nils-herrmann/clean_scirepeval huggingface dataset:
Topic Forest: a plan-based dialog management structure

There are many task-oriented dialog systems, but few of them can cope with the issues such as the multiple-topic issue, the topic changing issue, information sharing among different topics, and the difference in importance for different information items. To provide efficient solutions, a plan-based dialog management structure named Topic Forest is proposed, which makes the mixed-initiative dialog control easier. The Topic Forest based reasoning engine with a certain strategy for both remembering and forgetting is also described. The reasoning strategy is designed to be domain-independent; therefore it makes the dialog management model easy to be ported to other different domains.
DehazeNet: An End-to-End System for Single Image Haze Removal.

Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use.
Tumor Classification via Sparse Representation Based on Metasample

In this paper, we propose a new method for tumor classification using gene expression data. The new method expresses each testing sample as a linear combination of a set of metasamples extracted from all the training samples. Classification is achieved by a defined discriminating functions using the coefficient vector for the metasamples extracted from each category, which is obtained by l1-regularized least square. Since l1-normminimization could leads to sparse solution, our approach can be named as metasample based sparse representation classification (MSRC). The experimental results show that our method is efficient for tumor classification.
An adaptive Kalman filter technique for context-aware heart rate monitoring

Traditional physiological monitoring systems convert a person's vital sign waveforms, such as heart rate, respiration rate and blood pressure, into meaningful information by comparing the instant reading with a preset threshold or a baseline without considering the contextual information of the person. It would be beneficial to incorporate the contextual data such as activity status of the person to the physiological data in order to obtain a more accurate representation of a person's physiological status. In this paper, we proposed an algorithm based on adaptive Kalman filter that describes the heart rate response with respect to different activity levels. It is towards our final goal of intelligent detection of any abnormality in the person's vital signs. Experimental results are provided to demonstrate the feasibility of the algorithm.
The Study of Nonlinear DC Power Modulation Control in Multi-infeed HVDC Power Systems

A novel nonlinear DC power modulation control is proposed for multi-infeed HVDC in this paper. The idea is that the inter-area oscillations can be damped out by multi-infeed HVDC systems with COIs signals which means individual areas are driven to the stable equilibrium point. A three-area AC/DC power system is used as a sample to show the model derivation in COI coordinates. The back-stepping method is then applied to work out the nonlinear robust adaptive control law for DC power modulation in order to damp low frequency oscillations. Computer simulation results show that the proposed controller is superior to conventional DC power modulation controllers and can damp inter-area oscillations very well.
A Subspace Method for Fourier Based Image Registration

Image registration is the process of estimating the misalignment between two images and then aligning the image pair. A popular approach for coarse registration is Fourier based. This paper enhances the current Fourier based registration approach by employing the MUSIC algorithm for frequency estimation. A more accurate registration result is achieved at the expense of additional computational complexity. Performance is comparable to a mutual information based registration algorithm but requires less computation.
Robust Vehicle Detection in Vision Systems Based on Fast Wavelet Transform and Texture Analysis

Moving vehicle detection in digital image sequences is one of the key technologies of Intelligent Transportation Systems (ITS). However, problems arise due to the shadows of sunshine in daytime and the illuminations of vehicle headlights in nighttime. To begin with, a new auto- regression algorithm based on Gaussian Distribution hypotheses is proposed for background estimation. Furthermore, a pivot approach to eliminate shadows and illuminations from the foreground, which is the difference between dynamic image and background image, is investigated and studied. And in this proposed approach, image textures are extracted by fast wavelet transform (FWT) which is designed for discrete signal while grey level co-occurrence matrix (GLCM) is employed to measure and analyze the extracted textures. Subsequently, shadows and illuminations can be segmented since their textures differ from those of vehicles. Experiment results in real traffic scenes reveal that the techniques presented in this work are effective and efficient for vehicle detection.
Salient Feature Region: A New Method for Retinal Image Registration

Retinal image registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate registration of low-quality retinal images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This paper provides a new retinal image registration method based on salient feature region (SFR). We first propose a well-defined region saliency measure that consists of both local adaptive variance and gradient field entropy to extract the SFRs in each image. Next, an innovative local feature descriptor that combines gradient field distribution with corresponding geometric information is then computed to match the SFRs accurately. After that, normalized cross-correlation-based local rigid registration is performed on those matched SFRs to refine the accuracy of local alignment. Finally, the two images are registered by adopting high-order global transformation model with locally well-aligned region centers as control points. Experimental results show that our method is quite effective for retinal image registration.
Research on pricing method of public bicycle service: A case study in Guangzhou

