nils-herrmann/clean_scirepeval huggingface dataset:
AVC testing platform design and implementation for the regional power grid based on advanced digital power system simulator

The automatic voltage control (AVC) system provides real-time automatic control for the on-load transformer tap changer (OLTC) and capacitor/inductor banks, which is widely used for power system voltage monitoring and control in the regional grid. An advanced digital power system simulator (ADPSS)-based testing platform of the AVC controller is designed in this paper. The circuit model of the regional power grid is established, as well as the OLTC and the reactive power compensation devices. The data conversion interface is utilized for bidirectional data exchange from the remote meter and control signals between the ADPSS and the AVC system. The principle of the AVC voltage regulation and the ADPSS-based AVC testing platform are also introduced, followed by the data flow process of the OLTC and capacitor/inductor banks, which formulates the foundation for closed-loop testing of the AVC control system. To evaluate its rationality and availability, especially the effect of voltage regulation and control, an index system is established. Furthermore, functional modules of the assessment system and their implementation are presented. Test results show that all the modules in the assessment system possess good operational robustness and can perform automatic voltage control well on continually varying real-time simulated data of regional grid.
Enhancing nighttime surveillance video via gradient fusion

This paper presents an effective method to enhance the quality of dim light surveillance via gradient fusion. We simply take the advantage that surveillance cameras capture a large quantity of valuable information at the same viewpoint during the day. And it can be used to make the video at night easier to perceive. Based on a gradient domain technique, all the important local perceptual cues from the original video are automatically combined with the supporting daytime context, while avoiding traditional problems such as aliasing, ghosting and haloing. Experimental results show that our method outperforms the state-of-the-art ones, and can even handle some challenging conditions without altering the parameters.
Mining distinguishing patterns based on malware traces

The automatic generation of malicious behavior pattern based on system call trace is important to malware detection. This paper studied the existing generation method of malicious behavior specification. In order to reduce the complexity of pattern generation, it constructs graph which vertex label is unique, and uses these graphs to mine the pattern. To address the issue of limitation of the minimal contrast subgraph mining method, it uses multiple positive and negative samples, and proposes a mining method to mine distinguishing patterns based on mutual information. It designs the overall framework of mining process, and gives the mining algorithm. Finally, validation results demonstrate the effectiveness.
Two dimensional synthesis sparse model

Sparse representation has been proved to be very efficient in machine learning and image processing. Traditional image sparse representation formulates an image into a one dimensional (1D) vector which is then represented by a sparse linear combination of the basis atoms from a dictionary. This 1D representation ignores the local spatial correlation inside one image. In this paper, we propose a two dimensional (2D) sparse model to much efficiently exploit the horizontal and vertical features which are represented by two dictionaries simultaneously. The corresponding sparse coding and dictionary learning algorithm are also presented in this paper. The 2D synthesis model is further evaluated in image denoising. Experimental results demonstrate our 2D synthesis sparse model outperforms the state-of-the-art 1D model in terms of both objective and subjective qualities.
Relationship between shunt capacitor and small signal stability of power systems

In this paper, we studied the impact of shunt capacitors on power system small signal stability. Heffron-Phillips generator model was first applied to theoretical analysis of this problem. Explanation and some discussions to negative damping phenomena captured in past simulation studies were also presented. Then, simulations on a 10-bus system and New England 10-generator-39-bus system were used to demonstrated the analytical results. Some useful suggestions on the improvement of power small signal stability were given in the last based on the studies of this paper
Semi-supervised ranking SVM-assisted IGA with hierarchical evaluations

Interactive genetic algorithms (IGAs), combining the human's intelligent evaluations with the evolutionary optimization mechanisms, have been sufficiently investigated due to their powerful performance in solving problems with aesthetic criteria. User fatigue arising from intelligent evaluations, however, greatly restricts IGAs to successfully optimize complicated problems. Lot of efforts have been devoted to effectively alleviating user fatigue and improving the exploration performance by designing friendly evaluation modes or surrogate models. A large population IGA with a ranking learning based surrogate model is presented here. A hierarchical evaluation mode reflecting the preference orderings of the user is first presented. From the perspective of directly maintaining the correct preference ordering, a ranking function is constructed to estimate the fitness of all individuals. An enhanced semi-supervised ranking SVM is developed to obtain the ranking function with high prediction accuracy, in which the method of selecting most informative unlabeled samples is highlighted by naturally combining the merits of IGAs. The ranking function is managed based on a defined metric of prediction accuracy. The proposed algorithm is applied to a fashion design system to empirically demonstrate its strength in alleviating user fatigue and improving exploration.
Image hallucination with feature enhancement

