nils-herrmann/clean_scirepeval huggingface dataset:
Robust mean-shift tracking with corrected background-weighted histogram

The background-weighted histogram (BWH) algorithm proposed by Comaniciu et al. attempts to reduce the interference of background in target localisation in mean-shift tracking. However, the authors prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, that is, BWH does not introduce any new information because the mean-shift iteration formula is invariant to the scale transformation of weights. Then a corrected BWH (CBWH) formula is proposed by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background's interference in target localisation. The experimental results show that CBWH can lead to faster convergence and more accurate localisation than the usual target representation in mean-shift tracking. Even if the target is not well initialised, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventional target representation.
Negative Capacitance Ge PFETs for Performance Improvement: Impact of Thickness of HfZrOx

We report a comparative investigation of the negative capacitance (NC) Ge pFETs with different thicknesses of HfZrO<sub><italic>x</italic></sub> (HZO). Although the NC transistors with 6.6 nm HZO exhibit the sub-60 mV/decade subthreshold swing, the hysteresis inevitably occurs. The hysteresis-free characteristics are demonstrated in the NC Ge pFETs with 4.5 and 3.7 nm HZO. The influence of postannealing temperature on device performance is dependent on the thickness of HZO. With the postannealing at 450 °C, NC transistors with 4.5 and 3.7 nm HZO achieve 23% and 11% <inline-formula> <tex-math notation="LaTeX">${I}_{\textsf {DS}}$ </tex-math></inline-formula> improvement, respectively, over the control devices, at a <inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {GS}}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {TH}}$ </tex-math></inline-formula> of −1.0 V and a <inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {DS}}$ </tex-math></inline-formula> of –0.05 V. However, a higher postannealing temperature of 550 °C leads to the significant degradation in NC device with 4.5 nm HZO compared with the control device. It is further observed that, with the fixed HZO thickness and postannealing temperature, <inline-formula> <tex-math notation="LaTeX">${I}_{\textsf {DS}}$ </tex-math></inline-formula> of the NC Ge pFETs is improved as the <inline-formula> <tex-math notation="LaTeX">${C}_{G}$ </tex-math></inline-formula> peak gets increased.
Complete Binary Representation for 3-D Palmprint Recognition

3-D palmprint recognition has attracted a wide range of attentions due to its potential for civilian applications. Designing an effective representation is a key problem for the 3-D palmprint recognition. In this paper, we propose a complete binary representation (CBR) for the 3-D palmprint multiple-dimensional feature representation and recognition. First, we propose a multiple orientation binary representation (MOBR) to extract 2-D gray-level features of 3-D palmprint. Then, we propose a novel and effective compact surface type (CST) to characterize 3-D palmprint surface feature, and design a CST binary representation (CSTBR) to capture the surface consistency within a local patch. Finally, we develop a CBR method by integrating the MOBR and CSTBR, which can effectively represent both 2-D orientation-level and 3-D surface-level features of 3-D palmprint images. We conduct extensive intercomparison experiments to demonstrate the effectiveness of the proposed CBR method on a benchmark 3-D palmprint database. Also, we carry out multiple intracomparisons to validate the superiority of the MOBR- and CST-based representations.
Investigation of different post HK annealing impact on HK film property and device performance

HfO2 based high-permittivity gate dielectric has been introduced to CMOS logic device manufacturing since from 45nm node. However, these dielectrics are still under investigation and continuous optimization because of their relatively high oxygen vacancy concentration. Post Dielectric Annealing (PDA) after HK film may be a promising approach to reduce HK film trapped defect density and improve device performance as some literature reported recently. In the present work, different annealing conditions were applied on interface layer (IL)/HfO2 stack films, including soak annealing, spike annealing, and flash lamp based Milli-Second Annealing (MSA). Both blanket wafer and MOSCAP wafer characterization results show post HK MSA is an effective method to repair HK intrinsic defect, like oxygen vacancy, while it also beneficial for improving the Si/IL, IL/HK interface quality.
Bagging Evolutionary Feature Extraction Algorithm for Classification

Feature extraction is significant for pattern analysis and classification. Those based on genetic algorithms are promising owing to their potential parallelizability and possible applications in large scale and high dimensional data classification. Most recently, Zhao et al. presented a direct evolutionary feature extraction algorithm(DEFE) which can reduce the space complexity and improve the efficiency, thus overcoming the limitations of many genetic algorithm based feature extraction algorithms(EFE). However, DEFE does not consider the outlier problem which could deteriorate the classification performance, especially when the training sample set is small. Moreover, when there are many classes, the null space of within-class scatter matrix(Sw) becomes small, resulting in poor discrimination performance in that space. In this paper, we propose a bagging evolutionary feature extraction algorithm(BEFE) incorporating bagging into a revised DEFE algorithm to improve the DEFE's performance in cases of small training sets and large number of classes. The proposed algorithm has been applied to face recognition and testified using the Yale and ORLface databases.
Mesh simplification with hierarchical shape analysis and iterative edge contraction

