Automatic Generation of UML Diagrams From Product Requirements Described by Natural Language

2009 ◽  
Author(s):  
Lei Chen ◽  
Yong Zeng
Keyword(s):  
Natural Language ◽  
Semantic Information ◽  
Automatic Generation ◽  
Proof Of Concept ◽  
Requirements Modeling ◽  
Novel Approach ◽  
Graphic Language ◽  
Uml Diagrams ◽  
Future Work

In this paper, a novel approach is proposed to transform a requirement text described by natural language into two UML diagrams — use case and class diagrams. The transformation consists of two steps: from natural language to an intermediate graphic language called recursive object model (ROM) and from ROM to UML. The ROM diagram corresponding to a text includes the main semantic information implied in the text by modeling the relations between words in a text. Based on the semantics in the ROM diagram, a set of generation rules are proposed to generate UML diagrams from a ROM diagram. A software prototype R2U is presented as a proof of concept for this approach. A case study shows that the proposed approach is feasible. The proposed approach can be applied to requirements modeling in various engineering fields such as software engineering, automotive engineering, and aerospace engineering. The future work is pointed out at the end of this paper.

scholarly journals Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

2020 ◽  
Vol 10 (9) ◽  
pp. 3116 ◽  
Author(s):  
Raymond Moodley ◽  
Francisco Chiclana ◽  
Jenny Carter ◽  
Fabio Caraffini
Keyword(s):  
School Leadership ◽  
Educational Administration ◽  
School Attendance ◽  
Novel Approach ◽  
Negative Impacts ◽  
Using Data ◽  
The Uk ◽  
Leadership Research ◽  
Future Work

Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools.

scholarly journals Using Natural Language Preprocessing Architecture (NLPA) for Big Data Text Sources

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
María Novo-Lourés ◽  
Reyes Pavón ◽  
Rosalía Laza ◽  
David Ruano-Ordas ◽  
Jose R. Méndez
Keyword(s):  
Big Data ◽  
Natural Language ◽  
Semantic Information ◽  
Application Programming Interface ◽  
Unstructured Data ◽  
Application Programming ◽  
Text Preprocessing ◽  
Programming Interface ◽  
Proper Analysis

During the last years, big data analysis has become a popular means of taking advantage of multiple (initially valueless) sources to find relevant knowledge about real domains. However, a large number of big data sources provide textual unstructured data. A proper analysis requires tools able to adequately combine big data and text-analysing techniques. Keeping this in mind, we combined a pipelining framework (BDP4J (Big Data Pipelining For Java)) with the implementation of a set of text preprocessing techniques in order to create NLPA (Natural Language Preprocessing Architecture), an extendable open-source plugin implementing preprocessing steps that can be easily combined to create a pipeline. Additionally, NLPA incorporates the possibility of generating datasets using either a classical token-based representation of data or newer synset-based datasets that would be further processed using semantic information (i.e., using ontologies). This work presents a case study of NLPA operation covering the transformation of raw heterogeneous big data into different dataset representations (synsets and tokens) and using the Weka application programming interface (API) to launch two well-known classifiers.

scholarly journals A Model Workflow for GeoDeepDive: Locating Pliocene and Pleistocene Ice-Rafted Debris

2021 ◽  
Author(s):  
Simon Goring ◽  
Jeremiah Marsicek ◽  
Shan Ye ◽  
John Williams ◽  
Stephen Meyers ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
R Package ◽  
Regular Expressions ◽  
Learning Technology ◽  
Proof Of Concept ◽  
Spatiotemporal Information ◽  
Age Estimates

Machine learning technology promises a more efficient and scalable approach to locating and aggregating data and information from the burgeoning scientific literature. Realizing this promise requires provision of applications, data resources, and the documentation of analytic workflows. GeoDeepDive provides a digital library comprising over 13 million peer-reviewed documents and the computing infrastructure upon which to build and deploy search and text-extraction capabilities using regular expressions and natural language processing. Here we present a model GeoDeepDive workflow and accompanying R package to show how GeoDeepDive can be employed to extract spatiotemporal information about site-level records in the geoscientific literature. We apply these capabilities to a proof-of-concept subset of papers in a case study to generate a preliminary distribution of ice-rafted debris (IRD) records in both space and time. We use regular expressions and natural language-processing utilities to extract and plot reliable latitude-longitude pairs from publications containing IRD, and also extract age estimates from those publications. This workflow and R package provides researchers from the geosciences and allied disciplines a general set of tools for querying spatiotemporal information from GeoDeepDive for their own science questions.

scholarly journals Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 219
Author(s):  
Margarita Razgon ◽  
Alireza Mousavi
Keyword(s):  
Empirical Investigation ◽  
Rule Learning ◽  
Predictive Maintenance ◽  
Proof Of Concept ◽  
Compression Moulding ◽  
Industrial Case Study ◽  
Rule Based ◽  
Beverage Industry ◽  
Novel Approach

