nils-herrmann/clean_scirepeval huggingface dataset:
Denoising deep neural networks based voice activity detection

Recently, the deep-belief-networks (DBN) based voice activity detection (VAD) has been proposed. It is powerful in fusing the advantages of multiple features, and achieves the state-of-the-art performance. However, the deep layers of the DBN-based VAD do not show an apparent superiority to the shallower layers. In this paper, we propose a denoising-deep-neural-network (DDNN) based VAD to address the aforementioned problem. Specifically, we pre-train a deep neural network in a special unsupervised denoising greedy layer-wise mode, and then fine-tune the whole network in a supervised way by the common back-propagation algorithm. In the pre-training phase, we take the noisy speech signals as the visible layer and try to extract a new feature that minimizes the reconstruction cross-entropy loss between the noisy speech signals and its corresponding clean speech signals. Experimental results show that the proposed DDNN-based VAD not only outperforms the DBN-based VAD but also shows an apparent performance improvement of the deep layers over shallower layers.
Approximation for strategic single point weighted steiner tree problem

This paper studies the strategic version of single point weighted Steiner tree problem. We focus on Bayesian mechanism design setting for the problem. We find the optimal mechanism for it and prove that the optimal mechanism is incentive compatible, individually rational but computationally inefficient. We propose a class of approximation mechanisms and prove that the mechanisms are all incentive compatible, individually rational and computationally efficient. Based on this class of mechanisms, we give a deterministic mechanism with approximation ratio of 3 and a randomized mechanism with approximation ratio of 2.54.
A video copy detection algorithm combining local feature's robustness and global feature's speed

This paper presents a novel algorithm for fast and robust video copy detection. The idea is to use local features to estimate the copy transformation parameters first and then use the estimated parameters to guide the global-feature-based matching at a later stage. It is based on the fact that the copy transformations generally remain unchanged in a continuous video clip even in the whole video. Local-feature-based matching can find the candidates which are difficult to be detected only using global features. Furthermore, the matched local feature points can provide enough information to estimate the copy transformations. After the copy transformations are estimated, the subsequent detection can be accelerated by doing global-feature-based matching. The experimental results show that the proposed algorithm can get the same good robustness as the local-feature-based method but the faster detection speed.
An optimal spectrum sharing method for MIMO cognitive radio networks

This paper investigates the resource optimization problem for the multiple-input multiple-output (MIMO) cognitive radio (CR) networks. Different from the conventional works, a novel optimization metric, namely the bandwidth-power product (BPP), is used to achieve the optimal spectrum sharing. We apply the direct-channel singular value decomposition (DC-SVD) method to design the optimal source precoding matrix. Then the unified power and channel allocation problem is derived and found to be a mixed-integer programming problem. Hence, a sub-optimal and tractable algorithm with low complexity called the joint iterative power and channel optimization (JIPCO) algorithm is proposed. Furthermore, the discrete particle swarm optimization (DPSO) algorithm is introduced to approximately provide an upper bound. Numerical results show that the JIPCO algorithm achieves the acceptable performance with great reduction of computation complexity and validate the superiority of the proposed scheme compared to conventional power optimization scheme using the water-filling method.
A robust lane boundaries detection algorithm based on gradient distribution features

The paper presents a lane boundaries detection algorithm based on gradient distribution features. Firstly, a combination of EDF and Hough transform is used to obtain the linear models of lane boundaries. Secondly, without any prior knowledge, the width of road, the middle point of road and the other parameters are computed based on the linear models. Finally, bi-directional sliding window technique is applied to detect real lane markings. Experimental results indicate that the proposed method can enhance the adaptability to deal with the random and dynamic environment of road scenes, such as curved lane markings, sparse shadows, object occlusions and bad conditions of road painting.
A Double-Ended Interference Assessing System for Short Wave Transmission

This paper presents an short wave transmission quality assessing system based on speech signal in which there are some jamming signals to interrupt the normal communication. This work aims to give proper objective assessment of the intensity of interference signal which is consistent with subjective one. Except interference signal, there are also delays at receiver side, which cannot be determined in advance. Firstly, cepstral mean normalization and weighted Mel frequency cepstral coefficient are applied to reduce channel noise. Furthermore, a sliding strategy is proposed to find the best matching point during the objective assessment to deal with the delay problem. In objective and subjective mapping model, two kinds of fitting approaches, named two-pass least square fitting and Akima fitting, are combined for different condition. Experiments show the effectiveness of proposed assessment approach.
Analysis and research of the buried U-PVC pipe of the water supply function

