Relevant bibliographies by topics / Gab (Artificial language)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Gab (Artificial language)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Gab (Artificial language)"

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Garvía, Roberto. "Spelling reformers and artificial language advocates." Language Problems and Language Planning 41, no. 3 (2017): 287–303. http://dx.doi.org/10.1075/lplp.00007.gar.

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Abstract This article explores the shifting relations that took place from the last decades of the 19th to the first years of the 20th century between two of the most innovative language movements of the time: the spelling reform and the artificial language movements. The article focuses on the United States, the United Kingdom, Germany and France. Although both movements shared a similar language ideology which run counter to the organicist perception of language and emphasized its democratic function, the article shows how the shifting political environment in which they operated affected their relation. The article identifies three stages. In a first stage, and convinced that the reform of the spelling and the promotion of an artificial, neutral language were not mutually exclusive projects, the spelling reformers were favorably inclined towards artificial language projects. In a second stage relations began to skew when some reformers advocated for the “natural Esperanto” solution, which implied the promotion of a small language to the status of the international lingua franca. In the last stage, when nationalist sentiments and international rivalries mounted, the spelling reformers broke ties with the artificial language movement and worked to improve as much as possible the international standing of their own languages.
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Taher, Ali Abdul Kadhim, and Suhad Malallah Kadhim. "Improvement of genetic algorithm using artificial bee colony." Bulletin of Electrical Engineering and Informatics 9, no. 5 (2020): 2125–33. http://dx.doi.org/10.11591/eei.v9i5.2233.

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Genetic algorithm (GA) is a part of evolutionary computing that simulates the theory of evolution and natural selection, where this technique depends on a heuristic random search. This algorithm reflects the operation of natural selection, where the fittest individuals are chosen for reproduction so that they produce offspring of the next generation. This paper proposes a method to improve GA using artificial bee colony (GABC). This proposed algorithm was applied to random number generation (RNG), and travelling salesman problem (TSP). The proposed method used to generate initial populations for GA rather than the random generation that used in traditional GA. The results of testing on RNG show that the proposed GABC was better than traditional GA in the mean iteration and the execution time. The results of testing TSP show the superiority of GABC on the traditional GA. The superiority of the GABC is clear in terms of the percentage of error rate, the average length route, and obtaining the shortest route. The programming language Python3 was used in programming the proposed methods.
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Dissertations / Theses on the topic "Gab (Artificial language)"

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Predrag, Kojić. "Hidrodinamika i prenos mase u airlift reaktoru sa membranom." Phd thesis, Univerzitet u Novom Sadu, Tehnološki fakultet Novi Sad, 2016. http://www.cris.uns.ac.rs/record.jsf?recordId=100280&source=NDLTD&language=en.

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U okviru doktorske disertacije izvedena su eksperimentalna istraživanja osnovnih hidrodinamičkih i maseno-prenosnih karakteristika airlift reaktora sa spoljnom recirkulacijom sa ugrađenom višekanalnom cevnom membranom u silaznu cev (ALSRM). ALSRM je radio na dva načina rada: bez mehurova u silaznoj cevi (način rada A) i sa mehurovima u silaznoj cevi (način rada B) u zavisnosti od nivoa tečnosti u gasnom separatoru. Ispitivani su uticaji prividne brzine gasa, površinskih osobina tečne faze, tipa distributora gasa i prisustva mehurova gasa u silaznoj cevi na sadržaj gasa, brzinu tečnosti u silaznoj cevi i zapreminski koeficijent prenosa mase u tečnoj fazi u ALSRM. Rezultati su poređeni sa vrednostima dobijenim u istom reaktoru ali bez membrane (ALSR). Sadržaj gasa u uzlaznoj i silaznoj cevi određivan je pomoću piezometarskih cevi merenjem hidrostatičkog pritiska na dnu i vrhu uzlazne i silazne cevi. Brzina tečnosti merena je pomoću konduktometrijskih elektroda dok je zapreminski koeficijent prenosa mase dobijen primenom dinamičke metode merenjem promene koncentracije kiseonika u vremenu optičkom elektrodom. Eksperimentalni rezultati pokazuju da sadržaj gasa, brzina tečnosti i zapreminski koeficijent prenosa mase zavise od prividne brzine gasa, vrste alkohola i tipa distributora gasa kod oba reaktora. Višekanalna cevna membrana u silaznoj cevi uzrokovala je povećanje ukupnog koeficijenta trenja za 90% i time dovela do smanjenja brzine tečnosti u silaznoj cevi do 50%. Smanjena brzina tečnosti u silaznoj cevi povećala je sadržaj gasa do 16%. Predložene neuronske mreže i empirijske korelacije odlično predviđaju vrednosti za sadržaj gasa, brzinu tečnosti i zapreminski koeficijent prenosa mase.
An objective of this study was to investigate the hydrodynamics and the gas-liquid mass transfer coefficient of an external-loop airlift membrane reactor (ELAMR). The ELAMR was operated in two modes: without (mode A), and with bubbles in the downcomer (mode B), depending on the liquid level in the gas separator. The influence of superficial gas velocity, gas distributor’s geometry and various diluted alcohol solutions on hydrodynamics and gas-liquid mass transfer coefficient of the ELAMR was studied. Results are commented with respect to the external loop airlift reactor of the same geometry but without membrane in the downcomer (ELAR). The gas holdup values in the riser and the downcomer were obtained by measuring the pressures at the bottom and the top of the riser and downcomer using piezometric tubes. The liquid velocity in the downcomer was determined by the tracer response method by two conductivity probes in the downcomer. The volumetric mass transfer coefficient was obtained by using the dynamic oxygenation method by dissolved oxygen probe. According to experimental results gas holdup, liquid velocity and gas-liquid mass transfer coefficient depend on superficial gas velocity, type of alcohol solution and gas distributor for both reactors. Due to the presence of the multichannel membrane in the downcomer, the overall hydrodynamic resistance increased up to 90%, the liquid velocity in the downcomer decreased up to 50%, while the gas holdup in the riser of the ELAMR increased maximally by 16%. The values of the gas holdup, the liquid velocity and the gas-liquid mass transfer coefficient predicted by the application of empirical power law correlations and feed forward back propagation neural network (ANN) are in very good agreement with experimental values.
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Book chapters on the topic "Gab (Artificial language)"

