nils-herrmann/clean_scirepeval huggingface dataset:
CESMTuner: An Auto-tuning Framework for the Community Earth System Model

The growing scientific demands of climate predication and climate projection have promoted to manage the computational resources of climate model rationally. The Community Earth System Model (CESM) is one of the state-of-the-art and the most widely-used coupled models for simulating the earth system. Although considerable effort has been put to improve the scalability of single component, CESM is still struggling with the poor performance due to load balance across components. To solve this problem, an easy-used and easy-ported auto-tuning framework named CESM Tuner is proposed in this paper. It targets to reduce the time consumed of CESM as much as possible by looking for the optimal process configuration. In which, a novel process layout searching algorithm is presented that can look for the optimal process count of each component as well as the best process layout across components simultaneously. Moreover, a lightweight and accurate performance model is built to reduce searching overhead effectively. With the evaluation over Tian He-1A, CESM Tuner can achieve 58.49% performance improvement compared to the widely-used sequential process layout and achieve 38.23% performance improvement compared to the heuristic branch and bound algorithm based on the performance model of simply fitting each component's runtime.
An automatic tongue detection and segmentation framework for computer-aided tongue image analysis

Traditional Chinese Medicine (TCM) has a long history and has been recognized as a popular alternative medicine in western countries. Tongue diagnosis is a significant procedure in computer-aided TCM, where tongue image analysis plays a dominant role. In this paper, we proposed a fully automatic tongue detection and tongue segmentation framework, which is an essential step in computer-aided tongue image analysis. Comparing with other existing methods, our method is fully automatic without any need of adjusting parameters for different images and do not need any initialization.
Collaborative Representation Cascade for Single-Image Super-Resolution

Most recent learning-based single-image super-resolution methods first interpolate the low-resolution (LR) input, from which overlapped LR features are then extracted to reconstruct their high-resolution (HR) counterparts and the final HR image. However, most of them neglect to take advantage of the intermediate recovered HR image to enhance image quality further. We conduct principal component analysis (PCA) to reduce LR feature dimension. Then we find that the number of principal components after conducting PCA in the LR feature space from the reconstructed images is larger than that from the interpolated images by using bicubic interpolation. Based on this observation, we present an unsophisticated yet effective framework named collaborative representation cascade (CRC) that learns multilayer mapping models between LR and HR feature pairs. In particular, we extract the features from the intermediate recovered image to upscale and enhance LR input progressively. In the learning phase, for each cascade layer, we use the intermediate recovered results and their original HR counterparts to learn single-layer mapping model. Then, we use this single-layer mapping model to super-resolve the original LR inputs. And the intermediate HR outputs are regarded as training inputs for the next cascade layer, until we obtain multilayer mapping models. In the reconstruction phase, we extract multiple sets of LR features from the LR image and intermediate recovered. Then, in each cascade layer, mapping model is utilized to pursue HR image. Our experiments on several commonly used image SR testing datasets show that our proposed CRC method achieves state-of-the-art image SR results, and CRC can also be served as a general image enhancement framework.
A self-synchronization digital audio watermarking based on support vector regression

Synchronization attack is one of the key issues of digital audio watermarking. It is a challenging work to design a robust audio watermarking scheme against de-synchronization attacks. In this paper, a robust digital audio watermarking algorithm is presented, which can resist synchronization attack effectively. The features of the algorithm are as follows: (1) More steady features extraction and new embedded strategy are adopted to resist the synchronization attack effectively. (2) The candidate audio segments for embedding watermark are defined by using the feature points. (3) The digital watermark is embedded into a host audio by modulating the statistics average value of audio samples. (4) The algorithm can extract the watermark without the help of the original audio signal. (5) In digital watermark detection, the feature points are selected by the same technique as the embedding, and the digital watermark is extracted from the watermarked audio after the feature Points. Experimental results show that our synchronization audio watermarking scheme is not only inaudible and robust against various common signal processing (such as noise adding, resampling, re-quantization and MP3 compression and so on), but also robust against de-synchronization attacks such as random cropping, amplitude variation, time-scale modification, and jittering etc.
A multimodal deep architecture for large-scale protein ubiquitylation site prediction

