World Academy of Science, Engineering and Technology

Authors: Andy H. Clark

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This research looks into the relationship between language comprehension and production with a specific focus on the role of salience in shaping these processes. Salience, our most immediate perception of what is most probable out of all possible situations and outcomes strongly affects our perception and action in language production and comprehension. This study investigates the impact of geographic and emotional attachments to the target language on the differences in the learners’ comprehension and production abilities. Using quantitative research methods (Qualtrics, SPSS), this study examines preferential choices of two groups of Japanese English language learners: those residing in the United States and those in Japan. By comparing and contrasting these two groups, we hope to gain a better understanding of how salience of linguistics cues influences language processing.

Keywords: intercultural pragmatics, salience, production, comprehension, pragmatics, action, perception, cognition

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Authors: Nanyonjo Juliet

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Across Sub-Saharan Africa, it’s increasingly becoming an absolute necessity for either parents or governments to encourage learners, most particularly those attending high schools, to study a second or foreign language other than the “official language” or the language of instruction in schools. The major second or foreign languages under consideration include but are not necessarily limited to English, French, German, Arabic, Swahili/Kiswahili, Spanish and Chinese. The benefits of learning a second (foreign) language in the globalized world cannot be underestimated. Amongst others, it has been expounded to especially involve such opportunities related to traveling, studying abroad and widening one’s career prospects. Research has also revealed that beyond these non-cognitive rewards, learning a second language enables learners to become more thoughtful, considerate and confident, make better decisions, keep their brain healthier and generally – speaking, broaden their world views. The methodology of delivering a successful 2nd language – learning process by a professionally qualified teacher is located in motivation. We strongly believe that the psychology involved in teaching a foreign language is of paramount importance to a learner’s successful learning experience. The aim of this paper, therefore, is to explore and show the importance of motivation in the teaching and learning of a given 2nd (foreign) language in the local Ugandan high schools.

Keywords: second language, foreign language, language learning, language teaching, official language, language of instruction, globalized world, cognitive rewards, non-cognitive rewards, learning process, motivation

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Authors: E. Velasco

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Meeting high standards of writing in a Second Language (L2) is extremely important for many students who wish to undertake studies at universities in both English and non-English speaking countries. University lecturers in English speaking countries continue to express dissatisfaction with the apparent poor quality of essay writing skills displayed by English as a Second Language (ESL) students, whose essays are often criticised for their lack of cohesion and coherence. These critiques have extended to contexts such as Colombia, where many ESL students are criticised for their inability to write high-quality academic texts in L2-English, particularly at the tertiary level. If Colombian ESL students are expected to meet high standards of writing when studying locally and abroad, it makes sense to carry out specific research that can perhaps lead to recommendations to support their quest for improving argumentative strategies. Employing Corpus Linguistics methods within a Learner Corpus Research framework, and a combination of Log-Likelihood and Bayes Factor measures, this paper investigated argumentative essays written by Colombian ESL students. The study specifically aimed to analyse conjunctive adjuncts in argumentative essays to find out how Colombian ESL students connect their ideas in discourse. Results suggest that a) Colombian ESL learners need explicit instruction on specific areas of conjunctive adjuncts to counteract overuse, underuse and misuse; b) underuse of endophoric and evidential adjuncts highlights gaps between IELTS-like essays and good quality tertiary-level essays and published papers, and these gaps are linked to prior knowledge brought into writing task, rhetorical functions in writing, and research processes before writing takes place; c) both Colombian ESL learners and L1-English writers (in a reference corpus) overuse some adjuncts and underuse endophoric and evidential adjuncts, when compared to skilled L1-English and L2-English writers, so differences in frequencies of adjuncts has little to do with the writers’ L1, and differences are rather linked to types of essays writers produce (e.g. ESL vs. university essays). Ender Velasco: The pedagogical recommendations deriving from the study are that: a) Colombian ESL learners need to be shown that overuse is not the only way of giving cohesion to argumentative essays and there are other alternatives to cohesion (e.g., implicit adjuncts, lexical chains and collocations); b) syllabi and classroom input need to raise awareness of gaps in writing skills between IELTS-like and tertiary-level argumentative essays, and of how endophoric and evidential adjuncts are used to refer to anaphoric and cataphoric sections of essays, and to other people’s work or ideas; c) syllabi and classroom input need to include essay-writing tasks based on previous research/reading which learners need to incorporate into their arguments, and tasks that raise awareness of referencing systems (e.g., APA); d) classroom input needs to include explicit instruction on use of punctuation, functions and/or syntax with specific conjunctive adjuncts such as for example, for that reason, although, despite and nevertheless.

