Journal articles on the topic 'Linguistics – Computer network resources'
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1
Takahashi, Yusuke, and Ichiro Kobayashi. "Systemic-Functional Context-Sensitive Text Generation in the Framework of Everyday Language Computing." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 6 (November 20, 2006): 791–801. http://dx.doi.org/10.20965/jaciii.2006.p0791.
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The present text generator using resources based on Systemic Functional Linguistics (SFL). Resources are compiled in a database called the Semiotic Base, which deals with language in context. In contrast to previous SFL-based text generation systems, our comprehensive proposal contains the Context Base, which deals with context surrounding text, and covers all strata, from context to expression. Its text generation process maximizes the use of the Semiotic Base resources, i.e., system networks dealing with linguistic features. Our text generation system is resource-driven, draws heavily on information provided by the Semiotic Base, and minimizes information input.
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Wong, Jan, and Agnès Fauverge. "LEVERAGE: Reciprocal peer tutoring over broadband networks." ReCALL 11, no. 1 (May 1999): 133–42. http://dx.doi.org/10.1017/s0958344000002172.
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This paper reports on the experimental use of a broadband computer network hypermedia environment for language learning (French, English and Spanish). Using Web-based resources, students engage in a collaborative task over a network which offers high quality video-conferencing, application sharing and access to authentic multimedia resources. One of the main aims was to establish the practicalities of providing learners of languages with opportunities to engage in reciprocal peer tutoring. After outlining the pedagogical assumptions, and describing the set-up of the network-based learning environment, the trials are analysed, and the effectiveness of network-based language learning in supporting collaborative learning is discussed.
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Rains, Stephen A. "Language Style Matching as a Predictor of Perceived Social Support in Computer-Mediated Interaction Among Individuals Coping With Illness." Communication Research 43, no. 5 (January 13, 2015): 694–712. http://dx.doi.org/10.1177/0093650214565920.
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Several forms of computer-mediated communication (e.g., online support groups, blogs, social network sites) have been shown to be important resources for social support among individuals coping with illness. The reported study attempts to better understand social support processes in these settings by examining the implications of language style matching—a form of interpersonal coordination involving the degree to which speakers match one another’s use of function words (e.g., articles, prepositions, pronouns). Language style matching among a sample of health bloggers and their readers over a 3-month period was tested as a predictor of bloggers’ perceptions of support available from their readers. The results show that language style matching contributed to bloggers’ perceptions that their readers are willing and able to serve as a resource for specific forms of social support.
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Gupta, Vedika, Nikita Jain, Shubham Shubham, Agam Madan, Ankit Chaudhary, and Qin Xin. "Toward Integrated CNN-based Sentiment Analysis of Tweets for Scarce-resource Language—Hindi." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 5 (June 23, 2021): 1–23. http://dx.doi.org/10.1145/3450447.
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Linguistic resources for commonly used languages such as English and Mandarin Chinese are available in abundance, hence the existing research in these languages. However, there are languages for which linguistic resources are scarcely available. One of these languages is the Hindi language. Hindi, being the fourth-most popular language, still lacks in richly populated linguistic resources, owing to the challenges involved in dealing with the Hindi language. This article first explores the machine learning-based approaches—Naïve Bayes, Support Vector Machine, Decision Tree, and Logistic Regression—to analyze the sentiment contained in Hindi language text derived from Twitter. Further, the article presents lexicon-based approaches (Hindi Senti-WordNet, NRC Emotion Lexicon) for sentiment analysis in Hindi while also proposing a Domain-specific Sentiment Dictionary. Finally, an integrated convolutional neural network (CNN)—Recurrent Neural Network and Long Short-term Memory—is proposed to analyze sentiment from Hindi language tweets, a total of 23,767 tweets classified into positive, negative, and neutral. The proposed CNN approach gives an accuracy of 85%.
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Anees, Junaid, and Hao-Chun Zhang. "FLOC: Hesitant Fuzzy Linguistic Term Set Analysis in Energy Harvesting Opportunistic Clustering Using Relative Thermal Entropy and RF Energy Transfer." International journal of Computer Networks & Communications 14, no. 02 (March 31, 2022): 19–39. http://dx.doi.org/10.5121/ijcnc.2022.14202.
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Limited energy resources and sensor nodes’ adaptability with the surrounding environment play a significant role in the sustainable Wireless Sensor Networks. This paper proposes a novel, dynamic, selforganizing opportunistic clustering using Hesitant Fuzzy Linguistic Term Analysis- based Multi-Criteria Decision Modeling methodology in order to overcome the CH decision-making problems and network lifetime bottlenecks. The asynchronous sleep/awake cycle strategy could be exploited to make an opportunistic connection between sensor nodes using opportunistic connection random graph. Every node in the network observe the node gain degree, energy welfare, relative thermal entropy, link connectivity, expected optimal hop, link quality factor etc. to form the criteria for Hesitant Fuzzy Linguistic Term Set. It makes the node to evaluate its current state and make the decision about the required action (‘CH’, ‘CM’ or ‘relay’). Our proposed scheme leads to an improvement in network lifetime, packet delivery ratio and overall energy consumption against existing benchmarks.
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Ma, Tszinyan. "For what reason does Raskolnikov laugh?" Litera, no. 11 (November 2021): 89–97. http://dx.doi.org/10.25136/2409-8698.2021.11.36874.
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This article analyzes the resources of application of computer technologies for the purpose of visualization of the text semantic field “laughter”. The concept of “laughter” holds a special place in the works of F. M. Dostoevsky, as well as in one of the central semantic fields in the text semantic space of the novel “Crime and Punishment”. Examination of the semantic field within the space of a particular literary text allows determining the peculiarities of the authorial thesaurus within the structure of his linguistic identity. The subject of this article is the concept of “laughter” as the core of the corresponding text semantic field; determination of saturation of the text with the words belonging to a certain semantic field. Despite the fact that multiple questions related to the theory of semantic field are well studied, the scholars take interests in studying the linguistic material, denoted in field linguistics, namely development of the techniques for visualization of the semantic network through creating cloud tags, semantic text markup, synoptic patterns, rendering, etc. The article offers one of the techniques – visualization based on construction of a plot using a specifically developed computer software. The graphic image illustrates the distribution of units of the semantic field “laughter” in the novel, which enables new interpretations of the content of Dostoevsky’s novel.
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Kuznetsova, Yulia M., Valerii A. Mishlanov, Vladimir A. Salimovsky, and Natalia V. Chudova. "Category of speech system in modeling affective processes (based on the material of network communication)." Media Linguistics 9, no. 3 (2022): 190–209. http://dx.doi.org/10.21638/spbu22.2022.302.
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The goal of this paper is to convey the meaning of the category of speech system for computer cognitive modeling. By implementing it, the authors prove the importance of supplementing relational-situational analysis, based linguistically on the concept of functional syntax, with the technology of creating templates which are the formalization of those sections of stylisticspeech system that mark the mental processes under study. A review of works on speech system since the 1920s is given. It is shown that in recent decades, when studying speech system, the primary interest of linguists lies in the processes of verbal communication and in its main unit — a text. It is noted that the quantitative aspects of the language functioning in various areas and communicative situations are being examined not only in quantitative linguistics but also in the works on artificial intelligence. We prove the hypothesis that the given approach to the automatic analysis of texts is realized in all cases of social communicative practice, when to express the certain affective states or cognitive actions, we can find relatively stable ways of choosing and using linguistic means. From psychological point of view, this is the area of compressed internal actions, which, if necessary, can be expanded and expressed in the form of a verbal reaction or a verbal report. It is a broad research field covering the typologies underlying the known psychological inventories or recorded in descriptions of human behavior in various spheres of activity. The paper demonstrates the resources to analyze stylistic-speech system for the automatic search of professional crisis signs in social networks. The extensive material presented by the fragments of 86 network discussions describes the patterns of choice and use of multi-level language units to express the typical emotional states of currently experienced professional crisis. The linguistic markers of the mentioned states are considered in detail.
