nils-herrmann/clean_scirepeval huggingface dataset:
A Term-Mapping Framework for Data Mining in Heterogeneous Medical Data Sources

Medical informatics developed dramatically in recent years, and the resulting large volumes of data may contain useful information. Data mining is used to gain more value from data sources, but it would not work well using the same way in different medical information systems. It is one of the reasons that the data descriptions are various in different systems. This paper presents Term-mapping framework which can be applied in data mining. The framework connects data sources and standard terminology, and it would improve the efficiency of data mining remarkably.
Biometrics Encryption Based on Palmprint and Convolutional Code

Based on palmprint and convolutional code, a biometrics encryption method is proposed. Firstly, the period of randomly generated keys are extended. It is then encoded with our convolutional code to get what we call a pseudo-palm code. It will be locked by XORing it with the user&#xa1;&#xaf;s reference palmprint features code. The above two aspects of processing information is stored in the smart card. During the decoding phase, the user presents his palmprint features to unlock the key. After XORing with the date on the smart card, it is then decoded with convolutional code and voted to get original keys. The scheme adopts biometrics encryption method with palmprint and convolutional code, which is more secure than single password authentication, and is more accurate than single biometric authentication.
A novel 3-D space vector modulation scheme for three-level three-leg NPC converter in three-phase four-wire APF system

This paper proposes a novel 3-D space vector modulation scheme for three-level three-leg neutral point clamped converter used in the three-phase four-wire active power filter towards the performance optimization among load harmonic current compensation, mid-point voltage balancing, and Suppression of switching loss increase. Optimization functions for different objectives are analyzed based on the detailed state space equations of the system. Furthermore, a multi-objective optimization process is presented to show how the compensation for the load harmonic current, mid-point balancing control, and switching losses reduction can be optimized based on the solution space of linear equations. Lastly, the simulation and experimental results are given for verification purpose.
A high-voltage half-bridge gate drive circuit for GaN devices with high-speed low-power and high-noise-immunity level shifter

A high-voltage half-bridge gate drive circuit for E-mode GaN device with high-speed low-power and high-noise-immunity level shifter is proposed in this paper. It adopts digital-level detection concept by combining fast-slewing and output-maintain circuits in high noise environments to achieve both small response time and high dVsw/dt noise immunity. The proposed gate driver is fabricated in 0.5μm 80V HV CMOS process where the active area is 1699×1522μm2. Simulation and experimental results of the level shifter demonstrate that propagation delay is only about 1.618ns and dVsw/dt noise immunity is up to 50V/ns with a low FOM equal to 0.04ns/μm·V.
Capacity study of multiuser cooperative networks with fixed-gain relays

In this study, the authors investigate the approximate capacity of a multiuser cooperative networks with fixed-gain relays over independent non-identically distributed Rayleigh fading channels. In order to reduce the complexity, they propose a two-step selection scheme. In this scheme, relay and user selection is processed step by step and relay selection is based on the outdated channel state information (CSI). Finally, the authors show that the closed-form bounds match perfectly with Monte Carlo simulations and although the performance of our scheme is inferior to the optimal joint selection scheme, it is still available in practical scenarios because its low complexity.
A high-speed digital underwater communication solution using electric current method

The limitations of underwater acoustic communication have been intensively discussed. In this scenario, electric-current communication method is considered as a feasible solution to explore as an alternative to acoustic communication in case of short-range distance. Compared to underwater acoustic communication, electric-current communication method shows its advantages such as fast propagation, high band width and being not prone to noise due to mechanical movement. In this paper, a DSP based underwater modem using electric current method is designed, implemented and evaluated. According to the experiments, error-free communication is possible in a water tank with a data rate of 1Mbps.
A robust mixed H2/H∞ tracking control for 3-DOF permanent magnet spherical actuator

