nils-herrmann/clean_scirepeval huggingface dataset:
Towards Making Unlabeled Data Never Hurt

It is usually expected that learning performance can be improved by exploiting unlabeled data, particularly when the number of labeled data is limited. However, it has been reported that, in some cases existing semi-supervised learning approaches perform even worse than supervised ones which only use labeled data. For this reason, it is desirable to develop safe semi-supervised learning approaches that will not significantly reduce learning performance when unlabeled data are used. This paper focuses on improving the safeness of semi-supervised support vector machines (S3VMs). First, the S3VM-us approach is proposed. It employs a conservative strategy and uses only the unlabeled instances that are very likely to be helpful, while avoiding the use of highly risky ones. This approach improves safeness but its performance improvement using unlabeled data is often much smaller than S3VMs. In order to develop a safe and well-performing approach, we examine the fundamental assumption of S3VMs, i.e., low-density separation. Based on the observation that multiple good candidate low-density separators may be identified from training data, safe semi-supervised support vector machines (S4VMs) are here proposed. This approach uses multiple low-density separators to approximate the ground-truth decision boundary and maximizes the improvement in performance of inductive SVMs for any candidate separator. Under the assumption employed by S3VMs, it is here shown that S4VMs are provably safe and that the performance improvement using unlabeled data can be maximized. An out-of-sample extension of S4VMs is also presented. This extension allows S4VMs to make predictions on unseen instances. Our empirical study on a broad range of data shows that the overall performance of S4VMs is highly competitive with S3VMs, whereas in contrast to S3VMs which hurt performance significantly in many cases, S4VMs rarely perform worse than inductive SVMs.
Carbon-Aware Load Balancing for Geo-distributed Cloud Services

Recently, data center carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of the data centers to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed data centers. We jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analyses and real-world trace-driven empirical evaluation demonstrate its effectiveness in both minimizing electricity cost and reducing carbon emission.
Investigation of a Novel Large Area Dispersed Arc Plasma Source With Time-Resolved ICCD Imaging

Unsaturated time-resolved end-on images of argon arc plasma at 1 atm were captured by an intensified charge-coupled device equipped with a narrowband interference filter to evidence the homogeneity of the arc plasma dispersed by increasing the axial magnetic field. A relatively uniform diffuse current sheet was identified established between the concentric electrodes of our specified experimental geometry.
Fault Diagnosis Expert System Based on VXI Bus for Communication Circuit Board

A fault diagnose expert system base on VXI is designed to proceed automatically a certain type high-tech information electronic equipment fault detection and improve its efficiency and accuracy of diagnosis. This paper mainly introduces the research on algorithm and realization of the fault diagnose expert system of the communication circuit board of such equipment, and example proving on the hardware platform is described as well. By use of such method, it's quicker and more convenient to locate fault on the circuit boards on this equipment. It's proved that this expert system can solve the problems of high cost and long intervals of maintenance and keep the equipment in a stable status.
Wideband transient response of irregular conductive objects illuminated by a high-power EMP

This paper proposes a numerical method for solving time domain electric field integral equation (TD-EFIE) so as to capture the wideband transient surface current of irregular conductive objects. The methodology is based on curved triangle RWG (CRWG) basis function combined with Laguerre polynomials as temporal variation, instead of customary planar triangle RWG expansion with matching-on in time (MOT) scheme. Using the curved CRWG spatial basis function, arbitrary PEC geometry can be modeled more accurately than that of using conventional planar patches. Numerical examples for some typical metallic targets under the illumination of a high-power EMP show that our proposed method can get accurate EMP responses of different PEC objects but with fewer spatial unknowns.
A New Perspective of Web Usage Mining: Using Enterprise Proxy Log

Web Usage Mining is one of the important methods for web recommendations, but most of its studies are limited in using web server log, and its applications are limited in serving a particular web site. In this paper, based on mining the enterprise proxy log, we propose a novel WWW-oriented web recommendation system. Unlike other data sources, the enterprise proxy log is access history of visiting the whole World Wide Web. We firstly compare the differences between the web server log and the enterprise proxy log, and then an incremental data cleaning algorithm is proposed based on these differences. In the step of data mining, we present a clustering algorithm using hierarchical URL similarity. Experimental results show that this system can apply the technology of Web Usage Mining successfully in this new area.
The Use of Transform Domain LMS Algorithm to Improve The Performance of Blind Multi-user Detection