Keywords: argumentative essays, colombian english as a second language (esl) learners, conjunctive adjuncts, corpus linguistics

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Authors: Zayd Khayi

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This paper deals with the construction and analysis of the Tamazight text corpus. The grammatical structure of the Tamazight remains poorly understood, and a lack of comparative grammar leads to linguistic issues. In order to fill this gap, even though it is small, by constructed the diachronic corpus of the Tamazight language, and elaborated the program tool. In addition, this work is devoted to constructing that tool to analyze the different aspects of the Tamazight, with its different dialects used in the north of Africa, specifically in Morocco. It also focused on three Moroccan dialects: Tamazight, Tarifiyt, and Tachlhit. The Latin version was good choice because of the many sources it has. The corpus is based on the grammatical parameters and features of that language. The text collection contains more than 500 texts that cover a long historical period. It is free, and it will be useful for further investigations. The texts were transformed into an XML-format standardization goal. The corpus counts more than 200,000 words. Based on the linguistic rules and statistical methods, the original user interface and software prototype were developed by combining the technologies of web design and Python. The corpus presents more details and features about how this corpus provides users with the ability to distinguish easily between feminine/masculine nouns and verbs. The interface used has three languages: TMZ, FR, and EN. Selected texts were not initially categorized. This work was done in a manual way. Within corpus linguistics, there is currently no commonly accepted approach to the classification of texts. Texts are distinguished into ten categories. To describe and represent the texts in the corpus, we elaborated the XML structure according to the TEI recommendations. Using the search function may provide us with the types of words we would search for, like feminine/masculine nouns and verbs. Nouns are divided into two parts. The gender in the corpus has two forms. The neutral form of the word corresponds to masculine, while feminine is indicated by a double t-t affix (the prefix t- and the suffix -t), ex: Tarbat (girl), Tamtut (woman), Taxamt (tent), and Tislit (bride). However, there are some words whose feminine form contains only the prefix t- and the suffix –a, ex: Tasa (liver), tawja (family), and tarwa (progenitors). Generally, Tamazight masculine words have prefixes that distinguish them from other words. For instance, 'a', 'u', 'i', ex: Asklu (tree), udi (cheese), ighef (head). Verbs in the corpus are for the first person singular and plural that have suffixes 'agh','ex', 'egh', ex: 'ghrex' (I study), 'fegh' (I go out), 'nadagh' (I call). The program tool permits the following characteristics of this corpus: list of all tokens; list of unique words; lexical diversity; realize different grammatical requests. To conclude, this corpus has only focused on a small group of parts of speech in Tamazight language verbs, nouns. Work is still on the adjectives, prounouns, adverbs and others.

Keywords: Tamazight (Berber) language, corpus linguistic, grammar rules, statistical methods

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Authors: Guangyuan Zhao, Nan Huang, Xuesong Han, Xu Huang

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In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution.

Keywords: particle filter, impoverishment, state estimation, artificial bee colony algorithm

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Mubina Rauf

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Critical thinking is a highly valued outcome of university education. There is an agreement in literature that it is demonstrated through the abilities to highlight issues and assumptions, find links between ideas and concepts, make correct inferences, evaluate evidence or authority and deduce conclusions (Tsui, 2002). Although Critical thinking plays a significant role in developing all academic skills, its role in developing writing skills is significant (Kurfiss, 1988). SAW (student academic writing) is an observable output of critical thinking (Wilson K. , 2016). When students apply critical thinking to their writing, they present clear, accurate, significant and logical arguments constructing their own voice in the form of an essay or dissertation (Matsuda, 2001). This presentation will show how a rubric can be used to find evidence of critical thinking in SAW. Participants will experience how evidence-based written arguments supported by background knowledge and authorial voice can develop students into efficient critical thinkers. Participants will have an opportunity to use the rubric to find the evidence of critical thinking in SAW samples. This presentation is intended for classroom teachers with or without the basic knowledge of implementing critical thinking in academic settings. Participants will also learn tips how various features of critical thinking can be developed among students. After the session, the participants will be able to use or adapt the rubric according to their needs to find evidence of critical thinking in SAW within their context.

