English Article Style Recognition and Matching by Using Web Semantics

With the explosion of internet information, people feel helpless and difficult to choose in the face of massive information. However, the traditional method to organize a huge set of original documents is not only time-consuming and laborious, but also not ideal. The automatic text classification can liberate users from the tedious document processing work, recognize and distinguish different document contents more conveniently, make a large number of complicated documents institutionalized and systematized, and greatly improve the utilization rate of information. This paper adopts termed-based model to extract the features in web semantics to represent document. The extracted web semantics features are used to learn a reduced support vector machine. The experimental results show that the proposed method can correctly identify most of the writing styles.

Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach.

An Improved Fast Estimation of Satellite Phase Fractional Cycle Biases

Satellite phase fractional cycle biases (FCBs) are crucial to precise point positioning with ambiguity resolution (PPP–AR), and they can improve the accuracy and reliability of a solution. Traditional methods need multiple iterations and need to keep the same reference when estimating satellite phase fractional cycle biases. In this paper, we propose an improved fast estimation of FCB, which does not need any iterations and can select any reference when estimating FCB. We compare the suitability and precision of a traditional and a proposed method by BDS-3 experiments. The results of the FCB experiments show that the calculated time of the proposed method is less than the traditional method and that computation efficiency is increased by 34.71%. These two methods have a similar rate of fixed epochs and ambiguities in the static and dynamic models. However, the time to first fix (TTFF) of the proposed method decreased by 19.69% and 28.83% for the static and dynamic models, respectively. The results show that the proposed method has a better convergence time in PPP–AR.

Investigation of Hyperparameter Setting of a Long Short-Term Memory Model Applied for Imputation of Missing Discharge Data of the Daihachiga River

Missing observational data pose an unavoidable problem in the hydrological field. Deep learning technology has recently been developing rapidly, and has started to be applied in the hydrological field. Being one of the network architectures used in deep learning, Long Short-Term Memory (LSTM) has been applied largely in related research, such as flood forecasting and discharge prediction, and the performance of an LSTM model has been compared with other deep learning models. Although the tuning of hyperparameters, which influences the performance of an LSTM model, is necessary, no sufficient knowledge has been obtained. In this study, we tuned the hyperparameters of an LSTM model to investigate the influence on the model performance, and tried to obtain a more suitable hyperparameter combination for the imputation of missing discharge data of the Daihachiga River. A traditional method, linear regression with an accuracy of 0.903 in Nash–Sutcliffe Efficiency (NSE), was chosen as the comparison target of the accuracy. The results of most of the trainings that used the discharge data of both neighboring and estimation points had better accuracy than the regression. Imputation of 7 days of the missing period had a minimum value of 0.904 in NSE, and 1 day of the missing period had a lower quartile of 0.922 in NSE. Dropout value indicated a negative correlation with the accuracy. Setting dropout as 0 had the best accuracy, 0.917 in the lower quartile of NSE. When the missing period was 1 day and the number of hidden layers were more than 100, all the compared results had an accuracy of 0.907–0.959 in NSE. Consequently, the case, which used discharge data with backtracked time considering the missing period of 1 day and 7 days and discharge data of adjacent points as input data, indicated better accuracy than other input data combinations. Moreover, the following information is obtained for this LSTM model: 100 hidden layers are better, and dropout and recurrent dropout levels equaling 0 are also better. The obtained optimal combination of hyperparameters exceeded the accuracy of the traditional method of regression analysis.

A Connection Access Mechanism of Distributed Network based on Block Chain

Cross-border payment optimization technology based on block chain has become a hot spot in the industry. The traditional method mainly includes the block feature detection method, the fuzzy access method, the adaptive scheduling method, which perform related feature extraction and quantitative regression analysis on the collected distributed network connection access data, and combine the fuzzy clustering method to optimize the data access design, and realize the group detection and identification of data in the block chain. However, the traditional method has a large computational overhead for distributed network connection access, and the packet detection capability is not good. This paper constructs a statistical sequence model of adaptive connection access data to extract the descriptive statistical features of the distributed network block chain adaptive connection access data similarity. The performance of the strategy retrieval efficiency in the experiment is tested based on the strategy management method. The experiment performs matching query tests on the test sets of different query sizes. The different parameters for error rate and search delay test are set to evaluate the impact of different parameters on retrieval performance. The calculation method of single delay is the total delay or the total number of matches. The optimization effect is mainly measured by the retrieval delay of the strategy in the strategy management contract; the smaller the delay, the higher the execution efficiency, and the better the retrieval optimization effect.

