nils-herrmann/clean_scirepeval huggingface dataset:
Joint image compression and classification with vector quantization and a two dimensional hidden Markov model

We present an algorithm to achieve good compression and classification for images using vector quantization and a two dimensional hidden Markov model. The feature vectors of image blocks are assumed to be generated by a two dimensional hidden Markov model. We first estimate the parameters of the model, then design a vector quantizer to minimize a weighted sum of compression distortion and classification risk, the latter being defined as the negative of the maximum log likelihood of states and feature vectors. The algorithm is tested on both synthetic data and real image data. The extension to joint progressive compression and classification is discussed.
Machine Learning for Second Language Learning: Effects on Syntactic Processing in Garden-Path Sentences

With the rapid development of educational technologies, machine learning (ML) based second language learning (SLL) attracts the attention of many scholars from computational linguistics. Garden path (GP) sentence is a special sentence structure in which processing breakdown and backtracking are involved in the machine decoding. Faced with GP sentence, learners have to make original misinterpretation linger even after reanalysis has occurred, and have to face the fact that initial interpretations of GP sentence are often incorrect and do not correspond to the final output. On the basis of computational skills of N-S flowchart, recursive transition network, and well-formed substring table, this paper highlights the effects on syntactic processing in garden-path sentences when context-free grammar (CFG) driven ML system is introduced. There are two main conclusions to be drawn from the above discussion. Firstly, CFG-driven ML system can help SLL learners better understand the GP sentences in second language even no rich lingual environment is provided. Secondly, the hybrid knowledge, e.g. grammatical, cognitive and semantic information, needs to be integrated into ML system to help SLL learners achieve the "good enough" results in decoding GP sentences.
Optimum design of multi-finger SiGe HBT with non-uniform length for power application

With a three-dimensional thermal-electrical model, a non-uniform emitter length structure in multi-finger SiGe heterojunction bipolar transistors (HBTs) is presented to improve thermal stability. Compared with the traditional uniform emitter length design, the peak temperature of multi-finger SiGe HBT with non-uniform length is lowered. Therefore, it can operate at large current and has a higher power handling capability in power application.
Improved Variable EWMA Controller for General ARIMA Processes

The exponentially weighted moving average (EWMA) controller is a popular run-to-run control scheme in semiconductor manufacturing because of its effectiveness and simplicity. In this paper, we propose an improved variable EWMA controller design method. The optimal discount factor of the EWMA controller is determined by minimizing the mean square error of process output at each run. The proposed method overcomes some potential problems of Tseng's method and can be applied to general autoregressive integrated moving average processes. A numerical study is performed to investigate the performance and sensitivity of the proposed method. It is shown that the proposed method is more effective and robust than the conventional EWMA controller when the process parameters are estimated with uncertainty.
Energy window optimization in simultaneous technetium-99m TCT and thallium-201 SPECT data acquisition

In simultaneous Tc-99m TCT and Tl-201 SPECT data acquisition, the emission images are degraded by not only scatter from the emission source, but also cross-contamination from the transmission photons. Also, the transmission images are degraded by scatter from the transmission source and cross-contamination from the emission photons. In this work, a criterion based on maximizing a figure-of-merit (FOM) was used to reduce image degradations by optimizing the position and width of the energy windows used in the data acquisition in a triple-camera fan-beam SPECT system. The FOM determines a compromise between increased detection efficiency of primary photons and reduction of scatter and cross-contamination photons. Scatter in the projection data was modeled using Monte Carlo (MC) simulation methods. Experimental studies were performed to verify the simulations and to estimate the amount of Pb X-rays and scatter in the collimator which were not modeled in the MC program. Results suggest that cross-contamination degrades the emission and transmission data. However, the contamination does not significantly alter the optimal energy window settings, which should be centered at /spl sim/77 keV with a window width of /spl sim/34% for detecting the Ti emission photons, and centered at 140 keV with a window width of /spl sim/20% for detecting the Tc transmission photons.
Photonic Generation of UWB Pulses Using a Nonlinear Optical Loop Mirror and Its Distribution Over a Fiber Link

A novel approach is proposed for generating ultra-wideband (UWB) doublet pulses using a nonlinear optical loop mirror (NOLM). In this scheme, a polarization controller is used to shift the NOLM transfer function so that for a fixed operating condition (pump power), we can access the nonlinear portion of the transfer function. The UWB doublet pulses are then modulated using on-off keying and transmitted over a 10-km span of standard single-mode fiber to assess system performance. Error-free transmission is achieved with a power penalty of 1.5 dB compared with the back-to-back measurement.
Resilience Control of DC Shipboard Power Systems