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Navas-Loro, María, Ken Satoh, and Víctor Rodríguez-Doncel. "ContractFrames: Bridging the Gap Between Natural Language and Logics in Contract Law." In New Frontiers in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31605-1_9.

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Harper, Ciaran M., and S. Sarah Zhang. "Legal Tech and Lawtech: Towards a Framework for Technological Trends in the Legal Services Industry." In Market Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66661-3_11.

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AbstractThe use of legal technology (legal tech) and the lawtech ecosystem of legal start-ups has experienced tremendous growth in recent years. To provide a structured approach of analysing IT innovations in the legal sector, we propose a framework for lawtech applications, classifying them into three groups: internal, B2C and B2B applications. In the context of this framework, we examine technological trends in lawtech and their potential to support and transform processes in specific areas of business or personal law. We acknowledge that within lawtech there is a gap between the areas of interest of legal practitioners, IT professionals and academic researchers, and that some areas have received considerable attention by these groups, while other areas have been left relatively unexplored by one or more of these groups. However, the growing interest by legal practitioners in advanced technology such as artificial intelligence (AI) and natural language processing (NLP) is further closing the gap between academic research, IT professionals and legal practice.
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Keats, Jonathon. "Gene Foundry." In Virtual Words. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780195398540.003.0013.

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As the nineteenth century was the age of iron and the twentieth belonged to silicon, the present century will be identified with carbon. CO2 is the iconic greenhouse gas, imprinted on our vocabulary with talk of carbon footprints and allowances and offsets. For synthetic biologists, however, the carbon debacle has counterintuitively positioned this debased element as our savior. The future they foresee will supplant grimy factories of concrete and steel with clean colonies of living cells. To use the terminology of Freeman Dyson, gray technology will be replaced by green. Among the most celebrated physicists of the twentieth century, Dyson has become one of the foremost promoters of synthetic biology, a field that technologically is to genetic engineering what genetic engineering is to crop cultivation. Conceptually the distinction is even more radical than that: whereas genetic engineering merely modifies preexisting creatures more precisely than selective breeding, synthetic biology aims to fabricate entirely new organisms from nonliving materials. Unconstrained by genetic history, these artificial life forms can be intelligently designed to produce fuels or pharmaceuticals with unprecedented efficiency. The sheer audacity of synthetic biology lends itself to hyperbole, aptly captured in a 2007 Nature editorial: “For the first time, God has competition.” Yet the language of synthetic biology, also known as bioengineering, hardly bespeaks a cosmic paradigm shift. DNA constructed at a so-called gene foundry gives specialized function to a generic cell referred to as a chassis. A Victorian industrialist would have no trouble following the metaphoric language. He might even find work as a bioengineering consultant: the quaint iron age phrasing reflects the old-fashioned framework underlying this brave new discipline. After all, radical as artificial life may be philosophically—and significant as it may be environmentally—it’s technically just a strenuous construction project, with manufacturing challenges akin to building a bridge or a steam engine. That may explain why one of the most successful synthetic biologists working today, the Stanford University professor Drew Endy, trained as a civil engineer. Together with colleagues at the Massachusetts Institute of Technology, Endy has methodically approached synthetic biology as a problem of developing reliable building blocks and assembly protocols.
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Jainulabudeen, S. A. K., H. Shalma, S. Gowri Shankar, D. Anuradha, and K. Soniya. "A Novel Technique to Regenerate Sculpture Using Generative Adversarial Network." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200149.

