Transparent Aspect-Level Sentiment Analysis Based on Dependency Syntax Analysis and Its Application on COVID-19

Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurant14, Laptop, Restaurant16, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on “government” and “lockdown” of 1,658,250 tweets about “#COVID-19” that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users’ positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users’ emotions over time based on the tweets and on our models.

SGTools: a suite of tools for processing and analyzing large data sets from in situ X-ray scattering experiments

In situ synchrotron small-angle X-ray scattering (SAXS) is a powerful tool for studying dynamic processes during material preparation and application. The processing and analysis of large data sets generated from in situ X-ray scattering experiments are often tedious and time consuming. However, data processing software for in situ experiments is relatively rare, especially for grazing-incidence small-angle X-ray scattering (GISAXS). This article presents an open-source software suite (SGTools) to perform data processing and analysis for SAXS and GISAXS experiments. The processing modules in this software include (i) raw data calibration and background correction; (ii) data reduction by multiple methods; (iii) animation generation and intensity mapping for in situ X-ray scattering experiments; and (iv) further data analysis for the sample with an order degree and interface correlation. This article provides the main features and framework of SGTools. The workflow of the software is also elucidated to allow users to develop new features. Three examples are demonstrated to illustrate the use of SGTools for dealing with SAXS and GISAXS data. Finally, the limitations and future features of the software are also discussed.

Evaluation of an Active LF Tracking System and Data Processing Methods for Livestock Precision Farming in the Poultry Sector

Tracking technologies offer a way to monitor movement of many individuals over long time periods with minimal disturbances and could become a helpful tool for a variety of uses in animal agriculture, including health monitoring or selection of breeding traits that benefit welfare within intensive cage-free poultry farming. Herein, we present an active, low-frequency tracking system that distinguishes between five predefined zones within a commercial aviary. We aimed to evaluate both the processed and unprocessed datasets against a “ground truth” based on video observations. The two data processing methods aimed to filter false registrations, one with a simple deterministic approach and one with a tree-based classifier. We found the unprocessed data accurately determined birds’ presence/absence in each zone with an accuracy of 99% but overestimated the number of transitions taken by birds per zone, explaining only 23% of the actual variation. However, the two processed datasets were found to be suitable to monitor the number of transitions per individual, accounting for 91% and 99% of the actual variation, respectively. To further evaluate the tracking system, we estimated the error rate of registrations (by applying the classifier) in relation to three factors, which suggested a higher number of false registrations towards specific areas, periods with reduced humidity, and periods with reduced temperature. We concluded that the presented tracking system is well suited for commercial aviaries to measure individuals’ transitions and individuals’ presence/absence in predefined zones. Nonetheless, under these settings, data processing remains a necessary step in obtaining reliable data. For future work, we recommend the use of automatic calibration to improve the system’s performance and to envision finer movements.

On Learning Interpreted Languages with Recurrent Models

Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.

OMI Collection 4: establishing a 17-year long series of detrended L1b data

The Ozone Monitoring Instrument (OMI) was launched on July 15, 2004, with an expected mission lifetime of 5 years. After more than 17 years in orbit the instrument is still functioning satisfactorily, and in principle can continue doing so for many years more. In order to continue the datasets acquired by OMI and the Microwave Limb Sounder the mission was extended up to at least 2023. Actions have been taken to ensure the proper functioning of the OMI instrument operations, the data processing, and the calibration monitoring system until the eventual end of the mission. For the data processing a new level 0 to level 1b data processor was built based on the recent developments for Tropospheric Monitoring Instrument (TROPOMI). With corrections for the degradation of the instrument now included, it is feasible to generate a new data collection to supersede the current collection 3 data products. This paper describes the differences between the collection 3 and collection 4 data. It will be shown that the collection 4 L1b data is a clear improvement with respect to the previous collections. By correcting for the gentle optical and electronic aging that has occurred over the past 17 years, OMI's ability to make trend-quality ozone measurements has further improved.