In eukaryotes, protein ubiquitylation is an important type of post-translation modification, in which the ubiquitin conjugates to a substrate protein. To have a better insight of the mechanisms underlying ubiquitylation, a key step is to identify protein ubiquitylation sites. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning provides multiple-layer networks and non-linear mapping operations to detect potential complex patterns in a data-driven way, especially for large-scale data. It provides a promising new method to predict ubiquitylation sites. In this paper, we proposed a multimodal deep architecture for protein ubiquitylation sites prediction. First, we designed different multiple layers to extract hidden informative patterns from three modalities, namely protein fragments, physico-chemical properties, and sequence profiles. Then, the deep representations corresponding to three modalities were merged to implement the classification. On the available largest scale protein ubiquitylation site database PLMD, the performance of our proposed method was measured with 66.7% sensitivity, 66.4% specificity, 66.43% accuracy, and 0.221 MCC value. A range of comparative experiments also showed that our proposed architecture outperformed several popular protein ubiquitylation site prediction tools. Our source code is freely available at https://github.com/jiagenlee/deepUbiquitylation.
Metal artifact reduction in CT based on adaptive steering filter and nonlocal sinogram inpainting

This paper proposed a novel metal artifact reduction strategy for computerized tomography (CT). Firstly, an adaptive steering filter is applied to suppress noise and smooth artifacts in the original CT image with metal artifacts. Secondly, the metal component and artifact component is extracted from the filtered image using Mutual Information Maximized Segmentation (MIMS) with different maximum class number. With tagged projection sinogram from the metal artifact part in original CT image, we complete the subtracted sinogram using a novel nonlocal means inpainting method. Then, a corrected image is obtained using the filtered back-projection of the completed sinogram. The final image is formed from a combination of the corrected image and the image component free of artifacts. Experiment with both phantom and clinical images show that CT metal artifacts can be significantly reduced using our proposed method.
Choosing enterprise advertisement medium approach of three-point interval number comparison matrix method

Aim at the choice problem of the company advertisement medium in reality , a type of new uncertain comparison matrix-three-point interval number comparison matrix is put forward this text and its consistency, weight estimation approach are studied. Based on the complete consistency definition of three-point interval number matrix, definitions of possible value error and low - upper error are introduced. Following these definitions, the weight estimation model is developed based on the minimal sum error. Finally according to the actual circumstance of company, this text carry on a solid example analysis towards advertising medium of choose.
Adaptive Cooperation Scheme Using Fairness Selection Relay

This work presents a novel adaptive cooperation scheme that takes both the channel state information and residual energy information into account to prolong the network lifetime while meeting the transimission rate requirement of destination. In order to achieve this aim, we will select the relay with the best instantaneous weighted channel conditions by attributting a weight coefficient to each relay. Numerical results demonstrate the performance of outage probabilities of the proposed schemes using simulations. It is shown that the scheme can improve fairness of selection and extend network lifetime.
Circular drogue pose estimation for vision-based navigation in autonomous aerial refueling

This paper presents a new vision pose estimation method based on the circular feature, and the method is applied in the autonomous aerial refueling to solve the drogue pose estimation problem by using the internal circular refueling port of drogue as the vision feature. Any optical marker on the refueling drogue is not required. In the proposed circular pose estimation method, the oblique conical surface equations in the camera frame and world frame are established respectively, and the constraint condition that rotation transformation doesn't change the eigenvalues of coefficient matrixes is used to solve the unknown parameters, including the coordinate of circle center and the unit normal vector of circle plane. The visual simulation test and ground test are designed and done. Results show that the proposed method has the plane position error below 2cm when the relative distance is below 8m in the visual simulation test, and has the relative distance error less than 6% in 94.71% of cases in the ground test. The proposed method is able to meet the requirements of the positioning accuracy in the autonomous aerial refueling procedure.
Conical beam control of arbitrary conical spiral antennas