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Dancing, music or any format of art has been a prominent thing from the past centuries. The many dynasties ruled the nation for centuries but every king encouraged the art one way or the other. The present day is just a minute part of the finest part of that era of art; the art of any form had been lost in the shadows to redeem the lost art we are going to use the latest technology like machine learning and artificial intelligence. The art lovers of the present age can seek the knowledge of lost art through this modern day technology. The retrieval of this art can only be done if there is a possibility to learn their language which helps in reading the old sculptures or the paintings on the walls of the ancient architecture. Now using the present day technology we are going to recoup that lost art through reading the walls of those structures where the art has been hidden for centuries. So at present we do not allow the art to continue to fall into shadow and extinguish later on, thus in this paper we present a DC-GAN model which has been created to inherit all the artistic skills of our ancestors by training from the key images of art designed as sculptures by our forefathers.
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Conference papers on the topic "Gab (Artificial language)"

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Feng, Xiaocheng, Xiachong Feng, Bing Qin, Zhangyin Feng, and Ting Liu. "Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/566.

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Neural networks have been widely used for high resource language (e.g. English) named entity recognition (NER) and have shown state-of-the-art results.However, for low resource languages, such as Dutch, Spanish, due to the limitation of resources and lack of annotated data, taggers tend to have lower performances.To narrow this gap, we propose three novel strategies to enrich the semantic representations of low resource languages: we first develop neural networks to improve low resource word representations by knowledge transfer from high resource language using bilingual lexicons. Further, a lexicon extension strategy is designed to address out-of lexicon problem by automatically learning semantic projections.Thirdly, we regard word-level entity type distribution features as an external language-independent knowledge and incorporate them into our neural architecture. Experiments on two low resource languages (including Dutch and Spanish) demonstrate the effectiveness of these additional semantic representations (average 4.8\% improvement). Moreover, on Chinese OntoNotes 4.0 dataset, our approach achieved an F-score of 83.07\% with 2.91\% absolute gain compared to the state-of-the-art results.
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Holt, George, and Stuart Bassler. "Preliminary Design of Axial Compressors Using Artificial Intelligence and Numerical Optimization Techniques." In ASME 1991 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/91-gt-334.

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This paper discusses an automated method for optimizing multistage axial compressor designs using a generic design software shell called Engineous®, coupled with an aerodynamic compressor design program called CUS5 (Compressor Unification Study 5). Engineous combines heuristic searching and numerical optimization techniques, and expert systems to explore the design space of turbomachinery components by iterating preliminary design analysis programs written in languages such as FORTRAN and C. The application design and development process and the lessons learned are discussed for the Engineous/CUS5 axial compressor application. This particular application calculates and maintains compressor stall prediction values while optimizing at the operating (design) point, and requires a design vs. stall comparison during each iteration. A trial compressor design problem was investigated using the Engineous application, and the preliminary results of the study are included. Also introduced is a method for varying flowpath walls using shape criteria rather than radii points.
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de Silva, Lavindra, Felipe Meneguzzi, and Brian Logan. "An Operational Semantics for a Fragment of PRS." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/27.

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The Procedural Reasoning System (PRS) is arguably the first implementation of the Belief--Desire--Intention (BDI) approach to agent programming. PRS remains extremely influential, directly or indirectly inspiring the development of subsequent BDI agent programming languages. However, perhaps surprisingly given its centrality in the BDI paradigm, PRS lacks a formal operational semantics, making it difficult to determine its expressive power relative to other agent programming languages. This paper takes a first step towards closing this gap, by giving a formal semantics for a significant fragment of PRS. We prove key properties of the semantics relating to PRS-specific programming constructs, and show that even the fragment of PRS we consider is strictly more expressive than the plan constructs found in typical BDI languages.
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Wang, Jingxian, Chengfeng Pan, Haojian Jin, et al. "Speech Recognition Using RFID Tattoos (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/664.

