nils-herrmann/clean_scirepeval huggingface dataset:
Gabor wavelet representation for 3-D object recognition

This paper presents a model-based object recognition approach that uses a Gabor wavelet representation. The key idea is to use magnitude, phase, and frequency measures of the Gabor wavelet representation in an innovative flexible matching approach that can provide robust recognition. The Gabor grid, a topology-preserving map, efficiently encodes both signal energy and structural information of an object in a sparse multiresolution representation. The Gabor grid subsamples the Gabor wavelet decomposition of an object model and is deformed to allow the indexed object model match with similar representation obtained using image data. Flexible matching between the model and the image minimizes a cost function based on local similarity and geometric distortion of the Gabor grid. Grid erosion and repairing is performed whenever a collapsed grid, due to object occlusion, is detected. The results on infrared imagery are presented, where objects undergo rotation, translation, scale, occlusion, and aspect variations under changing environmental conditions.
Modified wavefront reconstruction imaging formation for stand-off GPEN SAR

A modified wavefront reconstruction algorithm (MWRA) is proposed for imaging a subsurface layer for a vehicle- or air-borne SAR. The MWRA with refraction and dispersive effect compensation can improve image quality and locating accuracy compared with the traditional one. Associated with the relax-based depth estimation method, the MWRA can provide 3D location information of multiple buried targets. Some simulated results show the effectiveness of the modified algorithm.
Neuron crawler: An automatic tracing algorithm for very large neuron images

Automatic 3D neuron reconstruction for very large 3D light microscopy images remains to be a challenge in neuroscience. Few existing neuron tracing algorithms can be used with commonly available computers (laptops, desktops, or workstations) to efficiently and accurately reconstruct a neuron in image stacks that are tens of gigabytes or greater. We introduce a new automatic tracing algorithm called Neuron Crawler, which works by first tracing a region of interest (e.g., around the soma), and then iteratively tracing in adjacent image tiles to grow the neuron structure in 3D to its termination point within the image. Our experimental results show that Neuron Crawler can achieve reconstruction accuracy that is comparable to several state-of-the-art algorithms, but with much less computational cost.
Cost-Sensitive Face Recognition

Most traditional face recognition systems attempt to achieve a low recognition error rate, implicitly assuming that the losses of all misclassifications are the same. In this paper, we argue that this is far from a reasonable setting because, in almost all application scenarios of face recognition, different kinds of mistakes will lead to different losses. For example, it would be troublesome if a door locker based on a face recognition system misclassified a family member as a stranger such that she/he was not allowed to enter the house, but it would be a much more serious disaster if a stranger was misclassified as a family member and allowed to enter the house. We propose a framework which formulates the face recognition problem as a multiclass cost-sensitive learning task, and develop two theoretically sound methods for this task. Experimental results demonstrate the effectiveness and efficiency of the proposed methods.
The Design and Simulation of Roller for Machining Involute Spline

The involute spline made by cold-rolling have many advantages. This method can increase the production efficiency, save material, prolong the cutting tool's life, and improve the dimensional accuracy. Because the metal fiber continues between the spline's teeth, wearing resistance of the spline's teeth side is better and intensity of the teeth is higher. The cold rolling is the development trend of involute spline manufacturing. And the design of the roller is the key point in the cold rolling processing. This paper analyses the principle of cold rolling for involute spline and sets up the design model of roller. Because of the plastic deformation, the amendment method is introduced according to experiment data. On the basis of research of the main algorithm, the roller CAD system is developed for the involute spline and the roller simulation system is built. The roller CAD system and the simulation system have been put into practice and the roller design model and the amend method proved to be correct in the practice. The application meets the urgent need in the spline manufacturing
Modeling and simulation of hydro-mechanical continuously variable transmission system based on Simscape

The traditional methods of dynamic modeling for the simulation of hydro-mechanical continuously variable transmission (HMCVT) are cumbersome and complex. The physical simulation models of engine, hydraulic speed regulating mechanism, mechanical speed regulating mechanism and the load were established based on Simscape, and the dynamic characteristics of the components were analyzed in the paper. The simulation results show that with the change of displacement ratio e, the output speed of the system is continuous and stepless, and the output torque of the system is constant when the torque of load is constant. From the analysis results, it is concluded that the physical models are feasible. The research provides a theoretical foundation and a verification platform for further development and improvement of HMCVT shifting strategy.
Test and diagnosis on a secondary voltage loss fault of a 220kV capacitor voltage transformer