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Kachkou, Dz I. "Applying the language acquisition model to the solution small language processing tasks." Informatics 19, no. 1 (January 5, 2022): 96–110. http://dx.doi.org/10.37661/1816-0301-2022-19-1-96-110.
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The problem of building a computer model of a small language was under solution. The relevance of this task is due to the following considerations: the need to eliminate the information inequality between speakers of different languages; the need for new tools for the study of poorly understood languages, as well as innovative approaches to language modeling in the low-resource context; the problem of supporting and developing small languages.There are three main objectives in solving the problem of small natural language processing at the stage of describing the problem situation: to justify the problem of modeling language in the context of resource scarcity as a special task in the field of natural languages processing, to review the literature on the relevant topic, to develop the concept of language acquisition model with a relatively small number of available resources. Computer modeling techniques using neural networks, semi-supervised learning and reinforcement learning were involved.The paper provides a review of the literature on modeling the learning of vocabulary, morphology, and grammar of a child's native language. Based on the current understanding of the language acquisition and existing computer models of this process, the architecture of the system of small language processing, which is taught through modeling of ontogenesis, is proposed. The main components of the system and the principles of their interaction are highlighted. The system is based on a module built on the basis of modern dialogical language models and taught in some rich-resources language (e.g., English). During training, an intermediate layer is used which represents statements in some abstract form, for example, in the symbols of formal semantics. The relationship between the formal recording of utterances and their translation into the target low-resource language is learned by modeling the child's acquisition of vocabulary and grammar of the language. One of components stands for the non-linguistic context in which language learning takes place.This article explores the problem of modeling small languages. A detailed substantiation of the relevance of modeling small languages is given: the social significance of the problem is noted, the benefits for linguistics, ethnography, ethnology and cultural anthropology are shown. The ineffectiveness of approaches applied to large languages in conditions of a lack of resources is noted. A model of language learning by means of ontogenesis simulation is proposed, which is based both on the results obtained in the field of computer modeling and on the data of psycholinguistics.
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BURNAGE, GAVIN. "Approaches to university network-based language learning." ReCALL 13, no. 2 (November 2001): 167–78. http://dx.doi.org/10.1017/s0958344001000325a.
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This paper examines the provision of networked CALL services at the Faculty of Modern and Medieval Languages at the University of Cambridge. It argues that in such a context a broad, inclusive approach to networking – one which avoids any over-strict commitment to a single method or paradigm of network delivery, but makes the most of whatever is available – is often the most appropriate. This eclectic approach helps preserve the best of what has been created in the past while keeping up with everything technological advances offer, and respects the wide-ranging and widely-differing views and approaches of Faculty teaching staff. The aim of the MML Network Service is therefore to present a wide range of disparate resources in a single, coherent framework. While welcoming the many advantages the use of the World Wide Web brings, the article stresses that in a broad infrastructure both older programs which have proved their worth and newer, highly-developed programs which have come after them need not be lost while the functionality of the Web is still developing. It also lays out in general terms some technical suggestions for those seeking ideas on the implementation of networking strategies in a Windows-based local area or university-wide network, partly with a view to reinforcing the credentials for CALL and humanities computing generally to be afforded high-quality computing infrastructure both in the present and with whatever technological advances bring in the future.
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Tsvetkov, Yulia, and Shuly Wintner. "Identification of Multiword Expressions by Combining Multiple Linguistic Information Sources." Computational Linguistics 40, no. 2 (June 2014): 449–68. http://dx.doi.org/10.1162/coli_a_00177.
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We propose a framework for using multiple sources of linguistic information in the task of identifying multiword expressions in natural language texts. We define various linguistically motivated classification features and introduce novel ways for computing them. We then manually define interrelationships among the features, and express them in a Bayesian network. The result is a powerful classifier that can identify multiword expressions of various types and multiple syntactic constructions in text corpora. Our methodology is unsupervised and language-independent; it requires relatively few language resources and is thus suitable for a large number of languages. We report results on English, French, and Hebrew, and demonstrate a significant improvement in identification accuracy, compared with less sophisticated baselines.
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Wang, Wenya, and Sinno Jialin Pan. "Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction." Computational Linguistics 45, no. 4 (January 2020): 705–36. http://dx.doi.org/10.1162/coli_a_00362.
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In fine-grained opinion mining, extracting aspect terms (a.k.a. opinion targets) and opinion terms (a.k.a. opinion expressions) from user-generated texts is the most fundamental task in order to generate structured opinion summarization. Existing studies have shown that the syntactic relations between aspect and opinion words play an important role for aspect and opinion terms extraction. However, most of the works either relied on predefined rules or separated relation mining with feature learning. Moreover, these works only focused on single-domain extraction, which failed to adapt well to other domains of interest where only unlabeled data are available. In real-world scenarios, annotated resources are extremely scarce for many domains, motivating knowledge transfer strategies from labeled source domain(s) to any unlabeled target domain. We observe that syntactic relations among target words to be extracted are not only crucial for single-domain extraction, but also serve as invariant “pivot” information to bridge the gap between different domains. In this article, we explore the constructions of recursive neural networks based on the dependency tree of each sentence for associating syntactic structure with feature learning. Furthermore, we construct transferable recursive neural networks to automatically learn the domain-invariant fine-grained interactions among aspect words and opinion words. The transferability is built on an auxiliary task and a conditional domain adversarial network to reduce domain distribution difference in the hidden spaces effectively in word level through syntactic relations. Specifically, the auxiliary task builds structural correspondences across domains by predicting the dependency relation for each path of the dependency tree in the recursive neural network. The conditional domain adversarial network helps to learn domain-invariant hidden representation for each word conditioned on the syntactic structure. In the end, we integrate the recursive neural network with a sequence labeling classifier on top that models contextual influence in the final predictions. Extensive experiments and analysis are conducted to demonstrate the effectiveness of the proposed model and each component on three benchmark data sets.
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Fan, Min, and Shanwen Xu. "Research on Discourse Coherence based on the Analysis Model of Event Chain from the Perspective of Computational Linguistics." E3S Web of Conferences 189 (2020): 03025. http://dx.doi.org/10.1051/e3sconf/202018903025.
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With the rapid development of network technology, natural language processing has also entered a boom period. Probability and data-driven methods have been widely used in natural language processing. The need for people to extract and retrieve information from the Internet is also increasing, and more and more researchers are trying to use computers to process content related to discourse coherence. Based on the event chain of the text semantic structure representation, this paper proposes a text semantic structure representation model, on the basis of which, text coherent resources can be used for the task of text semantic analysis. Event chain is a necessary condition for discourse coherence, which can be transformed into a computable event chain analysis problem, and can be further formalized as discourse-oriented partial dependency analysis of sentences.
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Zhang, Weiyu, and Yin Ling Cheung. "The construction of authorial voice in writing research articles: A corpus-based study from an APPRAISAL theory perspective." International Journal of English Studies 18, no. 2 (December 28, 2018): 53–75. http://dx.doi.org/10.6018/ijes/2018/2/320261.
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This study explores voice from an APPRAISAL theory perspective. It aims to investigate how published research writers deploy ATTITUDE and GRADUATION resources to review existing literature in the field. The study is based on a corpus of literature reviews (LRs) from 204 research articles (RAs) in computer networks and communications (CNC) and second language writing (SLW). Findings show that 1) writers demonstrate a strong preference to express their attitude through APPRECIATION rather than AFFECT and JUDGEMENT resources; 2) more FORCE than FOCUS resources are used to upgrade attitudinal meanings realized through ATTITUDE resources or to evoke APPRECIATION; and 3) one-way ANOVA and post hoc tests have detected significant differences in the use of AFFECT and JUDGEMENT resources and in two sub-categories of FORCE and FOCUS resources. The study contributes to new knowledge by relating ATTITUDE and GRADUATION resources to the construction of voice in the disciplines of CNC and SLW.
