nils-herrmann/clean_scirepeval huggingface dataset:
Color Reproduction From Noisy CFA Data of Single Sensor Digital Cameras

Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are channel dependent. If untreated in demosaicking, sensor noises can cause color artifacts that are hard to remove later by a separate denoising process, because the demosaicking process complicates the noise characteristics by blending noises of different color channels. This paper presents a joint demosaicking-denoising approach to overcome this difficulty. The color image is restored from noisy mosaic data in two steps. First, the difference signals of color channels are estimated by linear minimum mean square-error estimation. This process exploits both spectral and spatial correlations to simultaneously suppress sensor noise and interpolation error. With the estimated difference signals, the full resolution green channel is recovered. The second step involves in a wavelet-based denoising process to remove the CFA channel-dependent noises from the reconstructed green channel. The red and blue channels are subsequently recovered. Simulated and real CFA mosaic data are used to evaluate the performance of the proposed joint demosaicking-denoising scheme and compare it with many recently developed sophisticated demosaicking and denoising schemes.
Design and implementation of a RFID security authentication protocol

In order to improve the data communication security of the channel between the tag and the reader of Radio Frequency Identification (RFID) technology, this article designs a mutual security authentication protocol aiming at illegal access, tracking, wiretap and other attack technologies. The Protocol adopts SM4 encryption algorithm to encrypt communication data, and the communication data contain random number, tag ID and other key data. This article designs software and hardware systems for protocol authentication as well, taking ATmega16 as the core processor to make the RFID tag card, and implementing communication through the wireless data transmission module and the card reader port. The test results show that, the mutual authentication protocol designed in this article can correctly implement the RFID authentication process, and effectively prevent the illegal access, tracking, wiretap, retransmission and other attacks.
A Novel Way to Browse: Image Travel Wizard

This paper aims at developing an image travel wizard, a hierarchical image guide that can assist people seeking information for travelling. This guide system intends to overcome many of the limitations of the currently available travel information and navigation websites. These traditional websites usually provide some text links with the name of cities and famous resorts for customer search or let users search their interests by inputting a destination name. However these methods require users to have both geographic knowledge and understanding of a specific place already. People sometimes do not have a specific goal about where to go in the mood of a relaxation tour, not to mention to have a name in mind. This image travel wizard provides cognitive images of different categories that are classified accurately into different travel categories in advance. It can lead the users to an ideal place of interest through four iterations. In each iteration, users only need to click one of the images among three to six choices which are represented on the same screen. Once the users choose one image for a specified category, all the images shown in the next iteration will be selected from this category. There is no need for users to know a place name in advance. These iterations can reduce the category scope gradually until the wizard provides users the final recommendation that precisely meets the users' needs.
Comparison and Analysis on Different Finite Element Models of Gear Interfacial Contact Temperature

The development of modern transmission has leaded to transmission gears scuffing becoming more serious. The finite element method (FEM) is proposed that can be used as the most economical and effective designing method to predict the gears temperature, determining whether the tooth scuffing or not, and analysis the thermal stress and the deformation of gears. As simulating the heat generation process of the friction in the gear engagement, the two kinds of flash temperature models are developed, which were the Block flash temperature model and the gear flash temperature model. The ISO and predicted result are compared for situations where gears engaged at different points to demonstrate the both two thermal models could calculate the gear transient temperature rise accurately. While through comparing the two models, it is found the gear flash temperature model compared to Block model, which is not only much closer to the actual gear tooth and the form of the heat load, but could superimpose gear bulk temperature model directly, so it could be more comprehensive analysis of the real temperature distribution of the gear, and solve the gear interfacial contact temperature more accurately
Commutation Torque Ripple Restraint in BLDC Motor over Whole Speed Range

The un-commutated phase current of the brushless DC motor (BLDCM) is distorted during phase current commutation intervals, the distortion results in commutation torque ripples. This paper proposes a novel control method to restrain the commutation torque ripple according to different speed ranges. In the proposed method, the un-commutated phase current is directly controlled through a chopping action with correct current feedback mode in the low speed range, indirectly controlled by decreasing the slope of the outgoing phase current in the middle speed range, and indirectly controlled not only by decreasing the slope of the outgoing phase current but also by modifying the reference current value in the high speed range. The un-commutated phase current is validly controlled over the entire speed range, so the commutation torque ripple is reduced. The effectiveness of the control method presented here is verified by the simulation and experimental results
Development of Hexapod Robot with one passive joint on foot