We present a novel mesh simplification algorithm. It decouples the simplification process into two phases: shape analysis and edge contraction. In the analysis phase, it imposes a hierarchical structure on a surface mesh by uniform hierarchical partitioning, marks the importance of each vertex in the hierarchical structure, and determines the affected regions of each vertex at the hierarchical levels. In the contraction phase, it also divides the simplification procedure into two steps: half-edge contraction and optimization. In the first step, memoryless quadric metric error and the importance of vertices in the hierarchical structure are combined to determine one operation of half-edge contraction. In the second step, it repositions the vertices in the half-edge simplified mesh by minimizing the multilevel synthesized quadric error on the corresponding affected regions from the immediately local to the more global. The experiments illustrate the competitive results.
A Compound Pressure Signal Acquisition System for Multichannel Wrist Pulse Signal Analysis

In traditional Chinese pulse diagnosis (TCPD), to analyze the health condition of a patient, a practitioner should put three fingers on the wrist of the patient to adaptively feel the fluctuations in the radial pulse at the styloid processes. Thus, for comprehensive pulse signal acquisition, we should efficiently and accurately capture pulse signals at different positions and under different pressures. However, most conventional pulse signal acquisition devices can only capture signal at one position and under a fixed pressure, and thus only capture limited pulse diagnostic information. In this paper, we propose our novel solution to the problems of sensor positioning, sensor array design, pressure adjustment, and mechanical structure design, resulting in a compound system for multichannel pulse signal acquisition. Compared with the other systems, our system provides a systematic solution to sensor positioning, is effective in measuring the width of the pulse, and can capture multichannel pulse signals together with subsignals under different hold down pressures. Experimental results show that multichannel signals can achieve higher classification performance than single-channel signals.
Ferroelectric HfZrOx Ge and GeSn PMOSFETs with Sub-60 mV/decade subthreshold swing, negligible hysteresis, and improved Ids

We report the first ferroelectric (FE) HfZrOx (HZO) Ge and GeSn pMOSFETs with sub-60 mV/decade subthreshold swing (SS) (40∼43 mV/decade), negligible hysteresis, and enhanced Ids. With a RTA at 450 oC, FE devices with reduced hysteresis of 40∼60 mV demonstrate the significantly improved SS and Ids characteristics compared to control devices without FE, owing to the negative capacitance (NC) effect induced by HZO. FE Ge and GeSn pFETs achieve 22% and 20% Ids enhancement than control devices, respectively, at the drive voltage of 1.0 V. NC effect in FE devices is proved by the gate leakage and inversion capacitance characteristics.
Combination of Heterogeneous Features for Wrist Pulse Blood Flow Signal Diagnosis via Multiple Kernel Learning

Wrist pulse signal is of great importance in the analysis of the health status and pathologic changes of a person. A number of feature extraction methods have been proposed to extract linear and nonlinear, and time and frequency features of wrist pulse signal. These features are heterogeneous in nature and are likely to contain complementary information, which highlights the need for the integration of heterogeneous features for pulse classification and diagnosis. In this paper, we propose a novel effective method to classify the wrist pulse blood flow signals by using the multiple kernel learning (MKL) algorithm to combine multiple types of features. In the proposed method, seven types of features are first extracted from the wrist pulse blood flow signals using the state-of-the-art pulse feature extraction methods, and are then fed to an efficient MKL method, SimpleMKL, to combine heterogeneous features for more effective classification. Experimental results show that the proposed method is promising in integrating multiple types of pulse features to further enhance the classification performance.
Touchless Multiview Fingerprint Acquisition and Mosaicking

Touchless fingerprint capture devices have the advantage over traditional touch-based approaches of being hygienic and preventing distortions resulting from the contact of fingers. Single-view acquisition systems bring in problems, such as scene difference and a limited effective area. This paper thus presents a touchless multiview fingerprint capture system that acquires three different views of fingerprint images at the same time. This device is designed by optimizing parameters regarding the captured fingerprint image quality and device size. A fingerprint mosaicking method is proposed to splice together the captured images of a finger to form a new image with a larger useful print area. Optimization design of our device is demonstrated by introducing our design procedure and comparing with current touchless multiview fingerprint acquisition devices. The efficiency of our device is further proved by comparing recognition accuracy between mosaicked images and touch-based fingerprint images. Experimental results also show that our proposed mosaicking method is more robust to low ridge-valley contrast fingerprint images than available methods. The effectiveness of our mosaicking method is further proved by comparing equal error rates with the mosaicking algorithms evaluated on our established database of 541 fingers.
An Optimal Pulse System Design by Multichannel Sensors Fusion

