nils-herrmann/clean_scirepeval huggingface dataset:
3D shape constraint for facial feature localization using probabilistic-like output

This work presents a method to automatically locate facial feature points under large variations in pose, illumination and facial expressions. First we propose a method to calculate probabilistic-like output for each pixel of image. This probabilistic-like output describes the possibility of the pixel to be the center of specified object. A Gaussian mixture model is used to approximate the distribution of probabilistic-like output. The centers of these Gaussians are assigned with a probabilistic-like measure and they are considered as candidate feature points. There might be one or more candidate feature points in each facial region. A 3D model of facial feature points is built to enforce constraints on the localization results of feature points. Compared with active shape model (ASM) and its variant methods, our method could accommodate larger variations in pose, lighting and face expressions. Moreover, it is less sensitive to initialization errors, accurate, and fast. It takes a computer with P4 CPU about 10 ms to locate the five feature points (two eye centers, two mouth corners and nose tip). The feature localization accuracy is comparable with the accuracy of manually labeled features and it is robust to noise (glasses, beards). Experiments on FERET gallery and PIE are reported in this paper as well.
A smooth switch method for battery energy storage systems between Vf mode and PQ mode by utilizing electromagnetic relay

Battery energy storage systems (BESS) plays a significant role in micro-grids which consist of renewable energy systems. As the interface between storage components and grids, converter takes a big cost in ESS. Cost abatement of each part in converters is an important part in economic consideration. For grid connection, solid state relays (SSR) and electromagnetic relays (EMR) are two common choice. SSR owns a quick response (less than few ms) while EMR is much cheaper and with low conduct loss, but suffers a much slower response (5 to 50 ms) due to its excitation and mechanical movement. For fast response (milliseconds) of converters, the switch that connects ESS and grid is ordinarily SSR. Grid connection relay is not one of the most expensive components in converters, but assuming that thousands of SSRs in ESS being replaced by EMRs, a huge cost would be saved. This paper mainly discuss a new smooth switch method between Grid-connected and off-grid states based on Vf and PQ control, which allows electromagnetic relay takes the place of solid state relay, so as to reduce the cost.
A novel precision marketing model based on telecom big data analysis for luxury cars

Traditional marketing approach mainly adopts advertising and telemarketing method to attract potential consumers. It will need a lot of manpower and resources, but cannot position the targeted consumers accurately. In this paper, a novel precision automotive marketing model based on telecom big data mining is proposed to predict the potential high-end luxury car buyers. Initially, both logistic regression algorithm and neural network mining algorithm are applied to build the prediction model. A comprehensive understanding of existing high-end luxury car owners and telecom users are also analyzed deeply before modeling. Then, we derive the key attributes from four dimensions, including user characteristics, communication behavior, terminal attribute and social circle. Based on the evaluation of correlation matrix, eight clever attributes are selected as input variables of the proposed model. Finally, the analysis results demonstrate that the proposed prediction model can achieve superior performance and match the pre-analysis conclusions better.
An Elimination Method of Polarization-Induced Phase Shift and Fading in Dual Mach–Zehnder Interferometry Disturbance Sensing System

We proposed an improved polarization control method to reduce polarization-induced phase shift (PIPS) and polarization-induced fading (PIF) in a dual Mach-Zehnder interferometry (DMZI) disturbance sensing system. PIPS and PIF will seriously affect the positioning accuracy of the DMZI sensor. This polarization control method uses two PCs to control the state of polarization at the inputs of clockwise Mach-Zehnder interferometer and counter-clockwise Mach-Zehnder interferometer, respectively, based on chaotic particle swarm optimization algorithm, which has advantages of high speed and easy implementation. We experimentally demonstrated that the polarization method can overcome the effect of PIPS and PIF, improve the positioning accuracy of the DMZI sensor efficiently with positioning error of ±20 m after polarization control. Compared with the traditional polarization control method in DMZI based on the criterion of visibility, the positioning error of the sensor using our proposed method has been reduced at least an order of magnitude.
Closed-form Green's functions for multilayered structure and its applications [aperture coupled patch antenna example]

An efficient set of closed-form multilayered media Green's functions with general electric and magnetic sources is presented. The spatial-domain Green's functions are obtained by the discrete complex-image method, which obviates the time-consuming numerical evaluation of the Sommerfeld integral. Besides, we present a generalized Sommerfeld identity by which the higher order Sommerfeld integrals can be used in closed form. Numerical results by the Green's function are presented to show the efficiency and accuracy of this method.
Block independent component analysis for face recognition