The conical beam forming characteristics of a two-arm and a four-arm conical spiral antenna with arbitrary configuration are researched in this paper. The antennas are analyzed based on the Nakano integral equation and the moment method. The results show that their beam direction can be changed by adjusting the winding denseness of the antennas. With the increase of the winding denseness their beam deviation from the axis increases to form conical shaped radiation patterns. The beam deviation of equiangular conical antennas from the axis is wider than that of Archimedean conical antennas. Archimedean conical antennas are better than equiangular conical antennas in beam conformity.
Electromagnetic imaging of lossless dielectric cylinder using confined iterative method

The inverse problem of reconstruction the permittivity distribution image of a lossless dielectric cylinder from scattered electric field is discussed. The discretization inverse scatter problem involves of very nonlinearity and ill-pose. Due to the high numerical complexity, the proposed method is based on the BIM (Born iterative method), joining the confined condition to the solution. This method descends the permittivity distribution errors and enhances the image accuracy in comparison with BIM. From the simulation data, some numerical results of image reconstruction are given to show the efficiency of the proposed method for image reconstruction.
Digital implementation of diode-clamped three-phase three-level SVPWM inverter

The conventional modulation strategies for diode-clamped three-phase three-level inverter have some disadvantages, such as switching loss of switching device. This paper presents a novel space vector pulse width modulation (SVPWM) scheme to reduce switching loss. Using this novel modulation strategy, the changing of switch states cause only one single phase voltage change every time. In this way, the inverter generates similar output voltage as that under the common strategies, whereas the switching loss is reduced. The proposed scheme has been digitally implemented by using TMS320F240 and its feasibility has been verified by the experimental results.
A Novel Fault Diagnosis Framework of Computing with Words Based on Lattice-Valued Fuzzy Automata

Fault diagnosis is crucial for complex man-made system. The discrete event system approach is an efficient method for this problem. However, the states, events and transition functions in the previous discussions were assumed to have crisp values. This prevents the use of the existing approach for some other important fields when uncertain problems need to deal with, such as uncertain states, fuzzy events. Existing studies on classical fuzzy automata for fuzzy discrete event system fault diagnosis cannot solve all the problems encountered, such as dealing with fuzzy event string, fuzzy subset of events and so on. In this paper, we propose a more general fault diagnosis framework for fuzzy discrete event system based on the lattice-valued fuzzy automata. In this framework, the system is modeled as a fuzzy discrete event system based on lattice-valued fuzzy automata. Based on this model, a fault diagnoser for system is constructed, which is also a lattice-valued fuzzy automaton that can be used to detect system faults online. The diagnosis model of computing with words is also established by extending its state transition functions on fuzzy subset of observable events. The heating, ventilating and air condition system simulation experiment is carried out, and the results show that the lattice-valued fuzzy diagnoser is a promising way for fault diagnosis of system with uncertain problems.
Medical Aided Diagnosis Using Electronic Medical Records Based on LDA and Word Vector Model

With rapid development of medical information, more and more attention has been paid to the intelligent diagnosis based on electronic medical records. The results of intelligent diagnosis can provide doctors with advice in the course of diagnosis and treatment, and avoid unnecessary mistakes. In the field of intelligent text aided diagnosis, how to extract and express a large amount of text information in the medical record plays a key role. The final result of classification and prediction is based on the characterization of text information using some mathematical models. In the past, we used single feature set to get the final results, which could not achieve our expect, and the results affected the reliability of intelligent diagnosis. This paper puts forward a kind of combined text representation method based on weighted LDA and word vector model, we get the combined feature set by this way and use it to classify liver disease. By combining the weighted LDA and the word vector model, we can make full use of the potential text information of the electronic medical records, improve the accuracy of classification prediction, and increase the reliability of intelligent diagnosis.
A three-dimensional reconstruction system based on panoramic vision and two-dimensional ranging laser

With respect to the limited field of view in the visual environment sensory perceptual system of the traditional three-dimensional reconstruction system, as well as such defects as high price, big size, cumbersome shape and low rate of imaging, a three-dimensional reconstruction system is designed based on panoramic vision and a two-dimensional ranging laser. By adopting the linear laser technology, a non-contact hardware system of laser scanning and measuring system is constructed based on panoramic vision, so as to achieve real-time scanning of objects; by adopting the corner detection technology to abstract image features, and to perform image matching and camera calibration, a three-dimensional model of objects is constructed. System software tests developed in the programming environment of Visual C++ show that this system could realize an excellent three-dimensional reconstruction based on panoramic vision and two-dimensional ranging laser.
Accumulative competition neural network for shortest path tree computation

