nils-herrmann/clean_scirepeval huggingface dataset:
Chinese Traditional Opera database for Music Genre Recognition

This paper introduces the database of Traditional Chinese Opera for Music Genre Recognition, which is still a gap in our knowledge. The database contains different songs which are from the 14 most popular kinds of Chinese Opera, including Sichuan Opera, Beijing Opera, etc. Each Opera is then annotated between music, song and speech which are identified by acoustic waveform and spectral analysis. This breakdown is used to distinguish different types of operas. The database can be used for research concerning music genre recognition. Moreover this database can be used to analyze the relationship between the operas by comparing acoustic attributes, which can provide new perspectives and evidence for research in population migration, cultural evolution and other social investigation study in the historical development of Chinese opera.
A deep bidirectional long short-term memory based multi-scale approach for music dynamic emotion prediction

Music Dynamic Emotion Prediction is a challenging and significant task. In this paper, We adopt the dimensional valence-arousal (V-A) emotion model to represent the dynamic emotion in music. Considering the high context correlation among the music feature sequence and the advantage of Bidirectional Long Short-Term Memory (BLSTM) in capturing sequence information, we propose a multi-scale approach, Deep BLSTM (DBLSTM) based multi-scale regression and fusion with Extreme Learning Machine (ELM), to predict the V-A values in music. We achieved the best performance on the database of Emotion in Music task in MediaEval 2015 compared with other submitted results. The experimental results demonstrated the effectiveness of our novel proposed multi-scale DBLSTM-ELM model.
Real Vehicle Routing and Dispatching with Dynamic Fuzzy Travel Times

The paper presents a model of the real world vehicle routing and dispatching problem. In real world, vehicle travel speeds are varying with period of time and can not be expressed as exact values. So the time-dependent and fuzzy travel speeds are introduced into the model. A dispatching period is divided into some time slices and each time slice is designated a triangular fuzzy speed. The travel time is calculated by fuzzy value integral to insure FIFO property. The method of comparing two triangular fuzzy numbers is applied to check whether or not customers' time windows are satisfied. A hybrid intelligent approach combining genetic algorithm and ant colony algorithm is proposed for solving the dispatching model. A chromosome of the genetic algorithm is regarded as a ant of ant colony algorithm, and its fitness is regarded as the pheromone that remains on way that it passes. And chromosomes are constructed by searching path method of ant colony algorithm. The approach is viable and valid by computing a real instance.
Temporal Variance Analysis for Action Recognition

Slow feature analysis (SFA) extracts slowly varying signals from input data and has been used to model complex cells in the primary visual cortex (V1). It transmits information to both ventral and dorsal pathways to process appearance and motion information, respectively. However, SFA only uses slowly varying features for local feature extraction, because they represent appearance information more effectively than motion information. To better utilize temporal information, we propose temporal variance analysis (TVA) as a generalization of SFA. TVA learns a linear transformation matrix that projects multidimensional temporal data to temporal components with temporal variance. Inspired by the function of V1, we learn receptive fields by TVA and apply convolution and pooling to extract local features. Embedded in the improved dense trajectory framework, TVA for action recognition is proposed to: 1) extract appearance and motion features from gray using slow and fast filters, respectively; 2) extract additional motion features using slow filters from horizontal and vertical optical flows; and 3) separately encode extracted local features with different temporal variances and concatenate all the encoded features as final features. We evaluate the proposed TVA features on several challenging data sets and show that both slow and fast features are useful in the low-level feature extraction. Experimental results show that the proposed TVA features outperform the conventional histogram-based features, and excellent results can be achieved by combining all TVA features.
A 140–190 GHz amplifier based on 0.5-um InP DHBT transistor

A four-stage amplifier MMIC operating at G-Band (140-220GHz) is demonstrated in this letter. The amplifier utilizes a 0.5-um single emitter InP DHBT technology. The transistor exhibits collector current density of 2 mA/um2, fT of 250 GHz and fmax of 320 GHz at Ic = 12 mA and VCE = 1.5V. The chip size is 1mm×1.1mm. Measurements show that the small signal gain is above 3 dB over 149-180 GHz frequency band, and return loss is lower than -10 dB from 140 GHz to 153 GHz.
Dimensionality Reduction for Registration of High-Dimensional Data Sets

Registration of two high-dimensional data sets often involves dimensionality reduction to yield a single-band image from each data set followed by pairwise image registration. We develop a new application-specific algorithm for dimensionality reduction of high-dimensional data sets such that the weighted harmonic mean of Cramér-Rao lower bounds for the estimation of the transformation parameters for registration is minimized. The performance of the proposed dimensionality reduction algorithm is evaluated using three remotes sensing data sets. The experimental results using mutual information-based pairwise registration technique demonstrate that our proposed dimensionality reduction algorithm combines the original data sets to obtain the image pair with more texture, resulting in improved image registration.
Image and video dehazing using view-based cluster segmentation

