nils-herrmann/clean_scirepeval huggingface dataset:
Constraint Programming Model of Tdrss Single Access Link Scheduling Problem

TDRS (Tracking and Data Relay Satellite) can be seen as a bridge between user spacecrafts and ground station. The TDRSS (Tracking and Data Relay Satellite System) single access link scheduling problem is an important application field of TDRSS. The important character of the scheduling problem lies in that there is time windows constraint between TDRS and user spacecrafts. Tasks can be completed only within given time windows. The paper builds the CP (constraint programming) model of the scheduling problem based on reasonable assumption. The CP model is solved with improved GA (genetic algorithm) based on path representation of valid genes. Computational experiments indicate that it is quite good to solve the scheduling problem by building the CP model and solving it with improved GA
Discriminative Metric Preservation for Tracking Low-Resolution Targets

Tracking low-resolution (LR) targets is a practical yet quite challenging problem in real video analysis applications. Lack of discriminative details in the visual appearance of the LR target leads to the matching ambiguity, which confronts most existing tracking methods. Although artificially enhancing the video resolution by superresolution (SR) techniques before analyzing might be an option, the high demand of computational cost can hardly meet the requirements of the tracking scenario. This paper presents a novel solution to track LR targets without explicitly performing SR. This new approach is based on discriminative metric preservation that preserves the data affinity structure in the high-resolution (HR) feature space for effective and efficient matching of LR images. In addition, we substantialize this new approach in a solid case study of differential tracking under metric preservation and derive a closed-form solution to motion estimation for LR video. In addition, this paper extends the basic linear metric preservation method to a more powerful nonlinear kernel metric preservation method. Such a solution to LR target tracking is discriminative, robust, and efficient. Extensive experiments validate the entrustments and effectiveness of the proposed approach and demonstrate the improved performance of the proposed method in tracking LR targets.
Internet Search by Active Feedback

Modern search engines with arrays of parallel servers can provide efficient Web information retrieval. However, as the returned results are usually enormous mass, finding target information is still time-consuming. To increase the correctness of returned results, different page ranking methods have been introduced. They waited and passively weighted users' feedback in page ranking to try to improve the precision of searching. Previous studies on Web information retrieval show that the average error ratios are over 20%. In this paper, we base on a heuristic from the librarian to present an interactive information retrieval technology called SAFE (search with active feedback). SAFE uses the concept of balanced tree to provide users extra keywords for feedback. These keywords can guide users to have a proper feedback in further searching. The proposed method can help information retrieval applications (not only Web but also desktop) to speed their responses. Additionally, the same idea can be extended to assist search engines to balance their workloads among parallel and distributed servers.
A Triband Low-Profile High-Gain Planar Antenna Using Fabry–Perot Cavity

A triband antenna with high gain and low profile is proposed. This antenna is based on the concept of Fabry–Perot cavity antenna (FPCA) and three resonances are obtained by employing two frequency selective surface (FSS) layers. Two FSS layers lead to two resonant frequencies by making each FSS layer satisfy the resonant condition of Fabry–Perot cavity. The third resonant frequency is obtained by exploiting the combined effects of the two FSS layers together. The operating principle of this proposed antenna is explained and the achievable frequency range is investigated. It is shown that the proposed method enables all three resonant frequencies to be independently tuned within certain frequency range. Because of the 0° reflection phase of the combined-FSSs, the present antenna has a low profile. To verify the design concept, one C-/X-/Ku-band FPCA is designed, fabricated and tested. Experimental results agree well with the simulated results. High gain with good impedance matching in three bands is obtained, which reaches a peak gain of 13.4 dBi at 5.2 GHz, 18.9 dBi at 9.7 GHz, and 20 dBi at 14.6 GHz. The overall height of the proposed antenna is approximately 0.36 wavelength at its lowest operating frequency.
Sequential Frequency Vector Based System Call Anomaly Detection

Although either of temporal ordering and frequency distribution information embedded in process traces can profile normal process behaviors, but none of ever published schemes uses both of them to detect system call anomaly. This paper claims combining those two kinds of useful information can improve detection performance and firstly proposes sequential frequency vector (SFV) to exploit both temporal ordering and frequency information for system call anomaly detection. Extensive experiments on DARPA-1998 and UNM dataset have substantiated the claim. It is shown that SFV contains richer information and significantly outperforms other techniques in achieving lower false positive rates at 100% detection rate.
Design strategies to improve performance of GIS Web services