Direct current (DC) network has been recognized as a promising technique especially for shipboard power systems (SPSs). Fast resilience control is required for an SPS to survive after faults. Towards this end, this paper proposes the indices of survivability and functionality based on which a two-phase resilience control method is derived. The on/off status of loads is determined in the first phase to maximize survivability, while the functionality of supplying loads are maximized in the second phase. Based on a comprehensive model of a DC-SPS, the two-phase method renders two mixed-integer non-convex problems. To make the problems tractable, we develop second-order-cone-based convex relaxations, thus converting the problems into mixed-integer convex problems. Though this approach does not necessarily guarantee feasible, hence global, solutions to the original nonconvex formulations, we provide additional mild assumptions, which ensures that the convex relaxations are exact when the line constraints are not binding. In the case of inexactness, we provide a simple heuristic approach to ensure feasible solutions. Numerical tests empirically confirm the efficacy of the proposed method.
Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data

We proposed a probabilistic approach to joint modeling of participants’ reliability and humans’ regularity in crowdsourced affective studies. Reliability measures how likely a subject will respond to a question seriously; and regularity measures how often a human will agree with other seriously-entered responses coming from a targeted population. Crowdsourcing-based studies or experiments, which rely on human self-reported affect, pose additional challenges as compared with typical crowdsourcing studies that attempt to acquire concrete non-affective labels of objects. The reliability of participants has been massively pursued for typical non-affective crowdsourcing studies, whereas the regularity of humans in an affective experiment in its own right has not been thoroughly considered. It has been often observed that different individuals exhibit different feelings on the same test question, which does not have a sole correct response in the first place. High reliability of responses from one individual thus cannot conclusively result in high consensus across individuals. Instead, globally testing consensus of a population is of interest to investigators. Built upon the agreement multigraph among tasks and workers, our probabilistic model differentiates subject regularity from population reliability. We demonstrate the method’s effectiveness for in-depth robust analysis of large-scale crowdsourced affective data, including emotion and aesthetic assessments collected by presenting visual stimuli to human subjects.
Composite Differential Evolution for Constrained Evolutionary Optimization

When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the $\boldsymbol {\varepsilon }$ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods.
Heat Leakage Analysis on Peltier Current Leads of Different Structures

In order to expand applications of Peltier current leads (PCLs), serial-connection, variable cross-section, and copper-aluminum composite designs of conduction-cooled PCLs were introduced in this paper. The simulation models for each design were established. The results showed that serial connection of thermoelectric semiconductors with the same type is unavailable in conduction-cooled PCLs. For specified lengths of PCLs in some special occasions, the variable cross-section design is quite available. However, its decrease of heat leakages is limited. And the copper-aluminum composite structure can also reduce heat leakages.
HPF: a transport protocol for supporting heterogeneous packet flows in the Internet

We present a new transport protocol called HPF for effectively supporting heterogeneous packet flows in the Internet environment. The following are the key features of HPF: HPF supports packet flows where different packets in the same transport connection have different quality-of service requirements in term of reliability, priority, and deadlines. HPP supports application-level framing, and provides APIs for applications to specify the priority, reliability and timing requirements of each frame. HPF enables the use of application-specified priorities as hints for network routers to preferentially drop low-priority packets during congestion. This ensures that 'important data' gets through preferentially during congestion. HPF decouples the congestion control and reliability mechanisms in order to support congestion control for unreliable and heterogeneous packet flows. Preliminary performance measurements in our experimental testbed show that HPF can provide effective support for heterogeneous packet flows in the presence of dynamic network resources.
Morphological Changes of Chondrocytes in Compressed Articular Cartilage Using Polarized Light Microscopy

The orientation and shape of chondrocytes in compressed articular cartilage were studied quantitatively by polarized light microscopy (PLM) at 0.34μm pixel-resolution. The morphological changes of the cell shapes, the cell orientation, and the territorial matrix surrounding the chondrocytes in articular cartilage were quantified.
Horn Antenna With Reconfigurable Beam-Refraction and Polarization Based on Anisotropic Huygens Metasurface

