nils-herrmann/clean_scirepeval huggingface dataset:
A cryptosystem based on palmprint feature

Biometric cryptography is a technique using biometric features to encrypt data, which can improve the security of the encrypted data and overcome the shortcomings of the traditional cryptography. This paper proposes a novel biometric cryptosystem based on palmprint features. In this system, the palmprint features, called DoG code, are extracted using Gaussian derivative filters. Then the Reed-Solomon error correcting technique and the logical XOR operation are employed to encrypt and decrypt the data. Experimental results show that this system can obtain a high security with a low false rejection rate.
Palmprint Recognition Using 3-D Information

Palmprint has proved to be one of the most unique and stable biometric characteristics. Almost all the current palmprint recognition techniques capture the 2-D image of the palm surface and use it for feature extraction and matching. Although 2-D palmprint recognition can achieve high accuracy, the 2-D palmprint images can be counterfeited easily and much 3-D depth information is lost in the imaging process. This paper explores a 3-D palmprint recognition approach by exploiting the 3-D structural information of the palm surface. The structured light imaging is used to acquire the 3-D palmprint data, from which several types of unique features, including mean curvature image, Gaussian curvature image, and surface type, are extracted. A fast feature matching and score-level fusion strategy are proposed for palmprint matching and classification. With the established 3-D palmprint database, a series of verification and identification experiments is conducted to evaluate the proposed method. The results demonstrate that 3-D palmprint technique has high recognition performance. Although its recognition rate is a little lower than 2-D palmprint recognition, 3-D palmprint recognition has higher anticounterfeiting capability and is more robust to illumination variations and serious scrabbling in the palm surface. Meanwhile, by fusing the 2-D and 3-D palmprint information, much higher recognition rate can be achieved.
Palmprint identification based on directional representation

In this paper, we propose a novel approach for palmprint identification, which contains two interesting components. Firstly, we propose the directional representation for appearance based approaches. The new representation is robust to drastic illumination changes and preserves important discriminative information for classification. We then generate virtual samples to enlarge the training set to compensate for matching errors caused by large rotations and translations. Based on these two strategies, the recognition performance of representative appearance based approaches can be improved significantly. Secondly, in order to improve the robustness of palmprint identification, we propose a fusion method combining proposed method and orientation based approaches e.g. Competitive Code, which can obtain very low Equal Error Rates.
Palmprint texture analysis based on low-resolution images for personal authentication

Biometrics identification is an emerging solution for solving security problems in our networked society. A new branch of biometric approach -palmprint technology, whereby the lines and points can be extracted from our palm for personal authentication- was proposed several years ago. In this paper, we develop a new feature extraction method based on low-resolution palmprint images. A 2-D Gabor filter is used to obtain the texture information and two palmprint images are compared in term of their hamming distance. The experimental results illustrate the effectiveness of our method.
Watermark extraction by magnifying noise and applying global minimum decoder

For the classical watermark embedment model I = 1 + /spl alpha/W, the corresponding watermark detection has its limitation in its need of a fixed parameter for extracting watermarks. If the extraction parameter is too large, we cannot extract the watermark from the image that contains watermarks; if it is too small, the extracted watermarks may be blurred. This paper proposes a novel watermark extraction method. First, we treat the watermark information as noise for the watermarked image in its spatial domain. We then magnify the noise before detection. Next, we recover the watermark information by adjusting the extracted data from the frequency domain according to our global minimum method. Experimental results show that our watermark extraction method is more valid and accurate than the classical method. It can greatly reduce extraction errors.
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising

Most of existing image denoising methods learn image priors from either an external data or the noisy image itself to remove noise. However, priors learned from an external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real-world noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real-world noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real-world noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real-world noisy images.
On the Dimensionality Reduction for Sparse Representation Based Face Recognition

Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the dimensionality reduction (DR) of face images under the framework of SRC. Although one important merit of SRC is that it is insensitive to DR or feature extraction, a well trained projection matrix can lead to higher FR rate at a lower dimensionality. An SRC oriented unsupervised DR algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed DR algorithm over PCA or random projection based DR under the SRC framework.
Device performance improvement with implantation balancing energy contamination and productivity