Keywords: critical thinking, Rubric, student academic writing, argumentation, text analysis

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Authors: Zahra Kazemi Saleh, Bénédicte Poulin-Charronnat, Annie Vinter

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This study examines the effects of aging on visual statistical learning, using eye-tracking techniques to investigate this cognitive phenomenon. Visual statistical learning is a fundamental brain function that enables the automatic and implicit recognition, processing, and internalization of environmental patterns over time. Some previous research has suggested the robustness of this learning mechanism throughout the aging process, underscoring its importance in the context of education and rehabilitation for the elderly. The study included three distinct groups of participants, including 21 young adults (Mage: 19.73), 20 young-old adults (Mage: 67.22), and 17 old-old adults (Mage: 79.34). Participants were exposed to a series of 12 arbitrary black shapes organized into 6 pairs, each with different spatial configurations and orientations (horizontal, vertical, and oblique). These pairs were not explicitly revealed to the participants, who were instructed to passively observe 144 grids presented sequentially on the screen for a total duration of 7 min. In the subsequent test phase, participants performed a two-alternative forced-choice task in which they had to identify the most familiar pair from 48 trials, each consisting of a base pair and a non-base pair. Behavioral analysis using t-tests revealed notable findings. The mean score for the first group was significantly above chance, indicating the presence of visual statistical learning. Similarly, the second group also performed significantly above chance, confirming the persistence of visual statistical learning in young-old adults. Conversely, the third group, consisting of old-old adults, showed a mean score that was not significantly above chance. This lack of statistical learning in the old-old adult group suggests a decline in this cognitive ability with age. Preliminary eye-tracking results showed a decrease in the number and duration of fixations during the exposure phase for all groups. The main difference was that older participants focused more often on empty cases than younger participants, likely due to a decline in the ability to ignore irrelevant information, resulting in a decrease in statistical learning performance.

Keywords: aging, eye tracking, implicit learning, visual statistical learning

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Authors: Tomás Sobrino, Lara García-Varela, Marta Aramburu-Núñez, Mónica Castro, Noemí Gómez-Lado, Mariña Rodríguez-Arrizabalaga, Antía Custodia, Juan Manuel Pías-Peleteiro, José Manuel Aldrey, Daniel Romaus-Sanjurjo, Ángeles Almeida, Pablo Aguiar, Alberto Ouro

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Introduction: Alzheimer’s disease (AD) and other tauopathies are primary causes of dementia, causing progressive cognitive deterioration that entails serious repercussions for the patients' performance of daily tasks. Currently, there is no effective approach for the early diagnosis and treatment of AD and tauopathies. This study suggests a theragnostic approach based on the importance of phosphorylated tau protein (p-Tau) in the early pathophysiological processes of AD. We have developed a novel theragnostic monoclonal antibody (mAb) to provide both diagnostic and therapeutic effects. Methods/Results: We have developed a p-Tau mAb, which was doped with deferoxamine for radiolabeling with Zirconium-89 (89Zr) for PET imaging, as well as fluorescence dies for immunofluorescence assays. The p-Tau mAb was evaluated in vitro for toxicity by MTT assay, LDH activity, propidium iodide/Annexin V assay, caspase-3, and mitochondrial membrane potential (MMP) assay in both mouse endothelial cell line (bEnd.3) and cortical primary neurons cell cultures. Importantly, non-toxic effects (up to concentrations of p-Tau mAb greater than 100 ug/mL) were detected. In vivo experiments in the tauopathy model mice (PS19) show that the 89Zr-pTau-mAb and 89Zr-Fragments-pTau-mAb are stable in circulation for up to 10 days without toxic effects. However, only less than 0.2% reached the brain, so further strategies have to be designed for crossing the Brain-Blood-Barrier (BBB). Moreover, an intraparenchymal treatment strategy was carried out. The PS19 mice were operated to implement osmotic pumps (Alzet 1004) at two different times, at 4 and 7 months, to stimulate the controlled release for one month each of the B6 antibody or the IgG1 control antibody. We demonstrated that B6-treated mice maintained their motor and memory abilities significantly compared with IgG1 treatment. In addition, we observed a significant reduction in p-Tau deposits in the brain. Conclusions /Discussion: A theragnostic pTau-mAb was developed. Moreover, we demonstrated that our p-Tau mAb recognizes very-early pathology forms of p-Tau by non-invasive techniques, such as PET. In addition, p-Tau mAb has non-toxic effects, both in vitro and in vivo. Although the p-Tau mAb is stable in circulation, only 0.2% achieve the brain. However, direct intraventricular treatment significantly reduces cognitive impairment in Alzheimer's animal models, as well as the accumulation of toxic p-Tau species.

