nils-herrmann/clean_scirepeval huggingface dataset:
Text encryption using ECG signals with chaotic Logistic map

Security of information has become a popular subject during the last decades. Due to electrocardiogram (ECG) signals varying from person to person, it could be applied as a new tool for biometric recognition. This paper introduces an individual feature of ECG with Logistic map for cryptography. The encryption system utilizes a portable instrument (Heart Pal) to collect ECG signal from encryption person, then using an intelligent algorithm based on the Chaos theory to generate initial keys for Logistic map, so that the user needn't to set up the initial values of the chaotic function. Simulation results show that the proposed system is capable of encrypting text information for secure communication efficiently and securely.
Improvement of the mutual coupling effect between biconical antenna and antenna mast

It is well known that mutual coupling effect and problems exist between transmitting and receiving antennas. One factor is the coupling effect between antennas and masts. It can be more than 1 dB for omni-directional antennas with vertical polarization. This effect can be improved by utilizing the average method of changing distance between antennas and antenna mast. According to the reference site method (RSM) proposed in the CISPR 16-1-4-2012, a comparison test is made between the Bureau of Standards, Metrology & Inspection (BSMI), Taiwan and the National Institute of Metrology (NIM), China. The RSM and geometrical specific correction factor from the Annex G & H of ANSI C 63.5-2006 was also introduced into this comparison. It shows that the average method can improve the effect of mutual coupling between antennas and masts to 0.5 dB. This case study will discuss the theory of the average method and the measured result. The result will help to improve the measurement uncertainty for the validation of semi-anechoic chambers.
Motion Vector Coding in the HEVC Standard

High Efficiency Video Coding (HEVC) is an emerging international video coding standard developed by the Joint Collaborative Team on Video Coding (JCT-VC). Compared to H.264/AVC, HEVC has achieved substantial compression performance improvement. During the HEVC standardization, we proposed several motion vector coding techniques, which were crosschecked by other experts and then adopted into the standard. In this paper, an overview of the motion vector coding techniques in HEVC is firstly provided. Next, the proposed motion vector coding techniques including a priority-based derivation algorithm for spatial motion candidates, a priority-based derivation algorithm for temporal motion candidates, a surrounding-based candidate list, and a parallel derivation of the candidate list, are also presented. Based on HEVC test model 9 (HM9), experimental results show that the combination of the proposed techniques achieves on average 3.1% bit-rate saving under the common test conditions used for HEVC development.
A 17 T NbTi-Nb/sub 3/Sn superconducting magnet with low-purity holmium core

A 14-T NbTi-Nb/sub 3/Sn superconducting magnet system with 31-mm bore has been constructed and tested. The magnet system consists of three coils one of NbTi and two of Nb/sub 3/Sn. Two different cross sections of Nb/sub 3/Sn wire constructed using the bronze method were chosen for the outer Nb/sub 3/Sn coil of 80-mm bore, and an (NbTi)/sub 3/Sn wire made by the high-tin-content Nb tube method was used to build the inner Nb/sub 3/Sn coil. The NbTi-Nb/sub 3/ magnet system has successfully achieved a 14.42 T central field at 4.2 K. To obtain a higher magnetic field, a pair of low-purity holmium cores of 25-mm diameter were used. The test of the NbTi-Nb/sub 3/Sn magnet with the holmium cores shows that the central field increases from 14 T to 17 T at 4.2 K. >
Trajectory parsing by cluster sampling in spatio-temporal graph

The objective of this paper is to parse object trajectories in surveillance video against occlusion, interruption, and background clutter. We present a spatio-temporal graph (ST-Graph) representation and a cluster sampling algorithm via deferred inference. An object trajectory in the ST-Graph is represented by a bundle of “motion primitives”, each of which consists of a small number of matched features (interesting patches) generated by adaptive feature pursuit and a tracking process. Each motion primitive is a graph vertex and has six bonds connecting to neighboring vertices. Based on the ST-Graph, we jointly solve three tasks: 1) spatial segmentation; 2) temporal correspondence and 3) object recognition, by flipping the labels of the motion primitives. We also adapt the scene geometric and statistical information as strong prior. Then the inference computation is formulated in a Markov chain and solved by an efficient cluster sampling. We apply the proposed approach to various challenging videos from a number of public datasets and show it outperform other state of the art methods.
Drone-Based Object Counting by Spatially Regularized Regional Proposal Network

Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification). In addition, most of prior work mainly focus on counting objects in static environments with fixed cameras. Motivated by the advent of unmanned flying vehicles (i.e., drones), we are interested in detecting and counting objects in such dynamic environments. We propose Layout Proposal Networks (LPNs) and spatial kernels to simultaneously count and localize target objects (e.g., cars) in videos recorded by the drone. Different from the conventional region proposal methods, we leverage the spatial layout information (e.g., cars often park regularly) and introduce these spatially regularized constraints into our network to improve the localization accuracy. To evaluate our counting method, we present a new large-scale car parking lot dataset (CARPK) that contains nearly 90,000 cars captured from different parking lots. To the best of our knowledge, it is the first and the largest drone view dataset that supports object counting, and provides the bounding box annotations.
Novel Package Technology of Ultra High Power Light-Emitting Diodes by Electroplating

Thermal management of devices package is now a critical problem for applications of high power light emitting diodes (LEDs). In this paper coppers were first electroplated on the novel package red, green, and blue LED chips directly. With the copper plating layer, the endurable injection current of these LED chips can be increased easily from conventional 350 mA to more than 1680 mA in room temperature; especially the input power of the GaN-based single chip blue LED can be increased to 12W. The relative luminous intensity at 350 mA of the novel red, green, and blue LEDs (RGB LEDs) have 53%, 69%, and 23% enhancement respectively compared with those of the conventional packaged LEDs. When the injection current of these LED chips were increased to 850mA , the relative luminous intensity of the novel RGB LEDs chips have 431% and 83%, and 18% enhancement respectively compared with those of the conventional packaged ones.
A new intelligent traffic control system for Taiwan

This paper introduces a novel traffic control system framework, the Mobile Intelligent Traffic Control System (MITCS), designed for Taiwan for the next generation. The system integrates micro-mechanical and electrical technologies with embedded system, wireless transmission, image processing, and solar power module. The aims of this study are to: 1) research and design a novel and multi-functional traffic controller in a box; 2) design a cost-effective fundamental contribution to communication network; 3) use image processing techniques to develop a non-intrusive vehicle detector for monitoring traffic dynamics; 4) utilize artificial intelligence to adapt traffic dynamics and to renew traffic control strategies; 5) propose a specific concept for mobile intelligent traffic control center. Finally, an experimental system was built which consisted of Virtual Traffic Police (VTP), Status Monitor Agent (SMA) and Traffic Control Integration Module (TCIM). The system will support a highly efficient, self-organized and self-coordinated traffic control mechanism.
Electromagnetic modeling and inversion application for oil and gas industry

An essential objective of collecting measurements in the oil and gas industry is to detect, locate and quantify the amount of oil and gas in the Earth subsurface. Electromagnetic measurements either at DC, induction, propagation, and dielectric frequencies play a central role because electromagnetic properties of hydrocarbon and water are very different. These electromagnetic measurements can be collected either from a single borehole (e.g., laterolog, induction logging, electromagnetic geosteering, and dielectric logging), multi-boreholes (e.g., cross-well electromagnetic, surface-to-borehole electromagnetic), or at surface (e.g., magnetotelluric, controlled-source electromagnetic). The process to convert electromagnetic measurements into electromagnetic property (e.g., resistivity/conductivity and permittivity) map of the subsurface is an ill-posed problem. Furthermore, due to the substantial differences in electromagnetic properties of Earth subsurface especially hydrocarbon and water, the inverse problem can only be properly solved using a full nonlinear inversion algorithm. In addition the number of data points can be very limited, hence making the ill-posed problem more severe.
Instance-Level Salient Object Segmentation

Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks. However, none of the existing methods is able to identify object instances in the detected salient regions. In this paper, we present a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image. Our method consists of three steps, estimating saliency map, detecting salient object contours and identifying salient object instances. For the first two steps, we propose a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours. Once integrated with multiscale combinatorial grouping and a MAP-based subset optimization framework, our method can generate very promising salient object instance segmentation results. To promote further research and evaluation of salient instance segmentation, we also construct a new database of 1000 images and their pixelwise salient instance annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks for salient region detection as well as on our new dataset for salient instance segmentation.
Maximal margin feature mapping via basic image descriptors for image classification