As developed a Hexapod Robot with one passive joint on foot the structure and the movement characteristic are introduced in this paper. According to the insect's body characteristic and the movement characteristic, we design the robot's mechanical structure, the software and hardware. The design of the aluminum frame of the robot not only reduces the weight of the robot but also ensures that the robot has a higher strength. In the hardware aspect, we choose Freescale Carle chip as the central control chip, and it is equipped with multi sensors, which makes the robot have a certain degree of autonomy. In software aspect, we programmed in the IAR, and developed the host computer software to debug and download the action group to the robot, which is convenient and efficient. On the other hand, the foot adopts a universal foot structure which can reduce the impact of foot and ground. Meanwhile, it increases the contact area of the foot and the ground. There is a rubber gasket in the robot's palm, which adds to the adhesion between the robot and the ground. This foot-end design ensures that robots can adapt to different ground structures with the same attitude, which greatly improves the stability of the robot during operation. And we analyze the kinematics of the leg mechanism and derive the kinematics formula of the foot. According to the formula, we make a MATLAB simulation of the stable space of the passive joint at the foot, and obtain the stable range of motion. At last, we carried on the prototype experiment; the experiment proves that when the robot stands in different postures, the foot can be fully contacted with the ground. And the experiment of straight walking, turning, attitude adjustment, obstacle avoidance, tracking and other functions show that the Hexapod Robot designed in this paper is provided with high stability, superior flexibility, and particularly good adaptability to different terrains.
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection

Feature representation and object category classification are two key components of most object detection methods. While significant improvements have been achieved for deep feature representation learning, traditional SVM/softmax classifiers remain the dominant methods for the final object category classification. However, SVM/softmax classifiers lack the capacity of explicitly exploiting the complex structure of deep features, as they are purely discriminative methods. The recently proposed discriminative dictionary pair learning (DPL) model involves a fidelity term to minimize the reconstruction loss and a discrimination term to enhance the discriminative capability of the learned dictionary pair, and thus is appropriate for balancing the representation and discrimination to boost object detection performance. In this paper, we propose a novel object detection system by unifying DPL with the convolutional feature learning. Specifically, we incorporate DPL as a Dictionary Pair Classifier Layer (DPCL) into the deep architecture, and develop an end-to-end learning algorithm for optimizing the dictionary pairs and the neural networks simultaneously. Moreover, we design a multi-task loss for guiding our model to accomplish the three correlated tasks: objectness estimation, categoryness computation, and bounding box regression. From the extensive experiments on PASCAL VOC 2007/2012 benchmarks, our approach demonstrates the effectiveness to substantially improve the performances over the popular existing object detection frameworks (e.g., R-CNN [13] and FRCN ), and achieves new state-of-the-arts.
Design and Implementation of Energy Consumption Simulation Software for Energy-Using Systems

As lacking clear understanding of energy-using characteristics for devices, it is difficult to reasonably assess their consumption levels of energy. In order to solve this problem, chart-module integration theory is applied to energy consumption simulation field to design a software system that supports dynamic energy consumption simulation in this article. Firstly, the overall architecture of the software mainly composed by drawing core engine is described. Then, the realization processes and methods of the graph component library and drawing core engine are illustrated in detail. Finally, the process of modeling and simulation of energy consumption based on the designed software is given. The software users can use graphs to represent energy consumption modules, and give them energy consumption related properties. Applications show that the software provides a unified graphical, integrated, visual environment for energy-using systems, which effectively enhances flexibility and maneuverability of energy simulation analysis.
A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data

Under the framework of spectral clustering, the key of subspace clustering is building a similarity graph, which describes the neighborhood relations among data points. Some recent works build the graph using sparse, low-rank, and ℓ2-norm-based representation, and have achieved the state-of-the-art performance. However, these methods have suffered from the following two limitations. First, the time complexities of these methods are at least proportional to the cube of the data size, which make those methods inefficient for solving the large-scale problems. Second, they cannot cope with the out-of-sample data that are not used to construct the similarity graph. To cluster each out-of-sample datum, the methods have to recalculate the similarity graph and the cluster membership of the whole data set. In this paper, we propose a unified framework that makes the representation-based subspace clustering algorithms feasible to cluster both the out-of-sample and the large-scale data. Under our framework, the large-scale problem is tackled by converting it as the out-of-sample problem in the manner of sampling, clustering, coding, and classifying. Furthermore, we give an estimation for the error bounds by treating each subspace as a point in a hyperspace. Extensive experimental results on various benchmark data sets show that our methods outperform several recently proposed scalable methods in clustering a large-scale data set.
A Method for Service Outsourcing Vendor Selection

