nils-herrmann/clean_scirepeval huggingface dataset:
A novel text-independent speaker verification method based on the global speaker model

This correspondence introduces a new text-independent speaker verification method, which is derived from the basic idea of pattern recognition that the discriminating ability of a classifier can be improved by removing the common information between classes. In looking for the common speech characteristics between a group of speakers, a global speaker model can be established. By subtracting the score acquired from this model, the conventional likelihood score is normalized with the consequence of more compact score distribution and lower equal error rates. Several experiments are carried out to demonstrate the effectiveness of the proposed method.
A Novel Multichannel Wrist Pulse System With Different Sensor Arrays

Wrist pulse has long been recognized as an important physiological symbol for judging the health status of patients. It is regarded as one of the four diagnosis techniques in traditional Chinese medicine and plays an important role in disease analysis. However, the pulse diagnosis skill is subjective and needs decades of experience to master. To make the pulse diagnosis more objective and easily understood by practitioners, many pulse-taking platforms were invented, but several disadvantages, including the simplicity and insufficiency, limit the applications. This paper proposes a novel wrist pulse system for the pulse diagnosis to overcome the shortcomings in previous pulse platforms. We integrated a pressure sensor with a photoelectric sensor to make a fusion sensor to feel pulse in different approaches and designed the multichannel sensor arrays structure to acquire more pulse information. Finally, pulse analysis algorithm and classification methods are introduced. Experiments on disease classification are carried out to test the system performance with multichannel and different sensor arrays. The results show that the novel system is not only able to distinguish between healthy pulse samples and subjects suffering from diabetes, but also good at obtaining more information than the conventional pulse system with single channel or simplex-type sensor.
Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms

Feature extraction is one of the most important problems in image recognition tasks. In many applications such as face recognition, it is desirable to eliminate the redundancy among the extracted discriminant features. In this paper, we propose two novel feature extraction approaches named local uncorrelated discriminant transform (LUDT) and weighted global uncorrelated discriminant transform (WGUDT) for face recognition, respectively. LUDT and WGUDT separately construct the local uncorrelated constraints and the weighted global uncorrelated constraints. Then they iteratively calculate the optimal discriminant vectors that maximize the Fisher criterion under the corresponding statistical uncorrelated constraints, respectively. The proposed LUDT and WGUDT approaches are evaluated on the public AR and FERET face databases. Experimental results demonstrate that the proposed approaches outperform several representative feature extraction methods.
Methodology to form agile strategies in manufacturing organisations

Since the concept of agile manufacturing was introduced in 1991, the benefits of implementing agile manufacturing in companies have been widely recognised by researchers and industry. Motivated by Sharifi and Zhang's research in assisting agile manufacturing implementation in 1999, this paper proposed a methodology to implement agile manufacturing in manufacturing organisations through benchmarking processes. Quantitative assessment tools are developed to continuously and objectively analyse changes in a company's business environment, as well as capabilities and performances in reactively and proactively coping with the changes. A mechanism is established for identifying relations between agility drivers, capabilities and performances. This paper also presents a case study which employs the proposed methodology to form agile business development strategies in a SME company.  Attaining and maintaining world-class manufacturing performance and staying ahead of competitions are the never- ending targets for manufacturing companies. The development of ways to achieve the targets has been an evolutionary process in responding to the changing criteria of business competition since the beginning of the industrial age. In the last decade, business economy has shifted from economies of scale toward economies of scope. Price is no longer the sole factor in winning competitions. Academic researchers and industry were seeking the decisive factors that could result in better performances in responding to the changing market conditions.
Robust sparse coding for face recognition

Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsity-constrained robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, and it is much more robust to outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is proposed to solve the RSC model. Extensive experiments on representative face databases demonstrate that the RSC scheme is much more effective than state-of-the-art methods in dealing with face occlusion, corruption, lighting and expression changes, etc.
ICP registration using principal line and orientation features for palmprint alignment