To avoid distortion in sky regions and make the sky and white objects clear, in this paper we propose a new image and video dehazing method utilizing the view-based cluster segmentation. Firstly, GMM(Gaussian Mixture Model)is utilized to cluster the depth map based on the distant view to estimate the sky region and then the transmission estimation is modified to reduce distortion. Secondly, we present to use GMM based on Color Attenuation Prior to divide a single hazy image into K classifications, so that the atmospheric light estimation is refined to improve global contrast. Finally, online GMM cluster is applied to video dehazing. Extensive experimental results demonstrate that the proposed algorithm can have superior haze removing and color balancing capabilities.
Strong Tracking Filter Simultaneous Localization and Mapping Algorithm

Simultaneous localization and mapping (SLAM) is a central and complex problem in robot research community. In SLAM, extended Kalman filter (EKF) implementation is widely used to localize the robot and build the environment map incrementally. In this paper, we propose a strong tracking filter (STF) SLAM algorithm. This algorithm applies STF to deal with the non-linear estimated problem in SLAM instead of EKF. It can make the performance of the nonlinear filter approximate to that of optimal linear Kalman Filter (KF), so it can construct high accuracy maps and locate the robot more accurately than EKF SLAM. Simulation experiments illustrate the superior performance of our approach compared to EKF SLAM algorithm.
Use of fuzzy logic in adaptive "morphological" controllers

In this paper, fuzzy logic-based adaptive morphological control (MC) is proposed. MC generalizes the idea of variable structure control (VSC). In the conventional approach to VSC, the control surface does not change the shape during a step response. A morphological type of VSC changes the shape of the control surface during each step response to increase system performance. This MC's "varying structure" property provides an extra degree of flexibility to combine different control goals such as robustness and minimum time control. A TSK type FLC with sliding mode controller (SMC) functions as the rule output function is developed, which is used as a controller in MC approach. Simulation results with a DC motor model are presented.<<ETX>>
Prediction of malignant and benign of lung tumor using a quantitative radiomic method.

Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features has the ability to capture intra-tumoural heterogeneity in a non-invasive way. Here we carry out a radiomic analysis of 150 features quantifying lung tumour image intensity, shape and texture. These features are extracted from 593 patients computed tomography (CT) data on Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI) dataset. By using support vector machine, we find that a large number of quantitative radiomic features have diagnosis power. The accuracy of prediction of malignant of lung tumor is 86% in training set and 76.1% in testing set. As CT imaging of lung tumor is widely used in routine clinical practice, our radiomic classifier will be a valuable tool which can help clinical doctor diagnose the lung cancer.
A new full aperture wavefront measuring method for conformal domes

Conformal windows technology is widely applied to meet the aerodynamics and optical performance requirements. The conformal optical domes have high steepness and high precision shapes. Thus it is necessary to improve the interferometric method to measure the form qualities of conformal domes precisely and effectively. In this paper we introduce a novel full aperture measuring method to test the transmission wavefront of the dome surface. An aspheric compensator is designed and placed behind the dome to reflect the aspheric testing wave back to the same path. It is practical to assemble the testing system with high precision for its simple structure. In addition, tolerance analysis is conducted, and the accuracy of this method is verified to be λ/30 (λ=3.39um). The influence of different error factors, including the thickness error and decenter error, on the testing wavefront is analyzed, and then guidance to assembly is given accordingly.
High selective metamaterial resonator based on complementary split ring resonator

In this work, a metamaterial resonator based on complementary split ring resonator (CSRR) loaded with square open-loop resonator (OLR) is presented theoretically and experimentally. It is shown through equivalent circuit model that the addition of OLR to the conventional CSRR-loaded cell generates transmission zero in the upper stop band and improves the upper band rejection level without increasing the traversal dimensions. Simulation results show that a passband with high selectivity is obtained. Moreover, the via-free characteristic of the basic cell enables easy and low-cost fabrication.
Research on the environmental impact factors of Hand-Foot-Mouth Disease in Shenzhen, China using RS and GIS technologies

The outbreak of Hand-Foot-Mouth Disease (HFMD) has brought a serious threat to people's health. There is no research considering both the natural and social environment to study the outbreak and propagation of HFMD quantitatively by now. In this study, we acquired environmental impact factors of HFMD using remote sensing and GIS. Then we analyzed the relationship between density of HFMD cases and environmental impact factors for year 2009 in Shenzhen. It was found that density of HFMD cases was significantly associated with population density, hospital density, road density, annual average of aerosol optical depth, and annual average of normalized difference vegetation index. A linear regression model was constructed with the R2 value of 0.677 using environmental impact factors, and both hospital density and annual average of aerosol optical depth were included in the model.
Neuroadaptive PI control with prescribed performance for a class of nonaffine systems with non-smooth input saturation