This paper presented a subspace algorithm called Block Independent Component Analysis (B-ICA) for face recognition. Unlike the traditional ICA, in which the whole face image is transformed into a vector before calculating the independent components (ICs), B-ICA partitions the facial images into blocks and stretches the block to a vector, which is taken as the training vector. Since the dimensionality of the training vector in B-ICA is much smaller than that in traditional ICA, it can reduce the face recognition error caused by the dilemma in ICA, i.e. the number of available training samples is greatly less than that of the dimension of training vector. Extensive experiments are performed on the well-known Yale and AR databases to validate the proposed method and the experimental results show that the B-ICA achieves higher recognition accuracy than ICA and other existing subspace methods.
A hybrid approach based on regression tree and radial-based function network for dynamic storage device performance prediction

Storage device performance prediction is a critical element of self-managed storage systems and application planning tasks, such as data assignment and configuration. We proposed a new hybrid method (RT-RBF), which combines regression tree (RT) and radial-based functions network(RBF), to model storage device performance. In our proposed algorithm, the RT is firstly used to split the large space of knowledge into several small width and disjoint sub-spaces, and an RBF network is then used for training each of these smaller sub-spaces. With this new method, the advantages of the two techniques are completely amalgamated to obtain a more accurate and incremental model without compromising prediction time. In addition, we consider the caching effect as a feature in workload characteristics. Experiments indicate that RT-RBF model as well as workload characteristics used in the storage device modeling can produce more accurate predictions than RT or RBF.
BLDC motor field orientation control system based on LPIDBP neural network

In order to improve the performance of BLDC motor system , a brushless direct current (BLDC) motor FOC control system based on LPIDBP neural network is designed in this paper, which combining LPIDBP neural network with field orientation control (FOC) strategy. The control strategy and implementation method are put forward, and the system simulation model is established. The simulation results show the control performance of designed BLDC motor FOC control system is good, the torque ripple and speed ripple is small. STM32F103B is used as the main control chip to design the BLDC motor FOC control system test platform, and a BLDC motor monitoring system is designed by VB(Visual Basic). The contrast testing speed curves from monitoring system show the best performance of BLDC motor FOC control system based on LPIDBP neural network ,the system response is the fastest, the speed ripple is the smallest, and the operation is the stablest.
Combination of improved Katz and mutual information for speech recognition based on lattice

In the language model based on Chinese, only the number of occurring count of n-gram can not account for the reliability. Some n-grams have strong meaning in Chinese although their occurring count is low in training data. Combined the number of count with mutual information, whether the syllables in n-gram are highly associated can be better described. Further, in most smoothing approaches, the discount idea is widely adopted, that is for less reliable n-grams, the probabilities calculated by training data may be over-estimated, so they should be discounted. In this paper, not only the over-estimated n-grams are discounted, but also the most reliable n-grams which may be under-estimated during training procedure are enhanced. For all these modification is based on Katz which is the most commonly used method in speech recognition, the proposed approach is named as improved Katz. From the experiment results, it can be drawn that the performance of improved Katz is better that only Katz.
Deep Belief Networks Based Voice Activity Detection

Fusing the advantages of multiple acoustic features is important for the robustness of voice activity detection (VAD). Recently, the machine-learning-based VADs have shown a superiority to traditional VADs on multiple feature fusion tasks. However, existing machine-learning-based VADs only utilize shallow models, which cannot explore the underlying manifold of the features. In this paper, we propose to fuse multiple features via a deep model, called deep belief network (DBN). DBN is a powerful hierarchical generative model for feature extraction. It can describe highly variant functions and discover the manifold of the features. We take the multiple serially-concatenated features as the input layer of DBN, and then extract a new feature by transferring these features through multiple nonlinear hidden layers. Finally, we predict the class of the new feature by a linear classifier. We further analyze that even a single-hidden-layer-based belief network is as powerful as the state-of-the-art models in the machine-learning-based VADs. In our empirical comparison, ten common features are used for performance analysis. Extensive experimental results on the AURORA2 corpus show that the DBN-based VAD not only outperforms eleven referenced VADs, but also can meet the real-time detection demand of VAD. The results also show that the DBN-based VAD can fuse the advantages of multiple features effectively.
An Empirical Study on the Relationship between Stock Market Returns and Macroeconomic Variables: The Evidence from China

This study will investigate whether current economic activities in china have explanatory power over stock returns, or not. Monthly data of Shanghai Securities Exchange Composite Index and a set of macroeconomic variables, including money supply (M2), Value-added of industry (VAI), are used in this study. Engel-Granger, Johansen-Juselius co-integration tests and Granger Causality test are used to explain the long-run relations between macroeconomic variables and stock index.
Chip rate estimation of DS/FH hybrid spread spectrum signal

