World Academy of Science, Engineering and Technology

Authors: Huinan Zhang, Wenjie Jiang

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Accurate estimation of tropical cyclone intensity is of great importance for disaster prevention and mitigation. Existing techniques are largely based on satellite imagery data, and research and utilization of the inner thermal core structure characteristics of tropical cyclones still pose challenges. This paper presents a two-stage tropical cyclone intensity estimation network based on the fusion of coarse and fine-grained features from microwave brightness temperature data. The data used in this network are obtained from the thermal core structure of tropical cyclones through the Advanced Technology Microwave Sounder (ATMS) inversion. Firstly, the thermal core information in the pressure direction is comprehensively expressed through the maximal intensity projection (MIP) method, constructing coarse-grained thermal core images that represent the tropical cyclone. These images provide a coarse-grained feature range wind speed estimation result in the first stage. Then, based on this result, fine-grained features are extracted by combining thermal core information from multiple view profiles with a distributed network and fused with coarse-grained features from the first stage to obtain the final two-stage network wind speed estimation. Furthermore, to better capture the long-tail distribution characteristics of tropical cyclones, focal loss is used in the coarse-grained loss function of the first stage, and ordinal regression loss is adopted in the second stage to replace traditional single-value regression. The selection of tropical cyclones spans from 2012 to 2021, distributed in the North Atlantic (NA) regions. The training set includes 2012 to 2017, the validation set includes 2018 to 2019, and the test set includes 2020 to 2021. Based on the Saffir-Simpson Hurricane Wind Scale (SSHS), this paper categorizes tropical cyclone levels into three major categories: pre-hurricane, minor hurricane, and major hurricane, with a classification accuracy rate of 86.18% and an intensity estimation error of 4.01m/s for NA based on this accuracy. The results indicate that thermal core data can effectively represent the level and intensity of tropical cyclones, warranting further exploration of tropical cyclone attributes under this data.

Keywords: Artificial intelligence, deep learning, data mining, remote sensing

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Authors: Huang Yajing

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Multimodal discourse refers to the phenomenon of using various senses such as hearing, vision, and touch to communicate through various means and symbolic resources such as language, images, sounds, and movements. With the development of modern technology and multimedia, language and technology have become inseparable, and foreign language teaching is becoming more and more modal. Teacher-student communication resorts to multiple senses and uses multiple symbol systems to construct and interpret meaning. The classroom is a semiotic space where multimodal discourses are intertwined. College English multi-modal teaching is to rationally utilize traditional teaching methods while mobilizing and coordinating various modern teaching methods to form a joint force to promote teaching and learning. Multimodal teaching makes full and reasonable use of various meaning resources and can maximize the advantages of multimedia and network environments. Based upon the above theories about multimodal discourse and multimedia technology, the present paper will propose a multi-modal teaching model for college English in China.

Keywords: multimodal discourse, multimedia technology, English education, applied linguistics

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Authors: Luisa Villani

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The proposal aims to express concerns about the connection between mind, body, society, and environment in the Flyers’ dance, a very well-known rotatory dance in Mexico, to create meanings and to make the apprehension of the world possible. The interaction among the brain, mind, body, and environment, and the intersubjective relation among them, means the world creates and recreates a social interaction. The purpose of this methodology, based on the embodied cognition theory, which was named “A Performance-Embodied Theory” is to find the principles and patterns that organize the culture and the rules of the apprehension of the environment by Totonac people while the dance is being performed. The analysis started by questioning how anthropologists can interpret how Totonacs transform their unconscious knowledge into conscious knowledge and how the scheme formation of imagination and their collective imagery is understood in the context of public-facing rituals, such as Flyers’ dance. The problem is that most of the time, researchers interpret elements in a separate way and not as a complex ritual dancing whole, which is the original contribution of this study. This theory, which accepts the fact that people are body-mind agents, wants to interpret the dance as a whole, where the different elements are joined to an integral interpretation. To understand incorporation, data was recollected in prolonged periods of fieldwork, with participant observation and linguistic and extralinguistic data analysis. Laban’s notation for the description and analysis of gestures and movements in the space was first used, but it was later transformed and gone beyond this method, which is still a linear and compositional one. Performance in a ritual is the actualization of a potential complex of meanings or cognitive domains among many others in a culture: one potential dimension becomes probable and then real because of the activation of specific meanings in a context. It can only be thought what language permits thinking, and the lexicon that is used depends on the individual culture. Only some parts of this knowledge can be activated at once, and these parts of knowledge are connected. Only in this way, the world can be understood. It can be recognized that as languages geometrize the physical world thanks to the body, also ritual does. In conclusion, the ritual behaves as an embodied grammar or a text in movement, which, depending on the ritual phases and the words and sentences pronounced in the ritual, activates bits of encyclopedic knowledge that people have about the world. Gestures are not given by the performer but emerge from the intentional perception in which gestures are “understood” by the audio-spectator in an inter-corporeal way. The impact of this study regards the possibility not only to disseminate knowledge effectively but also to generate a balance between different parts of the world where knowledge is shared, rather than being received by academic institutions alone. This knowledge can be exchanged, so indigenous communities and academies could be together as part of the activation and the sharing of this knowledge with the world.

