nils-herrmann/clean_scirepeval huggingface dataset:
Super-Resolution Without Dense Flow

Super-resolution is a widely applied technique that improves the resolution of input images by software methods. Most conventional reconstruction-based super-resolution algorithms assume accurate dense optical flow fields between the input frames, and their performance degrades rapidly when the motion estimation result is not accurate enough. However, optical flow estimation is usually difficult, particularly when complicated motion is presented in real-world videos. In this paper, we explore a new way to solve this problem by using sparse feature point correspondences between the input images. The feature point correspondences, which are obtained by matching a set of feature points, are usually precise and much more robust than dense optical flow fields. This is because the feature points represent well-selected significant locations in the image, and performing matching on the feature point set is usually very accurate. In order to utilize the sparse correspondences in conventional super-resolution, we extract an adaptive support region with a reliable local flow field from each corresponding feature point pair. The normalized prior is also proposed to increase the visual consistency of the reconstructed result. Extensive experiments on real data were carried out, and results show that the proposed algorithm produces high-resolution images with better quality, particularly in the presence of large-scale or complicated motion fields.
Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering

The clustering-based approach for detecting abnormalities in surveillance video requires the appropriate definition of similarity between events. The HMM-based similarity defined previously falls short in handling the overfitting problem. We propose in this paper a multi-sample-based similarity measure, where HMM training and distance measuring are based on multiple samples. These multiple training data are acquired by a novel dynamic hierarchical clustering (DHC) method. By iteratively reclassifying and retraining the data groups at different clustering levels, the initial training and clustering errors due to overfitting will be sequentially corrected in later steps. Experimental results on real surveillance video show an improvement of the proposed method over a baseline method that uses single-sample-based similarity measure and spectral clustering.
Tracking non-stationary appearances and dynamic feature selection

Since the appearance changes of the target jeopardize visual measurements and often lead to tracking failure in practice, trackers need to be adaptive to non-stationary appearances or to dynamically select features to track. However, this idea is threatened by the risk of adaptation drift that roots in its ill-posed nature, unless good constraints are imposed. Different from most existing adaptation schemes, we enforce three novel constraints for the optimal adaptation: (1) negative data, (2) bottom-up pair-wise data constraints, and (3) adaptation dynamics. Substantializing the general adaptation problem as a subspace adaptation problem, this paper gives a closed-form solution as well as a practical iterative algorithm. Extensive experiments have shown that the proposed approach can largely alleviate adaptation drift and achieve better tracking results.
Robust-inversion-based 2DOF-control design for output tracking: Piezoelectric actuator example

In this article, a novel robust-inversion-based two-degree-of-freedom (2DOF)-control approach for output tracking is proposed. The inversion-based feedforward control techniques have been successfully implemented in various applications. Usually the inverse feedforward input is applied by augmenting it with a feedback controller - to account for adverse effects such as dynamics variations and disturbances. However, currently such an integration, of the feedback control to the inversion-based feedforward control, is ad-hoc, and may not lead to an optimal complementation of the feedback control with the inversion-based feedforward control. The contribution of this article is a systematic development of designing the inversion-based feedforward-feedback 2DOF controller. First, a novel robust system-inversion method is proposed which achieves a guaranteed tracking performance of the feedforward control for bounded dynamics uncertainties. Secondly, the bound of the tracking error of the feedforward control is utilized in the Hinfin robust control framework to design the feedback controller which complements the feedforward control. Based on the concept of Bode's integral, it is shown that the feedback bandwidth can be improved from that by using feedback alone, with no loss of tracking precision and robustness stability. We illustrate the proposed approach through experiments on a piezotube actuator used for precision positioning on an atomic force microscope (AFM).
Design of a novel wide-area backup protection system

With the fast development of communication technology, some of the functions of power security defense system in China are promising to be enhanced. The proposal is to add an intelligent analyzing and controlling function in key process of protection functionality. Therefore, a novel wide-area backup protection system is reported in this paper. The proposed system comprises a master system and several local units. The system is arranged as three layers. Connected together via fiber-optic communication links, these devices can process intelligent algorithms based on data collected locally. The architecture and algorithm of the system are also introduced. The protection system is co-simulated using EMTP and NS2. The data generated by EMTP are then used in a program to validate the performance of the proposed technique regarding the speed of response, computational burden and reliability. Numerical simulation results corroborate the availability and superior performance of the proposed scheme
Multiple Collaborative Kernel Tracking