GIS systems are ubiquitous distributed systems, since geo-spatial information adheres to almost everything. Considering the characteristics of GIS, the following four design-decision issues are particularly crucial: transactional mode (synchronous versus asynchronous), service granularity (finegrained versus coarse-grained), delivery manner (chunk versus stream), and transmission formats (GML versus binary). We have shared our experience in making choices in these four dimensions.
Automatic Business Card Scanning with a Camera

In this paper, we present a system to automatically extract, rectify and enhance business card images. First the business card image patch is automatically segmented by minimizing a novel local-global variational energy. Second a quadrangle is fitted to the segmented image patch. With the four corner points of the quadrangle, we then estimate the physical aspect ratio of the business card and obtain a homography to rectify the quadrangle back to rectangular shape. We finally enhance the contrast of the rectified business card image using a S-shaped curve. Extensive experiments demonstrated the efficacy and robustness of our system.
Wavelength tuning of InAs/InAlGaAs quantum-dash-in-well laser using postgrowth intermixing

The first demonstration is reported of a bandgap tuned laser using InAs/InAlGaAs quantum-dash-in-well structures on an InP substrate, which utilises impurity-free induced intermixing. The intermixed laser exhibits comparable light–current characteristics after the bandgap is postgrowth-tuned by 100 nm.
Tracking articulated body by dynamic Markov network

A new method for visual tracking of articulated objects is presented. Analyzing articulated motion is challenging because the dimensionality increase potentially demands tremendous increase of computation. To ease this problem, we propose an approach that analyzes subparts locally while reinforcing the structural constraints at the mean time. The computational model of the proposed approach is based on a dynamic Markov network, a generative model which characterizes the dynamics and the image observations of each individual subpart as well as the motion constraints among different subparts. Probabilistic variational analysis of the model reveals a mean field approximation to the posterior densities of each subparts given visual evidence, and provides a computationally efficient way for such a difficult Bayesian inference problem. In addition, we design mean field Monte Carlo (MFMC) algorithms, in which a set of low dimensional particle filters interact with each other and solve the high dimensional problem collaboratively. Extensive experiments on tracking human body parts demonstrate the effectiveness, significance and computational efficiency of the proposed method.
Spoken language acquisition via human-robot interaction

This paper presents a subproject of a challenging project that explores teaching a computer human-intelligence. In the subproject, a multisensory mobile robot is used as the interface for human-computer interaction, and spoken language is taught to the computer through natural human-robot interaction. Different from state-of-the-art speech recognizers, our approach associates speech patterns directly with sensory inputs of the robot. This approach allows our system to learn multilingual speech patterns online. Further investigation of this project will include human-computer interaction that involves more modalities, and applications that use the proposed idea to train home appliances.
An Improved Algorithm of the Optimization Control Problem of Loss Systems

The optimization control problem of GI/M/n loss systems could be modeled with Markov decision processes (MDP); however, there have been no effective algorithms to solve the optimal policies of such large-scale problems by now. The model characteristic of the optimization control problem of GI/M/n loss systems is well analyzed, and the improved algorithm to solve optimal policies of the problem is proposed correspondingly. The algorithm is mainly improved in the selection rule of initial policy, the improvement rule of policy and the evaluation criterion of optimal policies, so both the storage space and computing time are reduced. Meanwhile the optimal solution of the MDP problem could be obtained by the improved algorithm. Finally, a simple comparison between the improved algorithm and conventional algorithm is given through an example. It can be concluded that the improvement algorithm is suitable to solve the large-scale optimization control problem of GI/M/n loss systems
Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG

A novel method for detecting ventricular premature contraction (VPC) from the Holter system is proposed using wavelet transform (WT) and fuzzy neural network (FNN). The basic ideal and major advantage of this method is to reuse information that is used during QRS detection, a necessary step for most ECG classification algorithm, for VPC detection. To reduce the influence of different artifacts, the filter bank property of quadratic spline WT is explored. The QRS duration in scale three and the area under the QRS complex in scale four are selected as the characteristic features. It is found that the R wave amplitude has a marked influence on the computation of proposed characteristic features. Thus, it is necessary to normalize these features. This normalization process can reduce the effect of alternating R wave amplitude and achieve reliable VPC detection. After normalization and excluding the left bundle branch block beats, the accuracies for VPC classification using FNN is 99.79%. Features that are extracted using quadratic spline wavelet were used successfully by previous investigators for QRS detection. In this study, using the same wavelet, it is demonstrated that the proposed feature extraction method from different WT scales can effectively eliminate the influence of high and low-frequency noise and achieve reliable VPC classification. The two primary advantages of using same wavelet for QRS detection and VPC classification are less computation and less complexity during actual implementation.
A simplified support vector clustering algorithm