The convergence speed of LMS algorithm is determined by the spread of the eigenvalues of the input signal's auto-correlation matrix, which is too slow to meet the need in many conditions. Transform domain LMS algorithm turns the auto-correlation matrix into unit matrix in transform domain, so it can speed up the convergence effectively. In this paper, transform domain LMS algorithm was applied in multi-user detection. The simulation result showed that the algorithm can improve the performance of multi-user detector.
Automatic Age Estimation Based on Facial Aging Patterns

While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.
Evaluating stochastic methods in power system operations with wind power

Wind power is playing an increasingly important role in electricity markets. However, it's inherent variability and uncertainty cause operational challenges and costs as more operating reserves are needed to maintain system reliability. Several operational strategies have been proposed to address these challenges, including advanced probabilistic wind forecasting techniques, dynamic operating reserves, and various unit commitment (UC) and economic dispatch (ED) strategies under uncertainty. This paper presents a consistent framework to evaluate different operational strategies in power system operations with renewable energy. We use conditional Kernel Density Estimation (KDE) for probabilistic wind power forecasting. Forecast scenarios are generated considering spatio-temporal correlations, and further reduced to lower the computational burden. Scenario-based stochastic programming with different decomposition techniques and interval optimization are tested to examine economic, reliability, and computational performance compared to deterministic UC/ED benchmarks. We present numerical results for a modified IEEE-118 bus system with realistic system load and wind data.
Communication-based Control Mode of Frequency Converter

In order to choose and apply frequency converter to the practical engineering situation rationally and correctly, especially for the effective control and adjustment of frequency converter, more attentions are paid to the comparison of two kinds of communication control modes for frequency converter, which are serial communication control and control based on fieldbus. The advantages and shortcomings of them are discussed. It is useful for the appropriate applications of the frequency converter and relative electronic devices under the practical situation
Location semantics prediction for living analytics by mining smartphone data

Automatic location semantics prediction for living analytics based on smartphone data has attracted extensive attention in just recent years. Basically, this task can be formulated as a multi-class classification problem, where different location/places are regarded as different labels. Previous studies were mostly based on common classification techniques directly, neglecting the critical challenging issue of class imbalance in such a problem (e.g., people go to offices much more often than they go to cinemas). It is also noteworthy that in contrast to common multi-class problems where the classes can be treated independently and interchangeably, the places for labeling usually have important correlations, which should be taken account in the classification/labeling process. Moreover, several activities may occur in the same place and thus the same place label might convey different semantics. In this paper, we address the above issues for location semantics prediction by proposing the FS-Mining (Frame-based Semantics Mining) approach. We treat the raw sensor data in the smartphone as a sequence of short and non-overlapping frames, based on which the user behavior at each place can be characterized and the place semantics can be modeled. To deal with the issues of label relation and class imbalance, a multi-level classification model with class-split and class-merge mechanisms was also developed. An ensemble strategy was also employed to further improve the performance. Experiments on the dataset of Nokia Mobile Data Challenge  demonstrate promising performances for the FS-Mining approach.
Performance of Multi-User DCSK Communication System Over Multipath Fading Channels

The bit error rate (BER) performances of an invariant delay multiple access DCSK (IDMA-DCSK) communication system over multipath fading channels are investigated. According to central limit theorem (CLT) we evaluate the BER formula of the IDMA-DCSK over Rayleigh and Ricean fading channels respectively. A two-ray fading channel model is used to simulate the IDMA-DCSK system. The theoretical and the simulation results match closely to each other, which supports the analyses approach.
Study on Area Estimation of Small Water Using MODIS Data

In the low-resolution MODIS satellite remote sensing images, the edge of a small water body is often blurred due to the resolution limitation. The existence of mixed pixels is not conducive to water information extraction and observation. Two Dimension Scattered Points (2DSP)-Classification(C) NDVI method is introduced to area estimation of the small water body. This method first analyzes band 1 and band 2 of MODIS data through 2-dimensional visualization, second, selects sample point sets of surface categories distinguished by eyes to train BP neural network and then, discriminates and classifies pixels in the study region to get mixed pixels, finally estimates water area by using linear spectral mixing model. Above-mentioned method is applied to estimate area of the West Lake in Hangzhou. Estimate error is discussed in detail. The results shows that 2DSP-C NDVI method which has a better adaptability and a higher precision in quantitative calculation for small water area than other water information extraction methods, is applicable to real-time monitoring of small size water such as small lakes and reservoirs.
Channel Estimator with Time Spread Tracking for OFDM systems