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This paper presents a radio-frequency (RF) based assistive technology for voice impairments (i.e., dysphonia), which occurs in an estimated 1% of the global population. We specifically focus on acquired voice disorders where users continue to be able to make facial and lip gestures associated with speech. Despite the rich literature on assistive technologies in this space, there remains a gap for a solution that neither requires external infrastructure in the environment, battery-powered sensors on skin or body-worn manual input devices. We present RFTattoo, which to our knowledge is the first wireless speech recognition system for voice impairments using batteryless and flexible RFID tattoos. We design specialized wafer-thin tattoos attached around the user's face and easily hidden by makeup. We build models that process signal variations from these tattoos to a portable RFID reader to recognize various facial gestures corresponding to distinct classes of sounds. We then develop natural language processing models that infer meaningful words and sentences based on the observed series of gestures. A detailed user study with 10 users reveals 86% accuracy in reconstructing the top-100 words in the English language, even without the users making any sounds.
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Pilarski, Sebastian, Martin Staniszewski, Frederic Villeneuve, and Daniel Varro. "On Artificial Intelligence for Simulation and Design Space Exploration in Gas Turbine Design." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2019. http://dx.doi.org/10.1109/models-c.2019.00029.

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Schwitter, Rolf. "Lossless Semantic Round-Tripping in PENG ASP." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/773.

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The PENG ASP system supports the writing of textual specifications with the help of a smart text editor that possesses knowledge about the structure of the specification language. Specifications written in PENG ASP are incrementally translated into executable answer set programs and vice versa. That means the system allows for lossless semantic round-tripping between a human-readable specification and an answer set program. This functionality is achieved by a single bi-directional logic grammar that serves at the same time as a text processor and a text generator. We demonstrate that the PENG ASP system can be used to bridge the gap between a (seemingly) informal specification and an executable answer set program.
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Yao, Kaichun, Libo Zhang, Tiejian Luo, Lili Tao, and Yanjun Wu. "Teaching Machines to Ask Questions." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/632.

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We propose a novel neural network model that aims to generate diverse and human-like natural language questions. Our model not only directly captures the variability in possible questions by using a latent variable, but also generates certain types of questions by introducing an additional observed variable. We deploy our model in the generative adversarial network (GAN) framework and modify the discriminator which not only allows evaluating the question authenticity, but predicts the question type. Our model is trained and evaluated on a question-answering dataset SQuAD, and the experimental results shown the proposed model is able to generate diverse and readable questions with the specific attribute.
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Li, Ziran, Zibo Lin, Ning Ding, Hai-Tao Zheng, and Ying Shen. "Triple-to-Text Generation with an Anchor-to-Prototype Framework." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/523.

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Generating a textual description from a set of RDF triplets is a challenging task in natural language generation. Recent neural methods have become the mainstream for this task, which often generate sentences from scratch. However, due to the huge gap between the structured input and the unstructured output, the input triples alone are insufficient to decide an expressive and specific description. In this paper, we propose a novel anchor-to-prototype framework to bridge the gap between structured RDF triples and natural text. The model retrieves a set of prototype descriptions from the training data and extracts writing patterns from them to guide the generation process. Furthermore, to make a more precise use of the retrieved prototypes, we employ a triple anchor that aligns the input triples into groups so as to better match the prototypes. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art baselines in terms of both automatic and manual evaluation, demonstrating the benefit of learning guidance from retrieved prototypes to facilitate triple-to-text generation.
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Wang, Ke, and Xiaojun Wan. "SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/618.

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Generating texts of different sentiment labels is getting more and more attention in the area of natural language generation. Recently, Generative Adversarial Net (GAN) has shown promising results in text generation. However, the texts generated by GAN usually suffer from the problems of poor quality, lack of diversity and mode collapse. In this paper, we propose a novel framework - SentiGAN, which has multiple generators and one multi-class discriminator, to address the above problems. In our framework, multiple generators are trained simultaneously, aiming at generating texts of different sentiment labels without supervision. We propose a penalty based objective in the generators to force each of them to generate diversified examples of a specific sentiment label. Moreover, the use of multiple generators and one multi-class discriminator can make each generator focus on generating its own examples of a specific sentiment label accurately. Experimental results on four datasets demonstrate that our model consistently outperforms several state-of-the-art text generation methods in the sentiment accuracy and quality of generated texts.
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Ji, Zhong, Kexin Chen, and Haoran Wang. "Step-Wise Hierarchical Alignment Network for Image-Text Matching." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/106.

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Image-text matching plays a central role in bridging the semantic gap between vision and language. The key point to achieve precise visual-semantic alignment lies in capturing the fine-grained cross-modal correspondence between image and text. Most previous methods rely on single-step reasoning to discover the visual-semantic interactions, which lacks the ability of exploiting the multi-level information to locate the hierarchical fine-grained relevance. Different from them, in this work, we propose a step-wise hierarchical alignment network (SHAN) that decomposes image-text matching into multi-step cross-modal reasoning process. Specifically, we first achieve local-to-local alignment at fragment level, following by performing global-to-local and global-to-global alignment at context level sequentially. This progressive alignment strategy supplies our model with more complementary and sufficient semantic clues to understand the hierarchical correlations between image and text. The experimental results on two benchmark datasets demonstrate the superiority of our proposed method.
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