nils-herrmann/clean_scirepeval huggingface dataset:
Motor Skill Development Alters Kinematics and Co-Activation Between Flexors and Extensors of Limbs in Human Infant Crawling

Hands and knees crawling is an important motor developmental milestone but the current clinical measures of motor function during crawling stage are relatively subjective. Objective metrics using kinematics and electromyography (EMG) in infant crawling may provide more stable and accurate measures of such developmental milestone, demonstrating changes in locomotion during age span. The purpose of this paper was to determine whether joint kinematics and the underlying co-activation between flexor and extensor in infant crawling are different for arms and legs across the infant age span. Surface EMG of two pairs of flexors and extensors from arms and legs and the corresponding joint kinematic data were collected in twenty health infants (11 males and 9 females, range 8–15 months), while they were crawling on hands and knees. Co-activation index of averaged EMG was used to quantify the simultaneous contractions between flexor and extensor muscles. Coefficient of variation of joint’s maximum vertical acceleration from multiple cycles was used to quantify the repeatability of kinematics during crawling. Our results indicated that the arm exhibited significantly higher co-activation and higher repeatability of joint movement than the leg, suggesting earlier development of arm compared to leg. Moreover, elder age groups, who had stronger walking ability developed, showed increased co-activation of the leg and significant increase in repeatability of the knee movement. These results were consistent with the rapid reinforcement of the leg during motor development from quadrupeds to bipedal walking. Furthermore, the EMG and kinematic parameters were significantly correlated with clinical variables. These results suggest that the EMG and kinematic analysis of infant crawling are useful in building effective assessment of infant’s motor function before independent walking.
ECA and 2DECA: Entropy contribution based methods for face recognition inspired by KECA

In this paper, two new methods: ECA and 2DECA are proposed for face recognition, which are inspired by KECA. In ECA (2DECA), features are selected in PCA (2DPCA) subspace based on the Renyi entropy contribution instead of cumulative variance contribution. Then the proposed methods are tested on the OLR, YALE and XM2VTS databases respectively. We also compare the performance of the related methods experimentally.
Multimodal News Story Clustering With Pairwise Visual Near-Duplicate Constraint

Story clustering is a critical step for news retrieval, topic mining, and summarization. Nonetheless, the task remains highly challenging owing to the fact that news topics exhibit clusters of varying densities, shapes, and sizes. Traditional algorithms are found to be ineffective in mining these types of clusters. This paper offers a new perspective by exploring the pairwise visual cues deriving from near-duplicate keyframes (NDK) for constraint-based clustering. We propose a constraint-driven co-clustering algorithm (CCC), which utilizes the near-duplicate constraints built on top of text, to mine topic-related stories and the outliers. With CCC, the duality between stories and their underlying multimodal features is exploited to transform features in low-dimensional space with normalized cut. The visual constraints are added directly to this new space, while the traditional DBSCAN is revisited to capitalize on the availability of constraints and the reduced dimensional space. We modify DBSCAN with two new characteristics for story clustering: 1) constraint-based centroid selection and 2) adaptive radius. Experiments on TRECVID-2004 corpus demonstrate that CCC with visual constraints is more capable of mining news topics of varying densities, shapes and sizes, compared with traditional k-means, DBSCAN, and spectral co-clustering algorithms.
Dynamic Analysis for Complex Information System Based on Nested Petri Nets

The analysis for complex information system includes both the structural system aspects and the system behavior, which can be achieved by nested Petri nets in the paper, i.e. Petri nets in which tokens can be nested relational data. Firstly, each place of a net represents a nested relation scheme, and the marking of each place is given as a nested relation of the respective type. Secondly, insert and delete operations in a nested relational database are expressed by transitions in a net, and the arcs of a net are inscribed with filter tables, which allow conditions to be formulated on the specified tuples. Then, the occurrence rule for transitions is defined by the operations union and intersection lattices of nested relations. Finally, the model is demonstrated on an example of an information system for planning and controlling projects in an enterprise.
Object tracking with kernel correlation filters based on mean shift

AbstractłIn order to enhance the robustness of kernel correlation filters(KCF) in complex background environment, this paper proposes a mean shift method with adaptive local object tracking algorithm. KCF algorithm has speed advantage by using the single template, we introduce the confidence map in the process of the tracking to determine the result of the current frame. If the result of confidence map in KCF algorithm is worse than the previous frame, then block mean shift algorithm is introduced to track the target again. The local information is utilized to get the final position of the target. The experimental results show that the proposed algorithm has the ability of adaptive local information fusion. The research indicates that the proposed method not only retains the KCF calculation efficiency, but also uses the mean shift to correct potential error frames on video sequences. Compared with other traditional tracking algorithms, the proposed method shows better performance.
A Monolithic High-Performance Buck Converter With Enhanced Current-Mode Control and Advanced Protection Circuits

