nils-herrmann/clean_scirepeval huggingface dataset:
An Optimized Tongue Image Color Correction Scheme

The color images produced by digital cameras are usually device-dependent, i.e., the generated color information (usually presented in RGB color space) is dependent on the imaging characteristics of specific cameras. This is a serious problem in computer-aided tongue image analysis because it relies on the accurate rendering of color information. In this paper, we propose an optimized correction scheme that corrects the tongue images captured in different device-dependent color spaces to the target device-independent color space. The correction algorithm in this scheme is generated by comparing several popular correction algorithms, i.e., polynomial-based regression, ridge regression, support vector regression, and neural network mapping algorithms. We test the performance of the proposed scheme by computing the CIE L*a*b* color difference (ΔE*ab) between estimated values and the target reference values. The experimental results on the colorchecker show that the color difference is less than 5 (ΔE*ab <; 5), while the experimental results on real tongue images show that the distorted tongue images (captured in various device-dependent color spaces) become more consistent with each other. In fact, the average color difference among them is greatly reduced by more than 95%.
Comparison of electrical performances of metal-insulator-metal capacitors with sputtered HfO2 and BZT-HfO2 dielectrics

Sputtered HfO<inf>2</inf> and co-sputtered BZT-HfO<inf>2</inf> dielectrics have been compared for high density metal-insulator-metal (MIM) capacitors, which are required for radio frequency and analog/mixed-signal integrated circuits. The MIM capacitor with BZT-HfO<inf>2</inf> shows a high permittivity value of 20.8, and the small quadratic voltage coefficient of capacitance of 1830 ppm/V<sup>2</sup> at 100kHz compared to that with HfO<inf>2</inf> dielectric. Further, the leakage characteristics of the capacitors with HfO<inf>2</inf> and BZT-HfO<inf>2</inf> dielectrics have been studied as a function of measurement temperature, and the conduction mechanisms are discussed.
The Mediating Role of Knowledge Management in Translating the Firm's Learning Orientation and Market Orientation to Business Performance

Knowledge management, market orientation, and learning orientation have all been considered important success factors to a firm's performance. Using data gathered from 307 managers in 105 organizations across Canada, this study develops and empirically tests a model that demonstrates that a firm's knowledge management mediates the positive influences from the firm's learning orientation and market orientation to market performance. The model highlights the important role played by knowledge management in translating organizational culture factors such as learning and market orientations to tangible business performance
Robust low-voltage program-erasable capacitors of Pd-Al2O3-Si with high density Ru-based nanocrystals embedded

Pd electrode-based metal-oxide-semiconductor (MOS) capacitors with high density Ru-based nanocrystals in atomic-layer-deposited Al2O3 dielectric have been fabricated and electrically characterized, exhibiting robust programming and erasing characteristics even under low voltages. Further, the tunable memory characteristics of the MOS capacitors are demonstrated by varying the tunneling-layer (T)/blocking-layer (B) thickness ratio, and the underlying mechanisms are addressed.
Effect of via-transitions on signal integrity using power transmission lines

Transmission lines carrying high-speed I/O signals that traverse through different metal layers in a printed circuit board suffer from return path discontinuities. As a result, significant amount of electromagnetic energy can couple to surrounding circuits and the power distribution network. In this work, we designed three printed circuit board (PCB) test vehicles using different power distribution topologies. One of them was based on a recently proposed power delivery method using power transmission lines (PTL). The other two were designed according to conventional power-ground plane pair approach. We show through measurement that PTL-based design provides less than -40dB of crosstalk between a signal line with multiple via-transitions and a nearby victim line. Measured data also showed that the PTL-based test vehicle had more than 68% of power supply noise reduction as compared to conventional designs.
Hierarchical multiscale LBP for face and palmprint recognition

Local binary pattern (LBP), fast and simple for implementation, has shown its superiority in face and palmprint recognition. To extract representative features, “uniform” LBP was proposed and its effectiveness has been validated. However, all “non-uniform” patterns are clustered into one pattern, so a lot of useful information is lost. In this study, the authors propose to build a hierarchical multiscale LBP histogram for an image. The useful information of “non-uniform” patterns at large scale is dug out from its counterpart of small scale. The main advantage of the proposed scheme is that it can fully utilize LBP information while it does not need any training step, which may be sensitive to training samples. Experiments on one public face database and one palmprint database show the effectiveness of the proposed method.
Learning Iteration-wise Generalized Shrinkage–Thresholding Operators for Blind Deconvolution

Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However, the existing approaches usually rely on carefully designed regularizers and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizers exhibit the structure-preserving smoothing capability, but fail to enhance salient edges. In this paper, under the MAP framework, we propose the iteration-wise ℓp-norm regularizers together with data-driven strategy to address these issues. First, we extend the generalized shrinkage-thresholding (GST) operator for ℓp-norm minimization with negative p value, which can sharpen salient edges while suppressing trivial details. Then, the iteration-wise GST parameters are specified to allow dynamical salient edge selection and time-varying regularization. Finally, instead of handcrafted tuning, a principled discriminative learning approach is proposed to learn the iterationwise GST operators from the training dataset. Furthermore, the multi-scale scheme is developed to improve the efficiency of the algorithm. Experimental results show that, negative p value is more effective in estimating the coarse shape of blur kernel at the early stage, and the learned GST operators can be well generalized to other dataset and real world blurry images. Compared with the state-of-the-art methods, our method achieves better deblurring results in terms of both quantitative metrics and visual quality, and it is much faster than the state-of-the-art patch-based blind deconvolution method.
Crystallization of amorphous silicon on glass substrate by microwave annealing for thin-film-transistor applications

There is a rising demand for low temperature polysilicon TFT these years due to the rapidly increasing market of high resolution display panels. In this paper, both low temperature microwave annealing and laser annealing were used to crystallize amorphous silicon film on glass substrate. It is found that both methods had successfully transferred the amorphous silicon into polysilicon according to Raman spectra results. The microwave crystallized polysilicon had smaller grain size and lower tensile stress than the laser crystallized one. After implantation and activation of BF2 and P, sheet resistance values of the BF2-implanted microwave crystallized samples were similar to that of laser crystallized ones. However, for the P implanted samples, the microwave crystallized samples had two to three magnitude higher sheet resistance compared with the laser crystallized ones.
LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation

We propose a novel reconstruction-based transfer learning method called latent sparse domain transfer (LSDT) for domain adaptation and visual categorization of heterogeneous data. For handling cross-domain distribution mismatch, we advocate reconstructing the target domain data with the combined source and target domain data points based on ℓ1-norm sparse coding. Furthermore, we propose a joint learning model for simultaneous optimization of the sparse coding and the optimal subspace representation. In addition, we generalize the proposed LSDT model into a kernel-based linear/nonlinear basis transformation learning framework for tackling nonlinear subspace shifts in reproduced kernel Hilbert space. The proposed methods have three advantages: 1) the latent space and the reconstruction are jointly learned for pursuit of an optimal subspace transfer; 2) with the theory of sparse subspace clustering, a few valuable source and target data points are formulated to reconstruct the target data with noise (outliers) from source domain removed during domain adaptation, such that the robustness is guaranteed; and 3) a nonlinear projection of some latent space with kernel is easily generalized for dealing with highly nonlinear domain shift (e.g., face poses). Extensive experiments on several benchmark vision data sets demonstrate that the proposed approaches outperform other state-of-the-art representation-based domain adaptation methods.
Design of a Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction

It has been reported that concentrations of several biomarkers in diabetics' breath show significant difference from those in healthy people's breath. Concentrations of some biomarkers are also correlated with the blood glucose levels (BGLs) of diabetics. Therefore, it is possible to screen for diabetes and predict BGLs by analyzing one's breath. In this paper, we describe the design of a novel breath analysis system for this purpose. The system uses carefully selected chemical sensors to detect biomarkers in breath. Common interferential factors, including humidity and the ratio of alveolar air in breath, are compensated or handled in the algorithm. Considering the intersubject variance of the components in breath, we build subject-specific prediction models to improve the accuracy of BGL prediction. A total of 295 breath samples from healthy subjects and 279 samples from diabetic subjects were collected to evaluate the performance of the system. The sensitivity and specificity of diabetes screening are 91.51% and 90.77%, respectively. The mean relative absolute error for BGL prediction is 21.7%. Experiments show that the system is effective and that the strategies adopted in the system can improve its accuracy. The system potentially provides a noninvasive and convenient method for diabetes screening and BGL monitoring as an adjunct to the standard criteria.
Towards Theory Building in Agile Manufacturing Strategy—A Taxonomical Approach