An improved channel estimator with time delay tracking measurement for OFDM systems is proposed by exploiting the inherent correlation between the cyclic prefix and port of the received OFDM signal. This scheme make use of real time measured size of inter-symbol interference (ISI) free window to estimate the maximum delay spread of channel, then a transform domain channel estimator is adopted to further reduce the noise variance. Due to the ISI-free window can be estimated before the application of FFT to the received signal, it is a parallel processing, and very suitable for the practical systems. Simulation result demonstrated that the proposed method can track the variation of delay spread of wireless channel, and improve the MSE of channel estimation significantly, thus give at least 1-dB improvement in ITU PA3 channel and 0.5-dB in ITU PB3 channel
Cognitive Tracking Waveform Design Based on Multiple Model Interaction and Measurement Information Fusion

To enhance maneuvering target tracking in modern battlefield, cognitive radar could adjust its waveforms and information processing manner. In this paper, a novel adaptive waveform design method based on multiple model interaction and measurement information fusion is developed. First, some latest measurements and virtual ones are collected to exploit more robust information. Second, the unknown target state is formulated via the multi-model idea, and the tracking framework is highlighted by the matrix-weighted multi-model fusion (MMF) in lieu of the probability-weighted way. Finally, the MMF output covariance matrix is selected as the ellipse metric, and ellipse parameters can be obtained by using the eigenvalue decomposition. Given these parameters, fractional Fourier transform is used to rotate the measurement error-ellipse to make them orthogonal, and further obtain the desirable rotating orientations for the cognitive transmitting waveform. Simulations show that compared with several algorithms, e.g., MIMM and IMM, our algorithm could further improve tracking performance and robustness.
Wiedemann Effect in ${\rm Fe}_{83}{\rm Ga}_{17}$  Alloys for Magnetostrictive Sensors

The application of the Wiedemann effect could make magnetostrictive sensors MS easily achieve the noncontact measurement. As we know, only a few theoretical studies on the Wiedemann effect, except for the material research, have been carried out. To study the essence of the Wiedemann effect, Fe83Ga17 alloys were applied to the magnetostrictive line, and the essentials determining the Wiedemann effect, including magnetostriction, hysteresis characteristics, and circumferential as well as longitudinal magnetic field characteristics, were studied by theoretical analyzes, experimental test, and simulative analysis, which could provide a basis for further studies on the internal relations between the Wiedemann effect and the measurement range of MS. In addition, the Wiedemann twist angle was measured and the flexural wave was captured to check the performance of the Wiedemann effect in Fe83Ga17 alloys.
Switch-Linear-Hybrid Power Converter and Its Application Prospect in Industry

A new power conversion mode is structured by hybridizing switch power conversion mode with linear amplification mode, so as to acquire their synthetic optimal performances. The theoretical analyses and experimental results are presented in this paper. They are proved that the new converters can match multi-kinds of loads (including nonlinear loads) with strong robustness, good waveforms and high efficiency. Its appliance prospect can be expected, particularly for the conditions where serious static and dynamic performances are required.
Improved transceiver structure based on PARC scheme for multi-stream MIMO systems

Per-antenna rate control (PARC) has been proved to be an efficient way to increase throughput of multi-stream MIMO systems. In time-varying channels, however, the channel quality indicator (CQI) fed back to the transmitter becomes outdated. That is, there is a mismatch between the selected transmission scheme and actual situation. Therefore, the system performance may decrease. In this paper, we proposed an improved transceiver structure for PARC based multi- stream MIMO system considering the channel delay effects of CQI feedback. The minimum-mean square error (MMSE) channel predictor based on pilot symbol assisted modulation (PSAM) for MIMO systems is used for prediction. This scheme compensates for the performance decrease caused by transmission delay and offers a more precise application of the modulation and coding set (MCS) selection scheme for modulation and rate control. Simulations are performed to demonstrate the performance for this proposed system.
Demand Dispatch and Probabilistic Wind Power Forecasting in Unit Commitment and Economic Dispatch: A Case Study of Illinois