A 220 kV Capacitor Voltage Transformer (CVT) quitted operation because of losing secondary voltage. After the accident, power-off diagnostic testing was performed and it was found that several data were abnormal such as the transformation ratio, the secondary winding's DC resistance and the capacitance values, showing various types of fault characteristics. However, according to the CVT's internal structure, further tests in different conditions revealed these abnormal data were not caused by multiple independent faults, but showing a relationship of mutual support. The common pointed cause of the failure was a short circuit between the metal shell and the initial terminal of the intermediate transformer. And the judgment of the fault was fully confirmed through the inspection and test under disassembled condition. Moreover, the reasons of the fault were analyzed. In addition, strengthening the operation monitoring and improving the structure of CVT were suggested to prevent such risk, and test methods for the similar fault were recommended.
Large Margin Distribution Learning with Cost Interval and Unlabeled Data

In many real-world applications, different types of misclassification usually suffer from different costs, but the accurate cost is often hard to be determined and usually one can only get an interval-estimation like that one type of mistake is about 5 to 10 times more serious than the other type. On the other hand, there are usually abundant unlabeled data available, leading to great research effort about semi-supervised learning. It is noticeable that cost interval and unlabeled data usually appear simultaneously in practice tasks; however, there is rare study tackling them together. In this paper, we propose the cisLDM approach which is able to handle cost interval and exploit unlabeled data in a principled way. Rather than maximizing the minimum margin like traditional large margin classifiers, cisLDM tries to optimize the margin distribution on both labeled and unlabeled data when minimizing the worst-case totalcost and the mean total-cost simultaneously according to the cost interval. Experiments on a broad range of datasets and cost settings exhibit the impressive performance of cisLDM. In particular, cisLDM is able to reduce 47 percent more total-cost than standard SVM and 27 percent more total-cost than cost-sensitive semi-supervised SVM which assumes the true cost value is known in advance.
A Framework for Secure Anycast Group Management

Anycast is a communication mode in which the same address is assigned to a group of servers and a request sent for a service is routed to the 'best" server. Because the group management and the routing protocol in the anycast are not so mature, there are distinct and significant security vulnerabilities in both the anycast model including denial of service, theft of service, eavesdropping, and masquerading. We propose a new framework for secure anycast group management, which overcome disadvantage of others anycast group management architecture, and can ensure the security of anycast server and the secure communication between anycast client and server. This method has been proved to be effective.
GLR test for minimum detectable velocity indication in OFDM radar

Minimum detectable velocity (MDV) of the target is always regarded as an important parameter in radar system that depends on an acceptable signal-to-interference ratio (SIR) loss. In this paper, we point out an asymptotically equivalent relationship between the generalized likelihood ratio (GLR) and the SIR for orthogonal frequency division multiplexing (OFDM) radar system. This relationship can simplify the post-signal processing procedure of moving target illumination, which makes the MDV could be obtained in parallel when detecting the target without separately calculating the SIR.
Switch-Linear Hybrid Power Conversion (I) - The Topologies Based on Source Follower

The paper presents a hybrid power conversion topology series. They are structured with linear source follower fed by switch-filter for getting synthetic good performances in high efficiency, THD, load robustness from step response and category changes. Special operating regulation of the power devices are analyzed, neither like in switch conversion nor like in linear amplifiers. Some comparison experiment results with present typical topologies are presented for proving that the novel topology series have a prospect future
Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud service, in this paper we propose a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation (JCC) resource allocation mechanism for network operator. Specifically, we first study the resource-efficient computation offloading problem for a user, in order to reduce user's resource occupation by determining its optimal communication and computation resource profile with minimum resource occupation and meanwhile satisfying the QoS constraint. We then tackle the critical problem of user admission control for JCC resource allocation, in order to properly select the set of users for resource demand satisfaction. We show the admission control problem is NP-hard, and hence develop an efficient approximation solution of a low complexity by carefully designing the user ranking criteria and rigourously derive its performance guarantee. To prevent the manipulation that some users may untruthfully report their valuations in acquiring mobile-edge cloud service, we further resort to the powerful tool of critical value approach to design truthful pricing scheme for JCC resource allocation. Extensive performance evaluation demonstrates that the proposed schemes can achieve superior performance for on-demand mobile-edge cloud computing.
Notice of RetractionThe Effect of Cutting Parameters on Surface Roughness when Diamond Cutting Stainless Steel