Ion implantation technology is widely used in semiconductor manufacturing process. Most of install base for high current implanters use deceleration technology to overcome space charge effect especially important for low energy implants. It is necessary to consider the Energy contamination (EC) effects on devices with thinner gate height. This paper use SRIM simulation and dopant profiles, offline sheet resistance to illustrate the selection of deceleration by considering EC tails beyond the gate height. The device effects using p-type Poly (PPoly) and Source Drain (PSD) implant steps are evaluated in state of art 28nm device flow. The device performance can gain 6% with optimized implant conditions. It demonstrated the necessary of balance device requirement and productivity using implanters with deceleration technologies. The newer generation implanters with EC filter technology, providing maximum productivity while meeting device requirement, was discussed briefly also.
H/sub /spl infin// fixed-lag smoothing and prediction for linear continous-time systems

This paper addresses the H/sub /spl infin// fixed-lag smoothing and prediction problems for linear continuous-time systems. We first present a solution to the optimal H/sub 2/ estimation problem for linear continuous-time systems with instantaneous and delayed measurements. It is then shown that the H/sub /spl infin// fixed-lag smoothing and prediction problems can be converted to the latter problem in Krein space. Therefore, the H/sub 2/ estimation is extended to give conditions on the existence of H/sub /spl infin// fixed-lag smoother and predictor based on innovation analysis and projection in Krein space and a solution for H/spl infin/ smoother or predictor is given in terms of a Riccati differential equation and matrix differential equations.
Effects of gate to drain capacitance to the AC characteristics of trench power MOSFET

For the low voltage power MOSFETs, the power loss and the switching frequency are the most important parameters, the two are separately determined by the conduction resistance RDS(on) and the gate charge QG. The gate charge mainly depends on the gate to drain charge QGD. The so-called figure-of-merit, which is defined as the product of the RDS(on) and QGD  is commonly used to quantifying the performance of the power MOSFETs for a specified off-state breakdown voltage (BVDS). The trench gate structure power MOSFET can attain low conduction resistance RDS(on) with high cell density, in this paper its structure and performance are discussed, due to there is a body diode between drain and source, the electrical characteristic of drain and source is not symmetric. On curve tracer, the device's capacitive effects both the gate capacitance and the PN junction capacitance which determines the gate charge QG are studied with its AC waveform. From this point, some abnormal cases are explained. Its C-V curve is also investigated, for the N- drift area under the gate which forms the gate to drain capacitance CGD, its high frequency C-V curve is different from that of the common MIS structure
An Iris Cryptosystem for Information Security

Securing information is a key issue in the field of network security and cryptography is one of the most effective ways to enhance information security. Biometric cryptography is a technique using biometric features to encrypt the data, which can improve the security of the encrypted data and overcome the shortcomings of the traditional cryptography. This paper proposes a novel biometric cryptosystem based on the most accurate biometric feature iris. In this system, a 256-dimension textural feature vector is extracted from the preprocessed iris image by using a set of 2-D Gabor filters. And then a modified fuzzy vault algorithm is employed to encrypt and decrypt the data. Experimental results demonstrate the feasibility of the proposed system.
A New Discriminative Sparse Representation Method for Robust Face Recognition via $l_{2}$ Regularization

Sparse representation has shown an attractive performance in a number of applications. However, the available sparse representation methods still suffer from some problems, and it is necessary to design more efficient methods. Particularly, to design a computationally inexpensive, easily solvable, and robust sparse representation method is a significant task. In this paper, we explore the issue of designing the simple, robust, and powerfully efficient sparse representation methods for image classification. The contributions of this paper are as follows. First, a novel discriminative sparse representation method is proposed and its noticeable performance in image classification is demonstrated by the experimental results. More importantly, the proposed method outperforms the existing state-of-the-art sparse representation methods. Second, the proposed method is not only very computationally efficient but also has an intuitive and easily understandable idea. It exploits a simple algorithm to obtain a closed-form solution and discriminative representation of the test sample. Third, the feasibility, computational efficiency, and remarkable classification accuracy of the proposed  $l_{2}$  regularization-based representation are comprehensively shown by extensive experiments and analysis. The code of the proposed method is available at http://www.yongxu.org/lunwen.html.
Multispectral palmprint recognition using wavelet-based image fusion

