Reviewerly/exHarmony huggingface dataset:
Paraphrase Generation with Chinese Short Text Dataset

An obstacle of conducting investigation on paraphrase generation is short of high-quality, publicly-available labeled dataset of sentential paraphrases, which is particularly serious for Chinese paraphrase generation research. Therefore, the study in Chinese paraphrase generation is the starting stage. This paper aimed to use a novel way to create Chinese paraphrase dataset, which contains 8K sentences pairs. The data source comes from a bank QA dataset, in which there are several sentences to express each problem. By calculating the similarity between the same semantic sentences, we can obtain paraphrase pairs to create Chinese paraphrase dataset. Then, we achieve paraphrase generation task by leveraging a classical Seq2Sseq model with attention mechanism. Following previous work and evaluate paraphrase generation result on our Chinese dataset. Experimental results not only show that the dataset is suitable for Chinese paraphrase generation task, but also provides a benchmark for further research on this research area.
Implementation of a 1440Hz video projector for calibrating optic flow sensors at high speed

In this paper, the results of experiments in which a high velocity movement is simulated using digital light processing methods (with a Digital Light Processing (DLP) video projector) are presented. The main aim is to simulate a fast movement in order to test optic flow (OF) sensors or event-based sensor more quickly and simply than with traditional methods based on moving wallpaper or LCD screen. To obtain an accurate movement resolution, specific pictures are generated and displayed by the DLP at a high frame rate. The results show that accurate projected movement speeds are obtained with velocities of up to 50m/s. Propositions to increase these results are discussed.
A Large-Scale VR Panoramic Dataset of QR Code and Improved Detecting Algorithm

With the rapid development of mobile payment and scanning technology, QR code has become widespread in both consumer and enterprise domains. However, there is the lack of corresponding research on detecting QR code in panoramic video due to the lack of high-quality datasets. To fill this gap, in this work we propose a large-scale panoramic QR code dataset to facilitate relevant research. Our dataset includes the following characteristics: (1) It is by far the largest dataset in terms of image quantity. (2) Compared with the existing datasets, ours is closer to the realistic setting and can derive a variety of research problems. In addition to the dataset, we propose a QR code detecting approach in a complex environment based on deep learning improving accuracy of QR code detection.
Research on Freight Big Data Application Based on Railway Data Service Platform

According to the current status of railway freight data assets, the overall architecture of railway freight big data application based on the railway data service platform is proposed, including the processing flows of data acquisition and access, assets directory construction, data modeling and management, data storage and analysis, etc. Besides, the key technologies, e.g. the construction of data model based on source and paradigm approaches, Greenplum-based Ad Hoc queries of data, freight-specific data models are also studied. Based on the data of some existing freight business systems and the distributed computing architecture of the railway data service platform, this paper conducted Ad Hoc queries case analysis and the application practice of freight-specific algorithm library, thus verified the feasibility of key technologies, and provided data and technical support for the comprehensive analysis and mining application of freight big data.
Weather Attribute-Aware Multi-Scale Image Generation with Residual Learning

We present image generation networks to generate images conforming to specified weather attributes. Taking weather attributes as the conditions, the proposed networks generate scene images with the help of a guided reference image. To generate higher-resolution images, we construct a multi-scale generation framework consisting of a global generator and a local enhancer. Furthermore, we integrate the idea of residual learning into the proposed framework, and aim at generating fine-grained texture. The evaluation shows performance comparison both from quantitative and qualitative perspectives. A comprehensive study including the impact of different attributes and extension of the proposed models is also provided. This work is kind of hybrid approach among various image generation studies.
Local Connectivity Enhanced Sparse Representation

During the past two decades, the subspace clustering problem has attracted much attention. Since the data set in real-world problems usually contains a lot of categories, it seems that the large subspace number (LSN) subspace clustering has great significance. According to the analysis in the recent work, concerning the spectral clustering-based methods, strong graph connectivity is highly desired by the LSN subspace clustering problem. However, previous works usually consider this property by the global structure of the representation matrix. In this paper, we attempt to address the local structure by the local difference of the representation matrix and propose Local Connectivity Enhanced Sparse Representation (LCESR). Our method can not only exhibit strong graph connectivity but also produce subspace-preserving affinities for independent subspaces, another favorable property concerned by many related works. Hence, it achieves state-of-the-art results in LSN subspace clustering. Besides, because the weight matrix in LCESR can take advantage of the label information available in the data set, it can also perform well in the problem of the graph-based semi-supervised learning. Extensive experimental results demonstrate the effectiveness of our proposed method.
Knowledge Link Inference of Graph Structure Based on Holographic Model

