Language Processing Data Research Articles

Leveraging Big Data and AI for Enhanced Business Decision-Making: Strategies, Challenges, and Future Directions

Big data and artificial intelligence (AI) are the buzzwords of the moment in business decision-making. In this paper, I will show how predictive analytics, real-time data processing and natural language processing (NLP) are key strategies that allow businesses to optimise their operations and customer interaction. Moreover, through this paper I will present some of the challenges involved in the adoption of AI, such as the ethical and legal questions about data privacy, as well as the real problem of integrating AI systems within legacy business models. Furthermore, future trends in AI will be presented, such as the advances in quantum computing and the rise of so-called autonomous AI systems, that will define the future of decision-making in logistics, finance and a variety of other sectors. Overall, by addressing both the strategic advantages and the pitfalls involved in the adoption of AI based on some real business cases, this paper will provide a complete picture of what AI and big data can bring to decision-making as a tool for business success. This topic is of paramount importance as these technologies have not only brought a new wave of innovation but are also increasing the importance of human oversight of AI systems.

Research on English Translation Teaching Mode Based on Artificial Intelligence

With the continuous development of artificial intelligence technology, its application in the field of education is becoming increasingly widespread. This article aims to explore the effectiveness of an AI based English translation teaching model and its impact on traditional teaching models. By analyzing the current application status of artificial intelligence in English translation teaching, this article summarizes the advantages of artificial intelligence in personalized learning, real-time feedback, language data processing, etc., while also pointing out its limitations in the teaching process. Finally, this article proposes the development direction and improvement suggestions of English translation teaching mode based on artificial intelligence, in order to provide useful reference for educators.

Komi – Russian and Karelian – Russian Code-Switching

Introduction. The language situation in Russia is unique with a fair number of languages spoken on its territory and all of them having different status and scope of use. This implies the relevance of the study that focuses on the analysis of code-switching in the spoken language of the bilinguals speaking Komi and Russian and Karelian and Russian. The novelty of the study is implied by the analysis of the code-switching in the language pairs mentioned above. This contributes to the development of the code-switching theory.Methodology and sources. The methods of language data collection and processing are sociolinguistic methods of interviewing, questionnaire, observation, quantitative and descriptive methods. The research material are scripted dialogues with bilinguals as well as the “Komi mu” and “Parma gor” 2022 issues.Results and discussion. The study of the Komi-Russian and Karelian-Russian code-switching was conducted using P. Muysken’s topology of code-mixing. P. Muysken sees code-switching as alternation, insertion, and congruent lexicalization according to the degree of the foreign word assimilation in the matrix language. The features of the spoken language of the Komi-Russian bilinguals are the frequent use of Russian discursive and introductory lexical items and adverbs; the nouns and the adjectives usually following the grammatical rules of Komi; the respondents giving preference to Russian versions when using numerals. For the spoken language of the Karelian-Russian bilinguals it is typical to address to Russian vocabulary, especially when mentioning dates, numbers, and using introductory words and phrases; Russian words are also being quite easily transformed according to the Karelian grammar through its case system.Conclusion. Spontaneous speech of the bilingual interviewees contains a great number of code-switching, that could be a convincing demonstration of the language shift and is conditioned by a number of extralinguistic and linguistic factors, such as language prestige, language functionality, language situation, the native language of the interlocutor, and the willing of the respondent to use a certain language.

Are population cycles recovering?

The dynamics of populations of small mammals of Central Siberia was analyzed. The studies were carried out at the Yenisei ecological station "Mirnoye" of the A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences. The time series analysis was performed by the wavelet transform using the statistical data processing language R. In the 20th century, the dynamics of the population of the community and some of its constituent species (Sorex araneus; S. caecutiens; S. isodon; S. tundrensis; S. minutus; Craseomys rufocanus; Clethrionomys rutilus; Microtus oeconomus; M. agrestis) were characterized by a 4-year periodicity. The type of dynamics changed to noncyclic by the nineties, but by 2022, four species (S.araneus, S.isodon, C.rutilus, and M.oeconomus) and the community as a whole showed a tendency toward recovery of population cycles. The remaining species were characterized by consistently low numbers with irregular low amplitude fluctuations.