Computer vision is one of the most important branches in modern industrial technology. Image classification plays an important role in computer vision since it utilizes the most advance technique in this area. However, most image classification methods only use the SIFT feature for further processing, which hinders the rich useful low-level image attributes to be captured. This paper proposes a maximal margin feature mapping framework that incorporates basic descriptors in the recognition system. This is fulfilled by optimizing an objective function that minimizes intra-class distance and maximizes inter-class distance as well as the reconstruction error. An efficient optimization algorithm is proposed to learn the transformation matrix. Experiments on three publicly available datasets are conducted. The preliminary results show the effectiveness of the proposed approach.
Constrained optimal control for wheeled cars

This paper considers the optimal control problem for a wheeled car (robot) subject to inequality constraints which are induced by hardware. Before solving the optimal control problem, we propose an equality constraint to substitute the inequality constraints with a new technique, which is developed to design an optimal controller for tracking while characterizing the reasonable input torque. It is shown that the problem of optimal control design with equality constraints imposing feasible range of input torque is easily solvable by applying the proposed technique.
Dynamic Pricing and Risk Analytics Under Competition and Stochastic Reference Price Effects

This paper investigates the pricing strategy of firms in the context of uncertain demand. In particular, there are two factors that affect demand dynamics, the influence of reference prices and the price of the competition. In the monopoly case, pricing policy is affected by reference-price effects and in the duopoly case, both competitive pricing and reference-price effects are present. In each case, the optimal price paths are derived and simulated. The implications of uncertainty are analyzed by comparing the deterministic policy with the stochastic policy. The random variations in price paths are investigated to provide a risk analysis for firms that work in such market conditions. With the advent of the big data era, information about consumers and competitors gives firms a greater control over uncertainty than ever before. Simulations will demonstrate that firms can lower the volatility of their price path if they gather and process this information. Furthermore, the feedback forms of the optimal price path are derived in both the absence and the presence of both competition and reference-price effects. In general, the impact that demand uncertainty has over the firm's pricing strategy is determined by a combination of the firm's discount rate, demand uncertainty, and demand-side/cost-side dynamics.
The Comprehensive Application Study of GRA and ISM in System Structure Analysis

Interpretative Structural Modeling (ISM) is an effective approach to analyze the complex system structure. The adjacency matrix represents the relations between and among system elements and the system digraph can be drawn by deduction of the reachability matrix. The adjacency matrix is usually built by experts’ experience. Because the process does not take the corresponding data of system elements into account, the adjacency matrix is lack of objectivity and accuracy. In this paper, the adjacency matrix is built using grey relational analysis (GRA). The grey incidence matrix is derived considering the relevant behavioral sequences as referential behavior sequence in turn and the system digraph is drawn using ISM. The paper gives the theoretical deduction and the specific calculation steps of the above approach. The validity of the method is verified by the application of China's water transport system.
Initial stages of Ni-driven nanostructures growth on Ag/Ge(111)-√3×√3 surface

Using STM, we have investigated early stages of nanostructures formation upon room temperature (RT) deposition of 0.12 ML Ni onto Ag/Ge(111)-√3×√3 surface. While RT growth mode is determined by cluster formation, annealing up to 200°C promotes cluster nucleation. Further annealing results in creation of hexagonal-shaped islands which conserve on the surface in the unchanged form even after 800°C annealing. Unusual thermal stability of these islands is explained in terms of Ni-Ge compounds formation.
Clothing Co-parsing by Joint Image Segmentation and Labeling

This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. The first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (ESVM) technique [23]. In the second phase (i.e. "region colabeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.
Flux dynamic behavior inside HTS bulks under rotating magnetic field

Melt texturing large-grain HTS materials has evolved in the last few years into a technology capable of producing large quantities of high-performance superconducting bulk materials. Such materials are used for developing novel engineering devices, such as high-speed bearing, flywheel system and motor. In these systems, however, it is very possible to generate rotating magnetic fields with the frequency higher than 50 Hz due to various inhomogeneities. Hence, the experimental investigation was presented on the change of the levitation force and the trapped-flux response of HTS bulks exposed to the rotating magnetic fields with different frequencies. Meanwhile, the experimental phenomena were qualitatively analyzed and discussed in this paper, based on HTS flux dynamics.
A fast fractal image coding using pyramid structure of image blocks

This work presents a fast search algorithm based on the pyramid structure to reduce the computational complexity of fractal encoding. An appropriate topological structure, L/sub 2/ norm pyramids of image blocks, is employed to exclude a large number of ineligible domain blocks so as to speed up fractal encoding time. Simulation results show that the proposed algorithm can produce a completely identical fractal code to that of the exhaustive search with much less computational load.
Numerical model and experimental study on comparison of semiconductor pulsed power devices