The anti-seismic characters of earthing U-PVC supply pipeline is analysed by the pull-off test of flexible joint static-load on the earthing U-PVC supply pipeline. Under the condition of earthing, the test of tensile static-load is going through five pipes 200mm dia., which is connected with cushion rubber, meanwhile, five displacement conditions have been done on each pipe. According to the test data of the pressure and the displacement, a relative curve about pressure-displacement has been accepted and its formula has been formed. Under the relationship between leakage-amount and the time on each condition, a leakage model is built. The analysis suggests that such a tube of flow can be used as an available model only when the supply pipe net with leakage is analysed. The formula about hydraulic pressure to displacement at the interface provides the reference information for the anti-seismic design of the supply pipeline.
Approximate Mean Value Analysis for multi-core systems

Mean Value Analysis (MVA) has long been a standard approach for performance analysis of computer systems. While the exact load-dependent MVA algorithm is an efficient technique for computer system performance modeling, it fails to address several features of multi-core platforms. In addition, the load-dependent MVA algorithm suffers from numerical difficulties under heavy load conditions. The goal of our paper is to find an efficient and robust method which is easy to use in practice and also achieves accuracy for performance prediction for multi-core platforms. Our contributions are: We present a flow-equivalent performance model designed specifically to address multi-core computer systems. We identify the influence on the CPU demand of the effects of Dynamic Frequency Scaling (DFS) and Hyper-Threading Technology (HTT). We adopt an approximation technique to estimate resource demands to parameterize the MVA algorithm. We use a modified Conditional MVA (CMVA) algorithm to address the potential numerical instability. To validate the application of our method, we investigate a case study of an e-commerce web server which is equipped with diverse classes of user requests. We show that our method achieves better accuracy compared with other commonly used MVA algorithms.
An Automatic Baseline Extraction Algorithm for Intensity Absorption Type Gas Sensing

A novel algorithm for automatic baseline extraction of gas absorption spectrum is proposed. It is based on an automatic signal-free regions recognition that uses a difference of Gaussians function, followed by a baseline fitting procedure based on segmented polynomial fitting and segmented kernel smoothing. This algorithm has been applied to both simulated data and experimental data. The results show that this algorithm can extract baseline of spectrum with a wide range of possible baseline types, is tolerant to low signal-to-noise ratio spectrum, and to signals of varying widths in a single spectrum. Moreover, the efficiency of the algorithm is tested by accurately detecting the absorbance of experimental CO 2 absorption spectrum and C 2H 2 absorption spectrum.
Abnormal Odor Detection in Electronic Nose via Self-Expression Inspired Extreme Learning Machine

The electronic nose (E-nose), as a metal oxide semiconductor gas sensor system coupled with pattern recognition algorithms, is developed for approximating artificial olfaction functions. Ideal gas sensors should be with selectivity, reliability, and cross-sensitivity to different odors. However, a new problem is that abnormal odors (e.g., perfume, alcohol, etc.) would show strong sensor response, such that they deteriorate the usual usage of E-nose for target odor analysis. An intuitive idea is to recognize abnormal odors and remove them online. A known truth is that the kinds of abnormal odors are countless in real-world scenarios. Therefore, general pattern classification algorithms lose effect because it is expensive and unrealistic to obtain all kinds of abnormal odors data. In this paper, we propose two simple yet effective methods for abnormal odor (outlier) detection: 1) a self-expression model (SEM) with <inline-formula> <tex-math notation="LaTeX">$ {l_{1}/l_{2}}$ </tex-math></inline-formula>-norm regularizer is proposed, which is trained on target odor data for coding and then a very few abnormal odor data is used as prior knowledge for threshold learning and 2) inspired by self-expression mechanism, an extreme learning machine (ELM) based self-expression (SE<inline-formula> <tex-math notation="LaTeX">$^{ {2}}$ </tex-math></inline-formula>LM) is proposed, which inherits the advantages of ELM in solving a single hidden layer feed-forward neural network. Experiments on several datasets by an E-nose system fabricated in our laboratory prove that the proposed SEM and SE<inline-formula> <tex-math notation="LaTeX">$^{ {2}}$ </tex-math></inline-formula>LM methods are significantly effective for real-time abnormal odor detection.
Characteristic and Community Structure Evolution on Chinese Character Network

We investigate the radicals of Chinese character based on complex network theory. In this paper, in the study of Chinese character network, scale-free effect and hierarchical structure features are found in this complex system. In addition, we study the community structure in Chinese character network, and two interesting results have been obtained: First, the relationship between the node number of the maximum communities and the number of communities in the corresponding networks is studied and it is linear. Second, when the number of nodes in the maximum communities are increasing, the increasing tendency of the number of its edges slows down, we predict the Chinese characters network have sparsity. The study effectively explains the characteristic and community structure evolution on Chinese character network.
Diffusion model and empirical analysis of China-Brazil Earth Resource Satellite image