Those motion parameters that cannot be recovered from image measurements are unobservable in the visual dynamic system. This paper studies this important issue of singularity in the context of kernel-based tracking and presents a novel approach that is based on a motion field representation which employs redundant but sparsely correlated local motion parameters instead of compact but uncorrelated global ones. This approach makes it easy to design fully observable kernel-based motion estimators. This paper shows that these high-dimensional motion fields can be estimated efficiently by the collaboration among a set of simpler local kernel-based motion estimators, which makes the new approach very practical.
Study of Multi-target Tracking and Data Association Based on Sequential Monte Carlo Algorithm

A new method based on sequential Monte Carlo algorithm is proposed for tracking multi-target and data association in non-linear system. The algorithm partitions the problem of multi-target tracking into two problems: single target tracking and data association. Single target tracking is implemented by using UKF and data association by using sequential Monte Carlo algorithm. Since Particle Filter has advantages in non-linear non-Gauss system, the proposed method performs well in the experiment.
Research on the Overall Framework of Knowledge Management

Dispute about conception and research area of knowledge management exists always. In order to distinguish and evaluate about the influence factor of the success or failure of knowledge management project, it is necessary to understand it in the point of system thought and to design an integrated entirely knowledge management framework model that is accepted at large. Firstly, this article starts with the research on the present knowledge management and the analysis of different views, discussing the system framework, connotation and extension of knowledge management with systemic viewpoint. Then the paper will develop a knowledge management framework model and propose a system framework of knowledge management system and its practical application.
Focused ultrasound-facilitated brain drug delivery using optimized nanodroplets

Focused ultrasound with nanodroplets could facilitate localized drug delivery after vaporization with potentially improved in vivo stability, drug payload, and minimal interference outside of the focal zone compared with microbubbles. While the feasibility of blood-brain barrier (BBB) opening using nanodroplets has been previously reported, characterization of the associated delivery has not been achieved. It was hypothesized that the outcome of drug delivery was associated with the droplet sensitivity to acoustic energy, and can be modulated with the boiling point of the liquid core. Therefore, in this study, highly-efficient octafluoropropane (OFP) and less-efficient decafluorobutane (DFB) nanodroplets were used in vivo for delivering molecules with a size relevant to proteins (40-kDa dextran) to the murine brain. It was found that successful delivery was achieved with OFP droplets at 300 kPa or higher safely using 1/4 dosage compared to DFB droplets at 900 kPa where inertial cavitation caused damage. As a result, the OFP droplets due to the higher vaporization efficiency served as better acoustic agents to deliver large molecules safely and efficiently to the brain compared with the DFB droplets.
Unsupervised Hierarchical Dynamic Parsing and Encoding for Action Recognition.

Generally, the evolution of an action is not uniform across the video, but exhibits quite complex rhythms and non-stationary dynamics. To model such non-uniform temporal dynamics, in this paper, we describe a novel hierarchical dynamic parsing and encoding method to capture both the locally smooth dynamics and globally drastic dynamic changes. It parses the dynamics of an action into different layers and encodes such multi-layer temporal information into a joint representation for action recognition. At the first layer, the action sequence is parsed in an unsupervised manner into several smooth-changing stages corresponding to different key poses or temporal structures by temporal clustering. The dynamics within each stage are encoded by mean-pooling or rank-pooling. At the second layer, the temporal information of the ordered dynamics extracted from the previous layer is encoded again by rank-pooling to form the overall representation. Extensive experiments on a gesture action data set (Chalearn Gesture) and three generic action data sets (Olympic Sports, Hollywood2, and UCF101) have demonstrated the effectiveness of the proposed method.
Application of Blind Sources Separation in plant leaves classification

This paper discussed the application of Blind Sources Separation (BSS) in plant leaves classification. Firstly, collection of two different types of plant leaves was performed using the Nexus-870 Fourier transform infrared spectroscopy, and wavelet analysis was adopted to compress the immense sample data, thus accelerating the data processing speed. Then the BSS algorithm FastICA algorithm was used on the compressed spectral data to increase the difference between the different signals. Finally, BP neural network algorithm was used to achieve the classification of plant species. Experiments showed that processing data in near-infrared spectroscopy through BSS can not only improve the speed and accuracy of BP neural network, but also enhance its classification correctness, and the classification results with the proposed method was satisfactory.
Improving Security and Privacy of Images on Cloud Storage by Histogram Shifting and Secret Sharing

Nowadays people can easily use any smart device at hand to capture scenery and then upload it to the cloud storage. Cloud storages are widely used for storing the generated multimedia content. However, the risk of potential private data leakage may exist since cloud storages are normally in a public domain. To enhance the security and privacy of images on the cloud storage, we proposed an integrated scheme evolving invisible digital watermarking and masking which are based on the histogram shifting method. The histogram modification based scheme can achieve reversible data hiding, to ensure the integrity and the confidentiality of the image data. Additionally, we use the secret sharing scheme to keep the secret keys to further improve the security of data access. The evaluation results show that the proposed system can effectively prevent the malicious user from accessing the private images.
Independent Gabor analysis of illumination insensitive isophote-angle for face recognition

