nils-herrmann/clean_scirepeval huggingface dataset:
An approach to concept-obtained text summarization

In this paper, we propose a practical approach for extracting the most relevant sentences from the original document to form a summary. The idea of our approach is to obtain concepts of words based on HowNet, and use concept as feature, instead of word. We use conceptual vector space model to form a rough summarization, and then calculate degree of semantic similarity of sentence for reducing its redundancy. Experimental results show that our approach is effective and efficient, and performance of the system is reliable.
Modelless Data Quality Improvement of Streaming Synchrophasor Measurements by Exploiting the Low-Rank Hankel Structure

This paper presents a new framework to improve the quality of streaming synchrophasor measurements with the existence of missing data and bad data. The method exploits the low-rank property of the Hankel structure to identify and correct bad data, as well as to estimate and fill in the missing data. The method is advantageous compared to existing methods in the literature that only estimate missing data by leveraging the low-rank property of the synchrophasor data observation matrix. The proposed algorithm can efficiently differentiate event data from bad data, even in the existence of simultaneous and consecutive bad data. The algorithm has been verified through numerical experiments on recorded synchrophasor datasets.
Staying-alive and energy-efficient path planning for mobile robots

As most mobile robots are powered by batteries, their energy and operation times are limited. Therefore, how to minimize energy consumption and keep mobile robots to stay alive becomes an important problem. In this paper, by applying a dynamic energy-evaluation scheme, in which we consider if a robot has enough energy to go to next location, finish the task and return to the docking station in the path planning, we propose two staying-alive and energy-efficient path planning approaches based on the greedy TSP and Tabu-search methods, respectively. The experimental results show that our Tabu-search-based approach is the best and can provide an effective path planning by which a robot can be guaranteed to stay alive and finish all tasks with the minimum energy.
VideoPuzzle: Descriptive One-Shot Video Composition

A large amount of short, single-shot videos are created by personal camcorder every day, such as the small video clips in family albums, and thus a solution for presenting and managing these video clips is highly desired. From the perspective of professionalism and artistry, long-take/shot video, also termed one-shot video, is able to present events, persons or scenic spots in an informative manner. This paper presents a novel video composition system “Video Puzzle” which generates aesthetically enhanced long-shot videos from short video clips. Our task here is to automatically composite several related single shots into a virtual long-take video with spatial and temporal consistency. We propose a novel framework to compose descriptive long-take video with content-consistent shots retrieved from a video pool. For each video, frame-by-frame search is performed over the entire pool to find start-end content correspondences through a coarse-to-fine partial matching process. The content correspondence here is general and can refer to the matched regions or objects, such as human body and face. The content consistency of these correspondences enables us to design several shot transition schemes to seamlessly stitch one shot to another in a spatially and temporally consistent manner. The entire long-take video thus comprises several single shots with consistent contents and ίuent transitions. Meanwhile, with the generated matching graph of videos, the proposed system can also provide an efficient video browsing mode. Experiments are conducted on multiple video albums and the results demonstrate the effectiveness and the usefulness of the proposed scheme.
Fast rate distortion optimized quantization method for HEVC

Rate-Distortion Optimized Quantization (RDOQ) brings significant improvement of coding performance in High Efficiency Video Coding (HEVC). However, it results in high computational complexity to determine the best quantization levels for each transform coefficient when applying rate and distortion optimization operation. In this paper, we firstly proposed an efficient way to skip the all zero coding units. Then we further propose a fast RDOQ scheme by skipping the optimization step of All Quantized Zero Blocks except DC Coefficient (AQZB-DC). Moreover, a rate difference model is established to select optimal quantization level for AQZB-DC blocks. Experiments are carried out on reference software HM 16.0 and the results show that the proposed method achieves 42.00% and 39.63% quantization time saving under Random Access (RA) and Low Delay (LD) configuration on average, while the BD-Rate loss is only 0.03% and 0.06%, respectively.
Parallel NURBS PO method based on the Ludwig algorithm and its application to the RCS computation

This paper applied parallel computation technique on the radar cross section (RCS) prediction. Nonuniform rational B-spline (NURBS) surfaces modeling technique is applied in target description. Modified Ludwig algorithm is adopted to calculate the integral of the induced current on the rational Bezier surface. The parallel computation technique is applied to accelerate the computational speed. Numerical example is also given to verify the efficiency of this method.
Analysis of Hierarchical EDF Pre-emptive Scheduling