Image alignment is a crucial step for palmprint recognition. Current key point based palmprint pre-processing methods, however, can only provide coarse alignment. The rotation of extracted region of interest (ROI) often causes the failure of genuine matching. To solve this problem, in this paper we propose to use iterative closest point (ICP) algorithm for palmprint alignment before matching of features. Using both the palm line and orientation features extracted by high order steerable filters, the proposed method has fast convergence speed and high registration accuracy, which result in greatly improved verification accuracy. Experimental results on Hong Kong PolyU palmprint database demonstrate the effectiveness of the proposed method.
A Novel Cryptosystem Based on Iris Key Generation

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 the most accurate biometric feature - iris. In encryption phase, a quantified 256-dimension textural feature vector is firstly extracted from the preprocessed iris image using a set of 2-D Gabor filters. At the same time, an error-correct-code (ECC) is generated using Reed-Solomon algorithm. Then the feature vector is translated to a cipher key using Hash function. Some general encryption algorithms use this cipher key to encrypt the secret information. In decryption phase, a feature vector extracted from the input iris is firstly corrected using the ECC. Then it is translated to the cipher key using the same Hash function. Finally, the corresponding general decryption algorithms use the key to decrypt the information. Experimental results demonstrate the feasibility of the proposed system.
MonogenicCode: A Novel Fast Feature Coding Algorithm with Applications to Finger-Knuckle-Print Recognition

Biometrics based personal authentication is an effective method for recognizing a person's identity. Recently, it is found that the finger-knuckle-print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one's finger, can serve as a distinctive biometric identifier. In this paper, a novel feature extraction and coding method, namely MonogenicCode, is presented based on the monogenic signal theory, and is applied to FKP recognition. For each image pixel, the associated MonogenicCode is a 3-bits vector obtained by binarizing the monogenic signal at this position, and it can reflect the local phase and orientation information at that position. Experiments conducted on our established FKP database indicate that this new method achieves competitive verification accuracy with state-of-the-art methods, while it needs the least time for feature extraction, making it the best choice for real-time applications.
Holistic orthogonal analysis of discriminant transforms for color face recognition

The key of color face recognition technique is how to effectively utilize the complementary information between color components and remove their redundancy. Present color face recognition methods generally reduce the correlations between color components in the image pixel level, and then extract the discriminant features from the uncorrelated color face images. In this paper, we propose a novel color face recognition approach based on the holistic orthogonal analysis (HOA) of discriminant transforms of color images. HOA can reduce the correlation of color information in the feature level. It in turn achieves the discriminant transforms of red, green and blue color images by using the Fisher criterion, and simultaneously makes the achieved transforms mutually orthogonal. Experimental results on the AR and FRGC-2 public color face image databases demonstrate that the proposed approach acquires better recognition performance than several representative color face recognition methods.
An Open-Source Platform for Underwater Image and Video Analytics

Global fisheries and the future of sustainable seafood are predicated on healthy populations of various species of fish and shellfish. Recent developments in the collection of large-volume optical data by autonomous underwater vehicles (AUVs), stationary camera arrays, and towed vehicles has made it possible for fishery scientists to generate species-specific, size-structured abundance estimates for different species of marine organisms via imagery. The immense volume of data collected by such devices quickly exceeds manual processing capacity and creates a strong need for automatic image analysis. This paper presents an open-source computer vision software platform designed to integrate common image and video analytics, such as stereo calibration, object detection and object classification, into a sequential data processing pipeline that is easy to program, multi-threaded, and generic. The system provides a cross-language common interface for each of these components, multiple implementations of each, as well as unified methods for evaluating and visualizing the results of different methods for accomplishing the same task.
Analysis of Brute-Force Break-Ins of a Palmprint Authentication System

Biometric authentication systems are widely applied because they offer inherent advantages over classical knowledge-based and token-based personal-identification approaches. This has led to the development of products using palmprints as biometric traits and their use in several real applications. However, as biometric systems are vulnerable to replay, database, and brute-force attacks, such potential attacks must be analyzed before biometric systems are massively deployed in security systems. This correspondence proposes a projected multinomial distribution for studying the probability of successfully using brute-force attacks to break into a palmprint system. To validate the proposed model, we have conducted a simulation. Its results demonstrate that the proposed model can accurately estimate the probability. The proposed model indicates that it is computationally infeasible to break into the palmprint system using brute-force attacks
Class-imbalance learning based discriminant analysis