This paper addresses a robust mixed H<inf>2</inf>/H<inf>∞</inf> control for 3-DOF Permanent Magnet (PM) Spherical Actuator in order to improve its trajectory tracking performance. The dynamic model of the PM spherical actuator is a multi-variable nonlinear system contains uncertainties such as model errors and disturbances inevitably, which will gravely influence the performance of the control system. Therefore, a mixed H<inf>2</inf>/H<inf>∞</inf> control algorithm which consists of H<inf>2</inf> and H<inf>∞</inf> control theory is design to compensate for these uncertainties. The robust mixed H<inf>2</inf>/H<inf>∞</inf> control algorithm aim at attenuate the effects of the model uncertainties and external perturbations. To illustrate the effectiveness of the proposed control algorithm, both simulations and experiments are conducted. The results have shown that the proposed control algorithm has better trajectory tracking performance and strong robustness to uncertainties.
Hierarchical optimal power flow control for loss minimization in hybrid multi-terminal HVDC transmission system

A hierarchical control scheme is proposed for optimal power flow control to minimize loss in a hybrid multiterminal HVDC (hybrid-MTDC) transmission system. In this scheme, the lower level is the droop control, which enables fast response to power fluctuation and ensures a stable DC voltage, and the upper level is power flow optimization control, which minimizes the losses during the operation of hybrid-MTDC and solves the contradiction between minimizing losses and preventing commutation failure. A 6-terminal hybrid-MTDC is also designed and simulated in PSCAD according to the potential demand of power transmission and wind farms integration in China to verify the proposed control strategy. First, the steady state analysis is conducted and then compared with simulation results. The analysis shows that the proposed control scheme achieves the desired minimum losses while at the same time satisfying system constraints. The proposed control scheme also guarantees that the hybrid-MTDC not only has a good dynamic response, but also remains stable during communication failure.
880 V/ $2.7~\text{m}\Omega\cdot\text{cm}^{\text{2}}$  MIS Gate Trench CAVET on Bulk GaN Substrates

In this letter, we report a high-voltage metal-insulator-semiconductor gate trench current aperture vertical electron transistor using metal–organic chemical vapor deposition regrown AlGaN/GaN as the channel and in-situ Si<sub>3</sub>N<sub>4</sub> as the gate dielectric. The device had a high breakdown voltage of 880 V and a low <inline-formula> <tex-math notation="LaTeX">$\text{R}_{\mathrm {on,sp}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$2.7~\text {m}\Omega \cdot \text {cm}^{2}$ </tex-math></inline-formula>. A low hysteresis of ~0.1 V was observed due to the high quality of the in situ Si<sub>3</sub>N<sub>4</sub>.
Action Recognition Using Multilevel Features and Latent Structural SVM

We first propose a new low-level visual feature, called spatio-temporal context distribution feature of interest points, to describe human actions. Each action video is expressed as a set of relative XYT coordinates between pairwise interest points in a local region. We learn a global Gaussian mixture model (GMM) (referred to as a universal background model) using the relative coordinate features from all the training videos, and then we represent each video as the normalized parameters of a video-specific GMM adapted from the global GMM. In order to capture the spatio-temporal relationships at different levels, multiple GMMs are utilized to describe the context distributions of interest points over multiscale local regions. Motivated by the observation that some actions share similar motion patterns, we additionally propose a novel mid-level class correlation feature to capture the semantic correlations between different action classes. Each input action video is represented by a set of decision values obtained from the pre-learned classifiers of all the action classes, with each decision value measuring the likelihood that the input video belongs to the corresponding action class. Moreover, human actions are often associated with some specific natural environments and also exhibit high correlation with particular scene classes. It is therefore beneficial to utilize the contextual scene information for action recognition. In this paper, we build the high-level co-occurrence relationship between action classes and scene classes to discover the mutual contextual constraints between action and scene. By treating the scene class label as a latent variable, we propose to use the latent structural SVM (LSSVM) model to jointly capture the compatibility between multilevel action features (e.g., low-level visual context distribution feature and the corresponding mid-level class correlation feature) and action classes, the compatibility between multilevel scene features (i.e., SIFT feature and the corresponding class correlation feature) and scene classes, and the contextual relationship between action classes and scene classes. Extensive experiments on UCF Sports, YouTube and UCF50 datasets demonstrate the effectiveness of the proposed multilevel features and action-scene interaction based LSSVM model for human action recognition. Moreover, our method generally achieves higher recognition accuracy than other state-of-the-art methods on these datasets.
Efficient CW Dual-Wavelength and Passively  $Q$ -Switched Tm:LuAG Lasers