A monolithic high-performance step-down dc-dc converter for high power point-of-load application is presented in this paper. A novel adaptive quadratic slope compensation method and an adaptive antijittering ramp scheme are proposed to enhance the performance of widely used peak current-mode control. A fast overcurrent detection method together with a robust driver control logic design has been introduced to increase the circuits' reliability. The dc-dc converter has been designed and fabricated in a 0.35-μm Bipolar-CMOS-DMOS process with an area of 6 mm2. With the proposed enhanced current-mode control, our design allows more flexibility on inductor selection, switching frequency, and output voltage. Experimental results have shown great adaptability for various application configurations. This converter has also shown fast line step response and low PWM jittering. It can operate at beyond 9-A peak current and up to 1.4-MHz switching frequency with supply voltage from 2.35 to 6 V. Measured peak efficiency is 96.6% and over 92% efficiency for 6-A output current is realized as well.
Optimal Test Case Generation for Simulink Models Using Slicing

Simulink is widely used for avionics and automotive systems design within model driven approach. For system verification and validation effectively, it is essential to generate test cases for Simulink models which guarantee high coverage of requirements and completeness required by safety-critical systems certification. However, for large-scale Simulink models, there is limited ability of test generation tools because state-space explosions occur in calculations, and structure coverage of test cases cannot meet the standard because of complex dependency relations in the model. To overcome these drawbacks, we propose a model slicing technique for optimal test case generation, which includes a static slicing algorithm to decrease model scale and generate tests for requirements, and a dynamic slicing algorithm to improve the structure coverage of test cases. Furthermore, we evaluate the effectiveness of slicing approach with avionics Simulink models. The experimental shows that complexity of Simulink models can be reduced and high structure coverage can be reached.
Processor Load Analysis for Mobile Multimedia Streaming: The Implication of Power Reduction

The software codec on mobile device introduces significant power consumption because the energy efficiency of general processor based system is much lower than that of the dedicated hardware such as ASIC based accelerator. Dynamical voltage scaling (DVS) is one of the most efficient techniques to promote the energy efficiency. Most existing papers on this topic use simple heuristics to predict processor load, and poor prediction accuracy is observed in experiments. We advocate intensive analysis on processor load before designing DVS framework and algorithm. Hence, we conduct load analysis on more than 600 processor load trace files for 57 test sequences and 98 representative clips from Internet. Basic statistical analysis and time series analysis are applied intensively to identify major characteristics of the processor load. The analysis shows that it is feasible to predict processor load using low order linear time series model if the load is sampled using feature period. Moreover, there is indeed significant potential to reduce the energy consumption. Based on the analysis results, we develop a fully adaptive DVS technique to adjust supply voltage online with controllable penalty
Gait Recognition Based on Difference Motion Slice

In this paper, we propose a novel gait recognition method, which applies to different viewing angles. In the method, difference motion slices that contain moving objects are extracted. With the difference motion slices we can get difference gait images, in which both static appearance shape information and dynamic information of habitual motion of the body can be expressed. Extensive experiments have been conducted on the NLPR database and the experimental results verified the high performance of the proposed method
The Mobile Agent Security Enhanced by Trusted Computing Technology

The mobile agent (MA) technology has been widely used to archive plenty of applications in distributed systems. The mobile agents are completely exposed in different hosts and easy to be attacked by different adversaries. In this paper, the requirements for security of MA are analyzed. Considering the security and safety problems both in software and hardware, we construct a way to build a trusted computing environment for MA by using the trusted computing technology. The trusted platform module (TPM) is integrated into the mobile agent platform. In the TPM enabled schemes for MA, some innovations, such as authentication, MA software integrity, secure group communication and secure storage, are provided to improve the security of MA
A Suboptimal CI Algorithm and its Application to Distributed Target Tracking