This paper presents a simplified support vector clustering (SVC) algorithm for improving the efficiency of the SVC training procedure. The cluster structure obtained by our proposed approach is controlled by two parameters: the parameter of kernel functions, denoted as q; and the percentage of data used to form the contour. The mechanisms we developed can efficiently search for suitable parameters without much trial-and-error effort for reaching a satisfactory clustering result. From observations of the behavior of the clustering, we found that 1) the search range of q is related to the densities of the clusters; 2) the number of boundary vectors has much relevance to the computation time; and 3) the shape of the original dataset affects the size of a reduced dataset. We have based our findings to develop a simplified SVC to identify optimal cluster configuration with suitable cluster contours. Computer simulations have been conducted on benchmark datasets to demonstrate the effectiveness of our proposed approach.
Discovery of Collocation Patterns: from Visual Words to Visual Phrases

A visual word lexicon can be constructed by clustering primitive visual features, and a visual object can be described by a set of visual words. Such a "bag-of-words" representation has led to many significant results in various vision tasks including object recognition and categorization. However, in practice, the clustering of primitive visual features tends to result in synonymous visual words that over-represent visual patterns, as well as polysemous visual words that bring large uncertainties and ambiguities in the representation. This paper aims at generating a higher-level lexicon, i.e. visual phrase lexicon, where a visual phrase is a meaningful spatially co-occurrent pattern of visual words. This higher-level lexicon is much less ambiguous than the lower-level one. The contributions of this paper include: (1) a fast and principled solution to the discovery of significant spatial co-occurrent patterns using frequent itemset mining; (2) a pattern summarization method that deals with the compositional uncertainties in visual phrases; and (3) a top-down refinement scheme of the visual word lexicon by feeding back discovered phrases to tune the similarity measure through metric learning.
An Efficient Semidefinite Relaxation Algorithm for Moving Source Localization Using TDOA and FDOA Measurements

In this letter, we address the moving source localization problem by using time-difference-of-arrival and frequency-difference-of-arrival measurements. The localization problem is first reformulated based on the robust least squares criterion and then perform semidefinite relaxation (SDR) to obtain a convex semidefinite programming problem, which can be solved efficiently via optimization toolbox. Unlike several existing SDR localization methods requiring the initial estimate, the proposed method does not require this priori knowledge. The simulation results also show the superior positioning performance of the proposed method at high noise level than other existing methods.
Improved propagator method for joint angle and Doppler estimation based on structured least squares

A new propagator method for joint angle and Doppler estimation based on structured least squares (SLS) is proposed. By defining a new propagator matrix the proposed approach reduces the loss of array aperture. In addition, SLS which will take the specific relationship between two parts of the equation into account is utilized to enhance the estimation performance when solving the rotational invariance matrix equations. Simulation results are provided verifying the effectiveness of the proposed method.
Logic performance of 40 nm InAs/InxGa1−xAs composite channel HEMTs

Current Si-CMOS technology has come to a limit that novel semiconductors as alternative channel materials (Ge, InSb, InxGa1−xAs) are urgently needed for high-speed and low-power logic devices for post CMOS era . Recent research shows III–V heterostructure field-effect transistors demonstrate aggressive merits due to its high electron mobility and rather mature process technology [2]. The outstanding low field electron transport characteristics of III–V materials make ultrahigh-speed switching at very low supply voltage possible . Here, we present the latest advancement of 40 nm InAs/InxGa1−xAs composite channel High Electron Mobility Transistor (HEMT) devices that have achieved excellent digital logic characteristics at very low power level.
TOPSIS-based synthetic evaluation method for customer satisfaction

Quantitative evaluation of service quality is very important in modern service industry, so there has important theoretical and practical significance of studying comprehensive evaluation method of customer satisfaction. This paper discusses customer satisfaction concept and constructs evaluation index system for customer satisfaction oriented information service, then describes the theories and procedures of TOPSIS evaluation method and entropy weighted method. Finally, this paper takes university library-a typical kind of information service industry for example, using TOPSIS method to evaluate the library's reader satisfaction, discusses the evaluation results and gives suggestions for improving its information service quality and level based on the results.
Auto Disturbance Rejection Control for a Variable Speed Refrigeration System