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Chen, Bo. "Design of Piano Intelligent Teaching System Based on Neural Network Algorithm." Mobile Information Systems 2022 (August 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/5991124.
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In recent years, with the improvement of economic level, music art has become a major focus of extracurricular teaching. Students of all ages are included. However, the high cost of piano teaching and the unique one-to-one teaching method of teachers and students lead to the lack of piano education resources. Learning piano has become a luxury activity. Therefore, using computer multimedia software to teach piano has become a feasible way to alleviate the current contradiction. For piano teaching, the main difficulties are the differences between teachers and students (i.e., the data change at both ends due to the network), the instability of the network system, and the neural network algorithm can solve these difficulties. Based on this point, this work aims to introduce neural network algorithm into piano teaching intelligent system. This paper first introduces the theoretical basis of the neural network algorithm, then expounds the algorithm flow and general framework of the algorithm in speech recognition, and explains the split explanation in combination with five aspects: preprocessing, character extraction, acoustic model, linguistic model, and decoding. Then, it introduces the system design of intelligent piano teaching and describes the general system requirements and product architecture. Finally, the intelligent piano teaching system is tested and applied to prove the effectiveness of the system. I hope this intelligent piano teaching system can provide more convenience for piano teaching.
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Monje, Leticia, Ramón A. Carrasco, Carlos Rosado, and Manuel Sánchez-Montañés. "Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain." Mathematics 10, no. 9 (April 23, 2022): 1428. http://dx.doi.org/10.3390/math10091428.
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Time series forecasting of passenger demand is crucial for optimal planning of limited resources. For smart cities, passenger transport in urban areas is an increasingly important problem, because the construction of infrastructure is not the solution and the use of public transport should be encouraged. One of the most sophisticated techniques for time series forecasting is Long Short Term Memory (LSTM) neural networks. These deep learning models are very powerful for time series forecasting but are not interpretable by humans (black-box models). Our goal was to develop a predictive and linguistically interpretable model, useful for decision making using large volumes of data from different sources. Our case study was one of the most demanded bus lines of Madrid. We obtained an interpretable model from the LSTM neural network using a surrogate model and the 2-tuple fuzzy linguistic model, which improves the linguistic interpretability of the generated Explainable Artificial Intelligent (XAI) model without losing precision.
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Du, Yuan-Wei, and Qiong Song. "Ecological Security Evaluation for Marine Ranching Based on the PLTS/ANP Method: A Case Study of Rongcheng." Complexity 2022 (January 22, 2022): 1–16. http://dx.doi.org/10.1155/2022/2134296.
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The evaluation index system of ecological security of marine ranching (MRES) is based on the assumption that there is independence among evaluation indexes in the existing studies, which ignores the complex interactive paths of marine ranching as an artificial ecosystem. In this study, the MRES evaluation network model that includes interdependent relationships is established based on the Driver-Pressure-State-Impact-Response model and the analytic network process method. Then, the probabilistic linguistic term sets and analytic network process methods are used to calculate the weights of the evaluation indexes of MRES. The overall evaluation value and the contribution rate of clusters are consequently defined and analyzed to reflect the performance of MRES. Finally, a case study is carried out for evaluating the MRES of marine ranches in Rongcheng by means of the proposed method. The conclusions are summarized as follows: (1) The weights of clusters are ranked as Responses > Impact > Driver > State > Pressure, and “scientific management of fishery resources” is the most important index; (2) the MRES performance of marine ranches in the city of Rongcheng is at the medium security grade on the whole, and all 11 samples are driven by the response.
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Naz, Sumera, Muhammad Akram, Mohammed M. Ali Al-Shamiri, and Muhammad Ramzan Saeed. "Evaluation of Network Security Service Provider Using 2-Tuple Linguistic Complex q -Rung Orthopair Fuzzy COPRAS Method." Complexity 2022 (June 26, 2022): 1–27. http://dx.doi.org/10.1155/2022/4523287.
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In recent years, network security has become a major concern. Using the Internet to store and analyze data has become an integral aspect of the production and operation of many new and traditional enterprises. However, many enterprises lack the necessary resources to secure information security, and selecting the best network security service provider has become a real issue for many enterprises. This research introduces a novel decision-making method utilizing the 2-tuple linguistic complex q-rung orthopair fuzzy numbers (2TLCq-ROFNs) to tackle this issue. We propose the 2TLCq-ROF concept by combining the complex q-rung orthopair fuzzy set with 2-tuple linguistic terms, including the fundamental definition, operational rules, scoring, and accuracy functions. Aggregation operators are the fundamental mathematical approach used to combine various inputs into a single output. Taking into account the interaction between the attributes, we develop the 2TLCq-ROF Hamacher (2TLCq-ROFH) operators by using the innovative operational rules. These operators include the 2TLCq-ROFH weighted average (2TLCq-ROFHWA), 2TLCq-ROFH ordered weighted average (2TLCq-ROFHOWA), 2TLCq-ROFH hybrid average (2TLCq-ROFHHA), 2TLCq-ROFH weighted geometric (2TLCq-ROFHWG), 2TLCq-ROFH ordered weighted geometric (2TLCq-ROFHOWG), and 2TLCq-ROFH hybrid geometric (2TLCq-ROFHHG) operators. In addition, we talk about the properties of 2TLCq-ROFH operators such as idempotency, commutativity, monotonicity, and boundedness and also examine their spatial cases. To tackle the problems of the 2TLCq-ROF multiattribute group decision-making (MAGDM) environment, we develop a novel approach according to the COPRAS (complex proportional assessment) model. Finally, to validate the feasibility of the given strategy, we employ a quantitative example related to select the best network security service provider. In comparison with existing approaches, the developed decision-making algorithm is most extensively used and reduces the loss of information.
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Zhu, Yinglin, Wenbin Zheng, and Hong Tang. "Interactive Dual Attention Network for Text Sentiment Classification." Computational Intelligence and Neuroscience 2020 (November 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/8858717.
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Text sentiment classification is an essential research field of natural language processing. Recently, numerous deep learning-based methods for sentiment classification have been proposed and achieved better performances compared with conventional machine learning methods. However, most of the proposed methods ignore the interactive relationship between contextual semantics and sentimental tendency while modeling their text representation. In this paper, we propose a novel Interactive Dual Attention Network (IDAN) model that aims to interactively learn the representation between contextual semantics and sentimental tendency information. Firstly, we design an algorithm that utilizes linguistic resources to obtain sentimental tendency information from text and then extract word embeddings from the BERT (Bidirectional Encoder Representations from Transformers) pretraining model as the embedding layer of IDAN. Next, we use two Bidirectional LSTM (BiLSTM) networks to learn the long-range dependencies of contextual semantics and sentimental tendency information, respectively. Finally, two types of attention mechanisms are implemented in IDAN. One is multihead attention, which is the next layer of BiLSTM and is used to learn the interactive relationship between contextual semantics and sentimental tendency information. The other is global attention that aims to make the model focus on the important parts of the sequence and generate the final representation for classification. These two attention mechanisms enable IDAN to interactively learn the relationship between semantics and sentimental tendency information and improve the classification performance. A large number of experiments on four benchmark datasets show that our IDAN model is superior to competitive methods. Moreover, both the result analysis and the attention weight visualization further demonstrate the effectiveness of our proposed method.
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CHESTERS, GRAHAM. "Editorial." ReCALL 13, no. 2 (November 2001): 145. http://dx.doi.org/10.1017/s095834400100012x.