Service outsourcing vendor selection is a decision making process based on many criteria. But the interrelationship among the criteria is complex. To solve the problem, this paper proposes a service outsourcing vendor selection method. The method evaluates the information content of service outsourcing vendor under each criteria by using the concept of axiom design theory. For the interrelationship among the criteria is complex, the method use choquet integral to synthesizing the information content, and obtains each service outsourcing vendor’s integrated information content. The service outsourcing vendor that has the least integrated information content is selected as the best one. Finally, the feasibility and validity of this method have been proved through an example.
AVS video decoding acceleration on ARM Cortex-A with NEON

Acceleration of Audio Video coding Standard (AVS) Jizhun profile decoder has been proposed for ARM Cortex-A with NEON. Data level parallelism is utilized to effectively use the SIMD capability of NEON. Key modules are redesigned to make them SIMD friendly. Our optimized C implementation is set as start point for the acceleration. Thanks to effective use of ARM Cortex-Ax architecture and NEON SIMD engine, AVS decoding has been accelerated considerably. Results are provided for the overall acceleration in decoding and individual acceleration for key modules of AVS decoder.
Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification

Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval. Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in the most previous methods, neglecting the significance level of different bits and restricting their practical flexibility. To address these issues, we propose a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images. We pose hashing learning as a problem of regularized similarity learning. In particular, we organize the training images into a batch of triplet samples, each sample containing two images with the same label and one with a different label. With these triplet samples, we maximize the margin between the matched pairs and the mismatched pairs in the Hamming space. In addition, a regularization term is introduced to enforce the adjacency consistency, i.e., images of similar appearances should have similar codes. The deep convolutional neural network is utilized to train the model in an end-to-end fashion, where discriminative image features and hash functions are simultaneously optimized. Furthermore, each bit of our hashing codes is unequally weighted, so that we can manipulate the code lengths by truncating the insignificant bits. Our framework outperforms state-of-the-arts on public benchmarks of similar image search and also achieves promising results in the application of person re-identification in surveillance. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with shorter code lengths.
Block Marking and Updating Coding in Test Data Compression for SoC

A novel test data compression coding scheme, block marking and updating coding, is proposed in this paper. Test data in the test set was divided into successive fixed-length vectors, called blocks, and then they were marked according to their compatibility compared with a reference vector. An operation that is similar to difference and a technique that strategically fills in don 't-care bits are combined to increase the probabilities of compatibility or inverse compatibility. It effectively compresses test data and its decompression structure is very simple. Experimental results of ISCAS-89 benchmark circuits show that the scheme is very effective.
Discussion of Energy Consumption and Management in Tianjin

By using a modified emergy-based ecological footprint model, this paper dynamic calculated and analyzed the emergy-based ecological footprint and carrying capacity of energy consumption of Tianjin city from 2000 to 2008, and on the basis of this assessed the dynamic changes of carbon emission structure of energy consumption of Tianjin. The result showed that in past nine years the ecological carrying capacity per capita has slowly increased from 0.109 hm2•person-1 in 2000 to 0.234 hm2•person-1 in 2008 , the ecological footprint per capita of the city has quickly increased from 0.413 hm2•person-1 in 2000 to 0.578 hm2•person-1 in 2008, the ecological deficit per capita has steeply rose from 0.304 hm2•person-1in 2000 to 0.345 hm2•person-1 in 2008, and the ecological footprint per ten thousand GDP has decreased from 0.243 hm2•person-1 in 2000 to 0.107 hm2•person-1 in 2008. Along with the fast development, carbon emissions of energy consumption also grew rapidly from 3.478 t•person-1 in 2000 to 4.985 t•person-1 in 2008, and carbon emission intensity decreased from 2.046 t/104yuan in 2000 to 0.923 t/104yuan in 2008. Thus, the investigation revealed that the fast development over the past nine years, energy consumption was far over the carrying capacity, while carbon emissions increased unceasingly, shows that non-sustainability energy consumption has been threatening the economic and social development of Tianjin. According to the results, the paper proposes Tianjin city to promote sustainable energy consumption patterns by measures including restructuring of energy consumption construct, reducing the energy intensity, changing the mode of economic growth and make full use of renewable resources, etc.
A design of S-band monolithic integrated switched filter bank

