based pedestrian detection.
Sparse Structural Similarity for Objective Image Quality Assessment

In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized by a set of responses of neurons in visual cortex. In this paper, the sparse representation is applied in decomposing natural images into multiple layers depending on the visual importance. Inspired by these observations, a novel IQA metric called sparse structural similarity is proposed by measuring the fidelity of the stimulation of visual cortices. Experimental results on public databases indicate that the proposed method is effective in predicting subjective evaluation and as compared to state-of-the-art FR-IQA methods.
A Study of the Relationship between Global Warming and Hurricane Activity Based on Time Series

Hurricane is a tropical cyclone whose maximal wind speed exceeds 32.7 ms-1. There are two main parameters of hurricane intensity: intensity of wind speed, frequency of the wind speed. Based on the time series model, this paper takes the relationship into account between two parameters and the annual global average temperature. By using the global annual average temperature data collected from NOAA 2000 to 2016 inclusive, the change rule of temperature warming is found out by time series, and the function relationship between temperature and hurricane activity is obtained through regression analyses. The final result is close to the true value, which shows that the hurricane activity has something to do with the phenomenon of global warming.
Multicast scheduling in buffered crossbar switches with multiple input queues

We consider the problem of scheduling multicast traffic in a buffered crossbar switch with multiple input queues at each input port. In this paper, we design and investigate a series of combinations of queuing policies and scheduling algorithms and report the simulation result. It is shown that a small number of input queues at each input port can dramatically improve the performance under burst multicast traffic in buffered crossbar switches. Under this architecture, it is feasible to design simple queuing policies and scheduling algorithms for high speed switches while keeping high performance and small size of buffer within crossbar.