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The reports of the demise of the CD-ROM have proved to be a little premature, if one judges by the contents of this issue of ReCALL. Whilst it is true that there is a strong emphasis in the articles by Huw Jarvis, Julie Belz and Lina Lee on the opportunities offered by network-based learning – and the trend towards such a focus is inevitable – it remains the case that the CD-ROM is still capable of rich and significant exploitation, as shown in the articles by Gunther Kaltenboeck and Birgit Winkler. Gavin Burnage argues for a ‘broad inclusive approach to networking’ which declines to abandon the old data-carriers, whether floppy-based DOS programs or CD-ROMs and argues for the pragmatic integration of a wide range of disparate resources into a single, coherent framework. DISSEMINATE, the macro-structure articulated here by Philippe Delcloque and Alexandre Bramoullé, is a concept with a similar integrative ambition but from an authoring perspective.
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Goltsova, Tatiyana A., and Ekaterina A. Protsenko. "Implementation of innovative approaches to the organization of independent work in a foreign language." Yaroslavl Pedagogical Bulletin 2, no. 125 (2022): 58–65. http://dx.doi.org/10.20323/1813-145x-2022-2-125-58-65.
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The article concerns the current problem of organizing students’ independent work in foreign languages by applying different innovative approaches. The article aims to study and try different pedagogical technologies and teaching methods widely used in the classroom but seldom applicable within independent students’ learning and cognitive activity. The authors give reasons for integrating new pedagogical methods and technologies in learning experiences and their advantages over the traditional approaches. Practical experience in the field of using different modern pedagogical methods and technologies while organizing students’ independent work is presented. The authors describe educational potential of Internet network resources, computer programs and mobile applications, using them in students’ independent work helps solve multiple tasks such as to expand vocabulary, to overcome difficulties in grammar, to reinforce pronunciation, auditive and grammatical skills. Moreover, tests accessible in the Internet can be used as assessment materials. The capabilities of gamification include such instruments as network services, computer games or interactive presentations working on-line as well as off-line. The article presents the results of using web-quest technology, its advantages over traditional forms of work in developing different language activities. Project method is also successfully used in practice for organizing independent students’ learning activity. The article stresses that innovative approaches make possible to prepare tasks that have true interdisciplinary nature and promote professional and scientific personal enrichment of students. Presented in the article experience of organizing students’ independent work can be used in non-linguistic educational establishments for the purpose of enhancing students’ independent learning of foreign languages.
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Gómez-García, Melchor, Moussa Boumadan, Roberto Soto-Varela, and Ángeles Gutiérrez-García. "What are the factors that influence the use of social networks as a means to serve pedagogy?" Texto Livre: Linguagem e Tecnologia 14, no. 1 (December 22, 2020): e25420. http://dx.doi.org/10.35699/1983-3652.2021.25420.
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Social networks are a key element in young people's and teenagers' leisure time. But their influence goes beyond the field of entertainment further reaching educational and training environments. These tools can support and improve student learning, but it is clear that they can also be an inconvenience or a potential danger for the youth. The purpose of this research is to describe the profiles of teachers who use social networks as resources for their teaching, both inside and outside the classroom. It also seeks to know the characteristics that influence their incorporation as learning elements. For this reason, a secondary exploitation of the PISA 2018 database is carried out. It contains the data of 21,621 teachers of the secondary education level from the 19 regions of Spain. Multilevel analysis is used as a method of data analysis. The results point out differences in use according to the Autonomous Communities, showing the importance of the age of the teacher and the number of years of experience, both for their use in the classroom and to cover their training needs. Quality teaching requires a commitment to technological resources; therefore, decisive to know these elements in order to design teachers and educational institutions.
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Dong, Jie. "Global Learning from the Periphery: An Ethnographic Study of a Chinese Urban Migrant School." Sustainability 12, no. 1 (January 3, 2020): 381. http://dx.doi.org/10.3390/su12010381.
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This paper presents an ethnographic study on the global learning practice of teachers and students in a Chinese internal migrant school. Rural children have relocated to urban centers with their parents on a massive scale over the past decades, as China undergoes rapid economic changes. Many migrant children have to attend privately run migrant schools which often function within limited budgets. Drawing on various types of data, this study investigates informal learning in a global context. In particular, the research focuses on a Scout program that is modeled on world Scouting movements and that is tailored for the migrant pupils’ educational demands. The data collection tools include participant observation, in-depth interview and document collection. The research finds that, with limited educational resources, the informants learn globally to improve the sustainable development of the migrant pupils, to fight against educational inequality, and to facilitate mutual understanding between the migrant and the urban communities. This paper concludes that global learning plays an important role in the informants’ ‘’up-scaling” progress facilitated by their linguistic capacity, computer literacy, and social network.
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Jia, Junwei, Zhilu Wang, Lianghui Xu, Jiajia Dai, Mingyi Gu, and Jing Huang. "An Interference-Resistant and Low-Consumption Lip Recognition Method." Electronics 11, no. 19 (September 26, 2022): 3066. http://dx.doi.org/10.3390/electronics11193066.
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Lip movements contain essential linguistic information. It is an important medium for studying the content of the dialogue. At present, there are many studies on how to improve the accuracy of lip language recognition models. However, there are few studies on the robustness and generalization performance of the model under various disturbances. Specific experiments show that the current state-of-the-art lip recognition model significantly drops in accuracy when disturbed and is particularly sensitive to adversarial examples. This paper substantially alleviates this problem by using Mixup training. Taking the model subjected to negative attacks generated by FGSM as an example, the model in this paper achieves 85.0% and 40.2% accuracy on the English dataset LRW and the Mandarin dataset LRW-1000, respectively. The correct recognition rates are improved by 9.8% and 8.3%, compared with the current advanced lip recognition models. The positive impact of Mixup training on the robustness and generalization of lip recognition models is demonstrated. In addition, the performance of the lip recognition classification model depends more on the training parameters, which increase the computational cost. The InvNet-18 network in this paper reduces the consumption of GPU resources and the training time while improving the model accuracy. Compared with the standard ResNet-18 network used in mainstream lip recognition models, the InvNet-18 network in this paper has more than three times lower GPU consumption and 32% fewer parameters. After detailed analysis and comparison in various aspects, it is demonstrated that the model in this paper can effectively improve the model’s anti-interference ability and reduce training resource consumption. At the same time, the accuracy is comparable with the current state-of-the-art results.
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Raskin, Jonathan D., and Salvatore Attardo. "Non-literalness and non-bona-fîde in language." Pragmatics and Cognition 2, no. 1 (January 1, 1994): 31–69. http://dx.doi.org/10.1075/pc.2.1.02ras.
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The paper is devoted to the study of humor as an important pragmatic phenomenon bearing on cognition, and, more specifically, as a cooperative mode of non-bona-fide communication. Several computational models of humor are presented in increasing order of complexity and shown to reveal important cognitive structures in jokes. On the basis of these limited implementations, the concept of a full-fledged computational model for the understanding and generation of humor is introduced and discussed in various aspects. The model draws upon the authors ' General Theory of Verbal Humor, with its six knowledge resources informing a joke, and on SMEARR, a sophisticated semantic-network-based computational lexical environment. The relevance of the approach to the interpretation, generation, and cognitive structure of humor is discussed in the broader context of the nature of the cooperative non-bona-fide modes of communication.
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Polat, Huseyin, and Saadin Oyucu. "Building a Speech and Text Corpus of Turkish: Large Corpus Collection with Initial Speech Recognition Results." Symmetry 12, no. 2 (February 17, 2020): 290. http://dx.doi.org/10.3390/sym12020290.