Reinforcement Learning-based Algorithms for Music Improvisation and Arrangement in Sensor Networks for the Internet of Things

The process of learning any new technology requires acquiring the best knowledge about the information of that technology. The better the knowledge humans get about digital technology, the more they become efficient in implementing technological development. In developing the musical rhythm and tuning, the application of programming technologies helps improve the quality. In constructing networking sites and sensing technologies, algorithmic learning processes help in effective development. This development occurs by making the systematic process of transforming a data processing language and data interpreter. Thus, it helps in performing programming effectively in the present as well as future purposes. Therefore, it reflects all the benefits of machine learning. Thus, the preference for machine learning increases technological impact. This development of the programming used in the computer makes humans learn about something easily and get the best information. The effectiveness of the technological development by the algorithm used in the data processing implements the best way to improve the technological language transformation from human language to computer operating language. There is a transnational perspective of the average beat commonness of each part of the music. “Reinforcement algorithms-based learning” incorporated with sensor networks has proposed compelling opportunities for improving “music improvisation” and interpretation.

Sensitivity Analysis of Mathematical Models

The construction of a mathematical model of a complicated system is often associated with the evaluation of inputs’ (arguments, factors) influence on the output (response), the identification of important relationships between the variables used, and reduction of the model by decreasing the number of its inputs. These tasks are related to the problems of Sensitivity Analysis of mathematical models. The author proposes an alternative approach based on applying Analysis of Finite Fluctuations that uses the Lagrange mean value theorem to estimate the contribution of changes to the variables of a function to the output change. The article investigates the presented approach on an example of a class of fully connected neural network models. As a result of Sensitivity Analysis, a set of sensitivity measures for each input is obtained. For their averaging, it is proposed to use a point-and-interval estimation algorithm using Tukey’s weighted average. The comparison of the described method with the computation of Sobol’s indices is given; the consistency of the proposed method is shown. The computational robustness of the procedure for finding sensitivity measures of inputs is investigated. Numerical experiments are carried out on the neuraldat data set of the NeuralNetTools library of the R data processing language and on data of the healthcare services provided in the Lipetsk region.

Issues in Distributive-Semantic Analysis of Skeleton Structures in Language Data Processing Tasks

Issues in Distributive-Semantic Analysis of Skeleton Structures in Language Data Processing Tasks

Informative Language Encoding by Variational Autoencoders Using Transformer

In natural language processing (NLP), Transformer is widely used and has reached the state-of-the-art level in numerous NLP tasks such as language modeling, summarization, and classification. Moreover, a variational autoencoder (VAE) is an efficient generative model in representation learning, combining deep learning with statistical inference in encoded representations. However, the use of VAE in natural language processing often brings forth practical difficulties such as a posterior collapse, also known as Kullback–Leibler (KL) vanishing. To mitigate this problem, while taking advantage of the parallelization of language data processing, we propose a new language representation model as the integration of two seemingly different deep learning models, which is a Transformer model solely coupled with a variational autoencoder. We compare the proposed model with previous works, such as a VAE connected with a recurrent neural network (RNN). Our experiments with four real-life datasets show that implementation with KL annealing mitigates posterior collapses. The results also show that the proposed Transformer model outperforms RNN-based models in reconstruction and representation learning, and that the encoded representations of the proposed model are more informative than other tested models.

Validation of Artificial Intelligence to Support the Automatic Coding of Patient Adverse Drug Reaction Reports, Using Nationwide Pharmacovigilance Data.

IntroductionAdverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients’ adverse drug reaction reports.ObjectivesWe aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal.MethodsReports coded by 27 Pharmacovigilance Centres between March 2017 and December 2020 were selected (n = 11,633). For each report, the Portable Document Format form containing free-text information filled by the patient, and the corresponding encodings of adverse event symptoms (in Medical Dictionary for Regulatory Activities Preferred Terms) and seriousness were obtained. This encoding by experts was used as the reference to train and evaluate models, which contained input data processing and machine-learning natural language processing to learn and predict encodings. We developed and compared different approaches for data processing and classifiers. Performance was evaluated using receiver operating characteristic area under the curve (AUC), F-measure, sensitivity, specificity and positive predictive value. We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period.ResultsInternal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0.97 and an F-measure of 0.80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0.97 and an F-measure of 0.78. For seriousness assessment, FastText + LGBM achieved an AUC of 0.85 and an F-measure of 0.63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0.84 and an F-measure of 0.63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results.ConclusionsOur artificial intelligence models showed promising performance to automatically code patient adverse drug reaction reports, with very similar results across approaches. Our system has been deployed by national health authorities in France since January 2021 to facilitate pharmacovigilance of COVID-19 vaccines. Further studies will be needed to validate the performance of the tool in real-life settings.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40264-022-01153-8.

Calculate central limit theorem for the number of empty cells after allocation of particles

This work belongs to the field of limit theorems for separable statistics. In particular, this paper considers the number of empty cells after placing particles in a finite number of cells, where each particle is placed in a polynomial scheme. The statistics under consideration belong to the class of separable statistics, which were previously considered in (Mirakhmedov: 1985), where necessary statements for the layout of particles in a countable number of cells were proved. The same scheme was considered in (Asimov: 1982), in which the conditions for the asymptotic normality of random variables were established. In this paper, the asymptotic normality of the statistics in question is proved and an estimate of the remainder term in the central limit theorem is obtained. In summary, the demand for separable statistics systems is growing day by day to address large-scale databases or to facilitate user access to data management. Because such systems are not only used for data entry and storage, they also describe their structure: file collection supports logical consistency; provides data processing language; restores data after various interruptions; database management systems allow multiple users.

Practicalities of language data collection and management in and around Indonesia

Researchers use different approaches when collecting and managing primary language materials during fieldwork. Yet it is important that this work is done in a transparent way, so that it can be used by other researchers, who might have other aims, as well as by the speaker community who might want to use or take note of the collected materials. In this article we use our research experience in language data collection in and around Indonesia in fieldwork projects of three kinds: descriptive fieldwork, linguistic surveys, and projects investigating language contact. Our aim is to provide an introductory and practical guide for students and professionals who are embarking on fieldwork in or around Indonesia. Describing practical methods of language data collection, processing, and management, our aim is to provide a guide for any research which involves the collection of language materials, including linguistic research, oral history or literature, and ethnography.

RESEARCH OF DYNAMICS OF DEMAND FOR LOANING OF INDIVIDUALS BY USING LANGUAGE INSTRUMENTS R

The article studies the assessment of the demand of individuals for lending services provided by second-tier banks in the Republic of Kazakhstan. The information source was the open data portal of the National Bank of the Republic of Kazakhstan. The statistical research tool selected time series analysis performed by the tools provided by the statistical data processing language R. Conclusions based on the data presented prove that the programming environment R has all the capabilities for quick, simple and clear analysis of time series when using specialized packages, including the forecast package. Time series forecasting is carried out by the method of exponential smoothing. Trends found in the ranks for various types of consumer lending show a general tendency towards instability and a slight decrease in demand for this type of service.

Development of a System for Postmarketing Population Pharmacokinetic and Pharmacodynamic Studies Using Real-World Data From Electronic Health Records.

Postmarketing population pharmacokinetic (PK) and pharmacodynamic (PD) studies can be useful to capture patient characteristics affecting PK or PD in real-world settings. These studies require longitudinally measured dose, outcomes, and covariates in large numbers of patients; however, prospective data collection is cost-prohibitive. Electronic health records (EHRs) can be an excellent source for such data, but there are challenges, including accurate ascertainment of drug dose. We developed a standardized system to prepare datasets from EHRs for population PK/PD studies. Our system handles a variety of tasks involving data extraction from clinical text using a natural language processing algorithm, data processing, and data building. Applying this system, we performed a fentanyl population PK analysis, resulting in comparable parameter estimates to a prior study. This new system makes the EHR data extraction and preparation process more efficient and accurate and provides a powerful tool to facilitate postmarketing population PK/PD studies using information available in EHRs.

