Language Tools Research Articles

The state-of-practice in requirements specification: an extended interview study at 12 companies

Requirements specification is a core activity in the requirements engineering phase of a software development project. Researchers have contributed extensively to the field of requirements specification, but the extent to which their proposals have been adopted in practice remains unclear. We gathered evidence about the state of practice in requirements specification by focussing on the artefacts used in this activity, the application of templates or guidelines, how requirements are structured in the specification document, what tools practitioners use to specify requirements, and what challenges they face. We conducted an interview-based survey study involving 24 practitioners from 12 different Swedish IT companies. We recorded the interviews and analysed these recordings, primarily by using qualitative methods. Natural language constitutes the main specification artefact but is usually accompanied by some other type of instrument. Most requirements specifications use templates or guidelines, although they seldom follow any fixed standard. Requirements are always structured in the document according to the main functionalities of the system or to project areas or system parts. Different types of tools, including MS Office tools, are used, either individually or combined, in the compilation of requirements specifications. We also note that challenges related to the use of natural language (dealing with ambiguity, inconsistency, and incompleteness) are the most frequent challenges that practitioners face in the compilation of requirements specifications. These findings are contextualized in terms of demographic factors related to the individual interviewees, the organization they are affiliated with, and the project they selected to discuss during our interviews. A number of our findings have been previously reported in related studies. These findings show that, in spite of the large number of notations, models and tools proposed from academia for improving requirements specification, practitioners still mainly rely on plain natural language and general-purpose tool support. We expect more empirical studies in this area in order to better understand the reason of this low adoption of research results.

A comparison of ChatGPT-generated articles with human-written articles.

ChatGPT (Generative Pre-trained Transformer) is an artificial intelligence language tool developed by OpenAI that utilises machine learning algorithms to generate text that closely mimics human language. It has recently taken the internet by storm. There have been several concerns regarding the accuracy of documents it generates. This study compares the accuracy and quality of several ChatGPT-generated academic articles with those written by human authors. We performed a study to assess the accuracy of ChatGPT-generated radiology articles by comparing them with the published or written, and under review articles. These were independently analysed by two fellowship-trained musculoskeletal radiologists and graded from 1 to 5 (1 being bad and inaccurate to 5 being excellent and accurate). In total, 4 of the 5 articles written by ChatGPT were significantly inaccurate with fictitious references. One of the papers was well written, with a good introduction and discussion; however, all references were fictitious. ChatGPT is able to generate coherent research articles, which on initial review may closely resemble authentic articles published by academic researchers. However, all of the articles we assessed were factually inaccurate and had fictitious references. It is worth noting, however, that the articles generated may appear authentic to an untrained reader.

Design and Implementation of a Web-based Document Management System

One area that has seen rapid growth and differing perspectives from many developers in recent years is document management. This idea has advanced beyond some of the steps where developers have made it simple for anyone to access papers in a matter of seconds. It is impossible to overstate the importance of document management systems as a necessity in the workplace environment of an organization. Interviews, scenario creation using participants' and stakeholders' first-hand accounts, and examination of current procedures and structures were all used to collect data. The development approach followed a software development methodology called Object-Oriented Hypermedia Design Methodology. With the help of Unified Modeling Language (UML) tools, a web-based electronic document management system (WBEDMS) was created. Its database was created using MySQL, and the system was constructed using web technologies including XAMPP, HTML, and PHP Programming language. The results of the system evaluation showed a successful outcome. After using the system that was created, respondents' satisfaction with it was 96.60%. This shows that the document system was regarded as adequate and excellent enough to achieve or meet the specified requirement when users (secretaries and departmental personnel) used it. Result showed that the system developed yielded an accuracy of 95% and usability of 99.20%. The report came to the conclusion that a suggested electronic document management system would improve user happiness, boost productivity, and guarantee time and data efficiency. It follows that well-known document management systems undoubtedly assist in holding and managing a substantial portion of the knowledge assets, which include documents and other associated items, of Organizations.

Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation.

Mental health problems are one of the various ills that afflict the world's population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology's effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.

Comments: AI Language Tools Hit the Books . . . and Technical Content?