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To build automatic speech recognition (ASR) systems with a low word error rate (WER), a large speech and text corpus is needed. Corpus preparation is the first step required for developing an ASR system for a language with few argument speech documents available. Turkish is a language with limited resources for ASR. Therefore, development of a symmetric Turkish transcribed speech corpus according to the high resources languages corpora is crucial for improving and promoting Turkish speech recognition activities. In this study, we constructed a viable alternative to classical transcribed corpus preparation techniques for collecting Turkish speech data. In the presented approach, three different methods were used. In the first step, subtitles, which are mainly supplied for people with hearing difficulties, were used as transcriptions for the speech utterances obtained from movies. In the second step, data were collected via a mobile application. In the third step, a transfer learning approach to the Grand National Assembly of Turkey session records (videotext) was used. We also provide the initial speech recognition results of artificial neural network and Gaussian mixture-model-based acoustic models for Turkish. For training models, the newly collected corpus and other existing corpora published by the Linguistic Data Consortium were used. In light of the test results of the other existing corpora, the current study showed the relative contribution of corpus variability in a symmetric speech recognition task. The decrease in WER after including the new corpus was more evident with increased verified data size, compensating for the status of Turkish as a low resource language. For further studies, the importance of the corpus and language model in the success of the Turkish ASR system is shown.
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Alshahrani, Hala J., Khaled Tarmissi, Hussain Alshahrani, Mohamed Ahmed Elfaki, Ayman Yafoz, Raed Alsini, Omar Alghushairy, and Manar Ahmed Hamza. "Computational Linguistics with Deep-Learning-Based Intent Detection for Natural Language Understanding." Applied Sciences 12, no. 17 (August 29, 2022): 8633. http://dx.doi.org/10.3390/app12178633.
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Computational linguistics explores how human language is interpreted automatically and then processed. Research in this area takes the logical and mathematical features of natural language and advances methods and statistical procedures for automated language processing. Slot filling and intent detection are significant modules in task-based dialogue systems. Intent detection is a critical task in any natural language understanding (NLU) system and constitutes the base of a task-based dialogue system. In order to build high-quality, real-time conversational solutions for edge gadgets, there is a demand for deploying intent-detection methods on devices. This mandates an accurate, lightweight, and fast method that effectively operates in a resource-limited environment. Earlier works have explored the usage of several machine-learning (ML) techniques for detecting intent in user queries. In this article, we propose Computational Linguistics with Deep-Learning-Based Intent Detection and Classification (CL-DLBIDC) for natural language understanding. The presented CL-DLBIDC technique receives word embedding as input and learned meaningful features to determine the probable intention of the user query. In addition, the presented CL-DLBIDC technique uses the GloVe approach. In addition, the CL-DLBIDC technique makes use of the deep learning modified neural network (DLMNN) model for intent detection and classification. For the hyperparameter tuning process, the mayfly optimization (MFO) algorithm was used in this study. The experimental analysis of the CL-DLBIDC method took place under a set of simulations, and the results were scrutinized for distinct aspects. The simulation outcomes demonstrate the significant performance of the CL-DLBIDC algorithm over other DL models.
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Ullah, Farhat, Xin Chen, Syed Bilal Hussain Shah, Saoucene Mahfoudh, Muhammad Abul Hassan, and Nagham Saeed. "A Novel Approach for Emotion Detection and Sentiment Analysis for Low Resource Urdu Language Based on CNN-LSTM." Electronics 11, no. 24 (December 8, 2022): 4096. http://dx.doi.org/10.3390/electronics11244096.
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Emotion detection (ED) and sentiment analysis (SA) play a vital role in identifying an individual’s level of interest in any given field. Humans use facial expressions, voice pitch, gestures, and words to convey their emotions. Emotion detection and sentiment analysis in English and Chinese have received much attention in the last decade. Still, poor-resource languages such as Urdu have been mostly disregarded, which is the primary focus of this research. Roman Urdu should also be investigated like other languages because social media platforms are frequently used for communication. Roman Urdu faces a significant challenge in the absence of corpus for emotion detection and sentiment analysis because linguistic resources are vital for natural language processing. In this study, we create a corpus of 1021 sentences for emotion detection and 20,251 sentences for sentiment analysis, both obtained from various areas, and annotate it with the aid of human annotators from six and three classes, respectively. In order to train large-scale unlabeled data, the bag-of-word, term frequency-inverse document frequency, and Skip-gram models are employed, and the learned word vector is then fed into the CNN-LSTM model. In addition to our proposed approach, we also use other fundamental algorithms, including a convolutional neural network, long short-term memory, artificial neural networks, and recurrent neural networks for comparison. The result indicates that the CNN-LSTM proposed method paired with Word2Vec is more effective than other approaches regarding emotion detection and evaluating sentiment analysis in Roman Urdu. Furthermore, we compare our based model with some previous work. Both emotion detection and sentiment analysis have seen significant improvements, jumping from an accuracy of 85% to 95% and from 89% to 93.3%, respectively.
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Mishra, Prakhar, Chaitali Diwan, Srinath Srinivasa, and G. Srinivasaraghavan. "Automatic Title Generation for Learning Resources and Pathways with Pre-trained Transformer Models." International Journal of Semantic Computing 15, no. 04 (December 2021): 487–510. http://dx.doi.org/10.1142/s1793351x21400134.
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To create curiosity and interest for a topic in online learning is a challenging task. A good preview that outlines the contents of a learning pathway could help learners know the topic and get interested in it. Towards this end, we propose a hierarchical title generation approach to generate semantically relevant titles for the learning resources in a learning pathway and a title for the pathway itself. Our approach to Automatic Title Generation for a given text is based on pre-trained Transformer Language Model GPT-2. A pool of candidate titles are generated and an appropriate title is selected among them which is then refined or de-noised to get the final title. The model is trained on research paper abstracts from arXiv and evaluated on three different test sets. We show that it generates semantically and syntactically relevant titles as reflected in ROUGE, BLEU scores and human evaluations. We propose an optional abstractive Summarizer module based on pre-trained Transformer model T5 to shorten medium length documents. This module is also trained and evaluated on research papers from arXiv dataset. Finally, we show that the proposed model of hierarchical title generation for learning pathways has promising results.
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Abbasi, Burhan Ud Din, Iram Fatima, Hamid Mukhtar, Sharifullah Khan, Abdulaziz Alhumam, and Hafiz Farooq Ahmad. "Autonomous schema markups based on intelligent computing for search engine optimization." PeerJ Computer Science 8 (December 8, 2022): e1163. http://dx.doi.org/10.7717/peerj-cs.1163.
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With advances in artificial intelligence and semantic technology, search engines are integrating semantics to address complex search queries to improve the results. This requires identification of well-known concepts or entities and their relationship from web page contents. But the increase in complex unstructured data on web pages has made the task of concept identification overly complex. Existing research focuses on entity recognition from the perspective of linguistic structures such as complete sentences and paragraphs, whereas a huge part of the data on web pages exists as unstructured text fragments enclosed in HTML tags. Ontologies provide schemas to structure the data on the web. However, including them in the web pages requires additional resources and expertise from organizations or webmasters and thus becoming a major hindrance in their large-scale adoption. We propose an approach for autonomous identification of entities from short text present in web pages to populate semantic models based on a specific ontology model. The proposed approach has been applied to a public dataset containing academic web pages. We employ a long short-term memory (LSTM) deep learning network and the random forest machine learning algorithm to predict entities. The proposed methodology gives an overall accuracy of 0.94 on the test dataset, indicating a potential for automated prediction even in the case of a limited number of training samples for various entities, thus, significantly reducing the required manual workload in practical applications.
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Buriboev, Abror, and Azamjon Muminov. "Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems." Sensors 22, no. 23 (December 5, 2022): 9502. http://dx.doi.org/10.3390/s22239502.