Agility has been widely accepted in manufacturing industry as a new competitive concept. However, how to develop a manufacturing strategy based around agility is not fully understood. This paper proposes a framework for the implementation of agility as a manufacturing strategy and describes the development and analysis of a numerical taxonomy of agility strategies using the framework. The taxonomy was developed with cluster analysis based on the relative importance attached to seven agility capabilities by a number of U.K. manufacturing companies. Three distinct clusters of strategy groups were observed across the industry studied: Quick, Responsive and Proactive Players. Quick Players are oriented towards a strong customer focus and quickness. They do not emphasize flexibility and responsiveness to changes and they give low priority to proactiveness and partnership. Responsive Players are preoccupied with flexibility and responsiveness to changes. They do not emphasize proactiveness and partnerships and they attach low importance to quickness. Proactive Players are characterized by high priorities on proactiveness and customer focus, high values attached to all capabilities, and high importance given to partnerships. The underlying dimensions of agile capabilities along which the three strategy groups differ were investigated based on factor analysis and canonical discriminant analysis. Changes/uncertainties in the business environment experienced by members of different strategic groups were compared, and "manufacturing strategy choices," in terms of manufacturing practices for achieving agility, employed by members of different strategic groups were studied. Business characteristics and typical cases for each strategy group were investigated
Monogenic-LBP: A new approach for rotation invariant texture classification

Analysis of two-dimensional textures has many potential applications in computer vision. In this paper, we investigate the problem of rotation invariant texture classification, and propose a novel texture feature extractor, namely Monogenic-LBP (M-LBP). M-LBP integrates the traditional Local Binary Pattern (LBP) operator with the other two rotation invariant measures: the local phase and the local surface type computed by the 1st-order and 2ndorder Riesz transforms, respectively. The classification is based on the image's histogram of M-LBP responses. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of M-LBP over the other state-of-the-art methods evaluated.
Evaluation of the Putative Ratio Rules for Facial Beauty Indexing

Understanding the rules of facial beauty is important for esthetic plastic surgery. Averageness and ideal proportions are the most investigated rules. In this paper, we integrate the findings on these two aspects to identify race invariant ideal facial proportions. Extensive research on the averageness hypothesis have verified that average faces are beautiful, which provides an objective way to generate representatives of beautiful faces. In order to ensure ethnic variety, 148 average faces from 61 countries/regions around the world have been collected to build the data set. 26 putative ratio rules, including golden ratio, neoclassical canons, etc., are collected to construct a candidate feature set. We first perform k-means clustering and then examine the 26 rules with respect to accuracy and universality on both the entire average face data set and individual clusters. The results show that: 1) the clustering result is consistent with the anthropologic divisions, 2) the top universal ratio features are consistent across different clusters, and 3) the accuracy of putative ratio rules can be improved by using data driven ideal values. The validity of the corrected ideal facial proportions has been verified on both synthesized faces and well-known beautiful faces in the real world.
Schottky barrier height tuning via nickel silicide as diffusion source dopant segregation scheme with microwave annealing

In this work, microwave annealing is explored to tune the Schottky barrier height between NiSi and Si via boron and arsenic dopant segregation using silicide as diffusion source scheme. The microwave annealing is found to be able to obtain equivalent electron and hole Schottky barrier heights at significantly lower temperature (>100 °C) compared with conventional rapid thermal annealing. A plausible interpretation has been further proposed to explain the impact of low temperature microwave annealing on formation of dopant segregated Schottky junctions. The effectiveness of Schottky barrier height tuning below 400 °C by microwave annealing paves the way to form advanced source/drain and contact structures for future ultra-scaled MOSFETs and monolithic 3D sequential integration.
Approximate entropy based pulse variability analysis

The dynamical analysis of pulse variability gives new insight into researches of cardiovascular system's dynamics. Firstly, long-term pulse variability analysis for the researches on cardiovascular system was proposed. Secondly, approximate entropy was applied to analyze three groups of long-term pulse waveform variabilities and we found that the pulses' approximate entropies of patients with cardiovascular disease preferred to smaller value and less irregularity. What more, the pulse variability of the patient with pacemaker newly implanted was also studied. Finally, the pulse variability's clinical value for cardiovascular system was concluded.
Study of unique pseudo buried layer in 0.18um SiGe BiCMOS process

SiGe is an important semiconductor material after Si and GaAs. SiGe HBT has obvious advantages over Si HBT in terms of frequency characteristics, DC characteristics and noise characteristics, especially compatible with mature Si process. In our low cost and high performance 0.18um SiGe BiCMOS with collector epitaxial layer free, a unique pseudo buried layer is developed to replace the conventional n-type buried layer under collector epitaxial layer. This pseudo buried layer is important to keep the high performance of SiGe HBT due to the skipping of collector epitaxial layer and conventional n-type buried layer. The lithography is the most crucial step in pseudo buried layer process. In this paper, we report the study result of lithography process of the pseudo buried layer. The lithography conditions suitable for fabrication are achieved. A negative tone photoresist is selected with 7770A thickness and 80 ∼ 100nm resolution. The mask bias is 100nm per side and STI side wall angle 82 degrees. The process window (CD + OVL) control is within 50nm and the recommended minimum window CD of the device is 300nm.
Dynamically Integrated Manufacturing Systems (DIMS)—A Multiagent Approach