Example-based super-resolution recovers missing high frequencies in a magnified image by learning the correspondence between co-occurrence examples at two different resolution levels. As high-resolution examples usually contain more details and are of higher dimensionality in comparison with low-resolution ones, the mapping from low-resolution to high-resolution is an ill-posed problem. Rather than imposing more complicated mapping constraints, we propose to improve the mapping accuracy by enhancing low-resolution examples in terms of mapped features, e.g., derivatives and primitives. A feature enhancement method is presented through a combination of interpolation with prefiltering and non-blind sparse prior deblurring. By enhancing low-resolution examples, unique feature information carried by high-resolution examples is decreased. This regularization reduces the intrinsic dimensionality disparity between two different resolution examples and thus improves the feature mapping accuracy. Experiments demonstrate our super-resolution scheme with feature enhancement produces high quality results both perceptually and quantitatively.
CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images

In this paper, we propose a novel two-step scheme to filter heavy noise from images with the assistance of retrieved Web images. There are two key technical contributions in our scheme. First, for every noisy image block, we build two three dimensional (3D) data cubes by using similar blocks in retrieved Web images and similar nonlocal blocks within the noisy image, respectively. To better use their correlations, we propose different denoising strategies. The denoising in the 3D cube built upon the retrieved images is performed as median filtering in the spatial domain, whereas the denoising in the other 3D cube is performed in the frequency domain. These two denoising results are then combined in the frequency domain to produce a denoising image. Second, to handle heavy noise, we further propose using the denoising image to improve image registration of the retrieved Web images, 3D cube building, and the estimation of filtering parameters in the frequency domain. Afterwards, the proposed denoising is performed on the noisy image again to generate the final denoising result. Our experimental results show that when the noise is high, the proposed scheme is better than BM3D by more than 2 dB in PSNR and the visual quality improvement is clear to see.
Towards probabilistic identification of zero-day attack paths

Zero-day attacks continue to challenge the enterprise network security defense. A zero-day attack path is formed when a multi-step attack contains one or more zero-day exploits. Detecting zero-day attack paths in time could enable early disclosure of zero-day threats. In this paper, we propose a probabilistic approach to identify zero-day attack paths and implement a prototype system named ZePro. An object instance graph is first built from system calls to capture the intrusion propagation. To further reveal the zero-day attack paths hiding in the instance graph, our system constructs an instance-graph-based Bayesian network. By leveraging intrusion evidence, the Bayesian network can quantitatively compute the probabilities of object instances being infected. The object instances with high infection probabilities reveal themselves and form the zero-day attack paths. The experiment results show that our system can effectively identify zero-day attack paths.
Short-term photovoltaic power forecasting with weighted support vector machine

The output power of the solar photovoltaic (PV) arrays has the property of uncertainty, and usually fluctuates with the changes of solar radiation and the ambient temperature. It is important to forecast the output power of the PV power station so as to coordinate the relationship between the conventionality power supply and the grid-connected PV power station. In this paper, a weighted Supported Vector Machine (WSVM) is adopted to forecast the short-term PV power, in which the 5 days with the most similarity to the day to be forecasted were selected as the training samples, and the weights of the samples for the WSVM are designed based on the similarities together with the time interval. The proposed algorithm is experimentally validated and the results empirically show that the output power forecasted by use of the WSVM is more efficient than that of the artificial neutral network (ANN) and more practicable.
Combining Prostate Cancer Region Predictions from MALDI Spectra Processing and Texture Analysis

We present a three-step method to predict Prostate cancer (PCa) regions on biopsy tissue samples based on high confidence, low resolution PCa regions marked by a pathologist. First, we apply a texture analysis technique on a high magnification optical image to predict PCa regions on an adjacent tissue slice. Second, we design a prediction model for the same purpose using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue imaging data from the adjacent slice. Finally, we fuse those two results to obtain the PCa regions that will assist MALDI imaging biomarker identification. Experiment results show that the texture analysis based prediction is sensitive (sen. 87.45%) but not specific (spe. 75%), and the prediction based on the MALDI spectra data is specific (spe. 100%) but less sensitive (sen. 50.98%). By combining those two results, a much better prediction for PCa regions on the adjacent slice can be achieved (sen. 80.39%, spe. 93.09%).
Quantum particle swarm algorithm for Many-objective optimization problem

Many-objective optimization problems widely exist in real world. However, there is lack of effective solutions to solve this problem because they contain more than three conflicting objectives. Based on quantum particle swarm optimization algorithm, this paper presents an efficient many-objective particle swarm optimization algorithm. In this algorithm, an improved quantum update method is introduced to update the particles' positions, a selection strategy based on the global difference order is proposed to update the global best position of particle, and a congestion sorting strategies is applied to update the external repository. By optimizing ZDT and DTLZ series functions, and comparing with representative algorithms such as TV-MOPSO, results indicate that the proposed algorithm is effective for solving many-objective optimization problems.
Flexible P-picture (FlexP) coding for the efficient fine-granular scalability (FGS)