A new independent Gabor analysis of the isophote angle is proposed for face recognition. The isophote is the curve of constant intensity in image, which represents the geometric object contour. Isophote angles are proved to be insensitive to the illumination; therefore, they are adopted in our method for recognition under low-level lighting conditions. Then, a bank of Gabor filters is built to exact Gabor representations of the isophote angles. Last, the higher-order statistical relationships among variables are extracted for classifier, where the cosine distance function computed in gradient domain achieves the best performance. The experimental results demonstrate that our method outperform the conventional methods under varying illumination and other outliers (pose, expression, distance, etc.), especially in the large scale face databases.
Sparse Unmixing of Hyperspectral Data Using Spectral A Priori Information

Given a spectral library, sparse unmixing aims at finding the optimal subset of endmembers from it to model each pixel in the hyperspectral scene. However, sparse unmixing still remains a challenging task due to the usually high mutual coherence of the spectral library. In this paper, we exploit the spectral a priori information in the hyperspectral image to alleviate this difficulty. It assumes that some materials in the spectral library are known to exist in the scene. Such information can be obtained via field investigation or hyperspectral data analysis. Then, we propose a novel model to incorporate the spectral a priori information into sparse unmixing. Based on the alternating direction method of multipliers, we present a new algorithm, which is termed sparse unmixing using spectral a priori information (SUnSPI), to solve the model. Experimental results on both synthetic and real data demonstrate that the spectral a priori information is beneficial to sparse unmixing and that SUnSPI can exploit this information effectively to improve the abundance estimation.
A Novel Two-Cap Filter for Routing Noise Suppression Using Extended EBG Analysis

This paper proposes a novel two-cap wideband filter that suppresses the high-frequency noise in a power trace routing. By combining electromagnetic bandgap (EBG) theory and decoupling capacitors, the EBG propagation characteristics are studied and a systematic design procedure is proposed. Advanced Design System (ADS) and gigahertz transverse electromagnetic (GTEM) cell measurement are used to validate the accuracy of the proposed analysis and the validity of the design. It is found that the filter gives a 67% stopband with 20-dB suppression, using only two capacitors that are spaced one-twentieth of a wavelength apart.
Finding the right exemplars for reconstructing single image super-resolution

Exemplar-based methods have shown their potential in synthesizing novel but visually plausible contents for image super-resolution (SR), by using the implicit knowledge conveyed by the exemplar database. In practice, however, it is common that unwanted artifacts and low quality results are produced due to the using of inappropriate exemplars. How are the “right” exemplars defined and identified? This fundamental issue has not be well addressed in these methods. This paper proposes a novel solution to this issue by learning a new distance metric in the LR space, such that affinity structure of the LR space under the new metric is as close to that of the HR space. Based on this learned best metric, appropriate exemplars can be identified. In addition, the proposed method is able to automatically determine the appropriate number of exemplars to use. Extensive experiments have shown that our method is able to handle regions with different properties and to obtain visually appealing super-resolution results with sharp details and smooth edges.
Design of a fast global orbit feedback system for the Advanced Light Source

The fast stability of the closed orbit of the electron beam at the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory fulfills user requirements so far without any fast active correction system. In the range between 0.1 and 500 Hz the integrated rms closed orbit motion is significantly below one tenth of one sigma beamsizes. For the future there is some user demand to improve this stability further. Moreover, the expansion of the capabilities of the ALS creates new sources of closed orbit noise. Therefore the design of a fast global orbit feedback system has been started in conjunction with a general upgrade of the ALS control system. It will initially operate with an update rate of 500 Hz - 1 kHz, will include 24 beam position monitors and corrector magnets in each plane and will use standard computer and networking architecture. The system design, measurements of transfer functions and tests with small prototype systems will be presented.
An Evolutionary Approach to the Multidepot Capacitated Arc Routing Problem

The capacitated arc routing problem (CARP) is a challenging vehicle routing problem with numerous real world applications. In this paper, an extended version of CARP, the multidepot capacitated arc routing problem (MCARP), is presented to tackle practical requirements. Existing CARP heuristics are extended to cope with MCARP and are integrated into a novel evolutionary framework: the initial population is constructed either by random generation, the extended random path-scanning heuristic, or the extended random Ulusoy's heuristic. Subsequently, multiple distinct operators are employed to perform selection, crossover, and mutation. Finally, the partial replacement procedure is implemented to maintain population diversity. The proposed evolutionary approach (EA) is primarily characterized by the exploitation of attributes found in near-optimal MCARP solutions that are obtained throughout the execution of the algorithm. Two techniques are employed toward this end: the performance information of an operator is applied to select from a range of operators for selection, crossover, and mutation. Furthermore, the arc assignment priority information is employed to determine promising positions along the genome for operations of crossover and mutation. The EA is evaluated on 107 instances with up to 140 nodes and 380 arcs. The experimental results suggest that the integrated evolutionary framework significantly outperforms these individual extended heuristics.
A new MCTS-based algorithm for multi-objective flexible job shop scheduling problem