Keywords: dance, flyers, performance, embodied, cognition

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

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The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

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Authors: Cinzia Russi, Chiyo Nishida

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Using data from online corpora (Spanish CREA, Italian CORIS), this paper examines the relatively understudied use of Spanish se and Italian si exemplified in (1) and (2), respectively. (1) El rojo es … el que se come a los demás. ‘The red (bottle) is the one that outshines/*eats the rest.’(2) … ebbe anche la saggezza di mangiarsi tutto il suo patrimonio. ‘… he even had the wisdom to squander/*eat all his estate.’ In these sentences, se/si accompanies the consumption verb comer/mangiare ‘to eat’, without which the sentences would not be interpreted appropriately. This se/si cannot readily be attributed to any of the multiple functions so far identified in the literature: reflexive, ergative, middle/passive, inherent, benefactive, and complete consumptive. In particular, this paper argues against the feasibility of a recent construction-based analysis of sentences like (1) and (2), which situates se/si within a prototype-based network of meanings all deriving from the central meaning of 'COMPLETE CONSUMPTION' (e.g., Alice se comió toda la torta/Alicesi è mangiata tutta la torta ‘John ate the whole cake’). Clearly, the empirical adequacy of such an account is undermined by the fact that the events depicted in the se/si-sentences at issue do not always entail complete consumption because they may lack an INCREMENTAL THEME, the distinguishing property of complete consumption. Alternatively, it is proposed that the sentences under analysis represent instances of verbal METAPHORICAL EXTENSION: se/si represents an explicit marker of this cognitive process, which has independently developed from the complete consumptive se/si, and the meaning extension is captured by the general tenets of Conceptual Metaphor Theory (CMT). Two conceptual domains, Source (DS) and target (DT), are related by similarity, assigning an appropriate metaphorical interpretation to DT. The domains paired here are comer/mangiare (DS) and comerse/mangiarsi (DT). The eating event (DS) involves (a) the physical process of xEATER grinding yFOOD-STUFF into pieces and swallowing it; and (b) the aspect of xEATER savoring yFOOD-STUFF and being nurtured by it. In the physical act of eating, xEATER has dominance and exercises his force over yFOOD-STUFF. This general sense of dominance and force is mapped onto DT and is manifested in the ways exemplified in (1) and (2), and many others. According to CMT, two other properties are observed in each pair of DS & DT. First, DS tends to be more physical and concrete and DT more abstract, and systematic mappings are established between constituent elements in DS and those in DT: xEATER corresponds to the element that destroys and yFOOD-STUFF to the element that is destroyed in DT, as exemplified in (1) and (2). Though the metaphorical extension marker se/si appears by far most frequently with comer/mangiare in the corpora, similar systematic mappings are observed in several other verb pairs, for example, jugar/giocare ‘to play (games)’ and jugarse/giocarsi ‘to jeopardize/risk (life, reputation, etc.)’, perder/perdere ‘to lose (an object)’ and perderse/perdersi ‘to miss out on (an event)’, etc. Thus, this study provides evidence that languages may indeed formally mark metaphor using means available to them.

Keywords: complete consumption value, conceptual metaphor, Italian si/Spanish se, metaphorical extension.

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Authors: Vidhi Jain

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Communication between speakers and non-speakers of American Sign Language (ASL) can be problematic, inconvenient, and expensive. This project attempts to bridge the communication gap by designing a portable glove that captures the user’s ASL gestures and outputs the translated text on a smartphone. The glove is equipped with flex sensors, contact sensors, and a gyroscope to measure the flexion of the fingers, the contact between fingers, and the rotation of the hand. The glove’s Arduino UNO microcontroller analyzes the sensor readings to identify the gesture from a library of learned gestures. The Bluetooth module transmits the gesture to a smartphone. Using this device, one day speakers of ASL may be able to communicate with others in an affordable and convenient way.

Keywords: sign language, morse code, convolutional neural network, American sign language, gesture recognition

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Authors: Saman Khairani

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Assessment is a pivotal component of the teaching and learning process, serving as a critical tool for evaluating student progress, diagnosing learning needs, and informing instructional decisions. In the context of English Language Teaching (ELT), effective assessment practices are essential to promote meaningful learning experiences and foster continuous improvement in language proficiency. This paper delves into various assessment strategies, explores associated challenges, and highlights best practices for assessing student learning in ELT. The paper begins by examining the diverse forms of assessment, including formative assessments that provide timely feedback during the learning process and summative assessments that evaluate overall achievement. Additionally, alternative methods such as portfolios, self-assessment, and peer assessment play a significant role in capturing various aspects of language learning. Aligning assessments with learning objectives is crucial. Educators must ensure that assessment tasks reflect the desired language skills, communicative competence, and cultural awareness. Validity, reliability, and fairness are essential considerations in assessment design. Challenges in assessing language skills—such as speaking, listening, reading, and writing—are discussed, along with practical solutions. Constructive feedback, tailored to individual learners, guides their language development. In conclusion, this paper synthesizes research findings and practical insights, equipping ELT practitioners with the knowledge and tools necessary to design, implement, and evaluate effective assessment practices. By fostering meaningful learning experiences, educators contribute significantly to learners’ language proficiency and overall success.