This paper proposes a flexible P-picture (FlexP) coding technique, where P-pictures are selectively used as references for other pictures. For a given reference bit-rate, the proposed FlexP technique can improve the quality of references by both allocating more bits to the selected P-pictures and reducing the number of selected P-pictures. In case that the reference bits for each selected P-picture are fixed, the proposed technique is able to reduce the reference bit-rate by selecting fewer P-pictures as references, meanwhile limit drifting error at low bit-rates. Therefore, the proposed FlexP technique provides more flexibility on bandwidth adaptation, as well as achieves a good trade-off between high coding efficiency and low drifting error at the enhancement layer especially when the R-D rate allocation is enabled. The experimental results show that the fine-granular scalable video coding with the proposed technique can gain more than 1 .0 dB.
Transcoding to FGS Streams from H.264/AVC Hierarchical B-Pictures

This paper presents a transcoder which transcodes to FGS streams from H.264/AVC hierarchical B-pictures. First, the DCT-domain architecture is designed for fast FGS transcoding. Then, we propose a mode decision method in DCT domain to achieve a trade-off between the performances at low bit-rate and high bit-rate. Experimental results demonstrated that our method can improve the coding performance up to 1 dB at low rate and only lose at worst 0.5 dB at high rate.
Image Coding with Parameter-Assistant Inpainting

This paper carves out an image compression approach that integrates our parameter-assistant inpainting (PAI) technique to exploit the visual redundancy inherent in color-gradation image regions. In our scheme, an input image is first classified at block level according to the degree of edge content as well as chromatic variation in each block. An exemplar selection approach is then adopted to skip a majority of the gradation blocks during encoding. Only their positions and certain parameters extracted for condensed description are encoded along with the reserved blocks. At the decoder side, the skipped regions are recovered through image inpainting, relying on both the delivered parameters and reserved regions. Experimental results show that our proposed method outperforms baseline JPEG at color-gradation regions by nearly 80% bits-saving, at similar visual quality levels.
Multivariate symmetric cryptography with 2-dimesion chaotic disturbation

Multivariate public key cryptosystem acts as a signature system rather than encryption system due to the minus mode used in system. A multivariate encryption system with determinate equations in central map and chaotic shell protection for central map and affine map is proposed in this paper. The outputs of two-dimension chaotic system are discretized on a finite field to disturb the central map and affine map in multivariate cryptosystem. The determined equations meet the shortage of indeterminate equations in minus mode and make the general attack methods are out of tenable condition. The analysis shows the proposed multivariate symmetric encryption system based on chaotic shell is able to resist general attacks.
Weighted motion estimation for efficiently coding scene transition video

Scene transition video has brought great challenges to current video coding methods because the traditional motion model of block displacement cannot efficiently represent transition motion. The paper first analyzes the features of static transitions. By incorporating the transition filter information into the coding scheme, the weighted motion estimation (WTME) technique is proposed to get accurate motion parameters for the transition video, thereby efficiently compensating both normal and transition motions among frames. Experimental results show that the proposed technique can significantly improve the coding performance of H.264 by up to 2.0 dB while coding scene transition video.
CGS quality scalability for HEVC

Scalable video coding provides an efficient way to serve video contents at different quality levels. Based on the development of emerging High Efficiency Video Coding (HEVC), we propose two coarse granular scalable (CGS) video coding schemes here. In scheme A, we present a multi-loop solution in which the fully reconstructed base pictures are utilized in the enhancement layer prediction. By inserting the reconstructed base picture (BP) into the list of reference pictures of the collocated enhancement layer frame, we enable the coarse granular quality scalability of HEVC with very limited changes. On the other hand, scheme B supports single loop decoding. It contains three inter-layer predictions similar to the scalable extension of H.264/AVC. Compared to scheme A, it decreases the decoding complexity by avoiding the motion compensation, deblocking filtering (DF) and adaptive loop filtering (ALF) in the base layer. The effectiveness of our proposed two coding schemes is evaluated by comparing with single-layer coding and simulcast.
Performance analysis of inter-vehicle communications in multilane traffic streams