This paper focuses on scheduling different hard real-time applications on a uniprocessor when the earliest deadline first algorithm is used as the local scheduler, and the global scheduler of the system could be fixed priority (FP) or earliest deadline first (EDF). Each application task could be periodic or sporadic, bound or unbound, with arbitrary relative deadline which could be less than, equal to or greater than its period. A number of different server types are considered. This paper presents an exact and efficient schedulability test for the application tasks based on the capacity demand criterion when the global scheduler could be FP or EDF, in some cases, it is necessary and sufficient. Schedulability tests which are necessary and sufficient for several types of dynamic servers are presented when the global scheduler is EDF.
On Delay-Power Tradeoff of Rate Adaptive Wireless Communications with Random Arrivals

In this paper, we study delay optimal scheduling of bursty data traffics over multi-state time-varying wireless channels, where bursty packet arrival in the network layer, queueing behavior in the data link layer, and rate adaptive transmission with flexible modulation in the physical layer are jointly considered from a cross-layer perspective. To achieve a minimum queueing delay under a power constraint, a probabilistic queue-aware and channel- aware cross-layer scheduling policy is proposed, and characterized by a Markov chain model, where the transmission rate, i.e., the number of packets delivered in each slot, is selected with probabilities based on the buffer and channel states in this slot. To reveal the optimal delay-power tradeoff, we formulate a non-linear optimization problem, which, however, is very challenging to solve. To make it tractable, we convert the optimization problem equivalently into a Linear Programming (LP) problem, which helps us achieve the optimal three-dimensional threshold-based scheduling policy analytically. It is found that the source should select one transmission rate jointly based on the channel state and the backlog in the queue.
Demonstration of wafer capping through glass frit bonding and its application on molded platform package

This paper demonstrates the glass frit bonded cavity wafer capping fabrication process and its application on molding platform package. The fabrication process demonstrated from cavity wafer fabrication, glass frit print, wafer bonding and to the prototype on molded platform package. Two commercial glass frit materials were tested and studied. Detail process characterization of cavity wafer fabrication, glass frit fabrication, wafer bonding and molding process were carried on and discussed in this work. The demonstration in this paper shows the feasibility of capping MEMS package through glass frit for adapting to traditional molded platform package.
Different Phenomena in UV-Induced Surface Flashover

Much of the work in the area of surface flashover of the past few decades has been devoted into determining which parameters are important in predicting flashover behavior, and the theoretical understanding involved in the process is not yet mature. In this paper, the flashover performance of the insulator in vacuum was studied in the UV-laser-illuminated case. A testbed comprised of a pulsed high-voltage supply, a vacuum chamber, and an excimer laser was assembled to test the flashover performance under pulsed-voltage and UV-laser irradiation. Two different voltage decrease patterns were measured, implying some underlying information of the flashover mechanism. In addition, a circuit model was used to simplify the surface flashover system, and the simulation results seem pretty good with the experimental results.
VideoWhisper: Toward Discriminative Unsupervised Video Feature Learning With Attention-Based Recurrent Neural Networks

We present VideoWhisper, a novel approach for unsupervised video representation learning. Based on the observation that the frame sequence encodes the temporal dynamics of a video (e.g., object movement and event evolution), we treat the frame sequential order as a self-supervision to learn video representations. Unlike other unsupervised video feature learning methods based on frame-level feature reconstruction that is sensitive to visual variance, VideoWhisper is driven by a novel video “sequence-to-whisper” learning strategy. Specifically, for each video sequence, we use a prelearned visual dictionary to generate a sequence of high-level semantics, dubbed “whisper,” which can be considered as the language describing the video dynamics. In this way, we model VideoWhisper as an end-to-end sequence-to-sequence learning model using attention-based recurrent neural networks. This model is trained to predict the whisper sequence and hence it is able to learn the temporal structure of videos. We propose two ways to generate video representation from the model. Through extensive experiments on two real-world video datasets, we demonstrate that video representation learned by V ideoWhisper is effective to boost fundamental multimedia applications such as video retrieval and event classification.
A Practical Performance Model for Hadoop MapReduce

An accurate performance model for MapReduce is increasingly important for analyzing and optimizing MapReduce jobs. It is also a precondition to implement cost-based scheduling strategies or to translate Hive like query jobs into sets of low cost MapReduce jobs. However, the multiple processing steps in MapReduce task, as well as the complexity of relationships among these steps and the difficulty to measure the computational complexity of MapReduce task, greatly challenges the development and application of a precise performance model. In this paper, we define the concept of relative computational complexity of MapReduce task to estimate the complexity of task, and illustrate the way to measure it. Then, we analyze the detail composition of MapReduce tasks and relationships among them, decompose the major cost items, and present a vector style cost model with equation to calculate each cost items. Moreover, we provide equations to estimate the task execution time based on cost vectors. The experiment on several Hadoop clusters confirms the effectiveness of our proposed performance model.
The Role of Switching Costs in O2O Platforms: Antecedents and Consequences