Robôs de conversação e o ethos

Com a evolução da tecnologia, é cada vez mais comum encontrar robôs de conversação com maior capacidade de argumentar em um diálogo. Embora estejam muito longe de se igualarem a seres humanos nesse quesito, há vários desses robôs com performances significativas ao realizar essa tarefa. Nesse sentido, este estudo tem por objetivo discutir basicamente o conceito de ethos em máquinas que simulam personalidades humanas pré-determinadas. O estudo baseia-se em consulta bibliográfica interdisciplinar voltada às áreas de Linguística, Discurso, Retórica, Processamento de Linguagem Natural, Inteligência Artificial e Processamento de Dados. A observação dos exemplos utilizados para comparação sugere que mais pesquisas na mesma direção devem ser realizadas, tendo em vista que a evolução dessas máquinas é exponencial e constante.

Стохастическая роевая кластеризация в задачах автоматизированной обработки данных, представленных на естественном языке

Consider natural language data processing technology based on non-linear dimensionality reduction method which takes into account the discriminating power of the solution found for given values of the categorical variable associated with each observation. Stochastic optimization method known as the “Particle swarm optimization” is proposed to found characteristics that ensure the best separation of observations in terms of a given quality functional. The basis for evaluating the quality of the solution lies in the purity of the clusters obtained with the k-means method, or with using self-organizing Kohonen feature maps.

Mathematical Model for Izhevsk Primary Housing Market

В статье рассматривается проблема экономико-математического анализа состояния и перспектив формирования и развития регионального рынка жилья, состояние которого серьезно влияет в том числе на социально-экономическое положение в регионе. С помощью статистических методов исследования панельных данных построены модели спроса и предложения первичного рынка жилья Ижевска. Анализ ситуации на рынке г. Ижевска проводится в сравнении с рядом других городов Приволжского федерального округа. Выделены основные факторы, значимо влияющие на состояние и развитие рынка. Формирование модели происходило в несколько этапов путем последовательного уточнения и варьирования факторами. Достаточно подробно рассматриваются факторы спроса и предложения, являющиеся в данном случае одними из определяющих. Для построения модели применялся язык статистической обработки данных R и соответствующее программное обеспечение. Проведенный анализ включает тщательную проверку основных критериев оценки полученных моделей, что позволяет достаточно квалифицированно судить об адекватности полученных результатов.

The magic words: Using computers to uncover mental associations for use in magic trick design.

The use of computational systems to aid in the design of magic tricks has been previously explored. Here further steps are taken in this direction, introducing the use of computer technology as a natural language data sourcing and processing tool for magic trick design purposes. Crowd sourcing of psychological concepts is investigated; further, the role of human associative memory and its exploitation in magical effects is explored. A new trick is developed and evaluated: a physical card trick partially designed by a computational system configured to search for and explore conceptual spaces readily understood by spectators.