Manufacturing businesses in today's market are facing immense pressures to react rapidly to dynamic variations in demand distributions across products and changing product mixes. To cope with the pressures requires dynamically integrated manufacturing systems (DIMS) that can manage optimal fulfillment of customer orders while simultaneously considering alternative system structures to suit changing conditions. This paper presents a multiagent approach to DIMS, where production planning and control decisions are integrated with systems reconfiguration and restructure. A multiagent framework, referred to as a hierarchical autonomous agent network, is proposed to model complex manufacturing systems, their structures, and constraints. It allows the hierarchical structures of complex systems to be modeled while avoiding centralized control in classical hierarchical/hybrid frameworks. Subsystems interact heterarchically with product orders to carry out optimal planning and scheduling. An agent coordination algorithm, operating iteratively under the control of a genetic algorithm, is developed to enable optimal planning and control decisions for order fulfillment to be made through interactions between agents. This algorithm also allows the structural constraints of systems to be relaxed gradually during agent interaction, so that planning and control are first carried out under existing constraints, but when satisfactory solutions cannot be found, subsystems are allowed to regroup to form new configurations. Frequently used configurations are detected and evaluated for system restructure. The approach also enables Petri-net models of new system structures to be generated dynamically and the structures to be evaluated through agent-based discrete event simulation.
Palm-Print Classification by Global Features

Three-dimensional (3-D) palm print has proved to be a significant biometrics for personal authentication. Three-dimensional palm prints are harder to counterfeit than 2-D palm prints and more robust to variations in illumination and serious scrabbling on the palm surface. Previous work on 3-D palm-print recognition has concentrated on local features such as texture and lines. In this paper, we propose three novel global features of 3-D palm prints which describe shape information and can be used for coarse matching and indexing to improve the efficiency of palm-print recognition, particularly in very large databases. The three proposed shape features are maximum depth of palm center, horizontal cross-sectional area of different levels, and radial line length from the centroid to the boundary of 3-D palm-print horizontal cross section of different levels. We treat these features as a column vector and use orthogonal linear discriminant analysis to reduce their dimensionality. We then adopt two schemes: 1) coarse-level matching and 2) ranking support vector machine to improve the efficiency of palm-print recognition. We conducted a series of 3-D palm-print recognition experiments using an established 3-D palm-print database, and the results demonstrate that the proposed method can greatly reduce penetration rates.
Texture classification via patch-based sparse texton learning

Texture classification is a classical yet still active topic in computer vision and pattern recognition. Recently, several new texture classification approaches by modeling texture images as distributions over a set of textons have been proposed. These textons are learned as the cluster centers in the image patch feature space using the K-means clustering algorithm. However, the Euclidian distance based the K-means clustering process may not be able to well characterize the intrinsic feature space of texture textons, which if often embedded into a low dimensional manifold. Inspired by the great success of l1-norm minimization based sparse representation (SR), in this paper we propose a novel texture classification method via patch-based sparse texton learning. Specifically, the dictionary of textons is learned by applying SR to image patches in the training dataset. The SR coefficients of the test images over the dictionary are used to construct the histograms for texture classification. Experimental results on benchmark database validate the effectiveness of the proposed method.
Highly Uniform Bipolar Resistive Switching With $ \hbox{Al}_{2}\hbox{O}_{3}$ Buffer Layer in Robust NbAlO-Based RRAM

The bipolar resistive switching characteristics of atomic-layer-deposited NbAlO-based devices have been investigated for nonvolatile memory applications. With the help of a thin Al2O3 buffer layer, highly uniform and reproducible bipolar resistance switching cycles could be observed. Four typical multilevel operations, with resistances being at 1000, 350, 145, and 75 ¿, respectively, are also successfully demonstrated by varying the current compliance during the set process. The resistance ratios of high-resistance state to low-resistance state are more than 103 within 5000 cycles during the test without any degradation. Moreover, the estimated retention lifetime at room temperature is sufficiently long to fulfill the typical ten-year requirement. Considering its excellent memory switching behavior, a resistance switching device composed of a NbAlO film with a thin Al2O3 buffer layer is a possible candidate to be integrated into future memory processes.