In this paper, we present a new horn antenna that can mechanically reconfigure the beam direction and polarization by combining a traditional linearly polarized horn antenna with an anisotropic Huygens metasurface. Three layers of Jerusalem-cross structures are integrated in a dielectric substrate to form the anisotropic Huygens metasurface; the entire structure has a thickness of about one fifteenth the working wavelength. By placing the metasurface in front of a traditional linearly polarized horn antenna, the main beam of the horn antenna is steered to a predetermined direction. Furthermore, the direction and polarization of the main beam are reconfigurable by rotating the anisotropic Huygens metasurface. The measured results of the antenna confirm the simulated results. Possible applications of the presented antenna include radar detection, base-station communications, and satellite communications.
Real-time driver eye detection method using Support Vector Machine with Hu invariant moments

In the development of advanced vehicle safety systems, monitoring the driverpsilas vigilance level and issuing an alert when he is not paying enough attention to the road is a promising way to reduce the road accidents. In such driver monitoring systems, developing a reliable real-time driver eye detection method is a crucial part. In this paper, a rear-time eye detection method using support vector machine (SVM) with Hu invariant moments is proposed. In the method binarization and heuristic rules to screen the contour are firstly used to find the region of interest (ROI) of the driverpsilas eye. Then the Hu invariant moments of the ROI are calculated and further used in developing the SVM model. The test sets from the experiment were used to validate the classification results. The validation results and conclusions about the performance of the method are presented.
Application of Random-SMOTE on Imbalanced Data Mining

The performance of many classifiers based on balanced data sets can't do well in imbalanced data sets. This article integrates the over-sampling method of Random-SMOTE (R-S), which is based on SMOTE method, in imbalanced data mining. We use the R-S method to increase the number of the minority randomly in the minority sample space until it is almost equal to the majority in data mining tasks. 5 UCI imbalanced data sets are balanced with the integrated data mining process. Log it algorithm is used for classification with these data sets. The result shows that the integrated use of R-S in data mining can improve the performance of the classifier significantly.
Rail vehicle ride comfort prediction based on bogie acceleration measurements

Ride comfort is one of the most significant dynamic performance characteristics of railway vehicles. It is imperative to monitor and predict the vehicle ride comfort online especially for modern rail vehicles. In this paper, an innovative online rail vehicle ride comfort prediction method is proposed. By using the acceleration measurements from the sensors equipped on the bogies, the dynamical acceleration of the carbody is estimated by using principle component analysis (PCA) and partial least square (PLS) techniques. The ride comfort is further calculated by using the estimated three way acceleration signals. The proposed method is validated by using the professional rail vehicle simulation software, SIMPACK.
Modeling of TIR radiative transfer in the soil-vegetation-atmosphere system: sensitivity to soil water content and LAI and simulation of complex scenes

The exploration of the full potential of multi-angular thermal infrared measurements is hampered by the scarcity of accurate and representative data sets. Acquisition of field data has several constraints, including lack of a suitable airborne sensor system. Development and evaluation of algorithms requires detailed and accurate radiometric data, which have to be collected in a very short time to limit noise due to the temporal variability of surface temperature controlled by the local heat balance within the canopy. Several canopy properties have to be determined, e.g. leaf area index (LAI), leaf inclination distribution, in addition to soil and foliage temperature. A data set for validation studies should comprise a range of canopies and canopy conditions. We have coped with these difficulties by using a detailed radiative transfer model (CUPID) of the soil-plant--atmosphere system to produce synthetic data sets over a wide range of canopies and hydro-meteorological conditions. These data have been first used to assess the sensitivity of the anisotropy in TIR radiance to LAI and to the soil water content. It was shown that a dry top soil and a wet root zone imply that the brightness temperature is highest at nadir viewing, opposite to the condition with a wet top soil and a drier root zone. The relative magnitude of the directional change in surface temperature compared with the difference between soil and foliage temperature was evaluated in detail over a range of LAI values. Next, the radiance data base created by RT modeling was used to produce realistic complex scenes. Land cover of a complex, irrigated agriculture scene was mapped beforehand using a TM image.
Multi-objective Optimization Algorithm Based on BBO for Virtual Machine Consolidation Problem