Keywords: ELT, formative, summative, fairness, validity, reliability

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Authors: Hailang Yang

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To ensure the confidentiality and integrity of speech signals in communication transmission, this paper proposes a voice signal encryption scheme based on chaotic theory. Firstly, the scheme utilizes chaotic mapping to generate a key stream and then employs the key stream to perform bitwise exclusive OR (XOR) operations for encrypting the speech signal. Additionally, the scheme utilizes a chaotic hash function to generate a Message Authentication Code (MAC), which is appended to the encrypted data to verify the integrity of the data. Subsequently, we analyze the security performance and encryption efficiency of the scheme, comparing and optimizing it against existing solutions. Finally, experimental results demonstrate that the proposed scheme can resist common attacks, achieving high-quality encryption and speed.

Keywords: chaotic theory, XOR encryption, chaotic hash function, Message Authentication Code (MAC)

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Authors: Eglė Žurauskaitė, Eglė Gudavičienė

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The goal of this paper is to reveal how polarization is constructed through the use of impoliteness strategies. In general, media helps to spread various ideas very fast, and it means that processes of polarization are best revealed in computer-mediated communication (CMC) contexts. For this reason, data for the research was collected from online texts about a current, very diverse topic in Lithuania - Lithuanian language policy and regulations, because this topic is causing a lot of tension in Lithuanian society. Computer-mediated communication allows users to edit their message before they send it. It means that addressees carefully select verbal expressions to convey their message. In other words, each impoliteness strategy and its verbal expression were created intentionally. Impoliteness strategies in this research are understood as various ways to reach a communicative goal: belittle the other. To reach the goal, the public opinions of various Lithuanian public figures (e. g., cultural people, politicians, officials) were collected from new portals in 2019–2023 and analyzed using both quantitative and qualitative approaches. First, problematic aspects of the language policy, for which public figures complain, were identified. Then instances when public figures take a defensive position were analyzed: how they express this position and what it reveals about Lithuanian culture. Findings of this research demonstrate how concepts of impoliteness theory can be applied in analyzing the process of polarization in Lithuanian society on issues related to the State language policy. Also, to reveal how polarization is constructed, these tasks were set: a) determine which impoliteness strategies are used throughout the process of creating polarization, b) analyze how they were expressed verbally (e. g., as an advice, offer, etc.).

Keywords: impoliteness, Lithuanian language policy, polarization, impoliteness strategies

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Authors: Xingxing Peng

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With the development of society, there are more and more online communication scenarios such as teleconference and online education. In the process of conference communication, the quality of voice communication is a very important part, and noise may cause the communication effect of participants to be greatly reduced. Therefore, voice noise reduction has an important impact on scenarios such as voice calls. This research focuses on the key technologies of the sound transmission process. The purpose is to maintain the audio quality to the maximum so that the listener can hear clearer and smoother sound. Firstly, to solve the problem that the traditional speech enhancement algorithm is not ideal when dealing with non-stationary noise, an adaptive speech noise reduction algorithm is studied in this paper. Traditional noise estimation methods are mainly used to deal with stationary noise. In this chapter, we study the spectral characteristics of different noise types, especially the characteristics of non-stationary Burst noise, and design a noise estimator module to deal with non-stationary noise. Noise features are extracted from non-speech segments, and the noise estimation module is adjusted in real time according to different noise characteristics. This adaptive algorithm can enhance speech according to different noise characteristics, improve the performance of traditional algorithms to deal with non-stationary noise, so as to achieve better enhancement effect. The experimental results show that the algorithm proposed in this chapter is effective and can better adapt to different types of noise, so as to obtain better speech enhancement effect.

Keywords: speech noise reduction, speech enhancement, self-adaptation, Wiener filter algorithm

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Happy Zulu

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Neuropsychological examination is an important way of formally assessing brain function. While there is so much documentation about the influence that some factors, such as age and education, have on neuropsychological tests (NP), not so much has been done to assess the influence that residency (rural/urban) may have. The specific objectives of this study were to establish if there is a significant difference in mean test scores on NP tests between rural and urban participants and to assess which tests on the Zambia Neurobehavioural Test Battery (ZNTB) are more affected by the participants‘ residency (rural/urban) and to determine the extent to which education, gender, and age predict test performance on NP tests for rural and urban participants. The participants (324) were drawn from both urban and rural areas of Zambia (Rural = 152 and Urban = 172). However, only 234 participants (Rural = 152 and Urban 82) were used for all the analyses in this particular study. The 234 participants were used as the actual proportion of the rural vs urban population in Zambia was 65% : 35%, respectively (CSO, 2003). The rural-urban ratio for the participants that were captured during the data collection process was 152 : 172, respectively. Thus, all the rural participants (152) were included and 90 of the 172 urban participants were randomly excluded so that the rural/urban ratio reached the desired 65% : 35 % which was the required ideal statistic for appropriate representation of the actual population in Zambia. Data on NP tests were analyzed from 234 participants, rural (N=152) reflecting 65% and urban (N=82) reflecting 35%. T-tests indicated that urban participants had superior performances in all the seven NP test domains, and all the mean differences in all these domains were found to be statistically significant. Residency had a large or moderate effect in five domains, while its effect size was small only in two of the domains. A standard multiple regression revealed that education, age and residency as predictor variables made a significant contribution to variance in performance on various domains of the ZNTB. However, the gender of participants was not a major factor in determining one‘s performance on neuropsychological tests. This particular report is part of an ongoing, larger, cutting-edge study aimed at formulating the normative data for Zambia with regard to performance on neuropsychological tests. This is necessary for appropriate, effective, and efficient assessment or diagnosis of various neurocognitive and neurobehavioural deficits that a number of people may currently be suffering from. It has been shown in this study that it is vital to make careful analyses of the variables that may be associated with one‘s performance on neuropsychological tests.

