Academic literature on the topic 'Conditional language modelling'
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Journal articles on the topic "Conditional language modelling"
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Rezaee, Abbas A., Majid Nemati, and Seyyed Ehsan Golparvar. "Discourse-Pragmatic and Processing-Related Motivators of the ordering of Reason Clauses in an Academic Corpus." Research in Language 16, no. 3 (September 30, 2018): 325–39. http://dx.doi.org/10.2478/rela-2018-0014.
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The present research is aimed at examining the relative importance of the competing motivators of the sequencing of reason clauses in a corpus of research articles of applied linguistics. All the finite reason clauses accompanied by their main clauses in this corpus were collected. Random forest of conditional inference trees is the statistical modelling in this study. The findings showed that sentence-final reason clauses outnumber sentenceinitial ones. Moreover, subordinator choice and bridging, which are discourse-pragmatic constraints on clause positioning, emerged as the two more powerful predictors of the ordering of reason clauses in this corpus. Furthermore, the complexity of the clause turned out to be a stronger processing related predictor than the length of the clause.
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Dyka, Natalia, and Oleksandra Glazova. "A new paradigm of postgraduate pedagogical education: realization of the competent approach." Continuing Professional Education: Theory and Practice, no. 3-4 (2018): 14–20. http://dx.doi.org/10.28925/1609-8595.2018.3-4.1420.
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The article describes the content characteristics of the basic concepts of research. The principal differences between competence and knowledge approaches in the educational process are revealed and a comparative characteristic is designed. The competence approach differs significantly from the knowledge of all the characteristics of the organization of the educational process: the main and fundamental difference is the focus on the result of schooling, and the result is not the amount of information learned, but the ability of a person to successfully act in different situations. Modelling the lessons that systematic and purposeful formation of subject and key competencies in students requires a different, substantially changed typology of Ukrainian language lessons. The technology of modelling of knowledge oriented Ukrainian language lessons is requested. The structure and models of modelling of competence-oriented tasks are presented. Situational tasks are an intermediate stage between the students' acquisition of theoretical knowledge and the formation of skills and abilities of the practical use of such knowledge in everyday life situations. The main task of such tasks – the formation of skills to navigate in the imaginary or conditional communication, to match the objectives and conditions of communication language means. In addition, the execution of such cases promotes the formation of not only communicative skills of students, but also the skills of the culture of communication, the adoption of rules of etiquette. Competently oriented tasks are distinguished primarily by the fact that they provide for the search activity of students, the implementation of methods of independent, pair, group research activities, the creation of conditions for the formation of objective and key competencies. Teachers are provided with counselling, teacher training, scientific and methodological clusters, trainings, seminars, webinars, etc. on the typology of competency-oriented lessons, simulation of lessons and structuring of competency-oriented tasks at teacher training courses.
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KRUGER, HAIDEE, and BERTUS VAN ROOY. "A multifactorial analysis of contact-induced change in speech reporting in written White South African English (WSAfE)." English Language and Linguistics 24, no. 1 (April 29, 2019): 179–209. http://dx.doi.org/10.1017/s1360674319000017.
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This article presents a corpus analysis of changes over a period of two centuries in speech-reporting constructions in written White South African English (WSAfE), a native variety of English that has been in contact with Afrikaans throughout its history. The analysis is based on register-differentiated comparable diachronic corpora of WSAfE, its parent variety, British English (BrE), and the contact language, Afrikaans. Three related reported-speech constructions are analysed, focusing on changes in the relative frequencies of variants of each construction. These constructions show ongoing change, with similar trajectories of change for WSAfE and BrE in some cases, but divergent trajectories in others. In the latter case, WSAfE and Afrikaans converge on similar frequency distributions, which follow from an accelerated rate of change or a slowing down of the rate of change for particular features in WSAfE in comparison to BrE. Descriptive findings are supported by conditional inference tree modelling. The effect of frequency on reinforcing similar patterns of change in WSAfE and Afrikaans, as well as simplification through the levelling of register differences in WSAfE and Afrikaans are proposed as explanations. The study highlights the importance of converging norms in a multilingual publication industry as a site of contact.
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Leung, K. Y., M. W. Mak, M. H. Siu, and S. Y. Kung. "Adaptive articulatory feature-based conditional pronunciation modeling for speaker verification." Speech Communication 48, no. 1 (January 2006): 71–84. http://dx.doi.org/10.1016/j.specom.2005.05.013.
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LADDHA, ABHISHEK, and ARJUN MUKHERJEE. "Aspect opinion expression and rating prediction via LDA–CRF hybrid." Natural Language Engineering 24, no. 4 (April 22, 2018): 611–39. http://dx.doi.org/10.1017/s135132491800013x.
