nils-herrmann/clean_scirepeval huggingface dataset:
Optimal uncertain intervals in unit commitment with wind power

It is important to select an appropriate representation of wind power uncertainty for unit commitment (UC). Unlike previous studies predefining the scope of uncertainty, this paper presents a method to co-optimize wind power uncertain intervals and unit commitment. The proposed method is aiming to select the optimal wind power uncertain intervals and achieve the optimal trade-off between economics and reliability in power system operation. An interval UC considering the costs of expected energy not served (EENS) and expected wind power curtailment (EWC) is modeled. Then, a reformulation and linearization method is proposed, transforming the model to a MILP problem. Numerical studies on a six-bus system and the modified IEEE 118-bus system illustrate the effectiveness of the proposed model.
Albumin nanoparticles fabricated from microscaled PLGA templates using layer‐by‐layer assembly

The size of particles is a principal parameter to design drug delivery vehicles. In this work, albumin nanoparticles were fabricated from microscaled poly[lactic-co-(glycolic acid)] (PLGA) templates using layer-by-layer (LbL) assembly, which are different from traditional LbL-based hollow capsules. The albumin nanoparticles exhibit a spherical shape and solid inner structure. The formation mechanism of these albumin nanoparticles was proposed based on PLGA templates, mechanical property of albumin and external sonication.
Agent-Based Electricity Market Simulation With Demand Response From Commercial Buildings

With the development of power system deregulation and smart metering technologies, price-based demand response (DR) becomes an alternative solution to improving power system reliability and efficiency by adjusting the load profile. In this paper, we simulate an electricity market with DR from different types of commercial buildings by using agent-based modeling and simulation (ABMS) techniques. We focus on the consumption behavior of commercial buildings with different levels of DR penetration in different market structures. The results indicate that there is a noticeable impact from commercial buildings with price-responsive demand on the electricity market, and this impact differs with different scales of DR participation under different levels of market competitions.
Thin Fe-C Alloy Solid Film Based Fiber Optic Corrosion Sensor

In last few years, some transmitted type fiber optic corrosion sensors (FOCS) have emerged, but the fragile character makes them difficultly use in practice. In this paper, a novel thin Fe-C alloy solid film based fiber optic corrosion sensor has been developed. The sensing section is located at one end of the fiber. A double layer classic slab model has derived. According to the model, the change of reflectivity with time series relates to the corrosion location and corrosion rate of the sensing film. Two methods which are electroless plating deposition and DC magnetron sputtering have applied to prepare the nickel-phosphorus and copper reflective film, respectively. Electroplating and RF magnetron sputtering methods have been used to deposit Fe-C alloy sensing film. A sensing system has been setup to detect optical signal reflected from thin film. According to the change of the reflected light, the sensor can detect the corrosion when it generates at the sensor's position
Directional Sub-pixel Edge Detection Based on Limited Window Local Entropy

To realize precise micro-vision positioning based on edge detection of micro-structure parts, a pixel-level edge points detection method based on limit local entropy was proposed. 2-3 pixel were extended in the gradient direction to both sides from the edge points to construct the 3 cubic spline interpolation function of edge transition zone. Using function derivation to obtain the floating value of sub-pixel coordinates, then, sub-pixel coordinate was located according to the positioning principle of directional sub-pixel coordinates. Finally, the fine grinding standard gauge block and LIGA micro-spring were taken as example, using traditional gradient edge detection and the method proposed in this paper to realize image edge detection, and achieve the width measurement. The results show that the edge positioning accuracy of the method proposed in this paper is higher than the traditional gradient edge detection method. And whether the pixel level edge positioning accuracy or sub-pixel edge positioning accuracy are much higher than the positioning accuracy required of micro-assembly systems (5µm).
Multi-Label Learning with Emerging New Labels