Keywords: alzheimer's disease, theragnosis, tau, PET, immunotherapy, tauopathies

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Authors: Jyoti Rani, Ashima Anand, Shivendra Shivani

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In recent years, physiological signals obtained in telemedicine have been stored independently from patient information. In addition, people have increasingly turned to mobile devices for information on health-related topics. Major authentication and security issues may arise from this storing, degrading the reliability of diagnostics. This study introduces an approach to reversible watermarking, which ensures security by utilizing the electrocardiogram (ECG) signal as a carrier for embedding patient information. In the proposed work, Pan-Tompkins++ is employed to convert the 1D ECG signal into a 2D signal. The frequency subbands of a signal are extracted using RDWT(Redundant discrete wavelet transform), and then one of the subbands is subjected to MSVD (Multiresolution singular valued decomposition for masking. Finally, the encrypted watermark is embedded within the signal. The experimental results show that the watermarked signal obtained is indistinguishable from the original signals, ensuring the preservation of all diagnostic information. In addition, the DnCNN (Denoising convolutional neural network) concept is used to denoise the retrieved watermark for improved accuracy. The proposed ECG signal-based watermarking method is supported by experimental results and evaluations of its effectiveness. The results of the robustness tests demonstrate that the watermark is susceptible to the most prevalent watermarking attacks.

Keywords: ECG, VMD, watermarking, PanTompkins++, RDWT, DnCNN, MSVD, chaotic encryption, attacks

Procedia PDF Downloads 49

Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Tanzeela Anbreen, Ammara Maqsood

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Reading into writing is one of university students' most required academic skills. The current study explored postgraduate university students’ writing quality using a corpus-based approach. Twelve postgraduate students’ literature review essays were chosen for the corpus-based analysis. These essays were chosen because students had to incorporate multiple reading sources in these essays, which was a new writing exercise for them. The students were provided feedback at least two times which comprised of the written comments by the tutor highlighting the areas of improvement and also by using the ‘track changes’ function. This exercise was repeated two times, and students submitted two drafts. This investigation included only the finally submitted work of the students. A corpus-based approach was adopted to analyse the essays because it promotes autonomous discovery and personalised learning. The aim of this analysis was to understand the existing level of students’ writing before the start of their postgraduate thesis. Text Inspector was used to analyse the quality of essays. With the help of the Text Inspector tool, the vocabulary used in the essays was compared to the English Vocabulary Profile (EVP), which describes what learners know and can do at each Common European Framework of Reference (CEFR) level. Writing quality was also measured for the Flesch reading ease score, which is a standard to describe the ease of understanding the writing content. The results reflected that students found writing essays using multiple sources challenging. In most essays, the vocabulary level achieved was between B1-B2 of the CEFL level. The study recommends that students need extensive training in developing academic writing skills, particularly in writing the literature review type assignment, which requires multiple sources citations.