Keywords: neuropsychology, neurobehavioural, residency, Zambia

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Authors: Moquan Gong

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In recent years, with the rapid development of deep learning, pre-training technology for natural language processing has made great progress. The early field of natural language processing has long used word vector methods such as Word2Vec to encode text. These word vector methods can also be regarded as static pre-training techniques. However, this context-free text representation brings very limited improvement to subsequent natural language processing tasks and cannot solve the problem of word polysemy. ELMo proposes a context-sensitive text representation method that can effectively handle polysemy problems. Since then, pre-training language models such as GPT and BERT have been proposed one after another. Among them, the BERT model has significantly improved its performance on many typical downstream tasks, greatly promoting the technological development in the field of natural language processing, and has since entered the field of natural language processing. The era of dynamic pre-training technology. Since then, a large number of pre-trained language models based on BERT and XLNet have continued to emerge, and pre-training technology has become an indispensable mainstream technology in the field of natural language processing. This article first gives an overview of pre-training technology and its development history, and introduces in detail the classic pre-training technology in the field of natural language processing, including early static pre-training technology and classic dynamic pre-training technology; and then briefly sorts out a series of enlightening technologies. Pre-training technology, including improved models based on BERT and XLNet; on this basis, analyze the problems faced by current pre-training technology research; finally, look forward to the future development trend of pre-training technology.

Keywords: natural language processing, pre-training, language model, word vectors

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Authors: Belkhadir Radouane

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In this study, author tried to explore the beliefs of some Moroccan teachers working in the delegations of Safi and Youcefia about the usefulness of first and second languages in learning the third language. More specifically, author attempted to see the extent to which these teachers believe that a first and second language can serve students in learning a third one. The first language in this context is Arabic, the second is French, and the third is English. The teachers’ beliefs were gathered through a questionnaire that was addressed via Google Forms. Then, the results were analyzed using the same application. It was found that teachers are positive about the usefulness of the first and second language in learning the third one, but most of them rarely use in a conscious way activities that serve this purpose.

Keywords: Bilinguilism, teachers beliefs, English as ESL, Morocco

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Authors: Celine Davis

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The novel coronavirus 2019 (COVID-19) is an infectious disease caused by the virus SARS-CoV-2, which has detrimental respiratory, cardiovascular, and neurological effects impacting over one million lives in the United States. New researches has emerged indicating long-term neurologic consequences in those who survive COVID-19 infections, including more than seven million Americans and another 27 million people worldwide. These consequences include attentional deficits, memory impairments, executive function deficits and aphasia-like symptoms which fall within the purview of speech-language pathology. The National Health Interview Survey (NHIS) is a comprehensive annual survey conducted by the National Center for Health Statistics (NCHS), a branch of the Centers for Disease Control and Prevention (CDC) in the United States. The NHIS is one of the most significant sources of health-related data in the country and has been conducted since 1957. The longitudinal nature of the study allows for analysis of trends in various variables over the years, which can be essential for understanding societal changes and making treatment recommendations. This current study will utilize NHIS data from 2020-2022 which contained interview questions specifically related to COVID-19. Adult cases of individuals between the ages of 18-50 diagnosed with COVID-19 in the United States during 2020-2022 will be identified using the National Health Interview Survey (NHIS). Multiple regression analysis of self-reported data confirming COVID-19 infection status and challenges with concentration, communication, and memory will be performed. Latent class analysis will be utilized to identify subgroups in the population to indicate whether certain demographic groups have higher susceptibility to cognitive-linguistic deficits associated with COVID-19. Completion of this study will reveal whether there is an association between confirmed COVID-19 diagnosis and heightened incidence of cognitive deficits and subsequent implications, if any, on activities of daily living. This study is distinct in its aim to utilize national survey data to explore the relationship between confirmed COVID-19 diagnosis and the prevalence of cognitive-communication deficits with a secondary focus on resulting activity limitations. To the best of the author’s knowledge, this will be the first large-scale epidemiological study investigating the associations between cognitive-linguistic deficits, COVID-19 and implications on activities of daily living in the United States population. These findings will highlight the need for targeted interventions and support services to address the cognitive-communication needs of individuals recovering from COVID-19, thereby enhancing their overall well-being and functional outcomes.