A monolithic integrated switched filter bank has been designed to realize the working center frequency tunable in S-band. The switched filter bank was designed using 8 eight-order step impedance resonators (SIR) band-pass filters and Microelectromechanical Systems(MEMS) switched circuit, combined with low temperature co-fired ceramic (LTCC) technology for monolithic integrated design. The monolithic integrated switched filter bank is only 74mm×24mm×2.5mm, which realizes the monolithic integration of the device and enhances the reliability. A tuning range of 12.9% from 2.7 to 3.05 GHz was achieved with interval of 50MHz and measured results show that the switched filter bank is in good working condition.
A Bayesian Network Model for Automatic and Interactive Image Segmentation

We propose a new Bayesian network (BN) model for both automatic and interactive image segmentation. A multilayer BN is constructed from an oversegmentation to model the statistical dependencies among superpixel regions, edge segments, vertices, and their measurements. The BN also incorporates various local constraints to further restrain the relationships among these image entities. Given the BN model and various image measurements, belief propagation is performed to update the probability of each node. Image segmentation is generated by the most probable explanation inference of the true states of both region and edge nodes from the updated BN. Besides the automatic image segmentation, the proposed model can also be used for interactive image segmentation. While existing interactive segmentation (IS) approaches often passively depend on the user to provide exact intervention, we propose a new active input selection approach to provide suggestions for the user's intervention. Such intervention can be conveniently incorporated into the BN model to perform actively IS. We evaluate the proposed model on both the Weizmann dataset and VOC2006 cow images. The results demonstrate that the BN model can be used for automatic segmentation, and more importantly, for actively IS. The experiments also show that the IS with active input selection can improve both the overall segmentation accuracy and efficiency over the IS with passive intervention.
An improved secure and efficient sequential authentication scheme in delay tolerant networks

An ordered multi-signature scheme allows multiple users to sign the same message sequentially to generate a constant-size digital signature, which convinces the verifiers of not only the signers who have signed the message but also the order in which they signed. In some applications of wired/wireless networks, ordered multi-signature is always used to construct sequential authentication schemes. However, the emerging networks such as delay tolerant networks and vehicle ad-hoc networks make the conventional multi-signature schemes unavailable and insecure since the signer set is not determined at the beginning and the initiated signer can not receive the feedback from the co-signers due to the long delay and undetermined end-to-end connections. To solve the above problem, we give a general analysis of the previous schemes and list desirable properties of an ordered multi-signature scheme. We propose an improved secure and efficient sequential authentication scheme based on our proposed ordered multi-signature which could be proven secure in the registered key model with the helps of random oracle. Public key is short in our scheme which is suitable to the applications under current wireless networks.
Autonomous following indoor omnidirectional mobile robot

This paper designed a follow-up indoor omni-directional mobile robot which can achieve any target indoor tracking. Compared with the existing scheme, this robot does not require target to wear specific markers in the autonomous tracking process, and also does not need any auxiliary markers. Only the ordinary camera on the robot body is needed, and the target is tracking by using the Shi-Tomasi algorithm and the Lucas-Kanade algorithm, the cost is relatively low, a wide range of applications. Users can choose their own tracking target of robot, which can be human or object, regardless of the state of tracking the target's movement, the robot can be moved to position away from the target setting. Finally, this paper verifies the feasibility of the system through experiments.
An Azimuth-Dependent Phase Gradient Autofocus (APGA) Algorithm for Airborne/Stationary BiSAR Imagery

In airborne/stationary bistatic-synthetic-aperture-radar imaging, translational invariance was no longer valid. After range cell migration correction, the range-compressed signal under the same range gate exhibited azimuth-dependent FM rates that made the motion-induced phase error difficult to separate from the echoes. To solve this problem, an azimuth-dependent phase gradient autofocus (PGA) algorithm was proposed. Different from the conventional PGA, the residual quadratic phase arising from the azimuth-dependent FM rates was additionally estimated and compensated. As the influence of the azimuth-dependent FM rates was greatly reduced, a phase gradient estimator was subsequently applied for accurate phase error retrieval. Acquired raw data were analyzed to verify the proposed algorithm.