Are you among the millions of ChatGPT users/experimenters around the world? Or impatiently parked on the waitlist? OpenAI launched ChatGPT in late November 2022 with little fanfare. Within a week, the company reported 1 million users. Within the first month, it exceeded 57 million users. Since then, it has been updated several times and a UBS analyst estimated in February that the artificial intelligence (AI) language tool reached 100 million users. The website exceeded 13 million daily visitors as of January. On 1 February, OpenAI announced a ChatGPT Plus monthly subscription plan to give priority access to the chatbot. Clearly, AI language tools are fulfilling the needs and/or satisfying the curiosity of novices and more experienced AI users. Microsoft took notice and in January confirmed the extension of its partnership with OpenAI, an investment rumored to be $10 billion (not confirmed by Microsoft). Microsoft Azure will also continue as the exclusive cloud provider for the tool since OpenAI uses Azure to train its models. ChatGPT (generative pretrained transformer) is not the only AI tool available. Microsoft upgraded its Bing AI search engine, which is powered by an upgraded model of ChatGPT. ChatSonic, also built on top of ChatGPT, can access the internet. Jasper Chat, based on GPT 3.5 with OpenAI as its partner, was built for advertising and marketing businesses. Google Bard is an experimental conversational AI service powered by Google’s own next-generation language model. Character AI revolves around the concept of personas, trained with conversations in mind. Users choose from various personalities (e.g., Elon Musk, Tony Stark, Socrates, US President Biden, Kayne West, etc.) vs. interacting with a single AI chatbot. YouChat is built into a search engine and trained on a ChatGPT model. It holds conversations with full access to the internet. Caktus AI, not available as a free service, is aimed toward students and touted by the company as “the first-ever educational AI tool.” It helps with student content ranging from essays to writing paragraphs and extends to discussions, questions, and coding. There’s also a bot for programmers, GitHub Copilot X, which suggests and completes code and functions in real time. There are many more options and sure to be plenty more developed with specific user types in mind (e.g., technical specialties, business communications, conversational, special interests). All will have their own pros and cons. A retired systems engineer, formerly with General Dynamics Canada, Rob Miller, wrote a column in Canada’s National Observer about his experience using ChatGPT as a research tool for a story about carbon capture and storage—he gave a mixed review. As AI tools gain attention and provide assistance to a wide range of authors and researchers, SPE recently announced its policy for authors who use the tools to generate content for their papers. AI-generated content may be used within SPE publications, but under specific conditions. Open-source libraries, such as those used in ChatGPT and others, are not fail-safe solutions, as evidenced on 20 March when OpenAI took the chatbot offline due to a bug “which allowed some users to see titles from another active user’s chat history,” according to the company. “It’s also possible that the first message of a newly created conversation was visible in someone else’s chat history if both users were active around the same time.” OpenAI also discovered that “the same bug may have caused the unintentional visibility of payment-related information of 1.2% of the ChatGPT Plus subscribers who were active during a specific 9-hour window. … it was possible for some users to see another active user’s first and last name, email address, payment address, the last four digits (only) of a credit card number, and credit card expiration date.” I’d like to hear about your experience with ChatGPT or any of the other options. How did you use it? What type of content were you generating? If you were aiming for assistance with technical content related to oil/gas/energy, how useful was the chatbot? Share your comments and pros and cons at JPT Comments.

Building a Stock Price Prediction Model using Random Forest Regression and Sentimental Analysis

Stock market performance can be influenced by stock prices in either a positive ornegative way because it is a very unpredictable area. The public's opinions and feelings may be affected differently by various occurrences, which could impact the direction in which stock price fluctuates. To increase predictability of stock market indicators, this study examines the potential usage of sentiment data from Twitter users. In this study, relationship between public perceptions about acompany stated in tweets and changes in stock prices are investigated. The resultsof the study demonstrates strong association between changes in stock prices and the opinionsshared by the general public in tweets. In this study, a system is createdthat collects tweets, analyses and assesses if tweets has positive or negativesentiment using a model called Random Forest Classifier. The output for the algorithm is then checked with the actual change in stock price the next day to ascertain the prediction's error. The goal of this research is to forecast future market behavior by performing sentiment analysis on a sample of tweets from the past fewdays. The results suggest that Random Forest model is suitable for stock price prediction along with sentimental analysis it generates the accuracy of 84.86% which is accessed by with three widely used measures for the regressor— Mean Squared Error , Root Mean Square Error , Mean Absolute Error and along with sentimental analysis by generating classification report. Keywords: Stock price prediction, Volatility , Time series, Random Forest Classifier, Sentimental Analysis, Natural Language Tool Kit, VADER, Bootstrap method, Back testing, hyper parameter tuning, snscrape, Lemmatization and tokenization.