In a multi-label learning task, an object possesses multiple concepts where each concept is represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is dynamic and new concepts may emerge in a data stream. In order to maintain a good predictive performance in this environment, a multi-label learning method must have the ability to detect and classify instances with emerging new labels. To this end, we propose a new approach called Multi-label learning with Emerging New Labels (MuENL). It has three functions: classify instances on currently known labels, detect the emergence of a new label, and construct a new classifier for each new label that works collaboratively with the classifier for known labels. In addition, we show that MuENL can be easily extended to handle sparse high dimensional data streams by simply reducing the original dimensionality, and then applying MuENL on the reduced dimensional space. Our empirical evaluation shows the effectiveness of MuENL on several benchmark datasets and MuENLHD on the sparse high dimensional Weibo dataset.
What Makes Objects Similar: A Unified Multi-Metric Learning Approach

Linkages are essentially determined by similarity measures that may be derived from multiple perspectives. For example, spatial linkages are usually generated based on localities of heterogeneous data. Semantic linkages, however, can come from even more properties, such as different physical meanings behind social relations. Many existing metric learning models focus on spatial linkages but leave the <italic>rich</italic> semantic factors unconsidered. We propose a Unified Multi-Metric Learning (<sc>Um</sc><inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ye-ieq1-2829192.gif"/></alternatives></inline-formula><sc>l</sc>) framework to exploit <italic>multiple types</italic> of metrics with respect to overdetermined similarities between linkages. In <sc>Um</sc><inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ye-ieq2-2829192.gif"/></alternatives></inline-formula><sc>l</sc>, types of combination operators are introduced for distance characterization from multiple perspectives, and thus can introduce flexibilities for representing and utilizing both spatial and semantic linkages. Besides, we propose a uniform solver for <sc>Um</sc><inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ye-ieq3-2829192.gif"/></alternatives></inline-formula><sc>l</sc>, and the theoretical analysis reflects the generalization ability of <sc>Um</sc><inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ye-ieq4-2829192.gif"/></alternatives></inline-formula><sc>l</sc> as well. Extensive experiments on diverse applications exhibit the superior classification performance and comprehensibility of <sc>Um</sc><inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ye-ieq5-2829192.gif"/></alternatives></inline-formula><sc>l</sc>. Visualization results also validate its ability to physical meanings discovery.
A Partial Feedback Scheme for MIMO Systems

Close loop transmission incorporating preceding, rate and power loading etc, can improve the performance of multiple-input multiple-output (MIMO) antenna system and draw much attention in recent research. However, the feedback overhead of channel information may significantly reduce the system throughput especially in FDD mode. In this paper, a statistical information based partial feedback scheme is proposed for MIMO communication system to reduce the amount of channel information feedback, which can be used to improve the scheduling and rate control of traffic. Simulations demonstrate that the proposed scheme overhead can be reduced greatly and still tracking the variation of channel.
Multi-focus image fusion based on multi-scale focus measures and generalized random walk

Multi-focus image fusion aims to produce an all-in-focus image by integrating a series of partially focused images of the same scene. A small defocused (focused) region is usually encompassed by a large focused (defocused) region in the partially focused image, however, many state-of-the-art fusion methods cannot correctly distinguish this small region. To solve this problem, we propose a novel multi-focus image fusion algorithm based on multi-scale focus measures and generalized random walk (GRW) in this paper. Firstly, the multi-scale decision maps are obtained with multi-scale focus measures. Then, multi-scale guided filters are used to make the decision maps accurately align the boundaries between focused and defocused regions. Next, the GRW is introduced to effectively combine the advantages of the decision maps in different scales. As a result, our method can effectively distinguish the small defocused (focused) regions encompassed by large focused (defocused) regions, and the boundaries can also be aligned accurately. Experimental results demonstrate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.
Image splicing detection based on image quality and analysis of variance

In this paper an image splicing detection scheme is proposed. The scheme is based on image quality and analysis of variance. Four kinds of noise used to simulated the image quality changes which caused by tampering of images, and analysis of variance is used to selected the image quality measures which are more sensitive to image blind splicing detection. Combined with the characteristic function moments of three-level wavelet sub-bands and the further decomposition coefficients of the first scale diagonal sub-band, we extracted all features from given image and it's predicted error image. SVM is adopted as the classifier to train and test the given images. The simulation results show the proposed scheme has good performance in the average detection accuracy increased by about 1.5% ∼15% than the existed methods.
New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning

One pass learning updates a model with only a single scan of the dataset, without storing historical data. Previous studies focus on classification tasks with a fixed class set, and will perform poorly in an open dynamic environment when new classes emerge in a data stream. The performance degrades because the classifier needs to receive a sufficient number of instances from new classes to establish a good model. This can take a long period of time. In order to reduce this period to deal with any-time prediction task, we introduce a framework to handle emerging new classes called One-Pass Class Incremental Learning (OPCIL). The central issue in OPCIL is: how to effectively adapt a classifier of existing classes to incorporate emerging new classes. We call it the new class adaptation issue, and propose a new approach to address it, which requires only one new class instance. The key is to generate pseudo instances which are optimized to satisfy properties that produce a good discriminative classifier. We provide the necessary propertiesand optimization procedures required to address this issue. Experiments validate the effectiveness of this approach.
A simplified approach for a downward-looking GB-InSAR to terrain mapping

In traditional ground-based inierferometric synthetic aperture radar (GB-InSAR) signal processing procedure, the image registration and phase unwrapping are the necessary steps to terrain mapping. In this paper, a simplified signal processing procedure for the downward-looking GB-InSAR to terrain mapping is proposed, which focuses pairs of complex radar images on the ground-range plane by the back-projection (BP) algorithm. Image registration and phase unwrapping are avoided by controllable baseline design and some prior parameters knowledge of radar system and illuminated terrain scene. Simulation experiment was carried out to test the simplified procedure, and the final simulated results demonstrated that this approach could acquire accurate and acceptable terrain mapping performances.
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels

Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples are known. In this paper, we propose an adaptive kernel selection method for kernel principal component analysis, which can effectively learn the kernels when the class labels of the training examples are not available. By iteratively optimizing a novel criterion, the proposed method can achieve nonlinear feature extraction and unsupervised kernel learning simultaneously. Moreover, a non-iterative approximate algorithm is developed. The effectiveness of the proposed algorithms are validated on UCI datasets and the COIL-20 object recognition database.
Partially Shared Latent Factor Learning With Multiview Data

Multiview representations reveal the fundamental attributes of the studied instances from different perspectives. Some common perspectives are reviewed by multiple views simultaneously, while some specific ones are reflected by individual views. That is, there are two kinds of properties embedded in the multiview data: 1) consistency and 2) complementarity. Different from most multiview learning approaches only focusing on either consistency or complementarity, this paper proposes a novel semisupervised multiview learning algorithm, called partially shared latent factor (PSLF) learning, which jointly exploits both consistent and complementary information among multiple views. In PSLF, a nonnegative matrix factorization (NMF)-based formulation is adopted to learn a compact and comprehensive partially shared latent representation, which is composed of common latent factors shared by multiple views and some specific latent factors to each view. With the learned representations of multiview data, we introduce a robust sparse regression model to predict the cluster labels of labeled data. By integrating the NMF-based model and the regression model, we obtain a unified formulation and propose a multiplicative-based alternative algorithm for optimization. In addition, PSLF can learn the weights of different views adaptively according to the reconstruction precisions of data matrices. Our experimental study indicates different multiview data that contains consistent and complementary information in different degrees. In addition, the encouraging results of the proposed algorithm are achieved in comparison with the state-of-the-art algorithms on real-world data sets.
Correction to "Automatic Age Estimation Based on Facial Aging Patterns"

“The authors would like to thank Y. Zhang, G. Li, and H. Dai for their help and Dr. A. Lanitis for providing the FG-NET Aging Database. Part of the work was done when Xin Geng was at the LAMDA Group, Nanjing University. Also, this research was partially supported by the National Science Foundation of China (60635030, 60325207) and the Foundation for the Author of National Excellent Doctoral Dissertation of China (200343).”
Metamorphic Testing for Adobe Data Analytics Software

It is challenging to test data analytics software because a test oracle might not be available. This study reports our experience of applying metamorphic testing to Adobe's data analytics software that is used for anomaly detection in a set of time series data. We make use of geometric transformations to build metamorphic relations and generate simple time series data as the source test cases. The results of this study show that metamorphic testing is highly effective for both verification and validation purposes. An investigation of the issues detected during metamorphic testing revealed three bugs in the software under test.
The portfolio analysis of electricity market with transmission constraints