Keywords: literature review essays, postgraduate students, corpus-based analysis, vocabulary proficiency

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Authors: Desta Kebede Ayana, Lies Sercu, Demelash Mengistu

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The study is set in Ethiopia, a sub-Saharan multilingual, multiethnic African country, which has seen a significant increase in the number of universities in recent years. The aim of this growth is to provide access to education for all cultural and linguistic groups across the country. However, there are challenges in promoting intercultural competence among students in this diverse context. The aim of the study is to investigate the interculturalization of Ethiopian Higher Education Institutions as perceived by university lecturers and administrators. In particular, the study aims to determine the level of support for this educational innovation and gather suggestions for its implementation and institutionalization. The researchers employed semi-structured interviews with administrators and lecturers from two large Ethiopian universities to gather data. Thematic analysis was utilized for coding and analyzing the interview data, with the assistance of the NVIVO software. The findings obtained from the grounded analysis of the interview data reveal that while there are opportunities for interculturalization in the curriculum and campus life, support for educational innovation remains low. Administrators and lecturers also emphasize the government's responsibility to prioritize interculturalization over other educational innovation goals. The study contributes to the existing literature by examining an under-researched population in an under-researched context. Additionally, the study explores whether Western perspectives of intercultural competence align with the African context, adding to the theoretical understanding of intercultural education. The data for this study was collected through semi-structured interviews conducted with administrators and lecturers from two large Ethiopian universities. The interviews allowed for an in-depth exploration of the participants' views on interculturalization in higher education. Thematic analysis was applied to the interview data, allowing for the identification and organization of recurring themes and patterns. The analysis was conducted using the NVIVO software, which aided in coding and analyzing the data. The study addresses the extent to which administrators and lecturers support the interculturalization of Ethiopian Higher Education Institutions. It also explores their suggestions for implementing and institutionalizing intercultural education, as well as their perspectives on the current level of institutionalization. The study highlights the challenges in interculturalizing Ethiopian universities and emphasizes the need for greater support and prioritization of intercultural education. It also underscores the importance of considering the African context when conceptualizing intercultural competence. This research contributes to the understanding of intercultural education in diverse contexts and provides valuable insights for policymakers and educational institutions aiming to promote intercultural competence in higher education settings.

Keywords: administrators, educational change, Ethiopia, intercultural competence, lecturers

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Authors: Armel Mbon

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This paper examines the ‘success’ of George Bernard Shaw's main character Professor Higgins' motivation in teaching Eliza Doolittle, a young Cockney flower girl, how to speak 'correct' English in no time in Pygmalion. Notice should be given that Shaw in whose writings, language issues feature prominently, does not believe there is such a thing as perfectly correct English, but believes in the varieties of spoken English as a source of its richness. Indeed, along with his fellow phonetician Colonel Pickering, Henry Higgins succeeds in teaching Eliza that he first judges unfairly, the dialect of the upper classes and Received Pronunciation, to facilitate her social advancement. So, after six months of rigorous learning, Eliza's speech and manners are transformed, and she is able to pass herself off as a lady. Such is the success of Professor Higgins’ motivation in linguistically transforming his learner in record time. On the other side, his motivation is unsuccessful since, by the end of the play, he cannot have Eliza he believes he has shaped to his so-called good image, for wife. So, this paper aims to show, in support of the psychological approach, that in motivation, feelings, pride and prejudice cannot be combined, and that one has not to pre-judge someone’s attitude based purely on how well they speak English.

Keywords: teaching, speak, in no time, success

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Authors: Mohammad Hosein Panahi, Naser Yazdani

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we present a technical trading strategy that leverages the FinBERT language model and financial news analysis with a focus on news related to a subset of Nasdaq 100 stocks. Our approach surpasses the baseline Range Break-out strategy in the Bitcoin market, yielding a remarkable 24.8% increase in the win ratio for all Friday trades and an impressive 48.9% surge in short trades specifically on Fridays. Moreover, we conduct rigorous hypothesis testing to establish the statistical significance of these improvements. Our findings underscore considerable potential of our NLP-driven approach in enhancing trading strategies and achieving greater profitability within financial markets.

