nils-herrmann/clean_scirepeval huggingface dataset:
An investigation of dual-energy transmission measurements for more accurate non-uniform attenuation compensation in cardiac SPECT

In this study we investigate the use of dual energy transmission imaging, in simultaneous transmission-emission tomography. Transmission projection data are acquired using the two major photopeaks of /sup 153/Gd, at 44 keV and 99 keV. The degree of contamination of the data in the 44 keV window, by scattered 99 keV transmission photons, is investigated using Monte Carlo simulations. A new method is proposed to convert the two transmission maps measured at 44 keV and 99 keV to one appropriate for /sup 99m/Tc. This transmission map is compared with one produced by scaling the 99 keV map, for the attenuation correction of /sup 99m/Tc emission data. The use of dual energy transmission imaging is shown to improve the quantitative accuracy of the reconstructed emission maps. Dual energy transmission imaging is also shown to be more effective at visualizing areas of bone, particularly in truncated regions of the object.<<ETX>>
A Maximum Entropy Markov Model for Prediction of Prosodic Phrase Boundaries in Chinese TTS

Hierarchical prosody structure generation is a key component for a speech synthesis system. One major feature of the prosody of Mandarin Chinese speech flow is prosodic phrase grouping. In this paper a method based on maximum entropy Markov model (MEMM) is proposed to predict prosodic phrase boundaries in unrestricted Chinese text. MEMM is described in detail that combines transition probabilities and conditional probabilities of states effectively. The conditional probabilities of states are estimated by maximum entropy (ME) theory. A comparison is conducted between the new model and maximum entropy model for prosody phrase break prediction. The experiments show that utilizing the same feature set, MEMM improves overall performance. The precision and recall ratio are improved.
Exploiting the Human–Machine Gap in Image Recognition for Designing CAPTCHAs

Security researchers have, for a long time, devised mechanisms to prevent adversaries from conducting automated network attacks, such as denial-of-service, which lead to significant wastage of resources. On the other hand, several attempts have been made to automatically recognize generic images, make them semantically searchable by content, annotate them, and associate them with linguistic indexes. In the course of these attempts, the limitations of state-of-the-art algorithms in mimicking human vision have become exposed. In this paper, we explore the exploitation of this limitation for potentially preventing automated network attacks. While undistorted natural images have been shown to be algorithmically recognizable and searchable by content to moderate levels, controlled distortions of specific types and strengths can potentially make machine recognition harder without affecting human recognition. This difference in recognizability makes it a promising candidate for automated Turing tests [completely automated public Turing test to tell computers and humans apart (CAPTCHAs)] which can differentiate humans from machines. We empirically study the application of controlled distortions of varying nature and strength, and their effect on human and machine recognizability. While human recognizability is measured on the basis of an extensive user study, machine recognizability is based on memory-based content-based image retrieval (CBIR) and matching algorithms. We give a detailed description of our experimental image CAPTCHA system, IMAGINATION, that uses systematic distortions at its core. A significant research topic within signal analysis, CBIR is actually conceived here as a tool for an adversary, so as to help us design more foolproof image CAPTCHAs.
Application of H∞ Control in Rudder/Flap Vector Robust Control for Ship Course

For the parameter uncertainty and the disturbance randomicity that exist in ship course motion, the robust controller of ship course is designed on the base of rudder/flap vector control. The system uncertainty is analyzed, and the choice of weighting functions is discussed chiefly. For the sea wave operating on the ship, its energy-concentrating frequency varies as the encountering angle. For different encountering angles, we design the different weighting functions. For the pole of nominal model existing in imaginary axis, we use the bilinear-transform method to handle it. The method of "2-Riccati" equations is adopted to solve the Hinfin controller. The system simulation is given, and the result shows that, compared with the PID controller, this system has a higher course precision, better robust performance. And the research has engineering application value.
Phase Space Reconstruction of Nonlinear Time Series Based on Kernel Method

A phase space reconstruction method KPCA-CA was proposed based on kernel principal component analysis (KPCA) and correlation analysis (CA) for nonlinear time series. On the basis of KPCA, the correlation was analyzed between every kernel principal component and output variable, and some kernel principal components were discontinuously chosen according to their correlation degree to form the phase space of nonlinear time series. The method was compared with other methods of phase space reconstruction. The experimental results show that modeling accuracy for nonlinear time series is highest based on the phase space reconstruction method proposed by the paper, proving the efficiency of the method
Improved variable EWMA controller design