Combining rule- and SMT-based reasoning for verifying floating-point Java programs in KeY

Deductive verification has been successful in verifying interesting properties of real-world programs. One notable gap is the limited support for floating-point reasoning. This is unfortunate, as floating-point arithmetic is particularly unintuitive to reason about due to rounding as well as the presence of the special values infinity and ‘Not a Number’ (NaN). In this article, we present the first floating-point support in a deductive verification tool for the Java programming language. Our support in the KeY verifier handles floating-point arithmetics, transcendental functions, and potentially rounding-type casts. We achieve this with a combination of delegation to external SMT solvers on the one hand, and KeY-internal, rule-based reasoning on the other hand, exploiting the complementary strengths of both worlds. We evaluate this integration on new benchmarks and show that this approach is powerful enough to prove the absence of floating-point special values—often a prerequisite for correct programs—as well as functional properties, for realistic benchmarks.

Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records

To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years. Thirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients' disease status progressed, and 589 (11%) patients' disease improved. This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.

A new era: 3D printing as an aesthetic language and creative tool in fashion and textile design

PurposeThis study aims to produce textile-like surfaces using fused deposition modelling (FDM) 3D printers and create a garment collection.Design/methodology/approachExperiments were conducted using different types of materials in FDM 3D printers until the sufficient flexibility was achieved to create textile-like structures. During the research, properties of polylactic acid (PLA), acrylonitrile butadiene styrene (ABS) and thermoplastic polyurethane (TPU) were observed. Geometrical patterns were printed and each of them gave a different result depending on the pattern. Based on the information obtained from the experiments, a garment collection with four total looks was designed inspired by Vivaldi’s “Four Seasons”.FindingsAmong the materials used, TPU, a flexible filament, yielded the best results. Because of the rigid properties of PLA and ABS, chain-like structures were printed to create relatively flexible surfaces, but the results were still not successful enough to create a clothing material. Therefore, TPU was preferred for the garment material selection.Originality/valueIn this study, combinations of 3D printed flexible structures and different types of fabrics were used to create a garment collection. It was concluded that, with the right material selection, 3D printing can be used as an alternative method to create a new aesthetic language in fashion design.

Modeling Languages for Internet of Things (IoT) Applications: A Comparative Analysis Study

Modeling languages have gained ever-increasing importance for the Internet of Things (IoT) domain for improving the productivity and quality of IoT developments. In this study, we analyzed 32 different modeling languages that have been designed for IoT software development in terms of a set of requirements that were categorized into three groups: language definition, language features, and tool support. Some key findings are as follows: (1) performance is the most supported quality property (28%); (2) most languages offer a visual notation set only, while 6% provide both textual and visual notation sets; (3) most languages (88%) lack formally precise semantic definitions; (4) most languages (94%) support the physical, deployment, and logical modeling viewpoints, while the behavior, logical, and information viewpoints are rarely supported; (5) almost none of the languages enable extensibility; (6) Java (34%) and C (21%) are the most preferred programming languages for model transformation; (7) consistency (77%) and completeness (64%) are the most supported properties for the automated checking of models; and (8) most languages (81%) are not supported with any websites for sharing case studies, source code, tools, tutorials, etc. The analysis results can be useful for language engineers, practitioners, and tool vendors for better understanding the existing languages for IoT, their weak and strong points, and IoT industries’ needs in future language and modeling toolset developments.

HUMAN BODY VOLUME AND ITS ASSESSMENT IN THE RUSSIAN LANGUAGE PICTURE OF THE WORLD AGAINST THE BACKGROUND OF CHINESE

The main purpose of this research is to find out the characteristic of human body volume in the Russian language picture of the world against the background of Chinese. In this article analyze language tools for expressing volume in the Russian language against the background of Chinese. The question is raised about the evaluation construction, which is reflecting the features of world perception and understanding of native speakers of the Russian language, also revealed cultural, emotional and aesthetic significance of different volume through evaluative attribute construction and described the peculiarity of the evaluation relationship to volume in the Russian language against the background of Chinese.

RESEARCH ON WATER QUALITY IN THE DNIESTER RIVER 2019-2021

The article examines the water quality of the Dniester River for the period 2019-2021. Are analyzed the data concerning the degree of pollution of the Dniester River and its tributaries. Is discussed the negative impact of the detected pollutants on human health. The results of the statistical analysis using R language tools are presented. Is proposed the statistical model for approximation of the analyzed data.