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AbstractIn this paper, we study the problem of aspect-based sentiment analysis. Our model simultaneously extracts aspect-specific opinion expressions and determines the rating for each aspect in reviews. Previous works have mainly focused on the problem of opinion phrase extraction and aspect rating prediction in a pipelined manner and are not able to capture the dependencies of aspect opinion expression on aspect rating and vice-versa. They are also unable to discover aspect-specific opinion expressions and their associated rating scores. We present a joint modelling approach to extract aspect-specific sentiment expression and aspect rating prediction simultaneously. This paper proposes a novel LDA–CRF hybrid model which employs discriminative conditional random field component for phrase extraction, a regression component for rating prediction and a generative component for grouping aspect–sentiment expressions (aspect-specific opinion expressions) into coherent topics. To show the effectiveness of our approach, we evaluate the performance of the model on both task: (i) aspect-specific opinion expressions and (ii) rating prediction on the dataset of hotel and restaurant reviews from TripAdvisor.com. Experimental results show that both task potentially reinforce each other and joint modeling outperformed state-of-the-art baselines for each individual tasks.
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Syahid, Ahmed, Mohammad Ali Tareq, and Aizul Nahar. "PREFERENCE MODELLING IN R: A TRIAL ON HOME BUYERS’ WILLINGNESS TO PAY." Journal on Innovation and Sustainability RISUS 12, no. 2 (June 18, 2021): 154–73. http://dx.doi.org/10.23925/2179-3565.2021v12i2p154-173.
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Modelling stated preferences is an almost mystical science and as there is no data explaining how the sustainable feature in homes would effectively encourage homebuyers to invest in sustainable housing, it is important to investigate the buyers’ willingness to pay (WTP) for sustainable housing. The study of stated preferences often requires the use of specialised software or proprietary programs, which can be difficult and/or expensive to use. This study proposes to re-purpose the ‘support.CEs’ package, a program written in the R programming language, from its agronomic roots to measure home buyer preferences for sustainable housing. These are demonstrated through a stated preference discrete choice experiment of choosing model houses with differing levels of energy savings, renewable energy generation, landscaping, soundproofing, ventilation, and price differences. A pilot study was performed using an online survey, constructed using the LMA design tool provided in the ‘support.CEs’ package. The survey was also separated into six blocks of six questions each to reduce the cognitive burden on respondents. The survey was distributed through social media channels. Preliminary results with a limited sample of 20 respondents with mixed income, age, and occupational demographics, analysed using the package’s clogit function, that performs conditional logit estimations, have shown that the results have a statistically reliable adjusted rho-squared value and that all coefficients show the expected signs. From this study, it can be concluded that the ‘support.CEs’ package can be used to model home buyer preferences and that adequate blocking allows for the measurement of a higher number of variables despite having smaller sample sizes.
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Murphy, Rachel M., Joanna E. Klopotowska, Nicolette F. de Keizer, Kitty J. Jager, Jan Hendrik Leopold, Dave A. Dongelmans, Ameen Abu-Hanna, and Martijn C. Schut. "Adverse drug event detection using natural language processing: A scoping review of supervised learning methods." PLOS ONE 18, no. 1 (January 3, 2023): e0279842. http://dx.doi.org/10.1371/journal.pone.0279842.
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To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP methods were extracted and critically appraised. Out of 1,065 articles screened for eligibility, 29 articles met the inclusion criteria. Most frequent tasks included named entity recognition (n = 17; 58.6%) and relation extraction/classification (n = 15; 51.7%). Clinical involvement was reported in nine studies (31%). Multiple NLP modelling approaches seem suitable, with Long Short Term Memory and Conditional Random Field methods most commonly used. Although reported overall performance of the systems was high, it provides an inflated impression given a steep drop in performance when predicting the ADE entity or ADE relation class. When annotating corpora, treating an ADE as a relation between a drug and non-drug entity seems the best practice. Future research should focus on semi-automated methods to reduce the manual annotation effort, and examine implementation of the NLP methods in practice.
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Thakur, Amey. "Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2307–25. http://dx.doi.org/10.22214/ijraset.2021.37723.
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Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these representations. The purpose of this research is to get the reader conversant with the GAN framework as well as to provide the background information on Generative Adversarial Networks, including the structure of both the generator and discriminator, as well as the various GAN variants along with their respective architectures. Applications of GANs are also discussed with examples. Keywords: Generative Adversarial Networks (GANs), Generator, Discriminator, Supervised and Unsupervised Learning, Discriminative and Generative Modelling, Backpropagation, Loss Functions, Machine Learning, Deep Learning, Neural Networks, Convolutional Neural Network (CNN), Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), Information Maximizing GAN (InfoGAN), Stacked GAN (StackGAN), Pix2Pix, Wasserstein GAN (WGAN), Progressive Growing GAN (ProGAN), BigGAN, StyleGAN, CycleGAN, Super-Resolution GAN (SRGAN), Image Synthesis, Image-to-Image Translation.