Palmprint is widely used in personal identification for an accurate and robust recognition. To improve the existing palmprint systems, the proposed system, which is the first on-line multispectral palmprint recognition system ever designed before, uses multispectral capture device to sense images under different illumination, including red, green, blue and infrared. We adopt competitive coding scheme as matching algorithm, which performs well in on-line palmprint recognition. Wavelet-based image fusion method is used as data-level fusion strategy in our scheme. Fused verifications show better effort on motion blurred source images than single channel. Experimental results of fusion images are also useful references for future work on multispectral palmprint recognition.
Analysis of pulse waveforms preprocessing

Pulse is one of the most important body information in Traditional Chinese Medicine (TCM). Pulse diagnosis using statistical learning theory is attracting more and more attention and taking over the traditional subjective judgments. This paper mainly presents the preprocessing algorithm of pulse waveforms. This step almost decides the whole performance of pulse analysis and pattern classification. As a result many research results about pulse preprocessing have been published, while most of the previous method only emphasize on certain aspects and hardly utilize the information in TCM pulse. We propose an integrated framework of preprocessing and introduce our method in each module. The definitions and implementations of pulse preprocessing algorithm are all presented. The proposed preprocessing method of pulse waveforms show a well performance in experiments compared with other previous methods.
Band Selection for Improvement of Dorsal Hand Recognition

In order to study the relationship between recognition performance of dorsal hand and spectra including visible and near-infrared light, a multispectral image capture system is established in this study, and it works well on the spectra with the wavelength from 520nm to 1040nm. The optimal band is considered to have the most effective features for recognition. Line feature extraction method is adopted regardless of the texture types. EER are calculated to reflect the differentiating ability across all bands after matching process. The spectrum of 880nm is testified to have the best performance of verification.
Modeling and optimization of inventory and sourcing decisions with risk assessment in perishable food supply chains

Today, the food industries are transforming towards interconnected global networks. Food safety scandals and globalization of food production has sparked heightened public attention to the risks in the supply chain from farms to consumers. In response, it is of great importance that food supply chains consistently provide high quality foods, safety guarantees and transparency. The assessment of the entire food supply chains should now be integrated with risks assessment, food quality and safety management, sustainability and traceability. For inventory management, the focus shifts from merely minimizing inventory costs, to carefully balancing with wastage and risks exposed. Consequently, this paper proposes a model to determine inventory and sourcing decisions in food supply chains. The model aims to minimize total costs and reduce wastes from unqualified items, while achieving necessary robustness in coping with risks generated. Food quality degradation is integrated in the final model for the purpose of product quality control throughout the food supply chain.
Reconstruction Based Finger-Knuckle-Print Verification With Score Level Adaptive Binary Fusion

Recently, a new biometrics identifier, namely finger knuckle print (FKP), has been proposed for personal authentication with very interesting results. One of the advantages of FKP verification lies in its user friendliness in data collection. However, the user flexibility in positioning fingers also leads to a certain degree of pose variations in the collected query FKP images. The widely used Gabor filtering based competitive coding scheme is sensitive to such variations, resulting in many false rejections. We propose to alleviate this problem by reconstructing the query sample with a dictionary learned from the template samples in the gallery set. The reconstructed FKP image can reduce much the enlarged matching distance caused by finger pose variations; however, both the intra-class and inter-class distances will be reduced. We then propose a score level adaptive binary fusion rule to adaptively fuse the matching distances before and after reconstruction, aiming to reduce the false rejections without increasing much the false acceptances. Experimental results on the benchmark PolyU FKP database show that the proposed method significantly improves the FKP verification accuracy.
Feature Band Selection for Multispectral Palmprint Recognition

Palm print is a unique and reliable biometric characteristic with high usability. Many palm print recognition algorithms and systems have been successfully developed in the past decades. Most of the previous works use the white light sources for illumination. Recently, it has been attracting much research attention on developing new biometric systems with both high accuracy and high anti-spoof capability. Multispectral palm print imaging and recognition can be a potential solution to such systems because it can acquire more discriminative information for personal identity recognition. One crucial step in developing such systems is how to determine the minimal number of spectral bands and select the most representative bands to build the multispectral imaging system. This paper presents preliminary studies on feature band selection by analyzing hyper spectral palm print data (420nm~1100nm). Our experiments showed that 2 spectral bands at 700nm and 960nm could provide most discriminate information of palm print. This finding could be used as the guidance for designing multispectral palm print systems in the future.
Multiscale competitive code for efficient palmprint recognition