Screening and identification of hub-gene associated with brain metastasis in breast cancer.

The presence of breast cancer in the brain, also known as brain metastasis (BMS), is the primary reason for a bad prognosis in cases of breast cancer. Breast cancer is the most prevalent malignant tumor seen in women in developing nations. At present, there is no effective method to inhibit brain metastasis of breast cancer. Therefore, it is necessary to conduct a systematic study on BMS of breast cancer, which will not provide ideas and sites for follow-up studies on the treatment and inhibition of BMS. In this study, data set GSE43837 was screened from gene expression omnibus database, and then R language tool was used for differential analysis of its expression spectrum, The gene ontology functional enrichment and Kyoto encyclopedia of genes and genomes signal pathway enrichment analyses, as well as the interactive gene retrieval tool for hub-gene analysis, were performed. According to the findings, the primary genes linked to breast cancer brain metastases are those that involve interactions between cytokines and their respective receptors and between neuroactive ligands and their respective receptors. The majority of the gene ontology enrichment took place in the extracellular structural tissues, the extracellular matrix tissues, and the second message-mediated signaling. We were able to identify 8 genes that are linked to breast cancer spreading to the brain. The gene score for matrix metallopeptidase1 (MMP-1) was the highest among them, and the genes MMP10, tumor necrosis factor alpha-inducible protein 8, collagen type I alpha 2 chain, vascular cell adhesion molecule 1, and TNF superfamily member 11 were all connected to 1 another in an interaction way. There is a possibility that the 8 key genes that were identified in this research are connected to the progression of BMS in breast cancer. Among them, MMP1 is 1 that has the potential to have a role in the diagnosis and treatment of BMS in breast cancer.

Результаты использования технологии машинного обучения для расчета оптической силы интраокулярных линз

Purpose. To evaluate possibility of using the mathematical models obtained as a result of deep learning of artificial neural networks (ANNmodels) to predict the optical power of modern intraocular lenses (IOL). Material and methods. The dataset included 455 depersonalized records of patients (26 columns of input factors and one column – output factor – calculation of IOL (dptr). For convenient construction of ANN models, a simulator program previously developed by the authors and Python language tools in the Google Colaboratory were used. Results. This article describes the possibility of using mathematical models obtained as a result of deep learning of ANN models to predict the optical power of modern IOLs, widely used in the surgical cataract treatment in ophthalmology. A distinctive feature of such ANN models in comparison with the wellknown formulas SRK II, SRK/T, Hoffer-Q, Holladay II, Haigis, Barrett is their ability to take into account a significant number of recorded input quantities, which makes it possible to reduce the mean relative error in calculating the optical power of IOL from 10 –12 to 3.5%. Conclusion. The resulting models, in contrast to the traditionally used formulas, reflect the regional specificity of patients to a much greater extent. They also make it possible to retrain and optimize the structure based on newly received data, which allows taking into account the non-stationarity of the object. Keywords: optical power of an intraocular lens, IOL, artificial neural networks, ANN-models, deep learning, training dataset

Identity Construction in Company Persuasive Discourse

Constructing an identity is an important part of a company or business. This study aims to analyze information hierarchy text and reveal discourse cognition in constructing corporate identity. Both of these concepts are used to see and reveal the agent’s wishes and communications made in showing identity. The research data was obtained by documenting persuasive discourse on the nestlé and aqua-Danone websites in Indonesia. Research results direct our minds that there are two types of hierarchies built by the company: hierarchy text based on general truth and hierarchy text based on product identity. Cognition built-in discourse is generally the same. It is built through concrete language to show efforts to save the environment. The language tools used are mental imagery words, action words, and concrete language. From these two studies, it can be seen that corporate identity can be shown directly and indirectly and the target to be built is to save the environment.

A Systematic Literature Review on the Role of Artificial Intelligence in Entrepreneurial Activity

New models of entrepreneurship are emerging because of increasing digitalization and the development of artificial intelligence (AI). There is a lack of existing research on the intersection between digitalization and entrepreneurship. Therefore, this systematic literature analysis aims to expand knowledge in this area and provide a semantic analysis of existing contributions. Following the SPAR-4-SLR protocol, it analyzes 520 scientific articles from the Dimensions.ai database up to July 2022. The methodology uses natural language processing (NLP) and tools such as bibliometrix and VosViewer, which reveal the main characteristics of the titles and texts of the abstracts and their links with the numbers of citations and with scientific impact. This study provides guidelines and clear recommendations for scientists to focus their scientific research on AI and entrepreneurship and entrepreneurs by including the link between AI and entrepreneurship in their strategies. As future lines of research, the authors highlight the potential of using NLP in bibliometric analysis.

