Miracle-dz/UniFAR-FTUs huggingface dataset:
Automation of Template Formation to Identify the Structure of Natural Language Documents

In the task of text borrowings and plagiarism detection, it is important to take into account the structure of the document. This allows getting a more accurate assessment of the text and reducing the volume of material for comparison. Using a template allows identifying the structure of the document. The paper presents a constructive synthesizing model for automating the construction of a structural template of a document. Possible implementations of some algorithms by means of programming in C# are considered. Their comparative assessment is performed. Possible modification of the template is presented to increase the importance of keywords and simplify the xml-tree, which is a template.
Machine Learning for Arabic Text To Speech Synthesis: a Tacotron Approach

A Text-to-Speech (TTS) method converts a language’s standard utterance to speech. In contrast, other systems provide symbolic linguistic representation, such as phoneme transcriptions into speech. Synthesized speech is generated by bringing together units of recorded speech that are saved in a repository. The speech units, which come in diphones or tablets, differ in size depending on the system. Even though this provides the most comprehensive performance range, it can also require more clarity. The storage of whole words or sentences in explicit usage areas is the foundation of high-quality production. There have been pre-trained end-to-end TTS systems applied to other languages. In this work, we study the use of an end-to-end Tacotron when applied Arabic text. Arabic is morphologically rich and vague, and it has many dialects that vary significantly from one another. There are no official spelling norms in the dialects, and uncorrected standard Arabic includes numerous spelling and grammar errors and hence is a very challenging problem.
Linguistic Methods in Social Media Marketing

This article deals with relevant linguistic methods that are commonly used at social media marketing. Nowadays the process of digitalization influences all spheres from business to education. Besides, it is extremely important that social media allows to share information with thousands of people in no time. That’s why studying the services used for social media marketing as well as applications of linguistic methods in the sphere becomes the topic of current interest.
Usage of Sentiment Analysis to Tracking Public Opinion

This study reveals the problems of analysis of public opinion. The description, use cases and efficiency estimation of software for sentiment analysis of public opinion have been presented. The relevance of the problem of sentiment analysis as one of the important tasks of computational linguistics is substantiated. An overview of the existing classical methods of sentiment analysis and some software applications that solve this problem is conducted. The business process model of analysis of public opinion is presented in the form of BPMN-diagram. The principles of operation of the developed classifier that used the lexicon-based method are described. The model of determining the tonality of the news in the form of an activity diagram was considered. The efficiency estimation of the developed lexicon-based classifier has been evaluated based on standard metrics (Recall, Precision). The obtained results have been compared with values of similar metrics based on the using of the Naïve Bayesian Classifier and Recurrent Neural Network Cmeans Classifier. The calculation of the Recall and Precision has been conducted for two cases: the sentiment analyzer used a dictionary of affective words without slang words and with slang words. Conducted numerical studies show increasing of the efficiency of the sentiment analyzer by 5-6% in the case of using a dictionary with slang words.
Parameter exploration improves the accuracy of long-read genome assembly

Long-molecule sequencing is now routinely applied to generate high-quality reference genome assemblies. However, datasets differ in repeat composition, heterozygosity, read lengths and error profiles. The assembly parameters that provide the best results could thus differ across datasets. By integrating four complementary and biologically meaningful metrics, we show that simple fine-tuning of assembly parameters can substantially improve the quality of long-read genome assemblies. In particular, modifying estimates of sequencing error rates improves some metrics more than two-fold. We provide a flexible software, CompareGenomeQualities, that automates comparisons of assembly qualities for researchers wanting a straightforward mechanism for choosing among multiple assemblies.
Automatic Delineation of Urban Growth Boundaries Based on Topographic Data Using Germany as a Case Study

Urban Growth Boundary (UGB) is a growth management policy that designates specific areas where growth should be concentrated in order to avoid urban sprawl. The objective of such a boundary is to protect agricultural land, open spaces and the natural environment, as well as to use existing infrastructure and public services more efficiently. Due to the inherent heterogeneity and complexity of settlements, UGBs in Germany are currently created manually by experts. Therefore, every dataset is linked to a specific area, investigation period and dedicated use. Clearly, up-to-date, homogeneous, meaningful and cost-efficient delineations created automatically are needed to avoid this reliance on manually or semi-automatically generated delineations. Here, we present an aggregative method to produce UGBs using building footprints and generally available topographic data as inputs. It was applied to study areas in Frankfurt/Main, the Hanover region and rural Brandenburg while taking full account of Germany’s planning and legal framework for spatial development. Our method is able to compensate for most of the weaknesses of available UGB data and to significantly raise the accuracy of UGBs in Germany. Therefore, it represents a valuable tool for generating basic data for future studies. Application elsewhere is also conceivable by regionalising the employed parameters.
Hotspot Detection and Spatiotemporal Evolution of Catering Service Grade in Mountainous Cities from the Perspective of Geo-Information Tupu