Pulse diagnosis, recognized as an important branch of traditional Chinese medicine (TCM), has a long history for health diagnosis. Certain features in the pulse are known to be related with the physiological status, which have been identified as biomarkers. In recent years, an electronic equipment is designed to obtain the valuable information inside pulse. Single-point pulse acquisition platform has the benefit of low cost and flexibility, but is time consuming in operation and not standardized in pulse location. The pulse system with a single-type sensor is easy to implement, but is limited in extracting sufficient pulse information. This paper proposes a novel system with optimal design that is special for pulse diagnosis. We combine a pressure sensor with a photoelectric sensor array to make a multichannel sensor fusion structure. Then, the optimal pulse signal processing methods and sensor fusion strategy are introduced for the feature extraction. Finally, the developed optimal pulse system and methods are tested on pulse database acquired from the healthy subjects and the patients known to be afflicted with diabetes. The experimental results indicate that the classification accuracy is increased significantly under the optimal design and also demonstrate that the developed pulse system with multichannel sensors fusion is more effective than the previous pulse acquisition platforms.
A pipelined window implementation of multilayer networks with time-sequence input patterns for speech recognition

Neural networks (NNs), as processors of time-sequence patterns, have been successfully applied to several speaker-dependent speech recognition systems. They can be efficiently implemented by a pipelined architecture. In this paper, we explore its window structure as a computation component in the architecture. Two types of windows, parallel and serial data flow window, and their computation units with both feedforward and feedback paths are developed. The implementations of the windows are easily matched to the VLSI medium. Examples of applications to the NNs and performance analysis are given to illustrate effectiveness of the proposed window computation.
Stateful Logic Operations Implemented With Graphite Resistive Switching Memory

Memristor-based logic operation has attracted a lot of interest ever since the material implication and NAND logic operations were successfully implemented with several memristive devices. Here, we present a memory device fabricated with graphite membrane, which has a simple two terminal planar structure and high ON/OFF ratio over <inline-formula> <tex-math notation="LaTeX">$10^{2}$ </tex-math></inline-formula>. We use the memory device to establish logic gates, and stateful logic operations were successfully carried out. This letter paves the way for carbon electronics to be used in new computation technology.
Bi-directional PCA with assembled matrix distance metric

Principal component analysis (PCA) has been very successful in image recognition. Recent researches on PCA-based methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose bi-directional PCA (BDPCA) with assembled matrix distance (AMD) metric to simultaneously deal with these two issues. For feature extraction, we propose a BDPCA approach which can reduce the dimension of the original image matrix in both column and row directions. For classification, we present an AMD metric to calculate the distance between two feature matrices. The results of our experiments show that, BDPCA with AMD metric is very effective in image recognition.
A public mesh watermarking algorithm based on addition property of Fourier transform

This paper presents a public mesh watermarking algorithm. First, the resultant watermarked image minus the original image is the watermark information. According to the addition property of the Fourier transform, a change of spatial domain will cause a change in the frequency domain. Then, the watermark information is scaled down and embedded in one part of the x-coordinate of the original mesh. Finally, the x-coordinate of the test mesh is amplified before extraction. Experimental results prove that our algorithm is resistant to a variety of attacks without the need for any preprocessing.
Iris-Based Medical Analysis by Geometric Deformation Features

Iris analysis studies the relationship between human health and changes in the anatomy of the iris. Apart from the fact that iris recognition focuses on modeling the overall structure of the iris, iris diagnosis emphasizes the detecting and analyzing of local variations in the characteristics of irises. This paper focuses on studying the geometrical structure changes in irises that are caused by gastrointestinal diseases, and on measuring the observable deformations in the geometrical structures of irises that are related to roundness, diameter, and other geometric forms of the pupil and the collarette. Pupil- and collarette-based features are defined and extracted. A series of experiments are implemented on our experimental pathological iris database, including manual clustering of both normal and pathological iris images, manual classification by nonspecialists, manual classification by individuals with a medical background, classification ability verification for the proposed features, and disease recognition by applying the proposed features. The results prove the effectiveness and clinical diagnostic significance of the proposed features and a reliable recognition performance for automatic disease diagnosis. Our research results offer a novel systematic perspective for iridology studies and promote the progress of both theoretical and practical work in iris diagnosis.
Computer Aided Tongue Diagnosis System