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Bernaisch, Tobias, Stefan Th Gries, and Joybrato Mukherjee. "The dative alternation in South Asian English(es)." English World-Wide 35, no. 1 (February 21, 2014): 7–31. http://dx.doi.org/10.1075/eww.35.1.02ber.
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The present paper focuses on the modelling of cross-varietal differences and similarities in South Asian English(es) and British English at the level of verb complementation. Specifically, we analyse the dative alternation with GIVE, i.e. the alternation between the double-object construction (John gave Mary a book) and the prepositional dative (John gave a book to Mary) as well as their passivised constructions with regard to the factors that potentially exert an influence on this alternation in seven varieties of English. The South Asian varieties under scrutiny are Bangladeshi English, Indian English, Maldivian English, Nepali English, Pakistani English and Sri Lankan English, while British English serves as the reference variety. The patterns of GIVE are annotated according to the following parameters including potential predictors of the dative alternation: syntactic pattern and semantic class of GIVE; syntactic complexity, animacy, discourse accessibility and pronominality of constituents (cf. Gries 2003b; Bresnan and Hay 2008). The choices of complementation patterns are then statistically modelled using conditional inference trees and a random-forest analysis. The results indicate that many of the predictors found to be relevant in British English are at play in the South Asian varieties, too. The syntactic pattern of GIVE is, in descending order, uniformly influenced by the predictors pronominality of recipient, length of recipient, semantic class of GIVE and length of patient. Interestingly, the predictor country is marginal in accounting for the dative alternation of GIVE across the varieties at hand. Based on this observation, we derive variety-independent protostructions, i.e. abstract combinations of (cross-varietally stable) features with high predictive power for a particular syntactic pattern, which we argue to be part of the lexicogrammatical “common core” (Quirk et al. 1985: 16) of English. The implications of the present paper are twofold. While the order of the predictors regarding their influence on the dative alternation is clearly compatible with earlier studies (cf. e.g. Green 1974; Ransom 1979; Hawkins 1994; Gries 2003b), the stability of the order across varieties of English calls for a) a more fine-grained gradation of linguistic forms and structures at the lexis-grammar interface as indicators of structural nativisation and b) a revision of earlier verb-complementational findings specific to individual or groups of varieties of South Asian English.
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Shnurovska, L. V. "Dynamics of Semantic and Pragmatic Framework of Modal Proposition: Linguistic and Cognitive Aspects." Scientific Journal of National Pedagogical Dragomanov University. Series 9. Current Trends in Language Development, no. 17 (August 21, 2018): 135–47. http://dx.doi.org/10.31392/npu-nc.series9.2018.17.10.
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The article outlines the linguocognitive background for semantic and pragmatic structural dynamics of the modal proposition in planes of relevance, ambiguity, force dynamics, as well as possible worlds theories. The integrated theoretical approaches entailed the development of a relatively admissible algorithm for interpreting the modal values in a vast number of pragmatic frameworks. Due to the algorithm, a modal proposition incorporates a logical relation and a propositional domain. Logical relation integrates semantic denotation and pragmatic implication and presupposition into the linguistic coherence; whereas propositional domain represents human belief-desire system and encodes the factual or desirable state of affairs in root modalities and the individual’s mental states in epistemic modalities. Propositional domain permanently updates and extends due to the constant modelling of the novel mental inputs. Structurally, the propositional domain incorporates modal operators building the proposition into the contextual framework and linking it to another proposition, i.e. the restrictor. We incorporate the notion of force dynamics to ground the link between the root and epistemic modalities. Here force serves as contributing or restricting facility to precondition the way root modalities encode the external reality and metaphorically transmit it into the language of thought producing epistemic modal values. In terms of the possible worlds theory we classified factual, regulative, desirable, and idealistic propositional domains to generally outline pragmatic extension of English modals. In the case study of distributional properties and pragmatic extensions of most commonly used English modals in their relationship to truth-conditional content, we speculated on and systematized the means via modal values such as necessity, ability, possibility, potentiality, ordering, desirability etc. are encoded in the live English speech.
Dissertations / Theses on the topic "Conditional language modelling"
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Mina, Petrić. "Modelling the influence of meteorological conditions on mosquito vector population dynamics (Diptera, Culicidae)." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2020. https://www.cris.uns.ac.rs/record.jsf?recordId=114757&source=NDLTD&language=en.