Keywords: cognition, COVID-19, language, limitations, memory, NHIS

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Authors: Maxime Vignon

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This study delves into the discourse surrounding Africatown ancestors’ will, through a rigorous discourse analysis, to reveal the intricate narratives shaping heritage and identity within this unique community. Situated near Mobile, Alabama, Africatown traces its origins to the Clotilda, the last known slave ship to bring captives from Africa to the United States. Through a meticulous examination of Africatown ancestors posthumous will, this analysis aims to uncover the spiritual linguistic nuances which will contribute to the preservation of Africatown's ancestral heritage and the construction of individual self and collective empowerment. This research will be grounded in Michel Foucault discourse. In its attempt to analyze the four core elements of discourse, this study explores the role of rituals and spiritual discourses within the will of Africatown ancestors. Additionally, it delves into the descendants’ public appearance scrutinizing the use of ancestral names and identities. Ultimately, this discourse analysis contributes to a nuanced understanding of how language and narrative from the ancestors would shape unity, a valuable insight into the complexities of identity formation and the enduring impact of the transatlantic slave trade on this resilient community.

Keywords: applied linguistics, culture, discourse analysis, identity, spirituality, transatlantic slave trade

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Naseer Ahmad

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This research investigates the intricate dynamics of language policy, ideology, and the choice of educational language as a medium of instruction in rural Pakistan. Focused on addressing the complexities of language practices in underexplored educational contexts, the study employed a case study approach, analyzing interviews with education authorities, teachers, and students, alongside classroom observations in English-medium and Urdu-medium rural schools. The research underscores the significance of understanding linguistic diversity within rural communities. The analysis of interviews and classroom observations revealed that language policies in rural schools are influenced by multiple factors, including historical legacies, societal language ideologies, and government directives. The dominance of Urdu and English as the preferred languages of instruction reflected a broader language hierarchy, where regional languages are often marginalized. This language ideology perpetuates a sense of linguistic inferiority among students who primarily speak regional languages. The impact of language choices on students' learning experiences and outcomes is a central focus of the research. It became evident that while policies advocate for specific language practices, the implementation often diverges due to multifarious socio-cultural, economic, and institutional factors. This disparity significantly impacts the effectiveness of educational processes, influencing pedagogical approaches, student engagement, academic outcomes, social mobility, and language choices. Based on the findings, the study concluded that due to policy and practice gap, rural people have complex perceptions and language choices. They perceived Urdu as a national, lingua franca, cultural, easy, or low-status language. They perceived English as an international, lingua franca, modern, difficult, or high-status language. They perceived other languages as mother tongue, local, religious, or irrelevant languages. This research provided insights that are crucial for theory, policy, and practice, addressing educational inequities and inclusive language policies. It set the stage for further research and advocacy efforts in the realm of language policies in diverse educational settings.

Keywords: language-in-education policy, language ideology, educational language choice, pakistan

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Authors: Louise Moles, Steve Riley, Sarah D. Lichenstein, Marzieh Babaeianjelodar, Robert Kohler, Annie Cheng, Corey Horien Abigail Greene, Wenjing Luo, Jonathan Ahern, Bohan Xu, Yize Zhao, Chun Chieh Fan, R. Todd Constable, Sarah W. Yip

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Background: Risks for depression are myriad and include both genetic and brain-based factors. However, relationships between these systems are poorly understood, limiting understanding of disease etiology, particularly at the developmental level. Methods: We use a data-driven machine learning approach connectome-based predictive modeling (CPM) to identify functional connectivity signatures associated with polygenic risk scores for depression (DEP-PRS) among youth from the Adolescent Brain and Cognitive Development (ABCD) study across diverse brain states, i.e., during resting state, during affective working memory, during response inhibition, during reward processing. Results: Using 10-fold cross-validation with 100 iterations and permutation testing, CPM identified connectivity signatures of DEP-PRS across all examined brain states (rho’s=0.20-0.27, p’s<.001). Across brain states, DEP-PRS was positively predicted by increased connectivity between frontoparietal and salience networks, increased motor-sensory network connectivity, decreased salience to subcortical connectivity, and decreased subcortical to motor-sensory connectivity. Subsampling analyses demonstrated that model accuracies were robust across random subsamples of N’s=1,000, N’s=500, and N’s=250 but became unstable at N’s=100. Conclusions: These data, for the first time, identify neural networks of polygenic depression risk in a large sample of youth before the onset of significant clinical impairment. Identified networks may be considered potential treatment targets or vulnerability markers for depression risk.

Keywords: genetics, functional connectivity, pre-adolescents, depression

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Anvikshaa Bisen, Krish Makkar

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Professionals misdiagnose autism by ticking off symptoms on a checklist without questioning the causes of said symptoms, and without understanding the innate neurophysiology of the autistic brain. A dysfunctional cingulate gyrus (CG) hyperfocuses attention in the left frontal lobe (logical/analytical) with no ability to access the right frontal lobe (emotional/creative), which plays a central role in spontaneity, social behavior, and nonverbal abilities. Autistic people live in a specialized inner space that is entirely intellectual, free from emotional and social distractions. They have no innate biological way of emotionally connecting with other people. Autistic people process their emotions intellectually, a process that can take 24 hours, by which time it is too late to have felt anything. An inactive amygdala makes it impossible for autistic people to experience fear. Because they do not feel emotion, they have no emotional memories. All memories are of events that happened about which they felt no emotion at the time and feel no emotion when talking about it afterward.