Online to offline is a new business mode combining the online shopping and the front line transactions. No previous studies have simultaneously examined these variables as antecedents of e-loyalty intention or the possible relationships among them within O2O platform. Our paper has examined the effects of inertia, perceived ease of use, customization and quality of offering on the consumer's switching costs. These findings provide a theoretical foundation for academics and also practical guidelines for service providers in dealing with the promotion of loyalty at O2O platforms.
Fast event identification through subspace characterization of PMU data in power systems

This paper proposes a data-driven fast event identification method based on the measurements of Phasor Measurement Units (PMUs). The central idea is to characterize an event by the low-dimensional subspace spanned by the dominant singular vectors of a data matrix that contains spatial-temporal blocks of PMU data. A dictionary of subspaces that correspond to different events are established off-line, and an event is identified online with the most similar subspace in the dictionary. Both theoretical and numerical analysis demonstrate that the subspace of PMU data is characteristic of system events and is robust to system initial conditions. This compact representation reduces the dictionary size and the computational time of the event identification method. The proposed method is evaluated numerically on simulated events in an IEEE 68-bus power systems.
Multi-objective optimization of six-bar mechanisms using NSGA-II

The non-dominated sorting genetic algorithm (NSGA-II) is applied to solving a multi-objective optimization problem of a six-bar mechanism that is used in a kind of presses, providing an approach to this problem. Mathematical model of the mechanism is set according to 4 objectives and 11 constraints, and the NSGA-II is flexibly used to deal with relationships between variables. Motion simulation is done to check the result showing that this trial is positive to this kind of problems.
Optimal Wavelet Solutions for Ill-posed Elliptic Equations

In this paper, a Cauchy problem for two-dimensional Lap lace equation in the strip 0 ≤ × ≤ 1 is considered. This is a classical severely ill-posed problem. Connecting Shannon wavelet bases with a spectral integral of the Hermitian operator, we can obtain a regularized solution. Moreover, some sharp stable estimates between the exact solution and it's approximation is also provided.
Extension Genetic Algorithm and Its Applications

This paper presents a novel classified method that is called extension genetic algorithm (EGA). The new method is a combination of extension theory and genetic algorithm (GA). In the past, we used the extension method in some clustered problems. With the method, we had to rely on experiences to set rules on classical domain and weight, which caused to increase two tedious and complicated steps in clustering process. In order to improve this defect, the paper uses the EGA to find the best parameter of classical domain. Through the simulations, we prove that this new method can eliminate try and error adjustment of modeling parameters and increase accuracy of the classification.
Efficient image and video re-coloring for colorblindness

There are about 8% of men and 0.8% of women suffering from colorblindness. These viewers have difficulty in discriminating several colors, and thus many colorful images and videos that have high qualities for normal viewers may not be readily perceptible for them. In this paper, we propose an efficient re-coloring approach which can modify the colors of images and videos to enhance their perceptibility for colorblind users. Given an image, the re-coloring is accomplished by two color rotation steps in CIELAB color space. We first perform a local color rotation such that information of b* axis can be enhanced, and then adopt a global color rotation to refine the results. We will show that this method is simple yet effective, and it is able to outperform the traditional re-coloring algorithms in both performance and computational efficiency. We apply the method to process video frames and adopt several strategies to further reduce computational cost to realize real-time video re-coloring. We also explore the structure information of video data to avoid the color inconsistency problem. Specifically, we enforce the color mapping function to be identical in each shot and vary smoothly in adjacent shots. We conduct experiments on real-world images and videos with diverse content, and empirical results demonstrate the effectiveness of the proposed methods.
PicWords: Render a Picture by Packing Keywords

In this paper, we propose a novel text-art system: input a source picture and some keywords introducing the information about the picture, and the output is the so-called PicWords in the form of the source picture composed of the introduction keywords. Different from traditional text-graphics which are created by highly skilled artists and involve a huge amount of tedious manual work, PicWords is an automatic non-photorealistic rendering (NPR) packing system. Given a source picture, we first generate its silhouette, which is a binary image containing a Yang part and a Yin part. Yang part is for keywords placing while the Yin part can be ignored. Next, the Yang part is further over-segmented into small patches, each of which serves as a container for one keyword. To make sure that more important keywords are put into more salient and larger image patches, we rank both the patches and keywords and construct a correspondence between the patch list and keyword list. Then, mean value coordinates method is used for the keyword-patch warping. Finally, certain post-processing techniques are adopted to improve the aesthetics of PicWords. Extensive experimental results well demonstrate the effectiveness of the proposed PicWords system.
Chinese Automatic Summarization Based on Thematic Sentence Discovery