Data Processing Languages for Business Intelligence. SQL vs. R

(ProQuest: ... denotes formulae omitted.)1 IntroductionOften seen as a reincarnation of Decision Sup-port Systems [1] and sometimes referred as Business Intelligence and Analytics [2], Busi-ness intelligence (BI) is a broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better deci-sions [3]. Figure 1 displays a classical BI ar-chitecture [4].Common business intelligence related tasks are:* data storage* data extraction-transformation-load from various sources in a different for-mats, more or less structured, to the stor-age layer* data processing* information integration* visualization* exploratory analysis* data mining/data science etc.Slightly outdated, the schema in figure 1 is still valid in suggesting the vast array of tech-nologies, processes and tools gathered (or re-branded) within BI umbrella. Chen et al [Chen 2012] identified three generations of BI and Analytics (BI&A) systems whore core tech-nologies have been:* data management and warehousing [5] [6]* text and web analytics for unstructured web contents [7]* mobile technologies [8].Implementation of BI platforms requires vast quantity of organizational resources. Some of the most important current BI solutions are shown in figure 2 [9]. As with Enterprise Re-source Planning applications, BI systems im-plementation requires extensive organiza-tional changes and business expertise and sometimes it requires full vendor participa-tion.Apart from impressive costs, BI platforms have the drawback of keeping captive the cus-tomer. Every organizational change and also new or updated external data source and ser-vice must be negotiated with BI platform pro-vider, which usually attracts new costs and also delays.In this paper we scrutinize two languages, SQL and R, involved not only in BI applica-tion development but especially in the de-mocratization of BI as they allow various types of data professionals and users to access and process vast quantity of data in an inter-active, ad-hoc, way. Using two reliable sources, their role and popularity in current BI market will be outlined, taking into account job demand and a survey concerning BI tools and languages usage. Next the range of BI ac-tivities that can be supported by each SQL and R will be presented. The main section will compare SQL and R features syntax for the most common data processing/reporting prob-lems, particularly important for BI users.2 Languages and Tools for Business Intel-ligenceThere is a vast array of tools, languages and technologies covering large extents of BI tasks. Some of them target regular users who are unable to write code and scripts in any pro-gramming language. Others are BI application developer's toolbox. But there some technol-ogies that serve both users and developers in data processing, integration, visualization and analysis. Comparison of BI tools and lan-guages is also problematic because they can be available as programming languages, de-velopment environments, ecosystems or inte-grated platforms.In evaluating the popularity of Business Intel-ligence languages and tools, we gather infor-mation from two reliable sources. Search en-gine www.indeed.com provides data about job trends. Figure 3 compares job demand in 2012-2016 interval for some of the most im-portant data processing and analysis lan-guages [10].SQL and R share most of the job postings. In 2012 SQL was by far the most demanded data language. Its share decreased slightly and seems to have stabilized since the end of 2014. R grew spectacularly in 2012-2014 interval, overpass SQL in 2014 for a brief period, and then fell back. Since 2014 it has fluctuated around 2% share. After SQL and R, the next popular is Python followed by SAS, SPSS, Stata and Julia. Currently there is still a visible lag between SQL-R group and the rest of the languages/tools, although Python seems to in-crease steadily and might catch up with the leading group. …

READING ALOUD STRATEGIES IN READING ENGLISH TEXTS

Reading aloud by a young language learner shows unique patterns as the evidence of his/her language data processing. This study, thus, explored the strategies applied by an Indonesian young language learner to read English written texts aloud to identify errors that actually bring certain benefits in her language learning process such as making intelligent guesses when she encountered unfamiliar words. It adopted qualitative case study design involving a seven-year old girl as the subject, who had been exposed to English four yearearlier.The data were gained through observing her reading and interviewing her after reading. The data from both techniques confirmed each other and provided in-depth data analysis.Next, the data were analyzed under the the framework synthesized from Littlewood (1984). The findings indicated that to read aloud the subject made use of three strategies among others: overgeneralization, transfer and simplification. This means that the subject employed her L2 oral proficiency and L1 reading ability to process the L2 data from reading. The study implies that educators need to pay moreattention on how children process the language data they gain and to povide appropriate learning environments in order to prepare them to be better readers, beside improving awareness in similarities and differences of L1 and L2. Keywords:young learner, reading strategies, read aloud

Compilation of Query Languages into MapReduce

The introduction of MapReduce as a tool for Big Data Analytics, combined with the new requirements of emerging application scenarios such as the Web 2.0 and scientific computing, has motivated the development of data processing languages which are more flexible and widely applicable than SQL. Based on the Big Data context, we discuss the points in which SQL is considered too restrictive. Furthermore, we provide a qualitative evaluation of how recent query languages overcome these restrictions. Having established the desired characteristics of a query language, we provide an abstract description of the compilation into the MapReduce programming model, which, up to minor variations, is essentially the same in all approaches. Given the requirements of query processing, we introduce simple generalizations of the model, which allow the reuse of well-established query evaluation techniques, and discuss strategies to generate optimized MapReduce plans.