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Meteorological conditions have a significant influence on the time of occurrence, abundance and activity of the mosquito vector. In the current context of climate change, it is of great importance to assess the impact of shifts in climatic conditions on the suitability for the establishment and annual activity of the vector species. Moreover, changes in the variability of meteorological elements and their extremes can generate unexpected changes in the mosquito vector population which in turn have an important effect on human health. One of the ways to put these causes and effects into perspective is to simulate the activity of the vector within a processbased framework which allows for the analysis of the contribution of individual factors on the different life stages of the vector. Such analysis is presented by use of sophisticated dynamical models simulating the characteristics of the biological population, forced by observed meteorological data, capturing the localmicro-environment of the vector habitat, and validated by the observed entomology.Numerical models are being developed to model vector population dynamics and the expected circulation of the virus within a closed system. Two modelling approaches are standardly applied to modelling vector population dynamics: Mechanistic and Stochastic. The advantage of mechanistic overstatistical models is that they can provide a deterministic framework allowing for the isolated evaluation of each input parameter and their effect on the modelled system. Mechanistic dynamical models are used to describe the biophysical processes or partof the process as a response to changes in the meteorological conditions.The work carried out in this thesis can be summarized as follows: (i) Analysis of the association between the most important abiotic drivers influencing the population dynamics, annual activity and time of occurrence of Culex pipiens and Aedes aegypti; (ii) Identifying the most important climatic factors and model settings as a function of climatic characteristics of the study region; (iii) Modelling the vector population dynamics and stability analysis of the dynamical system (iv) Analysis of different verification techniques and implications in terms of model application; (v) Feasibility analysis ofimproving the model with a Land-Surface Parametrization scheme and short-range forecasting of pest population dynamics.Meteorološki uslovi bitno utiču na vreme pojave, brojnost vektora i njihovu aktivnost. U uslovima evidentnih promene klime, od ogromne je važnosti sagledati uticaj očekivanih promena klime na pogodnost uslova na pojavu izabranih vektora.Takodje, značajne promene kolebanja meteoroloških elemenata u odnosu na višegodišnji prosek i sve češće pojave nepovoljnih vremenskih prilika dovode do neočekivanog ponašanja populacije komarca što značajno utiče na kvalitet života i zdravlje ljudi. Jedini način da se sagledaju uzroci i posledice navedenih pojava zasniva se na simulaciji aktivnosti i brojnosti vektora uz mogućnost testiranja uticaja svakog pojedinačnog faktora. Ovu mogućnost pružaju samo visoko sofistikovani dinamički modeli koju su prošli proces kalibracije i validacije zasnovanna izmerenim vrednostima meteoroloških elemenata i karakteristika biološke populacije.Sofistikovani modeli za simulaciju dinamike populacije vektora i očekivane cirkulacije vektorskih transmisivnih bolesti se koriste sa ciljem modeliranja potencijalnog rizika od zaraze i epidemije. Modeli zasimulaciju dinamike vektora mogu da se podele na dve glavne grupe: Mehanističke i Statističke. Prednost mehanističkih modela nad statističkim je što mogu da se koriste za evaluaciju uticaja izolovanog faktora na dinamički sistem i odgovarajuće promene brojnosti unutar svake faze u razvoju vektora. Mehanistički dinamički sistemi se koriste kako bi se opisao mehanizam biofizičkog procesa ili dela procesa u zavisnosti od forsirajuće veličine.Predmet istraživanja u ovom radu jeste identifikovanje najznačajnijih bioloških i fizičkih procesa kao i odgovarajućih faktora koji utiču na brojnost i aktivnost vektora roda Aedes i Culex. Ciljevi istraživanja mogu da se sumiraju na sledeći način: (i)analiza najznačajnijih meteoroloških parametara koji utiču na vreme pojave, brojnost i aktivnost vektora Aedes i Culex roda; (ii) definisanje najznačajnijih klimatskih faktora i stepena osetljivosti procesa na njih; (iii) modeliranje dinamike populacije vektora i analiza stabilnosti dinamičkog sistema; (iv) verifikacija i analiza metoda verifikacije i validacije dinamičkog modela; (v) kratkoročna prognoza dinamike populacije komarca i formulacija hidrološkog modula upotrebom SURFEX površinskešeme sa ECOCLIMAP fiziogeografskim podacima.
Books on the topic "Conditional language modelling"
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Walker, Caitlin. From Contempt to Curiosity: Creating the Conditions for Groups to Collaborate Using Clean Language and Systemic Modelling. Clean Publishing, 2014.
Find full textBook chapters on the topic "Conditional language modelling"
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Paquet, Hugo. "Bayesian strategies: probabilistic programs as generalised graphical models." In Programming Languages and Systems, 519–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_19.
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AbstractWe introduceBayesian strategies, a new interpretation of probabilistic programs in game semantics. This interpretation can be seen as a refinement of Bayesian networks.Bayesian strategies are based on a new form ofevent structure, with two causal dependency relations respectively modelling control flow and data flow. This gives a graphical representation for probabilistic programs which resembles the concrete representations used in modern implementations of probabilistic programming.From a theoretical viewpoint, Bayesian strategies provide a rich setting for denotational semantics. To demonstrate this we give a model for a general higher-order programming language with recursion, conditional statements, and primitives for sampling from continuous distributions and trace re-weighting. This is significant because Bayesian networks do not easily support higher-order functions or conditionals.