For current knowledge link inference methods, whether it is traditional translation models, semantic matching models, or convolutional neural network models, it is impossible to obtain rich semantic information. This paper mainly uses a pre-training layer based on the holographic model, and combines the knowledge structure to perform the knowledge link inference. Firstly, the pre-training layer is used as the model initialization. Secondly, the graph structure encoder layer not only combines the information of entities and relationships directly related to the current entity, but also considers the information including multi-hop neighbor nodes and auxiliary relationships. Finally, ConvKB is used as a decoder to score the triples. The model is evaluated on two benchmark datasets WN18RR and FB237, that is slightly better than the previous embedding models on some indicators.
Global Best-Guided Modified Cat Swarm Optimization Algorithm for Applications in Machine Learning

The cat swarm optimization (CSO) is regarded as one of the intellectual optimization techniques that have the ability to show a good performance in the machine learning environment. The modified and improved version of the cat swarm optimization algorithm proposed in this paper provides a unique characteristic of parallel computation. The modified algorithm uses two types of weight expression to calculate the new weight for each iteration of the algorithm. The weight expression which helps in getting a better value of global best during the evaluation procedure is taken into consideration in every iteration. This modified algorithm takes the best from both the modified weight expression. Simulation results show that this modified version of CSO performs reasonably well in comparison to normal CSO.
Design and Development of Multirotor UAV with Automatic Collision Avoidance using Stereo Camera

The operation of drones in cluttered environments like forests and hilly areas is extremely difficult; it is impossible to use drones autonomously without having built-in information to detect and avoid obstacles. The vision based obstacle avoidance algorithm is presented in this paper, with extensions to UAV navigation. The proposed method is incorporated on a stereo vison multi copter using a block matching algorithm. The stereo vision baseline is based on horizontal configuration and computes the depth using a sum of absolute difference algorithm. The image processing node (LabVIEW vi) and the controller node are run on a remote laptop. This vi computes the distance between the multirotor and an obstacle and transmits depth data to an onboard flight controller through the MAVLink protocol. The algorithm efficiency was tested using the software in the loop on Gazebo simulator to analyze the performance of the UAV. The hardware in loop results are also shown in this paper after the successful flight test.
A Systematic Study of Open Source and Commercial Text-to-Speech (TTS) Engines

The widespread availability of open source and commercial text-to-speech (TTS) engines allows for the rapid creation of telephony services that require a TTS component. However, there exists neither a standard corpus nor common metrics to objectively evaluate TTS engines. Listening tests are a prominent method of evaluation in the domain where the primary goal is to produce speech targeted at human listeners. Nonetheless, subjective evaluation can be problematic and expensive. Objective evaluation metrics, such as word accuracy and contextual disambiguation (is "Dr." rendered as Doctor or Drive?), have the benefit of being both inexpensive and unbiased. In this paper, we study seven TTS engines, four open source engines and three commercial ones. We systematically evaluate each TTS engine on two axes: (1) contextual word accuracy (includes support for numbers, homographs, foreign words, acronyms, and directional abbreviations); and (2) naturalness (how natural the TTS sounds to human listeners). Our results indicate that commercial engines may have an edge over open source TTS engines.
Road identifying by using electromagnetic sensor and camera in smart car field

In the early centuries of 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> , self-driving becomes increasingly popular. Because of that, some technologies related to that are being studied and applied. Smart car control technology is one of these technologies including embedded technology, automatic control technology and so on. As for smart car field, there are two important methods to identify road, which are using electromagnetic sensors and using camera. In each method, there are different hardware structures to collect data and different code for microprocessor to process data. So in this essay, it will introduce the background technologies of smart car firstly. After that, basic principle of using electromagnetic sensors and camera will be given. Moreover, it will explain the code about using camera to achieve direction control and speed control. In this part, it will also give some applications of using camera to identify road elements. Finally, it will give a new method to control smart car by according to advantages and disadvantages of using camera and using electromagnetic sensors.
Research on Multi-round task scheduling algorithm for Unmanned Combat Unit Based on TDMA Time Slot

In the future high-tech battlefield environment, unmanned combat units are required to perform their own operational tasks in an autonomous, real-time, and efficient manner, and give full play to their operational effectiveness. In this paper, each unmanned combat unit is equivalent to independent sensor nodes, and the local unmanned combat unit group is regarded as a hybrid structure sensor network. The maximum similarity of nodes is used to select the first node of each region, Multi-round task scheduling based on TDMA time slots. To achieve efficient and dynamic scheduling of multi-unmanned combat unit mission self-organization in the battlefield.
FPGA Based Hierarchical Fuzzy System