Applications Satellite Industry is national strategic high-tech industry, and the key tache for its industrialization is to deepen Image diffusion in more economic and social fields. Based on the market structure and diffusion process of CBERS (China-Brazil Earth Resource Satellite) image, this paper analyses the characteristics of distribution and establishes three diffusion models of network channels, price strategies and competitive factors respectively. Empirical result shows speed of diffusion is fastened through online channel, but hampered by TM Image, and free distribution policy has no obvious impact on it. It is cost-efficient that influence the decision of potential adopter deeply. Further research takes all three factors into the nested model, and gives the simplest form including network factor and the most accurate form including network and competition factors.
A Corrosion Energy Feature Extract Algorithm of Aircraft Skin Based on Wavelet

The mechanism of aircraft skin corrosion is studied and a corrosion detection algorithm based on wavelet analysis is proposed. First of all, the magneto-optic image is decomposed by the wavelet analysis. In the next place a feature vector is assigned whose components represent energy in each sub-image. Lastly, a 1-nearest neighbor method classifier is applied to classify the proceeding feature vector as either corresponding to a region of corrosion or corresponding to a region of non-corrosion. The experimental results demonstrate that the proposed algorithm is insensitive to noise and the features are easy to extract. The algorithm has high recognition ratios and robustness for corrosion detection and the most important is that it can meet the real-time request.
A secret sharing shuffling scheme based on polynomial

The threshold protocol based on polynomial needs shuffling scheme to share the secret securely. For avoiding the complexity on communication and operation of the complete-shuffling scheme, this scheme applies brief shuffling algorithm. This paper introduces random order sequence via the dynamic attribute of the feedback time and presents a partial-shuffling scheme. In order to defense the adversary attack against secret sharing process, a kind of dynamic correction mechanism is designed. Performance evaluation shows that the partial-shuffling scheme greatly decreases the secret sharing time and improves the efficiency, while the threshold and the adversary appearance probability are increasing.
Content-Based Image Annotation Refinement

Automatic image annotation has been an active research topic due to its great importance in image retrieval and management. However, results of the state-of-the-art image annotation methods are often unsatisfactory. Despite continuous efforts in inventing new annotation algorithms, it would be advantageous to develop a dedicated approach that could refine imprecise annotations. In this paper, a novel approach to automatically refining the original annotations of images is proposed. For a query image, an existing image annotation method is first employed to obtain a set of candidate annotations. Then, the candidate annotations are re-ranked and only the top ones are reserved as the final annotations. By formulating the annotation refinement process as a Markov process and defining the candidate annotations as the states of a Markov chain, a content-based image annotation refinement (CIAR) algorithm is proposed to re-rank the candidate annotations. It leverages both corpus information and the content feature of a query image. Experimental results on a typical Corel dataset show not only the validity of the refinement, but also the superiority of the proposed algorithm over existing ones.
An Autofocus Algorithm for Estimating Residual Trajectory Deviations in Synthetic Aperture Radar

Due to the accuracy limitation of the navigation system, deviations between the real trajectory and the measured one appear inevitably in airborne synthetic aperture radar (SAR), which degrades the image quality dramatically. To improve the focusing performance, these trajectory deviations should be well estimated and compensated. In this paper, a data-based autofocus approach is proposed to correct the residual 3-D trajectory deviations. This new approach mainly contains two processing stages. The first stage is the local phase error estimation procedure involving small images autofocusing. A gradient function considering smoothness regularization is developed to efficiently achieve the sharpness-maximizing local phase error functions. In the second stage, the local phase error functions are combined to retrieve the residual 3-D trajectory deviations by a proposed weighted total least square method. This approach has been applied on highly squinted and large-swath airborne SAR raw data, respectively. Both real data experiments generate well-focused SAR images by the estimated trajectory parameters, and thus, validate the effectiveness of the proposed autofocus approach.
ANN and space mapping for microwave modelling and optimization

Artificial neural network (ANN) and space mapping are recognized as two major recent advances in microwave CAD. ANNs can be trained to learn EM and physics behaviour from component data, and trained ANNs can be used in high-level circuit design. Space mapping has proved to be a breakthrough in engineering optimization allowing expensive EM optimization to be performed effectively with the help of “coarse” or surrogate models. Recent advance also led to neuro-space mapping, combining the advantages of ANN and space mapping for efficient modelling of microwave components. This paper presents an overview of the state-of-art of microwave modelling and design with ANN, space mapping and neuro-space mapping.
Automated Music Video Generation using WEB Image Resource