The reasonable rental price is helpful to improve the service level of urban public bicycle system and adjust the number of system users. Firstly, the framework for pricing public bicycle service from perspectives of the users, government and public bicycle company is developed and data collection is discussed. Secondly, three models are established considering benefits of users and public bicycle companies. The objectives of these models are maximizing amount of users, balancing income and expenses of public bicycle system and maximizing the benefit of bicycle companies respectively. Finally, a case study in Guangzhou was analyzed to verify the feasibility and effectiveness of the proposed models.
AmphiHex-I: Locomotory Performance in Amphibious Environments With Specially Designed Transformable Flipper Legs

An amphibious robot can locomote in amphibious environments, including walking on rough terrains, maneuvering underwater, and passing through soft muddy or sandy substrates in the littoral area between land and water. However, developing an amphibious robot is challenging, especially when it requires a high locomotory performance in soft substrates and a combination of different propulsion methods. To tackle such a challenge, an amphibious robot, known as AmphiHex-I, with novel transformable flipper-leg composite propulsion mechanisms has been proposed and developed. In this paper, locomotory performance of the flipper legs in amphibious environments, especially in the muddy terrain, is extensively studied with a walking platform in terms of structural parameters, kinematic parameters, and environmental properties. The results indicate that there exist an optimal rotation speed of the flipper legs with various shapes for a higher locomotion speed of the walking model in muddy terrain, which guides the design and the control of the transformable flipper legs. Through the versatile gaits of AmphiHex-I in amphibious environments, outdoor locomotion experiments validate the platform study and demonstrate that the robot's transformable flipper-leg propulsion mechanisms make it highly adaptive to a littoral environment such as the muddy terrain. Locomotion ability of AmphiHex-I endows its broad applications in the areas of resource exploration, disaster rescue, and reconnaissance in complex environments.
A Security Threat Analysis and Evaluation Model for Power Field Operation Terminal

With the rapid development of the business and the continuous improvement of the level in SGCC, the security research in the mobile field has been paid more and more attention. Although mobile work terminals can improve the efficiency of field operations, but a large number of mobile terminal access, leading to the terminal management difficult to increase, security has been greatly challenged. Therefore, based on the sandbox technology, this paper proposes a security threat analysis and evaluation model for power field operation terminals. The model introduces the function of related modules from the terminal layer, network layer and application layer respectively, and focuses on threat analysis and evaluation algorithm. By this algorithm, the malicious code or virus can be classified according to the degree of harm, and the solution of "rollback" or "push reminder" is adopted. The model is used to ensure the security of the software of power field operation terminal and has good effect.
Energy Minimization With One Dot Fuzzy Initialization for Marine Oil Spill Segmentation

Detecting marine oil spill regions in synthetic aperture radar (SAR) images has always been posed as a segmentation problem in terms of minimizing a certain energy function(al). As most energy minimization problems do not have analytical solutions, minimizing an energy function(al) is usually achieved in an iterative numerical manner. In this scenario, one key factor that affects the segmentation accuracy is the initialization for starting or constraining the numerical iterations. To guarantee accurate segmentation, a proper initialization that characterizes the marine oil spill layouts in an SAR image is required. However, marine oil spill regions are always complicatedly shaped, and it is inefficient to manually devise precise initializations for capturing various marine oil spill shapes. To address this problem and render efficient and robust segmentation, we develop a one dot fuzzy initialization strategy. In contrast to the normal practice of manually labeling a large amount of pixels (possibly lines or cycles of pixels subject to strict spatial conditions) as initialization, our strategy just requires one arbitrary pixel within a marine oil spill region as the initial dot. The intuition of our strategy is that the fuzzy connectedness between an arbitrary initial dot and the rest pixels enables the derivation of a physically homogeneous region that is consistent for initializing the energy minimization. In the light of this observation, we develop schemes for exploiting the one dot derived region to initialize both level sets for minimizing continuous energy functionals and graph cuts for minimizing discrete energy functions. Experimental results validate the robustness of our one dot fuzzy initialization strategy.
Fuzzy rule based depth controller for variable ballast system of autonomous underwater vehicles