Implicit understandings and trust in the doctor-patient relationship: a philosophy of language analysis of pre-operative evaluations.

The aim of this paper is to enhance doctors' awareness of implicit understandings between doctors and patients in the context of pre-operative communication of risks. This paper draws on insights from the philosophy of language - in particular pragmatic analysis tools - that make explicit the implicit understandings of the interaction. Mastering not only what is said but also what is unsaid allows doctors to improve their communication with their patients. I suggest that being aware of the implications of the interactions is useful for improving both the doctor's and the patient's experience, further strengthening the therapeutic alliance. In this article I analyze actual cases involving pre-operative evaluation before cardiac surgery from a philosophy of language perspective. The paper is structured as follows: a description of the relevant philosophy of language tools that I will apply; an overview of the risk-communication context; an explanation of the link between "the implicit dimension" and trust, addressing whether the doctor needs to convey the whole truth; and the analysis of real cases. In conclusion, I re-emphasize the importance of implicit meanings during risk communication.

Research on Intelligent System of Computer Aided Ideological and Political Course Teaching

This paper focuses on the design scheme of the auxiliary teaching system for ideological and political theory courses using J2EE architecture design. In this paper, the overall planning of the system's architecture, work flow, system function, data structure, etc. is carried out, and it is designed and implemented in detail. The system adopts B/S three-tier structure and uses JSP technology to design dynamic pages. Consider using JavaBean to encapsulate the key code of the program from the aspects of system security and code reusability. Use CASE tools and object-oriented modeling language in system design. Through UML modeling language, use VISIO, ROSE and other modeling tools to analyze and design the system to generate conceptual models and entities of the system. Through requirement analysis and detailed design, the user login module, network course module, online examination module, learning resource module and exchange forum module are finally realized. Finally, the thesis assists the realization of the functions of each module of the teaching system, and carries out corresponding tests and verifications, and the system is in the testing stage and plays a great role in practice.

Sourcing high tissue quality brains from deceased wild primates with known socio‐ecology

Abstract The selection pressures that drove dramatic encephalisation processes through the mammal lineage remain elusive, as does knowledge of brain structure reorganisation through this process. In particular, considerable structural brain changes are present across the primate lineage, culminating in the complex human brain that allows for unique behaviours such as language and sophisticated tool use. To understand this evolution, a diverse sample set of humans' closest relatives with varying socio‐ecologies is needed. However, current brain banks predominantly curate brains from primates that died in zoological gardens. We try to address this gap by establishing a field pipeline mitigating the challenges associated with brain extractions of wild primates in their natural habitat. The success of our approach is demonstrated by our ability to acquire a novel brain sample of deceased primates with highly variable socio‐ecological exposure and a particular focus on wild chimpanzees. Methods in acquiring brain tissue from wild settings are comprehensively explained, highlighting the feasibility of conducting brain extraction procedures under strict biosafety measures by trained veterinarians in field sites. Brains are assessed at a fine‐structural level via high‐resolution MRI and state‐of‐the‐art histology. Analyses confirm that excellent tissue quality of primate brains sourced in the field can be achieved with a comparable tissue quality of brains acquired from zoo‐living primates. Our field methods are noninvasive, here defined as not harming living animals, and may be applied to other mammal systems than primates. In sum, the field protocol and methodological pipeline validated here pose a major advance for assessing the influence of socio‐ecology on medium to large mammal brains, at both macro‐ and microstructural levels as well as aiding with the functional annotation of brain regions and neuronal pathways via specific behaviour assessments.

“This Clock Is Oop” Development of Translanguaging Strategies and Language Attitude in a Deaf Bimodal Bilingual Student

Bilingual students do not always use language exactly the way teachers expect. This is especially true for deaf students who use a signed language and a spoken language. When students mix languages, they are often told they are making mistakes. Instead of forcing students to keep languages completely separate, teachers can let students use any and all of their language tools. When students can practice language with all of the languages they know, they feel better about their language skills. This article describes the language of one deaf bilingual student over one year. The author taught the student how to carefully examine and discuss his own language. The student then wrote about pictures and videos that he was interested in. At the end of the year, the student was comfortable using many types of language tools. He also felt powerful about his own language skills.