Efficient diode-pumped Tm:LuAG lasers operating in continuous wave, dual-wavelength,and passive Q-switching regimes have been demonstrated. A maximum output power of 4.42 W with a beam quality of M2 ≤1.45 at 2021.2 nm was obtained, corresponding to a slope efficiency of 49.5%. Using a birefringent quartz plate, the Tm:LuAG laser emitted the highest output power of 2.3 W at dual-wavelength of 2022 and 2030 nm. With a SWCNT-SA employed as saturable absorber, passively Q-switched Tm:LuAG laser was realized, which generated the shortest pulses of 405 ns at 2021.6 nm and a maximum single pulse energy of 40.6 μJ at 2019.7 nm.
A simple and compact tri-band planar inverted-F antenna for personal communication handset applications

A simple and compact planar inverted-F antenna (PIFA) with size of 35mm×25 mm×6mm for personal communication handset applications is proposed in this paper. The radiating patch is folded at end and etched with two slits to create multiple resonant frequency bands. The antenna covers following frequency bands: Global System for Mobile communication (GSM900, 880–960 MHz)/Digital Communication System (DCS1800MHz, 1710–1880 MHz)/Bluetooth (2450MHz, 2400–2480 MHz). The proposed antenna has been designed and analyzed by using commercially available software, High Frequency Structure Simulator (HFSS) based on the finite element method algorithm. We also fabricate the proposed antenna and the measurement is carried out by using E8363B Vector Network Analyzer of Agilent. The simulated results agree quite well with the experimental results.
A New Computerized Method for Missed Cancer Detection in Screening Mammography

The development of computer-aided diagnosis (CAD) methods to improve a radiologist's decision-making in reading mammography is facing a bottle-neck problem. One critical reason is that almost all current CAD methods are actually developed to perform as an independent "reader" rather than a complementary assistant "second reader" to the radiologists. This paper proposes a new computer aided detection (CAD) method to improve early detection of breast cancer in screening mammograms by focusing on computerized analysis and detection of cancers missed by radiologists. The strategics taken in this study include (a) multi-mode detection by breast density classification, (b) breast area partition and region based adaptive detection, (c) weighted classification using the distinguishing features identified in missed cancer analysis. The results demonstrated that, due to the effective modification strategics taken in the new system, detection performance was improved significantly for mammograms at both detected and missed stages. With the focus on missed cancer analysis and detection, a bigger improvement was obtained in detecting missed cases even though the general detection performance is still lower than that at detected stage. It is also observed that the cancer could be detected earlier with less false positive signals by using the new CAD strategy.
Development of Communication Model for Social Robots Based on Mobile Service

This paper describes an interactive social agent platform which examines anthropomorphic robot features in the mobile phone usage context. Our system is smart phone based robot agent that operates on a mobile network and co-located ad-hoc networks. It helps remote users communicate interactively with each other through the robotic interface which utilizes facial expressions and body gestures. In this paper, we introduce the mobile environment as a service platform for social robots, and discuss design considerations for such a communication system. Then we illustrate the development of our system followed by its network protocols built on existing mobile services such as Telephony and Short Messaging Service (SMS). Usage scenarios and working prototypes of the implemented system are also presented. We are hopeful that our research will open a new discussion on socially interactive robot platforms, and thus, that such efforts will enrich the telecommunication and personal robot services in the near future.
Application of computational fluid dynamics tools to circuit-breaker flow analysis

This paper presents the results of the application of a computational fluid dynamics computer program to the simulation of cold and plasma flows in circuit breakers. The underlying physical model is based on the axisymmetric form of the conservation equations which are coupled with an arc model including ohmic heating and radiative exchanges of energy in the fluid. Various cases are computed, ranging from steady cold flow to unsteady arc-flow interaction. A good agreement has been found between the computed solutions and the available experimental data, demonstrating a great potential of these tools for circuit-breaker design and analysis in the future.
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction

We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F0 = F - h(X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F0. Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X) and F, we show that FME relaxes the hard linear constraint F = h(X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms.
Examination of Broiler Price Periods Using Wavelet Transforms and Variance Analysis

The Examination of broiler price periods plays a crucial important role in forecasting broiler price for the poultry breeding industry because it is beneficial to maximizing the profit and minimizing the risk. As wavelet transforms are well known to work well for reducing the noise of the data, in this paper we perform wavelet transforms to de-noise the broiler prices, and then use variance analysis to seek length of the broiler price periods.
A New Hidden Markov Model for Protein Quality Assessment Using Compatibility Between Protein Sequence and Structure.