Variable ballast (VB) systems of autonomous underwater vehicles (AUV) are designed to compensate for changes in weights, variable loads, and displacement which occur during dive so as to keep submersibles at design condition such as neutral buoyancy and zero-trim. VB is also designed to create positive/negative buoyancy, so AW will ascend/descend to a desired depth. VB is composed of variable ballast tanks which may be flooded or blown to attain the required buoyancy. By investigating the dynamic response of VB system, a robust and energy saving depth control strategy is formed. The key point is to find a critical depth point at which a certain amount of water needs to be added to or subtracted from the VB tank. The critical depth point has nonlinear relationship with the desired depth, initial set weight, and VB dynamic parameters. The use of fuzzy logic controller is investigated for VB depth control. Fuzzy rules are developed to get the critical depth point. They are derived from qualitative analysis of VB dynamic systems.<<ETX>>
A Hybrid Fuel Cell Power System

This paper proposes a hybrid fuel cell power system, which consists of a fuel cell, an isolated unidirectional converter, a bidirectional converter, an inverter, and a battery. The fuel cell and the battery are connected to the same voltage bus through an appropriate hybrid full-bridge LLC resonant unidirectional converter and a three-level buck/boost bidirectional converter, respectively. The battery is an auxiliary energy source, powers the load during the system's start state to make it easy for the system to cold start, and provides or absorbs the dynamic power when the load varies and the fuel cell cannot respond immediately, so the system dynamic characteristics are improved. Furthermore, the battery can also provide peak power at overload, so the power rating of the fuel cell can be decreased, which reduces the total system cost. In order to ensure that the system operates with high efficiency, this paper also proposes a power management control scheme, which controls the bidirectional converter operating under buck, boost, or shutdown mode according to the operation condition of the fuel cell and battery, so that the battery can be charged or discharged. The operation of the system during cold start and overload are analyzed in detail. A 1-kW fuel cell power system was built in the laboratory. Experimental results are shown to verify the theoretical analysis.
Semantically-Based Human Scanpath Estimation with HMMs

We present a method for estimating human scan paths, which are sequences of gaze shifts that follow visual attention over an image. In this work, scan paths are modeled based on three principal factors that influence human attention, namely low-level feature saliency, spatial position, and semantic content. Low-level feature saliency is formulated as transition probabilities between different image regions based on feature differences. The effect of spatial position on gaze shifts is modeled as a Levy flight with the shifts following a 2D Cauchy distribution. To account for semantic content, we propose to use a Hidden Markov Model (HMM) with a Bag-of-Visual-Words descriptor of image regions. An HMM is well-suited for this purpose in that 1) the hidden states, obtained by unsupervised learning, can represent latent semantic concepts, 2) the prior distribution of the hidden states describes visual attraction to the semantic concepts, and 3) the transition probabilities represent human gaze shift patterns. The proposed method is applied to task-driven viewing processes. Experiments and analysis performed on human eye gaze data verify the effectiveness of this method.
LTCC BPFs used in Ka band LTCC frequency synthesizer module

Two compact microwave band pass filters used in Ka band LTCC frequency synthesizer are presented in this paper. The filters are buried in the Ferro-A6 substrate. Modified SIR structure is employed to improve the stop band rejection and minimize the size of the LTCC BPF. The design processes are proposed. The measured results of the BPFs agree well with the simulation.
Automatic cell segmentation in microscopic color images using ellipse fitting and watershed

This paper presents an efficient and innovative method for the automated counting of cells in a microscopic image. The performance of watershed-based algorithms for the segmentation of clustered cells has been well demonstrated. The strength of our algorithm lies in the fact that it incorporates knowledge of color in the image. Our method uses the watershed transform with iterative shape alignment and is shown to be more accurate in retaining cell shape. We report a sensitivity of 97% and specificity of 96% when all color bands are used. Our methods could be of value to computer-based systems designed to objectively interpret microscopic images, since they provide a means for accurate cell segmentation.
Modified voltage equaliser based on Cockcroft–Walton voltage multipliers for series-connected supercapacitors