Catering services are an essential part of urban life. The spatial structure and evolution of catering services can reflect the characteristics of an urban structure to a certain extent. In this study, we selected the main urban area of Chongqing, a typical mountainous city, as the research area. According to data sources for 200,000 catering POI data points in 2015 and 2020, we extracted the hotspots according to catering service grade based on kernel density. We quantitatively analyzed the spatiotemporal structure of catering services in the mountainous city. In addition, we used digital field hierarchical structure Tupu and generalized symmetric structure Tupu to identify the spatial morphology and evolution characteristics to enhance the understanding of geoscience trends. The results showed that (1) the distribution of catering services was statically consistent with the “multi-center group” distribution of the mountainous city and dynamically similar to the “sprawling leap” development of the mountainous city where it developed from independent points to cross mountains and rivers. Moreover, we found that there was a tendency of adhering development between groups. (2) From the perspective of symmetrical distribution, the symmetrical distribution of the catering industry reflected a certain generalized symmetrical structure with mountains and rivers in the mountainous city. Furthermore, the city tended to develop symmetrically along the topography, thus forming the symmetry of economic geography.
Semantic techniques for discovering architectural patterns in building information models

Architectural patterns, a concept devised by the Viennese architect Christopher Alexander, have inspired the world of patterns, especially in software engineering. Two objectives have been addressed in this work: to realise a semantic representation of Alexander's patterns by developing an OWL ontology, and to devise a rule-based system for discovering the patterns into a concrete building model, represented in IFC format, one of the pillars of the BIM - an approach now widely spread in the architectural and civil engineering design world. This paper shows how semantic and logical inference rules have been applied to discover Alexander's pattern on a generic IFC model of a building.
Using Part-of-Speech Tagging for Building Networks of Terms in Legal Sphere

This paper considers an important formalization problem and building the terminological ontology of problem subject domains based on content-related text data. As an ontological model, we propose to use a linguistic network model of text representation, the so-called network of key terms. In this network, the nodes are keywords and phrases that appear in the text corpus, and the links between them are semantic-syntactic links between these terms in the text. Using systems of aggregation of thematic information flows from freely available information resources distributed in global computer networks, input sets of text data were prepared. In particular, this paper solves the important and urgent problem of computerized processing of legal information. The task of computerized processing of natural language texts lies at the intersection between linguistic theory and mathematical sciences. Therefore, a wider natural language processing based on Part-of-Speech tagging was used for extraction of the key terms. After the extraction, a statistical weighing of the formed words and phrases was performed. The horizontal visibility graph algorithm was used to build undirected links between key terms. This paper also considers a new method that allows determining the direction of links between terms and weighting these links in the undirected network of words and phrases. This method takes into account the parts of speech tagging and also obeys the principle of inclusion of a word or phrase in their corresponding extended phrases with more words. The approbation of the proposed method was carried out on the example of a freely available legal document «Universal Declaration of Human Rights». After extracting the key terms from this legal document and determining the direction and weight of links between words or phrases using the proposed methods the directed weighted network of terms was built. The considered in this work method for building the terminological networks can be used, in particular, in systems for automatic text structuring and summarizing of legal information, or systems for detecting the duplicates and contradictions in normative legal documents. It will promote the formation and improvement of conceptual and terminological apparatus in the legal sphere and harmonize national and international law
Automatic detection of cyberbullying on social media platforms

The presence of cyberbullying on the Internet has grown alarmingly in recent years. Teenagers and children are the most affected by this phenomenon that is often the cause of higher suicide rates and social isolation. The detection and prevention of cyberbullying depends firstly on its correct understanding and secondly on the correct selection of a classification model trained on features that have a high discrimination factor between cyberbullying and non-cyberbullying. In this paper, we aim to create an automatic detection model for cyberbullying posts that is not biased towards a specific social media platform or a certain type of bullying. We describe the method we used for selecting the best features for two different classifiers trained on datasets collected from Twitter and Formspring. Next, we explain how we use the predictions made by these classifiers for labelling a new dataset collected by us from Twitter. The results of the automatic classification of the dataset have been compared to the manual classification of a sample of data from it, resulting in a rate of agreement larger than 50% between automatic detection and human annotation.
Female-Specific Association of Plasma N-Terminal Pro-Brain Natriuretic Peptide With Organ Dysfunction and Prognosis in Sepsis: A Retrospective Study

