Reviewerly/exHarmony huggingface dataset:
Classification of Connectives Based on Combination Neural Network

The overlap rate of connectives between different categories of connectives can be viewed directly from the heat map. Therefore, it is necessary to explore a classification method to minimize ambiguity of connectives. A series of methods for automatic classification of connectives in sentence-level are compared in this paper. A combination method is chosen by which we can predict the categories of connectives in a sentence. In the process of automatic classification of connectives, a supervised machine learning algorithm---K-Nearest Neighbor (KNN) was used to classify connectives in order to reduce the ambiguous connectives. However, this method has not gained good effect of automatic classification of connectives. Therefore, authors took another three methods, including Forward Feed Neural Network (FFNN) algorithm, Backward Propagation Neural Network (BPNN) algorithm and a combination method of FFNN&BPNN algorithm. All of these three methods can get better accuracy than the result of KNN algorithm, while the highest accuracy gained by the combination method of FFNN&BPNN among these methods of the experiments. These works can find a maximally disambiguated approach for the classification of connectives that was abstract from Penn Discourse Treebank 3.0 (PDTB3.0).
Using a Phenomenological Approach to Forecast Tuberculosis Cases in the USA

Background: Measurement errors produce bias and uncertainty. These are transferred to the outcomes of fitted models that describe an observed phenomenon. The models describing tuberculosis epidemics often include a large number of variables and parameters whose values are measured. In such cases, the width of the confidence intervals of the outcomes may be so large that the model renders useless. In contrast, straightforward phenomenological models can provide, theoretically, very precise outputs.Method: We use a phenomenological approach to describe and forecast TB incidence numbers in the US as a function of time. The tested models have only two variables whose values are measured. We use a standard method to measure the size of the observed variability and a heuristic method to evaluate the magnitude of the variability which cannot be explained as random variability. Findings: We find numerical regularities that provide trustworthy tools to make a prediction (provided there is no change in current trends) and forecast the tuberculosis incidence trends with great precision. However, after assessing CDC measurement errors -CDC surveillance reports do not reveal the size of measurement errors, we conclude that CDC measurement errors are probably larger than the magnitude of the unexplained variability and that, therefore, our predictions are provided with an overestimated precision. Interpretation: The uncertainty of collected epidemiological data seems to be typically large. This means that not only highly elaborate epidemic models can produce large uncertainties, but less elaborate models also have the risk of producing them. We propose to use epidemic models with caution. A general strategy of implementation could consist of first modeling of the data using a straightforward phenomenological approach and add variables and parameters one by one until the model explains all the variability unless it is well justified.Funding Statement: This study was not funded.Declaration of Interests: None.
Rating Estimation from Review Texts Using Long Short-Term Memory

A lot of reviews of products have been posted on various web sites and services because of the spread of the Internet, and the estimation of ratings from review texts is actively performed. However, there are few such studies on Japanese review texts without limiting the product genre. In this paper, we propose a neural network model that takes as input a general Japanese product review text and estimates rating for it without limiting the product genre. By using Long Short-Term Memory (LSTM), which is one of the regression type neural network models that can handle sequential data, we analyze words in sentences considering their order. The rating estimation model is realized mainly by segmentation of texts, conversion to distributed representations, an LSTM layer, and a fully connected layer. In addition, we conduct evaluation experiments of the created model and consider the results.
Median-Shape Representation Learning for Category-Level Object Pose Estimation in Cluttered Environments

In this paper, we propose an occlusion-robust pose estimation method of an unknown object instance in an object category from a depth image. In a cluttered environment, objects are often occluded mutually. For estimating the pose of an object in such a situation, a method that de-occludes the unobservable area of the object would be effective. However, there are two difficulties; occlusion causes the offset between the center of the actual object and its observable area, and different instances in a category may have different shapes. To cope with these difficulties, we propose a two-stage Encoder-Decoder model to extract features with objects whose centers are aligned to the image center. In the model, we also propose the Median-shape Reconstructor as the second stage to absorb shape variations in a category. By evaluating the method with both a large-scale virtual dataset and a real dataset, we confirmed the proposed method achieves good performance on pose estimation of an occluded object from a depth image.
Channel-Wise Dense Connection Graph Convolutional Network for Skeleton-Based Action Recognition