In this paper, the delay phase multiplication algorithm is introduced, which is very suitable to detect direct sequence and frequency hopping hybrid spread spectrum signal, detection principle of this kind signal is also given simultaneously. In order to improve detection performance, circular correlation and adapti ve filtering algorithms are introduced. The improved algorithm has been successfully used to detect direct sequence and frequency hopping hybrid spread spectrum signal, the results show that the detection algorithm can not only detect direct sequence and frequency hopping hybrid spread spectrum signal with lower SNR, but at the same time achieve the code rate.
Decolorization study of coking wastewater by continuous electrocoagulation process

In this study, decolorization and chemical oxygen demand (COD) reduction of coking wastewater by electrocoagulation process has been investigated. Effects of operating parameters, such as current density, pH of solution, electrolyte concentration and hydraulic residence time (HRT) on decolorization by continuous electrocoagulation were further studied in order to optimize process performance. The UV-Vis spectrum data supported the decolorization and degradation of organic pollutants of coking wastewater. These results showed great potential of electrocoagulation process as a decolorization treatment of coking wastewater, and up to 91 % of color removal efficiency was obtained at 25°C after 80min reaction with a current density of 30mA ·cm−2, a p H of 8.0, an electrode distance of 1.0cm, a supporting electrolyte concentration of 0.5g ·L−1.
A C++ data dictionary for ACR-NEMA image information

A data dictionary for the ACR-NEMA standard has been developed using the object-oriented language C++. The dictionary consists of DictionaryEntry, HashTable; Dictionary, DictionaryLink, Linknode. The computerized data Dictionary could be used by a Picture Archiving and Communications System to parse and display messages on an imaging workstation or to construct ACR-NEMA images from proprietary format image data on a modality. The paper describes the data structures, algorithm, implementation of the computerized dictionary.
Design of big data processing system for spacecraft testing experiment

The ground and in-orbit testing experiment of spacecraft are of great significance to the development of China's space industry, and processing the data of testing experiment is an indispensable part in it. In order to solve the problem of large data volume, complex data structure and long time needed for analysis in the testing experiment of spacecraft, a big data processing system of testing experiment is designed based on the mainstream big data processing technology. It stores the test data in the distributed file system of the computer cluster, and uses the memory computing technology to realize parallel processing of the experimental data. This system can greatly reduce the time needed to process experimental data, improve the efficiency of the spacecraft testing experiment, and has a good application prospect in the field of spacecraft test.
Color Salient Points Detection Using Wavelet

Point matching techniques have been widely used in content-based image retrieval progress. In this paper, a new color image salient points detector is proposed based on wavelet transform and the Barnard detector. First, an image is divided into three channels and each channel is decomposed with wavelet transform separately. Second, for a given wavelet coefficient, a corresponding wavelet coefficient could be found at a finer scale according to the absolute maximum value. Barnard detector is used when finding potential salient points in the image. Finally, color salient points are extracted by using a self-adaptable threshold and the continuous points set reduction method. Experiments show that this method is robust and the extracted salient points can give a satisfying representation of an image. It can also improve the retrieval performance effectively
Automotive ABS/DYC Coordinated Control Under Complex Driving Conditions

This paper presents an antilock braking system/direct yaw-moment control (ABS/DYC) coordinated control scheme in order to shorten the braking distance while ensuring the vehicle stability during emergency braking under complex driving conditions. Particularly, the braking actuator failure and driving on low-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-split roads are considered. The proposed control scheme is composed of three cascaded controllers. The first-level controller is used to derive and trace the desired yaw rate for DYC control based on the driver’s intent, exert conventional ABS control to maximize the braking force without considering braking actuator fault occurrence, and synthesize constraint conditions such as braking actuator failure and so forth. The second-level controller is used to determine on which side to fully capitalize for the maximum tire/road adhesion utilization. The third-level controller is leveraged to realize optimal torque allocation to each actuation motor with the purpose of minimizing the tire load. The effectiveness of the proposed ABS/DYC coordinated control scheme is verified through both simulation and hardware-in-loop experimentation.
A clustering-based scheme for concurrent trace in debugging NoC-based multicore systems

