nils-herrmann/clean_scirepeval huggingface dataset:
MCMC-based scene segmentation method using structure of video

Video scene segmentation and classification are fundamental steps for multimedia retrieval, browsing and indexing. In this paper, we present a robust scene segmentation approach based on the Markov Chain Monte Carlo (MCMC) method using the structure of video sequences. In our method, there are two novel approaches to segment video sequences into scenes. The first approach is the use of the video structures to extract scene boundary candidates from shot boundaries. Then using the MCMC method to select the true scene boundaries from these candidates, highly-accurate scene segmentation becomes possible. It should be noted that when the prior probability concerning the number of scenes in a target video sequence is given correctly, the MCMC method can provide a more accurate scene segmentation result. Therefore, in the second approach of the proposed method, the parameter utilized in the prior probability is set to the optimal value by using Multiple Regression Analysis (MRA). Consequently, accurate scene segmentation becomes possible by using the above novel approaches. Experimental results performed by applying the proposed method to actual broadcast programs are shown to verify the effectiveness of the proposed method.
Autonomous navigation based on iSAM and GPS filter for AUV

As we all known, autonomous navigation system is the basic of AUV movement, besides, accurate position estimation and reliable measurement are the keys of it. In order to avoid accumulating position errors in the long voyage by using navigation algorithm, we can depend on GPS. Due to the influence of diving and floating process, GPS data would slip. The slips affect the accuracy of navigation seriously and cause errors for AUV in path planning. Little slips can be accepted, while we can't tolerate large slips up to several hundred meters. This paper proposed the GPS filter algorithm, which combined with navigation algorithm such as DR, EKF and iSAM (incremental smoothing and mapping). In order to verify the validity and feasibility of the combination of navigation algorithm and the proposed algorithm, Sea trials were carried out on the high seas of the Yellow Sea. Experimental results and analysis show that the autonomous navigation approach combined GPS filter with navigation algorithm improves the accuracy of navigation compared with conventional method, at the same time iSAM has a higher accuracy than the other two algorithms.
Clustering for Complex and Massive Data

For applications of clustering algorithms, the key techniques are to handle complicatedly distributed clusters and process massive data effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive densityreachable is presented in this paper, which can handle clusters of arbitrary shapes, sizes and densities. For very large databases, such as spatial database and multimedia database, the traditional clustering algorithms are of limitations in validity and scalability. According to the notion of clustering feature of BIRCH, an incremental clustering algorithm is designed and implemented, which solves the problems of effectiveness, space and time complexities of clustering algorithms for very large spatial databases. KeywordsDensity-based clustering; Complex cluster; Large spatial database; Subcluster feature; Incremental clustering
Performance analysis of fractional order DCSK secure communication system

The research of chaos theory and its application in communication is the frontier of science and engineering, which provides a novel research field for conventional communication theory and also presents a broad application platform for nonlinear science. The fractional order chaotic system which had higher confidentiality than integral system was applied to physics and engineering are just a recent subject of interest. The use of fractional order chaos in differential Chaos shift keying (DCSK) has led to better secure performance, anti-interference ability[1][2]. The performance of DCSK communication system based on fractional order chaos (in this paper we call it FO-DCSK) is characterized in terms of bit error rate (BER) versus the signal to noise rate (SNR) by theoretic analysis and Matlab numerical simulation. The results of simulation show the superiority of this technique.
An end-to-end model for Android malware detection

Malware detection has been a difficult problem for a very long time. Since the wide use of smart devices in recent years, the number of malwares is increasing rapidly. Most existing methods for malware detection rely too much on manual interventions (e.g. pre-defined features and patterns), which can be easily deceived. In this paper, we propose a novel end-to-end deep learning model to detect Android malwares. Our model takes the raw system call sequence, which is generated during the application's runtime, as input and decides whether the sequence is malicious without any manual intervention. We evaluate the model on 14231 Android applications and obtain a detection accuracy of 93.16%, which is 2.81% higher than the contrast experiment in which we implement the method proposed by other researchers.
Intracranial arterial dolichoectasia involving both the vertebro-basilar and carotid systems: A case report

Intracranial arterial dolichoectasia (IADE) is a cerebral vascular disease characterized by ectatic, extended and zigzag brain artery. It affects more frequently the posterior circulation system (vertebro-basilar system). Involvement of both the posterior and anterior circulation system (carotid systems) is rare. Here, we report a 71-year-old man who suffered from paroxysmal vertigo and diplopia. He was diagnosed as IADE by brain CT scan, MRI and CTA. This patient presented dolichoectasia involving both the basilar and carotid artery systems.
The study of traffic accident reproduction system