Skeleton-based action recognition task has drawn much attention for many years. Graph Convolutional Network (GCN) has proved its effectiveness in this task. However, how to improve the model's robustness to different human actions and how to make effective use of features produced by the network are main topics needed to be further explored. Human actions are time series sequence, meaning that temporal information is a key factor to model the representation of data. The ranges of body parts involved in small actions (e.g. raise a glass or shake head) and big actions (e.g. walking or jumping) are diverse. It's crucial for the model to generate and utilize more features that can be adaptive to a wider range of actions. Furthermore, feature channels are specific with the action class, the model needs to weigh their importance and pay attention to more related ones. To address these problems, in this work, we propose a two-stream channel-wise dense connection GCN (2s-CDGCN). Specifically, the skeleton data was extracted and processed into spatial and temporal information for better feature representation. A channel-wise attention module was used to select and emphasize the more useful features generated by the network. Moreover, to ensure maximum information flow, dense connection was introduced to the network structure, which enables the network to reuse the skeleton features and generate more information adaptive and related to different human actions. Our model has shown its ability to improve the accuracy of human action recognition task on two large datasets, NTU-RGB+D and Kinetics. Extensive evaluations were conducted to prove the effectiveness of our model.
Leasing as an effective tool for agricultural financing: within the example of Armenia

Abstract. Through the decades’ Armenian agriculture doesn’t have sufficient growth. Technologies and methodology used by farmers are not sufficient for modern production creation. Financing system of rural areas are less effective as lack of stable income and low-price collateral doesn’t let banking institutions to actively finance the sector. The main purpose of this study is to define problems of agricultural financing and determine leasing as an effective tool for agricultural financing. Methodology of this study is based on quantitative analysis among farmers and other borrowers to take out leasing role in financing process and other relevant issues. Empirical data analysis of different countries’ experience and survey among local rural habitants were made to identify the most effective tools of agricultural financing. As a result, lack of effective financial and technological assistance has identified as an obstacle of agricultural development in Armenia. The importance of agriculture in the development of the country's economy was substantiated. The importance of public awareness in increasing the applicability of leasing has been identified. As a scientific novelty, the thesis of leasing as an effective financial tool for agricultural lending has been proven. The specifics of leasing efficiency for agricultural lending are explained. Absence of collateral as the only advantage of leasing was rejected.
Research on the Recommendation System of Gobang Game Based on Machine Learning

Gobang is an ancient game of pure strategy for two players, which not only makes people relax physically and mentally, but also exercises thinking ability. Although the rules of Gobang are simple, its playing method is complex. If you want to win, you need to be careful step by step, which is a challenging test of the thinking ability of chess players. In many cases, amateur chess players cannot figure out the right positions to place chess pieces in a short period of time, and at the same time, chess players also need to have opponents of their own level to train themselves. Therefore, a Gobang chess recommendation system is very necessary. Existing methods of Gobang recommendation mainly focus on rule-based manners, which is inaccurate and inefficient. Hence, in this paper, a Gobang recommendation system based on machine learning is proposed. We provide the details of our proposed method, and evaluate it using real-world Gobang playing scenarios. The experimental results show that our proposed machine learning methods highly improves on the existing rule-based mechanism and the random forest based method achieves the best accuracy of 93.39%.
Performance Comparison Between Dimension Reduction and Feature Selection Approaches for Data Classification

Dimension reduction and feature selection are two important data preprocessing techniques in data mining applications. The effect of data preprocessing on classification algorithms such as Random Forest, Naive Bayes, Support Vector Machine, Convolutional Neural Network, Decision Tree, K-Nearest Neighbors, and Multi-layer Perceptron are presented. Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are used for dimension reduction. Gini Index and Entropy are used for feature selection. Experiments are done on three datasets. For Diabetic Retinopathy Debrecen Dataset, dimension reduction and feature selection do not have any significant influence on classification accuracy. For Online Shoppers Purchasing Intention Dataset, Entropy based feature selection has significant influence on classification accuracy. For SPECTF Heart Dataset, SVD dimension reduction has some influence on classification accuracy. Our experiment with PCA and SVD dimension reduction methods and Entropy based feature selection method produced higher classification accuracies than those produced by Backward Feature Elimination dimension reduction method reported earlier.
A Summary of convolution Neural Network Compression and Acceleration Technology

Although convolution neural network has achieved remarkable results in different application scenarios, there are a large number of parameters and computation in its structure, which limit its development in mobile and embedded devices. How to reduce parameters, compress model and optimize structure to improve network performance without losing accuracy has become a hot issue of convolution neural network. This paper summarizes and summarizes the convolution neural network structure optimization technology from five aspects: granularity pruning, weight quantization sharing, knowledge distillation, tensor decomposition and fine network design, and analyzes the technical core of it. Their advantages and disadvantages, applicable scenarios and optimization results are analyzed and summarized respectively, and the future research direction is prospected.
Deep Learning Analysis in Development of Handwritten and Plain Text Classification API

Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) are technologies that enable text recognition. The difference between OCR and HTR is one designed specifically for digital text and one designed for handwritten text. There are already various implementations of OCR and HTR online. However, such systems do not guarantee the systems are in premises. To solve this problem, the OCR and HTR system must be built from the scratch.
Implementation of BFP-Typed Apertometer: Reference Location and Accuracy

This work is to investigate the instrumentation implementing issues of back focal plane (BFP)-typed apertometer. Firstly, we introduce a general instrument architecture of the BFP-typed apertometer, including the hardware (optics and mechanics) of the system, operation procedures of control and measurement system, and also the digital image processing algorithm/software. Secondly, we are the first to discuss the gradual rising effect of total-internal-reflection (TIR) phenomenon, the criteria on how to “exactly” locate the reference ring on BFP images, and the corresponding accuracies. As a significantly important complement, we further discuss three important issues: 1) the influence of Goos-Hänchen shift on numerical aperture (NA) measurement; 2) comparison of one-dimension and two-dimension identification algorithms; and 3) comparison of center locating using Fourier correlation analysis (FCA) and Hough transformation (HT) algorithms.
Spoken Keyword Detection Based on Wasserstein Generative Adversarial Network

With the rapid development of artificial neural networks, it's applied to all areas of computer technologies. This paper combines deep neural network and keyword detection technology to propose a Wasserstein Generative Adversarial Network-based spoken keyword detection which is widely different from the existing methods. With the ability of Wasserstein Generative Adversarial Network (WGAN) to generates data autonomously, new sequences are generated, through which it analyzes whether keywords presence and where the keywords appear. In this method, the generator in WGAN fits the observation data to generate new data, and the discriminator classifies the generated data and the labels. The generator and discriminator are trained by combating learning. The method we propose is simple, does not require complex acoustic models, and does not need to be transcribed into text. It is also applicable to such languages without words. The TIMIT corpus and self-recorded Chinese corpus has been used for conducting experiments. Our method is compared with Convolutional Neural Network (CNN) and Deep Convolutional Generative Adversarial Network (DCGAN) and shows significant improvement over other techniques.
Instrumeteor: Authoring tool for Guitar Performance Video

To show off their playing, musicians publish musical performance videos on streaming services. In order to find out typical characteristics of guitar performance videos, we carried out a quantitative survey of guitar performance videos. Then, we discuss key problems of creating effects informed by the survey. According to the discussion, authoring videos with typical effects takes a long time even for experienced users because they typically need to combine multiple video tracks (e.g., lyrics and videos shot from multiple angles) into a single track. They need to synchronize all tracks with the musical piece and set transitions between them at the right timing, aware of the musical structure. This paper presents Instrumeteor, an authoring tool for musical performance videos. First, it automatically analyzes the musical structure in the tracks to align them on a single timeline. Second, it implements typical video effects informed by the survey. In this way, our tool reduces manual work and unleashes the musicians’ creativity.
Research on “concentric drum” project based on discrete model

Abstract The “concentric drum” project tests team members’ ability of cooperation, and the team members can hit the ball more times by working together. It is a typical dynamic problem. In this paper, the complete elastic collision model and the drum surface segmentation model are established to complete the discrete model. Through the optimization of the system, the good solution results of the problem are obtained gradually. The “concentric drum” strategy based on the discrete model has a certain reference for the solution of dynamic problems.
Is That You Again?

Cyber-Physical Systems (CPS) continues to add more intelligence to various applications, including healthcare. A recent wave of research included the development of pill logging systems that monitor when a pill is extracted. Automatic pill logging systems are an important technology for medication adherence, medication error prevention, and tracing. However, most existing systems do not consider how multi-bottle settings or long-term bottle use can affect their ability to log accurately, forcing frequent re-training/recalibration. In this paper, we present adaptive learning techniques for subject identification across a pair of smart pill bottle systems. We collect inertial signals from subjects taking medication pills and encode the activity signals by transforming them into 2D texture images. Then we use pre-trained Convolutional Neural Network (CNN) models for image-based classification tasks. We evaluate our approach with experiments executed by 10 subjects and achieved improved differentiation capacity over existing models by using deep learning models, modified through domain adaptation and transfer learning.
Advanced pedagogic support for the formation of self-development competence in university freshmen