Protein structure Quality Assessment (QA) is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA. In this work, we developed a new Hidden Markov Model (HMM) to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities: (1) encoding local structure of each position by jointly considering sequence and structure information, and (2) assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP, and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.
An Optimization Method for Inquiry of Vector Data Based on Client

The contradiction between distributed-GIS massive data and the limited bandwidth of network resources is becoming more and more serious, inquiry of distant vector data and transmission of massive data become the crisis key of the problem. Based on analyzing the method of massive data inquiry, combing with the query optimized method of spatial database and the cost function, we proposed a novel vector data inquiry method which is optimized and can transplant to the client. The experiment proved that this method is effective on improving the inquiry speed and maintaining a good network-load performance.
Basic Time-Domain Electromagnetic Emanation Models of Information Technology Equipment

The standing-wave dipole and the traveling-wave monopole in time domain are proposed to model information technology equipment. The standing-wave dipole model can be easily got from some other references and the traveling-wave monopole model is built based on Rothwell's work with some revision made. The condition, on which selection can be made, is included. Features of the transient near zone and far zone fields are discussed of both models through examples. Good agreement is got between analysis and computed results plotted in the paper. Index Terms-standing-wave dipole, traveling-wave monopole, current pulse, transient fields.
A New Scheme of Image Retrieval Based Upon Digital Watermarking

Watermark is a new effective digital copyright protection method and data security technology. A new scheme of image retrieval based upon digital watermarking has been proposed. First, embeds the binary watermark information in carried image, computes the value of NC (normalized correlation coefficient) between query watermark and extracted watermark,and gains the retrieval results. The watermarking algorithm is carried out in DCT (discrete cosine transform) domain, and extracting process is instantaneous and blind. The experimental results show that this method cannot only protect the copyright reliably, but also realize image retrieval effectively.
Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild

In many real-world face recognition scenarios, face images can hardly be aligned accurately due to complex appearance variations or low-quality images. To address this issue, we propose a new approach to extract robust face region descriptors. Specifically, we divide each image (resp. video) into several spatial blocks (resp. spatial-temporal volumes) and then represent each block (resp. volume) by sum-pooling the nonnegative sparse codes of position-free patches sampled within the block (resp. volume). Whitened Principal Component Analysis (WPCA) is further utilized to reduce the feature dimension, which leads to our Spatial Face Region Descriptor (SFRD) (resp. Spatial-Temporal Face Region Descriptor, STFRD) for images (resp. videos). Moreover, we develop a new distance metric learning method for face verification called Pairwise-constrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks (resp. volumes) from an image (resp. a video). Our work achieves the state-of-the-art performances on two real-world datasets LFW and YouTube Faces (YTF) according to the restricted protocol.
Bit error rate of a digital radio eavesdropper on computer CRT monitors

An eavesdropper on computer CRT (cathode ray tube) monitors can be used to intercept video information. Its anti-noise performance is analyzed in this paper. Baseband transmission models of digital signals are established according to the operating principle of the eavesdropper. The relationship between the eavesdropper's bit error rate and some parameters, such as intercept distance, superposition times and noise power, is discussed under the circumstances of ISI and ISI-freedom. Good agreement is obtained between experimental results and theoretical analysis.
A cloud detection method based on color model and undecimated wavelet transformation

A new cloud detection method based on color model and undecimated wavelet transform was proposed for the basic properties of the cloud area on remote sensing images and band features. First of all, modified HSI Color model transformation was applied to original images combined with the band information in order to extract approximate boundary of cloud area. Secondly, as in binary sampling process of wavelet decomposition, there was losing spectral information and texture information problems. In order to solve this, we used undecimated wavelet transform and the normalized double threshold value method to determine the exact boundary of cloud area. At last, our results were assessed using False Recognition Rate and Accuracy. The assessment was compared with cloud detection methods based on wavelet transform and single threshold. The Result means that proposed algorithm has high detection precision.