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Gaboardi, Marco, Shin-ya Katsumata, Dominic Orchard, and Tetsuya Sato. "Graded Hoare Logic and its Categorical Semantics." In Programming Languages and Systems, 234–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_9.
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AbstractDeductive verification techniques based on program logics (i.e., the family of Floyd-Hoare logics) are a powerful approach for program reasoning. Recently, there has been a trend of increasing the expressive power of such logics by augmenting their rules with additional information to reason about program side-effects. For example, general program logics have been augmented with cost analyses, logics for probabilistic computations have been augmented with estimate measures, and logics for differential privacy with indistinguishability bounds. In this work, we unify these various approaches via the paradigm of grading, adapted from the world of functional calculi and semantics. We propose Graded Hoare Logic (GHL), a parameterisable framework for augmenting program logics with a preordered monoidal analysis. We develop a semantic framework for modelling GHL such that grading, logical assertions (pre- and post-conditions) and the underlying effectful semantics of an imperative language can be integrated together. Central to our framework is the notion of a graded category which we extend here, introducing graded Freyd categories which provide a semantics that can interpret many examples of augmented program logics from the literature. We leverage coherent fibrations to model the base assertion language, and thus the overall setting is also fibrational.
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Felli, Paolo, Marco Montali, and Sarah Winkler. "CTL$$^*$$ Model Checking for Data-Aware Dynamic Systems with Arithmetic." In Automated Reasoning, 36–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_4.
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AbstractThe analysis of complex dynamic systems is a core research topic in formal methods and AI, and combined modelling of systems with data has gained increasing importance in applications such as business process management. In addition, process mining techniques are nowadays used to automatically mine process models from event data, often without correctness guarantees. Thus verification techniques for linear and branching time properties are needed to ensure desired behavior.Here we consider data-aware dynamic systems with arithmetic (DDSAs), which constitute a concise but expressive formalism of transition systems with linear arithmetic guards. We present a CTL$$^*$$ ∗ model checking procedure for DDSAs that addresses a generalization of the classical verification problem, namely to compute conditions on the initial state, called witness maps, under which the desired property holds. Linear-time verification was shown to be decidable for specific classes of DDSAs where the constraint language or the control flow are suitably confined. We investigate several of these restrictions for the case of CTL$$^*$$ ∗ , with both positive and negative results: witness maps can always be found for monotonicity and integer periodicity constraint systems, but verification of bounded lookback systems is undecidable. To demonstrate the feasibility of our approach, we implemented it in an SMT-based prototype, showing that many practical business process models can be effectively analyzed.
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Schreier, Daniel. "Englishes in Tristan da Cunha, St Helena, Bermuda and the Falkland Islands: PCE, non-PCE or both? Blurred Boundaries in the Atlantic." In Modelling World Englishes, 298–321. Edinburgh University Press, 2020. http://dx.doi.org/10.3366/edinburgh/9781474445863.003.0014.
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This chapter looks at the interplay of extra- and intra-territorial forces that shaped the evolution and sociolinguistic characteristics of four varieties of English spoken in the Atlantic Ocean: the Bermudas, St Helena, Tristan da Cunha and the Falkland Islands. It evaluates general and locally specific forces that operated in the formation of these varieties, with a focus on the nature of various co-existing dialects (ENL, ESL and EFL) in the early contact scenarios that straddle current dividing lines between social and ethnic communities in the respective communities. Some sort of historic ‘push and pull’ operated between extra- and intra-territorial forces in all four varieties. There are domains where the two types cannot be subdivided (attitudes to tourism and immigration) so that external factors (such as settlement policy) have provided the petri dish for the enactment of internal forces at a later stage. This is discussed with reference as to whether dialect contact conditions lend themselves to theorizing and how lesser-known varieties fit into current models of English as a world language.
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van Nederveen, Sander, Reza Beheshti, and Wim Gielingh. "Modelling Concepts for BIM." In Handbook of Research on Building Information Modeling and Construction Informatics, 1–18. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-928-1.ch001.
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Building Information Modelling (BIM) is potentially a great technology for the expression of knowledge, supporting interoperability and communication throughout the life-cycle of a building. In fact, Building Information Modelling is not a simple technology. It requires a sound understanding of a number of abstract modelling concepts. Next to being a technology, BIM can also be regarded as a method for making a low or non-redundant (i.e. with every fact represented only once) model of an artefact that is sufficient to realize it as well as simulating it before it actually becomes physical reality. This chapter discusses the modelling concepts of BIM: what is Building Information Modelling, what is a Building Information Model and what are its rationale and objectives? A clear distinction will be made between (a) that what is being modelled, such as requirements, function, boundary conditions, building configuration, connectivity, shape, processes lifecycle aspects and discipline views, and (b) how it can be modelled, such as through parametric models, part libraries, nD models, various representations and presentations, including visualizations. Finally, there is a brief discussion of relevant methods and languages for information modelling, such as ISO 10303 (STEP, EXPRESS), BuildingSMART (IFC, IFD and IDM), process modelling and recent ontology-based approaches.