Through investigating the progress of domestic and international traffic simulation systems, we constructed the simulation model of vehicle movement during traffic accident after analyzing the one-dimensional and two-dimensional collision image of the accident vehicle impact. In a virtual simulation based on the theory of OpenGL technology, we built a three-dimensional simulation model for reading, displaying and controlling. Meanwhile, we developed a road traffic scene and vehicle simulation of three-dimensional animation. Finally, we elaborated a whole system from the overall design and structure, and briefly introduced the system in the actual application of traffic simulation.
Grid services mediation

Grid services are software components that provide seamless access to a variety of grid resources such as computational resources and data sources. We present a new multi-agent framework which aims to ensure effective mediation of grid services. The framework comprises different services including search and discovery, planning and coordination, and service reliability and security. The main objectives of the framework are to dynamically discover grid services, devise effective planning and coordination strategies for the discovered services, and securely and reliably utilise those services.
Multi-domain feature extraction from surface EMG signals using nonnegative tensor factorization

To propose a multi-domain feature extraction method of surface EMG signals is of great significance to EMG-based human-computer interface (HCI). In this paper, nonnegative Tucker decomposition (NTD)-one model of nonnegative tensor factorization (NTF)-is used to extract multidomain features of sEMG signals for classification. In the first step the sEMG data are transformed into multidimensional information using continuous wavelet transform and the 4-D sEMG tensor is established. Then the tensor is decomposed into four components (spatial components, spectral components, temporal components and category components) and the core tensor is the feature extracted. The feature after being eliminated and compressed are fed into KNN, LDA and SVM classifiers for the identification of condition classification. An experiment about elbow movements of 10 healthy participants was carried out to verify the validity of this algorithm. The result implied that NTF is a meaningful and valuable multidomain feature extraction method to EMG-based HCIs.
Adaptive Carrier Frequency Offset and Channel Estimation for MIMO-OFDM Systems

The issues of carrier frequency offset (CFO) and channel estimation are very important for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless communication systems. In this paper, the problems of CFO and channel estimation are considered. An adaptive estimation algorithm is proposed. It can estimate CFO and channel parameters at the same time. Since the estimation method is real-time, it can work at a fast time-varying environment. However, the model becomes a huge nonlinear system if CFO and channel parameters are estimated simultaneously. It is a heavy load for the wireless communication system, even the extended Kalman filter can solve the nonlinear estimation. A multiple model, which includes many subchannel models, is constructed. The subchannel with CFO can be separately estimated. The accurate CFO is obtained by integrating estimated CFOs from subchannel models with weighting technology. The computational complexity can be reduced by this method. Finally, several simulations are given to prove the performance in comparison with other methods.
Document image segmentation using Gabor wavelet and kernel-based methods

The document image segmentation is an important component in the document image understanding. kernel-based methods have demonstrated excellent performances in a variety of pattern recognition problems. This paper applies kernel-based methods and Gabor wavelet to the document image segmentation. The feature image are derived from Gabor filtered images. Taking the computational complexity into account, we subject the sampled feature image to spectral clustering algorithm (SCA). The clustering results serve as training samples to train a support vector machine (SVM). The initial segmentation is obtained by assigning class labels to pixels of the feature image with the trained SVM. A proper post-processing is used to improve the segmentation result. Several representative document images scanned from popular newspapers and journals are employed to verify the effectiveness of our algorithm
Study of Fuzzy Theory in EPC General Contractor Risk Assessment

With international practice and needs of business own development, it’ s has become more and more common to carry out EPC project general contract in enterprises’ contract. The EPC model covers basically the entire project construction process and comparing with the traditional contract model, the risk of general contactor is the biggest undoubtedly. To the ambiguity and uncertainty of risk assessment information, this paper presents the fuzzy comprehensive risk evaluation method for EPC general contractor risk assessment. Through the analysis of the EPC model to determine the risk source and loss of general contractor and then use the fuzzy comprehensive evaluation method to realize the quantization of the possibility of risk occurrence and risk loss. By the quantified possibility of risk occurrence and loss consequences to calculate the risk gene and get the risk quantized valve ultimately. The example shows that fuzzy comprehensive risk evaluation method can realize quantitative treatment of fuzzy and uncertain information and can practically and effectively evaluate the EPC general contractor's risk.
A hierarchical multi-agent evaluation scheme for integrating distributed energy resources in distribution systems