When the fused variables are independent or the statistics of variables are known perfectly, Kalman filter is rigorous and yields minimum mean squared error estimate. But in most situations, it is impossible to guarantee that the fused variables are independent and even might be highly correlated. In that case, it is possible to "over estimate" the statistics. CI algorithm can achieve a consistent estimation, when the correlation between the fused variables are unknown. However the caculation of optimal of CI costs lots of time. A simple way to calculate a sub optimal is proposed to reduce the time complexity and get a sub optimal estimation. According to the geometrical explanation of CI, a simple calculation equation of is given. And the functions of three kinds of fusion algorithm are illustrated in an application of decentralized estimation with circle topology, where it is impossible to consistently use a Kalman filter or other algorithms that need independent constraints.
Filtering Strategy Research Based on BDS/DR Navigation System

Nowadays Beidou satellite has fully realized real-time positioning of vehicles. However, we found that when BDS was used alone, the unsteadiness in the satellite signal transmission process may cause the problem of error positioning. In order to solve the problem, we propose to use the BDS/DR vehicle integrated navigation system. Then we choose the Extended Kalman algorithm to filter the data which includes the speed and displacement acquired by Beidou satellite navigation system. The results of the simulation show that Extended Kalman algorithm can effectively increases the reliability and fault-tolerance ability of BDS/DR navigation system, and avoids the diverging risk of error caused by Dead Reckoning system positioning and improve the positioning accuracy of the system.
A GW Level High-Power Radial Line Helical Array Antenna

High-power radial line helical array antenna is a novel antenna developed to realize directional radiation of high-power microwaves. This antenna directly accepts an azimuthally symmetric output of a high-power microwave source and radiates circularly polarized microwave with a boresight peak. It has a high efficiency, a compact structure, and a notable power-handling capacity. Starting from its fundamental design theory, an antenna prototype consisting of 48 elements is proposed. Numerical simulation and experimental measurement are accomplished for the prototype. In the range of 3.7-4.1 GHz, the experimental result shows an antenna VSWR of below 1.2, an antenna gain of over 23.2 dB, an aperture efficiency of more than 65%, and an antenna axial ratio of below 1.6 dB. In addition, the high-power experiment proves that this antenna has a power-handling capacity of more than 1 GW.
Noise Clustering Using a New Distance

Based on a new distance, a novel noise-resistant fuzzy clustering algorithm, called alternative noise clustering (ANC) algorithm, is proposed. ANC is an extension of the noise clustering (NC) algorithm proposed by Dave. By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance (non-Euclidean distance) is introduced in NC algorithm. Based on robust statistical point of view and influence function, the non-Euclidean distance is more robust than the Euclidean distance. So the ANC algorithm is more robust than the NC algorithm. Moreover, with the new distance ANC can deal with noises or outliers better than NC and fuzzy c-means (FCM). The better performance of the proposed algorithm is shown by performing experiments on data sets
Solving the Power Economic Dispatch Problem With Generator Constraints by Random Drift Particle Swarm Optimization

This paper proposes the random drift particle swarm optimization (RDPSO) algorithm to solve economic dispatch (ED) problems from power systems area. The RDPSO is inspired by the free electron model in metal conductors placed in an external electric field, and it employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Many nonlinear characteristics of a power generator, such as the ramp rate limits, prohibited operating zones and nonsmooth cost functions are considered when the proposed method is used in practice for optimizing the generators' operation. The performance of the RDPSO method is evaluated on three different power systems, and compared with that of other optimization methods in terms of the solution quality, robustness, and convergence performance. The experimental results show that the RDPSO method performs better in solving the ED problems than any other tested optimization techniques.
Weighted PCA space and its application in face recognition

In this paper, we propose a new PCA based subspace approach for pattern recognition. The conventional PCA feature space is first converted to a WPCA feature space with unit variance by weighting the features and then face recognition is performed in the new space. Detailed theoretical derivation and analysis are presented and simulation results on AR and ORL face databases are given. The simulation results indicate that the proposed approach is superior to conventional PCA approach in recognition accuracy under the same computation complexity.
Achieving target of Stockholm Convention on PCBs elimination: Asia-Pacific case

Polychlorinated biphenyls (PCBs) are required to be controlled by the Stockholm Convention due to their significant potential impacts to the environment and human health. Parties to the Stockholm Convention are obliged to prohibit the production of PCBs totally, eliminate equipment and oils containing PCBs from use by 2025 and bring these under environmentally sound waste management by 2028. Pollution to the environment and harm to the human health from PCBs in developing countries is serious in Asia-Pacific Region. This paper first summarizes the information of basic national status of PCBs in countries through analysis of submitted National Implementation Plans (NIPs), including production, import, inventory, disposal of PCBs. Then investigate and assesses the progress of PCBs management and elimination in Asia-Pacific countries on the base of PCBs projects developed by Asia-Pacific countries. Based on the obstacles and challenges analysis, the paper finally makes some suggestions on PCBs management and elimination at the regional level, including cooperation among regional countries, information exchange and experiences share, and.
Multiple Scattering by Parallel Plasma Cylinders