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Soboleva, Aleksandra V. "A Cognitive Style-Inclusive Approach as a Means of Learner-Centered EFL Teaching Mode Implementation." In Handbook of Research on Curriculum Reform Initiatives in English Education, 122–35. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5846-0.ch008.
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This chapter describes a methodology for developing English as a foreign language (EFL) communicative skills with respect to students' cognitive styles in the context of intercultural communication. A cognitive style-inclusive approach is now considered one of the most efficient ways to tailor teaching frameworks to the needs of each student. The chapter explores a number of psychological and pedagogical conditions that contribute to the efficient implementation of a learner-centered paradigm in the context of EFL education at the university level. Such aspects as students' involvement in the intercultural context, modelling the conditions of intercultural dialogue, the variability of content types and forms of learning activity, the complexity of information processing strategies, and polymodal cognitive experience are described, and their potential value for the developed methodology analyzed. Ways these can support students' communicative and cognitive development in EFL courses, in addition to teachers' efforts to create a rich and effective learning environment, are explored.
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Hrytsenko, Pavlo Yu. "L. E. Kalnyn’: foundations of her scholarship’s hypertext." In Slavic Dialectology Studies. Issue 23. A tribute to Ludmila Kalnyn, 9–36. Institute of Slavic Studies of the RAS, 2021. http://dx.doi.org/10.31168/2618-8589.2021.23.02.
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The works of renowned Slavicist Ludmila Kalnyn’ constitute an integral hypertext, exhibiting the movement of ideas, goals, and methods of studying Slavonic dialects in the second half of the 20th c. and the early 21st c. These works feature innovative methodologies and techniques of dialect research, such as her precise technique of modelling individual subdialects (i.e. actual communicative dialect systems) and dialect language (i.e. a construct comprising individual subdialects); this promising method has proved to be effective and has been adopted by other dialect researchers. Furthermore, she formulated a range of problems solution of which was aimed to the structure and functioning of dialects, their status in the national language stratigraphy, and areal characteristics of items and phenomena found in dialects. The scope of dialect modelling includes subdialect phonological systems, syllable-internal structure and syllabification, and word as both the field and the factor of phonetic changes. Her modelling technique involves the sequential use of sets of distinctive features of the objects under study, also taking into account the regularity of manifestation of features in dialect speech and conditions of their neutralization. Empirical databases were created regarding Slavonic (viz. Russian, Ukrainian, Polish, Bulgarian and Sorbian) dialects, including their systemic descriptions. Special attention was given to the status of modern dialects against the background of other strata of the national language, and to their role in forming and present-day development of literary language. In her research, Kalnyn’ has demonstrated: – the advantage of the exhaustive systemic description of a dialect based on the system of distinctive features over descriptions of random subdialectal features against the background of standard language, the importance of uncontradictory explication of preconditions and chronotope of the origin of structural and functional specificity of modern Slavonic dialects; – the importance of joint approach combining synchronic systemic description with the elucidation of the history of every structural element and phenomenon, establishing their spread within national and general Slavonic continuum as a means of achieving the completeness of dialectal knowledge; – the heuristic value of identification and detailed analysis not only of entities and phenomena widely spread within a dialectal continuum, but also of locally limited ones of the ancient origin or, on the contrary, resulting from recent innovations; – the necessity for comprehensive dialectal studies of consistently considering social, historical, and cultural circumstances of a subdialect’s functioning within society. Many years of her active participation in the work on The General Slavonic Linguistic Atlas (including the identification and development of its theoretical foundations, production of numerous phonetic maps, especially interpretative and generalizing ones), analysis of phonetic Slavonic areas determined the dynamics of her views on many phenomena of Slavonic phonetics. At the same time, working in the international collective, and organizing international conferences dedicated to problems of Slavonic dialectology, led to the dissemination of L. E. Kalnyn’s ideas, theoretical generalizations, and practical experience. The trajectory of this experience’s emergence is insolubly linked with the conceptual development of present-day Slavonic dialectology.
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Prathap, Boppuru Rudra, Sujatha A K, Chandragiri Bala Satish Yadav, and Mummadi Mounika. "Polarity Detection on Real-Time News Data Using Opinion Mining." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200124.
Full textAbstract:
Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the user’s opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done.
Conference papers on the topic "Conditional language modelling"
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Caratozzolo, Francesco, Alberto Traverso, and Aristide F. Massardo. "Development and Experimental Validation of a Modelling Tool for Humid Air Turbine Saturators." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23338.