Keywords: autism, Asperger, Asd, neuropsychology, neuroscience

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Authors: Xu Zhang

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This article unveils an indicator of language contact between English and Cantonese in one of the Outer Circle Englishes, Hong Kong (HK) English, through an empirical investigation into 1000 tokens from the Global Web-based English (GloWbE) corpus, employing frequency analysis and logistic regression analysis. It is perceived that Cantonese and general Chinese are contextually marked by an integral underlying thinking pattern. Chinese speakers exhibit a reliance on semantic context over syntactic rules and lexical forms. This linguistic trait carries over to their use of English, affording greater flexibility to formal elements in constructing English sentences. The study focuses on the syntactic positioning of the focusing subjunct ‘also’, a linguistic element used to add new or contrasting prominence to specific sentence constituents. The English language generally allows flexibility in the relative position of 'also’, while there is a preference for close marking relationships. This article shifts attention to Hong Kong, where Cantonese and English converge, and 'also' finds counterparts in Cantonese ‘jaa’ and Mandarin ‘ye’. Employing a corpus-based data-driven method, we investigate the syntactic position of 'also' in both HK and GB English. The study aims to ascertain whether HK English exhibits a greater 'syntactic freedom,' allowing for a more distant marking relationship with 'also' compared to GB English. The analysis involves a random extraction of 500 samples from both HK and GB English from the GloWbE corpus, forming a dataset (N=1000). Exclusions are made for cases where 'also' functions as an additive conjunct or serves as a copulative adverb, as well as sentences lacking sufficient indication that 'also' functions as a focusing particle. The final dataset comprises 820 tokens, with 416 for GB and 404 for HK, annotated according to the focused constituent and the relative position of ‘also’. Frequency analysis reveals significant differences in the relative position of 'also' and marking relationships between HK and GB English. Regression analysis indicates a preference in HK English for a distant marking relationship between 'also' and its focused constituent. Notably, the subject and other constituents emerge as significant predictors of a distant position for 'also.' Together, these findings underscore the nuanced linguistic dynamics in HK English and contribute to our understanding of language contact. It suggests that future pedagogical practice should consider incorporating the syntactic variation within English varieties, facilitating leaners’ effective communication in diverse English-speaking environments and enhancing their intercultural communication competence.

Keywords: also, Cantonese, English, focus marker, frequency analysis, language contact, logistic regression analysis

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Authors: Kalpana Singh, Nikita Katiyar

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This research investigates the impact of Raga Bhairavi, a prominent musical scale in Indian classical music, on the mood and basic physiological parameters of research scholars at the University of Lucknow - India. The study focuses on the potential therapeutic effects of listening to Raga Bhairavi during morning hours. A controlled experimental design is employed, utilizing self-reporting tools for mood assessment and monitoring physiological indicators such as heart rate, oxygen saturation levels, body temperature and blood pressure. The hypothesis posits that exposure to Raga Bhairavi will lead to positive mood modulation and a reduction in physiological stress markers among research scholars. Data collection involves pre and post-exposure measurements, providing insights into the immediate and cumulative effects of the musical intervention. The study aims to contribute valuable information to the growing field of music therapy, offering a potential avenue for enhancing the well-being and productivity of individuals engaged in intense cognitive activities. Results may have implications for the integration of music-based interventions in academic and research environments, fostering a conducive atmosphere for intellectual pursuits.

Keywords: bio-musicology, classical music, mood assessment, music therapy, physiology, Raga Bhairavi

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Authors: Hsiu-Ling Hsu, En-Minh (John) Lan

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Pronunciation has received more and more language researchers’ and teachers’ attention because it is important for effective or even successful communication. How to consistently and correctly orally produce verbal morphology, such as English regular past tense inflection, has been a big challenge and troublesome for FL learners. The research aims to explore EFL (English as a foreign language) learners’ developmental trajectory of the inflectional morphology, that is, what mediating processes and strategies EFL learners use, to attain native-like prosodic structure of inflectional morphemes (e.g., –ed and –s suffixes) by comparing the differences among EFL learners at different English levels. This research adopted a self-repair analysis and Prosodic Transfer Hypothesis with three developmental stages as a theoretical framework. To answer the research questions, we conducted two experiments, grammatical tense test written production (Experiment 1) and read-aloud oral production (Experiment 2), and recruited 30 participants who were divided into three groups, low-, middle-, and advanced EFL learners. Experiment 1 was conducted to ensure that participants had learned the knowledge of forming the English regular past tense rules and Experiment 2 was carried out to compare the data across FL English learner groups at different English levels. The EFL learners’ self-repair data showed at least four interesting findings. First, low achievers were more sensitive to the plural suffix -s than the past tense suffix -ed. Middle achievers exhibited a greater responsiveness to the past tense suffix, while high achievers demonstrated equal sensitivity to both suffixes. Additionally, two strategies used by EFL English learners to produce verbs and nouns with inflectional morphemes were to delete internal syllable and to divide a four-syllable verb (e.g., ‘graduated’) into two prosodic structures (e.g., ‘gradu’ and ‘ated’ or ‘gradua’ and ‘ted’). Third, true vowel epenthesis was found only in the low EFL achievers. Moreover fortition (native-like sound) was observed in the low and middle EFL achievers. These findings and self-repair data disclosed mediating processes between the developmental stages and provided insight on how Taiwan EFL learners attained the adjunction prosodic structures of inflectional Morphemes in English.