Multivariable fuzzy system implementation suffers from amount of computation resulting from the high number of rules especially for large number of input variables. As number of inputs increases, the situation becomes worse because the number of rules increases exponentially with respect to input variables resulting in problem commonly called “rule explosion problem”. Another problem related to multivariable system is the complexity of rule construction of high dimensionality fuzzy system. Hierarchical system breaks high dimensionality fuzzy system into several low dimensional fuzzy systems such that the number of rules has linear proportion with the number of input variables. In this paper, hardware implementation of fuzzy system with hierarchical structure is performed to obtain powerful system take the advantages of both hardware speed and low dimensionality processing reduction of hierarchical implementation.
Exploring the Properties of Points Generation Network

With the development of deep learning, learning-based 3D reconstruction has attracted a substantial amount of attention and various single-image 3D reconstruction networks have been proposed. However, due to self-occlusion, the information captured in a single image is highly limited, resulting in inaccuracy and instability in reconstruction results. In this paper, a feature combination module is proposed to enable existing single-image 3D reconstruction networks to perform 3D reconstruction from multiview images. In addition, we study the impact of the number of the input multiview images as well as the network output points on reconstruction quality, in order to determine the required number of the input multiview images and the output points for reasonable reconstruction. In experiment, point cloud generations with different number of input images and output points are conducted. Experimental results show that the Chamfer distance decreases by 20%~30% with the optimal number of input multiview images of five and at least 1000 output points.
Algorithm for Reducing Storage in Blockchain based on Secret Sharing

Blockchain is a growing list of linked blocks. Since each participant stores the whole list of transactions, the storage space rapidly increases. Therefore, there is a need of changing the storage concept in blockchain. There are several solutions for this problem based on network coding and secret sharing. In this paper, we propose a novel use of Shamir's secret sharing scheme for reducing the storage cost in blockchain system.
Which Metric Is Suitable for Evaluating Your Multi-Threading Processors? In Terms of Throughput, Fairness, and Predictability

Simultaneous multithreading technology (SMT) can effectively improve the overall throughput and fairness through improving the resources usage efficiency of processors. Traditional works have proposed some metrics for evaluation in real systems, each of which strikes a trade-off between fairness and throughput. How to choose an appropriate metric to meet the demand is still controversial. Therefore, we put forward suggestions on how to select the appropriate metrics through analyzing and comparing the characteristics of each metric. In addition, for the new application scenario of cloud computing, the data centers have high demand for the quality of service for killer applications, which bring new challenges to SMT in terms of performance guarantees. Therefore, we propose a new metric P-slowdown to evaluate the quality of performance guarantees. Based on experimental data, we show the feasibility of P-slowdown on performance evaluation. We also demonstrate the benefit of P-slowdown through two use cases, in which we not only improve the performance guarantee level of SMT processors through the cooperation of P-slowdown and resources allocation strategy, but also use P-slowdown to predict the occurrence of abnormal behavior against security attacks.
Haiku Generation From Narratological Perspective

Haiku—a form of unrhymed poetry—is popular among the Japanese. A typical haiku is composed of 17 moras and three phrases. A haiku has the possibility of scratching the surface and uncovering a hidden message through an expression of events. According to Masaoka Shiki, a haiku is a kind of literature and has high affinity with our research on generating stories. In this chapter, the authors implemented the prototype system that has two functions: first, to produce multiple haikus from a single story, and second, to engender multiple stories from haikus. The system prototyped in this chapter is based on haiku theory, which is used by the authors in their research, and is rooted in the concept's co-occurrence information and frequency information used to generate a haiku. The method uses statistical information for selecting the words and creating the word network in the haiku. Through the aforementioned methods, the authors created a framework for a system of circulating haiku and stories and proposed a kind of narrative generation with narrative as an input.
Enlargement of Ubiquitous Security Organism for Cloud Computing

Cloud storage empowers clients to tenuously pile their information and appreciate the on-request excellent cloud requests deprived of the weight of neighborhood hardware and software the executives. But there is some security hazards headed for the precision of files or documents or information in the cloud in added with the loss of storage in the existing which utilize the homomorphic token. In order to discourse this delinquent, a new effective security mechanism is proposed called data aware routing (DAR). This scheme reduces the data contention by partitioning the data storage, and using the concept of logical IP, it makes the data secured in the cloud. It also preserves data integrity and reduces the transmission time by partitioning the data in the cloud. DAR proved to be an efficient scheme in terms of reducing the transmission time during data request
The Course Design of Applied Talent Statistics Based on Fuzzy Control and Grey Measure Model