Keywords: quantitative finance, technical analysis, bitcoin market, NLP, language models, FinBERT, technical trading

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Authors: Vishal Raj, Noorhan Abbas

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Ambiguity in NLP (Natural language processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential am-biguities. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a novel method to create an affinity matrix to calculate the affinity be-tween any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an al-gorithm to create the sense clusters of tokens using affinity matrix under hierar-chy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contex-tual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and chal-lenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: word sense disambiguation (wsd), contextual sense model (csm), most frequent sense (mfs), part of speech (pos), natural language processing (nlp), oov (out of vocabulary), lemma_pos (a token where lemma and pos of word are joined by underscore), information retrieval (ir), machine translation (mt)

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Authors: Girma Yohannis Bade, Olga Kolesnikova, Grigori Sidorov

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Named entity recognition (NER) is a building block for information extraction. Though the information extraction process has been automated using a variety of techniques to find and extract a piece of relevant information from unstructured documents, the discovery of targeted knowledge still poses a number of research difficulties because of the variability and lack of structure in Web data. NER, a subtask of information extraction (IE), came to exist to smooth such difficulty. It deals with finding the proper names (named entities), such as the name of the person, country, location, organization, dates, and event in a document, and categorizing them as predetermined labels, which is an initial step in IE tasks. This survey paper presents the roles and importance of NER to IE from the perspective of different algorithms and application area domains. Thus, this paper well summarizes how researchers implemented NER in particular application areas like finance, medicine, defense, business, food science, archeology, and so on. It also outlines the three types of sequence labeling algorithms for NER such as feature-based, neural network-based, and rule-based. Finally, the state-of-the-art and evaluation metrics of NER were presented.

Keywords: the role of NER, named entity recognition, information extraction, sequence labeling algorithms, named entity application area

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Authors: Soheil Behdarvandirad

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The relationship between depression and self-focused language has been well documented. The more depressed a person is, the more "I"s, "me"s, and "my"s they will use. The present study attempted to factor in the impact of mood and examine whether negative mood has self-focused impacts similar to those of depression. For this purpose, 160 Iranian native speakers of Farsi were divided into three experimental groups of negative, neutral, and positive groups. After completing the BDI-II inventory and depression measurement, they were presented with pretested mood stimuli (3 separate videos to induce the target moods). Finally, they were asked to write between 10 to 20 minutes about a topic that asked them to freely write about their state of life, how you feel about it and the reasons that had shaped their current life circumstances. While the significant correlation between depression and I-talk was observed, negative mood led to more we-talk in general and seemed to even push the participants away from self-rumination. It seems that it is an emotion-regulatory strategy that participants subconsciously adopt to distract themselves from the disturbing mood. However, negative mood intensified the self-focused language among depressed participants. Such results can be further studied by examining brain areas that are more involved in self-perception and particularly in precuneus.

Keywords: self-focused language, depression, mood, precuneus

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference, supervised learning

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Authors: Johann Van Niekerk

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The disciplines of conducting (instrumental or choral) and of singing presume a willingness toward an open posture and, in many cases, demand it for effective communication and technique. Yet, this very openness, with the "spread-eagle" gesture as an extreme, is oftentimes counterintuitive for musicians and within the trajectory of human evolution. Conversely, it is in this very gesture of "taking up space" that confidence-gaining techniques such as the popular "power pose" are based. This paper consists primarily of a literature review, exploring the topics of physical openness and vulnerability, considering the semiotics of the "spread-eagle" and its accompanying letter X. A major finding of this research is the discrepancy between evolutionary instinct towards physical self-protection and “folding in” and the demands of the discipline of physical and gestural openness, expansiveness and vulnerability. A secondary finding is ways in which encouragement of confidence-gaining techniques may be more effective in obtaining the required results than insistence on vulnerability, which is influenced by various cultural contexts and socialization. Choral conductors and music educators are constantly seeking ways to promote engagement and healthy singing. Much of the information and direction toward this goal is gleaned by students from conducting gestures and other pedagogies employed in the rehearsal. The findings of this research provide yet another avenue toward reaching the goals required for sufficient and effective teaching and artistry on the part of instructors and students alike.