In this paper, we define the portfolio of a generation company as the coordination of a set of generation units, which differ in production cost, capacity, and geographical location. A portfolio allows a generation company to coordinate its generators to bid strategically to earn profit more than what can be achieved without coordination. In a constrained transmission network, for example, a generation company can raise the electricity prices at a particular zone by congesting the inbound transmission lines through importing electricity from neighboring zones where other cheaper generation units of the company located. Understanding generation companies' bidding behaviors under such situation is therefore crucial to ensure market fairness. In this paper, we use an agent-based approach to model the electricity market as a multi-player game and investigate how the transmission capacity and installed capacity of generation companies affect their bidding strategies under both a monopoly market and a non-monopoly market. We demonstrate conditions under which generation companies will prefer a coordination strategy over a non-coordination one.
Drosophila gene expression pattern annotation through multi-instance multi-label learning.

In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local expression patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila gene expression pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches.
Preliminary results of low frequency Ultra-wideband bistatic SAR experiment

The low frequency ultra-wideband bistatic synthetic aperture radar (LF UWB BSAR) system has the well ability of penetrating the foliage, high-resolution imaging and providing the increased information, thus it is able to detect the vehicle-size targets concealed in the forest. Initial experiment of the LF UWB BSAR system has been carried out in late 2015. In this experiment, both monostatic and bistatic data were collected simultaneously by operating the vehicle-based radar in a strip-map SAR mode, in conjunction with the stationary ground-based receiver. This paper gives an overview of the LF UWB BSAR system, and then discusses the experiment and data processing, finally presents the initial experimental results.
ALMA: a new approach to data initial association

An adaptive limited memory approach (ALMA) is proposed to deal with the data initial association problem in tracking-while-scanning systems in high clutter environments. By using measurements in the window limited memory and exploiting the target detection probability and receiver noise sampling information, the initial track number is estimated. It in turn can be adopted to adjust the initial correlation region and the normalized residual square threshold adaptively; many false initial trajectories are therefore restrained. Computer simulation demonstrates that the real time data initial association problem can be settled by a combination of radar's dynamic characteristics and limited window measurements.
Grating/side lobes mitigation in sparse UWB MIMO array

With finite aperture size and limited antenna number, sparse ultra-wideband (UWB) multiple-input-multiple-output (MIMO) array based imaging radar suffers from azimuth side/grating lobes. Commonly used aperture-domain mitigation methods such as coherence factor (CF), phase CF (PCF) and their modifications encounter some defects including insufficient data, suppression of weaker targets and heavy computing burden. In this paper, we propose extended CF (ECF) methods based on the sub-band subaperture (SBSA) image data to combat side/grating lobes and simultaneously overcome the above defects. With rational division of sub-bands and sub-apertures, on one hand, sufficient SBSA image data can be achieved in the UWB MIMO array, which guarantees the good side/grating lobe mitigation; on the other hand, SBSA image data have a certain ability of multiple targets discrimination in the range and azimuth directions and the side effects on weaker targets can be relieved. Besides, SBSA image data can be formed efficiently by the inverse fast Fourier transformation (IFFT) assisted back projection. Simulations with an 8-element UWB MIMO array validate the proposed methods.
Steganalysis for Quantization Index Module Hiding Scheme Based on Guassian Distribution

Quantization index module is a normal type of information hiding scheme, but the Steganalysis methods are not accuracy enough to detect them. A novel steganalysis method is proposed on the basis of center limitation theorem. One concise statistics is constructed to detect this kind of steganagraphy algorithms. A DCT-based image steganographic method which is recently advanced as resisting statistical attack is studied and the experimental results indicate that this technology is applicable for the detection of the quantization index module embedding algorithm.
Chemistry and physics of semiconducting BaTiO/sub 3/ based PTCR ceramics [for sensors]

For fifty years, the semiconducting PTCR ceramics have been developed to be one of the most important sensor groups, not only used as one kind of conditional physical sensor but also as new prospective chemical sensors. Defect structure, formation and diffusion of the semiconducting BaTiO/sub 3/ are the necessary condition for the resistivity anomalous with PTC. The adsorption and desorption of gas on the ceramic surface are two sufficient premises, particularly in terms of defect chemistry in semiconducting BaTiO/sub 3/ ceramics and were described.