Concurrent trace is an emerging challenge when debugging multicore systems. In concurrent trace, trace buffer becomes a bottleneck since all trace sources try to access it simultaneously. In addition, the on-chip interconnection fabric is extremely high hardware cost for the distributed trace signals. In this paper, we propose a clustering-based scheme which implements concurrent trace for debugging Network-on-Chip (NoC) based multicore systems. In the proposed scheme, a unified communication framework eliminates the requirement for interconnection fabric which is only used during debugging. With clustering scheme, multiple concurrent trace sources can access distributed trace buffer via NoC under bandwidth constraint. We evaluate the proposed scheme using Booksim and the results show the effectiveness of the proposed scheme.
A Survey of Self-Interference Management Techniques for Single Frequency Full Duplex Systems

This paper presents a comprehensive survey of the literature on self-interference management schemes required to achieve a single frequency full duplex (FD) communication in wireless communication networks. A single frequency FD system often referred to as in-band FD system has emerged as an interesting solution for the next generation mobile networks, where the scarcity of available radio spectrum is an important issue. Although studies on the mitigation of self-interference have been documented in the literature, this is the first holistic attempt at presenting not just the various techniques available for handling self-interference that arises when an FD device is enabled, as a survey, but it also discusses other system impairments that significantly affect the self-interference management of the system, and not only in terrestrial systems, but also on satellite communication systems. The survey provides a taxonomy of self-interference management schemes and shows by means of comparisons the strengths and limitations of various self-interference management schemes. It also quantifies the amount of self-interference cancellation required for different access schemes from the first generation to the candidate fifth generation of mobile cellular systems. Importantly, the survey summarizes the lessons learnt, identifies and presents open research questions and key research areas for the future. This paper is intended to be a guide and take off point for further work on self-interference management in order to achieve FD transmission in mobile networks, including heterogeneous cellular networks, which is undeniably the network of the future wireless systems.
Attack Tree Based Android Malware Detection with Hybrid Analysis

This paper proposes an Android malware detection approach based on attack tree. Attack tree model is extended to provide a novel way to organize and exploit behavior rules. Connections between attack goals and application capability are represented by an attack tree structure and behavior rules are assigned to every attack path in the attack tree. In this way, fine-grained and comprehensive static capability estimation and dynamic behavior detection can be achieved. This approach employs a hybrid static-dynamic analysis method. Static analysis tags attack tree nodes based on application capability. It filters the obviously benign applications and highlights the potential attacks in suspicious ones. Dynamic analysis selects rules corresponding to the capability and conducts detection according to runtime behaviors. In dynamic analysis, events are simulated to trigger behaviors based on application components, and hence it achieves high code coverage. Finally, in this way, we implement an automatic malware detection prototype system called AM Detector. The experiment result shows that the true positive rate is 88.14% and the false positive rate is as low as 1.80%.
Adaptive mobile camera tracking using color and topological features

Various features may be used for tracking with a mobile camera, such as color, edge, contour, texture, locomotion, key point and so on. It has been proved that combination of multiple features can increase the robustness of a tracking system. By considering the characteristics of these features, we propose the approach of employing the color and topological properties of the target in tracking. First, the target is segmented into fragments. The color of the target is represented by a Gaussian Mixture Model(GMM), with every fragment corresponding to a Gaussian model. The topology of the target is represented by a tree-liked structure constructed from the spatial information of the fragments. During the tracking procedure, we extract the foreground by comparing the GMM of the target, and represent it using a GMM as well. After that, color and topological properties of the target are integrated in order to determine the association of the target's fragments with the foreground's fragments. The tracking result is the fragments of the foreground, which are associated with the target's fragments. At last, the color and topological properties of the target are updated by the tracking result. Experimental results demonstrate the effectiveness of this algorithm, even though when the locomotion of the target is unpredictable.
A 143GHz power amplifier based on over neutralization and power combining technique in 65nm CMOS

This paper presents a 143GHz power amplifier using eight-path power combiner in a 65nm CMOS. The Over neutralization technique is proposed to boost the gain of a differential pair without extra penalty on power consumption. A four-stage PA achieves a maximum gain of 17.3dB at 143GHz with a 3dB bandwidth of 4GHz. Based on the eight-path power combining technology, it delivers up to 13.2dBm saturated output power and obtains a peak PAE better than 5.6% at 143GHz, the P1dB is better than 8.6dBm. The whole circuit consumes 336mw of power from a 1.4V supply.
A knowledge management model for product design process

A tremendous amount of useful information is generated during the process of designing, but often only the final result is recorded. To effectively manage knowledge can help engineers rapidly redesign another similar product by modifying the original design process. This article brings forward a model to manage knowledge generated from the design process. This model supports the reuse of these design process knowledge through mapping the product composition and variation relation in practical product design to the product design process knowledge in effect. This article also gives an example of product design to validate the model