Keywords: inflectional morphology, prosodic structure, developmental trajectory, strategies and mediating processes, English as a foreign language

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Authors: Ren Boyuan

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This study focuses on how the Character and Citizenship Education textbook in Singapore primary schools deliver preferred and desired qualities to students and therefore reveals how discourse in textbooks can facilitate and perpetuate certain social practices. In this way, this study also serves to encourage the critical thinking of textbook writers and school educators by unveiling the nuanced message through language use that facilitates the perpetuation of social practices in a society. In Singapore, Character and Citizenship Education is a compulsory subject for primary school students. Under the framework of 21st Century Competencies, Character and Citizenship Education in Singapore aims to help students thrive in this fast-changing world. The Singapore government is involved in the development of CCE curriculum in schools from primary schools to pre-university. Inevitably, the CCE curriculum is not free from ideological influences. This qualitative study utilizes Fairclough’s three-dimensional theory and his framework of three assumptions to analyze the Character and Citizenship Education textbook for Primary 1 and to reveal ideologies in this textbook. Data for the analysis in this study are the textual parts of the whole textbook for Primary 1 students as this book is used at the beginning of citizenship education in primary schools. It is significant because it promotes messages about CCE to the foundation years of a child's education. The findings of this study show that the four revealed ideologies, namely pragmatism, communitarianism, nationalism, and multiculturalism, are not only dated back in the national history but also updated and explained by the current demands for Singapore’s thriving and prosperity in a sustainable term. This study ends with a discussion of the implications of this study. By pointing out the ideologies in this textbook and how they are embedded in the discourse, this study may help teachers and textbook writers realize the possible political involvement in the book and therefore develop their recognition of the implicit influence of lexical choice on their teaching and writing. In addition, by exploring the ideologies in this book and comparing them with ideologies in past textbooks, this study helps researchers in this area on how language influences readers and reflects certain social demands.

Keywords: citizenship education, critical discourse analysis, sociolinguistics, textbook analysis

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Authors: Mulligan T., Barker-Collo S., Gobson K., Jones K.

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There is a relatively scarce body of literature regarding adolescent experiences of traumatic brain injury (TBI). This qualitative study explored how sustaining a TBI at this unique stage of development might impact a young person as they navigate the challenges of adolescence and transition to adulthood, and what might support recovery. Thirteen young adults who sustained a mild-moderate TBI as an adolescent (aged 13 – 17 years), approximately 7.7 years (range = 6.7 – 8.0 years) prior, participated in the research. Semi-structured individual interviews were conducted to explore participants’ experiences surrounding and following their TBIs. Thematic analysis of interview data produced five key categories of findings: (1) Following their TBIs, many participants experienced problems with cognitive (e.g., forgetfulness, concentration difficulties), physical (e.g., migraines, fatigue) and emotional (e.g., depression, anxiety) functioning, which were often endured into adulthood. (2) TBI-related problems often adversely affected important areas of life for the participant, including school, work and friendships. (3) Changes following TBI commonly impacted identity formation. (4) Recovery processes evolved over time as the participants coped initially by just ‘getting on with it’, before learning to accept new limitations and, ultimately, growing from their TBI experiences. (5) While the presence of friends and family assisted recovery, struggles were often exacerbated by a lack of emotional support from others, in addition to the absence of any assistance or information-provision from professionals regarding what to expect following TBI. The findings suggest that even mild TBI sustained during adolescence can have consequences for an individual’s functioning, engagement in life and identity development, whilst also giving rise to post-traumatic growth. Recovery following adolescent TBI might be maximised by facilitating greater understanding of the injury and acknowledging its impacts on important areas of life, as well as the provision of emotional support and facilitating self-reflection and meaning-making.

Keywords: adolescent, brain Injury, qualitative, post-traumatic growth

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Authors: Promise Ekpo Osaine, Daniel Melesse

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Image captioning is an active area of research in the multi-modal artificial intelligence (AI) community as it connects vision and language understanding, especially in settings where it is required that a model understands the content shown in an image and generates semantically and grammatically correct descriptions. In this project, we followed a standard approach to a deep learning-based image captioning model, injecting architecture for the encoder-decoder setup, where the encoder extracts image features, and the decoder generates a sequence of words that represents the image content. As such, we investigated image encoders, which are ResNet101, InceptionResNetV2, EfficientNetB7, EfficientNetV2M, and CLIP. As a caption generation structure, we explored long short-term memory (LSTM). The CLIP-LSTM model demonstrated superior performance compared to the encoder-decoder models, achieving a BLEU-1 score of 0.904 and a BLEU-4 score of 0.640. Additionally, among the CNN-LSTM models, EfficientNetV2M-LSTM exhibited the highest performance with a BLEU-1 score of 0.896 and a BLEU-4 score of 0.586 while using a single-layer LSTM.