In order to improve the teaching quality of probability theory and statistics course under the goal of cultivating applied talents, a design model of probability theory and statistics course for applied talents based on SPSS software is proposed. The distributed structure model of probability theory and statistics course is constructed under the goal of cultivating applied talents, and the constraint parameters of teaching objectives of probability theory and statistics course are designed by using the method of quantitative characteristic analysis. The explanatory variable model and control variable model of probability theory and statistics course teaching are constructed, and the teaching planning design of probability theory and statistics course is carried out by using descriptive statistical analysis method and regression analysis method. The quantitative regression analysis model of course design and teaching evaluation of probability theory and statistics course is established, and the optimization design of applied talent probability theory and statistics course is carried out by combining fuzzy control and grey measure method. The effectiveness of probability theory and statistics course of applied talents is analyzed in SPSS software environment. The result of statistical analysis shows that this model can improve the positive effect of course teaching. The robustness of teaching results promotes the innovation and cultivation of probability theory and statistical applied talents.
Mining Robust Frequent Items in Data Streams

This paper studies the problem of robust frequent items mining in data streams that generalizes the traditional frequent items mining by considering the noise of datasets. That is, different items may correspond to the same entity because of noise; examples include different images of the same object and fluctuated data in the same setting measured by sensors. Our objective is to identify those items that correspond to the same entity and have an aggregated frequency exceeding a given threshold, which named as robust frequent items. To the best of our knowledge, there is no existing works on mining robust frequent items in a data stream. In this paper, we first propose a scheme by applying sampling and spatial partition to address the problem in low dimensional spaces. Furthermore, we extend the above algorithmic framework to high dimensional spaces by incorporating the locality sensitive hashing scheme to deal with the approximate nearest neighbor problem. We conduct evaluations using synthetic datasets and compare our scheme with two prior adapted schemes. Our results demonstrate that the efficiency of our algorithms outperforms the adaptive Space Saving by 14.8% and 9.8% in terms of precision and recall, respectively.
DOA Estimation of Wideband LFM Signals Based on Zoom-FRFT

The conventional direction of arrival (DOA) estimation methods of wideband linear frequency modulated (LFM) signals based on fractional Fourier transform (FRFT) have good performance, in which LFM signals are changed into narrowband stationary ones and time-variant steering vector is converted to time-invariant one. However, the accuracy of peak estimation is greatly reduced under the condition of low SNR and insufficient snapshots, which seriously affects the accuracy of DOA estimation. Thus, a method for the DOA estimation based on Zoom-FRFT is proposed which can improve the estimation accuracy without increasing the sample sizes and sampling rate, especially at the low signal-noise ratio (SNR). Through Zoom-FRFT, the algorithm extracts all peaks of the LFM signals and construct the fractional autocorrelation matrix in Zoom-FRF domain. Thereafter, the DOAs of coherent and uncorrelated LFM signals are estimated by the multiple signal classification technique (MUSIC). The effectiveness of the proposed algorithm was verified by theoretical analysis and Monte-Carlo simulation trials.
Research on Face Expression Detection Based on Improved Faster R-CNN

Because facial expression is easy to be confused, and is easily affected by environment, Angle and other factors, this paper proposes an improved Faster R-CNN based facial expression detection method. In this method, histogram equalization and adaptive histogram equalization are preprocessed for SFEW 2.0 of the facial expression data set, and the facial expression data is enhanced and expanded. Then the repetitive experimental optimization of the hyperparameters is carried out to improve the training and learning effect of the model and improve the detection accuracy. In the end, based on the regularization model structure optimization, Soft-max cross entropy classification loss function and L1 Smooth regression loss function with parameter constraint term were proposed. The regularization method was used to optimize parameter weight, improve detection accuracy, and an improved Faster R-CNN model adapted to face expression characteristics was obtained.
Detection of Blind Signature Using Recursive Sum

Digital signatures are suitable technology for public key encryption. Acceptance (non-repudiation) of digital messages and data origin authentication are one of the main usage of digital signature. Digital signature's security mainly depends on the keys (public and private). These keys are used to generate and validate digital signatures. In digital signature signing process is performed using signer's secret key. However, any attacker can present a blinded version of message encrypted with signer's public key and can get the original message. Therefore, this paper proposed a novel method to identify blinded version of digital signature. The proposed method has been tested mathematically and found to be more efficient to detect blind signatures.