Keywords: conducting, gesture, music, pedagogy, posture, vulnerability

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Dan Xie

Abstract:

The use of discourse markers (DMs) can play a crucial role in representing discourse interaction and pragmatic competence. Learners’ use of DMs and differences between native speakers (NSs) and non-native speakers (NNSs) in the use of various DMs have been the focus of considerable research attention. However, some commonly used DMs, such as you know, have not received as much attention in comparative studies, especially in the Chinese context. This study analyses data in two corpora (COLSEC and Spoken BNC 2014 (14-25)) to investigate how Chinese learners differ from NNSs in their use of the DM you know and its functions in speech. The results show that there is a significant difference between the two corpora in terms of the frequency of use of you know. In terms of the functions of you know, the study shows that six functions can all be present in both corpora, although there are significant differences between the five functional dimensions, especially in introducing a claim linked to the prior discourse and highlighting particular points in the discourse. It is hoped to show empirically how Chinese learners and NSs use DMs differently.

Keywords: you know, discourse marker, native speaker, Chinese learner

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Authors: Yufen Qin

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Text sentiment transfer is an important branch of text style transfer. The goal is to generate text with another sentiment attribute based on a text with a specific sentiment attribute while maintaining the content and semantic information unrelated to sentiment unchanged in the process. There are currently two main challenges in this field: no parallel corpus and text attribute entanglement. In response to the above problems, this paper proposed a novel solution: Mask-Prompt-Rerank. Use the method of masking the sentiment words and then using prompt regeneration to transfer the sentence sentiment. Experiments on two sentiment benchmark datasets and one formality transfer benchmark dataset show that this approach makes the performance of small pre-trained language models comparable to that of the most advanced large models, while consuming two orders of magnitude less computing and memory.

Keywords: language model, natural language processing, prompt, text sentiment transfer

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Authors: Rui Yin, Haojie Wang, Xun Li

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Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data.

Keywords: dense retrieval, query generation, unsupervised training, text retrieval

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Authors: Yulan Wu

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The spread of fake news on social media has posed significant societal harm to the public and the nation, with its threats spanning various domains, including politics, economics, health, and more. News on social media often covers multiple domains, and existing models studied by researchers and relevant organizations often perform well on datasets from a single domain. However, when these methods are applied to social platforms with news spanning multiple domains, their performance significantly deteriorates. Existing research has attempted to enhance the detection performance of multi-domain datasets by adding single-domain labels to the data. However, these methods overlook the fact that a news article typically belongs to multiple domains, leading to the loss of domain knowledge information contained within the news text. To address this issue, research has found that news records in different domains often use different vocabularies to describe their content. In this paper, we propose a fake news detection framework that combines domain knowledge and expert knowledge. Firstly, it utilizes an unsupervised domain discovery module to generate a low-dimensional vector for each news article, representing domain embeddings, which can retain multi-domain knowledge of the news content. Then, a feature extraction module uses the domain embeddings discovered through unsupervised domain knowledge to guide multiple experts in extracting news knowledge for the total feature representation. Finally, a classifier is used to determine whether the news is fake or not. Experiments show that this approach can improve multi-domain fake news detection performance while reducing the cost of manually labeling domain labels.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Bowen Ding, Yihao Kuang

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In recent years, with the development of large-scale pre-trained lan-guage models, building vector representations of text through deep neural network models has become a standard practice for natural language processing tasks. From the performance on downstream tasks, we can know that the text representation constructed by these models contains linguistic information, but its encoding mode and extent are unclear. In this work, a structural probe is proposed to detect whether the vector representation produced by a deep neural network is embedded with a syntax tree. The probe is trained with the deep metric learning method, so that the distance between word vectors in the metric space it defines encodes the distance of words on the syntax tree, and the norm of word vectors encodes the depth of words on the syntax tree. The experiment results on ELMo and BERT show that the syntax tree is encoded in their parameters and the word representations they produce.

Keywords: deep metric learning, syntax tree probing, natural language processing, word representations

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Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo

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SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models.