To circumvent the subjective and qualitative problems of traditional tongue diagnosis, we present a novel computer aided tongue diagnosis system (CATDS). In this system, a standard acquisition device as well as a new color correction method is utilized to capture qualified tongue images. The system is constituted by five components: user interface module, acquisition module, tongue image database, image preprocessing module and diagnosis engine. In contrast to existing CATDS, the proposed system aims to establish the relationship between quantitative features and diseases via the Bayesian networks. System tests are carried out on a group of 544 patients affected by 9 common diseases and 56 healthy volunteers. The results show that the system can properly identify six groups: healthy, pulmonary heart disease, appendicitis, gastritis, pancreatitis and bronchitis with accuracy higher than 75%. Moreover, the execution time for the whole diagnosis process including image preprocessing and diagnosis is less than 5 seconds
Data-Driven Facial Beauty Analysis: Prediction, Retrieval and Manipulation

Facial beauty analysis becomes an emerging research area due to many potential applications, such as aesthetic surgery plan, cosmetic industry, photo retouching, and entertainment. In this paper, we propose a data-driven facial beauty analysis framework that contains three application modules: prediction, retrieval, and manipulation. A beauty model is the core of the framework. With carefully designed features, the model can be built for different purposes. For prediction, we combine several low-level face representations and high-level features to form a feature vector and perform feature selection to optimize the feature set. The model built with the optimized feature set outperforms state-of-the-art methods. Then, we discuss two scenarios of beauty-oriented face retrieval: for recommendation and for beautification. Finally, we propose two approaches for facial beauty manipulation. One is an exemplar-based approach that uses the retrieved results. The other is a model-based approach that modifies facial features along the gradient of the beauty model. In this case, the model is built with the shape or appearance feature. Experimental results show that the exemplar-based approach is better for shape beautification; the model-based approach is suitable for texture beautification; and the combination of them can increase the attractiveness of a query face robustly.
A New SPA Attack on ECC with Regular Point Multiplication

Elliptic Curve Cryptography (ECC) is becoming widely deployed in embedded cryptographic devices because of the reduced number of key bits. However, the side-channel attacks especially simple side-channel analysis (SPA) can obtain secret keys by measuring power consumption. To resist this attack there appear a number of countermeasures such as Montgomery ladder and double-and-add-always algorithm. This paper proposes a new simple power analysis attack to these countermeasures by distinguishing the conditional subtraction of Montgomery modular multiplication (MMM). Experimental results on smart cards demonstrate that this attack method can retrieve secret keys easily in several seconds using one power trace. Several countermeasures that can resist this kind of SPA attack are also demonstrated in this paper.
Architectural Significant Package (ASP): fundamentals of object-oriented architecture modeling for real-time embedded applications

Architecture modeling for real-time embedded applications presents a challenging issue in object oriented software development. The paper discusses the architectural modeling concepts and practices through an example of a real-time embedded project. During the project, a practical concept: the Architectural Significant Package (ASP) has been developed. Various ASPS are identified and integrated to form an architectural foundation for the project. In addition to the fundamental issues on identifying an ASP, the paper explores how to integrate ASPS into a cohesive and reusable model. Some interesting observations on this engineering practice of using the ASP approach will help to improve future architecture modeling in the real-time embedded environment.
The Negative Effects of Whitening Transformation in Face Recognition

Whitening transformation has widely been used in face recognition. Typically it is used in one of four ways: as a data preprocessing tool before feature extraction, to calculate statistically uncorrelated discriminant vectors, as a transitional step in calculating discriminant vectors, or as a data post-processing tool after feature extraction. The experimental studies presented in this paper demonstrate, however, that researchers’ faith in this technique is misplaced and that in fact whitening transformation is inappropriately used, and actually has a negative effect in face recognition without proper analysis.
Tongue color visualization for local pixel

Tongue diagnosis is a unique and important diagnostic method in Traditional Chinese Medicine (TCM). It is used to observe abnormal changes in the tongue color for identifying syndrome patterns. However, due to its qualitative, subjective and experience-based nature, it is hard to represent and visualize tongue color in computerized tongue diagnosis. This will undoubtedly limit the application of tongue color analysis in clinical practice. In this paper, we proposed a visualization system for local pixel separation to recognize this intrinsic pathological trait: tongue color. This system captures a digital image, extracts a contour of the tongue and constructs a lookup table. Finally, this lookup table is employed to construct some figures of different classes for the representation and visualization tongue color. The experimental results can draw a conclusion: Tongue color is a kind of pixel distribution.
A system design methodology for fuzzy clustering neural networks

A system design methodology for fuzzy clustering neural networks (FCNN) is presented. This methodology emphasizes a coordination between model definition, architectural description, and systolic implementation. Two mapping strategies both from FCNN model to system architecture and from the given architecture to systolic array are discussed. The effectiveness of the methodology is illustrated by: 1) applying the design to an effective FCNN model, where a direct fuzzy competitive learning algorithm between the nodes is adopted; 2) developing the corresponding parallel architecture with special feedforward and feedback paths; 3) building the systolic array (SA) suitable for VLSI implementation.