Feature extraction is an important research topic in the field of pattern recognition. The class-specific idea tends to recast a traditional multi-class feature extraction and recognition task into several binary class problems, and therefore inevitably class imbalance problem, where the minority class is the specific class, and the majority class consists of all the other classes. However, discriminative information from binary class problems is usually limited, and imbalanced data may have negative effect on the recognition performance. For solving these problems, in this paper, we propose two novel approaches to learn discriminant features from imbalanced data, named class-balanced discrimination (CBD) and orthogonal CBD (OCBD). For a specific class, we select a reduced counterpart class whose data are nearest to the data of specific class, and further divide them into smaller subsets, each of which has the same size as the specific class, to achieve balance. Then, each subset is combined with the minority class, and linear discriminant analysis (LDA) is performed on them to extract discriminative vectors. To further remove redundant information, we impose orthogonal constraint on the extracted discriminant vectors among correlated classes. Experimental results on three public image databases demonstrate that the proposed approaches outperform several related image feature extraction and recognition methods.
Influence of under-bump metallurgy and solder alloys on the crack in the wafer level chip scale packaging

The finite element method (FEM) is employed to investigate the solder crack mechanism in wafer level chip scale packaging (WLCSP). The location of the initial crack is calculated and is compared to the experimental one. Moreover, the impact of the following three aspects, e.g. under-bump metallurgy (UBM) materials, solder alloy, and the thickness of the UBM layers on the initial crack growth in solder is investigated by calculating J-integral, respectively, which reflects the possibility of crack growth. It is concluded that the J-integral values in 96.5Sn3.5Ag solder and the Au-Ni-Cu-Ti UBM material are smaller than other cases.
Data Classification on Multiple Manifolds

Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum reconstruction error based classifier. Our method could be easily extended by involving various manifold learning methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the effectiveness of the proposed multiple manifold based approach.
Ferroelectric Negative Capacitance GeSn PFETs With Sub-20 mV/decade Subthreshold Swing

Negative capacitance (NC) GeSn pFETs integrated with HfZrO<sub><italic>x</italic></sub> (HZO) ferroelectric film is demonstrated with sub-20 mV/decade subthreshold swing (SS) over two orders of magnitude of <inline-formula> <tex-math notation="LaTeX">${I}_{\text {DS}}$ </tex-math></inline-formula>. The ratio of remnant polarization to coercivity in HZO is significantly improved with the increasing of the annealing temperature from 400 °C to 500 °C, which contributes to the effective reduction of hysteresis in ferroelectric NC GeSn transistors. Ferroelectric NC GeSn pFET annealed at 500 °C achieves a hysteresis of 70 mV while maintaining a steep SS dramatically lower than 60 mV/decade, and an improved <inline-formula> <tex-math notation="LaTeX">${I}_{\text {DS}}$ </tex-math></inline-formula> over control device without HZO.
Computerized analysis of tongue sub-lingual veins to detect lung and breast cancers

Among all cancers, lung and breast can be attributed to a large number of deaths worldwide. Given the various inconveniences and risks associated with traditional diagnostic methods, efficient non-invasive detection approaches using computerized methods are needed. With recent advances in medical biometrics, particularly focusing on the analysis of facial and tongue images to detect various diseases, there is a lack of studies in tongue sub-lingual veins. Therefore, this paper will analyze tongue sub-lingual veins in order to distinguish those that are healthy from those that have cancer (either lung or breast). Tongue sub-lingual veins are first captured using a uniquely designed device. Segmentation is then carried out to separate the vein foreground pixels from its background. This facilitates feature extraction in the form of color and geometry. As for the last step, classification is performed. Experimental results on a dataset consisting of 628 healthy samples, 81 samples with lung cancer, and 147 with breast cancer yielded an average accuracy of 82.07% at healthy vs. lung cancer, and 79.23% at healthy vs. breast cancer, proving the effectiveness of the proposed method.
Multi-operator spectrum sharing in next generation wireless communications networks: A short review and roadmap to future