In this paper, we analyze how demand dispatch combined with the use of probabilistic wind power forecasting can help accommodate large shares of wind power in electricity market operations. We model the operation of day-ahead and real-time electricity markets, which the system operator clears by centralized unit commitment and economic dispatch. We use probabilistic wind power forecasting to estimate dynamic operating reserve requirements, based on the level of uncertainty in the forecast. At the same time, we represent price responsive demand as a dispatchable resource, which adds flexibility in the system operation. In a case study of the power system in Illinois, we find that both demand dispatch and probabilistic wind power forecasting can contribute to efficient operation of electricity markets with large shares of wind power.
Scale-Aware Edge-Preserving Image Filtering via Iterative Global Optimization

Presently, few filters are able to smooth images in a scale-aware manner like Gaussian filtering while not blurring the edges of large-scale features, whereas this kind of filter can be important in many visual applications requiring scale-aware manipulation while avoiding halos. In this paper, we propose a filtering technique through iterative global optimization (IGO), enabling to achieve both good scale-aware and edge-preserving performance. Our method is based on a filtering idea of selective gradient suppression and guidance gradient correction in the framework of IGO, which has the advantages of avoiding halos and preventing oversharpening of edges, and a scale-aware measure can be introduced to further control the way of gradient suppression. The proposed measure is spatially varying and oriented by coarse-scale local extrema at each pixel to better preserve the natural boundaries of large-scale structures. Besides, we show that our method can be fast implemented with a sequence of 1-D filtering. In the experiments, we demonstrate the effectiveness of our method by comparing it with current state-of-the-art filtering methods and using it in a variety of applications.
Supervised nonlinear dimensionality reduction for visualization and classification

When performing visualization and classification, people often confront the problem of dimensionality reduction. Isomap is one of the most promising nonlinear dimensionality reduction techniques. However, when Isomap is applied to real-world data, it shows some limitations, such as being sensitive to noise. In this paper, an improved version of Isomap, namely S-Isomap, is proposed. S-Isomap utilizes class information to guide the procedure of nonlinear dimensionality reduction. Such a kind of procedure is called supervised nonlinear dimensionality reduction. In S-Isomap, the neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points, which is specially designed to integrate the class information. The dissimilarity has several good properties which help to discover the true neighborhood of the data and, thus, makes S-Isomap a robust technique for both visualization and classification, especially for real-world problems. In the visualization experiments, S-Isomap is compared with Isomap, LLE, and WeightedIso. The results show that S-Isomap performs the best. In the classification experiments, S-Isomap is used as a preprocess of classification and compared with Isomap, WeightedIso, as well as some other well-established classification methods, including the K-nearest neighbor classifier, BP neural network, J4.8 decision tree, and SVM. The results reveal that S-Isomap excels compared to Isomap and WeightedIso in classification, and it is highly competitive with those well-known classification methods.
Resource Allocation for Heterogeneous Cognitive Radio Networks with Imperfect Spectrum Sensing

In this paper we study the Resource Allocation (RA) in Orthogonal Frequency Division Multiplexing (OFDM)-based Cognitive Radio (CR) networks, under the consideration of many practical limitations such as imperfect spectrum sensing, limited transmission power, different traffic demands of secondary users, etc. The general RA optimization framework leads to a complex mixed integer programming task which is computationally intractable. We propose to address this hard task in two steps. For the first step, we perform subchannel allocation to satisfy heterogeneous users' rate requirements roughly and remove the intractable integer constraints of the optimization problem. For the second step, we perform power distribution among the OFDM subchannels. By exploiting the problem structure to speedup the Newton step, we propose a barrier-based method which is able to achieve the optimal power distribution with an almost linear complexity, significantly better than the complexity of standard techniques. Moreover, we propose a method which is able to approximate the optimal solution with a constant complexity. Numerical results validate that our proposal exploits the overall capacity of CR systems well subjected to different traffic demands of users and interference constraints with given power budget.
Robust Capon Filter Bank based three dimensional structure superresolution algorithm

A time domain 3D Rank Deficit Robust Capon Filter Bank (RD-RCFB) is presented. An additional preprocess and postprocess are adopted to transform the time domain model into the frequency domain model. And matrix vectorization is used to reduce the dimension. Optimal implement of the algorithm is discussed by comparing the cascading 1D RD-RCFB, cascading 2D RD-RCFB and 3D RD-RCFB. The algorithm outperforms the 3D Adaptive Sidelobe Reduction (ASR) and the 3D Amplitude and Phase Estimation of a Sinusoid(APES). Simulated planar aperture 3D image processing verified the algorithm.