Multi-objective flexible job-shop scheduling problem (MO-FJSP) is very important in both fields of production management and combinatorial optimization. Wu et al. proposed a Monte-Carlo Tree Search (MCTS) to solve MO-FJSP and successfully improved the performance of MCTS to find 17 Pareto solutions: 4 of Kacem 4×5, 3 of 10×7, 4 of 8×8, 4 of 10×10, and 2 of 15×10. This paper proposes a new MCTS-based algorithm for MO-FJSP problem by modifying their algorithm. Our experimental results show that our new algorithm significantly outperforms their algorithm for large problems, especially for Kacem 15×10. This shows that the new algorithm tends to have better potential of solving harder MO-FJSP problems.
Control system design and upgrade considerations for the Duke Free Electron Laser Laboratory

The Duke Free Electron Laser Laboratory currently operates two FELs: the OK-4/Duke Storage Ring FEL, and the Mark III IR FEL. Both of these systems were among the early adopters of the Experimental Physics and Industrial Control System (EPICS). The Mark III EPICS control system was completed and operational in September, 1993. The Duke Storage Ring was commissioned in November of 1994 using EPICS. These control systems have proven to be very robust with high reliability over the intervening years. An overview of these current control systems and an upgrade strategy are presented.
A Planar Integrated Folded Reflectarray Antenna With Circular Polarization

This communication presents the complete design of a circularly polarized (CP) folded reflectarray (FRA) antenna with an integrated planar structure for the first time in the open literature. To achieve circularly polarized, a printed meander-line polarizer is designed and integrated with the linearly polarized (LP) FRA. To achieve a low-profile planar structure, an integrated 2×2 planar array is designed as the feed source instead of a horn. Thus, the whole antenna, including the feed source, LPFRA, and meander-line polarizer, can be fully integrated and fabricated using low-cost printed circuit board technology. To validate the concept, a right-handed CPFRA operating in C-band is designed, fabricated, and measured. The broadside axial ratio (AR) of the proposed CPFRA is lower than 1 dB over a bandwidth from 5.22 to 5.46 GHz. In addition, the maximum gain of 22.8 dBic is obtained at 5.38 GHz with the antenna efficiency of 27%. The antenna is promising for applications in satellite communication due to advantages of low profile, easy fabrication, low cost, and high gain.
An Embedded Cloud scheme for nonhomologous applications in Internet of Things

The rapid development and popularization of the Internet of Things (IoT) have brought great data exchanging and storage needs. Conventional cloud platforms can hardly satisfy with nonhomologous application scenarios in the IoT. In order to deal with the information isolation problems between nonhomologous devices, this paper proposes a Distributed and Embedded Cloud Storage (DECS) scheme. It defines a new architecture of cloud system based on System-on-Chip (SoC) integration and provides an advanced information distribution strategy for the system to realize the interoperability between various IoT equipment. Experiments have been carried out in Network Simulator 2 (NS2) simulation environment, and the results suggest that the system can achieve higher transmission efficiency and validity.
One Formalized Method of Research on the Transformation Rules between Software Reliability and Safety

In one software system, the software’s reliability and safety are ultimately determined by the program operating states, which will be changed as the exterior and interior environment changing. in this paper, according with the environmental factor which lead to the changing of program states, the definition of the deviation loss caused by the changing of program states is presented, and in some conditions is proposed ¿ the formal method is used to describe the transition rules between the software reliability and safety. Finally, the formal proofs of these rules are given.
Novel batch algorithm of joint blind equalization, carrier phase recovery and timing recovery for short bursts

In this paper, a novel batch scheme of jointly performing blind equalization, carrier recovery and timing recovery for short bursts is presented. The algorithm embeds the timing recovery process inside the blind equalization process. It extends the cost function of modified constant modulus algorithm which accomplishes carrier phase recovery, to estimate and handle timing offset. The equalizer coefficients and estimated timing offset are iteratively updated in open-loop batch approach, because of that, the algorithm is suited for short bursts and can achieve fast and smooth convergence performance. Simulation results show the performance of the batch algorithm for joint blind equalization, carrier phase recovery and timing recovery.