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This work presents the re-engineering of the TRANSAT 1.0 code which was developed to perform off-design and transient condition analysis of Saturators and Direct Contact Heat Exchangers. This model, now available in the 2.0 release, was originally implemented in FORTRAN language, has been updated to C language, fully coded into MATLAB/Simulink® environment and validated using the extensive set of data available from the MOSAT project, carried out by the Thermochemical Power Group of the University of Genoa. The rig consists of a fully instrumented modular vertical saturator, which is controlled and monitored with a LABVIEW® computer interface. The simulation software showed fair stability in computation and in response to step variation of the main parameters driving the thermodynamic evolution of the air and water flows. Considering the actual mass flow rates, a geometric similitude was used to avoid calculation instability due to flows under 100 g/s. Overall the model proved to be reliable and accurate enough for energy system simulations.
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Pedersen, Lea Duedahl, Kenny Krogh Nielsen, Chungen Yin, Henrik Sørensen, and Ingar Fossan. "Modelling of Hot Surface Ignition Within Gas Turbines Subject to Flammable Gas in the Intake." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64698.
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Controlling risks associated with fires and explosions from leaks of flammable fluids at oil and gas facilities is paramount to ensuring safe operations. The gas turbine is a significant potential source of ignition; however, the residual risk is still not adequately understood. A model has been successfully developed and implemented in the commercial Computational Fluid Dynamics (CFD) code ANSYS CFX. This model is based on a combination of standard models, User Defined Functions (UDFs) and the CFX Expression Language (CEL). Prediction of ignition is based on a set of criteria to be fulfilled while complex kinetics is handled computationally easy by means of a reaction progress variable. The simulation results show a good agreement with the trends experimentally observed in other studies. It is found that the hot surface ignition temperature (HSIT) increases with increase in velocity and turbulence but decreases with increase in initial mixture temperature and pressure. The model shows a great potential in reliable prediction of the risk of hot surface ignition within gas turbines in the oil and gas industry. In the future, a dedicated experimental study will be performed not only to improve the understanding of the risk of hot surface ignition but also to collect experimental data under well-defined conditions to further validate or refine the model.
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Kumar, Ashutosh, and Robin Marlar Rajendran. "Expediting Chemical Enhanced Oil Recovery Processes with Prediction of Chemical Reaction Yield Using Machine Learning." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211832-ms.
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Abstract Chemical enhanced oil recovery involves enormous combinations of chemicals, surfactants, etc. The reservoir properties such as temperature, capillary pressure, permeability keeps changing, making the process of identification of suitable chemicals even more challenging. Data driven modelling holds solutions for the complexity involved in identification of most suitable parameters for chemical enhanced oil recovery. Over the last decade, Artificial Intelligence has found its numerous applications in different branches of chemistry. From the selection of chemicals to the operating conditions during synthesis all can be estimated by the use of deep learning models. This paper presents yield prediction which is of high economic significance for chemical enhanced oil recovery, because they enable calculation of investment versus return. These models give us the conversion of reaction into products before performing the lab experiment. This will help chemists in selecting high performance chemicals for specific reservoirs without spending time on costly iterative chemical processes. These models require application of deep learning architectures like transformers and natural language processing techniques like tokenization for the prediction task. Encoder models like BERT are used for receiving the information on chemical reactions in text-based form for a reaction which is then combined with a regression extension layer to give us the desired reaction yield. We demonstrate our model on a HTE dataset with an excellent prediction score. Efforts are also made on the USPTO patent dataset which covers a wide variety of chemical reaction space. The USPTO patent dataset consists of almost every chemical reaction published since late 1970s till 2006. Diverse techniques starting with Multi Layer Perceptrons, Sequence to sequence modelling, Long short term memory models and finally transformers are employed for the improvement of accuracy of patent reactions. The paper presents detailed comparative results of predicting chemical reaction yield, and the enhancements that it will bring to Chemical Enhanced Oil Recovery. Reaction yield prediction models receive very little attention in spite of their enormous potential of determining the reaction conversion rates and its contribution to chemical enhanced oil recovery processes . The paper introduces a novel approach of modelling chemical reaction yield with deep learning models to the petroleum community. Unprecedented result of accuracy beyond 90% in predicting chemical reactions yield and its significance in chemical enhanced oil recovery has been proposed in the paper.
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Salogni, A., P. Iora, and S. Campanari. "Dynamic Analysis and Control of a Planar IT-SOFC System." In ASME 2009 7th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2009. http://dx.doi.org/10.1115/fuelcell2009-85136.