Efficient spectrum utilization lies at the heart of next generation (xG) wireless communication networks. In this context, a thorough investigation into new and innovative spectrum management techniques as well as additional regulatory procedures are deemed necessary to evaluate the practical feasibility of these xG networks. This paper reviews the three possible modes for multi-operator spectrum sharing based on dynamic spectrum access (DSA) in 5th Generation (5G) networks, and identifies Licensed/Authorized Shared Access (LSA/ASA) mode to be practically most suitable candidate for better spectrum utilization. Existing spectrum sharing techniques exploiting time, frequency and space domain to realize efficient co-existence of multiple operators are discussed, and game theory (GT) framework has been recognized as an appropriate tool to model the interactions among multiple operators accessing the shared spectrum resources. In particular, the hybrid cooperativecompetitive game has been proposed as a promising model to incorporate key parameters that play a significant role in the practical Deployment of DSA-enabled xG networks.
A new audio coder using a warped linear prediction model and the wavelet transform

In this paper, we present results for a wavelet transform (WT) excited warped linear prediction (WLP) audio coder. In contrast to conventional LP, WLP allows for the control of frequency resolution to closely match the response of the human auditory system. The structure of the system is similar to the transform-coded excitation techniques used in wideband speech coding, where LP has been replaced with WLP. Quantisation of the wavelet coefficients is aided by a psychoacoustic model to minimise the perceptually significant noise due to quantisation error. For monophonic signals sampled at 44.1 kHz, the coder achieves near transparent quality for a variety of speech and music signals at an average bit-rate of 64 kb/s. When compared to MPEG layer III at the same bit-rate, the coder delivers superior quality. The power of the proposed coder resides in its easy scalability to lower bitrates.
Hybrid LPC and discrete wavelet transform audio coding with a novel bit allocation algorithm

This paper examines a new method for coding high quality digital audio signals based on a combination of linear predictive coding (LPC) and the discrete wavelet transform (DWT). In this method, a linear predictor is first used to model each audio frame. Then, the prediction error is analyzed using the DWT. The LPC coefficients and DWT coefficients are quantized using a novel bit allocation scheme which minimizes the overall quantization error with respect to the masking threshold. The proposed coder is capable of delivering near-transparent audio signal quality at encoding bit rates of around 90-96 kb/s. Objective and subjective results suggest that the proposed coder operating at 90-96 kb/s has a performance comparable to that of the MPEG layer II codec operating at 128 kb/s.
Performance analysis of adaptive lattice filters for impulsive signals

The existence of impulsive signals with /spl alpha/-stable non-Gaussian distributions has already been reported in some applications. A new adaptive lattice algorithm is proposed for adaptive filtering of /spl alpha/-stable AR processes. The performance of this algorithm is compared to that of other stochastic gradient-based algorithms for stable AR processes for different /spl alpha/'s. It is shown that the proposed algorithm achieves a superior convergence speed with respect to the other algorithms.
Fingerprint compression using a piecewise-uniform pyramid lattice vector quantization

A new compression algorithm for fingerprint images is introduced. Using lattice vector quantization (LVQ), a technique for determining the largest radius of the lattice and its scaling factor is presented. The design is based on obtaining the smallest possible expected total distortion (ETD) measure, using a given bit budget, while using the smallest codebook size. In the proposed piecewise-uniform pyramid LVQ, the wedge problem encountered with the pyramidal lattice point shells is resolved. At very low bit rates, for the coefficients with high-frequency content, the positive-negative mean (PNM) method is proposed to improve the resolution of the reconstructed image. The proposed algorithm results in a high compression ratio and a high reconstructed image quality with a low computational load compared to other existing algorithms.
Trends and Challenges in Mono and Multi Biometrics

Several systems require authenticating a person's identity before giving access to resources. Biometrics has long been known as a robust approach for person authentication. However, most monomodal biometrics are proven to exhibit one or more weaknesses. In this respect, evidence reconciliation from different biometric systems (referred to as multibiometrics) has attracted much attention lately. Multibiometric systems combine the information presented by multiple biometric sensors, algorithms, samples, units, or traits. In addition to improving recognition accuracy, these systems are expected to improve population coverage, reduce spoofing and be resilient to fault tolerance of different monomodal biometric systems. Here, we present an overview of the different biometric systems, enumerate the advantages and weaknesses of such systems, and some of the newly introduced biometrics. We then discuss the various sources of biometric information that can be combined as well as the different levels of fusion in a multibiometric system. It is becoming increasingly evident that multibiometric systems will be the technology for person identification in the 21st century.