The exponentially weighted moving average (EWMA) controller is a popular run-to-run (RtR) control scheme in semiconductor manufacturing because of its effectiveness and simplicity. In this paper we propose an improved variable EWMA controller design method. The optimal discount factor of the EWMA controller is determined by minimizing the mean square error (MSE) of process output at each run. The proposed method overcomes some potential problems of Tseng's method and can be applied to general autoregressive integrated moving average (ARIMA) processes. Simulation examples are given to show the effectiveness of the proposed method.
Reliability data analysis of bogie components of high speed train

A reliability model of components of bogie system including wheel and axle boxes device, suspension device, frame device, foundation brake device and driving device is proposed based on the real data of the CRH2 high speed train and the universal reliability model. Furthermore, Million kilometers' mean failure rate and mean distances between failures was computed. Reliability index of each component was analyzed. The reliability model parameters of each component were estimated using the maximum likelihood estimation method and the optimal failure distribution function was determined by A-D test method.
Functional waveguide mode transformers

Field transformers having irregular scattering elements in a rectangular waveguide are introduced as a means to achieve frequency-dependent mode conversion, mode-selective reflection, multiple mode conversion, and phase shifting. A stepwise wall profile provides for a large number of degrees of freedom and also induces large evanescent field content. A multiresolution optimization strategy allows good convergence during synthesis. The resulting structures are compact, with dimension of a few wavelengths, and can have functionality not achievable through periodic means. In some cases, more than one solution can be found that have different spectral characteristics, a result of the large number of degrees of freedom and the constraint set. This structure class may prove useful in microwave sources where multiple or higher order modes are excited, in waveguide transitions, in frequency-dependent switching, and as phase-shifting elements.
Discriminative training for improving letter-to-sound conversion performance

In this paper, we propose to use discriminative training (DT) for improving letter-to-sound (LTS) conversion performance. LTS is a critical component in both ASR and TTS for predicting the correct pronunciation of a word not included in the lexicon. For TTS applications, predicting the proper pronunciation of an out-of-vocabulary person/place name, especially a name with foreign origin can be challenging. We utilize discriminative training, which has been successfully used in speech recognition, to sharpen the baseline N-grams of grapheme-phoneme pairs. We address the problem in a unified framework of discriminative training. Two criteria, maximum mutual information (MMI) and minimum phoneme error (MPE), are investigated. Experimental results show that DT yields a small (3.8-4.6% relative) but consistent error reduction across all databases tested. In addition, we observe that by pinpointing the local errors in a finer resolution, we can obtain a better discriminative model.
A research on modeling feedback-based data scheduling in P2P streaming system

In order to evaluate the influence of neighbors' quality to P2P streaming system's performance during scheduling, this paper establishes a markov chain based differential function model to analyze the relationship between peer's average startup delay and probability neighbors have wanted data as well as neighbors' available upload bandwidth. This paper also proposes a scheduling feedback algorithm, which learns real-time information of neighbors' data ownership as well as service capacity during data transferring and feeds back to overlay to help its optimization. The algorithm could help overlay discover possible better Qos neighbors when scheduling. Numerical analysis and experiment shows better quality neighbors can improve system's average startup delay significantly during scheduling.
An Improved Method For Retrieving Land Surface Albedo Over Rugged Terrain

Land surface albedo is a very important parameter, which can be derived from a bidirectional reflectance distribution function (BRDF) model with angular integration of BRDF in a particular distribution of downward solar irradiance. The Algorithm for MODIS Bidirectional Reflectance Anisotropic of Land Surface (AMBRALS) utilizes a kernel-driven BRDF model to retrieve land surface albedo, but it does not take into account the topographic effect. This letter proposed an improved method by adding topographic factor to the AMBRALS, which removes the topographic effect on land surface reflectance and considers the topographic effect in retrieving land surface albedo. This method is applied to the HJ-1A/B CCD land surface reflectance data to retrieve the land surface albedo. The results are compared with those from the AMBRALS and with the ground measurements. The comparison results show that the proposed method performs well in general and better than the AMBRALS over rugged area.
New Multi-Modulus Algorithms for Blind Decision- Feedback Equalization of High-Order QAM Signals

Two new multi-modulus algorithms (MMA)-the dual-mode MMA and the stop-and-go dual-mode MMA for blind decision-feedback equalization of highorder quadrature amplitude modulation (QAM) signals are proposed. Simulations using a fractionallyspaced decision-feedback equalization (DFE) setting are used to compare the proposed scheme with the recently introduced multi-modulus algorithm. The proposed blind equalizers are shown to have faster convergence speed and improved steady-state mean square error, compared with the MMA.
Data mining in ditributed data environment