When using diamond tool to cut the special stainless steel, the influence of cutting parameters on machining surface roughness is so great. Experiments of cutting special stainless steel by ultrasonic vibration were taken to develop the law that cutting parameters influence on surface roughness of the work piece. The relation curves between cutting parameters and the work piece surface roughness were achieved by contrast experiments with different cutting speed, tool feeds, cutting depth and vibration parameters. The result shows that cutting parameters have an obvious effect on surface roughness. Under given Conditions, the smaller amplitude of the vibration, the worse the surface roughness, and the higher vibrating frequency, the better surface roughness, and the more cutting depth and feeds, the worse surface roughness. Among these parameters, tool feeds was main effect on the surface roughness.
Trajectory Prediction Based on Improved Sliding Window Polynomial Fitting Prediction Method

Aiming at the problem of high precision trajectory prediction in air traffic control automation system, a new method based on improved sliding window polynomial fitting prediction is proposed. This method improves the traditional sliding window polynomial fitting method and selects an appropriate historical data group for every predicted value, which provides every prediction with a suitable polynomial fitting equation and make the most use of the current value, and its prediction precision is higher than the traditional sliding window polynomial fitting prediction method. The simulation results show that the improved sliding window polynomial fitting prediction is better than traditional sliding window polynomial fitting prediction method and it can predict the civil aviation flight trajectory effectively.
CoTrade: Confident Co-Training With Data Editing.

Co-training is one of the major semi-supervised learning paradigms that iteratively trains two classifiers on two different views, and uses the predictions of either classifier on the unlabeled examples to augment the training set of the other. During the co-training process, especially in initial rounds when the classifiers have only mediocre accuracy, it is quite possible that one classifier will receive labels on unlabeled examples erroneously predicted by the other classifier. Therefore, the performance of co-training style algorithms is usually unstable. In this paper, the problem of how to reliably communicate labeling information between different views is addressed by a novel co-training algorithm named COTRADE. In each labeling round, COTRADE carries out the label communication process in two steps. First, confidence of either classifier's predictions on unlabeled examples is explicitly estimated based on specific data editing techniques. Secondly, a number of predicted labels with higher confidence of either classifier are passed to the other one, where certain constraints are imposed to avoid introducing undesirable classification noise. Experiments on several real-world datasets across three domains show that COTRADE can effectively exploit unlabeled data to achieve better generalization performance.
Multi-label Learning with Emerging New Labels

Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is open and new concepts may emerge with previously unseen instances. In order to maintain good predictive performance in this environment, a multi-label learning method must have the ability to detect and classify those instances with emerging new labels. To this end, we propose a new approach called Multi-label learning with Emerging New Labels (MuENL). It builds models with three functions: classify instances on currently known labels, detect the emergence of a new label in new instances, and construct a new classifier for each new label that works collaboratively with the classifier for known labels. Our empirical evaluation shows the effectiveness of MuENL.
Study on gout-specific local gene network and its relationship with nutrient metabolism modules

Gout has a high onset rate in coastal regions, and the interaction between gout susceptibility genes and (coastal) diet is needed to study. A molecular linkage between gout-specific local gene network (LGN) and nutrient metabolism modules was constructed based on a local gene network approach, which reveals a complex relationship between gout-specific-LGN and metabolic modules of nucleotide metabolism, lipid metabolism, amino acid metabolism, carbohydrate metabolism, cofactor and vitamin metabolism. Interestingly, nucleotide metabolism modules do link more gout genes than other metabolism modules, providing a primary technological platform to study the impact of diet on gout onset and to develop preventive personalized nutrition regimen.
Thinckness dependence of phase transition temperature of epitaxial ferroelectric films

Considering the electromechanical coupling boundary conditions, such as misfit strain in epitaxial ferroelectric thin films, surface eigenstrain relaxation and depolarization induced by surface charges, the phase transition temperature of epitaxial ferroelectric thin film is analyzed and discussed systematically via the dynamic Ginzburg-Landau equation (DGL). Linear stability analysis of the evolutionary trajectory of the system for the phase transition is adopted in this paper. Explicit expressions are then derived for the critical phase transition temperature under various electromechanical surface conditions. The important factors on phase transition temperature of ferroelectric films are discussed and compared with experimental data for BaTiO 3 thin film epitaxially grown on SrTiO 3 substrate.
On the Correlation between the Effectiveness of Metamorphic Relations and Dissimilarities of Test Case Executions