In this paper we propose a novel approach of rule learning called Relaxed Separate-and- Conquer (RSC): a modification of the standard Separate-and-Conquer (SeCo) methodology that does not require elimination of covered rows. This method can be seen as a generalization of the methods of SeCo and weighted covering that does not suffer from fragmentation. We present an empirical investigation of the proposed RSC approach in the area of Predictive Maintenance (PdM) of complex manufacturing machines, to predict forthcoming failures of these machines. In particular, we use for experiments a real industrial case study of a Continuous Compression Moulding (CCM) machine which manufactures the plastic bottle closure (caps) in the beverage industry. We compare the RSC approach with a Decision Tree (DT) based and SeCo algorithms and demonstrate that RSC significantly outperforms both DT based and SeCo rule learners. We conclude that the proposed RSC approach is promising for PdM guided by rule learning.

scholarly journals Improving Readability of Online Privacy Policies through DOOP: A Domain Ontology for Online Privacy

Digital ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 198-215
Author(s):  
Dhiren A. Audich ◽  
Rozita Dara ◽  
Blair Nonnecke
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Ontology ◽  
Online Privacy ◽  
Privacy Policy ◽  
Proof Of Concept ◽  
Privacy Policies ◽  
Privacy Concerns ◽  
Novel Approach

Privacy policies play an important part in informing users about their privacy concerns by operating as memorandums of understanding (MOUs) between them and online services providers. Research suggests that these policies are infrequently read because they are often lengthy, written in jargon, and incomplete, making them difficult for most users to understand. Users are more likely to read short excerpts of privacy policies if they pertain directly to their concern. In this paper, a novel approach and a proof-of-concept tool are proposed that reduces the amount of privacy policy text a user has to read. It does so using a domain ontology and natural language processing (NLP) to identify key areas of the policies that users should read to address their concerns and take appropriate action. Using the ontology to locate key parts of privacy policies, average reading times were substantially reduced from 29 to 32 min to 45 s.

A Novel Approach to the Automatic Designation of Predefined Census Enumeration Areas and Population Sampling Frames: A Case Study in Somalia

2019 ◽  
Author(s):  
Sarchil Qader ◽  
Veronique Lefebvre ◽  
Amy Ninneman ◽  
Kristen Himelein ◽  
Utz Pape ◽  
...  
Keyword(s):  
Population Sampling ◽  
Census Enumeration ◽  
Novel Approach

A Novel Approach of Plasma Nitrocarburizing Using a Solid Carbon Active Screen – a Proof of Concept

2017 ◽  
Vol 72 (5) ◽  
pp. 254-259 ◽  
Author(s):  
I. Burlacov ◽  
S. Hamann ◽  
H.-J. Spies ◽  
A. Dalke ◽  
J. Röpcke ◽  
...  
Keyword(s):  
Solid Carbon ◽  
Proof Of Concept ◽  
Novel Approach ◽  
Plasma Nitrocarburizing ◽  
Active Screen

scholarly journals Use of Oxidative Stress Responses to Determine the Efficacy of Inactivation Treatments on Cryptosporidium Oocysts

2021 ◽  
Vol 9 (7) ◽  
pp. 1463
Author(s):  
Tamirat Tefera Temesgen ◽  
Kristoffer Relling Tysnes ◽  
Lucy Jane Robertson
Keyword(s):  
Oxidative Stress ◽  
Stress Responses ◽  
Target Genes ◽  
Cryptosporidium Oocyst ◽  
Drinking Water Treatment ◽  
Proof Of Concept ◽  
Environmental Matrices ◽  
Oocyst Wall ◽  
Novel Approach ◽  
Cryptosporidium Oocysts

Cryptosporidium oocysts are known for being very robust, and their prolonged survival in the environment has resulted in outbreaks of cryptosporidiosis associated with the consumption of contaminated water or food. Although inactivation methods used for drinking water treatment, such as UV irradiation, can inactivate Cryptosporidium oocysts, they are not necessarily suitable for use with other environmental matrices, such as food. In order to identify alternative ways to inactivate Cryptosporidium oocysts, improved methods for viability assessment are needed. Here we describe a proof of concept for a novel approach for determining how effective inactivation treatments are at killing pathogens, such as the parasite Cryptosporidium. RNA sequencing was used to identify potential up-regulated target genes induced by oxidative stress, and a reverse transcription quantitative PCR (RT-qPCR) protocol was developed to assess their up-regulation following exposure to different induction treatments. Accordingly, RT-qPCR protocols targeting thioredoxin and Cryptosporidium oocyst wall protein 7 (COWP7) genes were evaluated on mixtures of viable and inactivated oocysts, and on oocysts subjected to various potential inactivation treatments such as freezing and chlorination. The results from the present proof-of-concept experiments indicate that this could be a useful tool in efforts towards assessing potential technologies for inactivating Cryptosporidium in different environmental matrices. Furthermore, this approach could also be used for similar investigations with other pathogens.

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