Based on L1 adaptive control theory, a novel block backstepping control for a class of uncertain multiple-input-multiple-output nonlinear system is proposed. The matched system parametric uncertainty and unmatched general uncertainty including modeling error and external disturbance are considered in the design. The L1 adaptive control is integrated with block backstepping to improve the transient performance in addition to stable tracking. The low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signal and ensure its smoothness. A reference system is introduced to prove the stability of the closed-loop system via L1 adaptive theory and Lyapunov theorem. Finally, numeral simulation results show the transient performance and feasibility of the proposed adaptive control. A neuroadaptive PI controller is developed for a class of nonaffine systems with bounded external disturbance and a symmetric non-smooth input saturation. Neural networks's (NNs's) capability of nonlinear function approximation and the use of exponentially decaying and hyperbolic functions in solving prescribed performance (PP) problems. Then, a transformed filtered error is designed to facilitate the control design and analysis. A stability analysis based on Lyapunov method guarantees uniformly ultimate bounded (UUB). Simulations are carried out to verify and clarify the theoretical findings.
DBLSTM-based multi-scale fusion for dynamic emotion prediction in music

Dynamic Music Emotion Prediction is crucial to the emerging applications of music retrieval and recommendation. Considering the influence of temporal context and hierarchical structure on emotion in music, we propose a Deep Bidirectional Long Short-Term Memory (DBLSTM) based multi-scale regression method. In this method, a post-processing component is utilised for individual DBSLTM output to further enhance the ability of temporal context processing and a fusion component is to integrate the output of all DBLSTM models with different scales. In addition, we investigate how the difference of sequence length between the training and predicting phase affects the performance of DBLSTM. We conduct our experiments on a public database of Emotion in Music task at MediaEval 2015, and the result shows that our method achieves significant improvement when compared with the state-of-art methods.
An extendable multi-purpose 3D neuromorphic fabric using nanoscale memristors

Neuromorphic computing offers an attractive means for processing and learning complex real-world data. With the emergence of the memristor, the physical realization of cost-effective artificial neural networks is becoming viable, due to reduced area and increased performance metrics than strictly CMOS implementations. In the work presented here, memristors are utilized as synapses in the realization of a multi-purpose heterogeneous 3D neuromorphic fabric. This paper details our in-house memristor and 3D technologies in the design of a fabric that can perform real-world signal processing (i.e., image/video etc.) as well as everyday Boolean logic applications. The applicability of this fabric is therefore diverse with applications ranging from general-purpose and high performance logic computing to power-conservative image detection for mobile and defense applications. The proposed system is an area-effective heterogeneous 3D integration of memristive neural networks, that consumes significantly less power and allows for high speeds (3D ultra-high bandwidth connectivity) in comparison to a purely CMOS 2D implementation. Images and results provided will illustrate our state of the art 3D and memristor technology capabilities for the realization of the proposed 3D memristive neural fabric. Simulation results also show the results for mapping Boolean logic functions and images onto perceptron based neural networks. Results demonstrate the proof of concept of this system, which is the first step in the physical realization of the multi-purpose heterogeneous 3D memristive neuromorphic fabric.
Shrub biomass estimation in Mu Us Sandland using simple GO model and multi-angle observations

Forest biomass is an important indicator in carbon sequestration capacity and forest carbon budget evaluation, but there is less focus on shrub biomass. Multi-angle images provide the volume scattering information which could improve inversion accuracy. HJ-1 satellites are the sun synchronous recurrent frozen orbit small satellite constellation for environment and disaster monitoring and forecasting and were launched on September 6, 2008, China, with a repetition cycle of two days. Now 6 angle observations have been built up from several days of HJ-1A / B images. Images were first calibrated both geometrically and atmospherically. A simplified Geometric-Optical(SGM) model was used for the parameters estimation. The background estimation was conducted from the LiSparse-RossThin kernel weights and the Walthall empirical model. Then, the three kernel weights of the LiSparse-RossThin model were retrieved for each location. Finally, the shrub cover and radius were retrieved from SGM model. Biomass was estimated from the shrub cover and radius. 21 plots for background estimation and verification were measured during July 2009. The R2 between simulated cover and measured cover is 0.21, but the RMSE of the model fitting is 0.09. The distribution of the shrub cover estimated from multi-angle HJ images is coincidence with fine resolution Google Earth image. So is the biomass distribution. The weak relationship may be because the higher spatial heterogeneity, the view angles' limitation, and the time variance among images. The multi-angle HJ images are low cost which is useful in far and extended sandland monitoring and evaluation. The results reported here also showed that the development of HJ satellite data for wide range of mapping application.
Public transport network optimization based on a Multi-objective Optimization Problems Evolutionary Algorithm