Keywords: gender classification, joint detection, SMPL-X, 3D reconstruction

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Authors: Wenbo Wang, Yi-Fang Brook Wu

Abstract:

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated fact-checking has emerged as a safeguard against the spread of misinformation and disinformation. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state-of-the-art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study introduces a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using general-purpose, comprehensive, and authoritative data; 2) developing a search function to automatically select relevant, new, and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the fact-checking task. The experimental results demonstrate that 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multi-head attention technique, contributes to improved performance of fact-checking. 2) SAC with auto-selected references outperforms existing fact-checking approaches with manual selected references. Future directions of this study include I) exploring knowledge graphs in Wikidata to dynamically augment the representations of claims and references without introducing too much noise, II) exploring semantic relations in claims and references to further enhance fact-checking.

Keywords: fact checking, claim verification, deep learning, natural language processing

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Authors: Kadidiatou Toure

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Mali has started experimentation of teaching the national languages at school through the convergent pedagogy in 1987. Then, it is in 1994 that it will become widespread with eleven of the thirteen former national languages used at primary school. The aim was to improve the Malian educational system because the use of French as the only medium of instruction was considered a contributing factor to the significant number of student dropouts and the high rate of repetition. The Convergent pedagogy highlights the knowledge acquired by children at home, their vision of the world and especially the knowledge they have of their mother tongue. That pedagogy requires the use of a specific medium only during classroom practices and teachers have been trained in this sense. The specific medium depends on the learning content, which sometimes is French, other times, it is the national language. Research has shown that bilingual learners do not only use the required medium in their learning activities, but they code switch. It is part of their learning processes. Currently, many scholars agree on the importance of CS in bilingual classes, and teachers have been told about the necessity of integrating it into their classroom practices. One of the challenges of the Malian bilingual education curriculum is the question of ‘effective languages management’. Theoretically, depending on the classrooms, an average have been established for each of the involved language. Following that, teachers make use of CS differently, sometimes, it favors the learners, other times, it contributes to the development of some linguistic weaknesses. The present research tries to fill that gap through a tentative model of didactization of CS, which simply means the practical management of the languages involved in the bilingual classrooms. It is to know how to use CS for effective learning. Moreover, the didactization of CS tends to sensitize the teachers about the functional role of CS so that they may overcome their own weaknesses. The overall goal of this research is to make code switching a real tool for bilingual education. The specific objectives are: to identify the types of CS used during classroom activities to present the functional role of CS for the teachers as well as the pupils. to develop a tentative model of code-switching, which will help the teachers in transitional classes of bilingual schools to recognize the appropriate moment for making use of code switching in their classrooms. The methodology adopted is a qualitative one. The study is based on recorded videos of teachers of 3rd year of primary school during their classroom activities and interviews with the teachers in order to confirm the functional role of CS in bilingual classes. The theoretical framework adopted is the typology of CS proposed by Poplack (1980) to identify the types of CS used. The study reveals that teachers need to be trained on the types of CS and the different functions they assume and on the consequences of inappropriate use of language alternation.

Keywords: bilingual curriculum, code switching, didactization, national languages

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Authors: Andrei Nosov

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This paper discusses the development directions of dialogue systems within the digitalisation of the railway industry, where technologies based on conversational AI are already potentially applied or will be applied. Conversational AI is one of the popular natural language processing (NLP) tasks, as it has great prospects for real-world applications today. At the same time, it is a challenging task as it involves many areas of NLP based on complex computations and deep insights from linguistics and psychology. In this review, we focus on dialogue systems and their implementation in the railway domain. We comprehensively review the state-of-the-art research results on dialogue systems and analyse them from three perspectives: type of problem to be solved, type of model, and type of system. In particular, from the perspective of the type of tasks to be solved, we discuss characteristics and applications. This will help to understand how to prioritise tasks. In terms of the type of models, we give an overview that will allow researchers to become familiar with how to apply them in dialogue systems. By analysing the types of dialogue systems, we propose an unconventional approach in contrast to colleagues who traditionally contrast goal-oriented dialogue systems with open-domain systems. Our view focuses on considering retrieval and generative approaches. Furthermore, the work comprehensively presents evaluation methods and datasets for dialogue systems in the railway domain to pave the way for future research. Finally, some possible directions for future research are identified based on recent research results.

Keywords: digitalisation, railway, dialogue systems, conversational AI, natural language processing, natural language understanding, natural language generation

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