Nowadays, more and more databases are distributed in networks. To produce knowledge with the databases, traditional data mining technology should be developed in model, algorithm, strategy, etc. The popular distributed data mining (DDM) systems are introduced and classified into three classes: DDM systems based on parallel data mining agents, DDM systems based on meta-learning, and DDM systems based on Grid. According to the analysis of the three main classes of DDM systems, the main problems decreasing the system utility are concluded; a novel DDM model and the related knowledge integrating model are proposed.
A new algorithm for three-finger force-closure grasp of polygonal objects

We prove a new necessary and sufficient condition for 2D three-finger equilibrium grasps and implement a geometrical algorithm for computing force-closure grasps of polygonal objects in this article. The algorithm is quite simple and only needs some algebraic calculations. An easily computable measure of how far a grasp is from losing force-closure is provided as well. Finally, we implement the algorithm and demonstrate its usefulness by an example.
Construction of multiple-stage time-hopping sequences in time-hopping spread spectrum ultra wideband

In this paper, based on chip synchronism, a novel time-hopping (TH) design for time-hopping spread spectrum ultra wideband (THSS-UWB) is presented, and multiple-stage TH sequences are constructed. By combining the existing TH sequences in various ways, a large number of new TH sequences with good TH correlation properties are designed. Especially, when the periods of TH sub-sequences composing multiple-stage TH sequences are equal to each other, the maximal correlation function values of multiple-stage TH sequences, designed in this paper, are not larger than the maximum among the maximal correlation function values of the TH sub-sequences. Also, both the number of multiple-stage TH sequence families and the number of multiple-stage TH sequences in every family are large, and the numbers increase rapidly when the hopping stage value increases. The paper provides the construction of multiple-stage TH sequences, and proves the correlation properties of the designed sequences.
A study on the elementary control methodologies for complex systems

This paper discusses a kind of elementary methodology for the control of complex systems, called emergence-oriented control (EOC), and its key schemes: the direct scheme, the system restructuring scheme and the system calibration scheme. Firstly, the schemes are dependent on emergent behaviors and the emergence mechanisms producing those behaviors. The second is that an efficiency control relies on not only corresponding emergence mechanism, but also controlled components and interactions among them. As a universal ontology, the EOC provides a powerful tool for identifying and resolving control problems in specific systems.
Evaluation of corrective reconstruction methods using a 3D cardiac-torso phantom and bull's-eye plots

The goal of the authors' investigation was to study the effectiveness of the corrective reconstruction methods in cardiac SPECT using a realistic phantom and to qualitatively and quantitatively evaluate the reconstructed images using bull's-eye plots. A 3D mathematical phantom which realistically models the anatomical structures of the cardiac-torso region of patients was used. The phantom allows simulation of both the attenuation distribution and the uptake of radiopharmaceuticals in different organs. Also, the phantom can be easily modified to simulate different genders and variations in patient anatomy. Two-dimensional projection data were generated from the phantom and included the effects of attenuation and detector response blurring. The reconstruction methods used in the study included the conventional filtered backprojection (FBP) with no attenuation compensation, and the first-order Chang algorithm, an iterative filtered backprojection algorithm (IFBP), the weighted least square conjugate gradient algorithm and the ML-EM algorithm with non-uniform attenuation compensation. The transaxial reconstructed images were rearranged into short-axis slices from which bull's-eye plots of the count density distribution in the myocardium were generated. The FBP reconstructed images clearly demonstrated the effects of attenuation on the myocardial distribution caused by anatomical structures and the inadequacy of the algorithm to provide accurate quantitative reconstructions. The images reconstructed using the Chang, IFBP, and ML-EM methods with attenuation compensation showed substantial improvement in terms of reduction in artifacts and improvement in quantitative accuracy. The results also demonstrate that the ML-EM method is the most robust in attenuation compensation for a wide range of anatomical variations when compared to the Chang and IFBP methods. The combination of the 3D mathematical phantoms and bull's-eye plots provides an effective tool for evaluation of corrective reconstruction methods for quantitative cardiac SPECT.
Research on Elementary Principals of Complex System Control