Considering the benefits of passengers and bus corporations, this paper established a mathematical model of public transport network optimization. A Multi-objective Optimization Problems Evolutionary Algorithm (MOPEA) is presented to solve optimization problems in the public transport network. In this algorithm, the theory of particle system changing from non-equilibrium to equilibrium is used to define the Rank function and Niche function, so all the individuals in the population have chance to participate in the evolving operation such as crossover and mutation to solve the global Pareto optimal solutions of public transport network optimization problems. This algorithm can avoid premature phenomenon of public transport network optimization problems. At the same time, diversity of objective functions is reserved. This algorithm can gain compromise optimal results of conflicting multi-objective optimization problem—Pareto optimal front, and avoid changing multi-objective functions into one objective function by inducting experiential weight coefficient. At last, an example was given and the result showed that this algorithm had more advantages than traditional evolutionary algorithms.
Adaptive feedforward control of the steer-by-wire system based on the online parameter estimator

The adaptive feedforward control method is presented in this paper to improve the tracking performance of the SBW system. Due to model nonlinearities, noise disturbance and mechanical complexity, the system parameters vary with bounded uncertainties. To get an accurate system model, an online adaptive parameter estimator is proposed to realize parameter identification of the control model, based on the output error identification method. Meanwhile, the parameter gradient projection method is applied to eliminate the parameter drift, while a full order state observer is developed to weaken the effects of noise disturbance during the parameter identification. The experiment results show the effectiveness and robustness of the adaptive estimator, and a better tracking performance for the SBW system is achieved by applying the adaptive feedforward control method.
Ultrathin planar metasurface for controlling electromagnetic wave with broad bandwidth

For conventional lens to control the propagation of electromagnetic (EM) wave, the designed surface geometries or spatial variation of the refractive index distribution is a must to provide the gradual phase changes accumulated along the transmission path. The required thickness and the complication in terms of fabrication limit its applicability in some cases. The metasurface with phase discontinuity[1] at the interface is proposed as a solution. The gradual phase changes are taken placed by the abrupt phase changes introduced at different place of the surface. If the metasurface has a constant phase gradient of dΦ/dx along X axis, the transmitted wave can be bended in the plane of XOZ with an anomalous refraction angle θt, which can be predicted by the generalized Snell's law (GSL)[1]. For the published designs, the realized efficiency is only a few percent, which can be hardly used.
Health assessment of the water-level-fluctuation zone (WLFZ) in the Three Gorges area based on spatial information technology

The WLFZ in the Three Gorges area is alternately controlled and influenced by both water ecosystem and terrestrial ecosystem. Its ecological vulnerability and instability are significant. Taking small watershed as a unit of assessment, we select population density, urbanization intensity, temperature, precipitation, and humidity and vegetation coverage to construct the fluctuating zone wetland ecosystem assessment index system. The comprehensive health index is calculated after extracting the main information of the six variables using principal component analysis. It is ranged into three levels: good, medium and poor. The results show that the health state of the entire Three Gorges area is at medium and poor levels. Regions at medium health state account for 58% of the entire area, and the remaining regions are at poor health. Taking Wanzhou District as the boundary, the health state of the southwest region is better than the northeast region.
A small voltage standing wave ratio millimeter wave gain equalizer using microstrip SIR

A millimeter wave gain equalizer is designed using microstrip open SIR with a small voltage standing wave ratio. The equalizer is fixed on ceramic substrate with thin film resistance integrated. The theory of microstrip open SIR is analyzed. New match method is analyzed and used to get a small voltage standing wave ratio at the in/output port. The maximum attenuation is 12.5dB at 34GHz. The VSWR at I/O port both less than 1.4 from 30GHz to 40GHz.
A New Millimeter-wave Multi-way Power Dividing/Combining Network Based on Waveguide-Microstrip E-plane Dual-Probe Structure

This paper presents a new millimeter-wave four- way power dividing/combining network, based on waveguide- microstrip E-plane dual-probe structure. With a good heat sinking property and an easy fabrication process, this compact E- plane dual-probe structure fulfills waveguide-to-microstrip transition and power dividing/combining simultaneously, and has a loss of about 0.2dB in whole Ka-band, indicated by the results of electromagnetic field simulation. Connecting these two dual- probe tri-port networks symmetrically with a waveguide E-T hybrid, a millimeter-wave four-way power dividing/combining network is fabricated, and the measured loss of 0.4dB is obtained in 32GHz-38GHz. With convenience of integrating solid-sate high power MMIC devices, this broadband low loss millimeter-wave four-way symmetric microstrip integrated power dividing/combining network can realize millimeter solid-state high power combining with a high efficiency.