Coding-based method, which encodes the responses of a bank of filters into bitwise features, has been very successful in palmprint representation and matching. Palmprints, however, are typically multiscale features, where the palm lines can be represented at a higher scale while the wrinkles at a lower scale. In this work, we present a mutliscale competitive code method for efficient palmprint representation and matching. In filterbank design, we adopt the log-Gabor wavelets since of its less overlapping in the frequency domain. In palmprint representation, competitive code is used to encoding the dominant orientation of the filter responses in each scale. In palmprint matching, a fusion rule is proposed to combine the distances obtained using different scales. Experimental results indicate that the proposed method achieves better recognition accuracy and faster matching speed while compared with several state-of-the-art methods.
A novel face recognition system using hybrid neural and dual eigenspaces methods

In this paper, we present an automated face recognition (AFR) system that contains two components: eye detection and face recognition. Based on invariant radial basis function (IRBF) networks and knowledge rules of facial topology, a hybrid neural method is proposed to localize human eyes and segment the face region from a scene. A dual eigenspaces method (DEM) is then developed to extract algebraic features of the face and perform the recognition task with a two-layer minimum distance classifier. Experimental results illustrate that the proposed system is effective and robust.
A new equation of saturation in RGB-to-HSI conversion for more rapidity of computing

In this paper, a new equation of saturation In RGB-to-HSI conversion is proposed on the basis of HSI color space, aimed to provide more rapidity of computing in real-time control systems due to fewer operations needed. The theoretical validity of new conversion equation is studied and testified. Besides its significant advantages over traditional equation on temporal performance, better mapping performances is mathematically explained as a minor advantage. It is shown in illustrative experiments that average 30% time was saved in computing when using new saturation conversion equation instead of traditional one.
Feature Selection for Monotonic Classification

Monotonic classification is a kind of special task in machine learning and pattern recognition. Monotonicity constraints between features and decision should be taken into account in these tasks. However, most existing techniques are not able to discover and represent the ordinal structures in monotonic datasets. Thus, they are inapplicable to monotonic classification. Feature selection has been proven effective in improving classification performance and avoiding overfitting. To the best of our knowledge, no technique has been specially designed to select features in monotonic classification until now. In this paper, we introduce a function, which is called rank mutual information, to evaluate monotonic consistency between features and decision in monotonic tasks. This function combines the advantages of dominance rough sets in reflecting ordinal structures and mutual information in terms of robustness. Then, rank mutual information is integrated with the search strategy of min-redundancy and max-relevance to compute optimal subsets of features. A collection of numerical experiments are given to show the effectiveness of the proposed technique.
Investigation of Temperature Dependence, Device Scalability, and Modeling of Semifloating-Gate Transistor Memory Cell

A semifloating-gate transistor had been proposed and its memory function has been demonstrated recently. In this paper, we further investigate its temperature dependency, device scalability, and device modeling. The high-temperature behavior is studied by measuring its endurance, retention, and disturbance immunity at 85 °C. The device scalability down to the 14-nm technology node is investigated by simulation. Its macrodevice model for circuit design is also developed. Finally, a memory array with the specific peripheral circuits is designed using the device model developed in this paper.
A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition

In this paper, we propose a two-phase test sample representation method for face recognition. The first phase of the proposed method seeks to represent the test sample as a linear combination of all the training samples and exploits the representation ability of each training sample to determine M “nearest neighbors” for the test sample. The second phase represents the test sample as a linear combination of the determined M nearest neighbors and uses the representation result to perform classification. We propose this method with the following assumption: the test sample and its some neighbors are probably from the same class. Thus, we use the first phase to detect the training samples that are far from the test sample and assume that these samples have no effects on the ultimate classification decision. This is helpful to accurately classify the test sample. We will also show the probability explanation of the proposed method. A number of face recognition experiments show that our method performs very well.