In this paper, we proposed a novel prototype of automated music video generation using Web image resource. In this prototype, the salient words/phrases of a song's lyrics are first automatically extracted and then used as queries to retrieve related high-quality images from Web search engines. To guarantee the coherence among the chosen images' visual representation and the music song, the returned images are further re-ranked and filtered based on their content characteristics such as color, face, landscape, as well as the song's mood type. Finally, those selected images are concatenated to generate a music video using the Photo2Video technique, based on the rhythm information of the music. Preliminary evaluations of the proposed prototype have shown promising results.
Development and validation research of an active sting damper in a transonic acoustic wind tunnel

In wind tunnels, the support systems holding up the aerodynamic models under testing, such as long cantilever stings, confer a reduction of airflow interference on experiment data. Nevertheless, given the low structural damping of the support systems, such support systems could adversely turn out to be the potential source of vibration, which makes the aerodynamic model testing vulnerable to test envelop limitation and datum quality degradation. Model flow separation and turbulence in wind tunnel could stimulate the low order resonant modes of a sting structure, leading to detrimental vibration. The presented research elaborates on the development and control strategy of an active sting damping system employing piezoelectric stack actuators and balance signal feedback. The experiments implemented both in laboratory and transonic wind tunnel environment reveal the consistency between designed vibration suppression target and experiment results, and assert the verification of control algorithm. It is demonstrated that the active sting damper, regulated by PD controller, has attenuated the oscillation with effect. For sting mode only, 90% energy elimination of sting mode frequency has been attained; moreover, the operational range of angle of attack has been expanded by 1.5 degrees.
Artificial Neural Network model design and topology analysis for FPGA implementation of Lorenz chaotic generator

This paper presents an Artificial Neural Network (ANN) design for a chaotic generator, and the training performances for a three layer ANN architecture with different number of hidden neurons. Chaotic systems can be synchronized and used for secure communication. Chaotic systems such as Lorenz attractor, Rossler attractor and Chen's system are generally implemented directly based on their definitions represented by a unique group of ordinary differential equations (ODEs). An feed forward ANN can be trained using the output values of a chaotic system. The training process is carried out on a computer and the weights are generated for all neurons in an ANN architecture. These weights are then used for a trained ANN architecture model to generate the expected output for the target chaotic system. The complexity of the ANN architecture defines the implementation cost and speed. Therefore it is beneficial to use less number of hidden neurons to achieve the target training performance. Lorenz attractor has its significance in studying chaotic systems and is used as the design subject in this paper. The 3-layer ANN has one input, one hidden and one output layer. The ANN architecture with 1 to 16 hidden neurons is designed and trained respectively using MATLAB Neural Network Toolbox with three training algorithms: Levenberg-Marquardt, Scaled Conjugate Gradient algorithm and Bayesian Regulation. The optimized ANN architecture can be used to improve the efficiency of the fixed-point implementation on an Field Programmable Gates Array (FPGA) device.
A multiscale hybrid model exploiting heterogeneous contextual relationships for image segmentation

We propose a framework that can conveniently capture heterogeneous relationships among multiple random variables. The framework is formulated based on a hybrid probabilistic graphical model. It allows using both directed links and undirected links to capture various types of relationships. Based on this framework, we develop a multiscale hybrid model for image segmentation. The multiscale model systematically captures the spatial relationships and causal relationships among such image entities as regions, edges, and vertices at different scales. We further show how to parameterize such a hybrid model and how to factorize its joint probability distribution according to the global Markov properties. Based on this factorization, we exploit the factor graph theory to perform joint probabilistic inference and solve for the image segmentation problem.
A Numerical Simulation Study on Wetting and Bridging of SnAgCu Solder Liquid

With size and pitch of the solder pad decreasing, solder bridging become one of the most common and critical defect in the high-density assembly process. Optimized geometry of solder with its pad is the guarantee of manufacturing process. By means numerical simulation technology, the wetting behavior of SnAgCu solder liquid on structured surface consisting of solder pad and solder mask was investigated. The solder liquid separation condition, that the solder height must below one and third quarter of the width of nonwetting zone, was verified. The simulation results demonstrate the validity of the criteria.
Design and implementation of an integrated system of mining GIS and AutoCAD

To improve mine production efficiency, a GIS integrated system based on AutoCAD was developed for mine based on software engineering principles. The developing process included requirement analysis, structural design, database design, functional design, system implementation and functional verification. The system has basic information management, image-text interactive queries of coal and slope GIS, automatic drawing, attribute analysis and calculation function, etc. Lastly, the system was applied in a mine in China, which can achieve perfect connection to the current AutoCAD platform, improve mine production design and provide data query, information analysis and technical decision support for mine.