hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity

In this paper, we propose a method of fusing sentence information and word frequency information for the SemEval 2021 Task 1-Lexical Complexity Prediction (LCP) shared task. In our system, the sentence information comes from the RoBERTa model, and the word frequency information comes from the Tf-Idf algorithm. Use Inception block as a shared layer to learn sentence and word frequency information We described the implementation of our best system and discussed our methods and experiments in the task. The shared task is divided into two sub-tasks. The goal of the two sub-tasks is to predict the complexity of a predetermined word. The shared task is divided into two subtasks. The goal of the two subtasks is to predict the complexity of a predetermined word. The evaluation index of the task is the Pearson correlation coefficient. Our best performance system has Pearson correlation coefficients of 0.7434 and 0.8000 in the single-token subtask test set and the multi-token subtask test set, respectively.

End-to-end ASR to jointly predict transcriptions and linguistic annotations

We propose a Transformer-based sequence-to-sequence model for automatic speech recognition (ASR) capable of simultaneously transcribing and annotating audio with linguistic information such as phonemic transcripts or part-of-speech (POS) tags. Since linguistic information is important in natural language processing (NLP), the proposed ASR is especially useful for speech interface applications, including spoken dialogue systems and speech translation, which combine ASR and NLP. To produce linguistic annotations, we train the ASR system using modified training targets: each grapheme or multi-grapheme unit in the target transcript is followed by an aligned phoneme sequence and/or POS tag. Since our method has access to the underlying audio data, we can estimate linguistic annotations more accurately than pipeline approaches in which NLP-based methods are applied to a hypothesized ASR transcript. Experimental results on Japanese and English datasets show that the proposed ASR system is capable of simultaneously producing high-quality transcriptions and linguistic annotations.

Using Noisy Self-Reports to Predict Twitter User Demographics

Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.

The Predict of optimal injection rate to prevent the gas hydrates formation in the natural gas transportation from the pipelines of Damascus North fields (Al Brij-1, Qara-1, Der Atih-1)

This paper includes was clarified the objectives of this study and the problems caused by gas hydrate formation problem in the studied gas transportation lines, and submit of the studied fields, contains all the required data in this research.

Proposed method to analysis and predict time series with a regular cyclical factor (Olive production in Syria)

Olive cultivation is witnessing a remarkable development in the Syrian Arab Republic in terms of area cultivated and the number of trees and the quality of cultivated varieties of olives. The result of this evolution Syria occupied first place in the Arab and olive production ranked fifth in the world after Spain, Italy, Greece and Turkey, by passing Tunisia, which occupies the first place was an Arab. Olive production as dependent variable is affected by much of the factors which can be considered independent: The number of trees and age of tree and tree type and amount of rainfall, temperature and location of olive cultivation…… However, the most important influence on the production of olive is a phenomenon alternate fruit bearing in fruit trees.This lead to the affected by a time series of olive production, in addition to the regular periodic of other factors, the general trend and random factors. This study aims to provide a new method for modeling and analysis of time series with a regular cyclical factors and its application to olive production in the Syrian Arab Republic. The study to develop an econometric model based on the proposed new method can be used to predict the production of olive in Syria, and predict the size of production until 2016. ...

Employment State Space model to predict the Wheat production in Syria

This study aims at develop the model to predict the production of wheat in Syria which is based on the model State Space. The study to develop a model and predict the production of wheat in the Syrian Arab Republic until 2016. As it turns out to compare the model State Space with the models used in the analysis of time series priority model State Space for modeling wheat production in Syria. ...