It is essential for electric vehicle to equalise the voltages of battery or supercapacitor (SC) cells in series connection. Comparing with other equalisation methods based on energy transfer, Cockcroft–Walton (CW) voltage multiplier brings great benefits to the simplification of equalisation structure. Voltage equaliser formed by voltage multiplier can realise voltage balance automatically without voltage detection and control unit. However, the traditional (CW) voltage multiplier structure has two demerits. One is incapable of realising the voltage balancing for all the cells in series; the other one is the large inrush current of parallel capacitors that reduces the lifetime of the equaliser. To solve these problems, a modified voltage equalisation circuit is proposed. The new equaliser is constituted by a half-bridge frontend and a multiplier backend. The half-bridge generates the alternating voltage, and the multiplier accomplishes the voltage balancing. The operation principles, circuit analysis and parameters calculation are examined in detail. The performance is evaluated by simulation and experimental with three SCs in series. The accordance between simulation and experiment further proves that the voltage balancing is achieved for all the series cells and the inrush current of capacitors is declined at the turning on of power switches.
Generating reflective vortex waves in microwave region by metasurface

In this paper, anomalous reflection for circularly polarized (CP) waves is proposed to achieve, where the reflected waves possess orbital angular momentum (OAM) along their direction of propagation based on the Pancharatnam-Berry (geometrical) phase. Based on the phase gradient metasurface (PGM), we firstly presented a polarization-converting metasurface reflector for circularly polarized waves. On the basis of the reflector, two reflective vortex plates were designed. Under normal incidence, high-efficiency vortex waves are obtained by illuminating one vortex plate. Under oblique incidence, high-efficiency anomalous reflection of votex waves occurs for circularly polarized waves with 0° reflection angle by illuminating the other votex plate. The results of experiment and simulation are both accordant with theoretical prediction, which convincingly verifies the high-efficiency anomalous reflection of votex waves of CP waves. This technology makes a remarkable progress which is beneficial to put metasurface into practical application.
An ultra wide band power divider/combiner based on Y-structure waveguide

In this paper, an improved power divider/combiner based on Y-structure waveguide is proposed at ka-band (26–40 GHz). To widen the bandwidth of the divider/combiner, a stepped-impedance waveguide transformer is used, and a vertical resistive card is inserted into the divider/combiner to realize high isolation. According to EM simulations, the power divider/combiner illustrates a 42.4% effective bandwidth (ranging from 26 to 40 GHz), the return loss is better than −24 dB, and the isolation is better than −21 dB. This structure has properties of not only a wide band, low insertion loss and high output power, but also a small size, a high power combining efficiency and efficient heat sinking, and has potential for millimeter-wave broad-band high power combination.
A Word Similarity Based Belief Network IR Model with Two Term Layers

the retrieval performance of an information system usually increases when it uses the relationships among the terms in a given document collection. To resolve the problem that Belief Network model does not use term relationships, this paper presents an extended model with two term layers by using word similarity among document index terms, and discusses the new model's topology and information retrieval process. The experimental result shows that the extended model has better performance than the original one.
Multi-scale convolutional neural networks for crowd counting

Crowd counting on static images is a challenging problem due to scale variations. Recently deep neural networks have been shown to be effective in this task. However, existing neural-networks-based methods often use the multi-column or multi-network model to extract the scale-relevant features, which is more complicated for optimization and computation wasting. To this end, we propose a novel multi-scale convolutional neural network (MSCNN) for single image crowd counting. Based on the multi-scale blobs, the network is able to generate scale-relevant features for higher crowd counting performances in a single-column architecture, which is both accuracy and cost effective for practical applications. Complemental results show that our method outperforms the state-of-the-art methods on both accuracy and robustness with far less number of parameters.
Mission Planning of Long Distance Guidance for Space Rendezvous

Space rendezvous is the foundation of Space operation and application. In this paper, mission planning of long distance guidance stage for space rendezvous is discussed. Two-impulse rendezvous is solved by analyzing Gauss problem. Based on Battin-Vaughan algorithm, ergodic calculation is performed for different combination of initial position and transfer time. To carry out the mission planning for rendezvous, three-dimensional surface specified by transfer time, initial true anomaly and characteristic velocity is discussed as well as characteristic velocity isograms. Simulations are conducted for a special mission. Linear regression is applied to obtain approximate analytic formula of fuel-optimal rendezvous. Results indicate that the strategy introduced in the paper is universal and efficient and, can provide powerful tools for mission planning. Keywords-space rendezvous; mission planning; BattinVaughan algorithm; linear regression; fuel-optimal rendezvous