For the problem of coupling in the variable speed refrigeration system, this paper proposes a dynamic feed forward decoupling control method Based on active disturbance rejection control (ADRC). Influence of the compressor frequency was regarded as disturbance of superheat control system, the variable speed compressor and electronic expansion valve ADRC controllers were designed. Comparison between the ADRC temperature control and traditional PI temperature control was made. Compared with PI control, the ADRC control is more stable and accurate in regulating chamber temperature although its speed is slightly slow. Simulation result shows that the overshoot of the superheat declines from 22.7% to 5.15% when the influence of the variation of the compressor frequency is compensated with decoupling ADRC control.
Fabrication and characterization of 3D aspheric microlenses with arbitrary surface profiles based on a novel excimer laser contour scanning method

This paper presents a new method for fabricating 3D microstructures with an excimer laser micromachining system. A novel mask contour scanning method is developed for obtaining precise 3D microstructures with pre-described continuous surface profile. Five different microlenses with spherical and parabolic surfaces profiles with dimension less than 500 /spl mu/m are fabricated on polycarbonate (PC) samples. The surface profiles are measured and compared with their theoretical counterparts. Excellent agreements both in profile shapes and dimensions are achieved. The surface roughness (Ra) of the machined surfaces is also measured and is less than 10 nm. The machining profile accuracy and surface smoothness of this proposed micromachining method show great potentials in fabricating micro-optic components such as aspheric microlenses or microlens arrays.
Measured humic acid in water by the potassium permanganate oxidation flow-injection chemiluminescence

When potassium permanganate oxidized humic acid, it has been discovered that they happened chemiluminescence reaction. Kinetic curve of system of KMnO<sub>4</sub>-humic acid is mapped and the reactions' conditions are optimized. Meanwhile, a new method of chemiluminescence analysis of determination of humic acid in natural water, and linear range of the method is 1×10<sup>-1</sup>~1×10<sup>2</sup>mg/L (R=0.9989) and the detection limit is 8×10<sup>-8</sup>g/ml. Doing parallel determination eleven times at the concentration of 20mg/L humic acid, finds that the relative standard deviation of RSD is 2.98%.
A current cycle feedback iterative learning control approach to AFM imaging

In this article, we proposed a novel current cycle feedback (CCF) iterative learning control (ILC) approach to achieve high-speed imaging on atomic force microscope (AFM). AFM-imaging requires precision positioning of the AFM probe relative to the sample in 3-D (x-y-z) dimension. It has been demonstrated that with advanced control techniques such as the inversion-based iterative-control (IIC) technique, precision positioning of the AFM probe in the lateral (x-y) direction can be successfully achieved. Additional challenges, however, must be overcome to achieve precision positioning of the AFM-probe in the vertical direction. The main contribution of this article is the development of the CCF-ILC approach to the AFM z-axis control. Particularly, the proposed CCF-ILC controller design utilizes the developed robust-inversion technique to minimize the model uncertainty effect on the feedforward control, and remove the causality constraints existing in other CCF-ILC approaches. Experimental results for AFM imaging are presented and discussed to illustrate the proposed method.
Context-Aware Visual Tracking

Enormous uncertainties in unconstrained environments lead to a fundamental dilemma that many tracking algorithms have to face in practice: Tracking has to be computationally efficient, but verifying whether or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either effective but computationally intensive by using sophisticated image observation models or efficient but vulnerable to false alarms. This greatly challenges long-duration robust tracking. This paper presents a novel solution to this dilemma by considering the context of the tracking scene. Specifically, we integrate into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties, at least in a short time interval: 1) persistent co-occurrence with the target, 2) consistent motion correlation to the target, and 3) easy to track. Regarding these auxiliary objects as the context of the target, the collaborative tracking of these auxiliary objects leads to efficient computation as well as strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases.
DH-MAC: A Dynamic Channel Hopping MAC Protocol for Cognitive Radio Networks

In this paper, we propose a channel-hopping MAC protocol called dynamic hopping MAC (DH-MAC) to improve the utilization of spectrum in cognitive radio networks. In DH-MAC, each secondary user (SU) switches its operation channel with a channel-hopping sequence which is adaptive to the activity of primary users (PUs). Our proposed DH-MAC mechanism enjoys the following merits. 1) It needs no dedicated common control channel for operation. 2) It supports multiple rendezvous for multiple simultaneous transmission pairs of SUs within the interfering range. 3) The rendezvous channel of two SUs will not be blocked by PUs for a long time. 4) It allows SUs a higher probability to operate on available channels as compared with existing schemes. 5) One cognitive radio transceiver is sufficient. The simulation results show that DH-MAC outperforms existing schemes in terms of higher aggregate throughput and more robustness to the disturbance of PUs.