Inter-vehicle communications (IVC) based on mobile ad hoc networks have attracted increasing attention of researchers recently. In this paper, we study the communication performance of IVC networks in multilane dynamic traffic streams. We are concerned with the influence of several traffic factors on multilane communications, such as vehicles' high mobility, traffic directions or relative speed, and traffic density. So several simulations were conducted in network simulator 2 with Monte Carlo method for a penetration rate of 10%. The simulation results show that vehicles' high mobility does not greatly affect the communication of vehicles on different lanes; traffic streams running to opposite directions will distinctly affect each other's communication performance, but the unidirectional traffic streams with a relative speed of 30km/h has no obvious impact; under the bidirectional condition, the increasing of traffic density on one lane cannot markedly improve the communication performance of the IVC networks. These results help to better understand the communication of multilane traffic streams under high mobility scenarios.
Fractional compensation for spatial scalable video coding

This paper proposes a novel fractional compensation approach for spatial scalable video coding. It simultaneously exploits inter layer correlation and intra layer correlation by learning-based mapping. Instead of using an enhancement layer reconstruction as an entire reference, a set of reference pairs are generated from high-frequency components of both base layer and enhancement layer reconstructions at previous frame. The reference set, which consists of lowresolution and high-resolution patches, can be generated in both encoder and decoder by on-line learning. During the encoding of enhancement layer, a prediction is first gotten from base layer, from which low-resolution patches are extracted. These patches are then used as indices to find the matched high-resolution patches from the reference set. Finally, the prediction enhanced by the high-resolution patches is used for coding. The proposed approach does not need any motion bits. With our proposed FC approach, the performance of H.264 SVC can be improved up to 2.4dB in spatial scalable coding.
Multi-Dimensional Sparse Models

Traditional synthesis/analysis sparse representation models signals in a one dimensional (1D) way, in which a multidimensional (MD) signal is converted into a 1D vector. 1D modeling cannot sufficiently handle MD signals of high dimensionality in limited computational resources and memory usage, as breaking the data structure and inherently ignores the diversity of MD signals (tensors). We utilize the multilinearity of tensors to establish the redundant basis of the space of multi linear maps with the sparsity constraint, and further propose MD synthesis/analysis sparse models to effectively and efficiently represent MD signals in their original form. The dimensional features of MD signals are captured by a series of dictionaries simultaneously and collaboratively. The corresponding dictionary learning algorithms and unified MD signal restoration formulations are proposed. The effectiveness of the proposed models and dictionary learning algorithms is demonstrated through experiments on MD signals denoising, image super-resolution and texture classification. Experiments show that the proposed MD models outperform state-of-the-art 1D models in terms of signal representation quality, computational overhead, and memory storage. Moreover, our proposed MD sparse models generalize the 1D sparse models and are flexible and adaptive to both homogeneous and inhomogeneous properties of MD signals.
Single image super-resolution via 2D nonlocal sparse representation

Image super-resolution based on sparse model with patch clustering and nonlocal similarity provides promising performance. However, the traditional one dimensional (1D) sparse model enforces a 1D dictionary for every cluster of patches to capture complex structures and different features in images. The total dictionary will take expensive memory, which can be alleviated at cost of representation power. Recently, two dimensional (2D) sparse model has been proved to efficiently represent images and save memory usage. In this paper, we propose to integrate 2D sparse model with patch clustering and nonlocal similarity into a variational framework as 2D nonlocal sparse representation (2DNSR) for image SR to save memory cost and ensure SR performances. We also present a 2DNSR algorithm for image SR where each group of similar patches decompose on the respective 2D dictionaries. Experimental results on image SR demonstrate our proposed 2D nonlocal representation outperforms 2D sparse model and achieves competitive performance to state-of-the-art 1D nonlocal sparse models whereas with much less memory costs.
Spatially Scalable Video Coding For HEVC

Spatially scalable video coding (SSVC) provides an efficient way to transmit one video at different resolutions. Based on the emerging High Efficiency Video Coding (HEVC), we propose an SSVC scheme to support both single-loop (SL) and multiloop (ML) solutions by enabling different interlayer prediction mechanisms. Specifically, we employ two interlayer prediction modes: quadtree-based prediction mode (Q-mode) and learning-based prediction mode (L-mode). The Q-mode is investigated to exploit the interlayer redundancy based on the quadtree coding structure of HEVC. Due to the high correlation between layers, Q-mode utilizes the coded information from the base layer quadtree, including coding unit split, prediction unit partition, motion information, and partial texture information of transform unit, to predict the enhancement layer quadtree. By enabling Q-mode, we provide a basic SL solution for low complexity applications. Besides the correlation explored in Q-mode, we employ an extra L-mode to further improve the coding performance. In L-mode, the temporal-spatial correlation is exploited simultaneously by visual patch-based learning and mapping at pixel level. This helps us achieve more accurate prediction signals based on the coarse base layer reconstruction within an ML structure. Experimental results show the effectiveness of our SSVC scheme compared with the simulcast case and other HEVC-based SSVC schemes.