Modern distribution grid is a combination of distributed generators (DGs), energy storage systems, and controllable loads connected to mid-voltage or low-voltage network that can operate in deregulated manners. High penetration of renewable resources at distribution level places new challenges on the planning and economic operation of the distribution systems. This paper proposes a hierarchical control framework based on multi-agent method to allow more flexible control on the distributed energy resources and adjustable loads at the user end while minimizing the computation burden on the distribution grid operator. The impact of large-scale DG integration is evaluated in terms of important factors such as voltage stability and line loading rate. A multi-agent network is structured based on the critical line identification technology for the purpose of reducing the control complexity in the reliable system operation. The developed mechanism coordinates the operation among different generation units and loads. The adjustable DG and loads at the customer side and controllable reactive power devices are deployed to limit the voltage deviation within the acceptable range. Simulation results clearly indicate that the agent-based hierarchical control scheme is effective in distributed energy resources management among multiple clusters of DG and variable load.
Tibetan-Mandarin bilingual speech recognition based on end-to-end framework

Tibetan-Mandarin bilingual speech recognition is addressed in this paper. Because there is a great difference between the phoneme sets of these languages, it is difficult to find a universal phoneme set for the bilingual acoustic model (AM) in the conventional hidden Markov model (HMM) framework. The end-to-end framework based on connectionist temporal classification (CTC) loss function is proposed to solve this problem by using the character as the modeling unit instead of the phoneme. However, the sparseness problem of model units is an intractable and ineluctable fact in CTC model training, particularly under low-resource conditions. This paper explores two methods to address this problem. First, different model units are selected. The Tibetan characters and the Mandarin non-tonal syllables are used as the CTC output units. Second, an adding noise algorithm is applied to the bilingual part of the training corpus to augment Mandarin speech. The experiments are carried out on the hybrid IFLYTEK Tibetan-Mandarin corpus. Obvious improvements can be observed by using the proposed methods.
The verification test of thermal distortion decoupling design for single panel of space micro-strip array antenna

This paper studied the structural characters of space micro-strip array antenna. It is analyzed how the thermal stress between the panel and the metal feed line (TEM line) at the back greatly affects the distortion of single panel. To verifying the effectiveness of decoupling design for thermal distortion, a verification test in low temperature and in vacuum is designed with measuring the strain on the surface of TEM line. The verification test has been done for two completely assembling panels with thermal decoupling-design. The methodology, testing facility and procedure and parameters of the test are described in detail. The curves of the test verify the tightness of the test method. The strain results show the thermal-decoupling design is effective for the space micro-strip array antenna
Transfer of human control strategy based on similarity measure

We address the problem of transferring human control strategies (HCS) from an expert model to an apprentice model. The proposed algorithm allows us to develop useful apprentice models that incorporate some of the robust aspects of the expert HCS models. We first describe our experimental platform, a real-time graphic driving simulator, for collecting and modeling human control strategies. Then, we discuss an adaptive neural network learning architecture for abstracting HCS models. Next, we define a hidden Markov model (HMM) based similarity measure which allows us to compare different human control strategies. This similarity measure is combined subsequently with simultaneously perturbed stochastic approximation to develop our proposed transfer learning algorithm. In this algorithm, an expert HCS model influences both the structure and the parametric representation of the eventual apprentice HCS model. Finally, we describe some experimental results of the proposed algorithm.
Distributed model predictive control for polytopic uncertain systems with randomly occurring actuator saturation and packet loss

In this study, a distributed model predictive control algorithm is presented for the polytopic uncertain system subject to randomly occurring actuator saturation and packet loss. The global system is decomposed into several subsystems, and a novel distributed controller model is established to account for both the actuator saturation and packet loss in a unified representation by using two sets of Bernoulli distributed white sequences with known conditional probabilities. By transforming the non-linear feedback law into a convex hull of linear feedback laws, the distributed controllers for subsystems are obtained by solving a linear matrix inequality (LMI) optimisation problem. Finally, two simulation examples are employed to show the effectiveness of the techniques proposed in this study.
Mutual information analysis of EEG of children with attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurobehavioral disorders in children and electroencephalography (EEG) is one of most common used neuroimaging techniques as the most accessible and informative method. EEG can be of great help in ADHD studies. Considering the brain as the most complicated information processing system, information theories can be applied on EEG analysis. In this study, a total of 50 children (9 girls, mean age: 10.44±0.75) with ADHD and 58 age, gender and handedness matched normal children were recruited. This study aimed to (a) characterize the critical differences between ADHD children and a control group in connectivity patterns by applying mutual information (MI) analysis and (b) examine the classification accuracy that can be achieved with EEG MI measures. Statistical and classification results revealed that that in most regions MIs of ADHD children were higher than control group asymmetrically but connections with lower MIs in right frontal and left occipital were also of great importance. Those findings may be related to underlying neuropsychological deviation of ADHD in both coupling and isolation between different brain modules. With 28 selected MI features, the classifier achieved accuracy of 85.7%. Combining with appropriate feature selection algorithm, MI might be an adequate biomarker candidate for assisting diagnoses of children with ADHD.
Modelling, parameters identification and sliding mode control for the pitch control system of an remotely operated vehicle