Cloud computing is a promising technology having ability to influence the way of the provision of computing and storage resources through virtual machine (VM). VM Consolidation is an efficient way to improve power efficiency and quality guarantee for on-demand services. However, it is an integer programming problem and as well as a NP-hard problem to find optimal solutions within polynomial time. In this paper, the VM consolidation problem is formulated as a multi-objective optimization problem, which has three conflicting objectives, i.e., reducing power consumption, achieving good load balancing and shortening VM migration time. We propose a multi-objective optimization algorithm based on biogeography-based optimization (BBO) for the VM consolidation problem, which is named as  Multi-objective Biogeography-Based Optimization algorithm hybrid with Differential Evolution. It utilizes cosine migration model, differential strategies and Gaussian mutation model to improve the quality of habitats and the ability of finding optimal solutions. Experiments have been conducted to evaluate the effectiveness of MBBO/DE using synthetic and real-world instances. Experimental results show that MBBO/DE obtains a better performance while simultaneously reducing power consumption and achieving good load balancing within a satisfactory time as compared to genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO) and BBO.
Robust Controllability of Temporal Constraint Networks under Uncertainty

Temporal constraint networks are embedded in many planning and scheduling problems. In dynamic problems, a fundamental challenge is to decide whether such a network can be executed as uncertainty is revealed over time. Very little work in this domain has been done in the probabilistic context. In this paper, we propose a temporal constraint network (TCN) model where durations of uncertain activities are represented by random variables. We wish to know whether such a network is robust controllable, i.e. can be executed dynamically within a given failure probability, and if so, how one might find a feasible schedule as the uncertainty variables are revealed dynamically. We present a computationally tractable and efficient approach to solve this problem. Experimentally, we study how the failure probability is affected by various network properties of the underlying TCN, and the relationship of failure rates between robust and weak controllability
An integrated feature extraction algorithm for condition monitoring of railway point machine

Railway point machines are crucial parts of rail transit systems. To monitoring their health conditions, how to extract relevant features is extremely important. This paper proposes an integrated method for feature extraction, which utilizes Empirical Mode Decomposition and Singular Value Decomposition (EMD-SVD) to process the signals of point machines. In this method, the power supply signal is first decomposed to a number of intrinsic mode functions and a residue to form a feature matrix. Singular values of this feature matrix are obtained by SVD, at which point Mahalanobis distance (MD) is employed. This method can avoid the segemetation of signals and process them entirely without any manual intervention. Its performance and effectiveness has also been validated via a test rig.
A New Face Detection Method with GA-BP Neural Network

In this paper, the BP neural network improved by the genetic algorithm (GA) is applied to the problem of human face detection. GA is used to optimize the initial weights of the BP neural network to make full use of its global optimization and local accurate searching of the BP algorithm. Matlab Software and its neural network toolbox are used to simulate and compute. The experiment results show that the GA-BP neural network has a good performance for face detection. Furthermore, compared with the conventional BP algorithm, the GA-BP learning algorithm has more rapid convergence and better assessment accuracy of detecting quality.
A Survey on Scheduling Workflows in Cloud Environment

Cloud computing is increasingly popular in recent years. As an application of cloud computing, cloud workflow is widely applied in scientific research and commercial field. And the cloud workflow scheduling problem is a major concern of the research on cloud workflow application. To study the state of the art of cloud workflow scheduling, some efforts are made and presented in this paper. Firstly, the retrieval is conducted to analyze the trend of the amount of related papers. Then, taxonomies are made from different aspects of cloud workflow scheduling such as the type of cloud environment, the type of cloud workflow scheduling algorithms and the constraints and objectives of cloud workflow scheduling. Finally, several research challenges in this area are proposed.
Removing Label Ambiguity in Learning-Based Visual Saliency Estimation

Visual saliency is a useful clue to depict visually important image/video contents in many multimedia applications. In visual saliency estimation, a feasible solution is to learn a “feature-saliency” mapping model from the user data obtained by manually labeling activities or eye-tracking devices. However, label ambiguities may also arise due to the inaccurate and inadequate user data. To process the noisy training data, we propose a multi-instance learning to rank approach for visual saliency estimation. In our approach, the correlations between various image patches are incorporated into an ordinal regression framework. By iteratively refining a ranking model and relabeling the image patches with respect to their mutual correlations, the label ambiguities can be effectively removed from the training data. Consequently, visual saliency can be effectively estimated by the ranking model, which can pop out real targets and suppress real distractors. Extensive experiments on two public image data sets show that our approach outperforms 11 state-of-the-art methods remarkably in visual saliency estimation.