Supplemental Digital Content is available in the text. Objectives: The plasma level of N-terminal pro-brain natriuretic peptide is regulated by sex hormones. It has been controversial whether N-terminal pro-brain natriuretic peptide is a prognosis marker for sepsis. The aim of this study is to examine the sex-dependent association of plasma N-terminal pro-brain natriuretic peptide with organ dysfunction and mortality of sepsis patients. Design: In this retrospective study, the association between plasma N-terminal pro-brain natriuretic peptide concentration on the day of sepsis diagnosis and the degree of organ dysfunction, occurrence of septic shock, or 30-day mortality in both male and female patients was analyzed. Setting: This study was conducted in the Sepsis Laboratory at the Huaihe Hospital of Henan University in China. Patients: Diagnoses of sepsis, and septic shock, were based on the recently revised criteria (Sepsis 3.0). All sepsis patients (517) hospitalized in the respiratory ICU of the Huaihe Hospital from June 2016 to December 2019 were enrolled in this study. Interventions: None. Measurements and Main Results: No significant difference was found in the age, occurrence rate of septic shock, 30-day mortality, or degree of organ dysfunction between male and female patients. Median concentration of plasma N-terminal pro-brain Natriuretic peptide was higher by 93.48% in female than male patients. A significant association was found between N-terminal pro-brain natriuretic peptide and septic shock or 30-day mortality in female, but not in male patients of community- or hospital-acquired sepsis. N-terminal pro-brain natriuretic peptide levels correlated to functional deficiencies of the cardiac and nervous systems, only in female patients. Conclusions: The plasma N-terminal pro-brain natriuretic peptide level is a female-specific prognosis indicator of septic shock and mortality.
Using Natural Language Processing for Supply Chain Mapping: a Systematic Review of Current Approaches

Purpose: The COVID-19 crisis has shown that the global supply chains are not as resilient as expected. First investigations indicate that the main contributing factor is a lack of visibility into the supply chain's lower tiers. Simultaneously, the willingness to share data in the supply chain is low as companies mainly consider their data as proprietary. However, large amounts of data are available on the internet. The amount of this data is steadily increasing;however, the problem remains, that this data is hardly structured. Therefore, this paper investigates current approaches to use this data for supply chain transparency and derives further research directions. Methodology: The paper uses a systematic review of the literature followed by content analysis. The research process further follows established frameworks in the literature and is subdivided into distinct stages. Findings: Descriptive and clustering results show a fragmented research field, where current approaches disconnect from prior research. We classify the methods using a simple taxonomy and show developments from rule-based to supervised techniques and horizontal to vertical mining approaches. The techniques with rule-based-matching procedures mainly suffer from low recall. The current approaches do not satisfy yet essential requirements on supply chain mapping based on natural language. Originality: To the best of the authors' knowledge, no prior research has been attempted to review textual data usage for supply chain mapping. Therefore, this paper's main contribution is to fill this gap and add further evidence to the use of data-driven supply chain management methods. © 2021 CEUR-WS. All rights reserved.
A GIS Assessment of the Suitability of Tilapia and Clarias Pond Farming in Tanzania

Aquaculture production in Tanzania has increased in recent years, responding to an increased demand for fish, but the scale and productivity of smallholder aquaculture remains below the level needed to support significant sector growth in Tanzania. This study assesses, through geospatial analyses, the suitability for freshwater pond farming of Oreochromis niloticus and Clarias gariepinus in Tanzania, by assessing the geographical distribution of seven criteria (water availability, water temperature, soil texture, terrain slope, availability of farm inputs, potential farm-gate sales, and access to local markets) identified as important for fish pond farming. The criteria were developed and standardized from 15 sub-criteria, which were classified into a four-level suitability scale based on physical scores. The individual weights of the different criteria in the overall GIS suitability assessment were determined through a multi-criteria evaluation. The final results were validated and compared through field observations, interviews with 89 rural and 11 urban aquaculture farmers, and a questionnaire survey with 16 regional fisheries officers. Our results indicate that there is a good potential for aquaculture in Tanzania. Almost 60% of Tanzania is assessed as being suitable and 40% as moderately suitable for small-scale subsistence pond farming, which is the dominating fish farming practice currently. The corresponding figures for medium-scale commercial farming, which many regions expect to be the dominating farming method within ten-years, were 52% and 47% respectively. The availability of water was the most limiting factor for fish pond farming, which was confirmed by both farmers and regional fisheries officers, and assessed as being “suitable” in only 28% of the country. The availability of farm-gate sales and local markets were “moderate suitable” to “suitable” and were seen as a constraint for commercial farms in rural areas. The availability of farm inputs (agriculture waste and manure) was overall good (26% very suitable and 32% suitable), but high-quality fish feed was seen as a constraint to aquaculture development, both by farmers and regional fisheries officers. Soil, terrain, and water temperature conditions were assessed as good, especially at low altitudes and in regions close to the sea and south of Lake Victoria.
Spontaneous Polymorphic Nucleation of d-Mannitol in Aqueous Solution Monitored with Raman Spectroscopy and FBRM