In this paper, the problem of the Super-twisting sliding mode control (ST-SMC) is concerned for the pitch control system of a remotely operated vehicle(ROV). A novel kinetic model of ROV's pitch motion is established by using stress analysis. The purpose of the addressed pitch control problem is to design a Super-twisting sliding mode controller to drive the state of the pitch angle control system for a remotely operated vehicle to the desired value. A sliding surface is firstly constructed and an ST-SMC law is synthesized to ensure the reaching condition. Moreover, the total control condition is also given out to make the effect of the controller better. Then, by using the Lyapunov stability theory, the stability of the addressed pitch control problem is guaranteed. A simulation example is given to illustrate the effectiveness of the proposed design scheme.
The Adaptation Schemes In PR-SVM Based Language Recognition

Phonetic-based systems usually convert the input speech into token (i.e. word, phone etc.) sequence and determine the target language from the statistics of the token sequences on different languages. Generally, there are two kinds of statistical representation for token sequences, N-gram language model (PR-LM) and support vector machines (PR- SVM) to perform language classification. In this paper we focus on PR-SVM method. One problem of the PR-SVM is that the statistical representation based on utterance is sparse and inaccurate. To tackle this issue, the adaptation schemes in PR-SVM framework are proposed in this paper. There are two schemes to be used: 1) Adaptation from the Universal N-gram Language Model (UNLM) trained on all languages; 2) Adaptation from the Low-Order N-gram Language Model (LONLM). The experimental results on 2007 NIST LRE tasks show that our method achieves significant gains over the unadapted model.
Semiconductor disk laser with a diamond heatspreader

A semiconductor disk laser with a transparent chemical vapor deposited diamond heatspreader is presented. The gain region of the semiconductor disk includes 16 non-compensated compressively strained InGaAs quantum wells. 710mW continuous-wave output power at 1043nm is produced using a 3% output coupler and 6100mW incident pump power. The slope-efficiency is 14.2% and the optical-to-optical conversion efficiency is 11.6%. The effect of the diamond heatspreader on the laser is also analyzed.
Study on the Spatial Variability and the Sampling Scheme of Soil Nutrients in the Field Based on GPS and GIS

With the introduction of GPS and GIS, the spatial variability of soil properties was measured and analyzed using traditional statistics method and geo-statistics. 63 sampling points were measured on a 50 m grid in the soil surface (0~20 cm) of a 13.3 hectare winter-wheat field and were positioned by the GPS receiver. The results show that all soil nutrients are normal distribution and represent moderate spatial variability. The organic matter, total-N, NH4-N, and available K have strong spatial correlation, while the available P exhibits moderate spatial variability, and the range of soil properties is 265.8-349.7 m. The standard errors of the 63 sampling points were respectively computed in kriging interpolation method. The results indicate that the errors increased with the decrease of the sampling points. The 50 m times 100 m and 100 m times 100 m sampling schemes were recommended for their higher precision and economical efficiency. Thus, they are preferable in the large-scale sampling
Position sensorless technology of switched reluctance motor drives including mutual inductance

To reduce the effect of estimation position angle error caused by mutual inductance on sensorless control of switched reluctance motor (SRM) in clear command channel mode, a SRM rotor position estimation method is presented. Using this method, the effect of mutual inductance can be eliminated without measuring mutual inductance. First, different working states are analysed. When working at single-phase excitation, mutual inductance can be ignored, inductance model method is used to estimate the position angle. When working at two-phase excitation situation, mutual inductance is non-zero, based on flux linkage-difference a new method is used to eliminate mutual inductance influence. Finally, the proposed method is verified by experiments. Experiment results show that compared with the mutual inductance ignored methods, the proposed method has higher estimation accuracy. This method can realise stable and reliable running of SRM sensorless control in wider speed range.