A Simple Syntactic Approach for the Generation of Indexing Phrases

A syntactic approach is described for generating indexing phrases usable for the content identification of natural-language texts. The phrase generation method is based on a simple language analysis system that determines the syntactic function of individual text words with a high degree of accuracy, and chooses of indexing phrases based on weights assigned to the phrase components. The proportion of phrases that appear to be acceptable for content identification ranges from 96 to 98 percent

Diffusion Analysis and Incentive Method for Mobile Crowdsensing User Based on Knowledge Graph Reasoning

Aiming at the problem that the mobile crowdsensing (MCS) system relies on a specific platform with a large user group presupposed, this paper proposes a sensing user diffusion analysis and incentive method based on knowledge graph reasoning. We consider motivating users to participate under the constraint of limited budget so that the platform and users can get the most benefits. In this paper, we focus on socially aware users represented by self-organizing social networks, combine the knowledge graph to establish a knowledge graph for the crowdsensing system, use rules to derive user influence, and optimize user contributions. With the goal of maximizing social welfare, we propose a social awareness reverse auction (SARA) mechanism, in which the total contribution of users is the key to select winners, and the winners are paid based on critical prices. Through experimental simulations, we verify that SARA is close to the optimal social welfare under budget constraints

Text Linking and Retrieval Experiments for Textbook Components

Experiments are described designed to retrieve individual paragraphs of textbook material in answer to user-submitted queries. The retrieval strategies are based on the global comparison of paragraph texts, as well as on the local processing of text sentences. Furthermore, the retrieved items may be freely chosen, or may alternatively be restricted to certain areas in a clustered arrangement of book paragraphs. The retrieval results indicate that high retrieval values are obtainable for the more refined retrieval strategies, ranging between 0.70 and 0.80 in search precision

Research on sentiment analysis method of opinion mining based on multi-model fusion transfer learning

Abstract With the popularity of social media, opinion mining has gradually become a popular research field. Among these fields, sentiment analysis mining is an important research direction in the field of opinion mining. It aims to reveal the public's sentiment tendency, and attitude towards specific topics or events by analyzing text data generated by users on online platforms and digital media. However, the large amount of opinion data usually lacks effective annotation, which limits the learning and construction of opinion models. Therefore, focusing on the problem of the scarcity of labeled data in opinion analysis, this paper proposes a mining method for public opinion sentiment analysis based on multi-model fusion transfer learning, that can make full use of the limited labeled data to improve the learning efficiency of sentiment features by integrating the advantages of different models. Additionally, it introduces a transfer learning strategy to enable the models of the target domains to perform better in the absence of labeled data. Furthermore, the attention mechanism is combined to strengthen the acquisition of key features of the emotional colors and improve the accuracy of sentiment analysis. Specifically, the paper uses the ERNIE model to generate dynamic representations of the text word vectors in the dataset. It also uses TextCNN and BiGRU to construct a joint model for extracting local and overall features of the text word vectors. The parameters of the feature layer of the trained model are migrated to the target domain through transfer learning. The attention mechanism is combined with the model to identify the extreme elements of the sentiment. Finally, the local and overall features are fused to achieve comprehensive mining of public opinion and emotional information. This method can effectively improve the accuracy and generalization of public opinion analysis in cases of data scarcity. In the experimental part, the paper conducts comparisons and analyses in eight aspects: word embedding model, model combination, attention mechanism, transfer learning, source domain dataset, target domain dataset, model training, and baseline model. The four indicators, namely accuracy, precision, recall, and F1-measure are used to evaluate the performance of the method. The experiments are thorough and detailed, demonstrating the effective improvement of opinion mining performance

Evaluation of the relationships and uncertainties of airborne and ground-based sea ice surface temperature measurements against remotely sensed temperature records

Sea ice surface temperature (IST) is an important indicator of environmental changes in the Arctic Ocean. In this study, the relative performance of four mainstream IST records, i.e. airborne IST, infrared radiometer measured IST (IR IST), longwave radiation derived IST (LWR IST), and snow and ice mass balance array buoy derived IST (Buoy IST), were evaluated against the MODIS IST product. Bias, standard deviation (STD), and root mean square error (RMSE) were used to evaluate the data quality. Results revealed that airborne IST had the best accuracy, which was 0.21 K colder than MODIS IST, with STD of 1.46 K and RMSE of 1.47 K. Ground-based ISTs were biased with each other but all warmer than the MODIS IST. The IR IST had the best overall accuracy (bias = 0.55 K; STD = 1.52 K; RMSE = 1.61 K), while the LWR IST was the noisiest measurement with the largest outlier data percent. Besides, co-located IR and LWR ISTs were more consistent than any type of evaluated IST against MODIS IST (correlation coefficient = 0.99). Airborne and IR ISTs are thus the premier choice for monitoring the rapidly changing Arctic sea ice, together with satellite observations

Temperature Effects on the Deformation Mechanisms in a Ni-Co-Based Superalloys

The tensile properties of a Ni-Co-based superalloy were investigated from room temperature to 900 °C. From 25 to 650 °C, the yield strength and tensile strength of the alloy decreased slightly, while the elongation decreased sharply. From 760 °C to 900 °C, the yield strength and tensile strength were greatly reduced, while the elongation also had a low value. With the increase in temperature, the deformation mechanism transformed from anti-phase boundary shearing to stacking fault shearing, and then from deformation twinning to Orowan bypassing, respectively. Deformation twins were generated in the deformed alloy with high-density stacking faults and they can contribute to the high strength. The alloy in this study has good mechanical properties and hot working characteristics below 760 °C and can be used as a turbine disk, turbine blade, combustion chamber, and other aircraft structural parts

Overexpression Populus d-Type Cyclin Gene PsnCYCD1;1 Influences Cell Division and Produces Curved Leaf in Arabidopsis thaliana

d-type cyclins (CYCDs) are a special class of cyclins and play extremely important roles in plant growth and development. In the plant kingdom, most of the existing studies on CYCDs have been done on herbaceous plants, with few on perennial woody plants. Here, we identified a Populus d-type cyclin gene, PsnCYCD1;1, which is mainly transcribed in leaf buds and stems. The promoter of PsnCYCD1;1 activated GUS gene expression and transgenic Arabidopsis lines were strongly GUS stained in whole seedlings and mature anthers. Moreover, subcellular localization analysis showed the fluorescence signal of PsnCYCD1;1-GFP fusion protein is present in the nucleus. Furthermore, overexpression of the PsnCYCD1;1 gene in Arabidopsis can promote cell division and lead to small cell generation and cytokinin response, resulting in curved leaves and twisted inflorescence stems. Moreover, the transcriptional levels of endogenous genes, such as ASs, KNATs, EXP10, and PHB, were upregulated by PsnCYCD1;1. Together, our results indicated that PsnCYCD1;1 participates in cell division by cytokinin response, providing new information on controlling plant architecture in woody plants