The characteristics applied in pulsed power area for the power semiconductor devices of RSD (reversely switched dynistor), thyristor and IGBT are compared synthetically in this paper. By establishing the two-dimensional electro-thermal coupling models for the three types of devices, the turn-on performances of each one at low and high current density are discussed, separately. The results show that the special type device RSD always acquires the lowest turn-on voltage and power dissipation. This advantage is more obvious with the current density increase. The experimental results have approved the trend.
A general class of sliding surface for sliding mode control

In the conventional sliding mode control, the relative degree of the chosen sliding surface is usually one. This paper addresses a general class of sliding surface whose relative degree is no longer restricted to one. We show that when the relative degree is more than two, it is inevitable that there will exist a limit cycle for the nonlinear sign function. For the case of relative degree two, the system possesses poor phase margin. However, these disadvantages can be overcome by soothing out the control nonlinearity in a suitable boundary layer with a saturation function that ensures globally exponential stability of the overall system.
An expressive deep model for human action parsing from a single image

This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images. Its main challenges lie in the large variations in human poses and appearances, as well as the lack of temporal motion information. Addressing these problems, we propose to develop an expressive deep model to naturally integrate human layout and surrounding contexts for higher level action understanding from still images. In particular, a Deep Belief Net is trained to fuse information from different noisy sources such as body part detection and object detection. To bridge the semantic gap, we used manually labeled data to greatly improve the effectiveness and efficiency of the pre-training and fine-tuning stages of the DBN training. The resulting framework is shown to be robust to sometimes unreliable inputs (e.g., imprecise detections of human parts and objects), and outperforms the state-of-the-art approaches.
Experimental measurement for turn-off time of RSD switch

The turn-off time of the RSD(reversely switched dynistor) switch is investigated by experiments in this paper. According to its special operating mode, the turn-off criterion of the RSD and a measuring method of the turn-off time are proposed. The measuring platform of the turn-off time is designed and established. The measuring precision is 1.1µs. The experimental results show that the turn-off time of the RSD is in the order from several tens to a hundred microseconds. The increase of the minority lifetime in n-base region makes the turn-off time increase significantly. The turn-off time also increases with enhancing the discharge voltage.
I2T: Image Parsing to Text Description

In this paper, we present an image parsing to text description (I2T) framework that generates text descriptions of image and video content based on image understanding. The proposed I2T framework follows three steps: 1) input images (or video frames) are decomposed into their constituent visual patterns by an image parsing engine, in a spirit similar to parsing sentences in natural language; 2) the image parsing results are converted into semantic representation in the form of Web ontology language (OWL), which enables seamless integration with general knowledge bases; and 3) a text generation engine converts the results from previous steps into semantically meaningful, human readable, and query-able text reports. The centerpiece of the I2T framework is an and-or graph (AoG) visual knowledge representation, which provides a graphical representation serving as prior knowledge for representing diverse visual patterns and provides top-down hypotheses during the image parsing. The AoG embodies vocabularies of visual elements including primitives, parts, objects, scenes as well as a stochastic image grammar that specifies syntactic relations (i.e., compositional) and semantic relations (e.g., categorical, spatial, temporal, and functional) between these visual elements. Therefore, the AoG is a unified model of both categorical and symbolic representations of visual knowledge. The proposed I2T framework has two objectives. First, we use semiautomatic method to parse images from the Internet in order to build an AoG for visual knowledge representation. Our goal is to make the parsing process more and more automatic using the learned AoG model. Second, we use automatic methods to parse image/video in specific domains and generate text reports that are useful for real-world applications. In the case studies at the end of this paper, we demonstrate two automatic I2T systems: a maritime and urban scene video surveillance system and a real-time automatic driving scene understanding system.
Polarimetry–Interferometry Diagnostic for Measurement of Magnetic and Density Perturbations in the High-Temperature Plasma Core

Diagnostic techniques to measure the internal structure of density and magnetic perturbations, both equilibrium and turbulent, are becoming increasingly important for assessing the confinement properties of high-temperature magnetically confined fusion plasmas. Recent advances in laser-based polarimetry-interferometry techniques now permit simultaneous measurements of both density and magnetic field with fast time response and high phase resolution. Images of intrinsic tearing instabilities driven by radial gradients in the parallel current density are shown for the combined measurements.