The spontaneous nucleation of d-mannitol polymorphs at different initial concentrations in an aqueous solution was investigated. Two in situ process analytical technology (PAT) tools, Raman spectroscopy and focused beam reflectance measurement (FBRM), were combined to monitor the process, allowing instantaneous detection of polymorphs after nucleation. It was found that the stable β form of mannitol was favored at low initial concentrations, whereas the metastable α form nucleated at high concentrations and the metastable δ polymorph crystallized when the concentration was in the medium level. To help understand this phenomenon, the thermodynamic and kinetic characteristics of different polymorphs were analyzed. The solubility of the two metastable forms was determined by an innovative method with the aid of Raman spectroscopy, whereas the interfacial energy between polymorphs and the bulk solution were obtained from the induction time measurements. Consequently, the critical excess free energy and other .
Spectral feature characterization and nitrogen content prediction in soils with different particle sizes and moisture contents.

The objective of this research is to analyze the influences of light source incidence angle, fiber height, moisture content, and particle size on loamy mixed soil spectra. Nitrogen (N) content calibration and cross-validation models at different moisture contents and particle sizes were obtained using partial least squares (PLS) analysis. Spectral data were collected using a spectrophotometer. Fiber height of 100 mm and light source angle at 45 ° were chosen to obtain the sharpest spectra without apparent scattering effect. The results show that moisture content and particle size strongly influenced the absorbance of the spectra, and a better N prediction model was obtained when the particle sizes were in the ranges of 0.5
Corrigendum to “The past, present, and future viscous heat dissipation available for Greenland subglacial conduit formation” published in The Cryosphere, 11, 303–317, 2017

Basal hydrology of the Greenland Ice Sheet (GIS) influences its dynamics and mass balance through basal lubrication and ice–bed decoupling or efficient water removal and ice–bed coupling. Variations in subglacial water pressure through the seasonal evolution of the subglacial hydrological system help control ice velocity. Near the ice sheet margin, large basal conduits are melted by the viscous heat dissipation (VHD) from surface runoff routed to the bed. These conduits may lead to efficient drainage systems that lower subglacial water pressure, increase basal effective stress, and reduce ice velocity. In this study we quantify the energy available for VHD historically at present and under future climate scenarios. At present, 345 km3 of annual runoff delivers 66 GW to the base of the ice sheet per year. These values are already ∼ 50 % more than the historical 1960–1999 value of 46 GW. By 2100 under IPCC AR5 RCP8.5 (RCP4.5) scenarios, 1278 (524) km3 of runoff may deliver 310 (110) GW to the ice sheet base. Hence, the ice sheet may experience a 5-to-7-fold increase in VHD in the near future which will enhance opening of subglacial conduits near the margin and will warm basal ice in the interior. The other significant basal heat source is geothermal heat flux (GHF), which has an estimated value of 36 GW within the present-day VHD area. With increasing surface meltwater penetration to the bed the basal heat budget in the active basal hydrology zone of the GIS will be increasingly dominated by VHD and relatively less sensitive to GHF, which may result in spatial changes in the ice flow field and in its seasonal variability.
Comparison Between Hydroxypropyl b-Cyclodextrin And Polyvinylpyrrolidone As Carriers For Carbamazepine Solid Despersions

Solid dispersions of carbamazepine were prepared in order to improve its aqueous solubility by simple kneading procedure. Hydroxypropyl b-cyclodextrin and polyvinyl pyrrolidone were used as carriers. Solid dispersions were stored at room temperature in a dessicator over anhydrous calcium chloride for one month. The changes in dispersions during storage were assessed by X-ray diffraction studies and dissolution studies. It was concluded from this study that hydroxypropyl b-cyclodextrin, as a carrier, reduced crystalline nature of carbamazepine in solid dispersions to a greater extent than polyvinyl pyrrolidone as a carrier and resulted in better and predictable dissolution profiles.
Knowledge graph construction and application in geosciences: A review

Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG construction and implementation in geosciences. It consists of four major parts: 1) concepts relevant to KG and approaches for KG construction, 2) KG application in data collection, curation, and service, 3) KG application in data analysis, and 4) challenges and trends of geo-science KG creation and application in the near future. For each of the first three parts, a list of concepts, exemplar studies, and best practices are summarized. Those summaries are synthesized together in the challenge and trend analyses. As artificial intelligence and data science are thriving in geosciences, we hope this review of geoscience KGs can be of value to practitioners in data-intensive geoscience studies.