Multiple scattering by plasma cylinders with a plane wave is investigated rigorously by applying boundary value method that verifies validity with finite element method simultaneously, based upon the properties of electromagnetic wave transmitted in the argon plasma by cathode discharge. Approximate expressions for the far field, considering the interaction between the plasma cylinders, are derived briefly after some mathematical manipulations. Sample numerical results are presented for the cases of different plasma densities, different plasma collisions, and incidence with different striking angles, frequencies, and dissimilar forms of plasma arrays. Comparisons of the scattering distributions are given in detail to demonstrate the characteristics of plasma arrays.
Sound source localization based on directivity of MEMS microphones

MEMS technology based microphones have gradually been commercialized. These microphones have many virtues of small size, high sensitivity, flexible packaging, and so on. The paper proposes a novel and simple method for sound source localization using the directivity of MEMS microphones. The multi-microphone structure, signal processing algorithm, and system error depression technology of the method are presented. Theoretically, the maximum sound source localization angle error is 2.1/spl deg/ if signal acquirement frequency is 30 times the sound wave frequency.
Preprocessing and Classifying of Mass Spectrometry-Based Proteomics Data Using Wavelet Transform and Decision Tree Learning

Advances in mass spectrometry-based proteomics have brought expectations for biomedical researchers. It can be used for identify proteomic patterns in body fluids to discriminate patients from control, the results are inspiring. However, most of the earlier studies are based on the direct application of original MS data, together with dimension reduction or feature selection methods. We deemed that only the peaks of MS data have real biological meaning, so it's important to obtain the ultimate proteomic pattern using the real peaks. In this paper, we proposed a workflow that combined wavelet transform, statistical analysis and decision tree learning to process MS data. Especially, the statistical analysis which have not been attached too much importance in most studies was investigated, the possible distribution law of the MS peaks was proposed.
Nonlinear model predictive control of ultra-supercritical once through boiler-turbine unit

The coordinated control system (CCS) plays an import role in the operation of ultra-supercritical once-through boiler-turbine unit. To overcome the operating issues of the CCS, such as multivariable coupling, nonlinearity and large-time delay, a collocation method based nonlinear model predictive control is proposed in this paper. The dynamic optimization problem is transcribed into a finite dimensional nonlinear programming problem and solved using interior point-based large scale nonlinear optimization algorithm. Simulation results on a 1000MW ultra-supercritical once-through boiler-turbine unit show the effectiveness of the proposed approach.
SAR Image Despeckling via Bivariate Shrinkage Based on Contourlet Transform

We propose a novel and efficient SAR image despeckling via bivariate shrinkage based on contourlet transform, which has been recently introduced. Contourlet transform is a flexible multi-scale, multi-direction and multi-resolution image decomposition that can be efficiently implemented via transform. A bivariate shrinkage with local variance estimation is applied to the decomposed contourlet coefficients of the logarithmically transformed image to estimate the best value for the noise-free signal. Experimental results show that compared with conventional wavelet despeckling algorithm, the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserves image details, and the significant information of original image like textures and contour details is well maintained.
Grid System Integrated with Trusted Computing Platform

Grid provides people the way to share large mount of distributed resources and services that belong to different local organizations. That is a good way to share many kinds of distributed resources via the network in the open environment, thus it makes security problems more complicate and more important for us than before. In this paper, we analyze the requirements of trusted computing in grid. Considering the security and safety problems both in software and hardware, we construct a way to promote the trusted computing environment for grid by integrating the trusted computing platform (TCP) into grid system. We propose a new prototype system, the Daonity, in which grid system is combined with trusted platform support service (TSS) and TSS is based on trusted platform module (TPM). In this design, better effect can be obtained in authentication, confidentiality and integrity in grid computing environment
Cloud: Robust and anti-block game and delay-sensitive wireless networks

Wireless multimedia quality of service (QoS) support the basic objective is that in the bandwidth limited circumstances, provision and users pay a considerable quality of service. To establish the right business model is the first problem to be solved. The so-called business model, that is, according to the characteristics of a variety of specific applications, can be divided into different types. For example, in support of QoS and the ATM is defined in several business models. Constant bit rate services, the bandwidth requirements of the most stringent requirements of other types of bandwidth in turn to relax.