Among the mandatory requirements for the implementation of the main professional educational programmes in higher education is the formation of a universal self-development competence, which provides for forecasting, building and implementing the self-development trajectory that best meets their personal needs and aspirations. However, the results of a sociological study show that most university freshmen are unable to predict and implement the trajectory of self-development and note the lack of opportunities in this matter from teachers. One of the assistance types to university students can be advanced pedagogic support for the formation of self-development competence in university freshmen. The uniqueness of pedagogic support, conceptual foundations of which were developed by Oleg Gazman, is that it determines students' self-determination in a situation of choice and the subsequent independent solution of their problems. The anticipatory nature of pedagogic support for the formation of self-development competence in university freshmen is to provide students with a reason to think even before they have a problem. At the moment when a difficult moment appears in their real life, they already have a set of methods and techniques for its successful resolution. As a result of the theoretical research a refined definition of advanced pedagogic support for the formation of self-development competence in university freshmen is proposed from the position of the existential approach (Mikhail Rozhkov).
Research on High Sparse Sampling CT Image Reconstruction Algorithm Based on Convolutional Neural Networks

Abstract CT plays an important role in medical diagnosis and industrial nondestructive testing. How to reduce the radiation to patients in the process of CT scanning has become a hot spot. An effective way is to use sparse sampling projection data to reconstruct the CT image. However, the image obtained by using the traditional CT reconstruction algorithm to deal with the sparse projection data has serious image distortion and artifact. This paper presents a CT reconstruction method based on CNN. This method learns the mapping relationship between the sparse projection data and the complete projection data in the training database, and uses the learned result to process the sparse sampling projection data. FBP algorithm is used to get the high resolution CT image. This paper uses CNN to carry out an end-to-end learning to repair the projection sinusoidal image with missing angle and improve the quality of CT image reconstruction.
FPGA-Based Implementation of Digital Filters for Image Denoising

Image processing has an important role in signal processing. The occurrence of noise in the image can degrade its quality and can cause loss of information. In this paper, design of Gaussian, mean, and median filters is considered. Gaussian and mean filters are linear filters and are designed using convolution between 3 × 3 image pixel matrix and kernel matrix. The median filter is a nonlinear filter. An SRAM-based FPGA implementation of such a filter is susceptible to memory bit flips that are caused by single-event upsets (SEUs). Hence, a protection method is needed to ensure proper working of the median filter. Here, the median filter design proposed in (Aranda et al. in J. IEEE Transactions on Nuclear Science 64:2219–2226, 2017 []) is used to check if the median value obtained is within a dynamic range. The performance of these three filters is evaluated by considering image quality metrics such as PSNR and correlation coefficient with four different noises. The results show choice of the filter for denoising, based on the type of noise present in the image. In recent times, FPGA technology has become a reliable method for implementing image processing algorithms due to their fast response compared to image processing in MATLAB. The noisy image to pixel conversion is done in MATLAB. The 3 × 3 sliding window approach is used for filter coding in Verilog, and the FPGA synthesis on virtex5 is done with Xilinx ISE 14.5. The images are reconstructed with filtered pixels in MATLAB.
A Design of the Settlement Mode of the Electric Vehicles’ Shared Private Piles Based on the Energy Block Chain

To deal with a series of settlement problems such as the insufficient security for the user’s transaction, the complex process of settlement and the difficulty in data analysis within the sharing mode of the electric vehicles’ private piles, this paper proposed a new settlement mode for the shared private piles of electric vehicles. This mode took the shared charging network of the vehicle networking and the pile networking as the background, built a decentralized energy block chain, optimized the structure of the settlement mode by using distributed ledger technology, and used the intelligent contract algorithm to improve the settlement efficiency of charging orders. At the same time, this mode considered the transaction records and subsequent data analysis in its design, and used the evidence-storage and traceability technology to facilitate the integration of charging information data and to realize the configuration optimization of charging network.
Rural E-commerce Investment and Financing Model Based on Blockchain and Data Mining

Rural smart e-commerce investment and financing model based on blockchain and data mining is presented in this paper. Using intelligence analysis to prevent financial risks is not only an urgent expectation in the financial field, but also an inherent need for the development of the information science. Blockchain is a chained structure that combines data chunks in chronological order in a sequential manner and is a distributed ledger that is guaranteed by cryptology. The proposed model is simulated compared with the latest models.