Keywords: multi-modal AI systems, image captioning, encoder, decoder, BLUE score

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Authors: Benjamen Nathaniel, Eludiora Safiriyu Ijiyemi, Egume Oneme Lucky

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A review on machine translation systems shows that a lot of computational artefacts has been carried out to translate written or spoken texts from a source language to Yorùbá language through Machine Translation systems. However, there are no work on French to Yorùbá language machine translation system; hence, the study investigated the process involved in the translation of French-to-Yorùbá language equivalent with the view to adopting a rule- based MT approach to build a Machine Translation framework from simple sentences administered through questionnaire. Articles and relevant textbooks were reviewed with key speakers of both languages interviewed to find out the processes involved in the translation of French language and their equivalent in Yorùbálanguage simple sentences using home domain terminologies. Achieving this, a model was formulated using phrase grammar structure, re-write rule, parse tree, automata theory- based techniques, designed and implemented respectively with unified modeling language (UML) and python programming language. Analysing the result, it was observed when carrying out the result that, the Machine Translation system performed 18.45% above Experimental Subject Respondent and 2.7% below Linguistics Expert when analysed with word orthography, sentence syntax and semantic correctness of the sentences. And, when compared with Google Machine Translation system, it was noticed that the developed system performed better on lexicons of the target language.

Keywords: machine translation (MT), rule-based, French language, Yoru`ba´ language

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Authors: Laura Mclean

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A recurring point of interest in computational phylogenetic analysis of Indo-European family trees is the inference of a Germano-Italo-Celtic clade in some versions of the trees produced. The presence of this clade in the models is intriguing as there is little evidence for innovations shared among Germanic, Italic, and Celtic, the evidence generally used in the traditional method to construct a subgroup. One source of this unexpected outcome could be the input to the models. The datasets in the various models used so far, for the most part, take as their basis the Swadesh list, a list compiled by Morris Swadesh and then revised several times, containing up to 207 words that he believed were resistant to change among languages. The judgments made by Swadesh for this list, however, were subjective and based on his intuition rather than rigorous analysis. Some scholars used the Swadesh 200 list as the basis for their Indo-European dataset and made cognacy judgements for each of the words on the list. Another dataset is largely based on the Swadesh 207 list as well although the authors include additional lexical and non-lexical data, and they implement ‘split coding’ to deal with cases of polymorphic characters. A different team of scholars uses a different dataset, IECoR, which combines several different lists, one of which is the Swadesh 200 list. In fact, the Swadesh list is used in some form in every study surveyed and each dataset has three words that, when they are coded as cognates, seemingly contribute to the inference of a Germano-Italo-Celtic clade which could happen due to these clades sharing three words among only themselves. These three words are ‘fish’, ‘flower’, and ‘man’ (in the case of ‘man’, one dataset includes Lithuanian in the cognacy coding and removes the word ‘man’ from the screened data). This collection of cognates shared among Germanic, Italic, and Celtic that were deemed important enough to be included on the Swadesh list, without the ability to account for possible reasons for shared cognates that are not shared innovations, gives an impression of affinity between the Germanic, Celtic, and Italic branches without adequate methodological support. However, by changing how cognacy is defined (ie. root cognates, borrowings vs inherited cognates etc.), we will be able to identify whether these three cognates are significant enough to infer a clade for Germanic, Celtic, and Italic. This paper examines the question of what definition of cognacy should be used for phylogenetic datasets by examining the Germano-Italo-Celtic clade as a case study and offers insights into the reconstruction of a Germano-Italo-Celtic clade.

Keywords: historical, computational, Italo-Celtic, Germanic

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Authors: Avantika Vats, Kushal Thakur

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This paper displays the point at issue, improvement, and utilization of a Monarch Butterfly Optimization (MBO) rather than a Genetic Algorithm (GA) in cognitive radio for the channel portion. This approach offers a satisfactory approach to get the accessible range of both the users, i.e., primary users (PUs) and secondary users (SUs). The proposed enhancement procedure depends on a nature-inspired metaheuristic algorithm. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. Southern Canada and the northern USA (land 1), and Mexico (Land 2). The positions of the monarch butterflies are modernizing in two ways. At first, the offsprings are generated (position updating) by the migration operator and can be adjusted by the migration ratio. It is trailed by tuning the positions for different butterflies by the methods for the butterfly adjusting operator. To keep the population unaltered and minimize fitness evaluations, the aggregate of the recently produced butterflies in these two ways stays equivalent to the first population. The outcomes obviously display the capacity of the MBO technique towards finding the upgraded work values on issues regarding the genetic algorithm.

Keywords: cognitive radio, channel allocation, monarch butterfly optimization, evolutionary, computation

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