This paper points out the fundamental problems when controlling complex systems, concludes the principles for dealing with the problems in control of complex systems, which begins by reviewing on definitions and general properties of complex systems, and primary concepts of general system control. Furthermore, the relation between elementary control paradigms and general properties of complex systems is discussed, which determines that control goals are tightly dependent on emergence mechanisms, interacting components and interactions among them. Consequently, this paper indicates three kinds of elementary schemes for complex system control. The work presented in this paper provides a basis for further research on complex system control.
Efficient Distributed Data Clustering on Spark

Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation. The proposed method is based on the bootstrap trials. We implemented this method as an Intelligent Bootstrap Library (IBL) on Spark to support efficient data clustering. Intensive evaluations show that IBL can provide a 2x speed-up over the state of art solution with the same error bound.
Comparison between electropositive and electronegative cold atmospheric-pressure plasmas: a modelling study

Cold atmospheric-pressure He + N2 and He + O2 plasmas are chosen as the representatives for electropositive and electronegative plasmas, of which the discharge characteristics are studied and then compared to each other by fluid models. As the increase of the impurity (N2 or O2) fraction from 0 to 10%, for He + N2 plasmas the electron density and ion density increase, the spatiotemporal distributions of electron density, ion density, electron temperature and electron generation rate change a little. On contrast, for He + O2 plasmas the electron density decreases, the ion density first increases and then decreases, the electron temperature increases in the bulk region, but decreases in the sheath region, and the plasmas transform from γ mode to α mode as the significant change of electron generation rate distributions. Larger electric field is needed in the bulk region to sustain the electronegative plasma, so the electrical characteristics of He + O2 plasmas transform form capacitive to resistive with increasing O2 fraction. Meanwhile, the ion-coupling power increases dramatically, which can be estimated by a formula based on the electronegativity. A new criterion for determining the sheath boundary, |∇E| = 5 kV/cm2, is put forward, which is found suitable for both the electropositive and electronegative plasmas.
A Petri-Net Model for Formal Modeling of WS-BPEL Processes

Business Process Execution Language (WS-BPEL) is one of the most popular standards for Web service composition. To formalize WS-BPEL gives the way to further describe, reason, analyze and verify complex service compositions. In this paper, we propose a translation-based approach for formalization of WS-BPEL processes, which employs the Petri net as the intermediate representation. A set of translation rules is defined for constructs and activities so that processes specified in BPEL can be translated into the equivalent Petri nets.
Primary Video Object Segmentation via Complementary CNNs and Neighborhood Reversible Flow

This paper proposes a novel approach for segmenting primary video objects by using Complementary Convolutional Neural Networks (CCNN) and neighborhood reversible flow. The proposed approach first pre-trains CCNN on massive images with manually annotated salient objects in an end-to-end manner, and the trained CCNN has two separate branches that simultaneously handle two complementary tasks, i.e., foregroundness and backgroundness estimation. By applying CCNN on each video frame, the spatial foregroundness and backgroundness maps can be initialized, which are then propagated between various frames so as to segment primary video objects and suppress distractors. To enforce efficient temporal propagation, we divide each frame into superpixels and construct neighborhood reversible flow that reflects the most reliable temporal correspondences between superpixels in far-away frames. Within such flow, the initialized foregroundness and backgroundness can be efficiently and accurately propagated along the temporal axis so that primary video objects gradually pop-out and distractors are well suppressed. Extensive experimental results on three video datasets show that the proposed approach achieves impressive performance in comparisons with 18 state-of-the-art models.
A source coding approach to classification by vector quantization and the principle of minimum description length

An algorithm for supervised classification using vector quantization and entropy coding is presented. The classification rule is formed from a set of training data {(X/sub i/, Y/sub i/)}/sub i=1//sup n/, which are independent samples from a joint distribution P/sub XY/. Based on the principle of minimum description length (MDL), a statistical model that approximates the distribution P/sub XY/ ought to enable efficient coding of X and Y. On the other hand, we expect a system that encodes (X, Y) efficiently to provide ample information on the distribution P/sub XY/. This information can then be used to classify X, i.e., to predict the corresponding Y based on X. To encode both X and Y, a two-stage vector quantizer is applied to X and a Huffman code is formed for Y conditioned on each quantized value of X. The optimization of the encoder is equivalent to the design of a vector quantizer with an objective function reflecting the joint penalty of quantization error and misclassification rate. This vector quantizer provides an estimation of the conditional distribution of Y given X, which in turn yields an approximation to the Bayes classification rule. This algorithm, namely discriminant vector quantization (DVQ), is compared with learning vector quantization (LVQ) and CART/sup R/ on a number of data sets. DVQ outperforms the other two on several data sets. The relation between DVQ, density estimation, and regression is also discussed.