USING BLOCK PSEUDOREVERSION IN SEQUENTIAL DATA PROCESSING

Предложен алгоритм последовательной обработки данных на основе блочного псевдообращения матриц полного столбцового ранга. Показывается, что формула блочного псевдообращения, лежащая в основе алгоритма, является обобщением одного шага алгоритма Гревиля псевдообращения в невырожденном случае и потому может быть использована для обобщения метода нахождения весов нейросетевой функции LSHDI (linear solutions to higher dimensional interlayer networks), основанного на алгоритме Гревиля. Представленный алгоритм на каждом этапе использует найденные на предыдущих этапах псевдообратные к блокам матрицы и, следовательно, позволяет сократить вычисления не только за счет работы с матрицами меньшего размера, но и за счет повторного использования уже найденной информации. Приводятся примеры применения алгоритма для восстановления искаженных работой фильтра (шума) одномерных сигналов и двумерных сигналов (изображений). Рассматриваются случаи, когда фильтр является статическим, но на практике встречаются ситуации, когда матрица фильтра меняется с течением времени. Описанный алгоритм позволяет непосредственно в процессе получения входного сигнала перестраивать псевдообратную матрицу с учетом изменения одного или нескольких блоков матрицы фильтра, и потому алгоритм может быть использован и в случае зависящих от времени параметров фильтра (шума). Кроме того, как показывают вычислительные эксперименты, формула блочного псевдообращения, на которой основан описываемый алгоритм, хорошо работает и в случае плохо обусловленных матриц, что часто встречается на практике The paper proposes an algorithm for sequential data processing based on block pseudoinverse of full column rank matrixes. It is shown that the block pseudoinverse formula underlying the algorithm is a generalization of one step of the Greville’s pseudoinverse algorithm in the nonsingular case and can also be used as a generalization for finding weights of neural network function in the LSHDI algorithm (linear solutions to higher dimensional interlayer networks). The presented algorithm uses the pseudoinversed matrixes found at each step, and therefore allows one to reduce the computations not only by working with matrixes of smaller size but also by reusing the already found information. Examples of application of the algorithm for signal and image reconstruction are given. The article deals with cases where noise is static but the algorithm is similarly well suited to dynamically changing noises, allowing one to process input data in blocks on the fly, depending on changes. The block pseudoreverse formula, on which the described algorithm is based, works well in the case of ill-conditioned matrixes, which is often encountered in practice

MICROSCOPE Mission scenario, ground segment and data processing

Testing the Weak Equivalence Principle (WEP) to a precision of 10-15 requires a quantity of data that give enough confidence on the final result: ideally, the longer the measurement the better the rejection of the statistical noise. The science sessions had a duration of 120 orbits maximum and were regularly repeated and spaced out to accommodate operational constraints but also in order to repeat the experiment in different conditions and to allow time to calibrate the instrument. Several science sessions were performed over the 2.5 year duration of the experiment. This paper aims to describe how the data have been produced on the basis of a mission scenario and a data flow process, driven by a tradeoff between the science objectives and the operational constraints. The mission was led by the Centre National d’Etudes Spatiales (CNES) which provided the satellite, the launch and the ground operations. The ground segment was distributed between CNES and Office National d’Etudes et de Recherches Aerospatiales (ONERA). CNES provided the raw data through the Centre d’Expertise de Compensation de Trainee (CECT: Drag-free expertise centre). The science was led by the Observatoire de la Coote d’Azur (OCA) and ONERA was in charge of the data process. The latter also provided the instrument and the Science Mission Centre of MICROSCOPE (CMSM).

Influence of AI and Machine Learning in Insurance Sector

The Aim of this research is to identify influence, usage, and the benefits of AI (Artificial Intelligence) and ML (Machine learning) using big data analytics in Insurance sector. Insurance sector is the most volatile industry since multiple natural influences like Brexit, pandemic, covid 19, Climate changes, Volcano interruptions. This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. Empirical quantitative research method is used to verify the model with the sample of UK insurance sector analysis. This research will conclude some practical insights for Insurance companies using AI, ML, Big data processing and Cloud computing for the better client satisfaction, predictive analysis, and trending.