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Several crucial system design and deployment decisions, including workload management, sizing, capacity planning, and dynamic rule generation in dynamic systems such as computers, depend on predictive analysis of resource consumption. An analysis of the computer components’ utilizations and their workloads is the best way to assess the performance of the computer’s state. Especially, analyzing the particular or whole influence of components on another component gives more reliable information about the state of computer systems. There are many evaluation techniques proposed by researchers. The bulk of them have complicated metrics and parameters such as utilization, time, throughput, latency, delay, speed, frequency, and the percentage which are difficult to understand and use in the assessing process. According to these, we proposed a simplified evaluation method using components’ utilization in percentage scale and its linguistic values. The use of the adaptive neuro-fuzzy inference system (ANFIS) model and fuzzy set theory offers fantastic prospects to realize use impact analyses. The purpose of the study is to examine the usage impact of memory, cache, storage, and bus on CPU performance using the Sugeno type and Mamdani type ANFIS models to determine the state of the computer system. The suggested method is founded on keeping an eye on how computer parts behave. The developed method can be applied for all kinds of computing system, such as personal computers, mainframes, and supercomputers by considering that the inference engine of the proposed ANFIS model requires only its own behavior data of computers’ components and the number of inputs can be enriched according to the type of computer, for instance, in cloud computers’ case the added number of clients and network quality can be used as the input parameters. The models present linguistic and quantity results which are convenient to understand performance issues regarding specific bottlenecks and determining the relationship of components.
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Grincheva, Natalia. "Mapping museum ‘Soft Power’: Adding geo-visualization to the methodological framework." Digital Scholarship in the Humanities 34, no. 4 (December 24, 2018): 730–51. http://dx.doi.org/10.1093/llc/fqy072.
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Abstract The article proposes, justifies, and tests a new methodological framework to measure museum ‘soft power’ by employing geo-visualization as a new method empowered by the rapid development of digital humanities. This research not only demystifies the buzz term of ‘soft power’ that is frequently applied in relation to contemporary museums and their international cultural engagements but also develops an evaluation framework to assess museum capacities to exert global impacts. Specifically, the article draws on the academic scholarship outlining a plethora of approaches for ‘soft power’ evaluation, including Resources, Outputs, Perceptions, and Networks evaluation models. It argues for a new integrative approach that can comprehensively combine different methods to construct a more advanced tool to measure museum ‘soft power’. The article draws on preliminary results of developing a digital mapping system to assess museum soft power. It shares findings from the pilot project, Australian Center of the Moving Image (ACMI) on the Global Map, designed in collaboration with the ACMI in Melbourne.
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Colla, Davide, Enrico Mensa, and Daniele P. Radicioni. "LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items." Computational Linguistics 46, no. 2 (June 2020): 289–333. http://dx.doi.org/10.1162/coli_a_00375.
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We present LESSLEX, a novel multilingual lexical resource. Different from the vast majority of existing approaches, we ground our embeddings on a sense inventory made available from the BabelNet semantic network. In this setting, multilingual access is governed by the mapping of terms onto their underlying sense descriptions, such that all vectors co-exist in the same semantic space. As a result, for each term we have thus the “blended” terminological vector along with those describing all senses associated to that term. LESSLEX has been tested on three tasks relevant to lexical semantics: conceptual similarity, contextual similarity, and semantic text similarity. We experimented over the principal data sets for such tasks in their multilingual and crosslingual variants, improving on or closely approaching state-of-the-art results. We conclude by arguing that LESSLEX vectors may be relevant for practical applications and for research on conceptual and lexical access and competence.
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Dvoynikova, Anastasia, Maxim Markitantov, Elena Ryumina, Mikhail Uzdiaev, Alena Velichko, Dmitry Ryumin, Elena Lyakso, and Alexey Karpov. "Analysis of infoware and software for human affective states recognition." Informatics and Automation 21, no. 6 (November 24, 2022): 1097–144. http://dx.doi.org/10.15622/ia.21.6.2.
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The article presents an analytical review of research in the affective computing field. This research direction is a component of artificial intelligence, and it studies methods, algorithms and systems for analyzing human affective states during interactions with other people, computer systems or robots. In the field of data mining, the definition of affect means the manifestation of psychological reactions to an exciting event, which can occur both in the short and long term, and also have different intensity. The affects in this field are divided into 4 types: affective emotions, basic emotions, sentiment and affective disorders. The manifestation of affective states is reflected in verbal data and non-verbal characteristics of behavior: acoustic and linguistic characteristics of speech, facial expressions, gestures and postures of a person. The review provides a comparative analysis of the existing infoware for automatic recognition of a person’s affective states on the example of emotions, sentiment, aggression and depression. The few Russian-language, affective databases are still significantly inferior in volume and quality compared to electronic resources in other world languages. Thus, there is a need to consider a wide range of additional approaches, methods and algorithms used in a limited amount of training and testing data, and set the task of developing new approaches to data augmentation, transferring model learning and adapting foreign-language resources. The article describes the methods of analyzing unimodal visual, acoustic and linguistic information, as well as multimodal approaches for the affective states recognition. A multimodal approach to the automatic affective states analysis makes it possible to increase the accuracy of recognition of the phenomena compared to single-modal solutions. The review notes the trend of modern research that neural network methods are gradually replacing classical deterministic methods through better quality of state recognition and fast processing of large amount of data. The article discusses the methods for affective states analysis. The advantage of multitasking hierarchical approaches is the ability to extract new types of knowledge, including the influence, correlation and interaction of several affective states on each other, which potentially leads to improved recognition quality. The potential requirements for the developed systems for affective states analysis and the main directions of further research are given.
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Efendiieva, S. M., K. H. Havrylieva, and K. S. Buhaenko. "ORGANIZATION OF STUDENTS’ INDEPENDENT ENGLISH LANGUAGE LEARNING IN THE COURSE OF PREPARATION TO SUBTEST OF LICENSING EXAMINATION «KROK 1. DENTISTRY»." Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії 20, no. 4 (December 30, 2020): 169–73. http://dx.doi.org/10.31718/2077-1096.20.4.169.
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Over two decades Ukrainian system of medical professional training has been providing a single pathway for primary licensure of all graduates of medical schools by licensing examinations «Krok 1», «Krok 2» and «Krok 3». Since their inceptions, the examinations provide the valid and reliable independent assessment of medical examinations in order to demonstrate they have knowledge and clinical skills for the unsupervised general practice of medicine, and thus, to grant the primary license to practice medicine. The aim of this study is to analyze ways of the organization of student’s self-learning at the Department of Foreign Languages with Latin and Medical Terminology, Ukrainian Medical Stomatological Academy, which will promote the confidence in own English language command and ensure demonstrating high performance of English subtest «Krok 1». Independent learning using additional texts should help students, concerning with previously studied elements of terminology, lexis and grammar, to figure out faster phrases and constructions in texts based on the contex and extra linguistic clues.
The analysis of the factors influencing the students’ performance when doing the Krok tests, we have found the guided language exposure and scheduled amount of time for independent work according to the curriculum can help to overcome typical linguistic obstacles. Moreover, the English language instructors elaborated the questionnaire and surveyed the students to identify self-learning routine peculiarities, resources commonly used for self-training to licensing examination, which are the most effective from their point of view. The study included 155 third year dental students (2019-2020 academic year). The students actively use mobile applications from the playmarket, namely K-test, which they can use for independent practice at individual pace. Computer technologies can significantly improve the efficiency of foreign language teaching and learning and help us in organizing independent work of the students. According to result obtained, testkrokorg.ua internet resource ranks the second position. 19.35% of students chose this site as it gives an opportunity to check the knowledge, to get graded and to get the right answers. 17.41% of the respondents used testcentr.org.ua by reviewing and uploading Krok test booklets. 11.61% of the students used social network groups, which post materials, news and results to licensing examinations. 9.67% of students are found to prefer booklets; methodical instructions for students’ training that are on the site of our department. 3.2% of the responders suggest that lifehacks in YouTube contains the experience of bloggers and due to them the youth can learn new things. The obtained results have shown that the computer appliances are of great recourses for independent learning when used purposefully and guided by the instructors.