In this paper, we propose a practical approach for extracting the most relevant sentences from the original document to form a summary. The idea of our approach is to obtain summary based on similarity of thematic sentences, which use terms as features rather than words, and employs term length term frequency (TLTF) to compute weight of terms to obtain features. Furthermore, it uses an improved k-means method to cluster sentences, and compute similarity of thematic sentences according to clustering results. Experimental results indicate a clear superiority of the proposed method over the traditional method under the proposed evaluation scheme.
Economic Loss for Water Contaminations in East Lake Based on Logistic Model

Water Contaminations bring not only great damage to the ecological balance, but also serious losses to the national economy. The results of existing methods, which are used to calculate Economic Loss of water contaminations, are not able to reflect the problem explicitly. In view of deficiencies of those calculation methods, and on the base of mechanism model of pollution loss, the logistic equation and environment value theory, this paper first proposes to construct Logistic Model, which is invented by Belgian mathematician Verhulst in 1938, to measure Economic Loss of water contaminations applied to East Lake. The Logistic Model can not only suggest the relationship between pollutants concentration, discharge amount of pollutants, the value of environmental resource and economic loss; but also can quantitatively weigh the loss degree about environmental pollution for resources value. It can reflect the real truth objectively by using Logistic Model to perform “S”-type nonlinear behavior of Economic Loss. At the same time, the calculation method is simple, universal and easily to understand, especially when there are multiple water contaminations in water. The experimental results in this paper can describe the Economic Loss of water contaminations in East Lake accurately and comprehensively, in addition, it is able to help the relevant departments to establish measures to prevent and control water pollution.
Improving Short Text Classification through Better Feature Space Selection

Nowadays people are overwhelmed by more and more short information from lots of different applications, especially with the rapid development of mobile systems. One way to alleviate this issue is an automatic classification of the short texts before they are delivered to users. Several methods have been proposed to classify the short texts, and they are largely based on expanding the short texts to longer ones with external resources to solve the sparseness problem. Different from these studies, we tackle the sparseness problem by selecting a better feature space in which the feature vectors of the short texts are denser, and our method needs no external resources at all. The experimental results on an open dataset show that this method can significantly improve the short text classification accuracy comparing with the baseline, especially when the dimension of the feature space is low.
Mach-Zehnder interferometer in embedded-core optical fiber

A novel Mach-Zehnder interferometer (MZl) based on a pair of long period fiber gratings (LPGs) in an embedded-core hollow optical fiber (ECOF) are proposed and demonstrated experimentally. Two similar LPGs with peak attenuation of approximately 3dB are fabricated in an ECOF by using high-frequency CO2 laser to form an in-fiber MZI. The dependences of the resonant peak on the temperature and the axial strain were studied. Experimental results show that sensitivities of the temperature and the axial strain are 64.3 pm/°C and 0.4 pm/με, respectively.
Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization

The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts. This makes FGVC very challenging because: (i) part annotation and detection require expert guidance and are very expensive; (ii) parts are of different sizes; and (iii) the part interactions are complex and of higher-order. To address these issues, we propose an end-to-end framework based on higherorder integration of hierarchical convolutional activations for FGVC. By treating the convolutional activations as local descriptors, hierarchical convolutional activations can serve as a representation of local parts from different scales. A polynomial kernel based predictor is proposed to capture higher-order statistics of convolutional activations for modeling part interaction. To model inter-layer part interactions, we extend polynomial predictor to integrate hierarchical activations via kernel fusion. Our work also provides a new perspective for combining convolutional activations from multiple layers. While hypercolumns simply concatenate maps from different layers, and holistically-nested network uses weighted fusion to combine side-outputs, our approach exploits higher-order intra-layer and inter-layer relations for better integration of hierarchical convolutional features. The proposed framework yields more discriminative representation and achieves competitive results on the widely used FGVC datasets.
An inter satellite link handover management scheme based on link remaining time

In this paper, an inter satellite link handover management scheme based on link remaining time (HM-LRT) is proposed. Since the satellites move in a regular pattern, the remaining time of inter satellite links going down can be predicted. Based on link remaining time and other information, our link handover management scheme changes the data transmission routes to prevent data packets from going into links that are going to be handed off, so as to reduce the data loss and guarantee the QoS of the satellite communication network. Experiments show that the routing algorithm with HM-LRT outperforms the original routing algorithm.