Metamorphic testing (MT) is a property-based automated software testing method. It alleviates the oracle problem by testing programs against metamorphic relations (MRs), which are necessary properties among multiple executions of the target program. For a given problem, usually more than one MR can be identified. It is therefore of practical importance for testers to know the nature of good MRs, that is, which MRs are likely to have higher chances of revealing failures. To address this issue we investigate the correlation between the fault-detection effectiveness of MRs and the dissimilarity (distance) of test case execution profiles. Empirical study results reveal that there is a strong and statistically significant positive correlation between the fault-detection effectiveness and the distance. The findings of this research can help to develop automated means of selecting/prioritizing MRs for cost-effective metamorphic testing.
Color image enhancement based on retinex theory with guided filter

Color image enhancement is widely used in digital image processing. Retinex performs well in color image enhancement, however, traditional Gaussian filter-based retinex algorithms exist some problems such as halo artifacts and detail loss. To solve these problems, we propose an improved retinex image enhancement algorithm based on the guided filter, which is processed in IHS color space. We replace Gaussian filter with the guided filter to get the detail information in different fine scales to better enhance different bands of high-frequency information. Then, we also extract a certain amount of low-frequency information through the decomposition with guided filter in the log domain, while the retinex method based on Gaussian filter only extracts the high-information to enhance the image. Next, we enhance the high-frequency information of the image and combine the enhanced high-frequency information and low-frequency information to get the combined image. Finally, we stretch the combined image to enhance the contrast of the image. In this way, we get the result image with enhanced details and contrast. Compared with some existing retinex methods in image enhancement, our algorithm can avoid the halo artifacts and detail loss.  Image Enhancement, Retinex, The Guided Filter.
Arch Axis Optimum Design of Steel Box-Concrete Composite Arch Bridge Based on Improved Genetic Algorithms

In view of shortcomings such as premature convergence and oscillation in Simple Genetic Algorithms (SGA), the article adopts the method of adaptive adjusting for the probabilities of crossover and mutation. So the Improved Genetic Algorithms (IGA) is formed by adding the transgenic operator to SGA. And the optimum design program of steel box-concrete composite arch bridge based on improved genetic algorithms is simultaneously developed. The arch section optimum of steel box-concrete composite arch bridge is firstly conducted by using the program. Then the arch axis optimum is conducted by using the optimized arch section parameters. Recomputation of the optimized steel box-concrete composite arch bridge is conducted by using the fem program in the end. The numerical experiment shows that the optimization result meets the demands, the mechanical behavior of arch rib is improved and the project cost is obviously reduced.
Study on flood damage cause and new waterway regulating structures in mountain river

Flood damage of regulating structures is a general phenomenon and it is difficult to be resolved. Block of the dam face is moved by fluid, erosion of foundation and bank around the dam is the main causes for the damage. For reducing the degree of damage, the methods of design and build techniques of traditional rock-fill dam,new structure are summarized and discussed. The new structures such as geotextile bag, mold-concrete have many advantages and good application prospect.
Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble

Comprehensibility is very important when machine learning techniques are used in computer-aided medical diagnosis. Since an artificial neural network ensemble is composed of multiple artificial neural networks, its comprehensibility is worse than that of a single artificial neural network. In this paper, C4.5 Rule-PANE, which combines an artificial neural network ensemble with rule induction by regarding the former as a preprocess of the latter, is proposed. At first, an artificial neural network ensemble is trained. Then, a new training data set is generated by feeding the feature vectors of original training instances to the trained ensemble and replacing the expected class labels of original training instances with the class labels output from the ensemble. Additional training data may also be appended by randomly generating feature vectors and combining them with their corresponding class labels output from the ensemble. Finally, a specific rule induction approach, i.e., C4.5 Rule, is used to learn rules from the new training data set. Case studies on diabetes, hepatitis , and breast cancer show that C4.5 Rule-PANE could generate rules with strong generalization ability, which benefits from an artificial neural network ensemble, and strong comprehensibility, which benefits from rule induction.