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Kowsher, Md, Md Shohanur Islam Sobuj, Md Fahim Shahriar, Nusrat Jahan Prottasha, Mohammad Shamsul Arefin, Pranab Kumar Dhar, and Takeshi Koshiba. "An Enhanced Neural Word Embedding Model for Transfer Learning." Applied Sciences 12, no. 6 (March 10, 2022): 2848. http://dx.doi.org/10.3390/app12062848.
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Due to the expansion of data generation, more and more natural language processing (NLP) tasks are needing to be solved. For this, word representation plays a vital role. Computation-based word embedding in various high languages is very useful. However, until now, low-resource languages such as Bangla have had very limited resources available in terms of models, toolkits, and datasets. Considering this fact, in this paper, an enhanced BanglaFastText word embedding model is developed using Python and two large pre-trained Bangla models of FastText (Skip-gram and cbow). These pre-trained models were trained on a collected large Bangla corpus (around 20 million points of text data, in which every paragraph of text is considered as a data point). BanglaFastText outperformed Facebook’s FastText by a significant margin. To evaluate and analyze the performance of these pre-trained models, the proposed work accomplished text classification based on three popular textual Bangla datasets, and developed models using various machine learning classical approaches, as well as a deep neural network. The evaluations showed a superior performance over existing word embedding techniques and the Facebook Bangla FastText pre-trained model for Bangla NLP. In addition, the performance in the original work concerning these textual datasets provides excellent results. A Python toolkit is proposed, which is convenient for accessing the models and using the models for word embedding, obtaining semantic relationships word-by-word or sentence-by-sentence; sentence embedding for classical machine learning approaches; and also the unsupervised finetuning of any Bangla linguistic dataset.
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Tinedo Rodríguez, Antonio Jesús. "Actors’ and Actresses’ Perceptions on the Applications of Discourse Analysis from a Qualitative and Quantitative Outlook to Musical Theatre." Epos : Revista de filología, no. 37 (December 21, 2021): 211–23. http://dx.doi.org/10.5944/epos.37.2021.30948.
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Homo loquens has the ability of language. Language is present in all the aspects of life, including performing arts. The main goal of this proposal is to assess the perception of actors and actresses on a resource which has been developed to help them make the most of the text; the fundaments of this resource are in the linguistic theory. The main goal of this acting resource which combines a quantitative and a qualitative approach to the text is to foster critical thinking. The goal is thus to approach the dramatic text from different perspectives to provoke deep reflections on the text through discourse analysis. Subsidiarily, an interview to a professional actress has been carried out to know her thoughts on the resource and her perception was positive. As a conclusion, it might be interesting to explore the relationships between drama and linguistics.
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Blanchete, Ilham, and Mohammed Mourchid. "The use of Arabic linguistic resources to develop learning applications." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (January 1, 2022): 562. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp562-571.
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<span>This paper aims at explaining how Arabic linguistic resources are generated and exploited to enhance Arabic acquisition. We have adopted the root and pattern approach to generate our resources using the linguistic NooJ platform. This work has been carried out in two phases: generating the linguistic resources and developing the application that exploits the pre-built resources. First, we have generated three different resources: comprehensive verbs and masdar resources linked to each other. A nouns-and-adjectives resource, where nouns and adjectives are linked to their broken plural forms. NooJ calls these resources to apply linguistic analysis to a given corpus and returns detailed annotations, which provides accurate morphological, syntactical, and semantic properties of each analyzed word. We have also used the mixed nature of Arabic masdar to implement transformational rules, which generate nominal sentences from verbal ones and vice versa. Second, we have developed an application that provides valuable learning functionalities, like full/semi verb conjugation, the extraction of broken plural forms of a given singular form, the extraction of masdar forms of a given verb form, and return words that share the same root. The developed application can be used by teachers, students/learners, and computational linguists interested in Arabic acquisition.</span>
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Bietti, Lucas M., and John Sutton. "Interacting to remember at multiple timescales." Coordination, Collaboration and Cooperation 16, no. 3 (December 30, 2015): 419–50. http://dx.doi.org/10.1075/is.16.3.04bie.
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Everyday joint remembering, from family remembering around the dinner table to team remembering in the operating theatre, relies on the successful interweaving of multiple cognitive, bodily, social and material resources, anchored in specific cultural ecosystems. Such systems for joint remembering in social interactions are composed of processes unfolding over multiple but complementary timescales, which we distinguish for analytic purposes so as better to study their interanimation in practice: (i) faster, lower-level coordination processes of behavioral matching and interactional synchrony occurring at timescale t1; (ii) mid-range collaborative processes which re-evoke past experiences in groups, unfolding at timescale t2; (iii) cooperative processes involved in the transmission of memories over longer periods occurring at timescale t3; and (iv) cultural processes and practices operating within distributed socio-cognitive networks over evolutionary and historical timeframes, unfolding at timescale t4. In this paper we survey studies of how the processes operating across these overlapping and complementary timescales constitute joint remembering in social interactions. We describe coordination, collaboration, cooperation, and culture as complementary aspects of interacting to remember, which we consider as a complex phenomenon unfolding over multiple timescales (t1, t2, t3, t4).
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Goffe, William L. "Computer Network Resources for Economists." Journal of Economic Perspectives 8, no. 3 (August 1, 1994): 97–119. http://dx.doi.org/10.1257/jep.8.3.97.
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This paper outlines resources for economists on the Internet, the large and very rapidly expanding computer network that is revolutionizing research in many fields. Even at this early stage, there are a number of very useful resources (such as large databases and mailing lists) for economists. Directions are given on how to retrieve the complete and current list of resources for economists, and the paper also contains a minimal set of directions on how to use the Internet. References are also given to more detailed general guides on the Internet.
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Gardas, Bhaskar B., Arash Heidari, Nima Jafari Navimipour, and Mehmet Unal. "A Fuzzy-Based Method for Objects Selection in Blockchain-Enabled Edge-IoT Platforms Using a Hybrid Multi-Criteria Decision-Making Model." Applied Sciences 12, no. 17 (September 5, 2022): 8906. http://dx.doi.org/10.3390/app12178906.
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The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud server load, slow response times, and a lack of global mobility. Some of these flaws can be addressed with edge computing. In addition, node selection helps avoid common difficulties related to IoT, including network lifespan, allocation of resources, and trust in the acquired data by selecting the correct nodes at a suitable period. On the other hand, the IoT’s interconnection of edge and blockchain technologies gives a fresh perspective on access control framework design. This article provides a novel node selection approach for blockchain-enabled edge IoT that provides a quick and dependable node selection. Moreover, fuzzy logic to approximation logic was used to manage numerical and linguistic data simultaneously. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. The suggested fuzzy-based technique employs three input criteria to select the correct IoT node for a given mission in IoT-edge situations. The outcomes of the experiments indicate that the proposed framework enhances the parameters under consideration.
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Bura, Aman, and Yang Dacheng . "GPRS Network Resources Optimization." Information Technology Journal 5, no. 5 (August 15, 2006): 970–75. http://dx.doi.org/10.3923/itj.2006.970.975.
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AbuBaker, Ayman, Yazeed Yasin Ghadi, and Nader Santarisi. "Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 2564. http://dx.doi.org/10.11591/ijece.v12i3.pp2564-2570.
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<span>The paper presents an intelligent system to enhance mass lesions in digitized mammogram images. This system can assist radiologists in detecting mass lesions in mammogram images as an early diagnosis of breast cancer. In this paper, the early detection of mass lesion is visually detected by enhancing mass lesions in mammogram images using hybrid neuro-fuzzy technique. Fuzzified engine is proposed as a first step to convert all pixels in mammogram image to a fuzzy value using three linguistic labels. After that, artificial neural networks are used instead of the inference engine to accurately detect the mass lesions in the mammogram images in a short time. Finally, five linguistic labels are used as a defuzzifier engine to restore the mammogram image. Processed mammogram images are extensively evaluated using two different types of mammogram resources, mammographic image analysis society (MIAS) and University of South Florida (USF) databases. The results show that the proposed intelligent computer aided diagnosis system can successfully enhance the mass lesions in mammogram images with minimum number of false positive regions.</span>
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Stilman, Boris, Vladimir Yakhnis, and Oleg Umanskiy. "Linguistic Geometry: The Age of Maturity." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 6 (September 20, 2010): 684–99. http://dx.doi.org/10.20965/jaciii.2010.p0684.