Nonlocal Boundary Conditions Are Applied to the Analysis of Curve Equations

The traditional curve equation solution method has a low accuracy, so the non-local boundary conditions are applied to the curve equation solution. Firstly, the solution coordinate system is established, and then the key parameters are determined to solve the curve equation. Finally, the curve equation is solved by combining the non-local boundary conditions. The experiment proves that the method of this design is more accurate than the traditional method in solving simple curve equation or complex curve equation.

Towards the analysis of coral skeletal density-banding using deep learning

X-ray micro–computed tomography (µCT) is increasingly used to record the skeletal growth banding of corals. However, the wealth of data generated is time consuming to analyse for growth rates and colony age. Here we test an artificial intelligence (AI) approach to assist the expert identification of annual density boundaries in small colonies of massive Porites spanning decades. A convolutional neural network (CNN) was trained with µCT images combined with manually labelled ground truths to learn banding-related features. The CNN successfully predicted the position of density boundaries in independent images not used in training. Linear extension rates derived from CNN-based outputs and the traditional method were consistent. In the future, well-resolved 2D density boundaries from AI can be used to reconstruct density surfaces and enable studies focused on variations in rugosity and growth gradients across colony 3D space. We recommend the development of a community platform to share annotated images for AI.

Comparison of the Effects of Sugar-free Solutions, Cola, Mouthwash on the Surface Hardness and Roughness of Temporary Crown Materials

The aim of this study is to investigate the effect of polishing with different solutions on the surface roughness and hardness of two different polymethylmethacrylate temporary restoration materials. In the study, two different temporary crown materials prepared in the CAD / CAM system and prepared by the traditional method were used to test a total of 224 pieces of 10 mm diameter and 2 mm thickness. After the surface roughness and micro hardness values were measured, samples were randomly divided into seven groups among themselves; After waiting 24 h, 1 and 3 weeks, values were measured again. Data were evaluated using 3-way analysis of variance (ANOVA) and Tukey HSD test. The temporary restorative materials surface hardness and roughnesses are important to be able to stay in oral cavity without any changes. And it is also important to determine which of the materials (prepared by temporary conventional materiels or by the CAD/CAM) are less effected by the liquids in oral cavity.

Application of concave induction cooking to improve the texture and flavor of braised pork

Long-term cooking may reduce the eating and nutritional quality attributes of meat products due to excessive oxidation. This study aimed to investigate the feasibility of concave induction to improve the quality of braised pork belly. Pork belly cubes were subjected to concave induction cooking (2000 W) or plane induction cooking (2000 W, traditional) for 60 min, 90 min, 120 min or 150 min. Then texture, fatty acid profile, lipid and protein oxidation, volatile flavor and sensory test in braised meat were evaluated. Compared with traditional method, concave induction cooking showed higher heating performance with shorter time to achieve a setting temperature. Compared with traditional cooking for 150 min, concave induction cooking for 60 min did not only produce a comparable volatile flavor and sensory scores, but also give better quality attributes, including lower hardness, chewiness, thrombogenicity values, PUFA/SFA value, lipid and protein oxidation. E‑nose results showed that samples cooked by concave induction for 60 min and 90 min showed a great similarity to those cooked by plane induction for 150 min. Concave induction cooking for 60 min also showed advantages to retain higher abundances of other volatile compounds including 2-pentylfuran, (E, E)-3,5-octadien‑2- one, 2, 3-octanedione, 2-decahydro‑1,6- dimethylnaphthalene when compared with plane induction cooking for 150 min.