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This paper analyzes the dynamic behaviour of a 5 kW fuel cell system based on planar co-flow Intermediate Temperature Solid Oxide Fuel Cell (IT-SOFC) stack, with internal reforming. The system is composed by the SOFC stack, a combustor of the cell exhausts, two heat exchangers for fuel and air preheating and the related control valves, where the air temperature at the stack exit and the fuel utilization is controlled by means of a PI (proportional integral) device. The model of the stack is based on a lumped parameters dynamic model of a single cell, which is composed of the fuel and air channels, the electrochemically active three layer region representative of the anode, the cathode and the electrolyte. The stack model is first used here for a qualitative steady-state validation, reproducing the cell characteristic curve. Then it is presented the dynamic model of the system, which has been implemented using an a-causal software based on the open-source Modelica modelling language, which allows for future integration in complex power-plant configurations. After a description of the plant layout and of the dynamic model, we present and discuss the results obtained by applying the PI controls to different load changes and with different tuning of the controller parameters, evidencing the amplitudes of load changes, the extent of the transient phase to the new steady-state conditions, the internal cell temperature distribution and the thermal gradients along the PEN structure, giving the possibilities to adapt the control system to the requirements of specific SOFC technologies.
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Salogni, A., and D. Alberti. "Dynamic Analysis of a Planar SOFC Stack Fuelled by Biogas." In ASME 2010 8th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2010. http://dx.doi.org/10.1115/fuelcell2010-33255.
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This paper analyzes the dynamic behaviour of a 50 kW stack using planar co-flow solid oxide fuel cells with direct internal reforming fuelled by a biologically derived gaseous mixture of methane and carbon dioxide. The system modelled is composed by the SOFC stack, a catalytic burner, the heat recovery system and the control device aimed to keep the air temperature at the stack exit and the fuel utilization near to the set values. The model has been implemented using standard and user-defined components of an a-causal software based on the open-source Modelica modelling language. After a brief introduction to the production of the gaseous fuel derived from the anaerobic digestion of pig manure, data obtained from a case study on a pig farm situated in Lombardia (Italy) are presented, focusing on the yield of methane which can be exploited. The steady-state performance of the SOFC system fuelled by pure methane are compared with those obtained for the biogas working conditions, showing that the stack voltage is affected by greater concentration losses. Then, starting from a steady-state delivered current of 750 mA cm−2, the dynamic behaviour of the system when a load change of −150 mA cm−2 occurs is investigated for both pure methane and biogas fuelling hypothesis. The results of the simulations show that the transient phase is only marginally affected by the composition of the fuel, which causes a delay of about 50 s in the voltage transient. Finally, the effect obtained by imposing a linear variation in the fuel composition, which can be representative of a modification in the biological degradation of the organic substrate within the anaerobic digester, is discussed. After an initial transient, which is comparable with that obtained for a variation in the load current, the SOFC system is capable to restore the initial delivered power, provided that the required amount of fuel can be supplied to the anode.
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Benato, Alberto, Leonardo Pierobon, Fredrik Haglind, and Anna Stoppato. "Dynamic Performance of a Combined Gas Turbine and Air Bottoming Cycle Plant for Off-Shore Applications." In ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/esda2014-20105.
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When the Norwegian government introduced the CO2 tax for hydrocarbon fuels, the challenge became to improve the performance of off-shore power systems. An oil and gas platform typically operates on an island (stand-alone system) and the power demand is covered by two or more gas turbines. In order to improve the plant performance, a bottoming cycle unit can be added to the gas turbine topping module, thus constituting a combined cycle plant. This paper aims at developing and testing the numerical model simulating the part-load and dynamic behavior of a novel power system, composed of two gas turbines and a combined gas turbine coupled with an air bottoming cycle plant. The case study is the Draugen off-shore oil and gas platform, located in the North Sea, Norway. The normal electricity demand is 19 MW, currently covered by two gas turbines generating each 50% of the power demand, while the third turbine is on stand-by. During oil export operations the power demand increases up to 25 MW. The model of the new power plant proposed in this work is developed in the Modelica language using basic components acquired from ThermoPower, a library for power plant modelling. The dynamic model of the gas turbine and the air bottoming cycle turbogenerator includes dynamic equations for the combustion chamber, the shell-and-tube recuperator and the turbine shafts. Turbines are modelled by the Stodola equation and by a correlation between the isentropic efficiency and the non-dimensional flow coefficient. Compressors are modelled using quasi steady-state conditions by scaling the maps of axial compressors employing a similar design point. The recuperator, which recovers the exhaust heat from the gas turbine, is modelled using correlations relating the heat transfer coefficient and the pressure drop at part-load with the mass flow rate. Thermodynamic variables and dynamic metrics, such as the rise time and the frequency undershooting/ overshooting, are predicted. Considering a load ramp of 0.5 MW/s, an undershooting of 4.9% and an overshooting of 3.0% are estimated. The rise time is approximately 30 s. Moreover, findings suggest that decreasing the core weight of the recuperator leads to limiting the frequency fluctuations, thus minimizing the risk of failure of the power system.