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This paper describes the current status of research and applications of Linguistic Geometry (LG), a type of game theory scalable to the level of real life defense systems. LG is compared to conventional gaming approaches with respect to their applicability to real world problems. LG generates winning strategies for all sides in a conflict in real time by constructing them out of a limited set of blocks called zones. Several examples of zones are introduced. The paper describes the process of discovery of new zones essential for various types of military operations, which represent different classes of abstract board games. We also included a brief explanation of scalability of the LG applications. This paper describes a universal tool kit, LG-PACKAGE, which allows a user to build his/her own applications of LG. This tool kit includes GDK (Game Development Kit), GIK (Game Integration Kit), GRT (Game Resource Tool), GST (Game Solving Tool), GNS (Game Network Services) and GMI (Game Mobile Interface). In the end we included a description of the most advanced experiments with the LG applications conducted by DARPA and US Army and assessments of these experiments by military experts.
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Krause, Sebastian, Leonhard Hennig, Andrea Moro, Dirk Weissenborn, Feiyu Xu, Hans Uszkoreit, and Roberto Navigli. "Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs." Journal of Web Semantics 37-38 (March 2016): 112–31. http://dx.doi.org/10.1016/j.websem.2016.03.004.
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Bishop, Michael P., Roger M. Hubbard, James L. Ward, Mark S. Binkley, and Thomas K. Moore. "Computer Network Resources for Physical Geography Instruction." Journal of Geography 92, no. 3 (May 1993): 102–9. http://dx.doi.org/10.1080/00221349308979632.
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Volkova, Svetlana. "Nologisms in modert english based on the internet resources." Vìsnik Marìupolʹsʹkogo deržavnogo unìversitetu. Serìâ: Fìlologìâ 12, no. 21 (2019): 112–16. http://dx.doi.org/10.34079/2226-3055-2019-12-21-112-116.
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This article explores English neologisms in the spheres of information technologies based on the Internet resources. Neologisms and their most essential classification are observed and studied in the article. The most widely used classifications of neologisms according to their ways and methods of creation are clarified. The meanings of neologisms and their influence on the language and language flow in the modern world are identified as well. Due to rapidly developed computer technologies and the Internet, the neologisms and new words are created very quickly and constantly. It is important to state that English is enriched about 800 words yearly. However, it would take a long time to consolidate the position of new words in a language, and to make their content comprehensible to an average citizen. The similar situation is observed with the use of widely used words in a new meaning in other spheres of communication. Along with new inventions and devices, new software is emerging, for example, we now are not able to imagine our lives without search engines and systems, as well as the internet, or devices such as a smartphone. All these phenomena, accompanied by the formation of new vocabulary units, and information resources that have a target audience, namely professional users of information technology products. They provide the spread of the latest vocabulary (i.e. neologisms), its socialization (acceptance in society), and then lexicalization (consolidation in language). We can speculate that neologisms are one of the linguistic means that reflects cultural and technological progress in society. Modern humanity lives in the age of information, the main feature of which is computer technology. That is why the problem of the existence and functioning of the Internet language is urgent. The issue of computer language usage has been studied since the first computer was created in the mid-twentieth century. The researches were carried out by prominent scientists in different countries of the world. It might be added that various aspects of the impact of computer communication on language and vice versa were researched, as well as the relation between the development of lexical units and their ways of appearing in language. But it is essential to note that there are linguistic classifications of neologisms. The first one is according to the way of formation, namely, it names a new subject and gives the old concept a new name in order to renew and refresh the language. The emergence of neologisms of the first type is necessary to name a new phenomena in society, but the second type arises involuntarily. They could also be divided into lexical, authorial, individual-stylistic, phonological, loanwords, semantic and syntactic neologisms. They are also morphological according to the methods of creation, for example, defriend, web master, googlable and facebookian. As for shortening and abbreviation, we could define that such as methods are most widely used in English (CPU – Computing processing unit). To sum up, neologism is one way to increase the vocabulary of the language and a means of generating names for new phenomena in society. They appear in all areas of our lives, but they have gained the most development in the field of information technology because this area is the fastest growing in the world today. Since the primary purpose is to name a new subject, most modern neologisms belong to the lexical group, and they are called a new phenomenon because in our time, a new information revolution is taking place and this process is accompanied by the formation of new concepts and phenomena that need new words. It is also not surprising that the percentage of authorial neologisms is now growing, which has expanded significantly with the development of social networks and independent authors. They form a certain initial group of speakers, sometimes quite large, as the accounts of certain IT journalists have up to one and a half million followers, so the spread of these neologisms is extremely rapid.
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Bogdanchikov, Andrey, Dauren Ayazbayev, and Iraklis Varlamis. "Classification of Scientific Documents in the Kazakh Language Using Deep Neural Networks and a Fusion of Images and Text." Big Data and Cognitive Computing 6, no. 4 (October 24, 2022): 123. http://dx.doi.org/10.3390/bdcc6040123.
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The rapid development of natural language processing and deep learning techniques has boosted the performance of related algorithms in several linguistic and text mining tasks. Consequently, applications such as opinion mining, fake news detection or document classification that assign documents to predefined categories have significantly benefited from pre-trained language models, word or sentence embeddings, linguistic corpora, knowledge graphs and other resources that are in abundance for the more popular languages (e.g., English, Chinese, etc.). Less represented languages, such as the Kazakh language, balkan languages, etc., still lack the necessary linguistic resources and thus the performance of the respective methods is still low. In this work, we develop a model that classifies scientific papers written in the Kazakh language using both text and image information and demonstrate that this fusion of information can be beneficial for cases of languages that have limited resources for machine learning models’ training. With this fusion, we improve the classification accuracy by 4.4499% compared to the models that use only text or only image information. The successful use of the proposed method in scientific documents’ classification paves the way for more complex classification models and more application in other domains such as news classification, sentiment analysis, etc., in the Kazakh language.
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Macleod, Roderick A. "Network resources and access tools." New Review of Information Networking 10, no. 1 (May 2004): 85. http://dx.doi.org/10.1080/13614570412331311996.
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MacLeod, Roderick A. "Network resources and access tools." New Review of Information Networking 9, no. 1 (January 2003): 79–80. http://dx.doi.org/10.1080/1361457042000186921.
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Niu, Yiming, Wenyong Du, and Zhenying Tang. "Computer Network Security Defense Model." Journal of Physics: Conference Series 2146, no. 1 (January 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2146/1/012041.
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Abstract With the rapid development of the Internet industry, hundreds of millions of online resources are also booming. In the information space with huge and complex resources, it is necessary to quickly help users find the resources they are interested in and save users time. At this stage, the content industry’s application of the recommendation model in the content distribution process has become the mainstream. The content recommendation model provides users with a highly efficient and highly satisfying reading experience, and solves the problem of information redundancy to a certain extent. Knowledge tag personalized dynamic recommendation technology is currently widely used in the field of e-commerce. The purpose of this article is to study the optimization of the knowledge tag personalized dynamic recommendation system based on artificial intelligence algorithms. This article first proposes a hybrid recommendation algorithm based on the comparison between content-based filtering and collaborative filtering algorithms. It mainly introduces user browsing behavior analysis and design, KNN-based item similarity algorithm design, and hybrid recommendation algorithm implementation. Finally, through algorithm simulation experiments, the effectiveness of the algorithm in this paper is verified, and the accuracy of the recommendation has been improved.