Shortest path tree (SPT) computation is an important combinatorial optimization problem with numerous applications. A novel neural network model called accumulative competition neural network (ACNN) is proposed in this paper to compute the SPT in a given weighted graph. Comparing with the other neural network based search algorithms, the algorithm presented here features in much less number of neurons needed, much less iterations of the network needed, the simplicity of neuron model, the simplicity of the topology structure of the network and the global optimal solution result. Finally, examples for searching the shortest path tree in weighted graphs are given. All the results have shown the high performance of the ACNN in searching the SPT in weighted graph.
Automatic Face Naming by Learning Discriminative Affinity Matrices From Weakly Labeled Images

Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.
A New ANN-based Detection Algorithm of the Masses in Digital Mammograms

Breast cancer has become one of the most dangerous carcinomas for middle-aged and older women in China recently. Mammography is its most reliable detection method in the clinic, and computer-aided diagnosis (CAD) could assist the radiologists in reading the mammograms. In this paper, a new algorithm based on two ANNs (artificial neural networks), was proposed to detect the masses automatically. It firstly built up two mass models to represent the masses with different backgrounds and features, and used different detection methods on different type of masses: for those masses inside the fatty tissue, iterative thresholding was applied to locate them; for those masses in the denser tissue, black hole registration based on discrete wavelet transform (DWT) were used instead. Then, filling dilation was used to extract the whole masses from the backgrounds, which was adjusted adaptively by ANFIS (adaptive-network-based fuzzy inference system). At last, the segmented suspicious masses were filtrated with a MLP (multilayer perceptrons) classifier. With these two ANNs, the detection process were well adjusted and improved, and the final diagnosis result showed that the CAD scheme could simultaneously achieve comparatively high detection precision and low false positive rate, even when the special masses were dealt with.
A multi-sensor fusion SLAM approach for mobile robots

The ability to simultaneously localize a robot and accurately map its surroundings is considered to be key prerequisite of truly autonomous robots. This paper presents a novel method, which enhances the use of external mechanisms by considering a multi-sensor system, composed of sonar and a CCD camera. Monocular vision provides redundant information about the location of the geometric entities detected by the sonar sensor. Hough transform is utilized to extract features from raw sonar data and vision image. The information is fused at the level of features. This technique improves the reliability significantly and precision of the environment observations used for the simultaneous localization and map building problem for mobile robots. Experimental results validate the favorable performance of this approach.
Automatic Prostate Segmentation on MR Images with Deeply Supervised Network

Accurate and efficient segmentation of prostate image plays an important role in the diagnosis of prostate cancer. Since convolutional neural network demonstrates superior performance in computer vision applications, we present a multi-layer deeply supervised deconvolution network (DSDN) which completes end-to-end training to automatically segment magnetic resonance (MR) images. We put additional deeply supervised layers to supervise the performance of hidden layers. During training, the backpropagation process of gradient information in the additional deeply supervised layers accelerates the parameters update for hidden layers, which makes the trained model has strong capacity of features learning as well as passes the extracted features from shallow layers to higher layers effectively. A set of experiments using prostate magnetic resonance (MR) images is carried out to demonstrate that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches.
A hash-based RFID security protocol for strong privacy protection

RFID (Radio Frequency Identification) tags are small, wireless electronic devices that help identify objects and people. Privacy protection and integrity assurance become rather crucial in the RFID systems, because these RFID tags may have a wide transmission range, making them subject to unauthorized scanning by malicious readers and various other attacks. Hence, Ha et al. proposed an RFID protocol and proved that their protocol can provide the forward privacy service. However, in this paper, it is shown that an attacker can track a target tag by observing unsuccessful previous session of the tag. That is, Ha et al.'s RFID protocol fails to provide the forward privacy protection as claimed. Therefore, to overcome the privacy weaknesses of Ha et al.'s RFID protocol, an RFID protocol based on the cryptographic hash functions is proposed. Moreover, the proposed RFID protocol is evaluated according to both the privacy attribute and the implementation performance.